From a03e98c13405e10146f0193bc5c6ed281f58f3f3 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 27 May 2026 13:26:54 +0200
Subject: [PATCH 001/173] Add validation run of the best ckpt

---
 configs/train.yaml |  2 ++
 src/train.py       | 18 ++++++++++++++++++
 2 files changed, 20 insertions(+)

diff --git a/configs/train.yaml b/configs/train.yaml
index 1058d9a..75778d8 100644
--- a/configs/train.yaml
+++ b/configs/train.yaml
@@ -43,6 +43,8 @@ train: True
 # lightning chooses best weights based on the metric specified in checkpoint callback
 test: True
 
+validate: True
+
 # simply provide checkpoint path to resume training
 ckpt_path: null
 
diff --git a/src/train.py b/src/train.py
index 949e21b..92207bd 100644
--- a/src/train.py
+++ b/src/train.py
@@ -135,6 +135,24 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
 
     train_metrics = trainer.callback_metrics
 
+    if cfg.get("validate"):
+        # Run validation with the best ckpt
+        log.info("Validating the best ckpt!")
+        ckpt_path = trainer.checkpoint_callback.best_model_path
+        if ckpt_path == "":
+            log.warning("Best ckpt not found! Using current weights for testing...")
+            ckpt_path = None
+
+        trainer.validate(
+            model=model,
+            datamodule=datamodule,
+            ckpt_path=ckpt_path,
+            weights_only=False,
+        )
+
+        val_metrics = trainer.callback_metrics
+        wandb_logger.log({f"best_val_{k}": v for k, v in val_metrics.items()})
+
     if cfg.get("test"):
         log.info("Starting testing!")
         ckpt_path = trainer.checkpoint_callback.best_model_path

From 1428e92f29124c45ea098a7c4e98547a3866ba1c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:01:54 +0200
Subject: [PATCH 002/173] Add avr embedding modalities

---
 src/data/base_dataset.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index f92eca6..f09a90a 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -9,8 +9,8 @@
 import torch
 from torch.utils.data import Dataset
 
+from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError
 from src.utils.data_utils import center_crop_npy
-from src.utils.errors import MissingDataError
 
 TORCH_DTYPES = {
     "float32": torch.float32,
@@ -143,6 +143,16 @@ def get_records(self) -> dict[str, Any]:
         for modality, params in self.modalities.items():
             if modality == "coords":
                 columns.extend(["lat", "lon"])
+            elif modality in ["aef_avr", "tessera_avr"]:
+                df = pd.read_csv(params.get("path", KeyError))
+                df["name_loc"] = df["name_loc"].astype(str)
+
+                self.df["name_loc"] = self.df["name_loc"].astype(str)
+
+                self.df = self.df.merge(df, on="name_loc", how="left")
+
+                max_no = 128 if modality == "tessera_avr" else 64
+                columns.extend([f"avr_{i}" for i in range(0, max_no)])
             else:
                 # Add paths
                 self.add_modality_paths_to_df(
@@ -233,7 +243,6 @@ def setup_tessera(self) -> None:
             tessera_from_df,
         )
 
-
         print("\n\nSetting up Tessera data...\n\n")
         download_missing_tiles = False
 
@@ -291,12 +300,11 @@ def setup_tessera(self) -> None:
                                 tessera_con=gt,
                             )
                             continue
-                        except:
+                        except NoTileError or PartialTileError:
                             print(f"Tile for {fname} could not be retrieved.")
                 self.records.pop(i)
                 print(f"No tile found for {fname} thus it will not be used.")
 
-
     @final
     def setup_aef(self) -> None:
         """Download full dataset or the missing AEF tiles.

From e6c141e969a58a686d656a99e1faa24bdf16b4c7 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:02:24 +0200
Subject: [PATCH 003/173] Make search in power of 2, plus add max batch size

---
 configs/callbacks/batch_size_finder.yaml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/configs/callbacks/batch_size_finder.yaml b/configs/callbacks/batch_size_finder.yaml
index 55533d3..d9c117b 100644
--- a/configs/callbacks/batch_size_finder.yaml
+++ b/configs/callbacks/batch_size_finder.yaml
@@ -1,5 +1,6 @@
 # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.BatchSizeFinder.html
 
 _target_: lightning.pytorch.callbacks.BatchSizeFinder
-mode: 'binsearch'
-init_val: 16
\ No newline at end of file
+mode: "power"
+init_val: 16
+max_val: 1024

From 8e035d8f09403e7d143d67bdfcbd972030130a3e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:02:59 +0200
Subject: [PATCH 004/173] Add average embedding modalities

---
 src/data/butterfly_dataset.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index 50b3348..23de9a2 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -45,7 +45,7 @@ def __init__(
             dataset_name="s2bms",
             seed=seed,
             cache_dir=cache_dir,
-            implemented_mod={"s2", "tessera", "coords", "aef"},
+            implemented_mod={"s2", "tessera", "coords", "aef", "aef_avr", "tessera_avr"},
             mock=mock,
             dtype=dtype,
         )
@@ -183,6 +183,16 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 formatted_row["eo"][modality] = self.load_tessera(row["tessera_path"])
             elif modality == "aef":
                 formatted_row["eo"][modality] = self.load_aef(row["aef_path"])
+            elif modality == "aef_avr":
+                formatted_row["eo"][modality] = torch.tensor(
+                    [row[f"avr_{i}"] for i in range(64)],
+                    dtype=getattr(torch, self.modalities["aef_avr"].get("dtype")),
+                )
+            elif modality == "tessera_avr":
+                formatted_row["eo"][modality] = torch.tensor(
+                    [row[f"avr_{i}"] for i in range(128)],
+                    dtype=getattr(torch, self.modalities["tessera_avr"].get("dtype")),
+                )
 
         if self.use_target_data:
             formatted_row["target"] = torch.tensor(

From 695bd3c30434b8bbcffc4b61bbc69a354108982e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:03:55 +0200
Subject: [PATCH 005/173] Add no gradient computation option if model is not
 trained

---
 src/models/components/text_encoders/clip_text_encoder.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 5052ca5..234ea4f 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -31,6 +31,7 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
 
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
+        """Forward pass through the text encoder."""
         # Get text inputs
         text_input = batch.get("text")
 
@@ -50,7 +51,11 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
             device = next(self.model.parameters()).device
             text_tokens = {k: v.to(device) for k, v in text_tokens.items()}
 
-            text_embeds = self.model.get_text_features(**text_tokens)
+            if any(p.requires_grad for p in self.model.parameters()):
+                text_embeds = self.model.get_text_features(**text_tokens)
+            else:
+                with torch.inference_mode():
+                    text_embeds = self.model.get_text_features(**text_tokens)
 
             # Project
             if self.projector is not None:

From 54bb273f1af9398cd5af7cebf78a5f728cb511d4 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:04:24 +0200
Subject: [PATCH 006/173] Allow identity encoder for the average embeddings

---
 src/models/components/geo_encoders/encoder_wrapper.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/models/components/geo_encoders/encoder_wrapper.py b/src/models/components/geo_encoders/encoder_wrapper.py
index a44a3fa..d8d467d 100644
--- a/src/models/components/geo_encoders/encoder_wrapper.py
+++ b/src/models/components/geo_encoders/encoder_wrapper.py
@@ -4,9 +4,7 @@
 import torch.nn as nn
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
-from src.models.components.geo_encoders.identity_encoder import IdentityEncoder
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
-from src.utils.errors import IllegalArgumentCombination
 
 
 class EncoderWrapper(BaseGeoEncoder):
@@ -98,10 +96,6 @@ def _setup(self) -> List[str]:
 
             # Configure adapter/projector if requested
             if "projector" in branch:
-                if isinstance(encoder, IdentityEncoder):
-                    raise IllegalArgumentCombination(
-                        "Identity encoder cannot have linear projector"
-                    )
                 projector = branch["projector"]
 
                 projector.set_input_dim(input_dim=branch_dim)

From fea48d5b4ad91807b5b4053e187572ec5161fe8b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:05:38 +0200
Subject: [PATCH 007/173] Compute average embeddings into csv

---
 src/data_preprocessing/get_avr_embeds.py | 61 ++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 src/data_preprocessing/get_avr_embeds.py

diff --git a/src/data_preprocessing/get_avr_embeds.py b/src/data_preprocessing/get_avr_embeds.py
new file mode 100644
index 0000000..9eee19a
--- /dev/null
+++ b/src/data_preprocessing/get_avr_embeds.py
@@ -0,0 +1,61 @@
+import glob
+
+import numpy as np
+import pandas as pd
+import rasterio
+import torch
+
+from src.utils.data_utils import center_crop_npy
+
+
+def _load_tiff(tiff_file_path: str, dtype: np.dtype) -> np.ndarray:
+    """Load tiff files as desired dtype np arrays."""
+    with rasterio.open(tiff_file_path) as f:
+        im = f.read()
+        assert isinstance(im, np.ndarray)
+        if im.dtype != np.dtype(dtype):
+            im = im.astype(dtype=dtype, copy=False)
+    return im
+
+
+def _load_npy(filepath: str, dtype: np.dtype) -> np.ndarray:
+    """Load npy files as desired dtype np arrays."""
+    arr = np.load(filepath).transpose(2, 0, 1)
+    if arr.dtype != np.dtype(dtype):
+        arr = arr.astype(dtype=dtype, copy=False)
+    return arr
+
+
+def main():
+    """Compute average embeddings into csv file."""
+    size = 128
+    mod = "aef"
+    format = "tif"
+
+    # save aef averages
+    paths = glob.glob(f"data/s2bms/eo/{mod}/*.{format}")
+
+    rows = []
+    for p in paths:
+        if format == "npy":
+            im = _load_npy(p, np.dtype("float32"))
+        else:
+            im = _load_tiff(p, np.dtype("float32"))
+        im = center_crop_npy(im, (64 if mod == "aef" else 128, size, size))
+        tile = torch.from_numpy(im)
+        tile = tile.nanmean(dim=(-2, -1))  # shape [64]
+        row = {f"avr_{i}": v.item() for i, v in enumerate(tile)}
+        row["name_loc"] = f"UKBMS_loc-{p.split('-')[-1].split('.')[0]}"
+        rows.append(row)
+
+    df = pd.DataFrame(rows)
+    df.to_csv(f"data/s2bms/eo/{mod}_avr_{size}.csv", index=False)
+    print(f"Saved to data/s2bms/eo/{mod}_avr_{size}.csv")
+
+
+if __name__ == "__main__":
+    import os
+
+    os.chdir("../..")
+    print(os.getcwd())
+    main()

From 0d1c4cd79b8bd0b61d53611e50ad314645aa378d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:06:35 +0200
Subject: [PATCH 008/173] Adapt identity encoder for average embeddings

---
 .../geo_encoders/identity_encoder.py          | 32 +++++++++++++++----
 1 file changed, 25 insertions(+), 7 deletions(-)

diff --git a/src/models/components/geo_encoders/identity_encoder.py b/src/models/components/geo_encoders/identity_encoder.py
index 29f9bfe..19fbf4a 100644
--- a/src/models/components/geo_encoders/identity_encoder.py
+++ b/src/models/components/geo_encoders/identity_encoder.py
@@ -11,24 +11,25 @@ def __init__(
         self,
         geo_data_name="aef",
     ) -> None:
-        """Encoder to avreage tile values into a 1D vector.
+        """Encoder to pass through the data with no changes.
 
         :param geo_data_name: modality name
         """
         super().__init__()
 
-        self.dict_n_bands_default = {"s2": 4, "aef": 64, "tessera": 128}
+        self.dict_n_bands_default = {"aef": 64, "tessera": 128, "aef_avr": 64, "tessera_avr": 128}
         self.allowed_geo_data_names: list[str] = list(self.dict_n_bands_default.keys())
         assert (
             geo_data_name in self.allowed_geo_data_names
         ), f"geo_data_name must be one of {self.allowed_geo_data_names}, got {geo_data_name}"
         self.geo_data_name = geo_data_name
 
+        self.output_dim = self.dict_n_bands_default[geo_data_name]
+
     @override
     def _setup(self) -> List[str]:
         """Configures modules and returns newly initialised, trainable module names."""
 
-        self.output_dim = None
         self.geo_encoder = nn.Identity()
         return []
 
@@ -37,9 +38,6 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Data forward pass through the encoder."""
         tile = batch.get("eo", {}).get(self.geo_data_name)
 
-        if self.output_dim is None:
-            self.output_dim = tile.shape
-
         feats = self.geo_encoder(tile)
         if self.extra_projector:
             feats = self.extra_projector(feats)
@@ -47,4 +45,24 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
 
     @property
     def device(self):
-        return
+        if self.extra_projector:
+            devices = {p.device for p in self.extra_projector.parameters()}
+            if len(devices) > 1:
+                raise RuntimeError("Extra encoder is on multiple devices")
+            elif len(devices) == 0:
+                return None
+            return devices.pop()
+        else:
+            return None
+
+    @property
+    def dtype(self):
+        if self.extra_projector:
+            dtypes = {p.dtype for p in self.extra_projector.parameters()}
+            if len(dtypes) > 1:
+                raise RuntimeError("Extra encoder has multiple dtypes")
+            elif len(dtypes) == 0:
+                return None
+            return dtypes.pop()
+        else:
+            return None

From 90ebd95dab15af69d40808dcf1562a898a6d2168 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:07:05 +0200
Subject: [PATCH 009/173] Avoid gradient computation where not needed

---
 src/models/text_alignment_model.py | 36 +++++++++++++++++++++---------
 1 file changed, 26 insertions(+), 10 deletions(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 7a67cde..02ead9a 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -1,6 +1,5 @@
 from typing import Dict, Tuple, override
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 
@@ -124,8 +123,9 @@ def setup_retrieval_evaluation(
                 return
 
         # Encode concepts if text branch is frozen
-        with torch.no_grad():
+        with torch.inference_mode():
             self.concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            self.concept_embeds = F.normalize(self.concept_embeds, dim=1)
 
     @override
     def forward(
@@ -185,10 +185,12 @@ def _step(self, batch: Dict[str, torch.Tensor], mode: str = "train"):
         self.log_dict(metrics, batch_size=local_batch_size, **self.log_kwargs)
 
         if mode in ["val", "test"]:
+            aux = batch.get("aux", {}).get("aux")
             self.outputs_epoch_memory.append(
                 {
-                    "geo_feats": geo_feats.detach(),
-                    "aux_vals": batch.get("aux", {}).get("aux").detach(),
+                    # Store on CPU to avoid holding the whole epoch on GPU.
+                    "geo_feats": geo_feats.detach().cpu(),
+                    "aux_vals": aux.detach().cpu() if aux is not None else None,
                 }
             )
 
@@ -198,8 +200,11 @@ def _on_epoch_end(self, mode: str, verbose=0):
 
         # Combine batches
         geo_feats = torch.cat([x["geo_feats"] for x in self.outputs_epoch_memory], dim=0)
+        geo_feats = geo_feats.to(self.device, non_blocking=True)
 
-        aux_vals = torch.cat([x["aux_vals"] for x in self.outputs_epoch_memory], dim=0)
+        aux_vals = torch.cat([x["aux_vals"] for x in self.outputs_epoch_memory], dim=0).to(
+            self.device, non_blocking=True
+        )
 
         # Rank on similarity
         similarity = self.concept_similarities(geo_feats)
@@ -247,23 +252,34 @@ def on_test_epoch_end(self):
         return self._on_epoch_end("test")
 
     def concept_similarities(self, geo_embeds, concept=None) -> torch.Tensor:
+        device_type = geo_embeds.device.type
+        is_bf16 = self.trainer.precision == "bf16-mixed" and device_type == "cuda"
+
         # Get concept embeddings
         if concept is not None:
             # If only one concept is provided
             if isinstance(concept, str):
                 concept = [concept]
-            with torch.no_grad():
-                concept_embeds = self.text_encoder({"text": concept}, mode="train")
+
+            with torch.inference_mode():
+                with torch.autocast(
+                    device_type=device_type, dtype=torch.bfloat16, enabled=is_bf16
+                ):
+                    concept_embeds = self.text_encoder({"text": concept}, mode="train")
+            concept_embeds = F.normalize(concept_embeds, dim=1)
 
         elif self.concept_embeds is not None:
             concept_embeds = self.concept_embeds
         else:
-            with torch.no_grad():
-                concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            with torch.inference_mode():
+                with torch.autocast(
+                    device_type=device_type, dtype=torch.bfloat16, enabled=is_bf16
+                ):
+                    concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            concept_embeds = F.normalize(concept_embeds, dim=1)
 
         # Similarity
         geo_embeds = F.normalize(geo_embeds, dim=1)
-        concept_embeds = F.normalize(concept_embeds, dim=1)
         similarity_matrix = concept_embeds @ geo_embeds.T
 
         return similarity_matrix

From 545c3ce3d3123ada73f993890de92443788236a0 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 29 May 2026 18:05:25 +0200
Subject: [PATCH 010/173] Inference -> no_grad

---
 src/models/components/text_encoders/clip_text_encoder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 234ea4f..764af9a 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -54,7 +54,7 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
             if any(p.requires_grad for p in self.model.parameters()):
                 text_embeds = self.model.get_text_features(**text_tokens)
             else:
-                with torch.inference_mode():
+                with torch.no_grad():
                     text_embeds = self.model.get_text_features(**text_tokens)
 
             # Project

From 4fa833dbc864c0158ac5522f93898da158c01f42 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 09:30:25 +0200
Subject: [PATCH 011/173] Download GEE data in parallel

---
 scripts/download_gee_data.py              | 46 +++++++++++++++++++++++
 scripts/download_gee_parallel.sh          | 25 ++++++++++++
 src/data_preprocessing/create_aux_data.py |  4 ++
 3 files changed, 75 insertions(+)
 create mode 100644 scripts/download_gee_data.py
 create mode 100644 scripts/download_gee_parallel.sh

diff --git a/scripts/download_gee_data.py b/scripts/download_gee_data.py
new file mode 100644
index 0000000..f5b9ecc
--- /dev/null
+++ b/scripts/download_gee_data.py
@@ -0,0 +1,46 @@
+import argparse
+import os
+import sys
+
+import pandas as pd
+
+from src.data_preprocessing.create_aux_data import get_aux_data_from_coords_list
+
+
+def main(start=0, stop=2000):
+    path_csv = os.path.join(os.environ["DATA_DIR"], "s2bms/source/", "unlabelled_samples_10k.csv")
+    assert os.path.exists(path_csv), f"CSV file with locations does not exist: {path_csv}"
+    df_samples = pd.read_csv(path_csv)
+    assert (
+        start >= 0 and stop > start
+    ), f"Invalid start ({start}) and stop ({stop}) values. Ensure that 0 <= start < stop."
+    if start >= len(df_samples):
+        print(
+            f"Start index ({start}) exceeds number of available samples ({len(df_samples)}). No data to process."
+        )
+        return
+    if stop > len(df_samples):
+        print(
+            f"Warning: stop index ({stop}) exceeds number of available samples ({len(df_samples)}). Adjusting stop to {len(df_samples)}."
+        )
+        stop = len(df_samples)
+    coords_list = [(float(row.lon), float(row.lat)) for _, row in df_samples.iterrows()]
+    coords_list = coords_list[start:stop]
+    name_list = df_samples.name.values[start:stop]
+
+    _, __ = get_aux_data_from_coords_list(
+        coords_list=coords_list,
+        name_list=name_list,
+        save_file=True,
+        save_filename=f"aux_data_unlabelled_samples_10k_{start}_{stop}.csv",
+        patch_size=2560,
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--start", type=int, required=True)
+    parser.add_argument("--stop", type=int, required=True)
+    args = parser.parse_args()
+    print(f"Starting download of GEE data for locations from index {args.start} to {args.stop}...")
+    main(start=args.start, stop=args.stop)
diff --git a/scripts/download_gee_parallel.sh b/scripts/download_gee_parallel.sh
new file mode 100644
index 0000000..d0793a6
--- /dev/null
+++ b/scripts/download_gee_parallel.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+set -a
+# shellcheck disable=SC1091
+source "$(dirname "$0")/../.env"
+set +a
+
+N=10000        # max total points
+P=20           # number of processes
+CHUNK=$((N / P))
+
+for ((i=0; i<P; i++)); do
+    START=$((i * CHUNK))
+    END=$(( (i+1) * CHUNK ))
+
+    # last chunk takes the remainder
+    if [ $i -eq $((P-1)) ]; then
+        END=$N
+    fi
+
+    echo "Launching $START -> $END"
+    python download_gee_data.py --start $START --stop $END &
+done
+
+wait
+echo "All workers finished."
diff --git a/src/data_preprocessing/create_aux_data.py b/src/data_preprocessing/create_aux_data.py
index 3831b3f..6fe9091 100644
--- a/src/data_preprocessing/create_aux_data.py
+++ b/src/data_preprocessing/create_aux_data.py
@@ -70,6 +70,7 @@ def get_aux_data_from_coords_list(
     if save_file:
         save_path = os.path.join(save_folder, save_filename)
         assert os.path.exists(save_folder), f"Save folder does not exist: {save_folder}"
+        assert not os.path.exists(save_path), f"Save file already exists: {save_path}"
         save_every_n = 100  # save every n samples to avoid data loss
         print(f"Will save auxiliary data to {save_path} every {save_every_n} samples")
     else:
@@ -80,6 +81,9 @@ def get_aux_data_from_coords_list(
         desc="Collecting auxiliary data",
     ) as pbar:
         for i_coords, coords in enumerate(coords_list):
+            assert (
+                type(coords) in [tuple, list] and len(coords) == 2
+            ), f"Coordinates must be a tuple or list of (lon, lat), got type {type(coords)} with value {coords}"
             try:
                 result = get_aux_data_from_coords(coords, patch_size=patch_size)
                 result_keys = list(result.keys())

From 1e0974e621be929017f37743885f7e3de4c2e505 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 09:47:59 +0200
Subject: [PATCH 012/173] Sample unlabelled aux data for UK s2bms

---
 .pre-commit-config.yaml                       |   4 +-
 ...vdP-sample-alignment-locations-S2BMS.ipynb | 352 ++++++++++++++++++
 2 files changed, 354 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5ebe893..12eb7c8 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -76,7 +76,7 @@ repos:
         args:
           [
             "--extend-ignore",
-            "E203,E402,E501,F401,F841,RST2,RST301",
+            "E203,E266,E402,E501,F401,F841,RST2,RST301",
             "--exclude",
             "logs/*,data/*",
           ]
@@ -142,6 +142,6 @@ repos:
       - id: nbqa-flake8
         args:
           [
-            "--extend-ignore=E203,E402,E501,F401,F841",
+            "--extend-ignore=E203,E266,E402,E501,F401,F841",
             "--exclude=logs/*,data/*",
           ]
diff --git a/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb b/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb
new file mode 100644
index 0000000..f3adbbb
--- /dev/null
+++ b/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb
@@ -0,0 +1,352 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"../\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import geopandas as gpd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "from sklearn.metrics import euclidean_distances\n",
+    "\n",
+    "import src.data_preprocessing.caption_generation as cg\n",
+    "import src.data_preprocessing.create_aux_data as cad\n",
+    "import src.data_preprocessing.data_utils as du\n",
+    "import src.data_preprocessing.gee_utils as gu\n",
+    "from src.data.base_caption_builder import BaseCaptionBuilder, DummyCaptionBuilder\n",
+    "from src.data.base_datamodule import BaseDataModule\n",
+    "from src.data.butterfly_caption_builder import ButterflyCaptionBuilder\n",
+    "from src.data.butterfly_dataset import ButterflyDataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BD = ButterflyDataset(\n",
+    "    modalities={\"coords\": None}, data_dir=os.path.join(os.environ[\"DATA_DIR\"]), use_aux_data=\"all\"\n",
+    ")\n",
+    "\n",
+    "# BCB = ButterflyCaptionBuilder(\n",
+    "#     data_dir=os.path.join(os.environ[\"DATA_DIR\"], \"s2bms\"),\n",
+    "#     templates_fname=\"v5.json\",\n",
+    "#     concepts_fname=\"v4.json\",\n",
+    "#     seed=42,\n",
+    "# )\n",
+    "\n",
+    "# BM = BaseDataModule(\n",
+    "#     dataset=BD,\n",
+    "#     batch_size=32,\n",
+    "#     split_mode=\"from_file\",\n",
+    "#     saved_split_file_name=os.path.join(\n",
+    "#         os.environ[\"DATA_DIR\"], \"s2bms\", \"splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
+    "#     ),\n",
+    "#     caption_builder=BCB,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = \"/Users/tplas/data/Countries_December_2025_Boundaries_UK_BFE_4990233815767821569/CTRY_DEC_2025_UK_BFE.shp\"\n",
+    "gdf_uk = gpd.read_file(pth)\n",
+    "## merge multiple polygons into one (for the UK mainland)\n",
+    "gdf_uk = gdf_uk.dissolve()\n",
+    "gdf_uk = gdf_uk.to_crs(epsg=4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = \"../data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
+    "split_indices_original = torch.load(pth, weights_only=False)\n",
+    "\n",
+    "split_names = [\"train\", \"val\", \"test\"]\n",
+    "gdf_splits = {}\n",
+    "for split_name in split_names:\n",
+    "    gdf_splits[split_name] = BD.df[\n",
+    "        np.isin(BD.df.name_loc, split_indices_original[f\"{split_name}_indices\"])\n",
+    "    ]\n",
+    "    gdf_splits[split_name] = gpd.GeoDataFrame(\n",
+    "        gdf_splits[split_name],\n",
+    "        geometry=gpd.points_from_xy(gdf_splits[split_name].lon, gdf_splits[split_name].lat),\n",
+    "        crs=\"EPSG:4326\",\n",
+    "    )\n",
+    "\n",
+    "gdf_s2bms = gpd.GeoDataFrame(\n",
+    "    BD.df,\n",
+    "    geometry=gpd.points_from_xy(BD.df.lon, BD.df.lat),\n",
+    "    crs=\"EPSG:4326\",\n",
+    ")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "gdf_uk.plot(ax=ax, color=\"lightgrey\", edgecolor=\"black\")\n",
+    "for split_name in split_names:\n",
+    "    gdf_splits[split_name].plot(ax=ax, label=split_name, markersize=20)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def average_dist_nearest_neighbour(n_samples):\n",
+    "    points_sampled = (\n",
+    "        gdf_uk.sample_points(n_samples, random_state=42).explode().reset_index(drop=True)\n",
+    "    )\n",
+    "    gdf_points_proj = points_sampled.to_crs(\"EPSG:27700\")\n",
+    "    coords_points = np.array([(geom.x, geom.y) for geom in gdf_points_proj.geometry])\n",
+    "\n",
+    "    # --- Compute distance matrix in km ---\n",
+    "    dist_matrix = euclidean_distances(coords_points, coords_points) / 1000\n",
+    "    np.fill_diagonal(dist_matrix, np.inf)\n",
+    "    nearest_distances = np.min(dist_matrix, axis=1)\n",
+    "    return nearest_distances.mean()\n",
+    "\n",
+    "\n",
+    "for n_samples in [1000, 2000, 5000, 10000, 20000]:\n",
+    "    avg_dist = average_dist_nearest_neighbour(n_samples)\n",
+    "    print(f\"Average distance to nearest neighbour for {n_samples} samples: {avg_dist:.2f} km\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_samples = 10000\n",
+    "points_sampled = gdf_uk.sample_points(n_samples, random_state=42).explode().reset_index(drop=True)\n",
+    "\n",
+    "gdf_s2bms_proj = gdf_s2bms.to_crs(\"EPSG:27700\")  # British National Grid (UK data)\n",
+    "gdf_points_proj = points_sampled.to_crs(\"EPSG:27700\")\n",
+    "coords_s2bms = np.array([(geom.x, geom.y) for geom in gdf_s2bms_proj.geometry])\n",
+    "coords_points = np.array([(geom.x, geom.y) for geom in gdf_points_proj.geometry])\n",
+    "dist_matrix = euclidean_distances(coords_s2bms, coords_points) / 1000\n",
+    "\n",
+    "dist_df = pd.DataFrame(dist_matrix, index=gdf_s2bms_proj.index, columns=gdf_points_proj.index)\n",
+    "\n",
+    "## Assign each sampled point to the data split of the closest S2BMS point, if the closest point is within a certain distance threshold (e.g., 5 km).\n",
+    "distance_threshold_km = 5\n",
+    "assigned_splits = []\n",
+    "\n",
+    "for point_idx in dist_df.columns:\n",
+    "    closest_s2bms_idx = dist_df[point_idx].idxmin()\n",
+    "    closest_distance_km = dist_df.loc[closest_s2bms_idx, point_idx]\n",
+    "\n",
+    "    if closest_distance_km <= distance_threshold_km:\n",
+    "        # Get the split of the closest S2BMS point\n",
+    "        split = None\n",
+    "        for split_name in split_names:\n",
+    "            if closest_s2bms_idx in gdf_splits[split_name].index:\n",
+    "                split = split_name\n",
+    "                break\n",
+    "        assigned_splits.append(split)\n",
+    "    else:\n",
+    "        assigned_splits.append(None)  # No close S2BMS point, so no split assigned\n",
+    "\n",
+    "## Assign the remaining points to new splits, based on some centroid-based clustering:\n",
+    "inds_unassigned = [i for i, split in enumerate(assigned_splits) if split is None]\n",
+    "n_centroids = int(len(inds_unassigned) / 10)  # one centroid per 10 unassigned points\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=n_centroids, random_state=42)\n",
+    "kmeans.fit(coords_points[inds_unassigned])\n",
+    "centroid_labels = kmeans.labels_\n",
+    "\n",
+    "target_ratios = {\"train\": 0.7, \"val\": 0.15, \"test\": 0.15}\n",
+    "centroid_to_split = {}\n",
+    "for centroid_label in np.unique(centroid_labels):\n",
+    "    inds_in_centroid = [\n",
+    "        inds_unassigned[i]\n",
+    "        for i in range(len(inds_unassigned))\n",
+    "        if centroid_labels[i] == centroid_label\n",
+    "    ]\n",
+    "    if len(inds_in_centroid) == 0:\n",
+    "        continue\n",
+    "    assigned_split = random.choices(\n",
+    "        population=list(target_ratios.keys()), weights=list(target_ratios.values()), k=1\n",
+    "    )[0]\n",
+    "    centroid_to_split[centroid_label] = assigned_split\n",
+    "\n",
+    "for i, centroid_label in enumerate(centroid_labels):\n",
+    "    assert assigned_splits[inds_unassigned[i]] is None  # Only assign if not already assigned\n",
+    "    assigned_splits[inds_unassigned[i]] = centroid_to_split[centroid_label]\n",
+    "\n",
+    "gdf_new_points = gpd.GeoDataFrame(\n",
+    "    {\"split\": assigned_splits}, geometry=points_sampled.geometry, crs=\"EPSG:4326\"\n",
+    ")\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "print(Counter(assigned_splits))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Sample points randomly within gdf_uk\n",
+    "\n",
+    "## convert from multipoint to geodataframe\n",
+    "\n",
+    "## calculate distance between sampled points and points in gdf_splits:\n",
+    "# distances = {}\n",
+    "# for split_name in split_names:\n",
+    "\n",
+    "colour_splits = {\n",
+    "    \"train\": \"blue\",\n",
+    "    \"val\": \"orange\",\n",
+    "    \"test\": \"green\",\n",
+    "}\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "gdf_uk.plot(ax=ax, color=\"lightgrey\", edgecolor=\"black\")\n",
+    "for split_name in split_names:\n",
+    "    gdf_new_points[gdf_new_points.split == split_name].plot(\n",
+    "        ax=ax,\n",
+    "        label=f\"sampled {split_name}\",\n",
+    "        markersize=5,\n",
+    "        alpha=0.5,\n",
+    "        color=colour_splits[split_name],\n",
+    "    )\n",
+    "    gdf_splits[split_name].plot(\n",
+    "        ax=ax, label=split_name, markersize=10, color=colour_splits[split_name], edgecolor=\"black\"\n",
+    "    )\n",
+    "# points_sampled.plot(ax=ax, label=\"sampled points\", color=\"red\", markersize=5)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf_new_points[\"lon\"] = gdf_new_points.geometry.x\n",
+    "gdf_new_points[\"lat\"] = gdf_new_points.geometry.y\n",
+    "gdf_new_points[\"coords\"] = list(zip(gdf_new_points.lon, gdf_new_points.lat))\n",
+    "gdf_new_points[\"name\"] = [\n",
+    "    f\"unlabelled-sample_{i}_{split}\" for i, split in enumerate(gdf_new_points.split)\n",
+    "]\n",
+    "gdf_new_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "save_file = False\n",
+    "if save_file:\n",
+    "    gdf_new_points.to_csv(\n",
+    "        os.path.join(os.environ[\"DATA_DIR\"], \"s2bms/source/\", \"unlabelled_samples_10k.csv\"),\n",
+    "        index=False,\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Merge csv aux files:\n",
+    "save_file = False\n",
+    "folder_csv = os.path.join(os.environ[\"DATA_DIR\"], \"s2bms/source/\")\n",
+    "assert os.path.exists(folder_csv), f\"Folder {folder_csv} does not exist\"\n",
+    "csv_files = [f for f in os.listdir(folder_csv) if f.endswith(\".csv\")]\n",
+    "aux_files = [f for f in csv_files if f.startswith(\"aux_data_unlabelled_samples\")]\n",
+    "\n",
+    "for ii, fn in enumerate(aux_files):\n",
+    "    df_aux = pd.read_csv(os.path.join(folder_csv, fn))\n",
+    "    if ii == 0:\n",
+    "        df_aux_all = df_aux\n",
+    "    else:\n",
+    "        df_aux_all = pd.concat([df_aux_all, df_aux], ignore_index=True)\n",
+    "\n",
+    "df_samples = pd.read_csv(os.path.join(folder_csv, \"unlabelled_samples_10k.csv\"))\n",
+    "\n",
+    "df_merge = pd.merge(df_samples, df_aux_all, on=\"name\", how=\"left\")\n",
+    "df_merge = df_merge.drop(columns=[\"coords_y\", \"coords_x\", \"geometry\"])\n",
+    "dict_rename = {\n",
+    "    **{\n",
+    "        col: \"aux_\" + col for col in df_merge.columns if col not in [\"name\", \"lon\", \"lat\", \"split\"]\n",
+    "    },\n",
+    "    **{\"name\": \"name_loc\"},\n",
+    "}\n",
+    "df_merge = df_merge.rename(columns=dict_rename)\n",
+    "\n",
+    "if save_file:\n",
+    "    df_merge.to_csv(\n",
+    "        os.path.join(folder_csv, \"model_ready_s2bms-unlabelled-20260529.csv\"), index=False\n",
+    "    )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aether",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ca466431d36cc004b1f3285c78de6efe275c89d0 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:07:55 +0200
Subject: [PATCH 013/173] Add ability to load unlabelled data

# Conflicts:
#	src/data/butterfly_dataset.py
---
 configs/data/butterfly_coords_text.yaml       |  1 +
 .../butterfly_coords_text_unlabelled.yaml     | 27 +++++++++++++++++++
 src/data/base_datamodule.py                   | 10 ++++++-
 src/data/base_dataset.py                      |  6 ++++-
 src/data/butterfly_dataset.py                 | 13 +++++++--
 5 files changed, 53 insertions(+), 4 deletions(-)
 create mode 100644 configs/data/butterfly_coords_text_unlabelled.yaml

diff --git a/configs/data/butterfly_coords_text.yaml b/configs/data/butterfly_coords_text.yaml
index d471997..52cae6d 100644
--- a/configs/data/butterfly_coords_text.yaml
+++ b/configs/data/butterfly_coords_text.yaml
@@ -6,6 +6,7 @@ dataset:
   data_dir: ${paths.data_dir}/
   modalities:
     coords:
+  use_unlabelled_data: false
   use_target_data: false
   use_aux_data: "all"
   seed: ${seed}
diff --git a/configs/data/butterfly_coords_text_unlabelled.yaml b/configs/data/butterfly_coords_text_unlabelled.yaml
new file mode 100644
index 0000000..e3c944e
--- /dev/null
+++ b/configs/data/butterfly_coords_text_unlabelled.yaml
@@ -0,0 +1,27 @@
+_target_: src.data.base_datamodule.BaseDataModule
+
+dataset:
+  _target_: src.data.butterfly_dataset.ButterflyDataset
+
+  data_dir: ${paths.data_dir}/
+  modalities:
+    coords:
+  use_unlabelled_data: true
+  use_target_data: false
+  use_aux_data: "all"
+  seed: ${seed}
+  cache_dir: ${paths.cache_dir}
+
+caption_builder:
+  _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+  templates_fname: v3.json
+  concepts_fname: v4.json
+  data_dir: ${paths.data_dir}/s2bms
+  seed: ${seed}
+
+batch_size: 16
+num_workers: 8
+pin_memory: true
+split_mode: "from_df"
+save_split: false
+seed: ${seed}
diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 7d80c66..a1ae875 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -2,7 +2,7 @@
 import os
 import time
 from functools import partial
-from typing import Any, Dict, List, Tuple
+from typing import Any, Tuple
 
 import numpy as np
 import pandas as pd
@@ -125,6 +125,14 @@ def split_data(self) -> None:
                     "test_indices": self.data_test.dataset.df.name_loc,
                 }
 
+        elif self.hparams.split_mode == "from_df":
+            assert hasattr(
+                self.dataset.df, "split"
+            ), "Dataset dataframe must have a 'split' column for 'from_df' split mode."
+            train_indices = self.dataset.df[self.dataset.df.split == "train"].index
+            val_indices = self.dataset.df[self.dataset.df.split == "val"].index
+            test_indices = self.dataset.df[self.dataset.df.split == "test"].index
+
         elif self.hparams.split_mode == "spatial_clusters":
             min_dist = self.hparams.spatial_split_distance_m
             coords = np.array([self.dataset.df.lat, self.dataset.df.lon]).T
diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index f09a90a..d81cee0 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -92,7 +92,11 @@ def __init__(
 
         # Set data attributes
         self.registry_path = os.path.join(data_dir, "registry.txt")
-        self.data_dir = os.path.join(data_dir, dataset_name)
+        if "unlabel" in dataset_name:
+            dataset_dirname = dataset_name.split("-unlabel")[0]
+        else:
+            dataset_dirname = dataset_name
+        self.data_dir = os.path.join(data_dir, dataset_dirname)
         self.cache_dir = cache_dir or os.path.join(data_dir, "cache")
         for d in [self.data_dir, self.cache_dir]:
             os.makedirs(d, exist_ok=True)
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index 23de9a2..ede0327 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, List, override
+from typing import Any, Dict, override
 
 import numpy as np
 import pooch
@@ -16,6 +16,7 @@ def __init__(
         self,
         data_dir: str,
         modalities: dict,
+        use_unlabelled_data: bool = False,
         use_target_data: bool = True,
         use_aux_data: Any = None,
         seed: int = 12345,
@@ -37,12 +38,20 @@ def __init__(
             used for aux, target
         """
 
+        assert not (
+            use_unlabelled_data and use_target_data
+        ), "Joint use of unlabelled and target data is not supported yet."
+        if use_unlabelled_data:
+            dataset_name = "s2bms-unlabelled-20260529"
+        else:
+            dataset_name = "s2bms"
+
         super().__init__(
             data_dir=data_dir,
             modalities=modalities,
             use_target_data=use_target_data,
             use_aux_data=use_aux_data,
-            dataset_name="s2bms",
+            dataset_name=dataset_name,
             seed=seed,
             cache_dir=cache_dir,
             implemented_mod={"s2", "tessera", "coords", "aef", "aef_avr", "tessera_avr"},

From af4f68959936aa8222889ea6ba00599d585dd556 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 10:17:02 +0200
Subject: [PATCH 014/173] adding unlabbelled s2bms data, bypassing hook

---
 .../model_ready_s2bms-unlabelled-20260529.csv | 9920 +++++++++++++++++
 1 file changed, 9920 insertions(+)
 create mode 100644 data/s2bms/model_ready_s2bms-unlabelled-20260529.csv

diff --git a/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv b/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv
new file mode 100644
index 0000000..3ed238c
--- /dev/null
+++ b/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv
@@ -0,0 +1,9920 @@
+split,lon,lat,name_loc,aux_bioclim_01,aux_bioclim_02,aux_bioclim_03,aux_bioclim_04,aux_bioclim_05,aux_bioclim_06,aux_bioclim_07,aux_bioclim_08,aux_bioclim_09,aux_bioclim_10,aux_bioclim_11,aux_bioclim_12,aux_bioclim_13,aux_bioclim_14,aux_bioclim_15,aux_bioclim_16,aux_bioclim_17,aux_bioclim_18,aux_bioclim_19,aux_corine_frac_111,aux_corine_frac_112,aux_corine_frac_121,aux_corine_frac_122,aux_corine_frac_123,aux_corine_frac_124,aux_corine_frac_131,aux_corine_frac_132,aux_corine_frac_133,aux_corine_frac_141,aux_corine_frac_142,aux_corine_frac_211,aux_corine_frac_212,aux_corine_frac_213,aux_corine_frac_221,aux_corine_frac_222,aux_corine_frac_223,aux_corine_frac_231,aux_corine_frac_241,aux_corine_frac_242,aux_corine_frac_243,aux_corine_frac_244,aux_corine_frac_311,aux_corine_frac_312,aux_corine_frac_313,aux_corine_frac_321,aux_corine_frac_322,aux_corine_frac_323,aux_corine_frac_324,aux_corine_frac_331,aux_corine_frac_332,aux_corine_frac_333,aux_corine_frac_334,aux_corine_frac_335,aux_corine_frac_411,aux_corine_frac_412,aux_corine_frac_421,aux_corine_frac_422,aux_corine_frac_423,aux_corine_frac_511,aux_corine_frac_512,aux_corine_frac_521,aux_corine_frac_522,aux_corine_frac_523,aux_corine_frac_1,aux_corine_frac_2,aux_corine_frac_3,aux_corine_frac_4,aux_corine_frac_5,aux_corine_frac_11,aux_corine_frac_12,aux_corine_frac_13,aux_corine_frac_14,aux_corine_frac_21,aux_corine_frac_22,aux_corine_frac_23,aux_corine_frac_24,aux_corine_frac_31,aux_corine_frac_32,aux_corine_frac_33,aux_corine_frac_41,aux_corine_frac_42,aux_corine_frac_51,aux_corine_frac_52,aux_total_population,aux_pop_density,aux_maxdist_road,aux_meandist_road,aux_corine_frac_lowlevel_top_1,aux_corine_frac_lowlevel_top_2,aux_corine_frac_lowlevel_top_3,aux_corine_frac_lowlevel_top_4,aux_corine_frac_lowlevel_top_5,aux_corine_frac_midlevel_top_1,aux_corine_frac_midlevel_top_2,aux_corine_frac_midlevel_top_3,aux_corine_frac_highlevel_top_1,aux_corine_frac_highlevel_top_2,aux_corine_frac_highlevel_top_3
+train,-8.096958260447545,54.45894114090544,unlabelled-sample_0_train,8.9,6.5,40.0,35.57,17.6,1.5,16.1,5.300000000000001,10.0,13.5,4.6000000000000005,1320.0,155.0,75.0,23.0,432.0,237.0,274.0,398.0,0.0,0.0013651037843966,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1134066068721112,0.0,0.0,0.6152662018323722,0.0,0.0,0.0595889293686033,0.0,0.0,0.0,0.0,0.0398470503452985,0.0,0.0,0.0,0.0,0.0,0.0,0.1705261077972178,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0013651037843966,0.7286728087044835,0.0994359797139019,0.1705261077972178,0.0,0.0013651037843966,0.0,0.0,0.0,0.0,0.0,0.1134066068721112,0.6152662018323722,0.0595889293686033,0.0398470503452985,0.0,0.1705261077972178,0.0,0.0,0.0,175.0,26.0,1926.0,686.0,corine_frac_243,corine_frac_412,corine_frac_231,corine_frac_312,corine_frac_324,corine_frac_24,corine_frac_41,corine_frac_23,corine_frac_2,corine_frac_4,corine_frac_3
+train,-8.057715240394087,54.48539534806826,unlabelled-sample_1_train,8.6,6.5,40.0,35.76,17.400000000000002,1.3,16.1,4.9,9.7,13.2,4.3,1343.0,158.0,76.0,23.0,441.0,240.0,277.0,407.0,0.0,0.012026502259188,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5562368499285384,0.0,0.0,0.0168652429744439,0.0,0.0,0.0,0.0,0.0,0.0838650912448677,0.0,0.0796763717843416,0.0,0.0,0.0,0.0,0.0,0.0,0.179118593239623,0.0,0.0,0.0,0.0470525345823237,0.0251588139866733,0.0,0.0,0.0,0.012026502259188,0.5731020929029824,0.1635414630292094,0.179118593239623,0.0722113485689971,0.012026502259188,0.0,0.0,0.0,0.0,0.0,0.5562368499285384,0.0168652429744439,0.0,0.1635414630292094,0.0,0.179118593239623,0.0,0.0722113485689971,0.0,132.0,20.0,1613.0,640.0,corine_frac_231,corine_frac_412,corine_frac_322,corine_frac_324,corine_frac_511,corine_frac_23,corine_frac_41,corine_frac_32,corine_frac_2,corine_frac_4,corine_frac_3
+train,-8.031894965557141,54.48896517200197,unlabelled-sample_2_train,8.6,6.5,40.0,35.63,17.5,1.3,16.2,5.0,9.8,13.3,4.4,1337.0,158.0,76.0,23.0,440.0,240.0,276.0,406.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5182388990004458,0.0,0.0,0.0645901570354962,0.0,0.0,0.0,0.0,0.0,0.0090200707589826,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3486238614607291,0.0,0.0,0.0,0.0,0.059527011744346,0.0,0.0,0.0,0.0,0.5828290560359422,0.0090200707589826,0.3486238614607291,0.059527011744346,0.0,0.0,0.0,0.0,0.0,0.0,0.5182388990004458,0.0645901570354962,0.0,0.0090200707589826,0.0,0.3486238614607291,0.0,0.059527011744346,0.0,71.0,10.0,1932.0,738.0,corine_frac_231,corine_frac_412,corine_frac_243,corine_frac_512,corine_frac_322,corine_frac_23,corine_frac_41,corine_frac_24,corine_frac_2,corine_frac_4,corine_frac_5
+train,-8.006842924418319,54.480716585892914,unlabelled-sample_3_train,8.9,6.5,40.0,35.46,17.7,1.6,16.1,5.300000000000001,10.0,13.5,4.6000000000000005,1308.0,154.0,74.0,23.0,429.0,235.0,270.0,396.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5869727402296303,0.0,0.0,0.0198979987920515,0.0,0.0,0.0187945993013082,0.0361047108492057,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0134203975439379,0.3248095532838662,0.0,0.0,0.0,0.0,0.6068707390216819,0.0548993101505139,0.0,0.3382299508278042,0.0,0.0,0.0,0.0,0.0,0.0,0.5869727402296303,0.0198979987920515,0.0548993101505139,0.0,0.0,0.0,0.0,0.3382299508278042,0.0,92.0,14.0,1064.0,452.0,corine_frac_231,corine_frac_512,corine_frac_313,corine_frac_243,corine_frac_312,corine_frac_23,corine_frac_51,corine_frac_31,corine_frac_2,corine_frac_5,corine_frac_3
+train,-7.977071177671213,54.39116061204077,unlabelled-sample_4_train,8.4,6.7,40.0,36.12,17.400000000000002,1.0,16.400000000000002,4.800000000000001,9.6,13.1,4.1000000000000005,1338.0,157.0,77.0,22.0,437.0,244.0,277.0,405.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2049513568251426,0.0,0.3807642880090316,0.0,0.0,0.2846290708755841,0.0,0.0,0.0,0.0,0.0,0.0,0.1296552842902416,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8703447157097584,0.1296552842902416,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2049513568251426,0.6653933588846158,0.0,0.1296552842902416,0.0,0.0,0.0,12.0,1.0,1586.0,729.0,corine_frac_321,corine_frac_324,corine_frac_312,corine_frac_412,corine_frac_112,corine_frac_32,corine_frac_31,corine_frac_41,corine_frac_3,corine_frac_4,corine_frac_2
+train,-7.901483551542825,54.44563049147996,unlabelled-sample_5_train,7.9,6.6000000000000005,40.0,36.46,17.0,0.6000000000000001,16.400000000000002,4.2,9.1,12.7,3.6,1379.0,162.0,80.0,22.0,451.0,252.0,285.0,420.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2422496838391828,0.0,0.2532582428412417,0.0462879458922592,0.0,0.4582041274273161,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2422496838391828,0.7577503161608172,0.0,0.0,0.0,0.0,0.0,30.0,4.0,2117.0,978.0,corine_frac_324,corine_frac_321,corine_frac_312,corine_frac_322,corine_frac_212,corine_frac_32,corine_frac_31,corine_frac_14,corine_frac_3,corine_frac_4,corine_frac_2
+train,-7.900407049855181,54.49425478167862,unlabelled-sample_6_train,8.9,6.5,40.0,35.980000000000004,17.8,1.6,16.2,5.300000000000001,10.1,13.6,4.6000000000000005,1280.0,150.0,72.0,23.0,420.0,230.0,265.0,388.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0009337841697896,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9990662158302104,0.0,0.0,0.0,0.0,0.0009337841697896,0.0,0.0,0.9990662158302104,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0009337841697896,0.0,0.0,0.0,0.0,0.0,0.9990662158302104,0.0,0.0,0.0,2157.0,1376.0,corine_frac_512,corine_frac_243,corine_frac_334,corine_frac_324,corine_frac_111,corine_frac_51,corine_frac_24,corine_frac_12,corine_frac_5,corine_frac_2,corine_frac_1
+train,-7.894263646778579,54.403693418280625,unlabelled-sample_7_train,7.800000000000001,6.7,40.0,36.78,16.900000000000002,0.4,16.5,4.1000000000000005,9.0,12.6,3.4000000000000004,1388.0,163.0,81.0,22.0,453.0,255.0,287.0,423.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1507321552555441,0.0,0.442331705842075,0.4069361389023808,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1507321552555441,0.8492678447444558,0.0,0.0,0.0,0.0,0.0,16.0,2.0,940.0,399.0,corine_frac_321,corine_frac_322,corine_frac_312,corine_frac_335,corine_frac_211,corine_frac_32,corine_frac_31,corine_frac_42,corine_frac_3,corine_frac_2,corine_frac_5
+train,-7.874660928559555,54.381273477593915,unlabelled-sample_8_train,7.4,6.7,40.0,36.71,16.6,0.0,16.6,3.6,8.5,12.2,3.0,1418.0,166.0,84.0,22.0,461.0,263.0,294.0,431.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0003941556813629,0.0,0.0,0.1397449444063645,0.0,0.4391635243117024,0.3296632393806065,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0910341362199635,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0003941556813629,0.9085717080986734,0.0910341362199635,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0003941556813629,0.1397449444063645,0.768826763692309,0.0,0.0910341362199635,0.0,0.0,0.0,21.0,3.0,3369.0,1993.0,corine_frac_321,corine_frac_322,corine_frac_312,corine_frac_412,corine_frac_243,corine_frac_32,corine_frac_31,corine_frac_41,corine_frac_3,corine_frac_4,corine_frac_2
+train,-7.870675177991031,54.704574357243246,unlabelled-sample_9_train,7.6,6.300000000000001,39.0,35.96,16.3,0.4,15.9,3.9,8.6,12.2,3.2,1408.0,165.0,82.0,23.0,461.0,255.0,292.0,427.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8999093778300697,0.0,0.0,0.0,0.0,0.1000906221699302,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8999093778300697,0.1000906221699302,0.0,0.0,0.0,0.0,0.0,27.0,4.0,4999.0,4442.0,corine_frac_312,corine_frac_324,corine_frac_213,corine_frac_133,corine_frac_411,corine_frac_31,corine_frac_32,corine_frac_12,corine_frac_3,corine_frac_1,corine_frac_2
+train,-7.796500781270221,54.63601108737685,unlabelled-sample_10_train,8.200000000000001,6.4,40.0,36.08,17.0,1.0,16.0,4.6000000000000005,9.4,12.9,3.9,1312.0,153.0,75.0,23.0,429.0,237.0,273.0,397.0,0.0,0.0,0.0856137407149008,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3945017503062643,0.0,0.0,0.0,0.0,0.0086156105175786,0.0,0.0,0.0,0.0,0.0,0.0,0.5112688984612562,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0856137407149008,0.0,0.403117360823843,0.5112688984612562,0.0,0.0,0.0856137407149008,0.0,0.0,0.0,0.0,0.0,0.0,0.3945017503062643,0.0086156105175786,0.0,0.5112688984612562,0.0,0.0,0.0,14.0,2.0,4999.0,4289.0,corine_frac_412,corine_frac_312,corine_frac_121,corine_frac_324,corine_frac_131,corine_frac_41,corine_frac_31,corine_frac_12,corine_frac_4,corine_frac_3,corine_frac_1
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+train,-5.723001232424415,57.43859733954579,unlabelled-sample_937_train,6.6000000000000005,5.9,35.0,39.52,15.5,-1.1,16.6,2.6,8.1,11.9,1.9,1706.0,208.0,86.0,28.0,582.0,281.0,327.0,521.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0196391947545147,0.0,0.0,0.0,0.0,0.0,0.0424485318160559,0.0,0.0,0.0,0.9379122734294294,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0620877265705706,0.9379122734294294,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0196391947545147,0.0424485318160559,0.9379122734294294,0.0,0.0,0.0,1.0,0.0,2884.0,1248.0,corine_frac_412,corine_frac_333,corine_frac_321,corine_frac_131,corine_frac_423,corine_frac_41,corine_frac_33,corine_frac_32,corine_frac_4,corine_frac_3,corine_frac_2
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+test,-5.713397054209106,56.807727261437606,unlabelled-sample_942_test,6.7,6.0,35.0,41.47,15.9,-1.2000000000000002,17.1,2.5,8.4,12.2,1.7000000000000002,2003.0,245.0,98.0,29.0,687.0,324.0,381.0,617.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0023083735009165,0.1341908871824071,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8635007393166763,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1364992606833236,0.8635007393166763,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0023083735009165,0.1341908871824071,0.0,0.8635007393166763,0.0,0.0,0.0,1.0,0.0,4108.0,2920.0,corine_frac_412,corine_frac_321,corine_frac_313,corine_frac_411,corine_frac_112,corine_frac_41,corine_frac_32,corine_frac_31,corine_frac_4,corine_frac_3,corine_frac_2
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+test,-4.971962700238833,50.26777129067146,unlabelled-sample_1507_test,10.2,5.7,33.0,42.02,19.5,2.4000000000000004,17.1,6.0,14.0,15.7,5.1000000000000005,1052.0,127.0,58.0,29.0,371.0,177.0,188.0,347.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.865840708983767,0.0,0.0,0.0,0.0,0.0,0.0031407233734755,0.0,0.0,0.0,0.0,0.0159831335099246,0.0,0.1150354341328327,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8689814323572426,0.1310185676427574,0.0,0.0,0.0,0.0,0.0,0.0,0.865840708983767,0.0,0.0031407233734755,0.0,0.1310185676427574,0.0,0.0,0.0,0.0,0.0,0.0,27.0,4.0,2475.0,1183.0,corine_frac_211,corine_frac_313,corine_frac_311,corine_frac_231,corine_frac_335,corine_frac_21,corine_frac_31,corine_frac_23,corine_frac_2,corine_frac_3,corine_frac_5
+val,-4.969922984247881,58.599367197731034,unlabelled-sample_1508_val,8.4,6.0,37.0,36.35,17.400000000000002,1.2000000000000002,16.2,6.7,9.4,13.4,4.3,1056.0,116.0,58.0,24.0,345.0,181.0,216.0,304.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9979808568534756,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0020191431465243,0.0,0.0,0.0,0.9979808568534756,0.0020191431465243,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9979808568534756,0.0,0.0,0.0020191431465243,0.0,0.0,5000.0,5000.0,corine_frac_412,corine_frac_523,corine_frac_335,corine_frac_331,corine_frac_313,corine_frac_41,corine_frac_52,corine_frac_33,corine_frac_4,corine_frac_5,corine_frac_3
+test,-4.969486118965985,54.84952193844149,unlabelled-sample_1509_test,8.6,6.6000000000000005,36.0,42.07,18.2,0.1,18.1,9.3,10.3,14.0,3.4000000000000004,1085.0,119.0,62.0,24.0,350.0,192.0,240.0,291.0,0.0,0.0,0.0054194240894107,0.0,0.0,0.0104672500455733,0.0,0.0,0.0,0.0,0.0,0.7924527646098111,0.0,0.0,0.0,0.0,0.0,0.1782516274871637,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.013408933768041,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0158866741349841,0.9707043920969748,0.0,0.013408933768041,0.0,0.0,0.0158866741349841,0.0,0.0,0.7924527646098111,0.0,0.1782516274871637,0.0,0.0,0.0,0.0,0.013408933768041,0.0,0.0,0.0,51.0,7.0,1011.0,360.0,corine_frac_211,corine_frac_231,corine_frac_412,corine_frac_124,corine_frac_121,corine_frac_21,corine_frac_23,corine_frac_12,corine_frac_2,corine_frac_1,corine_frac_4
+train,-4.96917543956037,56.43579599757162,unlabelled-sample_1510_train,7.9,6.5,34.0,44.56,18.0,-0.7000000000000001,18.7,3.3000000000000003,10.1,13.9,2.4000000000000004,1883.0,234.0,94.0,30.0,650.0,296.0,346.0,599.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0044181741871658,0.0,0.0,0.0,0.0,0.0,0.1408917773471689,0.0,0.56636864314229,0.1467643048955342,0.0,0.0397180985490493,0.0,0.0,0.0,0.0,0.0,0.0,0.1018390018787917,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0044181741871658,0.8937428239340424,0.1018390018787917,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0044181741871658,0.0,0.1408917773471689,0.7528510465868735,0.0,0.1018390018787917,0.0,0.0,0.0,1.0,0.0,4506.0,2923.0,corine_frac_321,corine_frac_322,corine_frac_312,corine_frac_412,corine_frac_324,corine_frac_32,corine_frac_31,corine_frac_41,corine_frac_3,corine_frac_4,corine_frac_2
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+val,-4.965700499356473,58.561382903493296,unlabelled-sample_1512_val,7.9,6.0,36.0,36.49,17.0,0.6000000000000001,16.400000000000002,6.2,8.8,12.9,3.8,1099.0,122.0,61.0,24.0,359.0,190.0,225.0,318.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0418246328500717,0.0,0.0,0.0,0.9581753671499282,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0418246328500717,0.9581753671499282,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0418246328500717,0.9581753671499282,0.0,0.0,0.0,0.0,0.0,5000.0,5000.0,corine_frac_412,corine_frac_333,corine_frac_222,corine_frac_212,corine_frac_321,corine_frac_41,corine_frac_33,corine_frac_11,corine_frac_4,corine_frac_3,corine_frac_2
+train,-4.965649888070034,58.07560833679343,unlabelled-sample_1513_train,7.800000000000001,6.2,36.0,39.52,17.0,-0.1,17.1,5.800000000000001,9.1,13.1,3.1,1127.0,126.0,63.0,25.0,369.0,194.0,232.0,326.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0308020187371346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1669937724144092,0.0,0.0,0.0,0.0,0.01496919025577,0.0,0.0,0.0,0.7856586587442639,0.0,0.0,0.0,0.0,0.0015763598484222,0.0,0.0,0.0,0.0,0.0308020187371346,0.1819629626701793,0.7856586587442639,0.0015763598484222,0.0,0.0,0.0,0.0,0.0,0.0,0.0308020187371346,0.0,0.0,0.1669937724144092,0.01496919025577,0.7856586587442639,0.0,0.0015763598484222,0.0,45.0,6.0,1736.0,628.0,corine_frac_412,corine_frac_322,corine_frac_231,corine_frac_333,corine_frac_512,corine_frac_41,corine_frac_32,corine_frac_23,corine_frac_4,corine_frac_3,corine_frac_2
+test,-4.9653781055367725,54.84657207152494,unlabelled-sample_1514_test,8.6,6.6000000000000005,36.0,42.04,18.2,0.1,18.1,9.3,10.3,14.1,3.4000000000000004,1078.0,118.0,62.0,24.0,349.0,191.0,238.0,289.0,0.0,0.0,0.02382939203047,0.0,0.0,0.0193298114204348,0.0,0.0,0.0,0.0,0.0,0.7864812789437353,0.0,0.0,0.0,0.0,0.0,0.1328214041153251,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0375381134900346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0431592034509049,0.9193026830590604,0.0,0.0375381134900346,0.0,0.0,0.0431592034509049,0.0,0.0,0.7864812789437353,0.0,0.1328214041153251,0.0,0.0,0.0,0.0,0.0375381134900346,0.0,0.0,0.0,134.0,20.0,1011.0,405.0,corine_frac_211,corine_frac_231,corine_frac_412,corine_frac_121,corine_frac_124,corine_frac_21,corine_frac_23,corine_frac_12,corine_frac_2,corine_frac_1,corine_frac_4
+train,-4.964023460641854,56.47856137365169,unlabelled-sample_1515_train,5.9,6.5,34.0,45.46,16.3,-2.7,19.0,1.2000000000000002,7.9,12.0,0.3,2113.0,267.0,107.0,30.0,734.0,341.0,386.0,683.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0160016524513358,0.5331767466357905,0.0,0.0,0.0,0.0,0.0701935376117189,0.0,0.0,0.0,0.3806280633011547,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6193719366988453,0.3806280633011547,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5491783990871263,0.0701935376117189,0.3806280633011547,0.0,0.0,0.0,0.0,0.0,5000.0,5000.0,corine_frac_322,corine_frac_412,corine_frac_333,corine_frac_321,corine_frac_132,corine_frac_32,corine_frac_41,corine_frac_33,corine_frac_3,corine_frac_4,corine_frac_1
+train,-4.963827101946062,55.71723884356622,unlabelled-sample_1516_train,8.1,6.6000000000000005,36.0,42.52,17.900000000000002,-0.1,18.0,3.7,10.1,13.8,2.9000000000000004,1548.0,173.0,84.0,26.0,504.0,257.0,311.0,451.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3586349131160355,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6413650868839644,0.0,0.0,0.3586349131160355,0.0,0.6413650868839644,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3586349131160355,0.0,0.0,0.0,0.6413650868839644,0.0,0.0,4994.0,3287.0,corine_frac_523,corine_frac_333,corine_frac_131,corine_frac_242,corine_frac_222,corine_frac_52,corine_frac_33,corine_frac_31,corine_frac_5,corine_frac_3,corine_frac_4
+test,-4.963664067206131,57.77703601757713,unlabelled-sample_1517_test,4.3,6.2,34.0,42.3,14.1,-3.8,17.900000000000002,0.2,5.7,10.1,-0.7000000000000001,1615.0,193.0,89.0,25.0,541.0,279.0,317.0,497.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0235479040078193,0.3647876547771946,0.0,0.0,0.0,0.6116644412149861,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3883355587850139,0.6116644412149861,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3883355587850139,0.6116644412149861,0.0,0.0,0.0,1.0,0.0,4999.0,3486.0,corine_frac_412,corine_frac_333,corine_frac_332,corine_frac_421,corine_frac_141,corine_frac_41,corine_frac_33,corine_frac_23,corine_frac_4,corine_frac_3,corine_frac_2
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+test,-4.962191237967779,57.79088349714012,unlabelled-sample_1519_test,4.7,6.2,34.0,42.18,14.5,-3.4000000000000004,17.900000000000002,0.6000000000000001,6.2,10.5,-0.3,1556.0,185.0,86.0,25.0,521.0,269.0,306.0,476.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0454622414381582,0.4394434192782053,0.0,0.0,0.0,0.5150943392836363,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4849056607163636,0.5150943392836363,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4849056607163636,0.5150943392836363,0.0,0.0,0.0,0.0,0.0,4999.0,4159.0,corine_frac_412,corine_frac_333,corine_frac_332,corine_frac_112,corine_frac_421,corine_frac_41,corine_frac_33,corine_frac_42,corine_frac_4,corine_frac_3,corine_frac_2
+train,-4.961346350979111,58.29272307064754,unlabelled-sample_1520_train,7.0,6.1000000000000005,36.0,38.37,16.3,-0.5,16.8,3.4000000000000004,8.200000000000001,12.3,2.6,1187.0,134.0,66.0,24.0,388.0,205.0,242.0,348.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1712282983693873,0.0,0.0,0.0,0.0,0.0,0.123261968197391,0.0,0.0,0.0,0.6888251311634546,0.0,0.0,0.0,0.0,0.016684602269767,0.0,0.0,0.0,0.0,0.0,0.2944902665667784,0.6888251311634546,0.016684602269767,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1712282983693873,0.123261968197391,0.6888251311634546,0.0,0.016684602269767,0.0,0.0,0.0,4445.0,3303.0,corine_frac_412,corine_frac_321,corine_frac_333,corine_frac_512,corine_frac_335,corine_frac_41,corine_frac_32,corine_frac_33,corine_frac_4,corine_frac_3,corine_frac_5
+train,-4.9597420672363315,51.935216034243126,unlabelled-sample_1521_train,9.0,5.300000000000001,31.0,42.94,18.4,1.4,17.0,7.0,10.4,14.8,4.0,953.0,109.0,57.0,25.0,321.0,171.0,193.0,274.0,0.0,0.0028017635652372,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9955087596934598,0.0,0.0,0.0,0.0,0.001689476741303,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0028017635652372,0.9955087596934598,0.001689476741303,0.0,0.0,0.0028017635652372,0.0,0.0,0.0,0.0,0.0,0.9955087596934598,0.0,0.001689476741303,0.0,0.0,0.0,0.0,0.0,0.0,101.0,15.0,2151.0,914.0,corine_frac_231,corine_frac_112,corine_frac_311,corine_frac_522,corine_frac_512,corine_frac_23,corine_frac_11,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
+test,-4.956833844034762,56.99623068594042,unlabelled-sample_1522_test,5.4,6.4,33.0,45.92,15.9,-3.3000000000000003,19.200000000000003,0.8,7.4,11.7,-0.2,1905.0,240.0,98.0,29.0,660.0,310.0,352.0,611.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.274061767027719,0.342818007503216,0.0,0.0,0.0,0.0,0.170668607592192,0.0,0.0,0.0,0.2124516178768729,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7875483821231272,0.2124516178768729,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6168797745309351,0.170668607592192,0.2124516178768729,0.0,0.0,0.0,0.0,0.0,4999.0,4251.0,corine_frac_322,corine_frac_321,corine_frac_412,corine_frac_333,corine_frac_332,corine_frac_32,corine_frac_41,corine_frac_33,corine_frac_3,corine_frac_4,corine_frac_5
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+test,-4.722225796162161,50.462260258841134,unlabelled-sample_1807_test,10.2,5.7,33.0,42.49,19.6,2.4000000000000004,17.2,6.0,11.5,15.7,5.1000000000000005,1032.0,122.0,59.0,27.0,357.0,180.0,193.0,330.0,0.0,0.5182018192791326,0.0028732279496491,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0017279078685883,0.2919169723622921,0.0,0.0,0.0,0.0,0.0,0.1841232350730328,0.0,0.0,0.0,0.0,0.0011568374673051,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.52280295509737,0.4760402074353249,0.0011568374673051,0.0,0.0,0.5182018192791326,0.0028732279496491,0.0,0.0017279078685883,0.2919169723622921,0.0,0.1841232350730328,0.0,0.0011568374673051,0.0,0.0,0.0,0.0,0.0,0.0,10483.0,1608.0,1154.0,218.0,corine_frac_112,corine_frac_211,corine_frac_231,corine_frac_121,corine_frac_142,corine_frac_11,corine_frac_21,corine_frac_23,corine_frac_1,corine_frac_2,corine_frac_3
+val,-4.7219584496069595,50.340220445206235,unlabelled-sample_1808_val,10.5,5.7,33.0,41.81,19.8,2.8000000000000003,17.0,6.4,14.4,16.0,5.5,1002.0,119.0,57.0,27.0,348.0,173.0,185.0,324.0,0.0,0.1598410794980432,0.0985840406431444,0.0,0.037235040015069,0.0,0.0,0.0,0.0,0.0,0.1621560206767018,0.0688057626978332,0.0,0.0,0.0,0.0,0.0,0.0609688559727461,0.0,0.0,0.025120561104789,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3872886393916731,0.4578161808329585,0.1548951797753684,0.0,0.0,0.3872886393916731,0.1598410794980432,0.1358190806582134,0.0,0.1621560206767018,0.0688057626978332,0.0,0.0609688559727461,0.025120561104789,0.0,0.0,0.0,0.0,0.0,0.0,0.3872886393916731,3171.0,486.0,1587.0,373.0,corine_frac_523,corine_frac_142,corine_frac_112,corine_frac_121,corine_frac_211,corine_frac_52,corine_frac_14,corine_frac_11,corine_frac_1,corine_frac_5,corine_frac_2
+test,-4.721511909965641,57.73555717087828,unlabelled-sample_1809_test,6.7,6.4,34.0,43.45,16.6,-1.8,18.4,2.4000000000000004,8.3,12.6,1.5,1194.0,135.0,66.0,24.0,390.0,207.0,248.0,349.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0632472535354469,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0595056588415299,0.0,0.0,0.0,0.0,0.0,0.0,0.8772470876230231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0632472535354469,0.0595056588415299,0.8772470876230231,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0632472535354469,0.0,0.0,0.0595056588415299,0.0,0.8772470876230231,0.0,0.0,0.0,1.0,0.0,4999.0,4075.0,corine_frac_412,corine_frac_231,corine_frac_324,corine_frac_244,corine_frac_323,corine_frac_41,corine_frac_23,corine_frac_32,corine_frac_4,corine_frac_2,corine_frac_3
+test,-4.720900390400221,55.26504880385152,unlabelled-sample_1810_test,7.800000000000001,6.7,36.0,43.33,17.7,-0.7000000000000001,18.4,5.5,9.8,13.5,2.5,1287.0,142.0,72.0,25.0,419.0,222.0,270.0,354.0,0.0,0.0207978084331386,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0646439473899786,0.0,0.0,0.0,0.0,0.0,0.0,0.3404001105275713,0.0,0.0,0.0,0.0,0.0028119091480621,0.0,0.04053666397419,0.0899573021037212,0.4408522584233379,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0854417558231172,0.3404001105275713,0.5741581336493113,0.0,0.0,0.0207978084331386,0.0,0.0,0.0646439473899786,0.0,0.0,0.3404001105275713,0.0,0.0433485731222522,0.5308095605270592,0.0,0.0,0.0,0.0,0.0,549.0,84.0,1951.0,690.0,corine_frac_322,corine_frac_231,corine_frac_321,corine_frac_142,corine_frac_313,corine_frac_32,corine_frac_23,corine_frac_14,corine_frac_3,corine_frac_2,corine_frac_1
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+test,-4.719834818622368,55.799870293880346,unlabelled-sample_1812_test,7.1000000000000005,6.7,36.0,43.47,17.2,-1.4,18.6,2.6,9.1,12.9,1.7000000000000002,1641.0,190.0,90.0,26.0,543.0,274.0,324.0,490.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1688708970465356,7.23228920528406e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8310567800614116,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1689432199385884,0.8310567800614116,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1689432199385884,0.0,0.8310567800614116,0.0,0.0,0.0,1.0,0.0,4998.0,3584.0,corine_frac_412,corine_frac_321,corine_frac_322,corine_frac_522,corine_frac_142,corine_frac_41,corine_frac_32,corine_frac_24,corine_frac_4,corine_frac_3,corine_frac_1
+train,-4.718045868196839,52.01917723708401,unlabelled-sample_1813_train,9.5,5.300000000000001,32.0,40.13,18.3,2.0,16.3,7.800000000000001,10.7,14.8,4.800000000000001,910.0,104.0,54.0,25.0,309.0,165.0,188.0,254.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0667713123852801,0.0,0.0,0.0,0.0,0.0,0.7472192437144057,0.0,0.0,0.0205602691636919,0.0,0.1654491747366221,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8345508252633778,0.1654491747366221,0.0,0.0,0.0,0.0,0.0,0.0,0.0667713123852801,0.0,0.7472192437144057,0.0205602691636919,0.1654491747366221,0.0,0.0,0.0,0.0,0.0,0.0,232.0,35.0,1712.0,760.0,corine_frac_231,corine_frac_311,corine_frac_211,corine_frac_243,corine_frac_141,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_5
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+val,-4.717013722059181,58.556927285753865,unlabelled-sample_1815_val,8.5,6.1000000000000005,37.0,36.53,17.3,1.0,16.3,6.7,9.5,13.4,4.3,976.0,107.0,55.0,23.0,316.0,169.0,206.0,275.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2908589669668258,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.043888626852021,0.0,0.0,0.0,0.5201932610399036,0.0,0.0,0.0,0.0,7.186384388681827e-07,0.0,0.0,0.1450584265028106,0.0,0.2908589669668258,0.043888626852021,0.5201932610399036,0.1450591451412495,0.0,0.0,0.0,0.0,0.0,0.0,0.2908589669668258,0.0,0.0,0.0,0.043888626852021,0.5201932610399036,0.0,7.186384388681827e-07,0.1450584265028106,30.0,4.0,1658.0,550.0,corine_frac_412,corine_frac_231,corine_frac_523,corine_frac_333,corine_frac_512,corine_frac_41,corine_frac_23,corine_frac_52,corine_frac_4,corine_frac_2,corine_frac_5
+val,-4.716239366214617,51.66596121331703,unlabelled-sample_1816_val,10.4,5.4,31.0,42.44,19.5,2.6,16.900000000000002,8.4,11.8,15.9,5.300000000000001,897.0,105.0,51.0,26.0,304.0,158.0,185.0,255.0,0.0,0.24170481135874,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2039104711302762,0.0,0.0,0.0,0.0,0.0,0.0,0.2958223571014857,0.0,0.0,0.0,0.0,0.0135699869490639,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0753538931454457,0.0,0.0,0.0,0.0,0.1696384803149883,0.4456152824890163,0.2958223571014857,0.0135699869490639,0.0753538931454457,0.1696384803149883,0.24170481135874,0.0,0.0,0.2039104711302762,0.0,0.0,0.2958223571014857,0.0,0.0135699869490639,0.0,0.0,0.0,0.0753538931454457,0.0,0.1696384803149883,4010.0,615.0,1486.0,267.0,corine_frac_231,corine_frac_112,corine_frac_142,corine_frac_523,corine_frac_423,corine_frac_23,corine_frac_11,corine_frac_14,corine_frac_1,corine_frac_2,corine_frac_5
+val,-4.715926219121865,50.661370541029896,unlabelled-sample_1817_val,10.4,5.6000000000000005,32.0,42.46,19.700000000000003,2.6,17.1,6.2,11.8,15.9,5.2,982.0,113.0,57.0,26.0,336.0,173.0,190.0,305.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3221556111821689,0.0,0.0,0.0,0.0,0.0,0.592763622890111,0.0,0.0,0.0,0.0,0.0777643220051109,0.0,0.0,0.0,0.0073105654781631,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.87844444603069e-06,0.0,0.9149192340722798,0.0850748874832741,0.0,5.87844444603069e-06,0.0,0.0,0.0,0.0,0.3221556111821689,0.0,0.592763622890111,0.0,0.0777643220051109,0.0073105654781631,0.0,0.0,0.0,0.0,5.87844444603069e-06,191.0,29.0,1260.0,409.0,corine_frac_231,corine_frac_211,corine_frac_311,corine_frac_322,corine_frac_523,corine_frac_23,corine_frac_21,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
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+train,-4.710362774287434,58.04318818402942,unlabelled-sample_1824_train,7.2,6.300000000000001,36.0,40.52,16.7,-0.8,17.5,5.1000000000000005,8.6,12.7,2.4000000000000004,1084.0,119.0,61.0,23.0,348.0,191.0,231.0,309.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0033803431012312,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9966196568987687,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0033803431012312,0.9966196568987687,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0033803431012312,0.0,0.9966196568987687,0.0,0.0,0.0,0.0,0.0,5000.0,5000.0,corine_frac_412,corine_frac_321,corine_frac_231,corine_frac_221,corine_frac_511,corine_frac_41,corine_frac_32,corine_frac_11,corine_frac_4,corine_frac_3,corine_frac_2
+train,-4.709520113688294,51.92922896758911,unlabelled-sample_1825_train,9.1,5.300000000000001,31.0,41.36,18.1,1.4,16.7,7.2,10.3,14.5,4.1000000000000005,991.0,113.0,59.0,25.0,335.0,179.0,202.0,283.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8286057456904444,0.0,0.0,0.0,0.0,0.0,0.0004722480367824,0.0,0.1704403432592573,0.0004816630135157,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8286057456904444,0.1713942543095555,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8286057456904444,0.0,0.0004722480367824,0.1709220062727731,0.0,0.0,0.0,0.0,0.0,160.0,24.0,1471.0,364.0,corine_frac_231,corine_frac_321,corine_frac_322,corine_frac_312,corine_frac_132,corine_frac_23,corine_frac_32,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
+train,-4.7088737393224696,58.048831588455975,unlabelled-sample_1826_train,7.2,6.300000000000001,36.0,40.52,16.7,-0.8,17.5,5.1000000000000005,8.6,12.7,2.4000000000000004,1084.0,119.0,61.0,23.0,348.0,191.0,231.0,309.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0050544080606042,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0494900882010407,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9454555037383552,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0050544080606042,0.0494900882010407,0.9454555037383552,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0050544080606042,0.0,0.0,0.0494900882010407,0.0,0.9454555037383552,0.0,0.0,0.0,0.0,0.0,5000.0,5000.0,corine_frac_412,corine_frac_321,corine_frac_231,corine_frac_123,corine_frac_241,corine_frac_41,corine_frac_32,corine_frac_23,corine_frac_4,corine_frac_3,corine_frac_2
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+val,-4.707820921161719,56.08264867384007,unlabelled-sample_1828_val,5.9,6.7,35.0,44.63,16.3,-2.6,18.9,1.4,7.9,11.9,0.5,1983.0,246.0,105.0,28.0,681.0,327.0,369.0,633.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6935452743557826,0.0,0.0,0.0,0.0,0.0,0.0167401548317377,0.0,0.0,0.0,0.2897145708124797,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7102854291875204,0.2897145708124797,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6935452743557826,0.0167401548317377,0.2897145708124797,0.0,0.0,0.0,1.0,0.0,4240.0,3037.0,corine_frac_321,corine_frac_412,corine_frac_333,corine_frac_422,corine_frac_242,corine_frac_32,corine_frac_41,corine_frac_33,corine_frac_3,corine_frac_4,corine_frac_5
+val,-4.705655029450847,56.013734023835234,unlabelled-sample_1829_val,8.200000000000001,6.7,36.0,42.93,18.2,-0.2,18.4,3.7,10.1,13.9,2.9000000000000004,1576.0,184.0,85.0,28.0,527.0,256.0,304.0,479.0,0.0018151313795256,0.2197829667529694,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0579528057452916,0.0,0.0,0.0,0.0,0.0,0.0,0.2059673807217999,0.0,0.0,0.0,0.0,0.0856247214974072,0.0073320067267831,0.0491496522316367,0.3723753349445863,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2795509038777866,0.2059673807217999,0.5144817154004134,0.0,0.0,0.221598098132495,0.0,0.0,0.0579528057452916,0.0,0.0,0.2059673807217999,0.0,0.1421063804558271,0.3723753349445863,0.0,0.0,0.0,0.0,0.0,3746.0,574.0,1213.0,320.0,corine_frac_321,corine_frac_112,corine_frac_231,corine_frac_311,corine_frac_142,corine_frac_32,corine_frac_11,corine_frac_23,corine_frac_3,corine_frac_1,corine_frac_2
+test,-4.705371886377996,50.52860073880012,unlabelled-sample_1830_test,10.1,5.7,33.0,42.45,19.5,2.3000000000000003,17.2,5.9,11.5,15.7,5.0,1031.0,121.0,60.0,26.0,355.0,181.0,195.0,327.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4701664697324106,0.0,0.0,0.0,0.0,0.0,0.393422815748358,0.0,0.0,0.0,0.0,0.1100526569688391,0.0,0.0,0.0,0.0,0.0,0.0263580575503921,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8635892854807687,0.1364107145192313,0.0,0.0,0.0,0.0,0.0,0.0,0.4701664697324106,0.0,0.393422815748358,0.0,0.1100526569688391,0.0263580575503921,0.0,0.0,0.0,0.0,0.0,88.0,13.0,2160.0,1266.0,corine_frac_211,corine_frac_231,corine_frac_311,corine_frac_324,corine_frac_522,corine_frac_21,corine_frac_23,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
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+val,-4.639914595488677,52.88110097436347,unlabelled-sample_1898_val,9.8,5.6000000000000005,35.0,37.57,18.2,2.3000000000000003,15.9,8.200000000000001,10.8,14.6,5.300000000000001,910.0,105.0,55.0,24.0,306.0,168.0,193.0,250.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.901283681892668,0.0,0.0,0.0,0.0,0.0360614369066203,0.0626548812007116,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.901283681892668,0.0987163181073319,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.901283681892668,0.0,0.0987163181073319,0.0,0.0,0.0,0.0,0.0,0.0,113.0,17.0,1742.0,506.0,corine_frac_231,corine_frac_312,corine_frac_311,corine_frac_324,corine_frac_221,corine_frac_23,corine_frac_31,corine_frac_13,corine_frac_2,corine_frac_3,corine_frac_1
+val,-4.639601816743907,57.816021376283295,unlabelled-sample_1899_val,6.4,6.4,34.0,43.36,16.400000000000002,-2.0,18.4,2.1,8.0,12.4,1.3,1167.0,131.0,65.0,23.0,379.0,205.0,245.0,339.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1694370763248014,0.3929410602006237,0.0,0.0074881591813326,0.0,0.0,0.1782402065236432,0.0,0.0,0.0,0.2518934977695991,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.748106502230401,0.2518934977695991,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5698662957067577,0.1782402065236432,0.2518934977695991,0.0,0.0,0.0,0.0,0.0,5000.0,5000.0,corine_frac_322,corine_frac_412,corine_frac_333,corine_frac_321,corine_frac_324,corine_frac_32,corine_frac_41,corine_frac_33,corine_frac_3,corine_frac_4,corine_frac_5
+val,-4.637391028693948,57.37469459813801,unlabelled-sample_1900_val,6.2,6.6000000000000005,33.0,46.73,16.8,-2.8000000000000003,19.6,1.4,8.1,12.6,0.4,1334.0,157.0,73.0,26.0,446.0,226.0,265.0,405.0,0.0,0.0,0.0501439326068574,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0930563718871607,0.0,0.0,0.8567996955059818,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0501439326068574,0.0,0.9498560673931424,0.0,0.0,0.0,0.0501439326068574,0.0,0.0,0.0,0.0,0.0,0.0,0.0930563718871607,0.8567996955059818,0.0,0.0,0.0,0.0,0.0,1.0,0.0,3998.0,2754.0,corine_frac_322,corine_frac_312,corine_frac_121,corine_frac_411,corine_frac_313,corine_frac_32,corine_frac_31,corine_frac_12,corine_frac_3,corine_frac_1,corine_frac_2
+test,-4.6370897440244345,56.84471161505693,unlabelled-sample_1901_test,4.5,6.6000000000000005,33.0,47.17,15.5,-4.4,19.9,-0.2,6.4,11.0,-1.3,1896.0,237.0,102.0,28.0,654.0,316.0,352.0,612.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0482044835297238,0.0,0.0012608313567068,0.2385772579116627,0.0,0.0,0.0,0.0,0.0089761894819969,0.0,0.0,0.0,0.7029812377199096,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2970187622800905,0.7029812377199096,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0482044835297238,0.2398380892683696,0.0089761894819969,0.7029812377199096,0.0,0.0,0.0,0.0,0.0,4999.0,4577.0,corine_frac_412,corine_frac_322,corine_frac_312,corine_frac_333,corine_frac_321,corine_frac_41,corine_frac_32,corine_frac_31,corine_frac_4,corine_frac_3,corine_frac_1
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+val,-4.635943186014227,54.80721889209489,unlabelled-sample_1903_val,7.9,6.6000000000000005,36.0,43.02,17.6,-0.6000000000000001,18.2,5.800000000000001,9.6,13.5,2.6,1207.0,133.0,68.0,25.0,398.0,211.0,248.0,341.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6033266587594645,0.0,0.0,0.0,0.0,0.0,0.1085279717785991,0.0292956456646334,0.2512165564592832,0.0075828832840581,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,5.0284053961562346e-05,0.0,0.0,0.0,0.0,0.6033266587594645,0.396623057186574,0.0,5.0284053961562346e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.6033266587594645,0.0,0.1378236174432326,0.2587994397433414,0.0,0.0,0.0,5.0284053961562346e-05,0.0,3.0,0.0,1688.0,924.0,corine_frac_231,corine_frac_321,corine_frac_312,corine_frac_313,corine_frac_322,corine_frac_23,corine_frac_32,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
+train,-4.635639654789387,55.13831159699917,unlabelled-sample_1904_train,7.2,6.7,36.0,43.32,17.2,-1.2000000000000002,18.4,5.0,9.1,12.9,1.9,1388.0,155.0,76.0,25.0,456.0,239.0,281.0,398.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7253774737470671,0.0,0.049837333874455,0.0,0.0,0.2247851923784779,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7253774737470671,0.2746225262529329,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4999.0,4589.0,corine_frac_312,corine_frac_324,corine_frac_321,corine_frac_331,corine_frac_132,corine_frac_31,corine_frac_32,corine_frac_33,corine_frac_3,corine_frac_5,corine_frac_1
+val,-4.634603018367825,58.37862462830462,unlabelled-sample_1905_val,7.6,6.2,37.0,38.11,16.7,0.0,16.7,5.7,8.8,12.8,3.2,1024.0,111.0,58.0,23.0,329.0,181.0,219.0,290.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0702663191445781,0.1786018185019578,0.0575843581521758,0.0,0.0,0.0,0.0,0.0605961097808861,0.0,0.0,0.0,0.632951394420402,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.367048605579598,0.632951394420402,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0702663191445781,0.2361861766541336,0.0605961097808861,0.632951394420402,0.0,0.0,0.0,0.0,0.0,1586.0,616.0,corine_frac_412,corine_frac_321,corine_frac_313,corine_frac_333,corine_frac_322,corine_frac_41,corine_frac_32,corine_frac_31,corine_frac_4,corine_frac_3,corine_frac_5
+train,-4.634251774737083,52.06196215312737,unlabelled-sample_1906_train,10.0,5.300000000000001,33.0,38.44,18.5,2.6,15.9,8.4,11.1,15.0,5.4,859.0,100.0,50.0,25.0,292.0,157.0,181.0,234.0,0.0,0.0520076205042152,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6939838878951845,0.0,0.0,0.0847486350814369,0.0,0.16003838437107,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0092214721480933,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0520076205042152,0.7787325229766214,0.16003838437107,0.0092214721480933,0.0,0.0520076205042152,0.0,0.0,0.0,0.0,0.0,0.6939838878951845,0.0847486350814369,0.16003838437107,0.0,0.0,0.0,0.0092214721480933,0.0,0.0,976.0,149.0,1145.0,448.0,corine_frac_231,corine_frac_311,corine_frac_243,corine_frac_112,corine_frac_421,corine_frac_23,corine_frac_31,corine_frac_24,corine_frac_2,corine_frac_3,corine_frac_1
+val,-4.633556270621205,54.93112385716146,unlabelled-sample_1907_val,7.9,6.7,36.0,43.07,17.8,-0.6000000000000001,18.4,5.800000000000001,9.8,13.5,2.6,1236.0,136.0,69.0,25.0,408.0,214.0,254.0,348.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0812389511956665,0.0,0.0,0.0,0.0,0.0,0.0,0.0764446458686094,0.0693827604581163,0.4689498370124376,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3039838054651701,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0812389511956665,0.6147772433391634,0.3039838054651701,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0812389511956665,0.0,0.0764446458686094,0.538332597470554,0.0,0.3039838054651701,0.0,0.0,0.0,11.0,1.0,2378.0,811.0,corine_frac_322,corine_frac_412,corine_frac_231,corine_frac_313,corine_frac_321,corine_frac_32,corine_frac_41,corine_frac_23,corine_frac_3,corine_frac_4,corine_frac_2
+test,-4.631177274657521,51.76829225219844,unlabelled-sample_1908_test,9.6,5.4,31.0,42.63,18.8,1.8,17.0,7.6,11.0,15.2,4.5,996.0,114.0,59.0,25.0,336.0,179.0,203.0,287.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9893703482829578,0.0,0.0,0.0,0.0,0.0,0.0106296517170422,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9893703482829578,0.0106296517170422,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9893703482829578,0.0,0.0106296517170422,0.0,0.0,0.0,0.0,0.0,0.0,188.0,28.0,995.0,377.0,corine_frac_231,corine_frac_312,corine_frac_121,corine_frac_324,corine_frac_321,corine_frac_23,corine_frac_31,corine_frac_52,corine_frac_2,corine_frac_3,corine_frac_4
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+train,-4.627515521501183,55.397712812912985,unlabelled-sample_1915_train,8.200000000000001,6.800000000000001,36.0,43.53,18.1,-0.3,18.4,8.5,10.2,13.9,2.8000000000000003,1145.0,130.0,61.0,26.0,382.0,196.0,250.0,299.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0129464001332279,0.0,0.0,0.0,0.0,0.0,0.8216866629468467,0.0,0.0,0.0,0.0,0.1030819634249918,0.0,0.0622849734949335,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8346330630800747,0.1653669369199253,0.0,0.0,0.0,0.0,0.0,0.0,0.0129464001332279,0.0,0.8216866629468467,0.0,0.1653669369199253,0.0,0.0,0.0,0.0,0.0,0.0,275.0,42.0,1157.0,421.0,corine_frac_231,corine_frac_311,corine_frac_313,corine_frac_211,corine_frac_411,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_4
+test,-4.62661796924556,56.887901098951644,unlabelled-sample_1916_test,6.800000000000001,6.7,33.0,47.84,17.6,-2.3000000000000003,19.9,1.9,9.0,13.3,0.9,1576.0,196.0,83.0,30.0,546.0,252.0,290.0,505.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5637153986338578,0.0,0.0,1.198160105342236e-07,0.0,0.3136074444083666,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.122677037141765,0.0,0.0,0.0,0.0,0.0,0.877322962858235,0.0,0.122677037141765,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5637153986338578,0.3136075642243772,0.0,0.0,0.0,0.122677037141765,0.0,1.0,0.0,2666.0,1218.0,corine_frac_312,corine_frac_324,corine_frac_512,corine_frac_322,corine_frac_331,corine_frac_31,corine_frac_32,corine_frac_51,corine_frac_3,corine_frac_5,corine_frac_4
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+val,-4.626443814626477,58.41361495309156,unlabelled-sample_1918_val,8.5,6.2,37.0,36.98,17.5,0.9,16.6,6.800000000000001,9.7,13.5,4.2,930.0,102.0,52.0,23.0,300.0,163.0,201.0,258.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0872974173521607,0.0,0.0,0.0934261250652125,0.0,0.0,0.0,0.0,0.0,0.2687998272297695,0.0,0.0,0.0,0.5005991080844646,0.0,0.0,0.0,0.0,0.0498775222683926,0.0,0.0,0.0,0.0,0.0,0.4495233696471428,0.5005991080844646,0.0498775222683926,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0872974173521607,0.0934261250652125,0.2687998272297695,0.5005991080844646,0.0,0.0498775222683926,0.0,0.0,0.0,1937.0,780.0,corine_frac_412,corine_frac_333,corine_frac_321,corine_frac_311,corine_frac_512,corine_frac_41,corine_frac_33,corine_frac_32,corine_frac_4,corine_frac_3,corine_frac_5
+train,-4.626384770773786,55.756202632115254,unlabelled-sample_1919_train,8.200000000000001,6.800000000000001,37.0,42.42,18.1,-0.1,18.2,5.9,10.2,13.8,3.0,1324.0,145.0,71.0,26.0,430.0,222.0,274.0,372.0,0.0,0.2426514458919087,0.0,0.0,0.0,0.0,0.0340986764534135,0.0351126131241072,0.0,0.0,0.0295179549362944,0.0786659898699811,0.0,0.0,0.0,0.0,0.0,0.514899643878788,0.0,0.0,0.0,0.0,0.0650536758455069,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.341380690405724,0.593565633748769,0.0650536758455069,0.0,0.0,0.2426514458919087,0.0,0.0692112895775207,0.0295179549362944,0.0786659898699811,0.0,0.514899643878788,0.0,0.0650536758455069,0.0,0.0,0.0,0.0,0.0,0.0,4837.0,742.0,1071.0,272.0,corine_frac_231,corine_frac_112,corine_frac_211,corine_frac_311,corine_frac_132,corine_frac_23,corine_frac_11,corine_frac_21,corine_frac_2,corine_frac_1,corine_frac_3
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+val,-4.531212817470831,52.78824575192998,unlabelled-sample_2062_val,9.9,5.6000000000000005,35.0,37.68,18.4,2.4000000000000004,16.0,8.4,11.0,14.7,5.4,900.0,104.0,55.0,24.0,303.0,167.0,192.0,244.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2204661436446078,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3178910491898604,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0060368532260978,0.0,0.0,0.0,0.0,0.4556059539394338,0.0,0.2204661436446078,0.3178910491898604,0.0060368532260978,0.4556059539394338,0.0,0.0,0.0,0.0,0.0,0.0,0.2204661436446078,0.0,0.0,0.3178910491898604,0.0,0.0,0.0060368532260978,0.0,0.4556059539394338,45.0,7.0,4153.0,2461.0,corine_frac_523,corine_frac_321,corine_frac_231,corine_frac_423,corine_frac_521,corine_frac_52,corine_frac_32,corine_frac_23,corine_frac_5,corine_frac_3,corine_frac_2
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+val,-4.466988555205417,50.35256903905397,unlabelled-sample_2159_val,10.2,5.800000000000001,34.0,40.63,19.4,2.6,16.8,6.2,13.9,15.4,5.300000000000001,1030.0,122.0,59.0,27.0,355.0,180.0,191.0,333.0,0.0,0.1907188693469535,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.402910036372005,0.0,0.0,0.0,0.0,0.0,0.1540336353154856,0.0,0.0,0.0,0.0,0.1282895198638566,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0661192847698933,0.0579286543318058,0.1907188693469535,0.5569436716874907,0.1282895198638566,0.0,0.1240479391016991,0.1907188693469535,0.0,0.0,0.0,0.402910036372005,0.0,0.1540336353154856,0.0,0.1282895198638566,0.0,0.0,0.0,0.0,0.0,0.1240479391016991,3902.0,598.0,1454.0,362.0,corine_frac_211,corine_frac_112,corine_frac_231,corine_frac_311,corine_frac_522,corine_frac_21,corine_frac_11,corine_frac_23,corine_frac_2,corine_frac_1,corine_frac_3
+test,-4.465469540151352,57.48789759351397,unlabelled-sample_2160_test,7.7,6.7,34.0,45.68,18.2,-1.2000000000000002,19.4,5.2,9.6,14.0,2.1,940.0,100.0,49.0,23.0,298.0,167.0,208.0,261.0,0.0,0.0861908414016753,0.0019840612567606,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2337005033744497,0.0,0.0,0.0,0.0,0.0,0.532121383263475,0.0,0.0,0.0548830364709407,0.0,0.0,0.0321278228813941,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0589923513513043,0.0,0.088174902658436,0.8207049231088656,0.0321278228813941,0.0,0.0589923513513043,0.0861908414016753,0.0019840612567606,0.0,0.0,0.2337005033744497,0.0,0.532121383263475,0.0548830364709407,0.0321278228813941,0.0,0.0,0.0,0.0,0.0,0.0589923513513043,1445.0,221.0,1314.0,332.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_522,corine_frac_243,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_5
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+test,-4.465265743360188,52.067716293408054,unlabelled-sample_2162_test,9.3,5.4,33.0,39.18,17.900000000000002,1.8,16.1,7.6,10.5,14.4,4.6000000000000005,1007.0,114.0,61.0,25.0,341.0,186.0,208.0,283.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9490168623218636,0.0,0.0,0.0,0.0,0.0509831376781364,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9490168623218636,0.0509831376781364,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9490168623218636,0.0,0.0509831376781364,0.0,0.0,0.0,0.0,0.0,0.0,177.0,27.0,1335.0,585.0,corine_frac_231,corine_frac_311,corine_frac_313,corine_frac_331,corine_frac_421,corine_frac_23,corine_frac_31,corine_frac_22,corine_frac_2,corine_frac_3,corine_frac_4
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+val,-4.464214436349405,56.15683782579624,unlabelled-sample_2164_val,8.1,6.9,36.0,43.87,18.4,-0.4,18.8,3.6,10.1,14.0,2.7,1519.0,180.0,81.0,28.0,514.0,246.0,287.0,473.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0485598344636074,0.6832456718995885,0.0,0.0,0.0,0.0,0.2681944936368039,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.731805506363196,0.2681944936368039,0.0,0.0,0.0,0.0,0.0,10.0,1.0,4558.0,2988.0,corine_frac_312,corine_frac_324,corine_frac_311,corine_frac_213,corine_frac_331,corine_frac_31,corine_frac_32,corine_frac_24,corine_frac_3,corine_frac_2,corine_frac_1
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+val,-4.462405541925681,57.83638643898307,unlabelled-sample_2168_val,5.800000000000001,6.5,34.0,43.76,16.0,-2.7,18.7,1.5,7.300000000000001,11.8,0.6000000000000001,1148.0,126.0,65.0,22.0,367.0,207.0,248.0,330.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0034113219517977,0.0,0.0,0.0,0.0,0.0214869975808491,0.0,0.0,0.0,0.975101680467353,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0248983195326468,0.975101680467353,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0034113219517977,0.0214869975808491,0.975101680467353,0.0,0.0,0.0,2.0,0.0,4999.0,4419.0,corine_frac_412,corine_frac_333,corine_frac_322,corine_frac_131,corine_frac_411,corine_frac_41,corine_frac_33,corine_frac_32,corine_frac_4,corine_frac_3,corine_frac_1
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+val,-4.280998265792521,55.44537913615625,unlabelled-sample_2460_val,7.6,6.9,37.0,43.07,17.7,-0.8,18.5,5.300000000000001,9.6,13.3,2.2,1273.0,142.0,69.0,26.0,420.0,217.0,260.0,360.0,0.0,0.1715493007379837,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.104990526637905,0.0,0.0,0.0,0.0,0.0,0.6589940244762383,0.0,0.0,0.0,0.0,0.0002124569407614,0.0,0.0642536912071113,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1715493007379837,0.7639845511141433,0.0644661481478728,0.0,0.0,0.1715493007379837,0.0,0.0,0.0,0.104990526637905,0.0,0.6589940244762383,0.0,0.0644661481478728,0.0,0.0,0.0,0.0,0.0,0.0,2186.0,335.0,1200.0,273.0,corine_frac_231,corine_frac_112,corine_frac_211,corine_frac_313,corine_frac_311,corine_frac_23,corine_frac_11,corine_frac_21,corine_frac_2,corine_frac_1,corine_frac_3
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+train,-4.160991378256882,54.81566139188775,unlabelled-sample_2634_train,8.4,6.800000000000001,37.0,42.93,18.3,0.0,18.3,6.2,10.2,14.0,3.1,1106.0,124.0,61.0,26.0,371.0,190.0,224.0,314.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1918888561009207,0.0,0.0,0.0,0.0,0.0,0.5088617126384549,0.0,0.0,0.2374580177641255,0.0,0.0,0.0,0.0,0.0521968867734355,0.0089723945089084,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0006221322141547,0.0,0.0,0.0,0.0,0.0,0.0,0.9382085865035013,0.061169281282344,0.0006221322141547,0.0,0.0,0.0,0.0,0.0,0.1918888561009207,0.0,0.5088617126384549,0.2374580177641255,0.0,0.061169281282344,0.0,0.0,0.0006221322141547,0.0,0.0,65.0,9.0,1343.0,533.0,corine_frac_231,corine_frac_243,corine_frac_211,corine_frac_321,corine_frac_322,corine_frac_23,corine_frac_24,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_4
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+train,-4.158572118734842,52.99921632216884,unlabelled-sample_2642_train,8.3,5.9,34.0,41.61,17.7,0.4,17.3,6.300000000000001,12.3,13.7,3.3000000000000003,1195.0,141.0,70.0,24.0,399.0,219.0,244.0,346.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0032962708245372,0.0,0.0,0.0,0.0,0.0,0.0234576111984699,0.0,0.7957312303401312,0.0,0.0,0.0,0.0,0.0,0.1775148876368616,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0032962708245372,0.9967037291754628,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0032962708245372,0.0,0.0234576111984699,0.7957312303401312,0.1775148876368616,0.0,0.0,0.0,0.0,5.0,0.0,4579.0,3425.0,corine_frac_321,corine_frac_333,corine_frac_312,corine_frac_231,corine_frac_511,corine_frac_32,corine_frac_33,corine_frac_31,corine_frac_3,corine_frac_2,corine_frac_5
+train,-4.157830509463616,51.96575145956628,unlabelled-sample_2643_train,9.1,5.6000000000000005,32.0,42.87,18.4,1.1,17.3,4.9,10.8,14.7,3.9,1234.0,146.0,72.0,25.0,415.0,225.0,249.0,365.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4608333189885089,0.0,0.0,0.0,0.0,0.0,0.2300829398802671,0.1773068983095037,0.0,0.0,0.0,0.1317768428217202,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4608333189885089,0.5391666810114911,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4608333189885089,0.0,0.4073898381897708,0.1317768428217202,0.0,0.0,0.0,0.0,0.0,75.0,11.0,2648.0,1353.0,corine_frac_231,corine_frac_312,corine_frac_313,corine_frac_324,corine_frac_112,corine_frac_23,corine_frac_31,corine_frac_32,corine_frac_3,corine_frac_2,corine_frac_1
+train,-4.157307639841964,56.92826313442177,unlabelled-sample_2644_train,5.1000000000000005,6.9,32.0,49.8,16.900000000000002,-4.4,21.3,0.1,6.9,12.0,-1.0,1420.0,167.0,80.0,25.0,474.0,248.0,281.0,441.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0490672375968399,0.70944075477052,0.0,0.0,0.0,0.0,0.1549680822967708,0.0,0.0,0.0,0.0865239253358691,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9134760746641308,0.0865239253358691,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.75850799236736,0.1549680822967708,0.0865239253358691,0.0,0.0,0.0,0.0,0.0,4999.0,3977.0,corine_frac_322,corine_frac_333,corine_frac_412,corine_frac_321,corine_frac_421,corine_frac_32,corine_frac_33,corine_frac_41,corine_frac_3,corine_frac_4,corine_frac_2
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+val,-4.155534567105521,50.504354252891424,unlabelled-sample_2646_val,9.8,5.9,34.0,41.25,19.3,2.2,17.1,5.800000000000001,13.7,15.2,4.9,1064.0,126.0,61.0,26.0,363.0,188.0,200.0,342.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1923395142784834,0.0,0.0,0.0,0.0,0.0,0.3126781713067657,0.0,0.0,0.0,0.0,0.187731914438654,0.0462415326443252,0.1315655867802037,0.0,0.1294432805515678,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5050176855852491,0.4949823144147509,0.0,0.0,0.0,0.0,0.0,0.0,0.1923395142784834,0.0,0.3126781713067657,0.0,0.3655390338631831,0.1294432805515678,0.0,0.0,0.0,0.0,0.0,50.0,7.0,1719.0,780.0,corine_frac_231,corine_frac_211,corine_frac_311,corine_frac_313,corine_frac_322,corine_frac_31,corine_frac_23,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_5
+train,-4.155490107503347,55.03271757319949,unlabelled-sample_2647_train,8.1,6.9,37.0,42.93,18.1,-0.3,18.4,5.800000000000001,10.0,13.7,2.8000000000000003,1238.0,143.0,68.0,27.0,418.0,209.0,240.0,364.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.611160443946368,0.0,0.0,0.0,0.0,0.388839556053632,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.611160443946368,0.388839556053632,0.0,0.0,0.0,0.0,0.0,2.0,0.0,4199.0,2409.0,corine_frac_312,corine_frac_324,corine_frac_123,corine_frac_244,corine_frac_223,corine_frac_31,corine_frac_32,corine_frac_11,corine_frac_3,corine_frac_2,corine_frac_4
+test,-4.155266526825181,51.01914213911934,unlabelled-sample_2648_test,10.7,5.800000000000001,33.0,43.08,20.3,2.8000000000000003,17.5,6.4,12.3,16.3,5.5,1037.0,120.0,61.0,25.0,353.0,184.0,209.0,314.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.397164396550147,0.0,0.0,0.0,0.0,0.0,0.602835603449853,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.397164396550147,0.0,0.602835603449853,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,134.0,20.0,1244.0,546.0,corine_frac_231,corine_frac_211,corine_frac_213,corine_frac_242,corine_frac_133,corine_frac_23,corine_frac_21,corine_frac_14,corine_frac_2,corine_frac_5,corine_frac_4
+train,-4.15524936354295,56.524044106744306,unlabelled-sample_2649_train,7.1000000000000005,7.0,34.0,47.6,18.2,-2.1,20.3,2.1,9.0,13.6,1.2000000000000002,1366.0,161.0,73.0,27.0,459.0,230.0,265.0,427.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5641017421259551,0.0,0.0,0.0,0.0,0.0789171798846629,0.0,0.0,0.0466918373726561,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3102892406167259,0.0,0.0,0.0,0.0,0.5641017421259551,0.125609017257319,0.0,0.3102892406167259,0.0,0.0,0.0,0.0,0.0,0.0,0.5641017421259551,0.0,0.0789171798846629,0.0466918373726561,0.0,0.0,0.0,0.3102892406167259,0.0,5.0,0.0,1728.0,583.0,corine_frac_231,corine_frac_512,corine_frac_311,corine_frac_321,corine_frac_423,corine_frac_23,corine_frac_51,corine_frac_31,corine_frac_2,corine_frac_5,corine_frac_3
+test,-4.154486158926415,51.07722336157475,unlabelled-sample_2650_test,11.2,5.9,33.0,42.83,20.700000000000003,3.3000000000000003,17.400000000000002,6.9,12.7,16.8,6.0,992.0,115.0,57.0,25.0,336.0,175.0,202.0,297.0,0.0,0.0635852038239799,0.0613545208224875,0.0,0.0,0.1390123924567112,0.0,0.0,0.0,0.0,0.0,0.0104338901602187,0.0,0.0,0.0,0.0,0.0,0.3452977785435219,0.0,0.0,0.0,0.0,0.0012119860337551,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.044286412402559,0.0,0.1200365893442413,0.0,0.0,0.0,0.2147812264125249,0.0,0.2639521171031788,0.3557316687037407,0.0012119860337551,0.1643230017468004,0.2147812264125249,0.0635852038239799,0.2003669132791988,0.0,0.0,0.0104338901602187,0.0,0.3452977785435219,0.0,0.0012119860337551,0.0,0.0,0.0,0.1643230017468004,0.0,0.2147812264125249,1140.0,174.0,2061.0,883.0,corine_frac_231,corine_frac_522,corine_frac_124,corine_frac_423,corine_frac_112,corine_frac_23,corine_frac_52,corine_frac_12,corine_frac_2,corine_frac_1,corine_frac_5
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+train,-4.0058837951852,52.52000910942172,unlabelled-sample_2923_train,9.7,5.9,35.0,39.32,18.4,1.8,16.6,7.9,11.2,14.7,4.800000000000001,1032.0,117.0,63.0,23.0,343.0,195.0,225.0,280.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5419521924560321,0.0,0.0,0.0,0.0,0.0,0.0874939119517538,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0967251353194478,0.196610628135446,0.0,0.0,0.0,0.0,0.0,0.07721813213732,0.0,0.0,0.629446104407786,0.0,0.2933357634548938,0.07721813213732,0.0,0.0,0.0,0.0,0.5419521924560321,0.0,0.0874939119517538,0.0,0.0,0.0,0.0,0.0967251353194478,0.196610628135446,0.0,0.07721813213732,65.0,9.0,2103.0,842.0,corine_frac_211,corine_frac_421,corine_frac_412,corine_frac_231,corine_frac_522,corine_frac_21,corine_frac_42,corine_frac_41,corine_frac_2,corine_frac_4,corine_frac_5
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+test,-4.004905759939442,50.39106299820504,unlabelled-sample_2925_test,10.1,6.0,35.0,40.41,19.5,2.4000000000000004,17.1,6.1000000000000005,13.8,15.3,5.2,1014.0,121.0,57.0,26.0,345.0,179.0,187.0,331.0,0.0,0.0112095723819945,0.143462065809826,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0042791021517734,0.5215045643816287,0.0,0.0,0.0,0.0,0.0,0.2487734842551472,0.0,0.0,0.0,0.0,0.0,0.0,0.0707712110196299,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.158950740343594,0.770278048636776,0.0707712110196299,0.0,0.0,0.0112095723819945,0.143462065809826,0.0,0.0042791021517734,0.5215045643816287,0.0,0.2487734842551472,0.0,0.0707712110196299,0.0,0.0,0.0,0.0,0.0,0.0,840.0,128.0,1148.0,383.0,corine_frac_211,corine_frac_231,corine_frac_121,corine_frac_313,corine_frac_112,corine_frac_21,corine_frac_23,corine_frac_12,corine_frac_2,corine_frac_1,corine_frac_3
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+val,-3.6155898260592094,57.18923904549057,unlabelled-sample_3606_val,5.9,7.1000000000000005,33.0,49.7,17.8,-3.5,21.3,0.9,7.5,12.9,-0.2,944.0,100.0,54.0,18.0,289.0,182.0,216.0,265.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3454711640664973,0.0,0.0,0.5981322281566024,0.0,0.0,0.0,0.0,0.0563966077769003,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3454711640664973,0.5981322281566024,0.0563966077769003,0.0,0.0,0.0,0.0,1.0,0.0,4999.0,4382.0,corine_frac_322,corine_frac_312,corine_frac_333,corine_frac_243,corine_frac_421,corine_frac_32,corine_frac_31,corine_frac_33,corine_frac_3,corine_frac_1,corine_frac_2
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+train,-3.574186432365258,52.76228702301994,unlabelled-sample_3669_train,6.800000000000001,6.2,32.0,45.86,17.2,-1.6,18.8,2.1,11.5,12.9,1.2000000000000002,1414.0,174.0,83.0,24.0,477.0,258.0,279.0,431.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0072350455084872,0.0,0.0,0.0190400416752055,0.0,0.0,0.0,0.0,0.0,0.0,0.9737249128163072,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0262750871836927,0.9737249128163072,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0262750871836927,0.0,0.9737249128163072,0.0,0.0,0.0,2.0,0.0,4998.0,3726.0,corine_frac_412,corine_frac_324,corine_frac_321,corine_frac_241,corine_frac_122,corine_frac_41,corine_frac_32,corine_frac_52,corine_frac_4,corine_frac_3,corine_frac_2
+train,-3.5730116584895923,50.61909492590426,unlabelled-sample_3670_train,9.5,6.2,33.0,43.41,19.8,1.3,18.5,5.0,13.8,15.3,4.3,932.0,109.0,54.0,24.0,312.0,170.0,175.0,301.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3563162207760327,0.0,0.0,0.0,0.0,0.0,0.4275431745193788,0.0,0.0,0.0433043380063038,0.0,0.1099969703986527,0.0628392962996319,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8271637333017154,0.1728362666982846,0.0,0.0,0.0,0.0,0.0,0.0,0.3563162207760327,0.0,0.4275431745193788,0.0433043380063038,0.1728362666982846,0.0,0.0,0.0,0.0,0.0,0.0,124.0,19.0,1011.0,417.0,corine_frac_231,corine_frac_211,corine_frac_311,corine_frac_312,corine_frac_243,corine_frac_23,corine_frac_21,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
+train,-3.57153756119352,55.57984579865585,unlabelled-sample_3671_train,7.4,7.2,38.0,42.62,17.8,-1.1,18.9,5.1000000000000005,9.1,13.0,2.1,1122.0,120.0,65.0,21.0,351.0,206.0,242.0,316.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0176990779147943,0.0,0.0,0.0,0.0,0.0,0.6982353127688008,0.0,0.0,0.0,0.0,0.0,0.0883490220859669,0.0,0.0,0.1361851178309228,0.0,0.059531469399515,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7159343906835951,0.2840656093164048,0.0,0.0,0.0,0.0,0.0,0.0,0.0176990779147943,0.0,0.6982353127688008,0.0,0.0883490220859669,0.1957165872304379,0.0,0.0,0.0,0.0,0.0,29.0,4.0,1550.0,538.0,corine_frac_231,corine_frac_322,corine_frac_312,corine_frac_324,corine_frac_211,corine_frac_23,corine_frac_32,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
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+train,-3.091766160540102,52.138439711542574,unlabelled-sample_4621_train,8.6,6.6000000000000005,33.0,47.33,19.5,-0.1,19.6,3.6,13.5,14.9,2.8000000000000003,1051.0,121.0,63.0,21.0,343.0,201.0,212.0,317.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3966234530628372,0.0,0.0,0.0,0.0,0.0,0.5591255630747499,0.0,0.0,0.0,0.0,0.0083453895444905,0.0,0.0,0.0359055943179223,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9557490161375872,0.0442509838624129,0.0,0.0,0.0,0.0,0.0,0.0,0.3966234530628372,0.0,0.5591255630747499,0.0,0.0083453895444905,0.0359055943179223,0.0,0.0,0.0,0.0,0.0,112.0,17.0,2062.0,768.0,corine_frac_231,corine_frac_211,corine_frac_321,corine_frac_311,corine_frac_521,corine_frac_23,corine_frac_21,corine_frac_32,corine_frac_2,corine_frac_3,corine_frac_5
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+test,-3.087411225582627,57.15030363554271,unlabelled-sample_4627_test,5.300000000000001,7.1000000000000005,36.0,43.77,15.8,-3.6,19.4,0.9,6.9,11.1,-0.1,1002.0,105.0,62.0,17.0,305.0,198.0,230.0,282.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0547144078270059,0.0,0.0,0.0,0.0,0.0,0.0005634081983772,0.0,0.1616330820735291,0.5892794978015515,0.0,0.1348743383413929,0.0,0.0,0.0310929064493132,0.0,0.0,0.0,0.0278423593088299,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0547144078270059,0.9174432328641638,0.0278423593088299,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0547144078270059,0.0,0.0005634081983772,0.8857869182164735,0.0310929064493132,0.0278423593088299,0.0,0.0,0.0,3.0,0.0,4888.0,3227.0,corine_frac_322,corine_frac_321,corine_frac_324,corine_frac_231,corine_frac_333,corine_frac_32,corine_frac_23,corine_frac_33,corine_frac_3,corine_frac_2,corine_frac_4
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+val,-3.0799115045904983,55.72479039850051,unlabelled-sample_4640_val,5.300000000000001,7.2,36.0,44.23,16.3,-3.4000000000000004,19.700000000000003,0.9,7.1000000000000005,11.2,-0.1,1248.0,136.0,80.0,18.0,391.0,241.0,271.0,360.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0,4035.0,2727.0,corine_frac_412,corine_frac_222,corine_frac_511,corine_frac_142,corine_frac_123,corine_frac_41,corine_frac_42,corine_frac_21,corine_frac_4,corine_frac_3,corine_frac_1
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+val,-3.076391183937682,57.28369204028655,unlabelled-sample_4642_val,5.0,7.0,36.0,43.16,15.5,-3.7,19.200000000000003,0.7000000000000001,6.5,10.7,-0.3,1037.0,108.0,65.0,16.0,316.0,206.0,239.0,292.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4481816586401341,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5518183413598658,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4481816586401341,0.5518183413598658,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4481816586401341,0.0,0.5518183413598658,0.0,0.0,0.0,1.0,0.0,4998.0,4805.0,corine_frac_412,corine_frac_322,corine_frac_511,corine_frac_523,corine_frac_132,corine_frac_41,corine_frac_32,corine_frac_11,corine_frac_4,corine_frac_3,corine_frac_5
+test,-3.075981803024547,53.52492554601972,unlabelled-sample_4643_test,9.2,6.7,35.0,44.65,19.4,0.5,18.9,9.8,5.6000000000000005,15.0,3.6,804.0,87.0,47.0,21.0,255.0,148.0,199.0,192.0,0.0,0.0726525665699921,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.034201104625172,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3043667804112976,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.550432846243264,0.0,0.0,0.0,0.0,0.0383467021502742,0.0726525665699921,0.034201104625172,0.3043667804112976,0.550432846243264,0.0383467021502742,0.0726525665699921,0.0,0.0,0.0,0.034201104625172,0.0,0.0,0.0,0.0,0.3043667804112976,0.0,0.0,0.550432846243264,0.0,0.0383467021502742,1364.0,209.0,2381.0,982.0,corine_frac_423,corine_frac_321,corine_frac_112,corine_frac_523,corine_frac_211,corine_frac_42,corine_frac_32,corine_frac_11,corine_frac_4,corine_frac_3,corine_frac_1
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+test,-3.075085452829711,57.05035916476153,unlabelled-sample_4646_test,7.1000000000000005,7.2,37.0,43.45,17.5,-1.7000000000000002,19.200000000000003,2.7,8.9,12.8,1.8,792.0,80.0,48.0,15.0,231.0,161.0,193.0,211.0,0.0,0.0005255073602788,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0114657077385722,0.0,0.0,0.0,0.0,0.0,0.0,0.3897093674653061,0.0,0.0,0.0,0.0,0.0273235065254591,0.5664458217491017,0.0,0.0,0.0045300891612818,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.011991215098851,0.3897093674653061,0.5982994174358427,0.0,0.0,0.0005255073602788,0.0,0.0,0.0114657077385722,0.0,0.0,0.3897093674653061,0.0,0.5937693282745609,0.0045300891612818,0.0,0.0,0.0,0.0,0.0,49.0,7.0,1076.0,352.0,corine_frac_312,corine_frac_231,corine_frac_311,corine_frac_142,corine_frac_322,corine_frac_31,corine_frac_23,corine_frac_14,corine_frac_3,corine_frac_2,corine_frac_1
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+train,-3.0741630697761315,54.83888500568203,unlabelled-sample_4648_train,8.6,7.1000000000000005,36.0,45.28,19.3,-0.2,19.5,9.0,5.2,14.5,2.9000000000000004,888.0,93.0,50.0,20.0,277.0,165.0,221.0,219.0,0.0,0.0122928544939392,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3660324473854939,0.0,0.0,0.0,0.0,0.0,0.6216746981205669,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0122928544939392,0.9877071455060608,0.0,0.0,0.0,0.0122928544939392,0.0,0.0,0.0,0.3660324473854939,0.0,0.6216746981205669,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,348.0,53.0,1317.0,359.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_213,corine_frac_242,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_5
+train,-3.0724354720488476,54.89077517959504,unlabelled-sample_4649_train,8.6,7.2,36.0,45.1,19.3,-0.2,19.5,8.9,5.2,14.4,2.9000000000000004,880.0,92.0,50.0,20.0,272.0,164.0,221.0,216.0,0.0,0.0550782749172444,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1133717497154559,0.0,0.0,0.0,0.0,0.0,0.7842373856174045,0.0,0.0,0.0,0.0,0.047312589749895,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0550782749172444,0.8976091353328605,0.047312589749895,0.0,0.0,0.0550782749172444,0.0,0.0,0.0,0.1133717497154559,0.0,0.7842373856174045,0.0,0.047312589749895,0.0,0.0,0.0,0.0,0.0,0.0,340.0,52.0,2145.0,865.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_311,corine_frac_423,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+test,-3.0724228318476885,54.474373397180514,unlabelled-sample_4650_test,6.800000000000001,6.9,35.0,45.48,17.400000000000002,-1.9,19.3,1.9,8.8,12.7,1.1,1282.0,144.0,76.0,22.0,415.0,235.0,273.0,369.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4843480172080741,0.4369324658151521,0.0,0.0,0.0,0.0,0.0471088296298499,0.0,0.0,0.0,0.0316106873469238,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9683893126530764,0.0316106873469238,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9212804830232264,0.0471088296298499,0.0316106873469238,0.0,0.0,0.0,3.0,0.0,3706.0,2323.0,corine_frac_321,corine_frac_322,corine_frac_333,corine_frac_412,corine_frac_221,corine_frac_32,corine_frac_33,corine_frac_41,corine_frac_3,corine_frac_4,corine_frac_1
+test,-3.0720257031234017,57.04751911834616,unlabelled-sample_4651_test,7.1000000000000005,7.2,37.0,43.45,17.5,-1.7000000000000002,19.200000000000003,2.7,8.9,12.8,1.8,792.0,80.0,48.0,15.0,231.0,161.0,193.0,211.0,0.0,0.0090364521228085,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0274056639386795,0.0,0.0,0.0,0.0,0.0,0.0,0.3408485002462738,0.0,0.0,0.0,0.0,0.027287140910578,0.5832311486667059,0.0121910941149541,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0364421160614881,0.3408485002462738,0.622709383692238,0.0,0.0,0.0090364521228085,0.0,0.0,0.0274056639386795,0.0,0.0,0.3408485002462738,0.0,0.622709383692238,0.0,0.0,0.0,0.0,0.0,0.0,65.0,10.0,1076.0,338.0,corine_frac_312,corine_frac_231,corine_frac_142,corine_frac_311,corine_frac_313,corine_frac_31,corine_frac_23,corine_frac_14,corine_frac_3,corine_frac_2,corine_frac_1
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+train,-2.902280563282161,56.47189359364516,unlabelled-sample_5006_train,8.1,7.4,37.0,44.12,18.7,-0.8,19.5,11.4,4.7,13.9,2.5,685.0,69.0,43.0,14.0,195.0,138.0,172.0,177.0,0.0,0.5401735045746281,0.1071589499354183,0.0,0.0,0.0,0.0,0.0,0.0,0.0540692061051949,0.0,0.0012692138718562,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2973291255129025,0.0,0.7014016606152413,0.0012692138718562,0.0,0.0,0.2973291255129025,0.5401735045746281,0.1071589499354183,0.0,0.0540692061051949,0.0012692138718562,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2973291255129025,11652.0,1787.0,766.0,134.0,corine_frac_112,corine_frac_522,corine_frac_121,corine_frac_141,corine_frac_211,corine_frac_11,corine_frac_52,corine_frac_12,corine_frac_1,corine_frac_5,corine_frac_2
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+train,-2.9008664405226643,54.56041197438165,unlabelled-sample_5009_train,6.9,7.0,35.0,45.42,17.7,-1.8,19.5,2.1,9.0,12.9,1.2000000000000002,1221.0,135.0,72.0,22.0,394.0,224.0,264.0,349.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0656598873233694,0.0,0.0,0.0,0.0,0.0565355623249732,0.0210290540714729,0.0,0.1878735028858626,0.5466032438447228,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1222987495495989,0.0,0.0,0.0,0.0,0.0656598873233694,0.8120413631270316,0.0,0.1222987495495989,0.0,0.0,0.0,0.0,0.0,0.0,0.0656598873233694,0.0,0.0775646163964461,0.7344767467305854,0.0,0.0,0.0,0.1222987495495989,0.0,14.0,2.0,2472.0,1226.0,corine_frac_322,corine_frac_321,corine_frac_512,corine_frac_231,corine_frac_311,corine_frac_32,corine_frac_51,corine_frac_31,corine_frac_3,corine_frac_5,corine_frac_2
+val,-2.900751447321686,51.812110489717895,unlabelled-sample_5010_val,9.8,6.800000000000001,34.0,46.43,20.8,1.1,19.700000000000003,4.9,11.9,16.0,4.1000000000000005,887.0,96.0,57.0,18.0,278.0,180.0,190.0,254.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4938372431334794,0.0,0.0,0.0,0.0,0.0,0.5061627568665206,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4938372431334794,0.0,0.5061627568665206,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,129.0,19.0,891.0,280.0,corine_frac_231,corine_frac_211,corine_frac_131,corine_frac_223,corine_frac_311,corine_frac_23,corine_frac_21,corine_frac_14,corine_frac_2,corine_frac_1,corine_frac_5
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+train,-2.8995264788059374,55.965492221168034,unlabelled-sample_5012_train,8.6,7.4,37.0,43.69,19.4,-0.3,19.700000000000003,11.9,5.1000000000000005,14.3,3.1,663.0,69.0,37.0,17.0,197.0,128.0,178.0,149.0,0.0,0.1045300680082273,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0450291769926243,0.823909628908019,0.0,0.0,0.0,0.0,0.0,0.009552953081569,0.0,0.0,0.0,0.0,0.0019462516181554,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0149689302772274,0.0,0.0,0.0,0.0,6.29911141773578e-05,0.1495592450008517,0.833462581989588,0.0019462516181554,0.0149689302772274,6.29911141773578e-05,0.1045300680082273,0.0,0.0,0.0450291769926243,0.823909628908019,0.0,0.009552953081569,0.0,0.0019462516181554,0.0,0.0,0.0,0.0149689302772274,0.0,6.29911141773578e-05,1715.0,263.0,1145.0,319.0,corine_frac_211,corine_frac_112,corine_frac_142,corine_frac_423,corine_frac_231,corine_frac_21,corine_frac_11,corine_frac_14,corine_frac_2,corine_frac_1,corine_frac_4
+train,-2.898916510818789,54.39426841433838,unlabelled-sample_5013_train,8.1,7.0,35.0,45.99,18.8,-0.7000000000000001,19.5,5.5,10.2,14.1,2.3000000000000003,1113.0,122.0,64.0,22.0,355.0,205.0,250.0,305.0,0.0,0.0299545254405242,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5394350669719734,0.0,0.0,0.0,0.0,0.2447127610794174,0.0,0.0,0.0985998982206355,0.0872977482874493,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0299545254405242,0.5394350669719734,0.4306104075875023,0.0,0.0,0.0299545254405242,0.0,0.0,0.0,0.0,0.0,0.5394350669719734,0.0,0.2447127610794174,0.1858976465080849,0.0,0.0,0.0,0.0,0.0,347.0,53.0,1977.0,705.0,corine_frac_231,corine_frac_311,corine_frac_321,corine_frac_322,corine_frac_112,corine_frac_23,corine_frac_31,corine_frac_32,corine_frac_2,corine_frac_3,corine_frac_1
+train,-2.8988873106094286,57.516474058367734,unlabelled-sample_5014_train,7.4,7.0,38.0,40.67,17.3,-1.0,18.3,5.4,3.9,12.8,2.5,806.0,80.0,50.0,15.0,233.0,163.0,208.0,205.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1506471958978543,0.0,0.0,0.0,0.0,0.0,0.1609120267852389,0.0,0.0,0.0,0.0,0.0,0.5744356317202228,0.0,0.0,0.0158849522183038,0.0,0.0599398324316865,0.0,0.0,0.0,0.0,0.0,0.0,0.0381803609466933,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3115592226830933,0.6502604163702133,0.0381803609466933,0.0,0.0,0.0,0.0,0.0,0.1506471958978543,0.0,0.1609120267852389,0.0,0.5744356317202228,0.0758247846499903,0.0,0.0381803609466933,0.0,0.0,0.0,20.0,3.0,2522.0,1435.0,corine_frac_312,corine_frac_231,corine_frac_211,corine_frac_324,corine_frac_412,corine_frac_31,corine_frac_23,corine_frac_21,corine_frac_3,corine_frac_2,corine_frac_4
+val,-2.8987293044606584,51.83190646062655,unlabelled-sample_5015_val,9.7,6.800000000000001,34.0,46.36,20.6,1.1,19.5,4.9,14.5,15.9,4.0,890.0,97.0,57.0,18.0,279.0,180.0,190.0,255.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5525555543890857,0.0,0.0,0.0,0.0,0.0,0.4474444456109143,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5525555543890857,0.0,0.4474444456109143,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,134.0,20.0,878.0,323.0,corine_frac_211,corine_frac_231,corine_frac_132,corine_frac_212,corine_frac_323,corine_frac_21,corine_frac_23,corine_frac_24,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.89799715965939,52.44230916932116,unlabelled-sample_5016_test,8.1,6.7,34.0,46.2,18.8,-0.7000000000000001,19.5,3.1,12.8,14.1,2.3000000000000003,936.0,103.0,61.0,17.0,292.0,194.0,201.0,267.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1326457111897026,0.0,0.0,0.0,0.0,0.0,0.6429997802921655,0.0,0.0,0.0,0.0,0.0,0.0008815304525075,0.1374690269234164,0.0860039511422078,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7756454914818682,0.2243545085181319,0.0,0.0,0.0,0.0,0.0,0.0,0.1326457111897026,0.0,0.6429997802921655,0.0,0.138350557375924,0.0860039511422078,0.0,0.0,0.0,0.0,0.0,93.0,14.0,2469.0,1250.0,corine_frac_231,corine_frac_313,corine_frac_211,corine_frac_321,corine_frac_312,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_4
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+test,-2.896885840748738,50.766523826481865,unlabelled-sample_5018_test,8.8,6.800000000000001,34.0,46.04,20.0,0.3,19.700000000000003,4.2,10.6,15.0,3.3000000000000003,922.0,104.0,57.0,21.0,299.0,181.0,186.0,284.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3904811308131216,0.0,0.0,0.0,0.0,0.0,0.5458088641645361,0.0,0.0,0.0,0.0,0.0,0.0,0.0637100050223423,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9362899949776576,0.0637100050223423,0.0,0.0,0.0,0.0,0.0,0.0,0.3904811308131216,0.0,0.5458088641645361,0.0,0.0637100050223423,0.0,0.0,0.0,0.0,0.0,0.0,116.0,17.0,2419.0,1608.0,corine_frac_231,corine_frac_211,corine_frac_313,corine_frac_243,corine_frac_312,corine_frac_23,corine_frac_21,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_4
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+train,-2.8965482296014966,56.41219729322787,unlabelled-sample_5020_train,8.200000000000001,7.4,37.0,44.32,18.8,-0.7000000000000001,19.5,11.5,4.800000000000001,14.1,2.6,674.0,67.0,43.0,14.0,192.0,136.0,170.0,173.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2060424267198235,0.6279467719231362,0.0,0.0,0.0,0.0,0.0,0.0427582431826912,0.0,0.0,0.0,0.0,0.0,0.0904357009187069,0.0,0.0328168572556421,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2060424267198235,0.6707050151058275,0.123252558174349,0.0,0.0,0.0,0.0,0.0,0.2060424267198235,0.6279467719231362,0.0,0.0427582431826912,0.0,0.0904357009187069,0.0328168572556421,0.0,0.0,0.0,0.0,0.0,262.0,40.0,1384.0,343.0,corine_frac_211,corine_frac_142,corine_frac_312,corine_frac_231,corine_frac_321,corine_frac_21,corine_frac_14,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.896051690524212,55.369703241025434,unlabelled-sample_5021_test,7.5,7.300000000000001,36.0,44.66,18.5,-1.4,19.9,2.9000000000000004,9.6,13.4,1.9,1011.0,104.0,63.0,17.0,305.0,198.0,236.0,274.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4477114382620579,0.0,0.0,0.0,0.0,0.0,0.1759134307113559,0.0,0.0,0.0,0.0,0.0497452957656751,0.0,0.0,0.2167545831806548,0.1098752520802561,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6236248689734138,0.3763751310265861,0.0,0.0,0.0,0.0,0.0,0.0,0.4477114382620579,0.0,0.1759134307113559,0.0,0.0497452957656751,0.326629835260911,0.0,0.0,0.0,0.0,0.0,31.0,4.0,2647.0,908.0,corine_frac_211,corine_frac_321,corine_frac_231,corine_frac_322,corine_frac_311,corine_frac_21,corine_frac_32,corine_frac_23,corine_frac_2,corine_frac_3,corine_frac_4
+train,-2.895956288545976,57.58731280137443,unlabelled-sample_5022_train,7.5,7.0,38.0,40.36,17.3,-0.9,18.2,5.5,4.1000000000000005,12.8,2.6,808.0,81.0,50.0,15.0,233.0,163.0,210.0,206.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0391212682566587,0.0,0.0,0.0,0.0,0.0,0.6492525683927645,0.0,0.0,0.0,0.0,0.0,0.1180461611805047,0.0,0.0835117611367918,0.0012293080016878,0.0,0.0583719990981799,0.0,0.0,0.0,0.0,0.0,0.0,0.0504669339334123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6883738366494232,0.2611592294171644,0.0504669339334123,0.0,0.0,0.0,0.0,0.0,0.0391212682566587,0.0,0.6492525683927645,0.0,0.1180461611805047,0.1431130682366597,0.0,0.0504669339334123,0.0,0.0,0.0,12.0,1.0,1826.0,819.0,corine_frac_231,corine_frac_312,corine_frac_321,corine_frac_324,corine_frac_412,corine_frac_23,corine_frac_32,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_4
+train,-2.895762288347153,54.51489130731171,unlabelled-sample_5023_train,6.4,7.0,35.0,45.74,17.2,-2.3000000000000003,19.5,1.5,8.3,12.3,0.6000000000000001,1334.0,151.0,80.0,22.0,435.0,245.0,281.0,390.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7083056221460071,0.2478601107887928,0.0,0.0,0.0,0.0,0.0438342670652,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9561657329348,0.0438342670652,0.0,0.0,0.0,0.0,2.0,0.0,3221.0,1679.0,corine_frac_321,corine_frac_322,corine_frac_333,corine_frac_142,corine_frac_112,corine_frac_32,corine_frac_33,corine_frac_13,corine_frac_3,corine_frac_4,corine_frac_1
+test,-2.8950813192712053,53.512324871055455,unlabelled-sample_5024_test,9.3,6.800000000000001,36.0,44.18,19.3,0.5,18.8,7.0,5.6000000000000005,15.0,3.7,869.0,91.0,52.0,20.0,269.0,161.0,217.0,215.0,0.0,0.042709704067696,0.0498628685961169,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0297061473343449,0.8777212800018421,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1222787199981579,0.8777212800018421,0.0,0.0,0.0,0.042709704067696,0.0498628685961169,0.0,0.0297061473343449,0.8777212800018421,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2010.0,308.0,887.0,257.0,corine_frac_211,corine_frac_121,corine_frac_112,corine_frac_142,corine_frac_324,corine_frac_21,corine_frac_12,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.894360103534126,54.202096224291694,unlabelled-sample_5025_train,8.5,7.0,35.0,45.92,19.1,-0.4,19.5,6.0,10.6,14.5,2.7,1111.0,122.0,65.0,22.0,357.0,206.0,252.0,300.0,0.0,0.0552609131075886,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0458371114219721,0.0,0.0,0.0,0.0,0.0,0.0,0.256724817903772,0.0,0.0,0.0,0.0,0.2959500868398284,0.0,0.0442928991959483,0.0,0.0134126118879338,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0750869672385377,0.0,0.0893060710174541,0.0,0.0,0.0,0.1241285213869646,0.0,0.1010980245295607,0.256724817903772,0.3536555979237106,0.1643930382559919,0.1241285213869646,0.0552609131075886,0.0,0.0,0.0458371114219721,0.0,0.0,0.256724817903772,0.0,0.3402429860357767,0.0134126118879338,0.0,0.0,0.1643930382559919,0.0,0.1241285213869646,907.0,139.0,1300.0,358.0,corine_frac_311,corine_frac_231,corine_frac_522,corine_frac_423,corine_frac_421,corine_frac_31,corine_frac_23,corine_frac_42,corine_frac_3,corine_frac_2,corine_frac_4
+train,-2.8937721038122906,55.68818890734513,unlabelled-sample_5026_train,7.1000000000000005,7.300000000000001,35.0,45.27,18.4,-1.9,20.3,2.5,9.2,13.2,1.5,924.0,92.0,59.0,15.0,271.0,187.0,222.0,241.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0577853206021698,0.0,0.0,0.0,0.0,0.0,0.5127097556670329,0.0,0.0,0.0,0.0,0.0,0.0032458053361919,0.0,0.3440605802147508,0.0821985381798545,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5704950762692027,0.4295049237307973,0.0,0.0,0.0,0.0,0.0,0.0,0.0577853206021698,0.0,0.5127097556670329,0.0,0.0032458053361919,0.4262591183946054,0.0,0.0,0.0,0.0,0.0,11.0,1.0,2863.0,1277.0,corine_frac_231,corine_frac_321,corine_frac_322,corine_frac_211,corine_frac_312,corine_frac_23,corine_frac_32,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_4
+train,-2.8934809319178627,51.01523302826968,unlabelled-sample_5027_train,9.7,6.9,35.0,45.81,20.8,1.1,19.700000000000003,4.9,11.5,15.8,4.2,827.0,89.0,49.0,18.0,259.0,163.0,180.0,240.0,0.0,0.0241661797086123,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6362921042714051,0.0,0.0,0.0,0.0,0.0,0.2208793733448349,0.0,0.0,0.0,0.0,0.1186623426751475,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0241661797086123,0.8571714776162401,0.1186623426751475,0.0,0.0,0.0241661797086123,0.0,0.0,0.0,0.6362921042714051,0.0,0.2208793733448349,0.0,0.1186623426751475,0.0,0.0,0.0,0.0,0.0,0.0,404.0,62.0,1652.0,576.0,corine_frac_211,corine_frac_231,corine_frac_311,corine_frac_112,corine_frac_124,corine_frac_21,corine_frac_23,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_1
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+train,-2.781284705273387,59.19497873672393,unlabelled-sample_5261_train,7.7,5.7,37.0,33.32,15.8,0.6000000000000001,15.2,6.4,8.4,12.0,3.7,997.0,117.0,50.0,29.0,341.0,161.0,227.0,268.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1176146177639019,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3364958422332367,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5458895400028614,0.0,0.1176146177639019,0.3364958422332367,0.0,0.5458895400028614,0.0,0.0,0.0,0.0,0.0,0.0,0.1176146177639019,0.0,0.0,0.3364958422332367,0.0,0.0,0.0,0.0,0.5458895400028614,7.0,1.0,1946.0,697.0,corine_frac_523,corine_frac_322,corine_frac_231,corine_frac_323,corine_frac_213,corine_frac_52,corine_frac_32,corine_frac_23,corine_frac_5,corine_frac_3,corine_frac_2
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+val,-2.7809055303078,56.59273278084118,unlabelled-sample_5263_val,7.6,7.300000000000001,37.0,43.33,18.1,-1.2000000000000002,19.3,3.0,4.2,13.3,2.1,732.0,73.0,46.0,14.0,209.0,150.0,182.0,191.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9668169510204044,0.0,0.0,0.0,0.0,0.0,0.0173430561227715,0.0,0.0,0.0,0.0,0.0158399928568239,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.984160007143176,0.0158399928568239,0.0,0.0,0.0,0.0,0.0,0.0,0.9668169510204044,0.0,0.0173430561227715,0.0,0.0158399928568239,0.0,0.0,0.0,0.0,0.0,0.0,39.0,6.0,1983.0,588.0,corine_frac_211,corine_frac_231,corine_frac_311,corine_frac_333,corine_frac_213,corine_frac_21,corine_frac_23,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
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+test,-2.7636539149103374,50.81916641527051,unlabelled-sample_5306_test,9.0,6.9,34.0,46.36,20.3,0.5,19.8,4.3,10.7,15.3,3.5,871.0,97.0,53.0,19.0,279.0,172.0,181.0,263.0,0.0,0.0418826007191384,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0795889180071129,0.0,0.0,0.0,0.0,0.0,0.8785284812737486,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0418826007191384,0.9581173992808616,0.0,0.0,0.0,0.0418826007191384,0.0,0.0,0.0,0.0795889180071129,0.0,0.8785284812737486,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,831.0,127.0,964.0,341.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_321,corine_frac_222,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_4
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+train,-2.760346490142812,57.53185127093147,unlabelled-sample_5313_train,7.800000000000001,7.0,38.0,40.1,17.6,-0.5,18.1,5.800000000000001,4.4,13.0,2.9000000000000004,785.0,78.0,49.0,16.0,227.0,158.0,202.0,199.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.081570674963094,0.0,0.0,0.0,0.0,0.0,0.7905873363004007,0.0,0.0,0.0,0.0,0.0,0.0017944278312581,0.1215830604095476,0.0,0.0,0.0,0.0044645004956994,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8721580112634948,0.1278419887365052,0.0,0.0,0.0,0.0,0.0,0.0,0.081570674963094,0.0,0.7905873363004007,0.0,0.1233774882408057,0.0044645004956994,0.0,0.0,0.0,0.0,0.0,246.0,37.0,1258.0,327.0,corine_frac_231,corine_frac_313,corine_frac_211,corine_frac_324,corine_frac_312,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_5
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+train,-2.7572846756524805,55.561876373319706,unlabelled-sample_5316_train,7.1000000000000005,7.300000000000001,35.0,45.46,18.4,-2.0,20.4,2.5,9.2,13.1,1.4,933.0,93.0,60.0,15.0,275.0,189.0,224.0,245.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.376380472936467,0.0,0.0,0.0,0.0,0.0,0.4610597093168293,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.056731780934012,0.1058280368126915,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8374401822532964,0.1625598177467035,0.0,0.0,0.0,0.0,0.0,0.0,0.376380472936467,0.0,0.4610597093168293,0.0,0.0,0.1625598177467035,0.0,0.0,0.0,0.0,0.0,15.0,2.0,2400.0,1051.0,corine_frac_231,corine_frac_211,corine_frac_322,corine_frac_321,corine_frac_142,corine_frac_23,corine_frac_21,corine_frac_32,corine_frac_2,corine_frac_3,corine_frac_1
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+val,-2.7395093787184597,54.07566548501113,unlabelled-sample_5347_val,8.8,7.0,36.0,45.69,19.4,0.0,19.4,6.300000000000001,10.9,14.7,3.0,1125.0,125.0,66.0,23.0,362.0,209.0,257.0,304.0,0.0,0.0883179883844708,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0100125206724204,0.0,0.0,0.0,0.0,0.0,0.0,0.883983456547415,0.0,0.0,0.0,0.0,0.0004772749375855,0.0,0.0172087594581083,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0983305090568912,0.883983456547415,0.0176860343956938,0.0,0.0,0.0883179883844708,0.0,0.0,0.0100125206724204,0.0,0.0,0.883983456547415,0.0,0.0176860343956938,0.0,0.0,0.0,0.0,0.0,0.0,1476.0,226.0,1046.0,288.0,corine_frac_231,corine_frac_112,corine_frac_313,corine_frac_142,corine_frac_311,corine_frac_23,corine_frac_11,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.737906653142122,55.40363953022104,unlabelled-sample_5348_test,7.5,7.300000000000001,36.0,45.09,18.6,-1.4,20.0,2.8000000000000003,9.6,13.4,1.8,923.0,92.0,58.0,16.0,273.0,185.0,222.0,243.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2392405757540611,0.0,0.0,0.0,0.0,0.0,0.1521235329147676,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4698366886618936,0.1387992026692777,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3913641086688287,0.6086358913311714,0.0,0.0,0.0,0.0,0.0,0.0,0.2392405757540611,0.0,0.1521235329147676,0.0,0.0,0.6086358913311714,0.0,0.0,0.0,0.0,0.0,5.0,0.0,2225.0,1145.0,corine_frac_321,corine_frac_211,corine_frac_231,corine_frac_322,corine_frac_331,corine_frac_32,corine_frac_21,corine_frac_23,corine_frac_3,corine_frac_2,corine_frac_1
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+val,-2.736011724605562,56.87654422374774,unlabelled-sample_5350_val,6.9,7.2,38.0,42.59,17.2,-1.7000000000000002,18.9,2.6,8.6,12.5,1.7000000000000002,811.0,81.0,51.0,14.0,235.0,167.0,198.0,215.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0116768440917862,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1045975363971829,0.8837256195110308,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0116768440917862,0.9883231559082136,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0116768440917862,0.0,0.0,0.9883231559082136,0.0,0.0,0.0,0.0,0.0,2.0,0.0,4999.0,4331.0,corine_frac_322,corine_frac_321,corine_frac_231,corine_frac_423,corine_frac_142,corine_frac_32,corine_frac_23,corine_frac_14,corine_frac_3,corine_frac_2,corine_frac_5
+train,-2.735728295169754,55.945046792915925,unlabelled-sample_5351_train,8.200000000000001,7.4,37.0,43.9,19.0,-0.7000000000000001,19.700000000000003,11.5,4.800000000000001,13.9,2.6,691.0,73.0,38.0,16.0,203.0,136.0,184.0,155.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0197786621436148,0.9802213378563852,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0197786621436148,0.9802213378563852,0.0,0.0,0.0,0.0,0.0,0.0,0.0197786621436148,0.9802213378563852,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,123.0,18.0,940.0,320.0,corine_frac_211,corine_frac_142,corine_frac_123,corine_frac_111,corine_frac_331,corine_frac_21,corine_frac_14,corine_frac_32,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.7349229602167324,57.63236212582333,unlabelled-sample_5352_test,8.0,6.9,38.0,39.73,17.7,-0.2,17.900000000000002,6.1000000000000005,4.7,13.3,3.2,775.0,78.0,48.0,16.0,226.0,156.0,199.0,197.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7411103391044906,0.0,0.0,0.0,0.0,0.0,0.0295718810225227,0.0,0.0,0.0,0.0,0.0,0.2293177798729866,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7706822201270134,0.2293177798729866,0.0,0.0,0.0,0.0,0.0,0.0,0.7411103391044906,0.0,0.0295718810225227,0.0,0.2293177798729866,0.0,0.0,0.0,0.0,0.0,0.0,60.0,9.0,958.0,305.0,corine_frac_211,corine_frac_312,corine_frac_231,corine_frac_313,corine_frac_222,corine_frac_21,corine_frac_31,corine_frac_23,corine_frac_2,corine_frac_3,corine_frac_5
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+test,-2.734667441362309,51.04200346701901,unlabelled-sample_5354_test,9.3,7.0,34.0,46.94,20.8,0.6000000000000001,20.200000000000003,4.5,11.3,15.7,3.7,811.0,87.0,49.0,17.0,251.0,164.0,180.0,233.0,0.0,0.0627514827514826,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.7179109179109175,0.0,0.0,0.0,0.0,0.0,0.2193375993375997,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0627514827514826,0.9372485172485172,0.0,0.0,0.0,0.0627514827514826,0.0,0.0,0.0,0.7179109179109175,0.0,0.2193375993375997,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1355.0,207.0,996.0,317.0,corine_frac_211,corine_frac_231,corine_frac_112,corine_frac_111,corine_frac_512,corine_frac_21,corine_frac_23,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.7342531429394525,54.401011613273695,unlabelled-sample_5355_test,7.2,7.0,35.0,46.13,18.0,-1.6,19.6,2.3000000000000003,9.2,13.2,1.4,1271.0,144.0,76.0,22.0,414.0,233.0,271.0,367.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4911170576606601,0.0,0.0,0.0,0.0,0.016345863355278,0.0,0.0,0.2809652331895644,0.2115718457944974,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4911170576606601,0.50888294233934,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4911170576606601,0.0,0.016345863355278,0.4925370789840618,0.0,0.0,0.0,0.0,0.0,29.0,4.0,3170.0,1394.0,corine_frac_231,corine_frac_321,corine_frac_322,corine_frac_311,corine_frac_512,corine_frac_32,corine_frac_23,corine_frac_31,corine_frac_3,corine_frac_2,corine_frac_4
+test,-2.733817522137183,53.51492121351875,unlabelled-sample_5356_test,9.0,6.9,36.0,43.85,19.1,0.3,18.8,6.7,5.4,14.6,3.5,985.0,105.0,60.0,19.0,305.0,188.0,243.0,257.0,0.0,0.0292807203650277,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0857415361846581,0.82603539579959,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0589423476507241,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1150222565496859,0.82603539579959,0.0589423476507241,0.0,0.0,0.0292807203650277,0.0,0.0,0.0857415361846581,0.82603539579959,0.0,0.0,0.0,0.0589423476507241,0.0,0.0,0.0,0.0,0.0,0.0,1116.0,171.0,1048.0,401.0,corine_frac_211,corine_frac_142,corine_frac_311,corine_frac_112,corine_frac_122,corine_frac_21,corine_frac_14,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.7335424177749283,53.08589793016943,unlabelled-sample_5357_train,8.700000000000001,6.800000000000001,35.0,44.31,18.9,0.0,18.9,6.4,5.1000000000000005,14.4,3.1,834.0,86.0,53.0,15.0,248.0,166.0,207.0,213.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3921459725210329,0.0,0.0,0.0,0.0,0.0,0.4807859053139221,0.0,0.0,0.0,0.0,0.0490591400267692,0.0,0.0780089821382756,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8729318778349551,0.1270681221650449,0.0,0.0,0.0,0.0,0.0,0.0,0.3921459725210329,0.0,0.4807859053139221,0.0,0.1270681221650449,0.0,0.0,0.0,0.0,0.0,0.0,272.0,41.0,1067.0,360.0,corine_frac_231,corine_frac_211,corine_frac_313,corine_frac_311,corine_frac_244,corine_frac_23,corine_frac_21,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_5
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+val,-2.5608246690024283,54.147055212829066,unlabelled-sample_5710_val,8.8,7.0,36.0,45.52,19.3,0.0,19.3,6.300000000000001,10.8,14.7,3.1,1182.0,134.0,68.0,22.0,379.0,217.0,264.0,330.0,0.0,0.0029357972645672,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9283857929596506,0.0,0.0,0.0,0.0,0.0686784097757821,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0029357972645672,0.9283857929596506,0.0686784097757821,0.0,0.0,0.0029357972645672,0.0,0.0,0.0,0.0,0.0,0.9283857929596506,0.0,0.0686784097757821,0.0,0.0,0.0,0.0,0.0,0.0,144.0,22.0,1294.0,566.0,corine_frac_231,corine_frac_311,corine_frac_112,corine_frac_521,corine_frac_243,corine_frac_23,corine_frac_31,corine_frac_11,corine_frac_2,corine_frac_3,corine_frac_1
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+train,-2.5555462235864823,53.83358491797361,unlabelled-sample_5722_train,8.700000000000001,6.9,36.0,43.99,18.9,0.1,18.8,6.5,10.6,14.4,3.2,1205.0,136.0,71.0,22.0,388.0,225.0,278.0,330.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.006898728526431,0.0,0.0,0.0,0.0,0.0,0.0,0.9929553256465732,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0001459458269956,0.0,0.0,0.0,0.006898728526431,0.9929553256465732,0.0,0.0,0.0001459458269956,0.0,0.0,0.0,0.006898728526431,0.0,0.0,0.9929553256465732,0.0,0.0,0.0,0.0,0.0,0.0,0.0001459458269956,0.0,498.0,76.0,701.0,238.0,corine_frac_231,corine_frac_142,corine_frac_512,corine_frac_244,corine_frac_422,corine_frac_23,corine_frac_14,corine_frac_51,corine_frac_2,corine_frac_1,corine_frac_5
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+val,-2.5166964529103217,53.7458238605129,unlabelled-sample_5788_val,8.700000000000001,6.9,36.0,44.33,18.8,0.1,18.7,6.4,10.6,14.4,3.1,1211.0,136.0,73.0,22.0,387.0,230.0,282.0,331.0,0.0890687717749324,0.3793170378602528,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0969319272654968,0.0,0.0,0.0,0.0,0.0,0.0,0.3688813200483272,0.0,0.0,0.0,0.0,0.0092570989510376,0.0,0.056543844099953,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5653177369006821,0.3688813200483272,0.0658009430509906,0.0,0.0,0.4683858096351852,0.0,0.0,0.0969319272654968,0.0,0.0,0.3688813200483272,0.0,0.0658009430509906,0.0,0.0,0.0,0.0,0.0,0.0,14095.0,2162.0,1018.0,262.0,corine_frac_112,corine_frac_231,corine_frac_142,corine_frac_111,corine_frac_313,corine_frac_11,corine_frac_23,corine_frac_14,corine_frac_1,corine_frac_2,corine_frac_3
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+val,-2.516109430569777,54.13788363778858,unlabelled-sample_5790_val,8.6,7.0,36.0,45.14,19.1,-0.1,19.200000000000003,3.9,10.7,14.5,2.9000000000000004,1213.0,139.0,70.0,22.0,391.0,222.0,267.0,344.0,0.0,0.0334970360239852,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0370316830958934,0.0,0.0,0.0,0.0,0.0,0.0,0.9294712808801212,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0705287191198787,0.9294712808801212,0.0,0.0,0.0,0.0334970360239852,0.0,0.0,0.0370316830958934,0.0,0.0,0.9294712808801212,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,370.0,56.0,854.0,231.0,corine_frac_231,corine_frac_142,corine_frac_112,corine_frac_512,corine_frac_421,corine_frac_23,corine_frac_14,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
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+test,-2.4745330476774554,57.30509027067229,unlabelled-sample_5885_test,7.7,7.1000000000000005,39.0,40.63,17.6,-0.5,18.1,5.6000000000000005,4.3,13.0,2.7,795.0,79.0,51.0,15.0,231.0,162.0,198.0,204.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4342179700309755,0.0,0.0,0.0,0.0,0.0,0.4699523060465661,0.0,0.0,0.0,0.0,0.0153242334438135,0.0079632521330225,0.0587983366971904,0.0,0.0,0.0,0.0137439016484317,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9041702760775416,0.0958297239224582,0.0,0.0,0.0,0.0,0.0,0.0,0.4342179700309755,0.0,0.4699523060465661,0.0,0.0820858222740265,0.0137439016484317,0.0,0.0,0.0,0.0,0.0,201.0,30.0,1088.0,293.0,corine_frac_231,corine_frac_211,corine_frac_313,corine_frac_311,corine_frac_324,corine_frac_23,corine_frac_21,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_4
+val,-2.474069412818274,51.4355624513562,unlabelled-sample_5886_val,9.7,7.2,36.0,46.3,21.0,1.0,20.0,4.9,6.0,15.8,4.1000000000000005,795.0,85.0,50.0,16.0,243.0,166.0,181.0,220.0,0.0,0.5091610346969963,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1211924961358324,0.0,0.0,0.0,0.0,0.0,0.3696464691671713,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5091610346969963,0.4908389653030037,0.0,0.0,0.0,0.5091610346969963,0.0,0.0,0.0,0.1211924961358324,0.0,0.3696464691671713,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,12347.0,1894.0,968.0,162.0,corine_frac_112,corine_frac_231,corine_frac_211,corine_frac_324,corine_frac_242,corine_frac_11,corine_frac_23,corine_frac_21,corine_frac_1,corine_frac_2,corine_frac_3
+val,-2.473837725652309,54.515274355396414,unlabelled-sample_5887_val,7.9,7.1000000000000005,36.0,45.39,18.6,-0.8,19.4,3.1,9.9,13.8,2.2,1080.0,117.0,65.0,20.0,342.0,203.0,241.0,302.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9939930007370484,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0060069992629515,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9939930007370484,0.0060069992629515,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9939930007370484,0.0,0.0,0.0060069992629515,0.0,0.0,0.0,0.0,0.0,78.0,11.0,4271.0,2819.0,corine_frac_231,corine_frac_321,corine_frac_522,corine_frac_334,corine_frac_133,corine_frac_23,corine_frac_32,corine_frac_14,corine_frac_2,corine_frac_3,corine_frac_5
+train,-2.473685454557039,56.765195034204126,unlabelled-sample_5888_train,8.0,7.300000000000001,38.0,41.87,18.3,-0.5,18.8,11.4,4.7,13.6,2.8000000000000003,702.0,69.0,43.0,15.0,201.0,141.0,180.0,176.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.8441442806702666,0.0,0.0,0.0,0.0,0.0,0.146971430091748,0.0,0.0,0.0,0.0,0.0,0.0088842892379853,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9911157107620148,0.0088842892379853,0.0,0.0,0.0,0.0,0.0,0.0,0.8441442806702666,0.0,0.146971430091748,0.0,0.0088842892379853,0.0,0.0,0.0,0.0,0.0,0.0,61.0,9.0,2051.0,1149.0,corine_frac_211,corine_frac_231,corine_frac_312,corine_frac_333,corine_frac_323,corine_frac_21,corine_frac_23,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_1
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+val,-2.470752549287063,52.12704418329531,unlabelled-sample_5892_val,9.3,7.1000000000000005,36.0,45.01,20.1,0.7000000000000001,19.4,4.6000000000000005,5.7,15.2,3.7,721.0,73.0,48.0,13.0,209.0,153.0,171.0,190.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2755491383707767,0.0,0.0,0.0,0.0794771440677843,0.0,0.5028142531722957,0.0,0.0,0.1421594643891431,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2755491383707767,0.0794771440677843,0.5028142531722957,0.1421594643891431,0.0,0.0,0.0,0.0,0.0,0.0,0.0,267.0,41.0,1460.0,559.0,corine_frac_231,corine_frac_211,corine_frac_243,corine_frac_222,corine_frac_123,corine_frac_23,corine_frac_21,corine_frac_24,corine_frac_2,corine_frac_5,corine_frac_3
+val,-2.470554900507985,51.1008850565313,unlabelled-sample_5893_val,9.3,7.2,35.0,47.05,21.0,0.5,20.5,4.5,11.2,15.6,3.7,790.0,86.0,48.0,17.0,246.0,162.0,175.0,227.0,0.0,0.0884873459867965,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2192464523842797,0.0,0.0,0.0,0.0,0.0,0.6922662016289237,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0884873459867965,0.9115126540132036,0.0,0.0,0.0,0.0884873459867965,0.0,0.0,0.0,0.2192464523842797,0.0,0.6922662016289237,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1542.0,236.0,1516.0,479.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_111,corine_frac_332,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.470411947041005,55.16248893710346,unlabelled-sample_5894_train,7.6,7.2,36.0,44.73,18.5,-1.1,19.6,2.9000000000000004,9.6,13.5,2.0,861.0,87.0,54.0,16.0,254.0,174.0,209.0,225.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6375805100371702,0.0,0.0008584500352227,0.2055182494213057,0.0,0.1083690516324036,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0476737388738976,0.0,0.0,0.0,0.0,0.0,0.9523262611261024,0.0,0.0476737388738976,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6375805100371702,0.3147457510889321,0.0,0.0,0.0,0.0476737388738976,0.0,8.0,1.0,2484.0,912.0,corine_frac_312,corine_frac_322,corine_frac_324,corine_frac_512,corine_frac_321,corine_frac_31,corine_frac_32,corine_frac_51,corine_frac_3,corine_frac_5,corine_frac_2
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+train,-2.353185839253233,53.73773617416718,unlabelled-sample_6114_train,8.700000000000001,6.9,35.0,45.6,19.3,0.1,19.200000000000003,4.0,10.7,14.7,3.1,1281.0,145.0,77.0,21.0,410.0,244.0,287.0,361.0,0.0,0.2967192114575762,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0197109223026488,0.0,0.0,0.0,0.0,0.0,0.0,0.5533173109036907,0.0,0.0,0.0,0.0,0.1302525553360841,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.316430133760225,0.5533173109036907,0.1302525553360841,0.0,0.0,0.2967192114575762,0.0,0.0,0.0197109223026488,0.0,0.0,0.5533173109036907,0.0,0.1302525553360841,0.0,0.0,0.0,0.0,0.0,0.0,6513.0,999.0,806.0,220.0,corine_frac_231,corine_frac_112,corine_frac_311,corine_frac_142,corine_frac_331,corine_frac_23,corine_frac_11,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
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+test,-2.259410935974076,55.03105367661223,unlabelled-sample_6302_test,7.5,7.2,37.0,44.54,18.3,-1.1,19.4,2.9000000000000004,9.4,13.4,1.9,850.0,86.0,55.0,15.0,250.0,173.0,205.0,223.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1039642885123646,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6942577019814282,0.0380920256920437,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1636859838141633,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1039642885123646,0.732349727673472,0.1636859838141633,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1039642885123646,0.0,0.0,0.732349727673472,0.0,0.1636859838141633,0.0,0.0,0.0,20.0,3.0,1612.0,637.0,corine_frac_321,corine_frac_412,corine_frac_231,corine_frac_322,corine_frac_112,corine_frac_32,corine_frac_41,corine_frac_23,corine_frac_3,corine_frac_4,corine_frac_2
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+train,-2.12439360754839,57.378523705888746,unlabelled-sample_6525_train,8.5,6.9,38.0,39.98,18.2,0.3,17.900000000000002,6.5,5.2,13.7,3.6,780.0,80.0,51.0,16.0,232.0,159.0,190.0,201.0,0.0,5.3741176741250046e-05,0.0,0.0,0.0,0.0,0.0396759065543386,0.0,0.0,0.0,0.0,0.3656546669868373,0.0,0.0,0.0,0.0,0.0,0.2577219540555474,0.0,0.0,0.0,0.0,0.0,0.3368937312265354,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0397296477310798,0.6233766210423848,0.3368937312265354,0.0,0.0,5.3741176741250046e-05,0.0,0.0396759065543386,0.0,0.3656546669868373,0.0,0.2577219540555474,0.0,0.3368937312265354,0.0,0.0,0.0,0.0,0.0,0.0,88.0,13.0,1081.0,409.0,corine_frac_211,corine_frac_312,corine_frac_231,corine_frac_131,corine_frac_112,corine_frac_21,corine_frac_31,corine_frac_23,corine_frac_2,corine_frac_3,corine_frac_1
+train,-2.123948704702374,57.40101019445731,unlabelled-sample_6526_train,8.200000000000001,6.9,38.0,40.03,18.0,0.1,17.900000000000002,6.2,9.6,13.5,3.4000000000000004,799.0,82.0,53.0,16.0,238.0,163.0,193.0,207.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.6334945100135655,0.0,0.0,0.0,0.0,0.0,0.3260015887407068,0.0,0.0,0.0,0.0,0.0,0.0405039012457277,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.9594960987542722,0.0405039012457277,0.0,0.0,0.0,0.0,0.0,0.0,0.6334945100135655,0.0,0.3260015887407068,0.0,0.0405039012457277,0.0,0.0,0.0,0.0,0.0,0.0,103.0,15.0,2354.0,1000.0,corine_frac_211,corine_frac_231,corine_frac_312,corine_frac_523,corine_frac_124,corine_frac_21,corine_frac_23,corine_frac_31,corine_frac_2,corine_frac_3,corine_frac_4
+val,-2.1238778540416865,51.39307436689836,unlabelled-sample_6527_val,9.7,7.4,36.0,46.16,21.1,0.9,20.200000000000003,5.0,6.0,15.8,4.1000000000000005,709.0,75.0,45.0,16.0,215.0,147.0,163.0,193.0,0.0,0.0504443829078993,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.3390029807897195,0.0,0.0,0.0,0.0,0.0,0.6105526363023812,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0504443829078993,0.9495556170921008,0.0,0.0,0.0,0.0504443829078993,0.0,0.0,0.0,0.3390029807897195,0.0,0.6105526363023812,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1843.0,282.0,1524.0,509.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_124,corine_frac_512,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.1237061424482424,54.42415397606697,unlabelled-sample_6528_train,5.800000000000001,7.0,36.0,45.56,16.7,-2.7,19.4,1.2000000000000002,10.3,11.8,0.2,1298.0,145.0,82.0,19.0,416.0,251.0,277.0,382.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0047386798852199,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.99526132011478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0047386798852199,0.99526132011478,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0047386798852199,0.0,0.99526132011478,0.0,0.0,0.0,3.0,0.0,4343.0,3167.0,corine_frac_412,corine_frac_322,corine_frac_323,corine_frac_423,corine_frac_333,corine_frac_41,corine_frac_32,corine_frac_42,corine_frac_4,corine_frac_3,corine_frac_5
+train,-2.122157224958317,53.298592182649095,unlabelled-sample_6529_train,9.5,7.0,36.0,46.37,20.3,0.9,19.4,4.7,5.800000000000001,15.6,3.8,932.0,95.0,62.0,14.0,274.0,197.0,226.0,245.0,0.0,0.2295321727724625,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0010247368194133,0.0,0.0,0.0,0.0,0.0,0.0,0.7694430904081241,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2305569095918758,0.7694430904081241,0.0,0.0,0.0,0.2295321727724625,0.0,0.0,0.0010247368194133,0.0,0.0,0.7694430904081241,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,3823.0,586.0,944.0,263.0,corine_frac_231,corine_frac_112,corine_frac_142,corine_frac_335,corine_frac_141,corine_frac_23,corine_frac_11,corine_frac_14,corine_frac_2,corine_frac_1,corine_frac_5
+train,-2.122013973724657,52.3564378875155,unlabelled-sample_6530_train,9.1,7.300000000000001,36.0,45.82,20.1,0.2,19.9,4.3,5.4,15.0,3.4000000000000004,703.0,69.0,47.0,11.0,196.0,152.0,178.0,179.0,0.0,0.0421719409357355,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0425070944219975,0.0,0.0,0.0,0.0,0.0,0.5534466049755541,0.0,0.0,0.0,0.0,0.2950409279505171,0.0,0.0668334317161957,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0421719409357355,0.5959536993975516,0.3618743596667128,0.0,0.0,0.0421719409357355,0.0,0.0,0.0,0.0425070944219975,0.0,0.5534466049755541,0.0,0.3618743596667128,0.0,0.0,0.0,0.0,0.0,0.0,146.0,22.0,1433.0,599.0,corine_frac_231,corine_frac_311,corine_frac_313,corine_frac_211,corine_frac_112,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_1
+train,-2.120976814250701,52.67414408964571,unlabelled-sample_6531_train,9.3,7.2,36.0,45.84,20.1,0.4,19.700000000000003,4.4,5.7,15.2,3.6,711.0,68.0,47.0,11.0,197.0,151.0,183.0,178.0,0.0,0.0108889456367152,0.1177111911737912,0.0010180424102974,0.0,0.0,0.034376728348142,0.0,0.0,0.0,0.0,0.5032400436651732,0.0,0.0,0.0,0.0,0.0,0.2392002642652182,0.0,0.0,0.0,0.0,0.0508510834550086,0.042713701045654,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1639949075689459,0.7424403079303914,0.0935647845006626,0.0,0.0,0.0108889456367152,0.1187292335840886,0.034376728348142,0.0,0.5032400436651732,0.0,0.2392002642652182,0.0,0.0935647845006626,0.0,0.0,0.0,0.0,0.0,0.0,762.0,116.0,1394.0,401.0,corine_frac_211,corine_frac_231,corine_frac_121,corine_frac_311,corine_frac_312,corine_frac_21,corine_frac_23,corine_frac_12,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.1208851670388515,53.031310146150666,unlabelled-sample_6532_train,8.8,7.0,35.0,46.21,19.6,0.1,19.5,3.9,5.1000000000000005,14.8,3.1,900.0,91.0,62.0,12.0,264.0,197.0,216.0,239.0,0.0,0.2701317598305346,0.0,0.0,0.0,0.0,0.0,0.0,0.0,4.215767666705123e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.614105238993848,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1157208434989503,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2701739175072017,0.614105238993848,0.1157208434989503,0.0,0.0,0.2701317598305346,0.0,0.0,4.215767666705123e-05,0.0,0.0,0.614105238993848,0.0,0.0,0.1157208434989503,0.0,0.0,0.0,0.0,0.0,7207.0,1105.0,694.0,183.0,corine_frac_231,corine_frac_112,corine_frac_322,corine_frac_141,corine_frac_333,corine_frac_23,corine_frac_11,corine_frac_32,corine_frac_2,corine_frac_1,corine_frac_3
+train,-2.120705536674104,57.529142614698486,unlabelled-sample_6533_train,8.4,6.800000000000001,37.0,39.62,18.2,0.3,17.900000000000002,6.4,9.7,13.6,3.5,812.0,84.0,53.0,16.0,243.0,163.0,194.0,212.0,0.0,0.0800082148681684,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4019321355553895,0.0,0.0,0.0,0.0,0.0,0.5106119297954684,0.0,0.0,0.0,0.0,0.0,0.0,0.0074477197809735,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0800082148681684,0.912544065350858,0.0074477197809735,0.0,0.0,0.0800082148681684,0.0,0.0,0.0,0.4019321355553895,0.0,0.5106119297954684,0.0,0.0074477197809735,0.0,0.0,0.0,0.0,0.0,0.0,787.0,120.0,1220.0,394.0,corine_frac_231,corine_frac_211,corine_frac_112,corine_frac_313,corine_frac_321,corine_frac_23,corine_frac_21,corine_frac_11,corine_frac_2,corine_frac_1,corine_frac_3
+test,-2.120410697279036,55.17137620109879,unlabelled-sample_6534_test,7.300000000000001,7.2,37.0,43.33,17.900000000000002,-1.2000000000000002,19.1,2.8000000000000003,9.1,13.0,1.9,835.0,84.0,55.0,15.0,244.0,172.0,204.0,219.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.331818362183999,0.0,0.0,0.0,0.0,0.0443411380001973,0.0172480016802465,0.0,0.3712065131611519,0.2352131171438497,0.0,0.0001728678305554,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.331818362183999,0.6681816378160009,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.331818362183999,0.0,0.0615891396804438,0.6065924981355572,0.0,0.0,0.0,0.0,0.0,20.0,3.0,2945.0,1952.0,corine_frac_321,corine_frac_231,corine_frac_322,corine_frac_311,corine_frac_312,corine_frac_32,corine_frac_23,corine_frac_31,corine_frac_3,corine_frac_2,corine_frac_5
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+train,-2.0656423911902557,54.14241617237899,unlabelled-sample_6622_train,6.800000000000001,7.0,35.0,45.71,17.7,-1.8,19.5,2.1,11.3,12.8,1.2000000000000002,1294.0,146.0,81.0,20.0,417.0,248.0,274.0,382.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2130519715153717,0.0,0.0,0.0,0.0,0.0043528274703198,0.0,0.0,0.6104668462019143,0.1721283548123942,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2130519715153717,0.7869480284846283,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2130519715153717,0.0,0.0043528274703198,0.7825952010143085,0.0,0.0,0.0,0.0,0.0,160.0,24.0,1535.0,701.0,corine_frac_321,corine_frac_231,corine_frac_322,corine_frac_311,corine_frac_122,corine_frac_32,corine_frac_23,corine_frac_31,corine_frac_3,corine_frac_2,corine_frac_4
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+val,-0.6115607146429651,52.8310111120981,unlabelled-sample_8523_val,9.3,7.300000000000001,33.0,50.15,21.3,-0.3,21.6,15.9,5.800000000000001,15.9,3.0,618.0,60.0,40.0,9.0,165.0,137.0,165.0,147.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1585948472866879,0.8031185017851633,0.0,0.0,0.0,0.0,0.0,0.0041016167134452,0.0,0.0,0.0,0.0,0.0341850342147035,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.1585948472866879,0.8072201184986085,0.0341850342147035,0.0,0.0,0.0,0.0,0.0,0.1585948472866879,0.8031185017851633,0.0,0.0041016167134452,0.0,0.0341850342147035,0.0,0.0,0.0,0.0,0.0,0.0,130.0,20.0,1184.0,405.0,corine_frac_211,corine_frac_142,corine_frac_311,corine_frac_231,corine_frac_112,corine_frac_21,corine_frac_14,corine_frac_31,corine_frac_2,corine_frac_1,corine_frac_3
+test,-0.6112136690547393,51.24949184040316,unlabelled-sample_8524_test,9.8,7.9,34.0,51.05,22.4,-0.2,22.6,4.2,15.2,16.5,3.4000000000000004,726.0,75.0,45.0,16.0,220.0,156.0,159.0,192.0,0.0010038566610443,0.4993769289600686,0.0540261709022946,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0932238865962741,0.0,0.0,0.0,0.0,0.0,0.2504297703246947,0.0,0.0,0.0,0.0,0.1019393865556234,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5544069565234077,0.3436536569209689,0.1019393865556234,0.0,0.0,0.500380785621113,0.0540261709022946,0.0,0.0,0.0932238865962741,0.0,0.2504297703246947,0.0,0.1019393865556234,0.0,0.0,0.0,0.0,0.0,0.0,15073.0,2312.0,960.0,147.0,corine_frac_112,corine_frac_231,corine_frac_311,corine_frac_211,corine_frac_121,corine_frac_11,corine_frac_23,corine_frac_31,corine_frac_1,corine_frac_2,corine_frac_3
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+train,-0.604700548501054,51.09109201421904,unlabelled-sample_8529_train,9.8,7.9,35.0,49.58,22.0,0.0,22.0,4.4,14.9,16.3,3.6,753.0,79.0,47.0,18.0,233.0,157.0,161.0,202.0,0.0,1.6848310642076517e-05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0320388314587339,0.0,0.0,0.0,0.0,0.0,0.4882562478152659,0.0,0.0,0.0,0.0,0.2156678496459156,0.0,0.2640202227694424,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.6848310642076517e-05,0.5202950792739999,0.4796880724153581,0.0,0.0,1.6848310642076517e-05,0.0,0.0,0.0,0.0320388314587339,0.0,0.4882562478152659,0.0,0.4796880724153581,0.0,0.0,0.0,0.0,0.0,0.0,174.0,26.0,1458.0,774.0,corine_frac_231,corine_frac_313,corine_frac_311,corine_frac_211,corine_frac_112,corine_frac_23,corine_frac_31,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_1
+train,-0.60313246280538,51.01484402392573,unlabelled-sample_8530_train,9.7,7.9,36.0,49.01,21.9,0.0,21.9,4.4,14.8,16.2,3.6,774.0,82.0,49.0,18.0,242.0,158.0,162.0,210.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0718131117158337,0.6078686364090148,0.0,0.0,0.0,0.0,0.0,0.0746301786456816,0.0,0.0,0.0,0.0,0.0697810317013716,0.0,0.1759070415280982,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0718131117158337,0.6824988150546965,0.2456880732294698,0.0,0.0,0.0,0.0,0.0,0.0718131117158337,0.6078686364090148,0.0,0.0746301786456816,0.0,0.2456880732294698,0.0,0.0,0.0,0.0,0.0,0.0,89.0,13.0,1504.0,484.0,corine_frac_211,corine_frac_313,corine_frac_231,corine_frac_142,corine_frac_311,corine_frac_21,corine_frac_31,corine_frac_23,corine_frac_2,corine_frac_3,corine_frac_1
+train,-0.6019936926499224,50.95255152012327,unlabelled-sample_8531_train,10.0,8.0,37.0,48.31,22.0,0.4,21.6,4.800000000000001,14.9,16.3,4.0,765.0,82.0,48.0,19.0,241.0,154.0,159.0,209.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.2113452545627993,0.0,0.0,0.0,0.0,0.0,0.3535151197909077,0.0,0.0,0.0,0.0,0.0285603445536314,0.0,0.1766847685637382,0.0,0.2298945125289232,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.5648603743537071,0.4351396256462929,0.0,0.0,0.0,0.0,0.0,0.0,0.2113452545627993,0.0,0.3535151197909077,0.0,0.2052451131173697,0.2298945125289232,0.0,0.0,0.0,0.0,0.0,105.0,16.0,2217.0,1288.0,corine_frac_231,corine_frac_322,corine_frac_211,corine_frac_313,corine_frac_311,corine_frac_23,corine_frac_32,corine_frac_21,corine_frac_2,corine_frac_3,corine_frac_5
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+val,1.7480416567083004,52.510184257319274,unlabelled-sample_9999_val,9.8,6.1000000000000005,32.0,45.57,20.1,1.2000000000000002,18.9,11.2,5.800000000000001,15.7,4.3,599.0,64.0,37.0,16.0,178.0,120.0,165.0,142.0,0.0,0.2645695484376261,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0489352240960401,0.2113904530136391,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0570892956895923,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.4180154787631023,0.3135047725336662,0.2113904530136391,0.0570892956895923,0.0,0.4180154787631023,0.2645695484376261,0.0,0.0,0.0489352240960401,0.2113904530136391,0.0,0.0,0.0,0.0,0.0570892956895923,0.0,0.0,0.0,0.0,0.4180154787631023,2405.0,369.0,1353.0,372.0,corine_frac_523,corine_frac_112,corine_frac_211,corine_frac_321,corine_frac_142,corine_frac_52,corine_frac_11,corine_frac_21,corine_frac_5,corine_frac_1,corine_frac_2

From 12495110238e897eafcbd1fcec2abeb51f53b8cf Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 14:46:18 +0200
Subject: [PATCH 015/173] Load unlabelled and original s2bms together

---
 .../butterfly_coords_text_unlabelled.yaml     |  3 +-
 src/data/base_datamodule.py                   |  9 ++++-
 src/data/base_dataset.py                      | 34 ++++++++++++-------
 src/data/butterfly_dataset.py                 |  4 +--
 4 files changed, 34 insertions(+), 16 deletions(-)

diff --git a/configs/data/butterfly_coords_text_unlabelled.yaml b/configs/data/butterfly_coords_text_unlabelled.yaml
index e3c944e..9ac97d0 100644
--- a/configs/data/butterfly_coords_text_unlabelled.yaml
+++ b/configs/data/butterfly_coords_text_unlabelled.yaml
@@ -22,6 +22,7 @@ caption_builder:
 batch_size: 16
 num_workers: 8
 pin_memory: true
-split_mode: "from_df"
+split_mode: "from_file"
 save_split: false
+saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth"
 seed: ${seed}
diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index a1ae875..2b23474 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -133,6 +133,13 @@ def split_data(self) -> None:
             val_indices = self.dataset.df[self.dataset.df.split == "val"].index
             test_indices = self.dataset.df[self.dataset.df.split == "test"].index
 
+            if self.hparams.save_split:
+                split_indices = {
+                    "train_indices": self.dataset.df.loc[train_indices, "name_loc"],
+                    "val_indices": self.dataset.df.loc[val_indices, "name_loc"],
+                    "test_indices": self.dataset.df.loc[test_indices, "name_loc"],
+                }
+
         elif self.hparams.split_mode == "spatial_clusters":
             min_dist = self.hparams.spatial_split_distance_m
             coords = np.array([self.dataset.df.lat, self.dataset.df.lon]).T
@@ -341,7 +348,7 @@ def save_split_indices(self, split_indices: dict[str, Any] | dict):
                 f"split_indices_{self.hparams.dataset_name}_{timestamp}.pth",
             ),
         )
-        print(f"Saved split indices to split_indices_{timestamp}.pth")
+        print(f"Saved split indices to split_indices_{self.hparams.dataset_name}_{timestamp}.pth")
 
     def load_split_indices(self, filepath: str = None) -> dict:
         """Load split indices from a file."""
diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index d81cee0..7338c93 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -28,7 +28,7 @@ def __init__(
         modalities: dict,
         use_target_data: bool = True,
         use_aux_data: Dict[str, List[str] | str] | str | None = None,
-        dataset_name: str = "BaseDataset",
+        dataset_name: str | List[str] = "BaseDataset",
         seed: int = 12345,
         mode: str = "train",
         cache_dir: str = None,
@@ -87,25 +87,31 @@ def __init__(
                 m_dtype = dtype
                 self.modalities[mod] = {"dtype": m_dtype}
 
-        # More precise dataset name (with modalities)
-        self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
-
         # Set data attributes
         self.registry_path = os.path.join(data_dir, "registry.txt")
-        if "unlabel" in dataset_name:
-            dataset_dirname = dataset_name.split("-unlabel")[0]
+        if type(dataset_name) is str:
+            dataset_name = [dataset_name]
+        if "unlabel" in dataset_name[0]:
+            dataset_dirname = dataset_name[0].split("-unlabel")[0]
         else:
-            dataset_dirname = dataset_name
+            dataset_dirname = dataset_name[0]
         self.data_dir = os.path.join(data_dir, dataset_dirname)
         self.cache_dir = cache_dir or os.path.join(data_dir, "cache")
-        for d in [self.data_dir, self.cache_dir]:
+        for d in [self.cache_dir]:
             os.makedirs(d, exist_ok=True)
 
         # Read model ready csv df
-        csv_filename = csv_name or f"model_ready_{dataset_name}.csv"
-        path_csv = os.path.join(self.data_dir, csv_filename)
-        assert os.path.exists(path_csv), f"{path_csv} does not exist."
-        self.df: pd.DataFrame = pd.read_csv(path_csv)
+        for i_ds, ds in enumerate(dataset_name):
+            csv_filename = csv_name or f"model_ready_{ds}.csv"
+            path_csv = os.path.join(self.data_dir, csv_filename)
+            assert os.path.exists(
+                path_csv
+            ), f"{path_csv} does not exist. (Expecting {ds} to exist in {self.data_dir})"
+            tmp_df: pd.DataFrame = pd.read_csv(path_csv)
+            if i_ds == 0:
+                self.df = tmp_df
+            else:
+                self.df = pd.concat([self.df, tmp_df], ignore_index=True)
 
         # Other attributes or placeholders
         self.pooch_cli = None
@@ -132,6 +138,10 @@ def __init__(
         else:
             self.use_aux_data = None
 
+        # More precise dataset name (with modalities)
+        if isinstance(dataset_name, list):
+            dataset_name = "+".join(dataset_name)
+        self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
         self.mode: str = mode  # 'train', 'val', 'test'
         self.records: dict[str, Any] = self.get_records()
 
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index ede0327..d21376c 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -40,9 +40,9 @@ def __init__(
 
         assert not (
             use_unlabelled_data and use_target_data
-        ), "Joint use of unlabelled and target data is not supported yet."
+        ), "Joint use of unlabelled and target data is not supported."
         if use_unlabelled_data:
-            dataset_name = "s2bms-unlabelled-20260529"
+            dataset_name = ["s2bms", "s2bms-unlabelled-20260529"]
         else:
             dataset_name = "s2bms"
 

From 14ed92299a3c79b67f063c4f80b76415f06e2dac Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 14:49:22 +0200
Subject: [PATCH 016/173] adding unlabbelled splits, bypassing hook

---
 ...bms-unlabelled-20260529_2026-05-29-1438.pth | Bin 0 -> 406157 bytes
 ...bms-unlabelled-20260529_2026-05-29-1433.pth | Bin 0 -> 543245 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth
 create mode 100644 data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth
new file mode 100644
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diff --git a/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth b/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth
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From a5b35b3df76b5089ac893ff25c9aa495d043f557 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 14:46:55 +0200
Subject: [PATCH 017/173] adding unlabbelled s2bms data splits, bypassing hook

---
 src/data/base_caption_builder.py      | 47 +++++++++++++++++++++++++++
 src/data/butterfly_caption_builder.py |  3 +-
 2 files changed, 49 insertions(+), 1 deletion(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 843db48..79de05b 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -168,3 +168,50 @@ def get_adjective_for_percentage(value: float) -> str:
         return "predominantly"
     else:
         return "almost entirely"
+
+def sample_adjective_for_percentage(percent: float) -> str:
+    """Convert a percentage (0-100) to a descriptive adjective, randomly sampled from synonyms."""
+    if not 0 <= percent <= 100:
+        raise ValueError(f"Percentage must be between 0 and 100, got {percent}")
+
+    synonyms = {
+        "none":         ["none", "zero", "absent", "nonexistent"],
+        "negligible":   ["negligible", "trivial", "trace", "barely any", "scarcely any"],
+        "minimal":      ["minimal", "tiny", "very little", "marginal", "meager"],
+        "slight":       ["slight", "small", "modest", "limited", "faint"],
+        "some":         ["some", "a bit of", "a portion of", "partial", "a measure of"],
+        "moderate":     ["moderate", "fair", "reasonable", "middling", "decent"],
+        "considerable": ["considerable", "notable", "meaningful", "appreciable", "marked"],
+        "substantial":  ["substantial", "solid", "sizable", "hefty", "goodly"],
+        "significant":  ["significant", "large", "strong", "pronounced", "prominent"],
+        "major":        ["major", "great", "high", "intense", "serious"],
+        "extensive":    ["extensive", "vast", "sweeping", "far-reaching", "immense"],
+        "complete":     ["complete", "total", "full", "entire", "absolute"],
+    }
+
+    if percent == 0:
+        key = "none"
+    elif percent < 10:
+        key = "negligible"
+    elif percent < 20:
+        key = "minimal"
+    elif percent < 30:
+        key = "slight"
+    elif percent < 40:
+        key = "some"
+    elif percent < 50:
+        key = "moderate"
+    elif percent < 60:
+        key = "considerable"
+    elif percent < 70:
+        key = "substantial"
+    elif percent < 80:
+        key = "significant"
+    elif percent < 90:
+        key = "major"
+    elif percent < 100:
+        key = "extensive"
+    else:
+        key = "complete"
+
+    return random.choice(synonyms[key])
\ No newline at end of file
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index d6dee33..af23298 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -7,6 +7,7 @@
 from src.data.base_caption_builder import (
     BaseCaptionBuilder,
     get_adjective_for_percentage,
+    sample_adjective_for_percentage,
 )
 from src.data.base_dataset import BaseDataset
 from src.data_preprocessing.data_utils import (
@@ -133,7 +134,7 @@ def _build_from_template(
 
             if "corine" in token:
                 if convert_corine_perc:
-                    adjective = get_adjective_for_percentage(value)
+                    adjective = sample_adjective_for_percentage(value)
                     formatted_desc = f"{adjective} {formatted_desc}"
                 else:
                     formatted_desc = formatted_desc + f' ({round(value)} {units if units else ""})'

From bee388c6aa29ca5d3cd9b87bd85ca2dc84be28ac Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Mon, 1 Jun 2026 10:14:23 +0200
Subject: [PATCH 018/173] Add avr embedding modalities

# Conflicts:
#	src/data/base_dataset.py
---
 src/data/base_dataset.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 7338c93..baf545b 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -11,6 +11,7 @@
 
 from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError
 from src.utils.data_utils import center_crop_npy
+from src.utils.errors import MissingDataError
 
 TORCH_DTYPES = {
     "float32": torch.float32,
@@ -314,8 +315,8 @@ def setup_tessera(self) -> None:
                                 tessera_con=gt,
                             )
                             continue
-                        except NoTileError or PartialTileError:
-                            print(f"Tile for {fname} could not be retrieved.")
+                        except NoTileError or PartialTileError as e:
+                            print(f"Tile for {fname} could not be retrieved. Error: {e}")
                 self.records.pop(i)
                 print(f"No tile found for {fname} thus it will not be used.")
 

From 991c6043f61727a061a2bfc747b2fa27643f5218 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 08:49:12 +0200
Subject: [PATCH 019/173] Add setup flag

---
 src/data/base_datamodule.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 2b23474..e8c14b8 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -68,6 +68,7 @@ def __init__(
             self.concept_configs = caption_builder.concepts
 
         self.split_data()
+        self._setup_flag = False
 
     @property
     def tabular_dim(self):
@@ -83,9 +84,10 @@ def setup(self, stage: str = "fit") -> None:
 
         Called by model trainer (trainer.fit()).
         """
-
-        # Set up the dataset (download requested modalities)
-        self.dataset.setup()
+        if not self._setup_flag:
+            # Set up the dataset (download requested modalities)
+            self.dataset.setup()
+            self._setup_flag = True
 
     @property
     def batch_size_per_device(self) -> None:

From 9052b2f059e805ce6a718f630c0ee1e7cca3c3e4 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 09:19:31 +0200
Subject: [PATCH 020/173] Re do unlabelled split

---
 ...ms-unlabelled-20260602_2026-06-02-0918.pth | Bin 0 -> 294641 bytes
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 111 +++++++++++++++++-
 2 files changed, 108 insertions(+), 3 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
new file mode 100644
index 0000000000000000000000000000000000000000..429e3b22f30eea24bd0c4487cf24133330d28780
GIT binary patch
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zE7}iJrmf8y8BH182?3_h)X@_WKFiRwT?kzyG=lPI$!J6{5v7@tMNkl>DTYcu66Ibs
z2pNaB<vxzeYRRCHRnrd~6qK4A|I?CwQ@}7&jX1=lHBG<I=&a&puac3uPwvCTQ%i;)
z=S7vK;~GRq+8PW;+64?pbSRUEX5%T8L~6dQ()&p9;NJC{$@gOSe%E~vyAQkWQtUng
zwfPv-=98}bG<Kg|gW7x^yDu`-=F1!)`RY0V$=9*_2Gr(TkJ^0aQJb?KwfVl|e(<Qx
z`5tPs?ope|vHLMcZGJMS&CedS`DK9G9GPs?w{Cl2{P5PRN4Et2YShPYT-et8LOHXU
zdLIXaC}`t+G(dRf+U13n_MO>h>Bws4kF?xeECCMl2E`TI|5FP2Etgo%W_C_KzSdfq
zPv@R&w%SW;tF_ru@aT>FL|)im|NZN<cbD<k=E63Ddx{F%PS;xVxIE1-FCAK4X|ATV
z+1)oL@33ywDX+kuqwXK?R^zYD71$TtT2O(7V`+1(wb*RcX2)~6>AQ5f>1F8d{`+>1
zl=*9O8TR6dQLM)YJw4_nsBHJV|G0GMq5s$a!Qk$KyeGY{-A?a<M-LvKPZ@D@ON+JH
znVas!kPS&#0flCn#F`^}Apsb|kjQ?#q^f12(UK&C_<&x^bHAq-chI{2YSeGZ{HXe~
zO6OY*y8S8E;$G>=Ck`(U^VDf`&@fzlYD>|Uw)1+JwHfe+^=-!rv%Y4Q4s+L`zRz&K
zQkeT-iVZW>F}~L@efW4$m3wn)n57Q5zDH%w`AT7y*Plv_`ozRtJ>~8gD-G1_L-75-
je`bR}2!4AycZuV^%#rHCiM*Vz<l|ZO36>;ZKd<{Avk=ui

literal 0
HcmV?d00001

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 6391757..a30ea17 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -186,16 +186,121 @@
     "m"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8",
+   "metadata": {},
+   "source": [
+    "# Re-do Unlabelled split"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8",
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\")\n",
+    "df = df[df.split == \"train\"]\n",
+    "\n",
+    "train_unlabelled = list(df.name_loc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth\"\n",
+    "split_indices = torch.load(pth, weights_only=False)\n",
+    "\n",
+    "print(\"Before test/val/train:\")\n",
+    "s = 0\n",
+    "for k, v in split_indices.items():\n",
+    "    if not k == \"clusters\":\n",
+    "        print(k, len(v))\n",
+    "        s += len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_indices[\"train_indices\"] = pd.Series(\n",
+    "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_test_indices = torch.load(\n",
+    "    \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth\",\n",
+    "    weights_only=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "split_indices[\"val_indices\"] = pd.Series(\n",
+    "    [i for i in list(val_test_indices[\"val_indices\"]) if \"UKBMS\" in i]\n",
+    ")\n",
+    "split_indices[\"test_indices\"] = pd.Series(\n",
+    "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"After test/val/train:\")\n",
+    "s = 0\n",
+    "for k, v in split_indices.items():\n",
+    "    if not k == \"clusters\":\n",
+    "        print(k, len(v))\n",
+    "        s += len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "torch.save(\n",
+    "    split_indices,\n",
+    "    \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth\",\n",
+    ")"
+   ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 5
 }

From e991dc9cf8bc6c11c34037bbd5e0569ee5cb5c42 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:52:18 +0200
Subject: [PATCH 021/173] Add size print statements

---
 src/data/base_datamodule.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index e8c14b8..12d822f 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -267,12 +267,15 @@ def split_data(self) -> None:
         if split_data_from_inds:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
             self.data_train.dataset.mode = "train"
+            print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
             self.data_val.dataset.mode = "val"
+            print(f"Validate dataset split size: {len(self.data_val)}")
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)
                 self.data_test.dataset.mode = "test"
+                print(f"Test dataset split size: {len(self.data_test)}")
             else:
                 self.data_test = None
 

From e726b1c8a59bc078ff3024ccfa93b51772792970 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:56:08 +0200
Subject: [PATCH 022/173] Make format hook changes

---
 .../yield_africa_tessera_preprocess.py        | 42 +++++++++++++------
 1 file changed, 30 insertions(+), 12 deletions(-)

diff --git a/src/data_preprocessing/yield_africa_tessera_preprocess.py b/src/data_preprocessing/yield_africa_tessera_preprocess.py
index 3a1e770..cea8a85 100644
--- a/src/data_preprocessing/yield_africa_tessera_preprocess.py
+++ b/src/data_preprocessing/yield_africa_tessera_preprocess.py
@@ -36,8 +36,8 @@
 import multiprocessing
 import os
 import socket
-import time
 import sys
+import time
 from pathlib import Path
 
 # Ensure the project root is on sys.path when the script is run directly.
@@ -45,7 +45,11 @@
 
 import pandas as pd
 
-from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError, get_tessera_embeds
+from src.data_preprocessing.tessera_embeds import (
+    NoTileError,
+    PartialTileError,
+    get_tessera_embeds,
+)
 
 log = logging.getLogger(__name__)
 
@@ -65,6 +69,7 @@
 def _init_worker(cache_dir: str, use_local_registry: bool, registry_dir: str) -> None:
     """Pool initializer: runs once per worker process to set up GeoTessera."""
     from geotessera import GeoTessera
+
     global _process_gt
     socket.setdefaulttimeout(60)
     embeddings_dir = str(Path(cache_dir) / "raw")
@@ -78,13 +83,18 @@ def _init_worker(cache_dir: str, use_local_registry: bool, registry_dir: str) ->
 def _worker_fetch(args: tuple) -> str:
     """Multiprocessing worker — reuses the per-process GeoTessera instance.
 
-    Must be a top-level function so it is picklable across processes.
-    Returning normally means success; raising means error (logged by caller).
+    Must be a top-level function so it is picklable across processes. Returning normally means
+    success; raising means error (logged by caller).
     """
     lon, lat, name_loc, year, save_dir, tile_size = args
     get_tessera_embeds(
-        lon=lon, lat=lat, name_loc=name_loc, year=year,
-        save_dir=save_dir, tile_size=tile_size, tessera_con=_process_gt,
+        lon=lon,
+        lat=lat,
+        name_loc=name_loc,
+        year=year,
+        save_dir=save_dir,
+        tile_size=tile_size,
+        tessera_con=_process_gt,
     )
     return name_loc
 
@@ -160,8 +170,8 @@ def fetch_tessera_tiles(
     # Per-task timeout: when a worker process exceeds this, it is killed and
     # the record added to stuck.txt.  Multiprocessing (unlike threading) allows
     # true process termination, so stuck downloads cannot block forward progress.
-    HEARTBEAT = 15      # seconds between "still fetching" log lines
-    TILE_TIMEOUT = 60   # seconds per record before the worker process is killed
+    HEARTBEAT = 15  # seconds between "still fetching" log lines
+    TILE_TIMEOUT = 60  # seconds per record before the worker process is killed
     # Note: stuck workers spin at 100% CPU and leak ~55 MB/s of rasterio
     # MemoryFile objects inside GeoTessera's fetch loop.  60s caps peak memory
     # at ~3 GB per stuck record and recovers 3x faster than the old 180s limit.
@@ -177,13 +187,17 @@ def fetch_tessera_tiles(
         else:
             stuck_records = set(stuck_file.read_text().splitlines())
             if stuck_records:
-                print(f"  Skipping {len(stuck_records)} previously-stuck record(s): {sorted(stuck_records)}")
+                print(
+                    f"  Skipping {len(stuck_records)} previously-stuck record(s): {sorted(stuck_records)}"
+                )
 
     rows = [row for _, row in df.iterrows() if row.name_loc not in stuck_records]
     done = 0
 
     _pool_initargs = (cache_dir, _use_local_registry, str(_default_registry_dir))
-    pool = multiprocessing.Pool(processes=workers, initializer=_init_worker, initargs=_pool_initargs)
+    pool = multiprocessing.Pool(
+        processes=workers, initializer=_init_worker, initargs=_pool_initargs
+    )
     try:
         for row in rows:
             args = (row.lon, row.lat, row.name_loc, int(row.year), str(save_dir), tile_size)
@@ -204,7 +218,9 @@ def fetch_tessera_tiles(
                     print(f"  Skipped {row.name_loc}: no TESSERA data for this location/year")
                     break
                 except PartialTileError:
-                    print(f"  Skipped {row.name_loc}: tile too close to mosaic edge, not enough context")
+                    print(
+                        f"  Skipped {row.name_loc}: tile too close to mosaic edge, not enough context"
+                    )
                     break
                 except Exception as exc:
                     print(f"  ERROR fetching {row.name_loc}: {exc}")
@@ -213,7 +229,9 @@ def fetch_tessera_tiles(
             if timed_out:
                 pool.terminate()
                 pool.join()
-                pool = multiprocessing.Pool(processes=workers, initializer=_init_worker, initargs=_pool_initargs)
+                pool = multiprocessing.Pool(
+                    processes=workers, initializer=_init_worker, initargs=_pool_initargs
+                )
                 with open(stuck_file, "a") as fh:
                     fh.write(row.name_loc + "\n")
                 print(

From 325f0ee7e224a8613bd8a769988daf881c416130 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:57:51 +0200
Subject: [PATCH 023/173] Remove full model name printing

---
 src/models/base_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index d0f92e8..350ae31 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -78,7 +78,7 @@ def setup(self, stage: str) -> None:
         """Updates model based data-bound configurations (through datamodule), This method is
         called after trainer is initialized and datamodule is available."""
         if self.setup_flag:
-            print(f"Model {self.__str__()} is already set up!")
+            print("Model is already set up!")
             return
 
         # If trainer is attached get num_classes and tabular_dim from datamodule (data-dependent)

From a67f56af616f3adba5231726a7be4e16bf0a691e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:09:52 +0200
Subject: [PATCH 024/173] Hook security

---
 src/utils/utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/utils/utils.py b/src/utils/utils.py
index f1771a2..a34d208 100644
--- a/src/utils/utils.py
+++ b/src/utils/utils.py
@@ -119,8 +119,9 @@ def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     if torch.cuda.is_available():
                         torch.cuda.empty_cache()
                         torch.cuda.ipc_collect()
-            except Exception:
+            except Exception as e:
                 # cleanup should never mask the original exception
+                print(e)
                 pass
 
         return metric_dict, object_dict

From 7d80c9bba5e3b509b493eb8800f794b7d907e203 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:23:28 +0200
Subject: [PATCH 025/173] Fix logging

---
 src/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index 92207bd..69d7c09 100644
--- a/src/train.py
+++ b/src/train.py
@@ -151,7 +151,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         )
 
         val_metrics = trainer.callback_metrics
-        wandb_logger.log({f"best_val_{k}": v for k, v in val_metrics.items()})
+        wandb_logger.log_metrics({f"best_val_{k}": v for k, v in val_metrics.items()})
 
     if cfg.get("test"):
         log.info("Starting testing!")

From 808d876576f88bd6eeeb57cdccc0e7ee27bdc17b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:23:53 +0200
Subject: [PATCH 026/173] Fix: getting size

---
 src/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index 69d7c09..c7b729a 100644
--- a/src/train.py
+++ b/src/train.py
@@ -118,7 +118,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     else:
                         data_name = "satclip"
                 else:
-                    data_name = f"{k}_{data_dict[k]['size']}"
+                    data_name = f"{k}_{data_dict[k].get('size', '')}"
             else:
                 ks = list(data_dict.keys())
                 data_name = str("".join([f'{k}_{data_dict[k].get("size", "")}' for k in ks]))

From e349b5a3c2eed68dbf56417bbaae717d95c92675 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:26:02 +0200
Subject: [PATCH 027/173] Hook fixes

---
 src/data/base_caption_builder.py              | 25 ++++++-------
 .../geo_encoders/average_encoder.py           |  2 +-
 .../components/geo_encoders/cnn_encoder.py    | 35 ++++++++++++-------
 src/models/components/geo_encoders/geoclip.py |  1 -
 .../components/pred_heads/base_pred_head.py   |  2 +-
 src/models/predictive_model.py                |  4 ++-
 6 files changed, 40 insertions(+), 29 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 79de05b..875cabc 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -169,24 +169,25 @@ def get_adjective_for_percentage(value: float) -> str:
     else:
         return "almost entirely"
 
+
 def sample_adjective_for_percentage(percent: float) -> str:
     """Convert a percentage (0-100) to a descriptive adjective, randomly sampled from synonyms."""
     if not 0 <= percent <= 100:
         raise ValueError(f"Percentage must be between 0 and 100, got {percent}")
 
     synonyms = {
-        "none":         ["none", "zero", "absent", "nonexistent"],
-        "negligible":   ["negligible", "trivial", "trace", "barely any", "scarcely any"],
-        "minimal":      ["minimal", "tiny", "very little", "marginal", "meager"],
-        "slight":       ["slight", "small", "modest", "limited", "faint"],
-        "some":         ["some", "a bit of", "a portion of", "partial", "a measure of"],
-        "moderate":     ["moderate", "fair", "reasonable", "middling", "decent"],
+        "none": ["none", "zero", "absent", "nonexistent"],
+        "negligible": ["negligible", "trivial", "trace", "barely any", "scarcely any"],
+        "minimal": ["minimal", "tiny", "very little", "marginal", "meager"],
+        "slight": ["slight", "small", "modest", "limited", "faint"],
+        "some": ["some", "a bit of", "a portion of", "partial", "a measure of"],
+        "moderate": ["moderate", "fair", "reasonable", "middling", "decent"],
         "considerable": ["considerable", "notable", "meaningful", "appreciable", "marked"],
-        "substantial":  ["substantial", "solid", "sizable", "hefty", "goodly"],
-        "significant":  ["significant", "large", "strong", "pronounced", "prominent"],
-        "major":        ["major", "great", "high", "intense", "serious"],
-        "extensive":    ["extensive", "vast", "sweeping", "far-reaching", "immense"],
-        "complete":     ["complete", "total", "full", "entire", "absolute"],
+        "substantial": ["substantial", "solid", "sizable", "hefty", "goodly"],
+        "significant": ["significant", "large", "strong", "pronounced", "prominent"],
+        "major": ["major", "great", "high", "intense", "serious"],
+        "extensive": ["extensive", "vast", "sweeping", "far-reaching", "immense"],
+        "complete": ["complete", "total", "full", "entire", "absolute"],
     }
 
     if percent == 0:
@@ -214,4 +215,4 @@ def sample_adjective_for_percentage(percent: float) -> str:
     else:
         key = "complete"
 
-    return random.choice(synonyms[key])
\ No newline at end of file
+    return random.choice(synonyms[key])
diff --git a/src/models/components/geo_encoders/average_encoder.py b/src/models/components/geo_encoders/average_encoder.py
index f3152bd..46258d5 100644
--- a/src/models/components/geo_encoders/average_encoder.py
+++ b/src/models/components/geo_encoders/average_encoder.py
@@ -36,7 +36,7 @@ def _setup(self) -> List[str]:
     def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Data forward pass through the encoder."""
         tile = batch.get("eo", {}).get(self.geo_data_name)
-        
+
         # NaNs vs 0s are specified in dataset configs and returned from BaseDataset class
         feats = self.geo_encoder(tile.nanmean(dim=(-2, -1)))
         if self.extra_projector:
diff --git a/src/models/components/geo_encoders/cnn_encoder.py b/src/models/components/geo_encoders/cnn_encoder.py
index 918b755..75c80c5 100644
--- a/src/models/components/geo_encoders/cnn_encoder.py
+++ b/src/models/components/geo_encoders/cnn_encoder.py
@@ -3,16 +3,13 @@
 import torch
 import torchvision.models as models
 from torch import nn
-from torchgeo.models import resnet50, ResNet50_Weights, ResNet18_Weights, resnet18
+from torchgeo.models import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
 from src.utils.errors import IllegalArgumentCombination
 
-RN_DIM = {
-    18 : 512,
-    34: 512,
-    50: 2048
-}
+RN_DIM = {18: 512, 34: 512, 50: 2048}
+
 
 class CNNEncoder(BaseGeoEncoder):
     """Convolutional neural network EO encoder. Adapted from PECL.
@@ -41,7 +38,12 @@ def __init__(
             assert resnet_version in [18, 34, 50], f"Unsupported resnet version: {resnet_version}"
             self.resnet_version = resnet_version
 
-            assert pretrained_cnn in ["imagenet", "IMAGENET1K_V1", 'SSL4EO_RGB_MOCO', None], f"Unsupported pretrained_cnn: {pretrained_cnn}"
+            assert pretrained_cnn in [
+                "imagenet",
+                "IMAGENET1K_V1",
+                "SSL4EO_RGB_MOCO",
+                None,
+            ], f"Unsupported pretrained_cnn: {pretrained_cnn}"
             self.pretrained_cnn = pretrained_cnn
 
             self.output_dim = RN_DIM[resnet_version]
@@ -52,7 +54,9 @@ def __init__(
         self.geo_data_name = geo_data_name
 
         self.set_n_input_bands(input_n_bands)
-        assert (self.input_n_bands >= 3 and type(self.input_n_bands) is int), f"input_n_bands must be int >=3, got {self.input_n_bands}"
+        assert (
+            self.input_n_bands >= 3 and type(self.input_n_bands) is int
+        ), f"input_n_bands must be int >=3, got {self.input_n_bands}"
 
     def set_n_input_bands(self, n_bands: int | None = None) -> None:
         """Sets number of input bands based on geo_data_name if n_bands is None.
@@ -77,6 +81,7 @@ def set_n_input_bands(self, n_bands: int | None = None) -> None:
     @override
     def _setup(self) -> List[str]:
         """Gets backbone model given configuration stored in self.
+
         :return: backbone model
         """
         trainable_modules = []
@@ -87,7 +92,9 @@ def _setup(self) -> List[str]:
                 if self.resnet_version == 18:
                     self.geo_encoder = resnet18(weights=ResNet18_Weights.SENTINEL2_RGB_MOCO)
                 elif self.resnet_version == 34:
-                    raise IllegalArgumentCombination('SSL4EO_RGB_MOCO weights are not available for RN-34')
+                    raise IllegalArgumentCombination(
+                        "SSL4EO_RGB_MOCO weights are not available for RN-34"
+                    )
                 else:
                     self.geo_encoder = resnet50(weights=ResNet50_Weights.SENTINEL2_RGB_MOCO)
             # Imagenet
@@ -123,7 +130,7 @@ def _setup(self) -> List[str]:
                             self.geo_encoder.conv1.weight[:, i, :, :] = weight[:, i % 3, :, :]
 
                 # Ensure replaced layer is not frozen
-                trainable_modules.append('geo_encoder.conv1')
+                trainable_modules.append("geo_encoder.conv1")
 
             # I think for features fc often is replaced with identity?
             self.geo_encoder.fc = nn.Identity()
@@ -145,8 +152,10 @@ def forward(
         :param batch: input batch
         :return: extracted features
         """
-        eo_data = batch.get("eo", KeyError(f"Batch must contain batch['eo']"))
-        eo_data = eo_data.get(self.geo_data_name,  KeyError(f"Batch must contain batch['eo']['{self.geo_data_name}']"))
+        eo_data = batch.get("eo", KeyError("Batch must contain batch['eo']"))
+        eo_data = eo_data.get(
+            self.geo_data_name, KeyError(f"Batch must contain batch['eo']['{self.geo_data_name}']")
+        )
         dtype = self.dtype
 
         if eo_data.dtype != dtype:
@@ -156,4 +165,4 @@ def forward(
         if self.extra_projector:
             feats = self.extra_projector(feats)
 
-        return feats
\ No newline at end of file
+        return feats
diff --git a/src/models/components/geo_encoders/geoclip.py b/src/models/components/geo_encoders/geoclip.py
index 9224d60..85eca81 100644
--- a/src/models/components/geo_encoders/geoclip.py
+++ b/src/models/components/geo_encoders/geoclip.py
@@ -2,7 +2,6 @@
 
 import torch
 from geoclip import LocationEncoder
-from torch.nn import functional as F
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
 
diff --git a/src/models/components/pred_heads/base_pred_head.py b/src/models/components/pred_heads/base_pred_head.py
index 02e2af6..29f19ae 100644
--- a/src/models/components/pred_heads/base_pred_head.py
+++ b/src/models/components/pred_heads/base_pred_head.py
@@ -76,4 +76,4 @@ def dtype(self) -> torch.dtype | None:
             raise RuntimeError("Prediction head has multiple dtypes")
         elif len(dtypes) == 0:
             return None
-        return dtypes.pop()
\ No newline at end of file
+        return dtypes.pop()
diff --git a/src/models/predictive_model.py b/src/models/predictive_model.py
index 9e3c28a..a956eb0 100644
--- a/src/models/predictive_model.py
+++ b/src/models/predictive_model.py
@@ -112,7 +112,9 @@ def _setup_encoders_adapters(self):
         self.trainable_modules.extend(new_modules)
 
         if self.normalize_features:
-            self.normalizer = nn.LayerNorm(self.geo_encoder.output_dim, dtype=self.geo_encoder.dtype)
+            self.normalizer = nn.LayerNorm(
+                self.geo_encoder.output_dim, dtype=self.geo_encoder.dtype
+            )
             self.trainable_modules.append("normalizer")
             print("Model set up to normalise geo_encoder features.")
 

From 633ff31c2beff85edd333e3bc1175b802603cc5b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:20:02 +0200
Subject: [PATCH 028/173] Save average embeddings to csv

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 67 +++++++++++++++++++
 1 file changed, 67 insertions(+)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index a30ea17..65e30f8 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -292,6 +292,73 @@
     "    \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth\",\n",
     ")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16",
+   "metadata": {},
+   "source": [
+    "# Average encodings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "from src.utils.data_utils import center_crop_npy\n",
+    "\n",
+    "mod = \"tessera\"\n",
+    "size = 128\n",
+    "\n",
+    "paths = glob.glob(f\"data/s2bms/eo/{mod}/*UKBMS*.npy\")\n",
+    "rows = []\n",
+    "for p in paths:\n",
+    "    arr = np.load(p).transpose(2, 0, 1)\n",
+    "    if arr.dtype != np.dtype(\"float32\"):\n",
+    "        arr = arr.astype(dtype=\"float32\", copy=False)\n",
+    "    arr = center_crop_npy(arr, (64 if mod == \"aef\" else 128, size, size))\n",
+    "    if np.isinf(arr).any():\n",
+    "        arr[np.isinf(arr)] = np.nan\n",
+    "    tensor = torch.from_numpy(arr)\n",
+    "    row = {f\"emb_{i}\": v.item() for i, v in enumerate(tensor.nanmean(dim=(-2, -1)))}\n",
+    "    row[\"name_loc\"] = p.split(\"/\")[-1].split(\".\")[0].replace(f\"{mod}_\", \"\")\n",
+    "    rows.append(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(rows)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(f\"data/s2bms/eo/avr_{mod}_{size}.csv\"):\n",
+    "    df.to_csv(f\"data/s2bms/eo/avr_{mod}_{size}.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 57cc2cb25839302fea0fa95c952b61d0971ae5f3 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:26:54 +0200
Subject: [PATCH 029/173] emb_ reading for csv of avr embeds

---
 src/data/base_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index baf545b..78f1490 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -167,7 +167,7 @@ def get_records(self) -> dict[str, Any]:
                 self.df = self.df.merge(df, on="name_loc", how="left")
 
                 max_no = 128 if modality == "tessera_avr" else 64
-                columns.extend([f"avr_{i}" for i in range(0, max_no)])
+                columns.extend([f"emb_{i}" for i in range(0, max_no)])
             else:
                 # Add paths
                 self.add_modality_paths_to_df(

From aa442644fddf9e0906cb5314823d12b9c158b85c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:27:40 +0200
Subject: [PATCH 030/173] Return name_loc parameter in dataset for tracing back
 data

---
 src/data/base_dataset.py      | 2 ++
 src/data/butterfly_dataset.py | 5 +++++
 2 files changed, 7 insertions(+)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 78f1490..a99b878 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -38,6 +38,7 @@ def __init__(
         use_features: bool = True,
         csv_name: str = None,
         dtype: str = "float32",
+        return_name_loc: bool = False,
     ) -> None:
         """Interface for any use case dataset.
 
@@ -145,6 +146,7 @@ def __init__(
         self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
         self.mode: str = mode  # 'train', 'val', 'test'
         self.records: dict[str, Any] = self.get_records()
+        self.return_name_loc: bool = return_name_loc
 
     @final
     def get_records(self) -> dict[str, Any]:
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index d21376c..5eb9a61 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -23,6 +23,7 @@ def __init__(
         cache_dir: str = None,
         mock: bool = False,
         dtype: str = "float32",
+        return_name_loc: bool = False,
     ) -> None:
         """A dataset implementation for the Butterfly diversity use case.
 
@@ -57,6 +58,7 @@ def __init__(
             implemented_mod={"s2", "tessera", "coords", "aef", "aef_avr", "tessera_avr"},
             mock=mock,
             dtype=dtype,
+            return_name_loc=return_name_loc,
         )
 
     def setup(self):
@@ -218,6 +220,9 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 else:
                     formatted_row["aux"][aux_cat] = [row[v] for v in vals]
 
+        if self.return_name_loc:
+            formatted_row["name_loc"] = row["name_loc"]
+
         return formatted_row
 
 

From 13fcdc14d0cbbd6dfc69c2c12ebcebc0d5c2c797 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:28:50 +0200
Subject: [PATCH 031/173] Freezing statement

---
 src/models/base_model.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 350ae31..9a3259c 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -104,8 +104,11 @@ def _setup(self, stage: str) -> None:
     @final
     def full_freezer(self):
         """Freeze the whole network."""
+        print("--------Frozen--------")
         for name, param in self.named_parameters():
             param.requires_grad = False
+        print("Full model")
+        print("------------------------")
 
         for name, module in self.named_modules():
             module.eval()

From 9c853668941068dc3c79898b0294ef69d760866e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:29:51 +0200
Subject: [PATCH 032/173] Add dtype conversion

---
 src/models/components/loss_fns/bce_loss.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/models/components/loss_fns/bce_loss.py b/src/models/components/loss_fns/bce_loss.py
index b4f6663..d3ddab1 100644
--- a/src/models/components/loss_fns/bce_loss.py
+++ b/src/models/components/loss_fns/bce_loss.py
@@ -21,9 +21,10 @@ def forward(
         mode: str | None = None,
         **kwargs,
     ) -> torch.Tensor or Dict[str, torch.Tensor]:
+        """Forward pass to get BCE loss."""
 
         labels = labels if labels is not None else batch.get("target")
-        loss = self.criterion(pred, labels)
+        loss = self.criterion(pred, labels.to(pred.dtype))
 
         if "return_label" in kwargs:
             return {f"{mode}_{self.name}": loss}

From 025e1aaae1db6529e8f64d19fb05259de997244b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:30:17 +0200
Subject: [PATCH 033/173] Make geoclip projector as optional argument

---
 src/models/components/text_encoders/clip_text_encoder.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 764af9a..33254f0 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -10,7 +10,7 @@
 
 
 class ClipTextEncoder(BaseTextEncoder):
-    def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -> None:
+    def __init__(self, hf_cache_dir: str = "../.cache", use_geoclip_projector=True) -> None:
         super().__init__()
         self.processor = CLIPProcessor.from_pretrained(
             "openai/clip-vit-large-patch14",
@@ -22,12 +22,13 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
             cache_dir=hf_cache_dir,
         )
 
-        self.projector = GeoCLIP().image_encoder.mlp
+        if use_geoclip_projector:
+            self.projector = GeoCLIP().image_encoder.mlp
 
         self.model.vision_model = None
         self.model.visual_projection = None
 
-        self.output_dim = 512
+        self.output_dim = 512 if use_geoclip_projector else 768
 
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:

From 38312df18b49eb519b8e2128b77269bcbeaf6dc0 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:30:53 +0200
Subject: [PATCH 034/173] Fix dataset name creation for multi-modal encoders

---
 src/train.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index c7b729a..3e170c8 100644
--- a/src/train.py
+++ b/src/train.py
@@ -121,7 +121,11 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     data_name = f"{k}_{data_dict[k].get('size', '')}"
             else:
                 ks = list(data_dict.keys())
-                data_name = str("".join([f'{k}_{data_dict[k].get("size", "")}' for k in ks]))
+                ks_new = [
+                    f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
+                    for k in ks
+                ]
+                data_name = "-".join(map(str, ks_new))
 
             # Log details to wandb
             wandb_logger.log_metrics(

From d6515e3410239e93e768d7065bb60ec70e70f5d6 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:52:56 +0200
Subject: [PATCH 035/173] Data split and encoding merging for aef_256 averages

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 101 +++++++++++++++++-
 1 file changed, 97 insertions(+), 4 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 65e30f8..2d815ae 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -9,6 +9,7 @@
    "source": [
     "import glob\n",
     "import os\n",
+    "import time\n",
     "\n",
     "import pandas as pd\n",
     "import torch\n",
@@ -298,7 +299,8 @@
    "id": "16",
    "metadata": {},
    "source": [
-    "# Average encodings"
+    "# Extend split with unlabelled data\n",
+    "\n"
    ]
   },
   {
@@ -307,6 +309,97 @@
    "id": "17",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "if not os.path.exists(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\"):\n",
+    "    df_labelled = pd.read_csv(\"data/s2bms/eo/avr_aef_256.csv\")\n",
+    "    df = pd.read_csv(\"data/s2bms/eo/avr_aef_256_just_unlabelled.csv\")\n",
+    "    df = pd.concat([df, df_labelled])\n",
+    "\n",
+    "    df.to_csv(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\")\n",
+    "else:\n",
+    "    df = pd.read_csv(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth\"\n",
+    "split_indices = torch.load(pth, weights_only=False)\n",
+    "\n",
+    "print(\"Before test/val/train:\")\n",
+    "s = 0\n",
+    "for k, v in split_indices.items():\n",
+    "    if not k == \"clusters\":\n",
+    "        print(k, len(v))\n",
+    "        s += len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_indices[\"train_indices\"] = split_indices[\"train_indices\"][\n",
+    "    split_indices[\"train_indices\"].isin(df.name_loc)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"After inclusion of avail unlabelled data:\")\n",
+    "s = 0\n",
+    "for k, v in split_indices.items():\n",
+    "    if not k == \"clusters\":\n",
+    "        print(k, len(v))\n",
+    "        s += len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "name = f\"data/s2bms/splits/split_indices_s2bms_{time.strftime('%Y-%m-%d-%H-%M-%S')}_union_aef_tsr_val_test.pth\"\n",
+    "\n",
+    "torch.save(split_indices, name)\n",
+    "print(f\"Saved: {name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23",
+   "metadata": {},
+   "source": [
+    "# Average encodings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import numpy as np\n",
     "\n",
@@ -333,7 +426,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "18",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,7 +437,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19",
+   "id": "26",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -355,7 +448,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "27",
    "metadata": {},
    "outputs": [],
    "source": []

From dff9d2284b279080bbe418d011860ad341e45746 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:54:53 +0200
Subject: [PATCH 036/173] Fix embs from avr

---
 src/data/butterfly_dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index 5eb9a61..aaa53f3 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -196,12 +196,12 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 formatted_row["eo"][modality] = self.load_aef(row["aef_path"])
             elif modality == "aef_avr":
                 formatted_row["eo"][modality] = torch.tensor(
-                    [row[f"avr_{i}"] for i in range(64)],
+                    [row[f"emb_{i}"] for i in range(64)],
                     dtype=getattr(torch, self.modalities["aef_avr"].get("dtype")),
                 )
             elif modality == "tessera_avr":
                 formatted_row["eo"][modality] = torch.tensor(
-                    [row[f"avr_{i}"] for i in range(128)],
+                    [row[f"emb_{i}"] for i in range(128)],
                     dtype=getattr(torch, self.modalities["tessera_avr"].get("dtype")),
                 )
 

From c1a310f1f15d495155e327e819b29c323c516608 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:55:19 +0200
Subject: [PATCH 037/173] Add name_loc extraction in collate function

---
 src/data/collate_fns.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 2c001b4..1a008ab 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -36,6 +36,9 @@ def collate_fn(
     if batch[0].get("target") is not None:
         batch_collected["target"] = smart_stack([item["target"] for item in batch])
 
+    if batch[0].get("name_loc") is not None:
+        batch_collected["name_loc"] = smart_stack([item["name_loc"] for item in batch])
+
     # convert aux into captions
     if mode == "train":
         batch_collected["text"] = caption_builder.random(batch_collected["aux"])

From d46902ca9b3f2afe6db78ae50fb9e51f76666e7e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:55:27 +0200
Subject: [PATCH 038/173] Verbose early stopping

---
 configs/callbacks/early_stopping.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/callbacks/early_stopping.yaml b/configs/callbacks/early_stopping.yaml
index c826c8d..0ab2df1 100644
--- a/configs/callbacks/early_stopping.yaml
+++ b/configs/callbacks/early_stopping.yaml
@@ -5,7 +5,7 @@ early_stopping:
   monitor: ??? # quantity to be monitored, must be specified !!!
   min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
   patience: 3 # number of checks with no improvement after which training will be stopped
-  verbose: False # verbosity mode
+  verbose: true # verbosity mode
   mode: "min" # "max" means higher metric value is better, can be also "min"
   strict: True # whether to crash the training if monitor is not found in the validation metrics
   check_finite: True # when set True, stops training when the monitor becomes NaN or infinite

From 994a293fbe1e26d094b95d34112a44f181f3a140 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:06:42 +0200
Subject: [PATCH 039/173] Temp fix to get inference model working

---
 configs/inference.yaml        | 37 +++++++++++++--
 src/models/inference_model.py | 86 +++++++++++++++++++++++++----------
 2 files changed, 93 insertions(+), 30 deletions(-)

diff --git a/configs/inference.yaml b/configs/inference.yaml
index 45f5bcc..ef9fdd0 100644
--- a/configs/inference.yaml
+++ b/configs/inference.yaml
@@ -1,12 +1,14 @@
 defaults:
-  - _self_
   - paths: ${oc.env:STORAGE_MODE,local}
   - extras: default
   - hydra: default
+  - data: s2bms_prediction
+  - _self_
 
 # task name, determines output directory path
 task_name: "inference"
 tags: ["dev"]
+seed: 12345
 
 # If set, inference.py will load this merged checkpoint directly.
 #inference_ckpt_path: ${paths.log_dir}${task_name}/2026-04-07_12-56-16/inference_model.ckpt
@@ -14,12 +16,37 @@ tags: ["dev"]
 # If `inference_ckpt_path` is not set, stitch the inference model from:
 # - predictive ckpt (provides prediction_head weights)
 # - alignment ckpt (provides text_encoder weights + geo_encoder)
-predictive_ckpt_path: ${paths.log_dir}train/runs/2026-04-02_15-54-53/checkpoints/epoch_000.ckpt
-alignment_ckpt_path: ${paths.log_dir}train/runs/2026-04-02_15-40-03/checkpoints/epoch_000.ckpt
+predictive_ckpt_path: ${paths.checkpoint_root}/s2bms_prediction_ckpt/1cti8qah_epoch_037.ckpt
+alignment_ckpt_path: ${paths.checkpoint_root}/s2bms_alignment_ckpt/dmts2wpm_epoch_097.ckpt
 
 # If set, inference.py will save a merged inference checkpoint you can reload with
 # `inference_ckpt_path`.
-save_inference_ckpt_path: ${paths.log_dir}${task_name}/${now:%Y-%m-%d}_${now:%H-%M-%S}/inference_model.ckpt
+#save_inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/${now:%Y-%m-%d}_${now:%H-%M-%S}.ckpt
+inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_12-05-05.ckpt
 #save_inference_ckpt_path: null
+save_ckpt: true
+
+training_order: ["prediction_model", "alignment_model"]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
 
-training_order: ["alignment_model", "prediction_model"]
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+  num_workers: 8
+  persistent_workers: false
+  pin_memory: false
+  batch_size: 64
+  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
diff --git a/src/models/inference_model.py b/src/models/inference_model.py
index 5c0023e..717d51d 100644
--- a/src/models/inference_model.py
+++ b/src/models/inference_model.py
@@ -23,6 +23,7 @@ class InferenceModel(BaseModel):
     def __init__(
         self,
         geo_encoder: BaseGeoEncoder | None,
+        geo_encoder_prediction: BaseGeoEncoder | None,
         text_encoder: BaseTextEncoder | None,
         prediction_head: BasePredictionHead | None,
         num_classes: int | None,
@@ -56,6 +57,9 @@ def __init__(
             tabular_dim=None,
         )
 
+        if geo_encoder_prediction:
+            self.geo_encoder_prediction = geo_encoder_prediction
+
         # Params from alignment model
         self.match_to_geo = match_to_geo
         self.ks = ks
@@ -72,6 +76,8 @@ def _setup(self, stage: str) -> None:
             self.geo_encoder.setup()
         if hasattr(self, "text_encoder"):
             self.text_encoder.setup()
+        if hasattr(self, "geo_encoder_prediction"):
+            self.geo_encoder_prediction.setup()
 
         if hasattr(self, "text_encoder") and hasattr(self, "geo_encoder"):
             # Configure optional extra projection so text embeddings match geo embeddings.
@@ -86,9 +92,11 @@ def _setup(self, stage: str) -> None:
                 raise ValueError(
                     "InferenceModel requires `num_classes` to build the prediction head."
                 )
-            self.prediction_head.set_dim(
-                input_dim=self.geo_encoder.output_dim, output_dim=self.num_classes
-            )
+            if hasattr(self, "geo_encoder_prediction"):
+                input_dim = self.geo_encoder_prediction.output_dim
+            else:
+                self.geo_encoder.output_dim
+            self.prediction_head.set_dim(input_dim=input_dim, output_dim=self.num_classes)
             self.prediction_head.setup()
         print("------------------------")
 
@@ -101,21 +109,31 @@ def _step(
         """Step forward computation of the model."""
         pass
 
+    def forward_text(self, text: list[str]) -> torch.Tensor:
+        batch = {"text": text}
+        return self.text_encoder(batch, "train")
+
+    def forward_geo(self, batched_eo) -> Tuple[torch.Tensor, torch.Tensor | None]:
+        geo_feats = self.geo_encoder(batched_eo)
+        pred = None
+        if hasattr(self, "prediction_head"):
+            if hasattr(self, "geo_encoder_prediction"):
+                geo_feats_pr = self.geo_encoder_prediction(batched_eo)
+                pred = self.prediction_head(geo_feats_pr)
+            else:
+                pred = self.prediction_head(geo_feats)
+        return geo_feats, pred
+
     @override
     def forward(
         self,
         batch: Dict[str, torch.Tensor],
-        mode: str = "train",
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Model forward logic."""
-
-        # Embed modalities
+    ) -> Tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
         if hasattr(self, "geo_encoder"):
-            geo_feats = self.geo_encoder(batch)
+            geo_feats, pred = self.forward_geo(batch)
+
         if hasattr(self, "text_encoder"):
-            text_feats = self.text_encoder(batch, mode)
-        if hasattr(self, "prediction_head"):
-            pred_feats = self.prediction_head(geo_feats)
+            text_feats = self.text_encoder(batch, "test")
 
         # Change dtype of geo data if it does not match text dtype
         if (
@@ -125,7 +143,7 @@ def forward(
         ):
             geo_feats = geo_feats.to(text_feats.dtype)
 
-            return pred_feats, geo_feats, text_feats
+            return pred, geo_feats, text_feats
 
     def concept_similarities(self, geo_embeds, concept=None) -> torch.Tensor:
         # Get concept embeddings
@@ -188,16 +206,20 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
     pred_ckpt = torch.load(pred_ckpt_path, map_location="cpu", weights_only=False)
     align_ckpt = torch.load(align_ckpt_path, map_location="cpu", weights_only=False)
 
+    inference_hparams = {}
+    geo_encoder_prediction_flag = False
     # Sanity check: ensure geo encoder configs match.
-    align_ckpt["hyper_parameters"]["geo_encoder"] = pred_ckpt["hyper_parameters"].get(
-        "geo_encoder"
-    )
     if pred_ckpt["hyper_parameters"].get("geo_encoder") != align_ckpt["hyper_parameters"].get(
         "geo_encoder"
     ):
         log.warning("Geo encoder configs differ between checkpoints; results may be invalid.")
-        if input("Do you want to proceed? y/n").lower() == "n":
+        if input("Do you want to proceed? y/n").lower() != "y":
             return None
+        if input("Use separate geo_encoders for prediction and alignment? y/n").lower() == "y":
+            inference_hparams["geo_encoder_prediction"] = pred_ckpt["hyper_parameters"].get(
+                "geo_encoder"
+            )
+            geo_encoder_prediction_flag = True
 
     pred_trainable_modules = pred_ckpt["hyper_parameters"].get("trainable_modules", [])
     align_trainable_modules = align_ckpt["hyper_parameters"].get("trainable_modules", [])
@@ -209,7 +231,7 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
         raise ValueError("Models are not aligned: both checkpoints trained geo_encoder.")
 
     # Instantiate InferenceModel via hydra, using alignment encoder configs with prediction model head configs
-    inference_hparams = align_ckpt["hyper_parameters"]
+    inference_hparams.update(align_ckpt["hyper_parameters"])
     inference_hparams.update(
         {
             "_target_": "src.models.inference_model.InferenceModel",
@@ -217,6 +239,7 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
             "num_classes": pred_ckpt["hyper_parameters"].get("num_classes"),
         }
     )
+    inference_hparams["trainable_modules"] = None
     inference_hparams["text_encoder"]["hf_cache_dir"] = os.path.join(
         cfg.paths.cache_dir, "huggingface"
     )
@@ -228,10 +251,21 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
     text_state = {
         k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("text_encoder.")
     }
-    res = model.load_state_dict(text_state, strict=False)
-    log_model_loading("text_encoder", res)
+    if text_state:
+        res = model.load_state_dict(text_state, strict=False)
+        log_model_loading("text_encoder", res)
 
-    if cfg.training_order[0] == "prediction_model" and not geo_align_encoder_trained:
+    if geo_encoder_prediction_flag:
+        geo_state = {
+            k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
+        }
+        geo_pred_state = {
+            k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
+        }
+        if geo_pred_state:
+            res = model.load_state_dict(geo_pred_state, strict=False)
+            log_model_loading("geo_encoder_pred", res)
+    elif cfg.training_order[0] == "prediction_model" and not geo_align_encoder_trained:
         geo_state = {
             k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
         }
@@ -239,15 +273,17 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
         geo_state = {
             k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
         }
-    res = model.load_state_dict(geo_state, strict=False)
-    log_model_loading("geo_encoder", res)
+    if geo_state:
+        res = model.load_state_dict(geo_state, strict=False)
+        log_model_loading("geo_encoder", res)
 
     # Load prediction head weights from predictive ckpt.
     head_state = {
         k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("prediction_head.")
     }
-    res = model.load_state_dict(head_state, strict=False)
-    log_model_loading("Predictive_head", res)
+    if head_state:
+        res = model.load_state_dict(head_state, strict=False)
+        log_model_loading("Predictive_head", res)
 
     # Save model
     if save_ckpt:

From 491071ddf27e248a9705b93f146d082943381746 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:07:12 +0200
Subject: [PATCH 040/173] import clean up

---
 src/data/base_dataset.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index a99b878..80143c6 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -11,7 +11,6 @@
 
 from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError
 from src.utils.data_utils import center_crop_npy
-from src.utils.errors import MissingDataError
 
 TORCH_DTYPES = {
     "float32": torch.float32,

From 5bf32aa8c0d2a18a8dc095d02054f0e0d0b6aeae Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:09:55 +0200
Subject: [PATCH 041/173]  Temp fix to get inference model working

---
 src/inference.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/inference.py b/src/inference.py
index dc15b50..aafbce6 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -36,8 +36,21 @@ def main(cfg: DictConfig) -> Optional[float]:
     # Otherwise merge model from two checkpoints
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
-
+    model.to("mps")
     # TODO: do what you need with the inference model
+    if cfg.data:
+        datamodule = hydra.utils.instantiate(cfg.get("data"))
+        datamodule.setup()
+
+        concepts = [
+            c["concept_caption"] for c in datamodule.caption_builder.__dict__["concepts"]
+        ]  # or other source of concepts
+        text_embeds = model.forward_text(concepts)
+
+        # per location (batching uses location text generation)
+        for d in datamodule.data_train:
+            geo_embeds, pred = model.forward_geo(d)
+            # TODO: do what you need with the inference model
 
     return
 

From 793b11578afc13a6b43ee5d3d70a953eb85e0dd9 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:21:17 +0200
Subject: [PATCH 042/173]  Temp fix to get inference model working

---
 configs/inference.yaml | 2 +-
 src/inference.py       | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/configs/inference.yaml b/configs/inference.yaml
index ef9fdd0..47ad5e4 100644
--- a/configs/inference.yaml
+++ b/configs/inference.yaml
@@ -22,7 +22,7 @@ alignment_ckpt_path: ${paths.checkpoint_root}/s2bms_alignment_ckpt/dmts2wpm_epoc
 # If set, inference.py will save a merged inference checkpoint you can reload with
 # `inference_ckpt_path`.
 #save_inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/${now:%Y-%m-%d}_${now:%H-%M-%S}.ckpt
-inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_12-05-05.ckpt
+inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_13-17-38.ckpt
 #save_inference_ckpt_path: null
 save_ckpt: true
 
diff --git a/src/inference.py b/src/inference.py
index aafbce6..f082056 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -37,6 +37,7 @@ def main(cfg: DictConfig) -> Optional[float]:
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
     model.to("mps")
+
     # TODO: do what you need with the inference model
     if cfg.data:
         datamodule = hydra.utils.instantiate(cfg.get("data"))
@@ -49,7 +50,8 @@ def main(cfg: DictConfig) -> Optional[float]:
 
         # per location (batching uses location text generation)
         for d in datamodule.data_train:
-            geo_embeds, pred = model.forward_geo(d)
+            b = {"eo": {"aef_avr": d["eo"]["aef_avr"].unsqueeze(0).to("mps")}}
+            geo_embeds, pred = model.forward_geo(b)
             # TODO: do what you need with the inference model
 
     return

From b41a027994990c1d438f9573012181e66f5d0b07 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 14:15:08 +0200
Subject: [PATCH 043/173] Fix cache dir

---
 src/inference.py              | 2 +-
 src/models/inference_model.py | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/inference.py b/src/inference.py
index f082056..6de9d6c 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -32,7 +32,7 @@ def main(cfg: DictConfig) -> Optional[float]:
     # If a merged inference ckpt is provided, just load it.
     inference_ckpt_path = cfg.get("inference_ckpt_path")
     if inference_ckpt_path:
-        model = load_inference_model(inference_ckpt_path)
+        model = load_inference_model(inference_ckpt_path, cfg.paths.cache_dir)
     # Otherwise merge model from two checkpoints
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
diff --git a/src/models/inference_model.py b/src/models/inference_model.py
index 717d51d..f07f839 100644
--- a/src/models/inference_model.py
+++ b/src/models/inference_model.py
@@ -173,13 +173,15 @@ def _is_prefix_trained(trainable_modules: list[str], prefix: str) -> bool:
     return any(m.split(".")[0] == prefix for m in trainable_modules)
 
 
-def load_inference_model(inference_ckpt_path: str) -> InferenceModel:
+def load_inference_model(inference_ckpt_path: str, cache_path: str | None) -> InferenceModel:
     """Loads inference model from a merged checkpoint.
 
     :param inference_ckpt_path: path to inference model weights
     :return: an InferenceModel with pre-trained weights
     """
     inference_ckpt = torch.load(inference_ckpt_path, map_location="cpu", weights_only=False)
+    if cache_path:
+        inference_ckpt["hyper_parameters"]["text_encoder"]["hf_cache_dir"] = cache_path
     model = hydra.utils.instantiate(inference_ckpt["hyper_parameters"])
     model.setup("inference")
     res = model.load_state_dict(inference_ckpt["state_dict"], strict=False)

From 0f3a90e53612e637d1f133edcd5bb31e3437fb64 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 14:25:06 +0200
Subject: [PATCH 044/173] Add split file

---
 ...6-06-09-09-45-48_union_aef_tsr_val_test.pth | Bin 0 -> 385207 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth

diff --git a/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth b/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth
new file mode 100644
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literal 0
HcmV?d00001


From aeb2b3a19a42d2ab64ee2bd0d8a0a8d7a6d7a7ab Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:35:57 +0200
Subject: [PATCH 045/173] Fix adopt encoder

---
 .../components/geo_encoders/adopt_encoder.py   | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/models/components/geo_encoders/adopt_encoder.py b/src/models/components/geo_encoders/adopt_encoder.py
index 2a5bd88..969be07 100644
--- a/src/models/components/geo_encoders/adopt_encoder.py
+++ b/src/models/components/geo_encoders/adopt_encoder.py
@@ -1,5 +1,3 @@
-from typing import Dict, List, override
-
 import hydra
 import torch
 
@@ -19,18 +17,28 @@ def adopt_encoder(ckpt_path: str) -> BaseGeoEncoder:
     # Get skeleton
     geo_config = ckpt["hyper_parameters"].get("geo_encoder")
     encoder: BaseGeoEncoder = hydra.utils.instantiate(geo_config)
+
     print("---Adopted encoder------")
+    encoder.adopted = True
     encoder.setup()
     encoder.cfg_dict = geo_config
-    print("------------------------")
+    encoder.cfg_dict.update(
+        {
+            "adopted": True,
+            "save_weights": bool(
+                "geo_encoder" in "".join(ckpt["hyper_parameters"].get("trainable_modules", []))
+            ),
+        }
+    )
 
     # Load in the weights
     state_dict = {
-        k.replace("geo_encoder.", ""): v
+        k.replace("geo_encoder.", "", 1): v
         for k, v in ckpt["state_dict"].items()
         if "geo_encoder." in k
     }
     res = encoder.load_state_dict(state_dict, strict=False)
-    log_model_loading("geo_encoder_ckpt", res)
+    log_model_loading("geo_encoder_ckpt", res, True)
+    print("------------------------")
 
     return encoder

From a015517b7ad08af5f75ff26f5781dcb4c359062a Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:36:49 +0200
Subject: [PATCH 046/173] If adopted, do not train

---
 src/models/components/geo_encoders/base_geo_encoder.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/models/components/geo_encoders/base_geo_encoder.py b/src/models/components/geo_encoders/base_geo_encoder.py
index 8711140..2b6062b 100644
--- a/src/models/components/geo_encoders/base_geo_encoder.py
+++ b/src/models/components/geo_encoders/base_geo_encoder.py
@@ -19,7 +19,9 @@ def __init__(self) -> None:
 
         self.allowed_geo_data_names: list[str] | None = None
         self.geo_data_name: str | None = None
+        self.adopted: bool = False
 
+    @final
     def update_configs(self, cfg):
         if len(self.cfg_dict) == 0:
             self.cfg_dict = cfg
@@ -41,6 +43,8 @@ def setup(self, verbose=1) -> List[str]:
             if verbose > 0:
                 print(f"Model set up with {self.__str__()}")
             self.setup_flag = True
+            if self.adopted:
+                trainable_modules = []
             return trainable_modules
 
     @abstractmethod

From b5e5d3f18e56ff97c10e7d0dc064c31851c57d0a Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:38:13 +0200
Subject: [PATCH 047/173] Move projector to new group of encoders: projectors
 and adapters

---
 .../projectors_adapters/base_encoder.py       | 65 +++++++++++++++++++
 .../mlp_projector.py                          |  7 +-
 tests/test_dynamic_gate_fusion_encoder.py     |  3 +-
 tests/test_gated_fusion_encoder.py            |  3 +-
 tests/test_geo_encoders.py                    |  7 +-
 5 files changed, 72 insertions(+), 13 deletions(-)
 create mode 100644 src/models/components/projectors_adapters/base_encoder.py
 rename src/models/components/{geo_encoders => projectors_adapters}/mlp_projector.py (86%)

diff --git a/src/models/components/projectors_adapters/base_encoder.py b/src/models/components/projectors_adapters/base_encoder.py
new file mode 100644
index 0000000..cfa7da1
--- /dev/null
+++ b/src/models/components/projectors_adapters/base_encoder.py
@@ -0,0 +1,65 @@
+from abc import ABC, abstractmethod
+from typing import Dict, List, final
+
+import torch
+from torch import nn
+
+
+class BaseEncoder(nn.Module, ABC):
+    def __init__(self) -> None:
+        super().__init__()
+
+        # Modules
+        self.output_dim: int | None = None
+        self.setup_flag: bool = False
+        self.cfg_dict: Dict = {}
+        self.input_dim: int | None = None
+        self.cfg_dict: Dict = {}
+
+    @final
+    def set_input_dim(self, input_dim: int) -> None:
+        self.input_dim = input_dim
+
+    @final
+    def setup(self, verbose=1) -> List[str]:
+        """Configures modules.
+
+        Gets called in model.setup() method. Returns names of any new module configured to be added
+        to the trainable modules list.
+        """
+        if self.setup_flag:
+            print(f"Module {self.__str__()} is already set up.")
+            return []
+        else:
+            trainable_modules = self._setup()
+            if verbose > 0:
+                print(f"Model set up with {self.__str__()}")
+            self.setup_flag = True
+            return trainable_modules
+
+    @abstractmethod
+    def _setup(self) -> List[str]:
+        """Configures modules and returns newly initialised, trainable module names."""
+        pass
+
+    @abstractmethod
+    def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
+        pass
+
+    @property
+    def device(self) -> torch.device | None:
+        devices = {p.device for p in self.parameters()}
+        if len(devices) > 1:
+            raise RuntimeError("Encoder is on multiple devices")
+        elif len(devices) == 0:
+            return None
+        return devices.pop()
+
+    @property
+    def dtype(self) -> torch.dtype | None:
+        dtypes = {p.dtype for p in self.parameters()}
+        if len(dtypes) > 1:
+            raise RuntimeError("Encoder has multiple dtypes")
+        elif len(dtypes) == 0:
+            return None
+        return dtypes.pop()
diff --git a/src/models/components/geo_encoders/mlp_projector.py b/src/models/components/projectors_adapters/mlp_projector.py
similarity index 86%
rename from src/models/components/geo_encoders/mlp_projector.py
rename to src/models/components/projectors_adapters/mlp_projector.py
index a43316e..7517508 100644
--- a/src/models/components/geo_encoders/mlp_projector.py
+++ b/src/models/components/projectors_adapters/mlp_projector.py
@@ -3,10 +3,10 @@
 import torch
 from torch import nn
 
-from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
+from src.models.components.projectors_adapters.base_encoder import BaseEncoder
 
 
-class MLPProjector(BaseGeoEncoder):
+class MLPProjector(BaseEncoder):
     def __init__(
         self,
         output_dim: int,
@@ -29,9 +29,6 @@ def _setup(self) -> List[str]:
         self.configure_nn()
         return ["net"]
 
-    def set_input_dim(self, input_dim: int) -> None:
-        self.input_dim = input_dim
-
     def configure_nn(self) -> None:
         """Configure the MLP network."""
         assert self.input_dim is not None, "input_dim must be defined"
diff --git a/tests/test_dynamic_gate_fusion_encoder.py b/tests/test_dynamic_gate_fusion_encoder.py
index 6dff786..30e4893 100644
--- a/tests/test_dynamic_gate_fusion_encoder.py
+++ b/tests/test_dynamic_gate_fusion_encoder.py
@@ -17,6 +17,7 @@
 from src.models.components.geo_encoders.encoder_wrapper import EncoderWrapper
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
 
+
 # ---------------------------------------------------------------------------
 # Minimal stub encoder for testing — outputs a fixed-size embedding.
 # ---------------------------------------------------------------------------
@@ -169,7 +170,7 @@ def test_with_tabular_encoder():
 
     Both branches projected to the same output_dim (32) via per-branch projectors.
     """
-    from src.models.components.geo_encoders.mlp_projector import MLPProjector
+    from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
     tabular_dim = 23
     tab_enc = TabularEncoder(output_dim=32)
diff --git a/tests/test_gated_fusion_encoder.py b/tests/test_gated_fusion_encoder.py
index 9fc2169..4593d0d 100644
--- a/tests/test_gated_fusion_encoder.py
+++ b/tests/test_gated_fusion_encoder.py
@@ -15,6 +15,7 @@
 from src.models.components.geo_encoders.encoder_wrapper import EncoderWrapper
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
 
+
 # ---------------------------------------------------------------------------
 # Minimal stub encoder for testing — outputs a fixed-size embedding.
 # ---------------------------------------------------------------------------
@@ -137,7 +138,7 @@ def test_with_tabular_encoder():
 
     Both branches projected to the same output_dim (32) via per-branch projectors.
     """
-    from src.models.components.geo_encoders.mlp_projector import MLPProjector
+    from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
     tabular_dim = 23
     tab_enc = TabularEncoder(output_dim=32)
diff --git a/tests/test_geo_encoders.py b/tests/test_geo_encoders.py
index 57ff921..aba31f3 100644
--- a/tests/test_geo_encoders.py
+++ b/tests/test_geo_encoders.py
@@ -1,15 +1,10 @@
-import json
-import os
-
-import pandas as pd
-import pytest
 import torch
 
 from src.models.components.geo_encoders.average_encoder import AverageEncoder
 from src.models.components.geo_encoders.cnn_encoder import CNNEncoder
 from src.models.components.geo_encoders.geoclip import GeoClipCoordinateEncoder
-from src.models.components.geo_encoders.mlp_projector import MLPProjector
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
+from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
 
 # @pytest.mark.slow

From 9e425bbf1a261f6b4bcb17b2b12dd669af0d3f14 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:38:59 +0200
Subject: [PATCH 048/173] Introduce adapters to the alignment model

---
 src/models/base_model.py                      | 15 +++++--
 .../components/pred_heads/base_pred_head.py   |  7 ++++
 src/models/text_alignment_model.py            | 41 ++++++++++++++++---
 3 files changed, 55 insertions(+), 8 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 9a3259c..b7b6f47 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -218,12 +218,16 @@ def update_configs(self, cfg):
         """Update hyper-parameters from the model."""
         if hasattr(self, "geo_encoder"):
             self.geo_encoder.update_configs(cfg["geo_encoder"])
+        if hasattr(self, "geo_adapter"):
+            self.geo_adapter.cfg_dict = cfg["geo_adapter"]
 
         if hasattr(self, "text_encoder"):
             self.text_encoder.cfg_dict = cfg["text_encoder"]
+        if hasattr(self, "text_adapter"):
+            self.text_adapter.cfg_dict = cfg["text_adapter"]
 
-        if hasattr(self, "prediction_head"):
-            self.prediction_head.cfg_dict = cfg["prediction_head"]
+        if self.prediction_head and cfg.get("prediction_head"):
+            self.prediction_head.update_configs(cfg["prediction_head"])
 
     def on_save_checkpoint(self, checkpoint):
         """Save checkpoint.
@@ -263,11 +267,16 @@ def on_save_checkpoint(self, checkpoint):
 
         if hasattr(self, "geo_encoder"):
             checkpoint["hyper_parameters"]["geo_encoder"] = self.geo_encoder.cfg_dict
+        if hasattr(self, "geo_adapter"):
+            checkpoint["hyper_parameters"]["geo_adapter"] = self.geo_adapter.cfg_dict
+
         if hasattr(self, "prediction_head"):
             checkpoint["hyper_parameters"]["prediction_head"] = self.prediction_head.cfg_dict
+
         if hasattr(self, "text_encoder"):
             checkpoint["hyper_parameters"]["text_encoder"] = self.text_encoder.cfg_dict
-
+        if hasattr(self, "text_adapter"):
+            checkpoint["hyper_parameters"]["text_adapter"] = self.text_adapter.cfg_dict
         return
 
     def on_load_checkpoint(self, checkpoint):
diff --git a/src/models/components/pred_heads/base_pred_head.py b/src/models/components/pred_heads/base_pred_head.py
index 29f19ae..60d493b 100644
--- a/src/models/components/pred_heads/base_pred_head.py
+++ b/src/models/components/pred_heads/base_pred_head.py
@@ -77,3 +77,10 @@ def dtype(self) -> torch.dtype | None:
         elif len(dtypes) == 0:
             return None
         return dtypes.pop()
+
+    @final
+    def update_configs(self, cfg):
+        if len(self.cfg_dict) == 0:
+            self.cfg_dict = cfg
+        else:
+            print("Configs for prediction head not updated")
diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 02ead9a..0e14d7c 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -10,9 +10,8 @@
     RetrievalContrastiveValidation,
 )
 from src.models.components.metrics.metrics_wrapper import MetricsWrapper
-from src.models.components.text_encoders.base_text_encoder import (
-    BaseTextEncoder,
-)
+from src.models.components.projectors_adapters.base_encoder import BaseEncoder
+from src.models.components.text_encoders.base_text_encoder import BaseTextEncoder
 
 
 class TextAlignmentModel(BaseModel):
@@ -25,6 +24,8 @@ def __init__(
         scheduler: torch.optim.lr_scheduler,
         loss_fn: BaseLossFn,
         metrics: MetricsWrapper,
+        geo_adapter: BaseEncoder | None = None,
+        text_adapter: BaseEncoder | None = None,
         num_classes: int | None = None,
         tabular_dim: int | None = None,
         ks: list[int] | None = [5, 10, 15],
@@ -45,6 +46,7 @@ def __init__(
         :param match_to_geo: whether to match dimensions of text encoder to geo_encoder or visa-
             versa
         """
+
         super().__init__(
             trainable_modules=trainable_modules,
             geo_encoder=geo_encoder,
@@ -58,6 +60,8 @@ def __init__(
             tabular_dim=tabular_dim,
         )
 
+        self.geo_adapter = geo_adapter
+        self.text_adapter = text_adapter
         # Metrics
         self.ks = ks
         self.log_kwargs = dict(on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
@@ -75,12 +79,35 @@ def _setup(self, stage: str = "fit") -> None:
         # Set up encoders and missing adapters/projectors
         print("-------Model------------")
         new_modules = [f"geo_encoder.{i}" for i in self.geo_encoder.setup() or []]
+
+        if self.geo_adapter:
+            self.geo_adapter.set_input_dim(self.geo_encoder.output_dim)
+            new_modules.extend([f"geo_adapter.{i}" for i in self.geo_adapter.setup() or []])
+
         new_modules.extend([f"text_encoder.{i}" for i in self.text_encoder.setup() or []])
+        if self.text_adapter:
+            self.text_adapter.set_input_dim(self.text_encoder.input_dim)
+            new_modules.extend([f"text_adapter.{i}" for i in self.text_adapter.setup() or []])
+
         self.trainable_modules.extend(new_modules)
 
         # Extra projector for text encoder if eo and text dim not match
-        if self.geo_encoder.output_dim != self.text_encoder.output_dim:
-            if self.match_to_geo:
+        geo_branch_dim = (
+            self.geo_adapter.output_dim if self.geo_adapter else self.geo_encoder.output_dim
+        )
+        text_branch_dim = (
+            self.text_adapter.output_dim if self.text_adapter else self.text_encoder.output_dim
+        )
+
+        if geo_branch_dim != text_branch_dim:
+            if self.geo_adapter or self.text_adapter:
+                print(
+                    f"You opted to use: {'geo' if self.geo_adapter else '' and 'text' if self.text_adapter else ''} adapter(s)",
+                    "but you miss-configured output dimensions:"
+                    f"geo:{geo_branch_dim} vs text: {text_branch_dim}",
+                    "Please try again.",
+                )
+            elif self.match_to_geo:
                 self.text_encoder.add_projector(projected_dim=self.geo_encoder.output_dim)
                 self.trainable_modules.append("text_encoder.extra_projector")
             else:
@@ -137,7 +164,11 @@ def forward(
 
         # Embed modalities
         geo_feats = self.geo_encoder(batch)
+        if self.geo_adapter:
+            geo_feats = self.geo_adapter(geo_feats)
         text_feats = self.text_encoder(batch, mode)
+        if self.text_adapter:
+            text_feats = self.text_adapter(text_feats)
 
         # Change dtype of geo data if it does not match text dtype
         if geo_feats.dtype != text_feats.dtype:

From ac72313dbc4b10021eed0ceab40bb4dd3964ce5e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:39:32 +0200
Subject: [PATCH 049/173] Print not log when adopting encoder

---
 src/utils/logging_utils.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/src/utils/logging_utils.py b/src/utils/logging_utils.py
index f23f4df..18efa73 100644
--- a/src/utils/logging_utils.py
+++ b/src/utils/logging_utils.py
@@ -70,16 +70,25 @@ def _group_keys(keys: list[str]) -> dict[str, list[str]]:
     return grouped
 
 
-def log_model_loading(tag: str, result) -> None:
+def log_model_loading(tag: str, result, printing=False) -> None:
     """Log missing/unexpected keys from `load_state_dict`."""
     missing_keys, unexpected_keys = result
     if missing_keys:
         grouped = _group_keys(list(missing_keys))
         summary = {k: len(v) for k, v in grouped.items()}
-        log.warning(f"[{tag}] Missing keys: {summary}")
+        if printing:
+            print(f"[{tag}] Missing keys: {summary}")
+        else:
+            log.warning(f"[{tag}] Missing keys: {summary}")
     if unexpected_keys:
         grouped = _group_keys(list(unexpected_keys))
         summary = {k: len(v) for k, v in grouped.items()}
-        log.warning(f"[{tag}] Unexpected keys: {summary}")
+        if printing:
+            print(f"[{tag}] Unexpected keys: {summary}")
+        else:
+            log.warning(f"[{tag}] Unexpected keys: {summary}")
     if not missing_keys and not unexpected_keys:
-        log.info(f"[{tag}] Module weights loaded successfully")
+        if printing:
+            print(f"[{tag}] Module weights loaded successfully")
+        else:
+            log.info(f"[{tag}] Module weights loaded successfully")

From 8312d3b3c9efe53021cd5905d4449c41fd74a410 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:40:00 +0200
Subject: [PATCH 050/173] wandb logging updates

---
 src/utils/experiment_tracking.py | 28 +++++++++++++++++++++++-----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index a19ccc2..2654758 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -1,6 +1,5 @@
 import os
 import re
-import shutil
 from pathlib import Path
 
 import pandas as pd
@@ -8,11 +7,25 @@
 from omegaconf import DictConfig
 
 # Which wandb projects to log runs from
-PROJECTS = ["s2bms_prediction", "s2bms_alignment"]  # Important to keep updating!
+PROJECTS = ["s2bms_prediction"]  # , "s2bms_alignment"]  # Important to keep updating!
 EXPERIMENT_TRACKER_NAME = "experiment_tracker.csv"
 ENTITY = "aether_xai"
 
 
+def update_wandb_run_id(run_id, project_name, updates_dict):
+    api = wandb.Api()
+    entity = ENTITY
+
+    run = api.run(f"{entity}/{project_name}/{run_id}")
+
+    # Update the config
+    for key, value in updates_dict.items():
+        run.config[key] = value
+
+    run.config.save()
+    print(f"Successfully updated run {run_id}")
+
+
 def experiment_check(cfg: DictConfig):
     """Check if requested through configs experiments has already been run."""
     # Get experiments from wandb (cross-device sync)
@@ -102,7 +115,12 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                         else:
                             data_name = "satclip"
                     else:
-                        data_name = f"{k}_{data_dict[k]['size']}"
+                        ks = list(data_dict.keys())
+                        ks_new = [
+                            f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
+                            for k in ks
+                        ]
+                        data_name = "-".join(map(str, ks_new))
                     run.summary.update({"data_used": data_name})
 
                 # Add missing experiments to the table
@@ -209,6 +227,6 @@ def clean_local_ckpts(cfg: DictConfig, df: pd.DataFrame) -> None:
         if str(local_ckpt) not in keep:
             print(f"Do you want to remove {local_ckpt}? (y/n)")
             answer = input()
-            if answer != "y":
-                shutil.rmtree(local_ckpt)
+            if answer == "y":
+                os.remove(str(local_ckpt))
                 print(f"Removed {local_ckpt}.")

From 9dbc9ccf20bd37a10c13a0799e5a833aef33621d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:40:26 +0200
Subject: [PATCH 051/173] projectors adapters importable

---
 src/models/components/projectors_adapters/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 src/models/components/projectors_adapters/__init__.py

diff --git a/src/models/components/projectors_adapters/__init__.py b/src/models/components/projectors_adapters/__init__.py
new file mode 100644
index 0000000..e69de29

From 825f657fa5cf34925a5807e9867af5820b4df778 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:38:22 +0200
Subject: [PATCH 052/173] Validation loss based on random selection of
 templates

---
 src/data/base_caption_builder.py      | 30 +++++++++++++++++++++++----
 src/data/butterfly_caption_builder.py | 14 +++++++++----
 src/data/collate_fns.py               |  2 +-
 3 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 875cabc..a985c9b 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -8,18 +8,26 @@
 import torch
 
 from src.data.base_dataset import BaseDataset
+from src.utils.errors import IllegalArgumentCombination
 
 
 class BaseCaptionBuilder(ABC):
     def __init__(
-        self, templates_fname: str, concepts_fname: str, data_dir: str, seed: int
+        self,
+        templates_fname: str,
+        concepts_fname: str,
+        data_dir: str,
+        seed: int,
+        n_captions_for_validation: int | str = "all",
     ) -> None:
         """Interface of caption builder class for converting numerical auxiliary data values into
         textual descriptions from provided caption templates.
 
         :param templates_fname: path to a json file with caption templates.
+        :param concepts_fname: path to a json file with concepts.
         :param data_dir: directory where data is stored.
         :param seed: random seed.
+        :param n_captions_for_validation: number of captions to randomly sample for validation
         """
 
         self.data_dir = data_dir
@@ -39,6 +47,15 @@ def __init__(
         self.seed = seed
         random.seed(self.seed)
 
+        if n_captions_for_validation == "all":
+            self.n = self.__len__
+        elif n_captions_for_validation > len(self):
+            raise IllegalArgumentCombination(
+                f"Requested {n_captions_for_validation} captions exceeds template dictionary size"
+            )
+        else:
+            self.n = n_captions_for_validation
+
     @final
     def __len__(self):
         """Number of caption templates."""
@@ -111,15 +128,20 @@ def random(self, aux_values) -> List[str]:
 
         return formatted_rows
 
-    def all(self, aux_values) -> List[str]:
-        """Return a list of captions from all available templates."""
+    def sample_multiple_or_all(self, aux_values):
+        """Return self.n captions from randomly sampled templates for each data point."""
         formatted_rows = []
         for i in range(0, len(aux_values["aux"])):
             descriptions = []
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
 
-            for template_idx in range(0, len(self)):
+            template_ids = random.choices(
+                range(len(self.templates)),
+                k=self.n,
+            )
+
+            for template_idx in template_ids:
                 descriptions.append(
                     self._build_from_template(template_idx, aux=row_aux, top=row_top)
                 )
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index af23298..786a6fd 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -1,12 +1,11 @@
 import os
-from typing import Any, List, override
+from typing import List, override
 
 import pandas as pd
 import torch
 
 from src.data.base_caption_builder import (
     BaseCaptionBuilder,
-    get_adjective_for_percentage,
     sample_adjective_for_percentage,
 )
 from src.data.base_dataset import BaseDataset
@@ -18,9 +17,16 @@
 
 class ButterflyCaptionBuilder(BaseCaptionBuilder):
     def __init__(
-        self, templates_fname: str, concepts_fname: str, data_dir: str, seed: int
+        self,
+        templates_fname: str,
+        concepts_fname: str,
+        data_dir: str,
+        seed: int,
+        n_captions_for_validation: int | str = "all",
     ) -> None:
-        super().__init__(templates_fname, concepts_fname, data_dir, seed)
+        super().__init__(
+            templates_fname, concepts_fname, data_dir, seed, n_captions_for_validation
+        )
 
     @override
     def sync_with_dataset(self, dataset: BaseDataset) -> None:
diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 1a008ab..9cfe811 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -43,6 +43,6 @@ def collate_fn(
     if mode == "train":
         batch_collected["text"] = caption_builder.random(batch_collected["aux"])
     else:
-        batch_collected["text"] = caption_builder.all(batch_collected["aux"])
+        batch_collected["text"] = caption_builder.sample_multiple_or_all(batch_collected["aux"])
 
     return batch_collected

From c71cf5ec3bb566b48fb980c18e7c611004fecb80 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:40:13 +0200
Subject: [PATCH 053/173] fix config saving bug for missing parts

---
 src/models/base_model.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index b7b6f47..170ea6d 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -218,15 +218,19 @@ def update_configs(self, cfg):
         """Update hyper-parameters from the model."""
         if hasattr(self, "geo_encoder"):
             self.geo_encoder.update_configs(cfg["geo_encoder"])
-        if hasattr(self, "geo_adapter"):
+        if hasattr(self, "geo_adapter") and self.geo_adapter is not None:
             self.geo_adapter.cfg_dict = cfg["geo_adapter"]
 
         if hasattr(self, "text_encoder"):
             self.text_encoder.cfg_dict = cfg["text_encoder"]
-        if hasattr(self, "text_adapter"):
+        if hasattr(self, "text_adapter") and self.text_adapter is not None:
             self.text_adapter.cfg_dict = cfg["text_adapter"]
 
-        if self.prediction_head and cfg.get("prediction_head"):
+        if (
+            hasattr(self, "prediction_head")
+            and self.prediction_head
+            and cfg.get("prediction_head")
+        ):
             self.prediction_head.update_configs(cfg["prediction_head"])
 
     def on_save_checkpoint(self, checkpoint):

From 0f8c91ad05a6b43201956901af79458543b82178 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:41:03 +0200
Subject: [PATCH 054/173] Fix wandb interactions with alignment project

---
 src/utils/experiment_tracking.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index 2654758..8623a2f 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -7,7 +7,7 @@
 from omegaconf import DictConfig
 
 # Which wandb projects to log runs from
-PROJECTS = ["s2bms_prediction"]  # , "s2bms_alignment"]  # Important to keep updating!
+PROJECTS = ["s2bms_prediction", "s2bms_alignment"]  # Important to keep updating!
 EXPERIMENT_TRACKER_NAME = "experiment_tracker.csv"
 ENTITY = "aether_xai"
 
@@ -53,6 +53,7 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
     # Get existing experiment table
     df, df_path = open_experiment_df(cfg)
     ids = list(df.run_id) if len(df) > 0 else []
+    df_len = len(df)
 
     # Connect to wandb api
     api = wandb.Api()
@@ -92,9 +93,10 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                 if best_val_loss is None:
                     if len(results) > 0:
                         loss_per_epoch = results.groupby("epoch")["val_loss"].mean()
-                        if best_epoch != loss_per_epoch.idxmin():
-                            raise ValueError()
-                        best_val_loss = loss_per_epoch.min().item()
+                        if best_epoch:
+                            best_val_loss = loss_per_epoch[best_epoch]
+                        else:
+                            best_val_loss = loss_per_epoch.min().item()
                         run.summary.update({"best_val_loss": best_val_loss})
                 if project == "s2bms_prediction" and best_val_mse_loss is None:
                     mse_loss_per_epoch = results.groupby("epoch")["val_mse_loss"].mean()
@@ -137,9 +139,12 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                 )
 
     # Return experiment df
-    if len(df) > 0:
+    if df_len - len(df) > 0:
         print(f"Saved {len(runs_list)} runs to {df_path}.")
         return df
+    if len(df) > 0:
+        print("No runs updated.")
+        return df
     else:
         print("No runs found.")
         return None

From 9f033da28ecbe2e90ca29ed67c74027115d4a32f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:42:46 +0200
Subject: [PATCH 055/173] Remove extra projector for adapters/projectors

---
 src/models/components/projectors_adapters/mlp_projector.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/models/components/projectors_adapters/mlp_projector.py b/src/models/components/projectors_adapters/mlp_projector.py
index 7517508..1822038 100644
--- a/src/models/components/projectors_adapters/mlp_projector.py
+++ b/src/models/components/projectors_adapters/mlp_projector.py
@@ -46,6 +46,4 @@ def configure_nn(self) -> None:
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         feats = self.net(x)
-        if self.extra_projector:
-            feats = self.extra_projector(feats)
         return feats

From 8fb4a0b98c9f870b1e52c251e250ebe926b1a9fc Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Mon, 15 Jun 2026 08:54:42 +0200
Subject: [PATCH 056/173] Embed concepts once model is put on correct device

---
 src/models/text_alignment_model.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 0e14d7c..9c9b792 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -114,9 +114,12 @@ def _setup(self, stage: str = "fit") -> None:
                 self.geo_encoder.add_projector(projected_dim=self.text_encoder.output_dim)
                 self.trainable_modules.append("geo_encoder.extra_projector")
 
+        print("------------------------")
+
+    def on_fit_start(self):
         # Configure contrastive retrieval evaluation
         self.setup_retrieval_evaluation(verbose=0)
-        print("------------------------")
+        print("Retrieval evaluation configured")
 
     def setup_retrieval_evaluation(
         self,
@@ -153,6 +156,7 @@ def setup_retrieval_evaluation(
         with torch.inference_mode():
             self.concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
             self.concept_embeds = F.normalize(self.concept_embeds, dim=1)
+            self.concept_embeds = self.concept_embeds.to(self.device)
 
     @override
     def forward(

From ad4bf9618a3b31892f697baa42c624290e4c81cb Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:10:37 +0200
Subject: [PATCH 057/173] New data splits and their explanations

---
 data/s2bms/splits/explanations.txt            |  11 +++++++++++
 ...2bms+s2bms-unlabelled-2026-06-02-0918.pth} | Bin 294641 -> 294459 bytes
 ...06-11-15-23-51_union_aef_tsr_val_test.pth} | Bin 385207 -> 385437 bytes
 ...ms-unlabelled-20260529_2026-05-29-1433.pth | Bin 543245 -> 0 bytes
 4 files changed, 11 insertions(+)
 create mode 100644 data/s2bms/splits/explanations.txt
 rename data/s2bms/splits/{split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth => split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth} (99%)
 rename data/s2bms/splits/{split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth => split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth} (99%)
 delete mode 100644 data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth

diff --git a/data/s2bms/splits/explanations.txt b/data/s2bms/splits/explanations.txt
new file mode 100644
index 0000000..ed2f4b5
--- /dev/null
+++ b/data/s2bms/splits/explanations.txt
@@ -0,0 +1,11 @@
+split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth: sum of: original s2bms (with val and test subset for aef and tessera available tile union) and all unlabelled locations in the training split.
+
+split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth: subset of split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth where all train tiles are availble for aef
+
+split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth: all train/val/test unlabelled indices (not used, source data)
+
+split_indices_s2bms_2024-08-14-1459.pth: original S2BMS split (?,  not used, source data?)
+
+split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth: S2bms with val and test subset for aef and tessera available tile union
+
+split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth:  S2bms with val and test and train subset for aef and tessera available tile union (less tiles in the training, because tessera was missing some: use for tessera)
diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
similarity index 99%
rename from data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
rename to data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
index 429e3b22f30eea24bd0c4487cf24133330d28780..b1f1cdf1466eff9f3790b2cca8cd82ee3c3b6e80 100644
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diff --git a/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
similarity index 99%
rename from data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth
rename to data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
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diff --git a/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth b/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth
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OpXssTe--ib-TwmqV*51!


From 0e00413ac84928bf012fc75bbbe5c32cdf13392c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:12:12 +0200
Subject: [PATCH 058/173] Seaborn for visualisation

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 4e39772..44eed0a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,6 +50,7 @@ create-data = [
 
 visualisation = [
     "folium>=0.20.0",
+    "seaborn>=0.13.2",
 ]
 
 [tool.pytest.ini_options]

From 0d16c284a74906047ea549d861cd71a31d0c515d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:12:49 +0200
Subject: [PATCH 059/173] Fix cache dir to be in the data folder

---
 configs/paths/local.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/paths/local.yaml b/configs/paths/local.yaml
index c3683f3..c7e598e 100644
--- a/configs/paths/local.yaml
+++ b/configs/paths/local.yaml
@@ -23,4 +23,4 @@ work_dir: ${hydra:runtime.cwd}
 
 # huggingface cache directory
 # can be overridden via HF_HOME environment variable
-huggingface_cache: ${oc.env:HF_HOME,${paths.root_dir}/.cache/huggingface}
+huggingface_cache: ${oc.env:HF_HOME,${paths.cache_dir}/.cache/huggingface}

From d24ad391eccc8e68a68530bd2a4b6eb5f23e595d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:13:27 +0200
Subject: [PATCH 060/173] Return standardised aux values if requested

---
 src/data/collate_fns.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 9cfe811..06675b5 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -32,6 +32,10 @@ def collate_fn(
         batch_collected["aux"] = {
             k: smart_stack([item["aux"][k] for item in batch]) for k in batch[0]["aux"].keys()
         }
+        if caption_builder.stats is not None:
+            batch_collected["aux"]["aux_standardized"] = (
+                batch_collected["aux"]["aux"] - caption_builder.means
+            ) / caption_builder.stds
 
     if batch[0].get("target") is not None:
         batch_collected["target"] = smart_stack([item["target"] for item in batch])

From be1bded20a917e41c745706d42b4b06992bb8b49 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:13:50 +0200
Subject: [PATCH 061/173] Return column ids used in the location template

---
 src/data/collate_fns.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 06675b5..bf05aca 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -49,4 +49,9 @@ def collate_fn(
     else:
         batch_collected["text"] = caption_builder.sample_multiple_or_all(batch_collected["aux"])
 
+    # If requested to return aux_ids, recompile stacks
+    if caption_builder.return_aux_ids:
+        batch_collected["text_aux_ids"] = batch_collected["text"][1]
+        batch_collected["text"] = batch_collected["text"][0]
+
     return batch_collected

From c2c440f0e20a3c9e68df83807eeae7663dae3e53 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:28:18 +0200
Subject: [PATCH 062/173] Add statistics and return aux ids for caption

---
 src/data/base_caption_builder.py      | 109 ++++++++++++++++++--------
 src/data/butterfly_caption_builder.py |  31 +++++++-
 2 files changed, 108 insertions(+), 32 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index a985c9b..50c4afd 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -3,7 +3,7 @@
 import random
 import re
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, final
+from typing import Any, Dict, List, Tuple, final
 
 import torch
 
@@ -19,6 +19,8 @@ def __init__(
         data_dir: str,
         seed: int,
         n_captions_for_validation: int | str = "all",
+        return_aux_ids: bool = False,
+        stats_file: str | None = None,
     ) -> None:
         """Interface of caption builder class for converting numerical auxiliary data values into
         textual descriptions from provided caption templates.
@@ -28,6 +30,8 @@ def __init__(
         :param data_dir: directory where data is stored.
         :param seed: random seed.
         :param n_captions_for_validation: number of captions to randomly sample for validation
+        :param return_aux_ids: whether to return auxiliary column ids.
+        :param stats_file: path to a json file with statistics of the aux cols (train split).
         """
 
         self.data_dir = data_dir
@@ -56,6 +60,11 @@ def __init__(
         else:
             self.n = n_captions_for_validation
 
+        self.return_aux_ids = return_aux_ids
+
+        if stats_file:
+            self.stats = json.load(open(stats_file))
+
     @final
     def __len__(self):
         """Number of caption templates."""
@@ -106,48 +115,86 @@ def _build_from_template(
         """Build caption text from template and row of auxiliary data."""
         pass
 
-    def random(self, aux_values) -> List[str]:
-        """Return a caption from a randomly sampled template for each data point."""
-        formatted_rows = []
+    def random(self, aux_values) -> Tuple[List[str], List[int] | None]:
+        """Return a caption per location from a randomly sampled template for each data point.
 
+        :param aux_values: a batch of auxiliary values to use for random sampling.
+        :return: a batch of text captions and optionally aux col ids used for each of the caption.
+        """
         batch_size = len(aux_values["aux"])
 
-        template_ids = random.choices(
-            range(len(self.templates)),
-            k=batch_size,
-        )
-        for (
-            i,
-            template_idx,
-        ) in enumerate(template_ids):
+        # Location captions holders
+        formatted_location_captions = []
+
+        # Ids of used aux col ids per template (location)
+        if self.return_aux_ids:
+            ids = []
+
+        # Sample templates
+        template_ids = random.choices(range(len(self.templates)), k=batch_size)
+        for i, template_idx in enumerate(template_ids):
+            # Get aux and top values per location
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
-            formatted_rows.append(
-                self._build_from_template(template_idx, aux=row_aux, top=row_top)
-            )
 
-        return formatted_rows
+            # Get filled in template for location
+            if self.return_aux_ids:
+                filled_template, template_ids = self._build_from_template(
+                    template_idx, aux=row_aux, top=row_top
+                )
+                ids.append(template_ids)
+            else:
+                filled_template = self._build_from_template(template_idx, aux=row_aux, top=row_top)
+            formatted_location_captions.append(filled_template)
+
+        if self.return_aux_ids:
+            return formatted_location_captions, ids
+        return formatted_location_captions
+
+    def sample_multiple_or_all(self, aux_values) -> Tuple[List[str], List[int] | None]:
+        """Return self.n captions from randomly sampled templates for each data point.
 
-    def sample_multiple_or_all(self, aux_values):
-        """Return self.n captions from randomly sampled templates for each data point."""
-        formatted_rows = []
-        for i in range(0, len(aux_values["aux"])):
-            descriptions = []
+        :param aux_values: a batch of auxiliary values to use for random sampling.
+        :return: a batch of text captions and optionally aux col ids used for each of the caption.
+        """
+        batch_size = len(aux_values["aux"])
+
+        # Location captions holders
+        formatted_location_captions = []
+
+        # Ids of used aux col ids per template (location)
+        if self.return_aux_ids:
+            ids = []
+
+        for i in range(0, batch_size):
+            # Get aux and top values per location
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
 
-            template_ids = random.choices(
-                range(len(self.templates)),
-                k=self.n,
-            )
+            # Sample templates
+            template_ids = random.choices(range(len(self.templates)), k=self.n)
 
+            # Get filled in templates for location
+            filled_in_location_templates = []
+            ids_per_location = []
             for template_idx in template_ids:
-                descriptions.append(
-                    self._build_from_template(template_idx, aux=row_aux, top=row_top)
-                )
-            formatted_rows.append(descriptions)
-
-        return formatted_rows
+                if self.return_aux_ids:
+                    filled_template, template_ids = self._build_from_template(
+                        template_idx, aux=row_aux, top=row_top
+                    )
+                    ids_per_location.extend(filled_template)
+                else:
+                    filled_template = self._build_from_template(
+                        template_idx, aux=row_aux, top=row_top
+                    )
+                filled_in_location_templates.append(filled_template)
+
+            if self.return_aux_ids:
+                ids.append(template_ids)
+            formatted_location_captions.append(filled_in_location_templates)
+        if self.return_aux_ids:
+            return formatted_location_captions, ids
+        return formatted_location_captions
 
     def sync_concepts(self) -> List[str]:
         for concept in self.concepts:
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index 786a6fd..dd3ae3a 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -23,9 +23,17 @@ def __init__(
         data_dir: str,
         seed: int,
         n_captions_for_validation: int | str = "all",
+        return_aux_ids: bool = False,
+        stats_file: str | None = None,
     ) -> None:
         super().__init__(
-            templates_fname, concepts_fname, data_dir, seed, n_captions_for_validation
+            templates_fname,
+            concepts_fname,
+            data_dir,
+            seed,
+            n_captions_for_validation,
+            return_aux_ids,
+            stats_file,
         )
 
     @override
@@ -51,6 +59,20 @@ def sync_with_dataset(self, dataset: BaseDataset) -> None:
                     "units": units,
                 }
 
+        # If auxiliary value statistics are provided, make them into tensors
+        if self.stats:
+            max_id = len(aux_columns) - 1
+            means = torch.zeros(max_id + 1)
+            stds = torch.ones(max_id + 1)
+
+            for name, stats in self.stats.items():
+                idx = self.column_to_metadata_map["aux"][name]["id"]
+                means[idx] = stats["mean"]
+                stds[idx] = stats["std"]
+
+            self.means = means
+            self.stds = stds + 1e-8
+
         self.sync_concepts()
 
     def get_corine_column_keys(self):
@@ -119,6 +141,9 @@ def _build_from_template(
         template = self.templates[template_idx]
         tokens = self.tokens_in_template[template_idx]
         replacements = {}
+        if self.return_aux_ids:
+            ids = []
+
         for token in tokens:
             init_token = token
             if "top" in token:
@@ -132,6 +157,8 @@ def _build_from_template(
                 )
 
             idx = values_dict["id"]
+            if self.return_aux_ids:
+                ids.append(idx)
             value = aux[idx].item()
 
             formatted_desc = values_dict["description"].lower() or ""
@@ -149,6 +176,8 @@ def _build_from_template(
             replacements[init_token] = formatted_desc
 
         template = self._fill(template, replacements)
+        if self.return_aux_ids:
+            return template, ids
         return template
 
 

From 144b448396ae6770de0eaee9c2a3f7a463f9ab15 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:37:16 +0200
Subject: [PATCH 063/173] make geo and text adapters not logged in hparams

---
 src/models/base_model.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 170ea6d..44d65a4 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -45,7 +45,9 @@ def __init__(
         self.save_hyperparameters(
             ignore=[
                 "geo_encoder",
+                "geo_adapter",
                 "text_encoder",
+                "text_adapter",
                 "prediction_head",
                 "optimizer",
                 "scheduler",
@@ -269,17 +271,17 @@ def on_save_checkpoint(self, checkpoint):
             }
         )
 
-        if hasattr(self, "geo_encoder"):
+        if hasattr(self, "geo_encoder") and self.geo_encoder is not None:
             checkpoint["hyper_parameters"]["geo_encoder"] = self.geo_encoder.cfg_dict
-        if hasattr(self, "geo_adapter"):
+        if hasattr(self, "geo_adapter") and self.geo_adapter is not None:
             checkpoint["hyper_parameters"]["geo_adapter"] = self.geo_adapter.cfg_dict
 
-        if hasattr(self, "prediction_head"):
+        if hasattr(self, "prediction_head") and self.prediction_head is not None:
             checkpoint["hyper_parameters"]["prediction_head"] = self.prediction_head.cfg_dict
 
-        if hasattr(self, "text_encoder"):
+        if hasattr(self, "text_encoder") and self.text_encoder is not None:
             checkpoint["hyper_parameters"]["text_encoder"] = self.text_encoder.cfg_dict
-        if hasattr(self, "text_adapter"):
+        if hasattr(self, "text_adapter") and self.text_adapter is not None:
             checkpoint["hyper_parameters"]["text_adapter"] = self.text_adapter.cfg_dict
         return
 

From 058b65b4fee823d382d4d53c98bb7bee6e867368 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:56:59 +0200
Subject: [PATCH 064/173] Inspect text encoders

---
 ...T-experimenting-with-text-embeddings.ipynb | 302 ++++++++++++++++++
 1 file changed, 302 insertions(+)
 create mode 100644 notebooks/11-GT-experimenting-with-text-embeddings.ipynb

diff --git a/notebooks/11-GT-experimenting-with-text-embeddings.ipynb b/notebooks/11-GT-experimenting-with-text-embeddings.ipynb
new file mode 100644
index 0000000..91e2aa8
--- /dev/null
+++ b/notebooks/11-GT-experimenting-with-text-embeddings.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "os.chdir(\"..\")\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import os\n",
+    "\n",
+    "import hydra\n",
+    "import lightning as pl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from lightning import LightningDataModule, Trainer\n",
+    "\n",
+    "if os.environ.get(\"TOKENIZERS_PARALLELISM\") is None:\n",
+    "    os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hydra import compose, initialize\n",
+    "from hydra.core.hydra_config import HydraConfig\n",
+    "\n",
+    "with initialize(version_base=\"1.3\", config_path=\"../configs\"):\n",
+    "    cfg = compose(\n",
+    "        config_name=\"train.yaml\", return_hydra_config=True, overrides=[\"experiment=pycharm\"]\n",
+    "    )\n",
+    "\n",
+    "HydraConfig.instance().set_config(cfg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)\n",
+    "datamodule.setup()\n",
+    "\n",
+    "trainer: Trainer = pl.Trainer(accelerator=\"mps\", max_epochs=1)\n",
+    "trainer.datamodule = datamodule\n",
+    "\n",
+    "# Get model\n",
+    "model = hydra.utils.instantiate(cfg.model)\n",
+    "model.to(\"mps\")\n",
+    "model.trainer = trainer\n",
+    "model.setup(\"test\")\n",
+    "\n",
+    "is_llm = \"LLM\" in cfg.model.text_encoder._target_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions = [c[\"concept_caption\"] for c in datamodule.caption_builder.__dict__[\"concepts\"]]\n",
+    "b = {\"text\": captions}\n",
+    "text_feats = model.text_encoder(b, \"train\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_feats = F.normalize(text_feats)\n",
+    "cos_sim = text_feats @ text_feats.T\n",
+    "if is_llm:\n",
+    "    cos_sim = cos_sim.detach().cpu().to(torch.float32).numpy()\n",
+    "else:\n",
+    "    cos_sim = cos_sim.detach().cpu().detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(16, 8))\n",
+    "gs = fig.add_gridspec(1, 2, width_ratios=[1.5, 1])\n",
+    "\n",
+    "ax1 = fig.add_subplot(gs[0])\n",
+    "short_labels = [f\"C{i}\" for i in range(len(captions))]\n",
+    "\n",
+    "sns.heatmap(\n",
+    "    cos_sim,\n",
+    "    xticklabels=short_labels,\n",
+    "    yticklabels=short_labels,\n",
+    "    ax=ax1,\n",
+    "    cmap=\"viridis\",\n",
+    "    # vmin=-1, vmax=1,\n",
+    "    cbar_kws={\"label\": \"Cosine Similarity\"},\n",
+    ")\n",
+    "\n",
+    "ax1.set_title(\n",
+    "    f\"Cosine Similarity Matrix For Concepts With {'LLM' if is_llm else 'CLIP'} Text Encoder\"\n",
+    ")\n",
+    "\n",
+    "ax2 = fig.add_subplot(gs[1])\n",
+    "ax2.axis(\"off\")\n",
+    "\n",
+    "legend_text = \"\\n\".join([f\"C{i}: {cap}\" for i, cap in enumerate(captions)])\n",
+    "ax2.text(0, 1, legend_text, fontsize=10, va=\"top\", ha=\"left\", wrap=True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = datamodule.train_dataloader()\n",
+    "dataloader_cycle = itertools.cycle(dataloader)\n",
+    "\n",
+    "all_captions = []\n",
+    "all_embeddings = []\n",
+    "total_samples = 0\n",
+    "max_seq_len = 0\n",
+    "target_samples = 10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for batch in dataloader_cycle:\n",
+    "    with torch.no_grad():\n",
+    "        model.eval()\n",
+    "        if is_llm:\n",
+    "            text_input = batch.get(\"text\")\n",
+    "            text_feats = model.text_encoder(batch, \"train\")\n",
+    "        else:\n",
+    "            text_input = batch.get(\"text\")\n",
+    "        text_tokens = model.text_encoder.processor(\n",
+    "            text=text_input,\n",
+    "            return_tensors=\"pt\",\n",
+    "            padding=True,\n",
+    "            truncation=True,\n",
+    "            max_length=None,\n",
+    "        )\n",
+    "        if text_tokens.input_ids.shape[-1] <= 77:\n",
+    "            device = \"mps\"\n",
+    "            text_tokens = {k: v.to(device) for k, v in text_tokens.items()}\n",
+    "            text_embeds = model.text_encoder.model.get_text_features(**text_tokens)\n",
+    "            if model.text_encoder.projector is not None:\n",
+    "                text_embeds = model.text_encoder.projector(text_embeds)\n",
+    "            if model.text_encoder.extra_projector is not None:\n",
+    "                text_embeds = model.text_encoder.extra_projector(text_embeds)\n",
+    "        else:\n",
+    "            for i, t in enumerate(text_input):\n",
+    "                print(t, len(text_tokens[\"input_ids\"][i]))\n",
+    "\n",
+    "            if len(text_tokens[\"input_ids\"][i]) > max_seq_len:\n",
+    "                max_seq_len = len(text_tokens[\"input_ids\"][i])\n",
+    "            break\n",
+    "\n",
+    "    all_embeddings.append(text_feats.cpu())\n",
+    "    all_captions.extend(batch[\"text\"])\n",
+    "\n",
+    "    total_samples += text_feats.shape[0]\n",
+    "    print(total_samples)\n",
+    "\n",
+    "    # Break only when we have enough\n",
+    "    if total_samples >= target_samples:\n",
+    "        break\n",
+    "\n",
+    "# 3. Finalize\n",
+    "text_feats = torch.cat(all_embeddings, dim=0)[:target_samples]\n",
+    "captions = all_captions[:target_samples]\n",
+    "\n",
+    "text_feats = F.normalize(text_feats)\n",
+    "cos_sim = text_feats @ text_feats.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions_arr = np.array(captions)\n",
+    "identity_mask = captions_arr[:, None] == captions_arr[None, :]\n",
+    "lower_tri_mask = np.tril(np.ones(identity_mask.shape, dtype=bool), k=-1)\n",
+    "\n",
+    "final_mask = (~identity_mask) & lower_tri_mask\n",
+    "if is_llm:\n",
+    "    unique_sims = cos_sim.detach().cpu().to(torch.float32).numpy()[final_mask]\n",
+    "else:\n",
+    "    unique_sims = cos_sim.detach().cpu().numpy()[final_mask]\n",
+    "\n",
+    "row_idx, col_idx = np.where(final_mask)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.histplot(unique_sims, bins=50, kde=True)\n",
+    "plt.title(\n",
+    "    f\"Distribution of Cosine Similarities \\n(for random 10k training location captions with {'LLM' if is_llm else 'CLIP'} text encoder)\"\n",
+    ")\n",
+    "plt.xlabel(\"Cosine Similarity\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the lower triangle\n",
+    "lower_tri_indices = np.tril_indices(cos_sim.shape[0], k=-1)\n",
+    "\n",
+    "# Get indices of the unique pairs\n",
+    "row_idx, col_idx = lower_tri_indices\n",
+    "\n",
+    "# Sort the unique similarities\n",
+    "sorted_indices = np.argsort(unique_sims)  # Ascending order\n",
+    "\n",
+    "print(\"--- Highest Similarity Pairs ---\")\n",
+    "for i in sorted_indices[-5:][::-1]:\n",
+    "    r, c = row_idx[i], col_idx[i]\n",
+    "    print(f\"Score: {unique_sims[i]:.4f}\")\n",
+    "    print(f\"  - '{captions[r]}'\")\n",
+    "    print(f\"  - '{captions[c]}'\\n\")\n",
+    "\n",
+    "print(\"--- Lowest Similarity Pairs ---\")\n",
+    "for i in sorted_indices[:5]:\n",
+    "    r, c = row_idx[i], col_idx[i]\n",
+    "    print(f\"Score: {unique_sims[i]:.4f}\")\n",
+    "    print(f\"  - '{captions[r]}'\")\n",
+    "    print(f\"  - '{captions[c]}'\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 13aa425f75e7e812a44c70bd7e9931909f1617ce Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:58:05 +0200
Subject: [PATCH 065/173] Fix LLM downloading, flatten captions for efficient
 forward pass

---
 .../text_encoders/llm2clip_text_encoder.py    | 84 +++++++++++--------
 1 file changed, 50 insertions(+), 34 deletions(-)

diff --git a/src/models/components/text_encoders/llm2clip_text_encoder.py b/src/models/components/text_encoders/llm2clip_text_encoder.py
index 0f9af97..eccd667 100644
--- a/src/models/components/text_encoders/llm2clip_text_encoder.py
+++ b/src/models/components/text_encoders/llm2clip_text_encoder.py
@@ -1,8 +1,8 @@
 from typing import Dict, override
 
 import torch
+from huggingface_hub import snapshot_download
 from llm2vec import LLM2Vec
-from torch.nn import functional as F
 from transformers import AutoConfig, AutoModel, AutoTokenizer
 
 from src.models.components.text_encoders.base_text_encoder import BaseTextEncoder
@@ -18,25 +18,26 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
         :param output_normalization: output normalization type
         """
         super().__init__()
+        self._download(hf_cache_dir)
 
-        # Adapter and image encoder
         self.projector = AutoModel.from_pretrained(
             "microsoft/LLM2CLIP-Openai-L-14-224",
             trust_remote_code=True,
             dtype=torch.bfloat16,
-            revision="50ed31c5248d8ff124893719e37829d59376be81",  # pin revision for full reproducibility
+            revision="50ed31c5248d8ff124893719e37829d59376be81",
             cache_dir=hf_cache_dir,
         ).eval()
 
-        # TODO: If we want to reuse the vision  part this is the fix place
         self.projector.vision_model = None
         self.projector.visual_projection = None
 
-        # The LLM sentence encoder
         llm_model_name = "microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned"
+        llm_revision = "main"
+
         config = AutoConfig.from_pretrained(
             llm_model_name,
             trust_remote_code=True,
+            revision=llm_revision,
             cache_dir=hf_cache_dir,
         )
         config._attn_implementation = "eager"
@@ -45,13 +46,17 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
             llm_model_name,
             config=config,
             dtype=torch.bfloat16,
-            trust_remote_code=False,  # local code
+            trust_remote_code=False,
+            revision=llm_revision,
             cache_dir=hf_cache_dir,
         )
-        llm_model.config._name_or_path = (
-            "meta-llama/Meta-Llama-3-8B-Instruct"  # Workaround for LLM2VEC
+        llm_model.config._name_or_path = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+        self.processor = AutoTokenizer.from_pretrained(
+            llm_model_name,
+            revision=llm_revision,
+            cache_dir=hf_cache_dir,
         )
-        self.processor = AutoTokenizer.from_pretrained(llm_model_name)
 
         # Caption to vector with the llama LLM
         self.model = LLM2Vec(
@@ -60,6 +65,20 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
 
         self.output_dim = 1280
 
+    @staticmethod
+    def _download(hf_cache_dir: str) -> None:
+        llm_revision = "main"
+        snapshot_download(
+            "microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned",
+            revision=llm_revision,
+            cache_dir=hf_cache_dir,
+        )
+        snapshot_download(
+            "microsoft/LLM2CLIP-Openai-L-14-224",
+            revision="50ed31c5248d8ff124893719e37829d59376be81",
+            cache_dir=hf_cache_dir,
+        )
+
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
         """Forward pass through text encoder."""
@@ -67,32 +86,29 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
         text_input = batch.get("text")
 
         if mode == "train":
-            text_input = [text_input]
-        # Embed text and if not train average all templates
-        avr_embeds = []
-        for captions_per_row in text_input:
-            # LLM is frozen, no gradients needed
-            with torch.no_grad():
-                # Embed
-                text_embeds = self.model.encode(
-                    captions_per_row, convert_to_tensor=True, device=self.device
-                )
-
-                # Change dtype
-                text_embeds = text_embeds.to(
-                    dtype=self.projector.dtype, device=self.projector.device
-                )
-
-            # Project to align with ViT in LLM2CLIP
-            text_embeds = self.projector.get_text_features(text_embeds)
-
-            if self.extra_projector is not None:
-                text_embeds = self.extra_projector(text_embeds)
-
-            if mode != "train":
-                avr_embeds.append(text_embeds.mean(dim=0))
+            captions_flattened = text_input
+            num_per_location = 1
+        else:
+            num_per_location = len(text_input[0])
+            captions_flattened = [c for loc in text_input for c in loc]
+
+        # LLM is frozen, no gradients needed
+        with torch.no_grad():
+            # Embed
+            text_embeds = self.model.encode(
+                captions_flattened, convert_to_tensor=True, device=self.device
+            )
+            # Change dtype
+            text_embeds = text_embeds.to(dtype=self.projector.dtype, device=self.projector.device)
+
+        # Project to align with ViT in LLM2CLIP
+        text_embeds = self.projector.get_text_features(text_embeds)
+
+        if self.extra_projector is not None:
+            text_embeds = self.extra_projector(text_embeds)
 
         if mode != "train":
-            text_embeds = torch.stack(avr_embeds, dim=0)
+            text_embeds = text_embeds.reshape(-1, num_per_location, text_embeds.shape[-1])
+            text_embeds = text_embeds.mean(dim=1)
 
         return text_embeds

From 710624ccfe5c87fdc8478c3629877e0ddfff4b72 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:09:51 +0200
Subject: [PATCH 066/173] Add aux stats calculation

---
 data/s2bms/aux_stats/explanations             |   2 +
 data/s2bms/aux_stats/labelled.json            | 350 ++++++++++++++++++
 data/s2bms/aux_stats/unlabelled.json          | 350 ++++++++++++++++++
 ...9-GT-aef-tessera-datasplit-filtering.ipynb |  84 ++++-
 4 files changed, 768 insertions(+), 18 deletions(-)
 create mode 100644 data/s2bms/aux_stats/explanations
 create mode 100644 data/s2bms/aux_stats/labelled.json
 create mode 100644 data/s2bms/aux_stats/unlabelled.json

diff --git a/data/s2bms/aux_stats/explanations b/data/s2bms/aux_stats/explanations
new file mode 100644
index 0000000..7dcd42c
--- /dev/null
+++ b/data/s2bms/aux_stats/explanations
@@ -0,0 +1,2 @@
+labelled - s2bms aux statistical values, all splits (model ready csv used)
+unlabelled - model_ready_s2bms-unlabelled-20260529.csv aucx col stats.
diff --git a/data/s2bms/aux_stats/labelled.json b/data/s2bms/aux_stats/labelled.json
new file mode 100644
index 0000000..a7c4b12
--- /dev/null
+++ b/data/s2bms/aux_stats/labelled.json
@@ -0,0 +1,350 @@
+{
+    "aux_bioclim_01": {
+        "mean": 9.485303030303031,
+        "std": 0.6721779966099152
+    },
+    "aux_bioclim_02": {
+        "mean": 7.2680303030303035,
+        "std": 0.5689534430067483
+    },
+    "aux_bioclim_03": {
+        "mean": 35.41439393939394,
+        "std": 1.586124088724008
+    },
+    "aux_bioclim_04": {
+        "mean": 46.59897727272727,
+        "std": 2.687255141701679
+    },
+    "aux_bioclim_05": {
+        "mean": 20.717651515151516,
+        "std": 1.1752884954485494
+    },
+    "aux_bioclim_06": {
+        "mean": 0.4790151515151515,
+        "std": 0.7658998086098698
+    },
+    "aux_bioclim_07": {
+        "mean": 20.238636363636363,
+        "std": 1.334879250034158
+    },
+    "aux_bioclim_08": {
+        "mean": 6.2856818181818195,
+        "std": 2.7529953893203554
+    },
+    "aux_bioclim_09": {
+        "mean": 8.998333333333333,
+        "std": 3.6756061563541813
+    },
+    "aux_bioclim_10": {
+        "mean": 15.577500000000002,
+        "std": 0.8218775773099746
+    },
+    "aux_bioclim_11": {
+        "mean": 3.715833333333333,
+        "std": 0.7096641900030296
+    },
+    "aux_bioclim_12": {
+        "mean": 772.3674242424242,
+        "std": 166.23233555877164
+    },
+    "aux_bioclim_13": {
+        "mean": 82.1219696969697,
+        "std": 22.639867063458876
+    },
+    "aux_bioclim_14": {
+        "mean": 47.80530303030303,
+        "std": 9.264660651186507
+    },
+    "aux_bioclim_15": {
+        "mean": 16.395454545454545,
+        "std": 5.220067763022615
+    },
+    "aux_bioclim_16": {
+        "mean": 236.16439393939393,
+        "std": 64.95910725410491
+    },
+    "aux_bioclim_17": {
+        "mean": 156.06136363636364,
+        "std": 25.90148756409456
+    },
+    "aux_bioclim_18": {
+        "mean": 175.34015151515152,
+        "std": 28.665071638612304
+    },
+    "aux_bioclim_19": {
+        "mean": 209.8628787878788,
+        "std": 61.3431923583961
+    },
+    "aux_corine_frac_1": {
+        "mean": 0.20213172274287844,
+        "std": 0.2622152174551455
+    },
+    "aux_corine_frac_11": {
+        "mean": 0.131449230804889,
+        "std": 0.19692458689748327
+    },
+    "aux_corine_frac_111": {
+        "mean": 0.002066708826550188,
+        "std": 0.015426455759550888
+    },
+    "aux_corine_frac_112": {
+        "mean": 0.1293825219783388,
+        "std": 0.19208603443526043
+    },
+    "aux_corine_frac_12": {
+        "mean": 0.021657749491230706,
+        "std": 0.05664609590993616
+    },
+    "aux_corine_frac_121": {
+        "mean": 0.017434713807879223,
+        "std": 0.04776988400835917
+    },
+    "aux_corine_frac_122": {
+        "mean": 0.001136558901397231,
+        "std": 0.009031022158448492
+    },
+    "aux_corine_frac_123": {
+        "mean": 0.000593951118003407,
+        "std": 0.008097839066449621
+    },
+    "aux_corine_frac_124": {
+        "mean": 0.0024925256639508433,
+        "std": 0.02424888230931939
+    },
+    "aux_corine_frac_13": {
+        "mean": 0.005133385725656498,
+        "std": 0.022908293584375135
+    },
+    "aux_corine_frac_131": {
+        "mean": 0.0038053286910386527,
+        "std": 0.02005505861795493
+    },
+    "aux_corine_frac_132": {
+        "mean": 0.0007020107325349989,
+        "std": 0.00784779702298141
+    },
+    "aux_corine_frac_133": {
+        "mean": 0.0006260463020828476,
+        "std": 0.005599379490387964
+    },
+    "aux_corine_frac_14": {
+        "mean": 0.043891356721102184,
+        "std": 0.09133970055877526
+    },
+    "aux_corine_frac_141": {
+        "mean": 0.00835683035412779,
+        "std": 0.03782197884501229
+    },
+    "aux_corine_frac_142": {
+        "mean": 0.03553452636697439,
+        "std": 0.07987201745664521
+    },
+    "aux_corine_frac_2": {
+        "mean": 0.5513668735249658,
+        "std": 0.28888749379740486
+    },
+    "aux_corine_frac_21": {
+        "mean": 0.27125289923715873,
+        "std": 0.2592278341064256
+    },
+    "aux_corine_frac_211": {
+        "mean": 0.27125289923715873,
+        "std": 0.2592278341064256
+    },
+    "aux_corine_frac_212": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_213": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_22": {
+        "mean": 0.00029521325322733613,
+        "std": 0.005538286698952744
+    },
+    "aux_corine_frac_221": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_222": {
+        "mean": 0.00029521325322733613,
+        "std": 0.005538286698952744
+    },
+    "aux_corine_frac_223": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_23": {
+        "mean": 0.2693556068826145,
+        "std": 0.22135701215303863
+    },
+    "aux_corine_frac_231": {
+        "mean": 0.2693556068826145,
+        "std": 0.22135701215303863
+    },
+    "aux_corine_frac_24": {
+        "mean": 0.010463154151965193,
+        "std": 0.04648981891217871
+    },
+    "aux_corine_frac_241": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_242": {
+        "mean": 0.0008571032018131485,
+        "std": 0.011100841260662409
+    },
+    "aux_corine_frac_243": {
+        "mean": 0.009606050950152044,
+        "std": 0.0451850389834082
+    },
+    "aux_corine_frac_244": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_3": {
+        "mean": 0.18763915186359192,
+        "std": 0.2218640467985756
+    },
+    "aux_corine_frac_31": {
+        "mean": 0.1326034419391667,
+        "std": 0.17622735352719843
+    },
+    "aux_corine_frac_311": {
+        "mean": 0.07758368889077984,
+        "std": 0.11956864235605459
+    },
+    "aux_corine_frac_312": {
+        "mean": 0.024074879482920666,
+        "std": 0.07879226090801407
+    },
+    "aux_corine_frac_313": {
+        "mean": 0.030944873565466192,
+        "std": 0.08557877272730283
+    },
+    "aux_corine_frac_32": {
+        "mean": 0.05234222403966993,
+        "std": 0.13051904379199386
+    },
+    "aux_corine_frac_321": {
+        "mean": 0.01997022199030348,
+        "std": 0.07860757730724548
+    },
+    "aux_corine_frac_322": {
+        "mean": 0.028188850928719787,
+        "std": 0.0933870874196766
+    },
+    "aux_corine_frac_323": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_324": {
+        "mean": 0.004183151120646648,
+        "std": 0.022183926835860082
+    },
+    "aux_corine_frac_33": {
+        "mean": 0.0026934858847553027,
+        "std": 0.023595793228210708
+    },
+    "aux_corine_frac_331": {
+        "mean": 0.0002803712455412349,
+        "std": 0.003998976440776625
+    },
+    "aux_corine_frac_332": {
+        "mean": 0.0002450738837095281,
+        "std": 0.00462172998034797
+    },
+    "aux_corine_frac_333": {
+        "mean": 0.0021680407555045396,
+        "std": 0.02159957361528944
+    },
+    "aux_corine_frac_334": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_335": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_4": {
+        "mean": 0.022425011231547286,
+        "std": 0.08846422780948254
+    },
+    "aux_corine_frac_41": {
+        "mean": 0.009388587533040331,
+        "std": 0.05603561935110207
+    },
+    "aux_corine_frac_411": {
+        "mean": 0.004857271597739827,
+        "std": 0.031342017459139795
+    },
+    "aux_corine_frac_412": {
+        "mean": 0.004531315935300505,
+        "std": 0.046857757619709096
+    },
+    "aux_corine_frac_42": {
+        "mean": 0.013036423698506954,
+        "std": 0.06387428994076835
+    },
+    "aux_corine_frac_421": {
+        "mean": 0.003889856930039471,
+        "std": 0.02801528335105976
+    },
+    "aux_corine_frac_422": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_423": {
+        "mean": 0.009146566768467479,
+        "std": 0.048514994627419525
+    },
+    "aux_corine_frac_5": {
+        "mean": 0.03643724063701645,
+        "std": 0.11224646394216233
+    },
+    "aux_corine_frac_51": {
+        "mean": 0.007054246408139815,
+        "std": 0.034806375379943334
+    },
+    "aux_corine_frac_511": {
+        "mean": 0.0005161728592101356,
+        "std": 0.007576797316774769
+    },
+    "aux_corine_frac_512": {
+        "mean": 0.006538073548929678,
+        "std": 0.034070963868355536
+    },
+    "aux_corine_frac_52": {
+        "mean": 0.029382994228876635,
+        "std": 0.10842243548103303
+    },
+    "aux_corine_frac_521": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_522": {
+        "mean": 0.0028242237557749256,
+        "std": 0.026375433615836653
+    },
+    "aux_corine_frac_523": {
+        "mean": 0.026558770473101706,
+        "std": 0.10425819017910092
+    },
+    "aux_maxdist_road": {
+        "mean": 1458.9318181818182,
+        "std": 734.0698207881803
+    },
+    "aux_meandist_road": {
+        "mean": 566.6704545454545,
+        "std": 515.0149939651185
+    },
+    "aux_pop_density": {
+        "mean": 553.6886363636364,
+        "std": 1161.5734071331294
+    },
+    "aux_total_population": {
+        "mean": 3611.462121212121,
+        "std": 7570.499412264948
+    }
+}
diff --git a/data/s2bms/aux_stats/unlabelled.json b/data/s2bms/aux_stats/unlabelled.json
new file mode 100644
index 0000000..fff3178
--- /dev/null
+++ b/data/s2bms/aux_stats/unlabelled.json
@@ -0,0 +1,350 @@
+{
+    "aux_bioclim_01": {
+        "mean": 8.41094868434318,
+        "std": 1.3360238648244849
+    },
+    "aux_bioclim_02": {
+        "mean": 6.823540679503983,
+        "std": 0.6342809203166841
+    },
+    "aux_bioclim_03": {
+        "mean": 35.30023187821353,
+        "std": 1.8705837139247672
+    },
+    "aux_bioclim_04": {
+        "mean": 44.31602379272104,
+        "std": 3.867245781600649
+    },
+    "aux_bioclim_05": {
+        "mean": 18.89492892428672,
+        "std": 1.8813192879057345
+    },
+    "aux_bioclim_06": {
+        "mean": -0.18415162818832542,
+        "std": 1.3426995827004902
+    },
+    "aux_bioclim_07": {
+        "mean": 19.07908055247505,
+        "std": 1.7031810508984064
+    },
+    "aux_bioclim_08": {
+        "mean": 5.635991531404375,
+        "std": 3.39855862807548
+    },
+    "aux_bioclim_09": {
+        "mean": 8.56209295291864,
+        "std": 2.7633050326736015
+    },
+    "aux_bioclim_10": {
+        "mean": 14.243320899284202,
+        "std": 1.487126142294304
+    },
+    "aux_bioclim_11": {
+        "mean": 2.9512954934973283,
+        "std": 1.2902476291925045
+    },
+    "aux_bioclim_12": {
+        "mean": 985.1452767416071,
+        "std": 342.0898828772206
+    },
+    "aux_bioclim_13": {
+        "mean": 108.52001209799374,
+        "std": 45.83286506673684
+    },
+    "aux_bioclim_14": {
+        "mean": 57.933057767920154,
+        "std": 16.250538794976354
+    },
+    "aux_bioclim_15": {
+        "mean": 18.951305575158788,
+        "std": 5.894305670889796
+    },
+    "aux_bioclim_16": {
+        "mean": 310.954834156669,
+        "std": 128.45692376406268
+    },
+    "aux_bioclim_17": {
+        "mean": 185.16584333098095,
+        "std": 48.51704264351247
+    },
+    "aux_bioclim_18": {
+        "mean": 216.1958866821252,
+        "std": 54.40623636470098
+    },
+    "aux_bioclim_19": {
+        "mean": 277.1669523137413,
+        "std": 119.02393909179635
+    },
+    "aux_corine_frac_111": {
+        "mean": 0.0012381316493298737,
+        "std": 0.017396567488694732
+    },
+    "aux_corine_frac_112": {
+        "mean": 0.05024036318228254,
+        "std": 0.13242626320000173
+    },
+    "aux_corine_frac_121": {
+        "mean": 0.009470084490101288,
+        "std": 0.03977666167613134
+    },
+    "aux_corine_frac_122": {
+        "mean": 0.0008100089562997777,
+        "std": 0.008066359462878986
+    },
+    "aux_corine_frac_123": {
+        "mean": 0.0003993860671807121,
+        "std": 0.008277578227576089
+    },
+    "aux_corine_frac_124": {
+        "mean": 0.0011437095240403085,
+        "std": 0.014974850457210408
+    },
+    "aux_corine_frac_131": {
+        "mean": 0.0024881257927702608,
+        "std": 0.019627607488532123
+    },
+    "aux_corine_frac_132": {
+        "mean": 0.00018884120796530722,
+        "std": 0.0036346243381618244
+    },
+    "aux_corine_frac_133": {
+        "mean": 0.0005659480898787022,
+        "std": 0.00739866412271175
+    },
+    "aux_corine_frac_141": {
+        "mean": 0.0025903592606651414,
+        "std": 0.02008876942855595
+    },
+    "aux_corine_frac_142": {
+        "mean": 0.011157809712526666,
+        "std": 0.03689460353806993
+    },
+    "aux_corine_frac_211": {
+        "mean": 0.2576389518683456,
+        "std": 0.3267167437405614
+    },
+    "aux_corine_frac_212": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_213": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_221": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_222": {
+        "mean": 0.0003715637297008934,
+        "std": 0.008382979498378294
+    },
+    "aux_corine_frac_223": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_231": {
+        "mean": 0.27627695932709384,
+        "std": 0.2941670917165106
+    },
+    "aux_corine_frac_241": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_242": {
+        "mean": 0.0014760015337967132,
+        "std": 0.021106612715894486
+    },
+    "aux_corine_frac_243": {
+        "mean": 0.005364096217631734,
+        "std": 0.02946542868616639
+    },
+    "aux_corine_frac_244": {
+        "mean": 1.0015032939121665e-05,
+        "std": 0.0009974389585585735
+    },
+    "aux_corine_frac_311": {
+        "mean": 0.02141707060090821,
+        "std": 0.05388296072110032
+    },
+    "aux_corine_frac_312": {
+        "mean": 0.04905874518444355,
+        "std": 0.12687690033450122
+    },
+    "aux_corine_frac_313": {
+        "mean": 0.012682777670815144,
+        "std": 0.0457894393833769
+    },
+    "aux_corine_frac_321": {
+        "mean": 0.05954866447921192,
+        "std": 0.14481817436759822
+    },
+    "aux_corine_frac_322": {
+        "mean": 0.0748806007487766,
+        "std": 0.17334097442585797
+    },
+    "aux_corine_frac_323": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_324": {
+        "mean": 0.016518766078724344,
+        "std": 0.05970527218851874
+    },
+    "aux_corine_frac_331": {
+        "mean": 0.00037079828146080804,
+        "std": 0.010036436983798991
+    },
+    "aux_corine_frac_332": {
+        "mean": 0.00072720624369643,
+        "std": 0.01157786569314129
+    },
+    "aux_corine_frac_333": {
+        "mean": 0.012274641287433904,
+        "std": 0.06402570315588603
+    },
+    "aux_corine_frac_334": {
+        "mean": 7.092218256869019e-06,
+        "std": 0.0005991380211712938
+    },
+    "aux_corine_frac_335": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_411": {
+        "mean": 0.0011926928129083708,
+        "std": 0.0177629896973229
+    },
+    "aux_corine_frac_412": {
+        "mean": 0.09765702521201046,
+        "std": 0.23604396908149422
+    },
+    "aux_corine_frac_421": {
+        "mean": 0.0015827466271050973,
+        "std": 0.01956554800791187
+    },
+    "aux_corine_frac_422": {
+        "mean": 0.0,
+        "std": 0.0
+    },
+    "aux_corine_frac_423": {
+        "mean": 0.003933563237704212,
+        "std": 0.032531498501610204
+    },
+    "aux_corine_frac_511": {
+        "mean": 0.000561918266933462,
+        "std": 0.009163339943300926
+    },
+    "aux_corine_frac_512": {
+        "mean": 0.009717452251824635,
+        "std": 0.06122992674436525
+    },
+    "aux_corine_frac_521": {
+        "mean": 2.297982526156031e-05,
+        "std": 0.0014667058518795608
+    },
+    "aux_corine_frac_522": {
+        "mean": 0.0021231797373174512,
+        "std": 0.024802238531671218
+    },
+    "aux_corine_frac_523": {
+        "mean": 0.014291723592658335,
+        "std": 0.0710510070030012
+    },
+    "aux_corine_frac_1": {
+        "mean": 0.0802927679330406,
+        "std": 0.18169086219409286
+    },
+    "aux_corine_frac_2": {
+        "mean": 0.5411375877095079,
+        "std": 0.38787876580612907
+    },
+    "aux_corine_frac_3": {
+        "mean": 0.2474863627937278,
+        "std": 0.31398304905390934
+    },
+    "aux_corine_frac_4": {
+        "mean": 0.10436602788972815,
+        "std": 0.2382482307321956
+    },
+    "aux_corine_frac_5": {
+        "mean": 0.026717253673995443,
+        "std": 0.09617029289776713
+    },
+    "aux_corine_frac_11": {
+        "mean": 0.05147849483161241,
+        "std": 0.13756143932672826
+    },
+    "aux_corine_frac_12": {
+        "mean": 0.011823189037622089,
+        "std": 0.04688613460990673
+    },
+    "aux_corine_frac_13": {
+        "mean": 0.0032429150906142703,
+        "std": 0.02152271396563153
+    },
+    "aux_corine_frac_14": {
+        "mean": 0.01374816897319181,
+        "std": 0.04341235709705624
+    },
+    "aux_corine_frac_21": {
+        "mean": 0.2576389518683456,
+        "std": 0.3267167437405614
+    },
+    "aux_corine_frac_22": {
+        "mean": 0.0003715637297008934,
+        "std": 0.008382979498378294
+    },
+    "aux_corine_frac_23": {
+        "mean": 0.27627695932709384,
+        "std": 0.2941670917165106
+    },
+    "aux_corine_frac_24": {
+        "mean": 0.00685011278436757,
+        "std": 0.03645831010427902
+    },
+    "aux_corine_frac_31": {
+        "mean": 0.08315859345616691,
+        "std": 0.14797484710834283
+    },
+    "aux_corine_frac_32": {
+        "mean": 0.15094803130671286,
+        "std": 0.25041112285241357
+    },
+    "aux_corine_frac_33": {
+        "mean": 0.01337973803084801,
+        "std": 0.06768695209323439
+    },
+    "aux_corine_frac_41": {
+        "mean": 0.09884971802491883,
+        "std": 0.23626719477301794
+    },
+    "aux_corine_frac_42": {
+        "mean": 0.00551630986480931,
+        "std": 0.04218330504440695
+    },
+    "aux_corine_frac_51": {
+        "mean": 0.010279370518758095,
+        "std": 0.061858529662706774
+    },
+    "aux_corine_frac_52": {
+        "mean": 0.016437883155237348,
+        "std": 0.07527943347488333
+    },
+    "aux_total_population": {
+        "mean": 1384.9394092146385,
+        "std": 4642.19802237972
+    },
+    "aux_pop_density": {
+        "mean": 212.1150317572336,
+        "std": 712.2522496739724
+    },
+    "aux_maxdist_road": {
+        "mean": 2228.0529287226536,
+        "std": 1309.984693451521
+    },
+    "aux_meandist_road": {
+        "mean": 1230.6387740699668,
+        "std": 1292.7376322660916
+    }
+}
diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 2d815ae..2807a33 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -8,6 +8,7 @@
    "outputs": [],
    "source": [
     "import glob\n",
+    "import json\n",
     "import os\n",
     "import time\n",
     "\n",
@@ -290,7 +291,7 @@
    "source": [
     "torch.save(\n",
     "    split_indices,\n",
-    "    \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth\",\n",
+    "    \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth\",  # geoclip, satclip\n",
     ")"
    ]
   },
@@ -326,16 +327,8 @@
    "id": "18",
    "metadata": {},
    "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth\"\n",
+    "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)\n",
     "\n",
     "print(\"Before test/val/train:\")\n",
@@ -349,7 +342,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "20",
+   "id": "19",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -361,7 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "21",
+   "id": "20",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -376,11 +369,11 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "22",
+   "id": "21",
    "metadata": {},
    "outputs": [],
    "source": [
-    "name = f\"data/s2bms/splits/split_indices_s2bms_{time.strftime('%Y-%m-%d-%H-%M-%S')}_union_aef_tsr_val_test.pth\"\n",
+    "name = f\"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-{time.strftime('%Y-%m-%d-%H-%M-%S')}_union_aef_tsr_val_test.pth\"\n",
     "\n",
     "torch.save(split_indices, name)\n",
     "print(f\"Saved: {name}\")"
@@ -388,7 +381,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23",
+   "id": "22",
    "metadata": {},
    "source": [
     "# Average encodings"
@@ -397,7 +390,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "24",
+   "id": "23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -426,7 +419,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "25",
+   "id": "24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -437,7 +430,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "26",
+   "id": "25",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -445,12 +438,67 @@
     "    df.to_csv(f\"data/s2bms/eo/avr_{mod}_{size}.csv\", index=False)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "26",
+   "metadata": {},
+   "source": [
+    "# Stats for scoring"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "27",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "labelled = True\n",
+    "\n",
+    "output_dir = \"/Users/gabriele/Desktop/aether/data/s2bms/aux_stats/\"\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "if labelled:\n",
+    "    df = pd.read_csv(\"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms.csv\")\n",
+    "    output_path = output_dir + \"labelled.json\"\n",
+    "else:\n",
+    "    df = pd.read_csv(\n",
+    "        \"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\"\n",
+    "    )\n",
+    "    output_path = output_dir + \"unlabelled.json\"\n",
+    "\n",
+    "col = [i for i in df.columns if \"aux\" in i and \"top\" not in i]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists(output_path):\n",
+    "    print(f\"Output file already exists: {output_path}\")\n",
+    "else:\n",
+    "    stats = {}\n",
+    "    for col in col:\n",
+    "        stats[col] = {\n",
+    "            \"mean\": float(df[col].mean()),\n",
+    "            \"std\": float(df[col].std()),\n",
+    "        }\n",
+    "\n",
+    "        with open(output_path, \"w\") as f:\n",
+    "            json.dump(stats, f, indent=4)\n",
+    "\n",
+    "        print(f\"Saved global statistics for {len(stats)} variables to {output_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

From a27c52ebbb893f64cbc22dea54d7664b94e9f451 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:20:51 +0200
Subject: [PATCH 067/173] Create soft contrastive loss

---
 .../loss_fns/soft_contrastive_loss.py         | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 src/models/components/loss_fns/soft_contrastive_loss.py

diff --git a/src/models/components/loss_fns/soft_contrastive_loss.py b/src/models/components/loss_fns/soft_contrastive_loss.py
new file mode 100644
index 0000000..2a6fa1f
--- /dev/null
+++ b/src/models/components/loss_fns/soft_contrastive_loss.py
@@ -0,0 +1,95 @@
+from typing import Dict, List, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing_extensions import override
+
+from src.models.components.loss_fns.base_loss_fn import BaseLossFn
+
+
+class SoftContrastiveLoss(BaseLossFn):
+    def __init__(self, temperature: float = 0.07, sigma: float = 1.0):
+        super().__init__()
+
+        self.log_temp = nn.Parameter(torch.log(torch.tensor(temperature)))
+        self.sigma = sigma
+
+        self.name = "SoftContrastiveLoss"
+
+    @override
+    def forward(
+        self,
+        eo_mod: torch.Tensor,
+        text_mod: torch.Tensor,
+        aux_values: torch.Tensor,
+        aux_ids_per_caption: List[List[int]],
+        mode: str | None = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        """Forward computation."""
+
+        # Get target matrix
+        T = self._get_soft_target_matrix(aux_ids_per_caption, aux_values)
+
+        # Normalize targets
+        T_eo2text = T / T.sum(dim=1, keepdim=True)
+        T_text2eo = T / T.sum(dim=0, keepdim=True)
+
+        # Normalise inputs
+        eo_mod = F.normalize(eo_mod, dim=-1)
+        text_mod = F.normalize(text_mod, dim=-1)
+
+        # Clip temperature to not exceed 100
+        temperature = torch.clamp(self.log_temp.exp(), max=100)
+
+        # Get cosine similarity
+        dot_product = (eo_mod @ text_mod.T) / temperature
+
+        # Cross entropy: sum(-target * log_prob)
+        loss_eo2text = -(T_eo2text * F.log_softmax(dot_product, dim=1)).sum(dim=1).mean()
+        loss_text2eo = -(T_text2eo * F.log_softmax(dot_product, dim=0)).sum(dim=0).mean()
+
+        loss = (loss_eo2text + loss_text2eo) / 2
+        if "return_label" in kwargs:
+            return {f"{mode}_{self.name}": loss}
+        else:
+            return loss
+
+    def _get_soft_target_matrix(
+        self,
+        aux_values: torch.Tensor,
+        aux_ids_per_caption: list[list[int]] | None,
+    ) -> torch.Tensor:
+        """Puts together a target matrix based on auxiliary value similarity (either all, or
+        specific per caption template).
+
+        :param aux_values: auxiliary column values (standardised).
+        :param aux_ids_per_caption: list of ids of auxiliary columns used per caption template.
+        :return: target matrix which tells how each row should be similar to column based on aux
+            values (all or specific ones per caption template).
+        """
+        batch_size, n_aux_cols = aux_values.shape
+        device = aux_values.device
+
+        # Soft targets based on the squared difference between location i and j for all aux values
+        diffs = (aux_values.unsqueeze(1) - aux_values.unsqueeze(0)) ** 2
+
+        # Create a mask based on aux_columns used per location caption
+        if aux_ids_per_caption:
+            # Create a mask
+            mask = torch.zeros((batch_size, n_aux_cols), dtype=aux_values.dtype, device=device)
+            for j, used_cols in enumerate(aux_ids_per_caption):
+                if used_cols:
+                    mask[j, used_cols] = 1.0
+            mask = mask.unsqueeze(0)
+
+            # Calculate the masked average distance for the selected aux cols
+            dist = (diffs * mask).sum(dim=-1) / (mask.sum(dim=-1) + 1e-8)
+        else:
+            # Distance based on all aux columns
+            dist = diffs.mean(dim=-1)
+
+        # Convert distances to similarities using a Gaussian kernel
+        T = torch.exp(-dist / (2 * self.sigma**2))  # (N, N)
+        return T

From 6054f4b0b3530bd855dda0973abdb12797e3607a Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:30:58 +0200
Subject: [PATCH 068/173] Implement loss pass

---
 src/models/text_alignment_model.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 9c9b792..0591a7e 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -195,7 +195,21 @@ def _step(self, batch: Dict[str, torch.Tensor], mode: str = "train"):
             geo_feats, text_feats = feats[0], feats[1]
 
         # Get loss
-        loss = self.loss_fn(geo_feats, text_feats)
+        aux_values = batch["aux"].get("aux_standardized")
+        aux_ids_per_caption = batch.get("text_aux_ids")
+
+        if geo_feats.isnan().any():
+            print(geo_feats)
+            print(batch["name_loc"])
+            exit()
+
+        loss = self.loss_fn(
+            geo_feats,
+            text_feats,
+            mode=mode,
+            aux_values=aux_values,
+            aux_ids_per_caption=aux_ids_per_caption,
+        )
 
         # Get similarities
         with torch.no_grad():

From 5991757887c308e0fbb5ab6d4c69c8474de01190 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:31:18 +0200
Subject: [PATCH 069/173] Dimension miss match warning

---
 src/models/text_alignment_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 0591a7e..88f54cd 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -102,9 +102,9 @@ def _setup(self, stage: str = "fit") -> None:
         if geo_branch_dim != text_branch_dim:
             if self.geo_adapter or self.text_adapter:
                 print(
-                    f"You opted to use: {'geo' if self.geo_adapter else '' and 'text' if self.text_adapter else ''} adapter(s)",
-                    "but you miss-configured output dimensions:"
-                    f"geo:{geo_branch_dim} vs text: {text_branch_dim}",
+                    f"You opted to use:{' geo' if self.geo_adapter else '' and ' text' if self.text_adapter else ''} adapter",
+                    "but you miss-configured output dimensions:\n"
+                    f"geo: {geo_branch_dim} vs text: {text_branch_dim}\n",
                     "Please try again.",
                 )
             elif self.match_to_geo:

From e24d18b7442cf32e173f4d3b31504f40143a3bf8 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 10:15:01 +0200
Subject: [PATCH 070/173] Add ability to load unlabelled data

# Conflicts:
#	configs/data/butterfly_coords_text_unlabelled.yaml
#	src/data/base_datamodule.py
#	src/data/base_dataset.py
#	src/data/butterfly_dataset.py
---
 configs/data/butterfly_coords_text_unlabelled.yaml | 2 +-
 src/data/base_datamodule.py                        | 6 +-----
 src/data/butterfly_dataset.py                      | 2 +-
 3 files changed, 3 insertions(+), 7 deletions(-)

diff --git a/configs/data/butterfly_coords_text_unlabelled.yaml b/configs/data/butterfly_coords_text_unlabelled.yaml
index 9ac97d0..e8399d3 100644
--- a/configs/data/butterfly_coords_text_unlabelled.yaml
+++ b/configs/data/butterfly_coords_text_unlabelled.yaml
@@ -24,5 +24,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 12d822f..9127c36 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -68,7 +68,6 @@ def __init__(
             self.concept_configs = caption_builder.concepts
 
         self.split_data()
-        self._setup_flag = False
 
     @property
     def tabular_dim(self):
@@ -266,17 +265,14 @@ def split_data(self) -> None:
 
         if split_data_from_inds:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
-            self.data_train.dataset.mode = "train"
             print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
-            self.data_val.dataset.mode = "val"
             print(f"Validate dataset split size: {len(self.data_val)}")
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)
-                self.data_test.dataset.mode = "test"
-                print(f"Test dataset split size: {len(self.data_test)}")
             else:
+                print(f"Test dataset split size: {len(self.data_test)}")
                 self.data_test = None
 
         if self.hparams.save_split:
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index aaa53f3..ce49704 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -41,7 +41,7 @@ def __init__(
 
         assert not (
             use_unlabelled_data and use_target_data
-        ), "Joint use of unlabelled and target data is not supported."
+        ), "Joint use of unlabelled and target data is not supported yet."
         if use_unlabelled_data:
             dataset_name = ["s2bms", "s2bms-unlabelled-20260529"]
         else:

From 0e6e42662db88603f5e5cf221dc7b6f29594e3d6 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Tue, 9 Jun 2026 13:31:27 +0200
Subject: [PATCH 071/173] Download average AEF data

---
 scripts/download_gee_data.py              | 29 ++++++++++++++++-------
 scripts/download_gee_parallel.sh          |  2 +-
 src/data_preprocessing/create_aux_data.py |  4 ++++
 src/data_preprocessing/gee_utils.py       | 12 +++++++++-
 4 files changed, 37 insertions(+), 10 deletions(-)

diff --git a/scripts/download_gee_data.py b/scripts/download_gee_data.py
index f5b9ecc..9be9e55 100644
--- a/scripts/download_gee_data.py
+++ b/scripts/download_gee_data.py
@@ -4,10 +4,12 @@
 
 import pandas as pd
 
+from src.data_preprocessing import gee_utils as gu
 from src.data_preprocessing.create_aux_data import get_aux_data_from_coords_list
 
 
-def main(start=0, stop=2000):
+def main(start=0, stop=2000, content="alphaearth"):
+    assert content in ["aux_data", "alphaearth"], f"{content} not recognised."
     path_csv = os.path.join(os.environ["DATA_DIR"], "s2bms/source/", "unlabelled_samples_10k.csv")
     assert os.path.exists(path_csv), f"CSV file with locations does not exist: {path_csv}"
     df_samples = pd.read_csv(path_csv)
@@ -28,13 +30,24 @@ def main(start=0, stop=2000):
     coords_list = coords_list[start:stop]
     name_list = df_samples.name.values[start:stop]
 
-    _, __ = get_aux_data_from_coords_list(
-        coords_list=coords_list,
-        name_list=name_list,
-        save_file=True,
-        save_filename=f"aux_data_unlabelled_samples_10k_{start}_{stop}.csv",
-        patch_size=2560,
-    )
+    if content == "aux_data":
+        _, __ = get_aux_data_from_coords_list(
+            coords_list=coords_list,
+            name_list=name_list,
+            save_file=True,
+            save_filename=f"aux_data_unlabelled_samples_10k_{start}_{stop}.csv",
+            patch_size=2560,
+        )
+    elif content == "alphaearth":
+        _ = gu.download_list_coord(
+            coord_list=coords_list,
+            name_list=name_list,
+            pixel_patch_size=256,
+            list_collections=["alphaearth"],
+            name_group="aef-uk-unlabelled",
+            save_average_only=True,
+            path_save=os.path.join(os.environ["DATA_DIR"], "s2bms/source/alphaearth_av/"),
+        )
 
 
 if __name__ == "__main__":
diff --git a/scripts/download_gee_parallel.sh b/scripts/download_gee_parallel.sh
index d0793a6..59fa487 100644
--- a/scripts/download_gee_parallel.sh
+++ b/scripts/download_gee_parallel.sh
@@ -5,7 +5,7 @@ source "$(dirname "$0")/../.env"
 set +a
 
 N=10000        # max total points
-P=20           # number of processes
+P=50           # number of processes
 CHUNK=$((N / P))
 
 for ((i=0; i<P; i++)); do
diff --git a/src/data_preprocessing/create_aux_data.py b/src/data_preprocessing/create_aux_data.py
index 6fe9091..6ac8b65 100644
--- a/src/data_preprocessing/create_aux_data.py
+++ b/src/data_preprocessing/create_aux_data.py
@@ -1,6 +1,10 @@
 import os
 import sys
 
+from dotenv import load_dotenv
+
+load_dotenv()
+
 import numpy as np
 import pandas as pd
 from tqdm import tqdm
diff --git a/src/data_preprocessing/gee_utils.py b/src/data_preprocessing/gee_utils.py
index f1f73bf..780b374 100644
--- a/src/data_preprocessing/gee_utils.py
+++ b/src/data_preprocessing/gee_utils.py
@@ -296,6 +296,7 @@ def download_gee_image(
     sentinel_month_end="09",
     collection_name="sentinel2",
     resize_image=True,
+    save_average_only=True,
 ):
     """Download a GEE image for given coordinates and save it locally.
 
@@ -314,6 +315,8 @@ def download_gee_image(
     assert type(path_save) and os.path.exists(
         path_save
     ), f"path_save must be a valid path, got {path_save}"
+    if save_average_only:
+        resize_image = True
     gsd_resolution = 10
     patch_size = (
         pixel_patch_size + 20
@@ -386,7 +389,12 @@ def download_gee_image(
         ), im_crop.shape
         if verbose:
             print("New size: ", im_crop.shape)
-        im_crop.rio.to_raster(filepath)
+        if save_average_only:
+            im_crop_mean = im_crop.mean(dim=("x", "y"), keepdims=True)
+            im_crop_mean.rio.to_raster(filepath, dtype="float32")
+        else:
+            im_crop.rio.to_raster(filepath, dtype="float32")
+
         im_gee = im_crop
 
     return im_gee, filepath
@@ -401,6 +409,7 @@ def download_list_coord(
     start_index=0,
     stop_index=None,
     resize_image=True,
+    save_average_only=False,
     list_collections=["sentinel2", "alphaearth"],
     verbose=0,
 ):
@@ -449,6 +458,7 @@ def download_list_coord(
                     verbose=verbose,
                     resize_image=resize_image,
                     collection_name=im_collection,
+                    save_average_only=save_average_only,
                 )
             except Exception as e:
                 print(f"Image {name}, {im_collection} could not be downloaded, error: {e}")

From d83f2c4873eae93722b4522d3b10ea34e8c80a55 Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Tue, 16 Jun 2026 09:57:21 +0200
Subject: [PATCH 072/173] download 128 av data

---
 .../08-TvdP-curate-aef-average-data.ipynb     | 116 ++++++++++++++++++
 scripts/download_gee_data.py                  |  12 +-
 2 files changed, 126 insertions(+), 2 deletions(-)
 create mode 100644 notebooks/08-TvdP-curate-aef-average-data.ipynb

diff --git a/notebooks/08-TvdP-curate-aef-average-data.ipynb b/notebooks/08-TvdP-curate-aef-average-data.ipynb
new file mode 100644
index 0000000..09de15c
--- /dev/null
+++ b/notebooks/08-TvdP-curate-aef-average-data.ipynb
@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"../\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "import src.data_preprocessing.caption_generation as cg\n",
+    "import src.data_preprocessing.create_aux_data as cad\n",
+    "import src.data_preprocessing.data_utils as du\n",
+    "import src.data_preprocessing.gee_utils as gu\n",
+    "from src.data.base_caption_builder import BaseCaptionBuilder, DummyCaptionBuilder\n",
+    "from src.data.base_datamodule import BaseDataModule\n",
+    "from src.data.butterfly_caption_builder import ButterflyCaptionBuilder\n",
+    "from src.data.butterfly_dataset import ButterflyDataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datadir = \"/Users/tplas/data/aether_data/s2bms/source/alphaearth_av-128/\"\n",
+    "tiffiles = os.listdir(datadir)\n",
+    "tiffiles = [f for f in tiffiles if f.endswith(\".tif\")]\n",
+    "list_names = []\n",
+    "for i_f, f in tqdm(enumerate(tiffiles)):\n",
+    "    fp = os.path.join(datadir, f)\n",
+    "    im = du.load_tiff(fp)\n",
+    "    assert (\n",
+    "        im.ndim == 3 and im.shape[1] == 1 and im.shape[2] == 1\n",
+    "    ), f\"Expected image to have shape (bands, 1, 1), but got {im.shape}\"\n",
+    "    if i_f == 0:\n",
+    "        vals = np.squeeze(im)[:, None]\n",
+    "    else:\n",
+    "        vals = np.concatenate((vals, np.squeeze(im)[:, None]), axis=1)\n",
+    "    list_names.append(\"_\".join(f.split(\"_\")[1:4]))\n",
+    "\n",
+    "\n",
+    "n_embed = 64\n",
+    "assert (\n",
+    "    vals.shape[0] == n_embed\n",
+    "), f\"Expected number of bands to be {n_embed}, but got {vals.shape[0]}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_vals = pd.DataFrame(vals.T, columns=[\"emb_\" + str(i) for i in range(n_embed)])\n",
+    "df_vals[\"name_loc\"] = list_names\n",
+    "df_vals[\"ind_int\"] = [int(x.split(\"_\")[1]) for x in list_names]\n",
+    "df_vals = df_vals.sort_values(\"ind_int\").reset_index(drop=True)\n",
+    "df_vals = df_vals.drop(columns=[\"ind_int\"])\n",
+    "df_vals.to_csv(os.path.join(datadir, \"aef-uk-unlabelled_average-128.csv\"), index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_vals"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aether",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/scripts/download_gee_data.py b/scripts/download_gee_data.py
index 9be9e55..2c67b20 100644
--- a/scripts/download_gee_data.py
+++ b/scripts/download_gee_data.py
@@ -9,6 +9,14 @@
 
 
 def main(start=0, stop=2000, content="alphaearth"):
+    """Download GEE data for a list of coordinates and save to disk. Either auxiliary data or
+    alphaearth data can be downloaded, depending on the value of `content`.
+
+    Args:
+        start (int): Starting index of the coordinates to process.
+        stop (int): Ending index of the coordinates to process.
+        content (str): Type of data to download. Must be either "aux_data" or "alphaearth".
+    """
     assert content in ["aux_data", "alphaearth"], f"{content} not recognised."
     path_csv = os.path.join(os.environ["DATA_DIR"], "s2bms/source/", "unlabelled_samples_10k.csv")
     assert os.path.exists(path_csv), f"CSV file with locations does not exist: {path_csv}"
@@ -42,11 +50,11 @@ def main(start=0, stop=2000, content="alphaearth"):
         _ = gu.download_list_coord(
             coord_list=coords_list,
             name_list=name_list,
-            pixel_patch_size=256,
+            pixel_patch_size=128,
             list_collections=["alphaearth"],
             name_group="aef-uk-unlabelled",
             save_average_only=True,
-            path_save=os.path.join(os.environ["DATA_DIR"], "s2bms/source/alphaearth_av/"),
+            path_save=os.path.join(os.environ["DATA_DIR"], "s2bms/source/alphaearth_av-128/"),
         )
 
 

From e7d0120242951ad2df184cdcf180d2344c53906a Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Tue, 16 Jun 2026 09:59:44 +0200
Subject: [PATCH 073/173] update nbs

---
 notebooks/03-TvdP-load-butterfly-data.ipynb   |  78 +++++-
 .../05-TvdP-load-butterfly-captions.ipynb     | 236 ++++++++++++++++++
 ...vdP-sample-alignment-locations-S2BMS.ipynb |  18 +-
 3 files changed, 326 insertions(+), 6 deletions(-)
 create mode 100644 notebooks/05-TvdP-load-butterfly-captions.ipynb

diff --git a/notebooks/03-TvdP-load-butterfly-data.ipynb b/notebooks/03-TvdP-load-butterfly-data.ipynb
index a853f9c..ff770fb 100644
--- a/notebooks/03-TvdP-load-butterfly-data.ipynb
+++ b/notebooks/03-TvdP-load-butterfly-data.ipynb
@@ -22,6 +22,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
+    "\n",
     "import src.data_preprocessing.create_aux_data as cad\n",
     "import src.data_preprocessing.data_utils as du\n",
     "import src.data_preprocessing.gee_utils as gu\n",
@@ -39,7 +41,7 @@
    "outputs": [],
    "source": [
     "BD = ButterflyDataset(\n",
-    "    modalities={\"coords\": None}, data_dir=os.path.join(os.environ[\"DATA_DIR\"]), use_aux_data=True\n",
+    "    modalities={\"coords\": None}, data_dir=os.path.join(os.environ[\"DATA_DIR\"]), use_aux_data=\"all\"\n",
     ")"
    ]
   },
@@ -52,7 +54,8 @@
    "source": [
     "BCB = ButterflyCaptionBuilder(\n",
     "    data_dir=os.path.join(os.environ[\"DATA_DIR\"], \"s2bms\"),\n",
-    "    templates_fname=\"v2.json\",\n",
+    "    templates_fname=\"v3.json\",\n",
+    "    concepts_fname=\"v2.json\",\n",
     "    seed=42,\n",
     ")\n",
     "\n",
@@ -92,7 +95,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "batch[\"text\"]"
+    "import json\n",
+    "\n",
+    "concepts_path = os.path.join(os.environ[\"DATA_DIR\"], \"s2bms\", \"concept_captions/v2.json\")\n",
+    "\n",
+    "concepts = json.load(open(concepts_path))\n",
+    "concepts"
    ]
   },
   {
@@ -102,7 +110,69 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "BM.caption_builder._build_from_template(template_idx=2, row=BD[20][\"aux\"])"
+    "float(batch[\"aux\"][\"aux\"][0, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ind_aux = np.where(np.array(BD.use_aux_data[\"aux\"]) == \"aux_corine_frac_412\")[0]\n",
+    "# BD.mode\n",
+    "# ind_aux = 70\n",
+    "\n",
+    "list_vals = []\n",
+    "for i in range(len(BD)):\n",
+    "    aux_data = BD[i][\"aux\"][\"aux\"][ind_aux]\n",
+    "    list_vals.append(aux_data)\n",
+    "\n",
+    "list_vals\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "\n",
+    "\n",
+    "def find_elbow_point(vals):\n",
+    "    \"\"\"vals is a list of tensor values\"\"\"\n",
+    "    with torch.no_grad():\n",
+    "        vals = torch.tensor(vals).cpu().numpy()\n",
+    "        vals = vals[~np.isnan(vals)]  # remove NaN values\n",
+    "\n",
+    "        vals = np.sort(vals)\n",
+    "        vals = vals[vals > vals[0]]\n",
+    "        x = np.arange(len(vals)) / len(vals)\n",
+    "        y = vals\n",
+    "        slope = (y[-1] - y[0]) / (x[-1] - x[0])  # diagonal from first to last point\n",
+    "        intercept = y[0] - slope * x[0]\n",
+    "        orthogonal_slope = -1 / slope\n",
+    "\n",
+    "        intercepts_orthogonal = y - orthogonal_slope * x\n",
+    "        intersection_diagonal_orthogonal = (intercepts_orthogonal - intercept) / (\n",
+    "            slope - orthogonal_slope\n",
+    "        )\n",
+    "        distances = np.sqrt(\n",
+    "            (x - intersection_diagonal_orthogonal) ** 2 + (y - (slope * x + intercept)) ** 2\n",
+    "        )  # distance to diagonal\n",
+    "        elbow_index = np.argmax(distances)\n",
+    "        elbow_point = y[elbow_index]\n",
+    "        return elbow_point\n",
+    "\n",
+    "\n",
+    "theta_k = find_elbow_point(list_vals)\n",
+    "print(f\"Elbow point (theta_k) for aux index {ind_aux}: {theta_k}\")\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "points = np.sort(torch.tensor(list_vals).cpu().numpy())\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(np.sort(points), marker=\"o\", linestyle=\"-\", markersize=4)\n",
+    "## plot diagonal\n",
+    "\n",
+    "plt.axhline(theta_k, color=\"red\", linestyle=\"--\", label=f\"Elbow Point: {theta_k:.4f}\")\n",
+    "plt.title(\"Sorted Values with Elbow Point\")\n",
+    "plt.xlabel(\"Index\")\n",
+    "plt.ylabel(\"Value\")"
    ]
   }
  ],
diff --git a/notebooks/05-TvdP-load-butterfly-captions.ipynb b/notebooks/05-TvdP-load-butterfly-captions.ipynb
new file mode 100644
index 0000000..ce1bf75
--- /dev/null
+++ b/notebooks/05-TvdP-load-butterfly-captions.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "import os\n",
+    "import sys\n",
+    "\n",
+    "sys.path.append(\"../\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import src.data_preprocessing.caption_generation as cg\n",
+    "import src.data_preprocessing.create_aux_data as cad\n",
+    "import src.data_preprocessing.data_utils as du\n",
+    "import src.data_preprocessing.gee_utils as gu\n",
+    "from src.data.base_caption_builder import BaseCaptionBuilder, DummyCaptionBuilder\n",
+    "from src.data.base_datamodule import BaseDataModule\n",
+    "from src.data.butterfly_caption_builder import ButterflyCaptionBuilder\n",
+    "from src.data.butterfly_dataset import ButterflyDataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# BD = ButterflyDataset(\n",
+    "#     modalities={\"coords\": None}, data_dir=os.path.join(os.environ[\"DATA_DIR\"]), use_aux_data='all'\n",
+    "# )\n",
+    "\n",
+    "BD = ButterflyDataset(\n",
+    "    modalities={\"coords\": None},\n",
+    "    data_dir=os.path.join(os.environ[\"DATA_DIR\"]),\n",
+    "    use_aux_data=\"all\",\n",
+    "    use_target_data=False,\n",
+    "    use_unlabelled_data=True,\n",
+    ")\n",
+    "\n",
+    "BCB = ButterflyCaptionBuilder(\n",
+    "    data_dir=os.path.join(os.environ[\"DATA_DIR\"], \"s2bms\"),\n",
+    "    templates_fname=\"v5.json\",\n",
+    "    concepts_fname=\"v4.json\",\n",
+    "    seed=42,\n",
+    ")\n",
+    "\n",
+    "BM = BaseDataModule(\n",
+    "    dataset=BD,\n",
+    "    batch_size=32,\n",
+    "    split_mode=\"from_file\",\n",
+    "    saved_split_file_name=os.path.join(\n",
+    "        os.environ[\"DATA_DIR\"],\n",
+    "        \"s2bms\",\n",
+    "        \"splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth\",\n",
+    "    ),\n",
+    "    caption_builder=BCB,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for dp in BD:\n",
+    "    assert len(dp[\"target\"]) == 0\n",
+    "    assert len(dp[\"eo\"]) == 1\n",
+    "    assert len(dp[\"eo\"][\"coords\"]) == 2 and not any(dp[\"eo\"][\"coords\"].isnan())\n",
+    "    assert not any(dp[\"aux\"][\"aux\"].isnan())\n",
+    "    # assert all([type(x) == str for x in dp[\"aux\"][\"top\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(BM.data_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# BM.split_dir\n",
+    "split_files = os.listdir(BM.split_dir)\n",
+    "split_indices_joint = {}\n",
+    "\n",
+    "for ifile, filename in enumerate(split_files):\n",
+    "    if filename.endswith(\".pth\"):\n",
+    "        filepath = os.path.join(BM.split_dir, filename)\n",
+    "        split_indices = BM.load_split_indices(filepath=filepath)\n",
+    "        if \"clusters\" in split_indices.keys():\n",
+    "            del split_indices[\n",
+    "                \"clusters\"\n",
+    "            ]  # remove clusters if present, as they are not relevant for the joint split\n",
+    "        if ifile == 0:\n",
+    "            split_indices_joint = split_indices\n",
+    "        else:  # concatenate series and reset index per key\n",
+    "            for key in split_indices_joint.keys():\n",
+    "                split_indices_joint[key] = pd.concat(\n",
+    "                    [split_indices_joint[key], split_indices[key]], ignore_index=True\n",
+    "                )\n",
+    "for k, v in split_indices_joint.items():\n",
+    "    print(f\"{k}: {len(v)} samples\")\n",
+    "    split_indices_joint[k] = v.reset_index(drop=True)\n",
+    "\n",
+    "BM.save_split_indices(split_indices_joint)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# batch = next(iter(BM.train_dataloader()))\n",
+    "batch = next(iter(BM.val_dataloader()))\n",
+    "batch.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch[\"eo\"].keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BCB.column_to_metadata_map[\"aux\"][\"aux_corine_frac_243\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "template_id = 100\n",
+    "data_id_in_batch = 5\n",
+    "\n",
+    "cap_num = BCB._build_from_template(\n",
+    "    template_idx=template_id,\n",
+    "    aux=batch[\"aux\"][\"aux\"][data_id_in_batch, :],\n",
+    "    top=batch[\"aux\"][\"top\"][data_id_in_batch],\n",
+    "    convert_corine_perc=False,\n",
+    ")\n",
+    "cap_adj = BCB._build_from_template(\n",
+    "    template_idx=template_id,\n",
+    "    aux=batch[\"aux\"][\"aux\"][data_id_in_batch, :],\n",
+    "    top=batch[\"aux\"][\"top\"][data_id_in_batch],\n",
+    "    convert_corine_perc=True,\n",
+    ")\n",
+    "\n",
+    "print(cap_num)\n",
+    "print(cap_adj)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10",
+   "metadata": {},
+   "source": [
+    "\"setting with a mosaic of <aux_corine_frac_lowlevel_top_1>, <aux_corine_frac_lowlevel_top_2> and <aux_corine_frac_lowlevel_top_3>, with <aux_bioclim_12>, <aux_pop_density> and <aux_bioclim_08>.\",\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch[\"text\"]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "aether",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb b/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb
index f3adbbb..5607f57 100644
--- a/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb
+++ b/notebooks/07-TvdP-sample-alignment-locations-S2BMS.ipynb
@@ -296,7 +296,7 @@
    "outputs": [],
    "source": [
     "## Merge csv aux files:\n",
-    "save_file = False\n",
+    "save_file = True\n",
     "folder_csv = os.path.join(os.environ[\"DATA_DIR\"], \"s2bms/source/\")\n",
     "assert os.path.exists(folder_csv), f\"Folder {folder_csv} does not exist\"\n",
     "csv_files = [f for f in os.listdir(folder_csv) if f.endswith(\".csv\")]\n",
@@ -321,11 +321,25 @@
     "}\n",
     "df_merge = df_merge.rename(columns=dict_rename)\n",
     "\n",
+    "## remove any nan rows:\n",
+    "df_merge = df_merge.dropna()\n",
+    "\n",
     "if save_file:\n",
     "    df_merge.to_csv(\n",
-    "        os.path.join(folder_csv, \"model_ready_s2bms-unlabelled-20260529.csv\"), index=False\n",
+    "        os.path.join(os.environ[\"DATA_DIR\"], \"s2bms\", \"model_ready_s2bms-unlabelled-20260529.csv\"),\n",
+    "        index=False,\n",
     "    )"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_merge"
+   ]
   }
  ],
  "metadata": {

From cb9922d79c6114627fd0ac07312f5021a12d1d9b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 13:13:58 +0200
Subject: [PATCH 074/173] New datasplits

---
 data/s2bms/splits/explanations.txt            |  15 +-
 ..._val_test.pth => s2bms_union_val_test.pth} | Bin
 .../s2bms_unlabelled_union_val_test.pth       | Bin 0 -> 294269 bytes
 ...8-14-1459_union_aef_tsr_val_test_train.pth | Bin 68355 -> 0 bytes
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 393 +++++-------------
 5 files changed, 107 insertions(+), 301 deletions(-)
 rename data/s2bms/splits/{split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth => s2bms_union_val_test.pth} (100%)
 create mode 100644 data/s2bms/splits/s2bms_unlabelled_union_val_test.pth
 delete mode 100644 data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth

diff --git a/data/s2bms/splits/explanations.txt b/data/s2bms/splits/explanations.txt
index ed2f4b5..56c5134 100644
--- a/data/s2bms/splits/explanations.txt
+++ b/data/s2bms/splits/explanations.txt
@@ -1,11 +1,6 @@
-split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth: sum of: original s2bms (with val and test subset for aef and tessera available tile union) and all unlabelled locations in the training split.
+split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth: all train/val/test unlabelled indices (not used, source data)
+split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth: original S2BMS split and all train/val/test unlabelled indices (not used, source data)
+split_indices_s2bms_2024-08-14-1459.pth: original S2BMS split (not used, source data)
 
-split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth: subset of split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth where all train tiles are availble for aef
-
-split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth: all train/val/test unlabelled indices (not used, source data)
-
-split_indices_s2bms_2024-08-14-1459.pth: original S2BMS split (?,  not used, source data?)
-
-split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth: S2bms with val and test subset for aef and tessera available tile union
-
-split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth:  S2bms with val and test and train subset for aef and tessera available tile union (less tiles in the training, because tessera was missing some: use for tessera)
+s2bms_union_val_test: S2BMS labelled subset for this study. All val and test split datapoints are available for modalities of: coords, alphaearth and tessera.
+s2bms_unlabelled_union_val_test: s2bms_union_val_test + all train samples from the unlabelled dataset
\ No newline at end of file
diff --git a/data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth b/data/s2bms/splits/s2bms_union_val_test.pth
similarity index 100%
rename from data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth
rename to data/s2bms/splits/s2bms_union_val_test.pth
diff --git a/data/s2bms/splits/s2bms_unlabelled_union_val_test.pth b/data/s2bms/splits/s2bms_unlabelled_union_val_test.pth
new file mode 100644
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diff --git a/data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth b/data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth
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diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 2807a33..3f8f2dd 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -1,39 +1,35 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import glob\n",
-    "import json\n",
     "import os\n",
-    "import time\n",
     "\n",
     "import pandas as pd\n",
     "import torch\n",
     "\n",
     "os.chdir(\"..\")\n",
     "os.getcwd()"
-   ]
+   ],
+   "id": "90846f361f4da291"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Check how do aef and tessera files overlap\n",
     "\n",
-    "paths = glob.glob(\"data/s2bms/eo/aef/*.tif\")\n",
+    "paths = glob.glob(\"data/s2bms/eo/aef/*.npy\")\n",
     "aef = []\n",
     "for p in paths:\n",
     "    aef.append(os.path.basename(p).split(\".\")[0].split(\"-\")[-1])\n",
     "\n",
-    "\n",
     "paths = glob.glob(\"data/s2bms/eo/tessera/*.npy\")\n",
     "tsr = []\n",
     "for p in paths:\n",
@@ -44,28 +40,32 @@
     "tsr_left = list(set(tsr) - set(common))  # None\n",
     "aef_left = list(set(aef) - set(common))  # 12\n",
     "\n",
+    "# Filter out unlabelled data\n",
+    "aef_left = {i for i in tsr_left if 'sample' not in i}\n",
+    "aef_left = {i for i in aef_left if 'sample' not in i}\n",
+    "\n",
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
-   ]
+   ],
+   "id": "55ed47d0469d34b"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Load in original datasplit file\n",
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
-   ]
+   ],
+   "id": "498c9b97c339519f"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Print data split sizes\n",
     "s = 0\n",
@@ -74,14 +74,14 @@
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "692d1229fd40ad29"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Filter out remaining aef but missing tessera tiles form val and test splits\n",
     "\n",
@@ -101,52 +101,22 @@
     "        s += len(v)\n",
     "\n",
     "if os.path.exists(\n",
-    "    \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth\"\n",
+    "    \"data/s2bms/splits/s2bms_union_val_test.pth\"\n",
     "):\n",
     "    print(\"Already saved\")\n",
     "else:\n",
     "    torch.save(\n",
     "        split_indices,\n",
-    "        \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth\",\n",
+    "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
-   ]
+   ],
+   "id": "ebb3fd9525ff77dd"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Filter out remaining aef but missing tessera tiles form train splits\n",
-    "\n",
-    "split_indices[\"train_indices\"] = split_indices[\"train_indices\"][\n",
-    "    ~split_indices[\"train_indices\"].isin(aef_left_str)\n",
-    "]\n",
-    "\n",
-    "print(\"After test/val/train cleaning:\")\n",
-    "for k, v in split_indices.items():\n",
-    "    if not k == \"clusters\":\n",
-    "        print(k, len(v))\n",
-    "        s += len(v)\n",
-    "\n",
-    "if os.path.exists(\n",
-    "    \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth\"\n",
-    "):\n",
-    "    print(\"Already saved\")\n",
-    "else:\n",
-    "    torch.save(\n",
-    "        split_indices,\n",
-    "        \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth\",\n",
-    "    )"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6",
-   "metadata": {},
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Visualise data split\n",
     "df = pd.read_csv(\"data/s2bms/model_ready_s2bms.csv\")\n",
@@ -154,20 +124,19 @@
     "train_idx = split_indices[\"train_indices\"]\n",
     "train_df = df[df[\"name_loc\"].isin(map(str, train_idx))]\n",
     "\n",
-    "\n",
     "val_idx = split_indices[\"val_indices\"]\n",
     "val_df = df[df[\"name_loc\"].isin(map(str, val_idx))]\n",
     "\n",
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
-   ]
+   ],
+   "id": "97f802307e332d52"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import folium\n",
     "\n",
@@ -186,35 +155,33 @@
     "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"red\", fill=True).add_to(m)\n",
     "\n",
     "m"
-   ]
+   ],
+   "id": "67dba7e5ac46f0d2"
   },
   {
-   "cell_type": "markdown",
-   "id": "8",
    "metadata": {},
-   "source": [
-    "# Re-do Unlabelled split"
-   ]
+   "cell_type": "markdown",
+   "source": "# Re-do Unlabelled split",
+   "id": "5633920d88ef725"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "df = pd.read_csv(\"data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\")\n",
-    "df = df[df.split == \"train\"]\n",
+    "df_unlabelled = pd.read_csv(\"data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\")\n",
+    "df_unlabelled = df_unlabelled[df_unlabelled.split == \"train\"]\n",
     "\n",
-    "train_unlabelled = list(df.name_loc)"
-   ]
+    "train_unlabelled = list(df_unlabelled.name_loc)"
+   ],
+   "id": "60fad48574e0b090"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)\n",
@@ -225,39 +192,39 @@
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "bdafc8dde36245b3"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "split_indices[\"train_indices\"] = pd.Series(\n",
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
-   ]
+   ],
+   "id": "b25a1eaadc10bb3c"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "12",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "val_test_indices = torch.load(\n",
-    "    \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth\",\n",
+    "    \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    weights_only=False,\n",
     ")"
-   ]
+   ],
+   "id": "eb3f894e8062d298"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "split_indices[\"val_indices\"] = pd.Series(\n",
     "    [i for i in list(val_test_indices[\"val_indices\"]) if \"UKBMS\" in i]\n",
@@ -265,250 +232,94 @@
     "split_indices[\"test_indices\"] = pd.Series(\n",
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
-   ]
+   ],
+   "id": "9c2686272f684c0c"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(\"After test/val/train:\")\n",
-    "s = 0\n",
-    "for k, v in split_indices.items():\n",
-    "    if not k == \"clusters\":\n",
-    "        print(k, len(v))\n",
-    "        s += len(v)"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "15",
-   "metadata": {},
    "outputs": [],
-   "source": [
-    "torch.save(\n",
-    "    split_indices,\n",
-    "    \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth\",  # geoclip, satclip\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "16",
-   "metadata": {},
-   "source": [
-    "# Extend split with unlabelled data\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
-   "id": "17",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "if not os.path.exists(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\"):\n",
-    "    df_labelled = pd.read_csv(\"data/s2bms/eo/avr_aef_256.csv\")\n",
-    "    df = pd.read_csv(\"data/s2bms/eo/avr_aef_256_just_unlabelled.csv\")\n",
-    "    df = pd.concat([df, df_labelled])\n",
-    "\n",
-    "    df.to_csv(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\")\n",
-    "else:\n",
-    "    df = pd.read_csv(\"data/s2bms/eo/avr_aef_256_unlabelled.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth\"\n",
-    "split_indices = torch.load(pth, weights_only=False)\n",
-    "\n",
-    "print(\"Before test/val/train:\")\n",
+    "print(\"After test/val/train:\")\n",
     "s = 0\n",
     "for k, v in split_indices.items():\n",
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "bc5d30df078d7afd"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "19",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "split_indices[\"train_indices\"] = split_indices[\"train_indices\"][\n",
-    "    split_indices[\"train_indices\"].isin(df.name_loc)\n",
-    "]"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "20",
-   "metadata": {},
    "outputs": [],
-   "source": [
-    "print(\"After inclusion of avail unlabelled data:\")\n",
-    "s = 0\n",
-    "for k, v in split_indices.items():\n",
-    "    if not k == \"clusters\":\n",
-    "        print(k, len(v))\n",
-    "        s += len(v)"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
-   "id": "21",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "name = f\"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-{time.strftime('%Y-%m-%d-%H-%M-%S')}_union_aef_tsr_val_test.pth\"\n",
-    "\n",
-    "torch.save(split_indices, name)\n",
-    "print(f\"Saved: {name}\")"
-   ]
+    "if not os.path.exists(\"data/s2bms/splits/s2bms_unlabelled_union_val_test.pth\"):\n",
+    "    torch.save(\n",
+    "        split_indices,\n",
+    "        \"data/s2bms/splits/s2bms_unlabelled_union_val_test.pth\",\n",
+    "    )\n",
+    "else:\n",
+    "    print('Already saved')"
+   ],
+   "id": "ef7579bad784e241"
   },
   {
-   "cell_type": "markdown",
-   "id": "22",
    "metadata": {},
-   "source": [
-    "# Average encodings"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "23",
-   "metadata": {},
    "outputs": [],
+   "execution_count": null,
    "source": [
-    "import numpy as np\n",
+    "# Visualise data split\n",
+    "df_merged = pd.concat([df, df_unlabelled])\n",
     "\n",
-    "from src.utils.data_utils import center_crop_npy\n",
+    "train_idx = split_indices[\"train_indices\"]\n",
+    "train_df = df_merged[df_merged[\"name_loc\"].isin(map(str, train_idx))]\n",
     "\n",
-    "mod = \"tessera\"\n",
-    "size = 128\n",
+    "val_idx = split_indices[\"val_indices\"]\n",
+    "val_df = df_merged[df_merged[\"name_loc\"].isin(map(str, val_idx))]\n",
     "\n",
-    "paths = glob.glob(f\"data/s2bms/eo/{mod}/*UKBMS*.npy\")\n",
-    "rows = []\n",
-    "for p in paths:\n",
-    "    arr = np.load(p).transpose(2, 0, 1)\n",
-    "    if arr.dtype != np.dtype(\"float32\"):\n",
-    "        arr = arr.astype(dtype=\"float32\", copy=False)\n",
-    "    arr = center_crop_npy(arr, (64 if mod == \"aef\" else 128, size, size))\n",
-    "    if np.isinf(arr).any():\n",
-    "        arr[np.isinf(arr)] = np.nan\n",
-    "    tensor = torch.from_numpy(arr)\n",
-    "    row = {f\"emb_{i}\": v.item() for i, v in enumerate(tensor.nanmean(dim=(-2, -1)))}\n",
-    "    row[\"name_loc\"] = p.split(\"/\")[-1].split(\".\")[0].replace(f\"{mod}_\", \"\")\n",
-    "    rows.append(row)"
-   ]
+    "test_idx = split_indices[\"test_indices\"]\n",
+    "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
+   ],
+   "id": "1c1db0df607ce8cf"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "24",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.DataFrame(rows)\n",
-    "df"
-   ]
-  },
-  {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "25",
-   "metadata": {},
    "outputs": [],
-   "source": [
-    "if not os.path.exists(f\"data/s2bms/eo/avr_{mod}_{size}.csv\"):\n",
-    "    df.to_csv(f\"data/s2bms/eo/avr_{mod}_{size}.csv\", index=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26",
-   "metadata": {},
-   "source": [
-    "# Stats for scoring"
-   ]
-  },
-  {
-   "cell_type": "code",
    "execution_count": null,
-   "id": "27",
-   "metadata": {},
-   "outputs": [],
    "source": [
-    "labelled = True\n",
-    "\n",
-    "output_dir = \"/Users/gabriele/Desktop/aether/data/s2bms/aux_stats/\"\n",
-    "os.makedirs(output_dir, exist_ok=True)\n",
+    "m = folium.Map(location=[df.lat.mean(), df.lon.mean()], zoom_start=3)\n",
     "\n",
-    "if labelled:\n",
-    "    df = pd.read_csv(\"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms.csv\")\n",
-    "    output_path = output_dir + \"labelled.json\"\n",
-    "else:\n",
-    "    df = pd.read_csv(\n",
-    "        \"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\"\n",
-    "    )\n",
-    "    output_path = output_dir + \"unlabelled.json\"\n",
+    "# Train = blue\n",
+    "for _, row in train_df.iterrows():\n",
+    "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"blue\", fill=True).add_to(m)\n",
     "\n",
-    "col = [i for i in df.columns if \"aux\" in i and \"top\" not in i]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "28",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if os.path.exists(output_path):\n",
-    "    print(f\"Output file already exists: {output_path}\")\n",
-    "else:\n",
-    "    stats = {}\n",
-    "    for col in col:\n",
-    "        stats[col] = {\n",
-    "            \"mean\": float(df[col].mean()),\n",
-    "            \"std\": float(df[col].std()),\n",
-    "        }\n",
+    "# Val = orange\n",
+    "for _, row in val_df.iterrows():\n",
+    "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"orange\", fill=True).add_to(m)\n",
     "\n",
-    "        with open(output_path, \"w\") as f:\n",
-    "            json.dump(stats, f, indent=4)\n",
+    "# Test = red\n",
+    "for _, row in test_df.iterrows():\n",
+    "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"red\", fill=True).add_to(m)\n",
     "\n",
-    "        print(f\"Saved global statistics for {len(stats)} variables to {output_path}\")"
-   ]
+    "m"
+   ],
+   "id": "c6d12053d30b3585"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "29",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
-   "source": []
+   "execution_count": null,
+   "source": "",
+   "id": "f3ecad3216a5347f"
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }

From 30de73ba82473a3e76b6569388f433c509d24651 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:17:19 +0200
Subject: [PATCH 075/173] fix wrong variable name

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 47 +++++++++----------
 1 file changed, 22 insertions(+), 25 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 3f8f2dd..7f2707a 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "90846f361f4da291"
+   "id": "3ff87451c09cad24"
   },
   {
    "metadata": {},
@@ -24,7 +24,6 @@
    "execution_count": null,
    "source": [
     "# Check how do aef and tessera files overlap\n",
-    "\n",
     "paths = glob.glob(\"data/s2bms/eo/aef/*.npy\")\n",
     "aef = []\n",
     "for p in paths:\n",
@@ -41,13 +40,13 @@
     "aef_left = list(set(aef) - set(common))  # 12\n",
     "\n",
     "# Filter out unlabelled data\n",
-    "aef_left = {i for i in tsr_left if 'sample' not in i}\n",
-    "aef_left = {i for i in aef_left if 'sample' not in i}\n",
+    "tsr_left = {i for i in tsr_left if \"sample\" not in i}\n",
+    "aef_left = {i for i in aef_left if \"sample\" not in i}\n",
     "\n",
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "55ed47d0469d34b"
+   "id": "387048682be53c77"
   },
   {
    "metadata": {},
@@ -59,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "498c9b97c339519f"
+   "id": "2f928ce098e29e42"
   },
   {
    "metadata": {},
@@ -75,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "692d1229fd40ad29"
+   "id": "b20b330d8eb96023"
   },
   {
    "metadata": {},
@@ -100,9 +99,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)\n",
     "\n",
-    "if os.path.exists(\n",
-    "    \"data/s2bms/splits/s2bms_union_val_test.pth\"\n",
-    "):\n",
+    "if os.path.exists(\"data/s2bms/splits/s2bms_union_val_test.pth\"):\n",
     "    print(\"Already saved\")\n",
     "else:\n",
     "    torch.save(\n",
@@ -110,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "ebb3fd9525ff77dd"
+   "id": "e06274201911a4b9"
   },
   {
    "metadata": {},
@@ -130,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "97f802307e332d52"
+   "id": "9b1814217c6924d"
   },
   {
    "metadata": {},
@@ -156,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "67dba7e5ac46f0d2"
+   "id": "e7b2568c05994cfa"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "5633920d88ef725"
+   "id": "d154daaf41540c01"
   },
   {
    "metadata": {},
@@ -175,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "60fad48574e0b090"
+   "id": "ec824d3aca28ab13"
   },
   {
    "metadata": {},
@@ -193,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "bdafc8dde36245b3"
+   "id": "4c88d03385dd91f8"
   },
   {
    "metadata": {},
@@ -205,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "b25a1eaadc10bb3c"
+   "id": "cf0d84c6b650c85d"
   },
   {
    "metadata": {},
@@ -218,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "eb3f894e8062d298"
+   "id": "4737bc63d2339070"
   },
   {
    "metadata": {},
@@ -233,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "9c2686272f684c0c"
+   "id": "5464fde10bbe806b"
   },
   {
    "metadata": {},
@@ -248,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "bc5d30df078d7afd"
+   "id": "a4fa0c55639bb35b"
   },
   {
    "metadata": {},
@@ -262,9 +259,9 @@
     "        \"data/s2bms/splits/s2bms_unlabelled_union_val_test.pth\",\n",
     "    )\n",
     "else:\n",
-    "    print('Already saved')"
+    "    print(\"Already saved\")"
    ],
-   "id": "ef7579bad784e241"
+   "id": "48e572cbd118bdd5"
   },
   {
    "metadata": {},
@@ -284,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "1c1db0df607ce8cf"
+   "id": "8150a27114a42b8c"
   },
   {
    "metadata": {},
@@ -308,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "c6d12053d30b3585"
+   "id": "cb811e31c94cfad5"
   },
   {
    "metadata": {},
@@ -316,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "f3ecad3216a5347f"
+   "id": "d68389ead488033a"
   }
  ],
  "metadata": {},

From 8bd56eb391de891af90175059733bc22f0b3ca17 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:18:04 +0200
Subject: [PATCH 076/173] Split after getting missing data, in the setup() call

---
 src/data/base_datamodule.py | 22 ++++++++++++++++------
 src/data/base_dataset.py    | 32 ++++++++++++++++++--------------
 2 files changed, 34 insertions(+), 20 deletions(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 9127c36..fe8dd8b 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -67,7 +67,7 @@ def __init__(
             self.caption_builder.sync_with_dataset(self.dataset)
             self.concept_configs = caption_builder.concepts
 
-        self.split_data()
+        self._setup_flag = False
 
     @property
     def tabular_dim(self):
@@ -86,6 +86,7 @@ def setup(self, stage: str = "fit") -> None:
         if not self._setup_flag:
             # Set up the dataset (download requested modalities)
             self.dataset.setup()
+            self.split_data()
             self._setup_flag = True
 
     @property
@@ -252,10 +253,19 @@ def split_data(self) -> None:
             if test_indices is not None and not isinstance(test_indices, pd.Series):
                 raise NotImplementedError("Expected a pd series of name_locs for data splits.")
 
-            train_indices = np.where(self.dataset.df["name_loc"].isin(train_indices))[0]
-            val_indices = np.where(self.dataset.df["name_loc"].isin(val_indices))[0]
+            ds_records_names = [i["name_loc"] for i in self.dataset.records]
+            records_name_to_idx = {name: idx for idx, name in enumerate(ds_records_names)}
+
+            train_indices = np.array(
+                [records_name_to_idx[n] for n in train_indices if n in records_name_to_idx]
+            )
+            val_indices = np.array(
+                [records_name_to_idx[n] for n in val_indices if n in records_name_to_idx]
+            )
             if test_indices is not None:
-                test_indices = np.where(self.dataset.df["name_loc"].isin(test_indices))[0]
+                test_indices = np.array(
+                    [records_name_to_idx[n] for n in test_indices if n in records_name_to_idx]
+                )
 
             print(f"Dataset was split using indices from file: {self.saved_split_file_path}")
         else:
@@ -267,12 +277,12 @@ def split_data(self) -> None:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
             print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
-            print(f"Validate dataset split size: {len(self.data_val)}")
+            self.data_val.dataset.mode = "val"
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)
-            else:
                 print(f"Test dataset split size: {len(self.data_test)}")
+            else:
                 self.data_test = None
 
         if self.hparams.save_split:
diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 80143c6..da9e7b0 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -274,19 +274,22 @@ def setup_tessera(self) -> None:
 
         # If data does not exist or is empty → full download
         if not os.path.exists(dst_dir) or len(os.listdir(dst_dir)) == 0:
-            os.makedirs(dst_dir, exist_ok=True)
-
-            tessera_from_df(
-                self.df,
-                data_dir=dst_dir,
-                year=year,
-                tile_size=size,
-                cache_dir=self.cache_dir,
-            )
+            if download_missing_tiles:
+                os.makedirs(dst_dir, exist_ok=True)
+
+                tessera_from_df(
+                    self.df,
+                    data_dir=dst_dir,
+                    year=year,
+                    tile_size=size,
+                    cache_dir=self.cache_dir,
+                )
 
-            # TODO: if we compile the dataset and use zenodo (or sth else) then change to pooch downloading/loading
-            # TODO: in case of zenodo use may need to be moved to UC dataset subclasses
-            # or self.setup_tessera_from_pooch() <- per children class implementation
+                # TODO: if we compile the dataset and use zenodo (or sth else) then change to pooch downloading/loading
+                # TODO: in case of zenodo use may need to be moved to UC dataset subclasses
+                # or self.setup_tessera_from_pooch() <- per children class implementation
+            else:
+                print("Please download the missing Tessera tiles...")
 
         # Download missing rows (if any)
         else:
@@ -318,8 +321,9 @@ def setup_tessera(self) -> None:
                             continue
                         except NoTileError or PartialTileError as e:
                             print(f"Tile for {fname} could not be retrieved. Error: {e}")
-                self.records.pop(i)
-                print(f"No tile found for {fname} thus it will not be used.")
+                    else:
+                        self.records.pop(i)
+                        print(f"No tile found for {fname} thus it will not be used.")
 
     @final
     def setup_aef(self) -> None:

From 433fe8412d27841ecaeac028fd2ab7fe05646188 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:21:08 +0200
Subject: [PATCH 077/173] Add missing aef data filtering out

---
 src/data/base_dataset.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index da9e7b0..0012e39 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -335,6 +335,12 @@ def setup_aef(self) -> None:
         print("\n\nSetting up AEF data...\n\n")
 
         dst_dir = os.path.join(self.data_dir, "eo/aef")
+        avail_files = os.listdir(dst_dir)
+        for i, rec in enumerate(self.records):
+            fname = os.path.basename(rec["aef_path"])
+            if fname not in avail_files:
+                self.records.pop(i)
+                print(f"No tile found for {fname} thus it will not be used.")
 
         # TODO aef retrieval?
         # TODO: in case of zenodo use may need to be moved to UC dataset subclasses

From f1736a05a83cf8d7b95e90e79ceceae11eba26ac Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:27:50 +0200
Subject: [PATCH 078/173] Include setup method in the tests

---
 tests/test_datasets_and_datamodules.py | 3 ++-
 tests/test_geo_encoders.py             | 1 +
 tests/test_pred_heads.py               | 7 +------
 tests/test_yield_africa.py             | 2 ++
 4 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/tests/test_datasets_and_datamodules.py b/tests/test_datasets_and_datamodules.py
index 20c94be..acdae4a 100644
--- a/tests/test_datasets_and_datamodules.py
+++ b/tests/test_datasets_and_datamodules.py
@@ -1,4 +1,3 @@
-from src.data.base_dataset import BaseDataset
 from src.data.butterfly_dataset import ButterflyDataset
 from src.data.heat_guatemala_dataset import HeatGuatemalaDataset
 from src.data.satbird_dataset import SatBirdDataset
@@ -64,6 +63,7 @@ def test_datasets_generic_properties(request, tmp_path, sample_csv):
 
 def test_datamodule_random_split_and_loaders(create_butterfly_dataset):
     dataset, dm = create_butterfly_dataset
+    dm.setup()
 
     assert len(dm.data_train) == 4
     assert len(dm.data_val) == 1
@@ -77,6 +77,7 @@ def test_datamodule_random_split_and_loaders(create_butterfly_dataset):
 def test_random_split_is_deterministic(create_butterfly_dataset):
     dataset1, dm1 = create_butterfly_dataset
     dataset2, dm2 = create_butterfly_dataset
+    dm1.setup(), dm2.setup()
 
     assert dm1.data_train.indices == dm2.data_train.indices
     assert dm1.data_val.indices == dm2.data_val.indices
diff --git a/tests/test_geo_encoders.py b/tests/test_geo_encoders.py
index aba31f3..d4f9add 100644
--- a/tests/test_geo_encoders.py
+++ b/tests/test_geo_encoders.py
@@ -18,6 +18,7 @@ def test_geo_encoder_generic_properties(create_butterfly_dataset):
         "mlp_projector": MLPProjector,
     }
     ds, dm = create_butterfly_dataset
+    dm.setup()
     batch = next(iter(dm.train_dataloader()))
 
     for geo_encoder_name, geo_encoder_class in dict_geo_encoders.items():
diff --git a/tests/test_pred_heads.py b/tests/test_pred_heads.py
index 6f617c2..36149e0 100644
--- a/tests/test_pred_heads.py
+++ b/tests/test_pred_heads.py
@@ -1,12 +1,6 @@
-import json
-import os
-
-import pandas as pd
-import pytest
 import torch
 
 from src.models.components.geo_encoders.geoclip import GeoClipCoordinateEncoder
-from src.models.components.pred_heads.base_pred_head import BasePredictionHead
 from src.models.components.pred_heads.linear_pred_head import LinearPredictionHead
 from src.models.components.pred_heads.mlp_pred_head import MLPPredictionHead
 from src.models.components.pred_heads.mlp_regression_head import (
@@ -18,6 +12,7 @@
 def test_pred_head_generic_properties(create_butterfly_dataset):
     """Test required properties of the prediction head class."""
     ds, dm = create_butterfly_dataset
+    dm.setup()
     batch = next(iter(dm.train_dataloader()))
     eo_encoder = GeoClipCoordinateEncoder()
     eo_encoder.setup()
diff --git a/tests/test_yield_africa.py b/tests/test_yield_africa.py
index 69cc138..d311c7d 100644
--- a/tests/test_yield_africa.py
+++ b/tests/test_yield_africa.py
@@ -282,6 +282,7 @@ def test_yield_africa_dataset_aux_shape(yield_africa_dataset_with_aux):
 
 def test_yield_africa_datamodule_split_sizes(yield_africa_datamodule):
     dm = yield_africa_datamodule
+    dm.setup()
     assert len(dm.data_train) == 7
     assert len(dm.data_val) == 2
     assert len(dm.data_test) == 1
@@ -323,6 +324,7 @@ def make_dm():
         )
 
     dm1, dm2 = make_dm(), make_dm()
+    dm1.setup(), dm2.setup()
     assert dm1.data_train.indices == dm2.data_train.indices
     assert dm1.data_val.indices == dm2.data_val.indices
 

From 07b7a9d0ef84a8cfc5740784f5212ab279f1e1e5 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:31:07 +0200
Subject: [PATCH 079/173] Include setup method in the tests

---
 tests/test_captions.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tests/test_captions.py b/tests/test_captions.py
index 85951dd..454f533 100644
--- a/tests/test_captions.py
+++ b/tests/test_captions.py
@@ -49,6 +49,7 @@ def test_datamodule_uses_collate_when_aux_data(request, sample_csv, tmp_path):
         num_workers=0,
         pin_memory=False,
     )
+    dm.setup()
 
     batch = next(iter(dm.train_dataloader()))
     assert "text" in batch

From d4b92eb550b34de752e7478d70545ed6299d2a7b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 15:03:49 +0200
Subject: [PATCH 080/173] More test (local) fixes

---
 configs/experiment/prediction.yaml | 42 ++++++++++++++++++++++++++++++
 tests/test_text_encoders.py        |  6 +----
 tests/test_train.py                |  2 ++
 3 files changed, 45 insertions(+), 5 deletions(-)
 create mode 100644 configs/experiment/prediction.yaml

diff --git a/configs/experiment/prediction.yaml b/configs/experiment/prediction.yaml
new file mode 100644
index 0000000..a4f85bf
--- /dev/null
+++ b/configs/experiment/prediction.yaml
@@ -0,0 +1,42 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+defaults:
+  - override /model: s2bms_prediction
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_predictive
+
+# all parameters below will be merged with parameters from default configurations set above
+# this allows you to overwrite only specified parameters
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.geoclip.GeoClipCoordinateEncoder
+    geo_data_name: coords
+  trainable_modules: [prediction_head]
+  prediction_head:
+    _target_: src.models.components.pred_heads.linear_pred_head.LinearPredictionHead
+
+data:
+  dataset:
+    modalities:
+      coords:
+    use_target_data: true
+    use_aux_data: false
+  caption_builder:
+  pin_memory: false
+  batch_size: 8
+  persistent_workers: false
+  saved_split_file_name: "s2bms_union_val_test.pth"
+
+tags: ["prediction", "geoclip_coords"]
+
+seed: 12345
+
+trainer:
+  min_epochs: 1
+  max_epochs: 100
+
+logger: none
diff --git a/tests/test_text_encoders.py b/tests/test_text_encoders.py
index 0d09714..6ec31fa 100644
--- a/tests/test_text_encoders.py
+++ b/tests/test_text_encoders.py
@@ -1,11 +1,6 @@
-import json
-import os
-
-import pandas as pd
 import pytest
 import torch
 
-from src.models.components.text_encoders.base_text_encoder import BaseTextEncoder
 from src.models.components.text_encoders.clip_text_encoder import ClipTextEncoder
 from src.models.components.text_encoders.llm2clip_text_encoder import (
     LLM2CLIPTextEncoder,
@@ -18,6 +13,7 @@ def test_text_encoder_generic_properties(create_butterfly_dataset):
     """This test checks that all text encoders implement the basic properties and methods."""
     list_text_encoders = [ClipTextEncoder, LLM2CLIPTextEncoder]
     ds, dm = create_butterfly_dataset
+    dm.setup()
     batch = next(iter(dm.train_dataloader()))
     text_input = batch.get("text")
     assert text_input is not None, f"text input is None in the batch from datamodule. {batch}"
diff --git a/tests/test_train.py b/tests/test_train.py
index a60a1e6..52b5eca 100644
--- a/tests/test_train.py
+++ b/tests/test_train.py
@@ -88,6 +88,8 @@ def test_train_resume(tmp_path: Path, cfg_train: DictConfig) -> None:
     """
     with open_dict(cfg_train):
         cfg_train.trainer.max_epochs = 1
+        cfg_train.callbacks.model_checkpoint.save_last = True
+        cfg_train.callbacks.model_checkpoint.dirpath = cfg_train.paths.output_dir + "/checkpoints"
 
     HydraConfig().set_config(cfg_train)
     metric_dict_1, _ = train(cfg_train)

From 27b607b535869cad6b278ff216cdb9266134ef92 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 15:08:06 +0200
Subject: [PATCH 081/173] More test (local) fixes

---
 configs/experiment/prediction.yaml | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/configs/experiment/prediction.yaml b/configs/experiment/prediction.yaml
index a4f85bf..738b1c0 100644
--- a/configs/experiment/prediction.yaml
+++ b/configs/experiment/prediction.yaml
@@ -39,4 +39,9 @@ trainer:
   min_epochs: 1
   max_epochs: 100
 
-logger: none
+logger:
+  wandb:
+    tags: ${tags}
+    group: "predictive"
+  aim:
+    experiment: "predictive"

From dee6f203742ca0bdc1db032dd13e675844594226 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:07:55 +0200
Subject: [PATCH 082/173] Add ability to load unlabelled data

# Conflicts:
#	src/data/butterfly_dataset.py
---
 src/data/base_datamodule.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index fe8dd8b..5b96c0c 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -2,7 +2,7 @@
 import os
 import time
 from functools import partial
-from typing import Any, Tuple
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 import pandas as pd

From a39d9967f1f14d5cf60dcf87876c5caa49bc1025 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 09:19:31 +0200
Subject: [PATCH 083/173] Re do unlabelled split

---
 ...ms-unlabelled-20260602_2026-06-02-0918.pth | Bin 0 -> 294641 bytes
 ...9-GT-aef-tessera-datasplit-filtering.ipynb |  36 +++++++++---------
 2 files changed, 18 insertions(+), 18 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
new file mode 100644
index 0000000000000000000000000000000000000000..429e3b22f30eea24bd0c4487cf24133330d28780
GIT binary patch
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diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 7f2707a..22d64cc 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "3ff87451c09cad24"
+   "id": "b0764834f848adc"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "387048682be53c77"
+   "id": "da9901c3f55a0614"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "2f928ce098e29e42"
+   "id": "7fcfdc4df7afeb0f"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "b20b330d8eb96023"
+   "id": "d47ef20b12b1c42d"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "e06274201911a4b9"
+   "id": "38b0e7bd9091872"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "9b1814217c6924d"
+   "id": "f9565b92fb9fe136"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "e7b2568c05994cfa"
+   "id": "d9bf8e81b9c29f78"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "d154daaf41540c01"
+   "id": "f1d25550b54c5454"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "ec824d3aca28ab13"
+   "id": "ea2aebc21923c062"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "4c88d03385dd91f8"
+   "id": "9a24492342d4e682"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "cf0d84c6b650c85d"
+   "id": "e9ae95ee0c7bfd8d"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "4737bc63d2339070"
+   "id": "d07af23ee8483e47"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "5464fde10bbe806b"
+   "id": "62a4e6976240eb8d"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "a4fa0c55639bb35b"
+   "id": "8fbc02d6a89b9776"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "48e572cbd118bdd5"
+   "id": "99113dfd9a914d78"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "8150a27114a42b8c"
+   "id": "35c255cd85667ed5"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "cb811e31c94cfad5"
+   "id": "6f6f8be6305920a1"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "d68389ead488033a"
+   "id": "6608186ef0fcf07d"
   }
  ],
  "metadata": {},

From d8c52a12096cc586b99a9d71e946973688f2143f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:52:18 +0200
Subject: [PATCH 084/173] Add size print statements

---
 src/data/base_datamodule.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 5b96c0c..04d6a69 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -66,7 +66,6 @@ def __init__(
             self.caption_builder = caption_builder
             self.caption_builder.sync_with_dataset(self.dataset)
             self.concept_configs = caption_builder.concepts
-
         self._setup_flag = False
 
     @property
@@ -83,6 +82,7 @@ def setup(self, stage: str = "fit") -> None:
 
         Called by model trainer (trainer.fit()).
         """
+
         if not self._setup_flag:
             # Set up the dataset (download requested modalities)
             self.dataset.setup()
@@ -277,7 +277,7 @@ def split_data(self) -> None:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
             print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
-            self.data_val.dataset.mode = "val"
+            print(f"Validate dataset split size: {len(self.data_val)}")
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)

From ff101764ca4305d6b313d29a9ed137d4ad8d6ff3 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:20:02 +0200
Subject: [PATCH 085/173] Save average embeddings to csv

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 22d64cc..19adb92 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "b0764834f848adc"
+   "id": "8a1a78760caeb40e"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "da9901c3f55a0614"
+   "id": "ecd7be8abf4fea77"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "7fcfdc4df7afeb0f"
+   "id": "35be971899e28a39"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "d47ef20b12b1c42d"
+   "id": "68255192a949f836"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "38b0e7bd9091872"
+   "id": "d25bce3c1c10cf1e"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "f9565b92fb9fe136"
+   "id": "9074068bef218fae"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "d9bf8e81b9c29f78"
+   "id": "921f49cf81b69805"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "f1d25550b54c5454"
+   "id": "1d4fbc16b9d16a24"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "ea2aebc21923c062"
+   "id": "b439b11efd719fc8"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "9a24492342d4e682"
+   "id": "18e3d07d994e48"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "e9ae95ee0c7bfd8d"
+   "id": "7a218f6dc9589de6"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "d07af23ee8483e47"
+   "id": "de0c0cd94ef7e27e"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "62a4e6976240eb8d"
+   "id": "73757cfd30c12f71"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "8fbc02d6a89b9776"
+   "id": "853463f17bb3efd4"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "99113dfd9a914d78"
+   "id": "a9c01bec3cb5c8f0"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "35c255cd85667ed5"
+   "id": "d60dbd80e57e23a3"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "6f6f8be6305920a1"
+   "id": "b5f30100b87e54a7"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "6608186ef0fcf07d"
+   "id": "67d278abfd555add"
   }
  ],
  "metadata": {},

From 9064826df50936eb621a67d7ea68b09adcdab364 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:52:56 +0200
Subject: [PATCH 086/173] Data split and encoding merging for aef_256 averages

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 19adb92..b7355eb 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "8a1a78760caeb40e"
+   "id": "1a3b9ba890b72bc9"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "ecd7be8abf4fea77"
+   "id": "2bb566faa1064206"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "35be971899e28a39"
+   "id": "8a846aa182b5757d"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "68255192a949f836"
+   "id": "bec39d793f8585b4"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "d25bce3c1c10cf1e"
+   "id": "edd8bbcd7133b44"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "9074068bef218fae"
+   "id": "2515bf2704190e8"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "921f49cf81b69805"
+   "id": "e3f951186b92690d"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "1d4fbc16b9d16a24"
+   "id": "5f7be7aa20ac9578"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "b439b11efd719fc8"
+   "id": "5c93a1564305eb7a"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "18e3d07d994e48"
+   "id": "2dece183d5967f84"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "7a218f6dc9589de6"
+   "id": "6c5fa206a96bfcfc"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "de0c0cd94ef7e27e"
+   "id": "d81a1dee4509b9b2"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "73757cfd30c12f71"
+   "id": "79d4167e18dd9ab5"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "853463f17bb3efd4"
+   "id": "86e94fc68954091b"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "a9c01bec3cb5c8f0"
+   "id": "45dc6b6cee054fc7"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "d60dbd80e57e23a3"
+   "id": "364ab6b18525cbc1"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "b5f30100b87e54a7"
+   "id": "f09092c171f8805c"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "67d278abfd555add"
+   "id": "7bd42b1900f66111"
   }
  ],
  "metadata": {},

From 3338f3efe39888eec40a3ee36baf65d73e5c7353 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:10:37 +0200
Subject: [PATCH 087/173] New data splits and their explanations

---
 ...bms-unlabelled-20260602_2026-06-02-0918.pth | Bin 294641 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
deleted file mode 100644
index 429e3b22f30eea24bd0c4487cf24133330d28780..0000000000000000000000000000000000000000
GIT binary patch
literal 0
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From 081898d463c56b0f85dbc1a5fe2f2b02346d23db Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:09:51 +0200
Subject: [PATCH 088/173] Add aux stats calculation

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index b7355eb..eeaee12 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "1a3b9ba890b72bc9"
+   "id": "d3bb5040fb16e6e0"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "2bb566faa1064206"
+   "id": "26ba34dcd7d17ffa"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "8a846aa182b5757d"
+   "id": "2146aee63b37faa9"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "bec39d793f8585b4"
+   "id": "b86a3a2be38714b2"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "edd8bbcd7133b44"
+   "id": "b9d25047d1ff00b5"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "2515bf2704190e8"
+   "id": "5d61c7a5d9c701f7"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "e3f951186b92690d"
+   "id": "642619b6a081547e"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "5f7be7aa20ac9578"
+   "id": "d0dd48f19237cb23"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "5c93a1564305eb7a"
+   "id": "1b7ba5f0b29dd7b9"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "2dece183d5967f84"
+   "id": "55cfd62245fc1b3"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "6c5fa206a96bfcfc"
+   "id": "d1214ad3ee3ce389"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "d81a1dee4509b9b2"
+   "id": "45f4bae4613a25c3"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "79d4167e18dd9ab5"
+   "id": "2c389f095a48d5f4"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "86e94fc68954091b"
+   "id": "3691112f528cf7d6"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "45dc6b6cee054fc7"
+   "id": "b9920aafa44c8b6a"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "364ab6b18525cbc1"
+   "id": "5a665bf02111df49"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "f09092c171f8805c"
+   "id": "d77b22160e0c9e"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "7bd42b1900f66111"
+   "id": "510582b75948c883"
   }
  ],
  "metadata": {},

From de0a4eb5383cad9c3c0ba6ac3326b7657a422967 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 16:00:58 +0200
Subject: [PATCH 089/173] fix the splits

---
 configs/data/butterfly_coords.yaml            |   2 +-
 configs/data/butterfly_coords_text.yaml       |   2 +-
 .../data/butterfly_full_param_example.yaml    |   2 +-
 configs/data/butterfly_s2_rgb.yaml            |   2 +-
 .../s2bms_align_avr_aef_clip_text.yaml        |  82 +++++++++++++++++
 .../s2bms_align_avr_aef_clip_text_1.yaml      |  81 +++++++++++++++++
 .../s2bms_align_avr_aef_clip_text_unlab.yaml  |  83 ++++++++++++++++++
 ...s2bms_align_avr_aef_clip_text_unlab_1.yaml |  82 +++++++++++++++++
 .../experiment/s2bms_align_avr_aef_llm.yaml   |  83 ++++++++++++++++++
 configs/experiment/s2bms_experiment_1.yaml    |   2 +-
 configs/experiment/s2bms_experiment_10.yaml   |   2 +-
 configs/experiment/s2bms_experiment_10_1.yaml |   2 +-
 configs/experiment/s2bms_experiment_11.yaml   |   2 +-
 configs/experiment/s2bms_experiment_12.yaml   |   2 +-
 configs/experiment/s2bms_experiment_13.yaml   |   2 +-
 configs/experiment/s2bms_experiment_14.yaml   |   2 +-
 configs/experiment/s2bms_experiment_15.yaml   |  71 +++++++++++++++
 configs/experiment/s2bms_experiment_2.yaml    |   2 +-
 configs/experiment/s2bms_experiment_3.yaml    |   2 +-
 configs/experiment/s2bms_experiment_4.yaml    |   3 +-
 configs/experiment/s2bms_experiment_5.yaml    |   2 +-
 configs/experiment/s2bms_experiment_6.yaml    |   2 +-
 configs/experiment/s2bms_experiment_7.yaml    |   2 +-
 configs/experiment/s2bms_experiment_8.yaml    |   2 +-
 configs/experiment/s2bms_experiment_9.yaml    |   2 +-
 ...s2bms+s2bms-unlabelled-2026-06-02-0918.pth | Bin 294459 -> 0 bytes
 ...-06-11-15-23-51_union_aef_tsr_val_test.pth | Bin 385437 -> 0 bytes
 ...ms-unlabelled-20260529_2026-05-29-1433.pth | Bin 0 -> 543245 bytes
 28 files changed, 502 insertions(+), 19 deletions(-)
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_llm.yaml
 create mode 100644 configs/experiment/s2bms_experiment_15.yaml
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
 create mode 100644 data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth

diff --git a/configs/data/butterfly_coords.yaml b/configs/data/butterfly_coords.yaml
index f4acb51..f0ad2d4 100644
--- a/configs/data/butterfly_coords.yaml
+++ b/configs/data/butterfly_coords.yaml
@@ -16,5 +16,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_coords_text.yaml b/configs/data/butterfly_coords_text.yaml
index 52cae6d..5fad06f 100644
--- a/configs/data/butterfly_coords_text.yaml
+++ b/configs/data/butterfly_coords_text.yaml
@@ -24,5 +24,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_full_param_example.yaml b/configs/data/butterfly_full_param_example.yaml
index 78923f5..63320dd 100644
--- a/configs/data/butterfly_full_param_example.yaml
+++ b/configs/data/butterfly_full_param_example.yaml
@@ -32,5 +32,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_s2_rgb.yaml b/configs/data/butterfly_s2_rgb.yaml
index 1b4a627..4916c57 100644
--- a/configs/data/butterfly_s2_rgb.yaml
+++ b/configs/data/butterfly_s2_rgb.yaml
@@ -21,5 +21,5 @@ num_workers: 1
 pin_memory: false
 split_mode: "from_file" # default 'random'
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text.yaml
new file mode 100644
index 0000000..f60723b
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text.yaml
@@ -0,0 +1,82 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/unlabelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
new file mode 100644
index 0000000..d5417f8
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
@@ -0,0 +1,81 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.clip_loss.ClipLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/unlabelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++data.caption_builder.return_aux_ids: false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
new file mode 100644
index 0000000..f1d13dd
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
@@ -0,0 +1,83 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: unlab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_256_unlabelled.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    use_unlabelled_data: true
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_10k-unlabelled_coords_aef256.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
new file mode 100644
index 0000000..892897a
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
@@ -0,0 +1,82 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: unlab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.clip_loss.ClipLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_256_unlabelled.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    use_unlabelled_data: true
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_10k-unlabelled_coords_aef256.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++data.caption_builder.return_aux_ids: false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_llm.yaml b/configs/experiment/s2bms_align_avr_aef_llm.yaml
new file mode 100644
index 0000000..e2674da
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_llm.yaml
@@ -0,0 +1,83 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_llm
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 1280
+  text_encoder:
+    _target_: src.models.components.text_encoders.llm2clip_text_encoder.LLM2CLIPTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+trainer.num_sanity_val_steps: 0
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_experiment_1.yaml b/configs/experiment/s2bms_experiment_1.yaml
index 591b889..6c24802 100644
--- a/configs/experiment/s2bms_experiment_1.yaml
+++ b/configs/experiment/s2bms_experiment_1.yaml
@@ -38,7 +38,7 @@ data:
   pin_memory: true
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_10.yaml b/configs/experiment/s2bms_experiment_10.yaml
index 09ed4bf..3a69bfd 100644
--- a/configs/experiment/s2bms_experiment_10.yaml
+++ b/configs/experiment/s2bms_experiment_10.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 256
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_10_1.yaml b/configs/experiment/s2bms_experiment_10_1.yaml
index adbef0f..2ae5339 100644
--- a/configs/experiment/s2bms_experiment_10_1.yaml
+++ b/configs/experiment/s2bms_experiment_10_1.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 256
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_11.yaml b/configs/experiment/s2bms_experiment_11.yaml
index 4f0f986..550f2f3 100644
--- a/configs/experiment/s2bms_experiment_11.yaml
+++ b/configs/experiment/s2bms_experiment_11.yaml
@@ -41,7 +41,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_12.yaml b/configs/experiment/s2bms_experiment_12.yaml
index eb69c23..b208c7e 100644
--- a/configs/experiment/s2bms_experiment_12.yaml
+++ b/configs/experiment/s2bms_experiment_12.yaml
@@ -41,7 +41,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_13.yaml b/configs/experiment/s2bms_experiment_13.yaml
index c71624b..a6872a8 100644
--- a/configs/experiment/s2bms_experiment_13.yaml
+++ b/configs/experiment/s2bms_experiment_13.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_14.yaml b/configs/experiment/s2bms_experiment_14.yaml
index e5412c7..2de8821 100644
--- a/configs/experiment/s2bms_experiment_14.yaml
+++ b/configs/experiment/s2bms_experiment_14.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_15.yaml b/configs/experiment/s2bms_experiment_15.yaml
new file mode 100644
index 0000000..0be763a
--- /dev/null
+++ b/configs/experiment/s2bms_experiment_15.yaml
@@ -0,0 +1,71 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+#  Better toegther: AEF with average encoder and satclip, and MLP prediction head, for tile sizes 128, each with 3 random seeds
+
+defaults:
+  - override /model: s2bms_prediction
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_predictive
+
+tags:
+  - prediction
+  - aef_satclip
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: avr_aef_128_and_satclip_mlp
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.encoder_wrapper.EncoderWrapper
+
+    encoder_branches:
+      - encoder:
+          _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+          geo_data_name: aef_avr
+      - encoder:
+          _target_: src.models.components.geo_encoders.satclip.SatClipCoordinateEncoder
+          geo_data_name: coords
+          hf_cache_dir: ${paths.huggingface_cache}
+    #            return_dtype: float32
+    normalise: false
+    fusion_strategy: "concat"
+
+  trainable_modules: [prediction_head]
+  prediction_head:
+    _target_: src.models.components.pred_heads.mlp_pred_head.MLPPredictionHead
+    hidden_dim: 512
+
+data:
+  dataset:
+    modalities:
+      coords:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: true
+    use_aux_data: false
+
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 4
+  num_workers: 7
+  batch_size: 128
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "predictive"
+    project: "s2bms_prediction"
+  aim:
+    experiment: "predictive"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345, 404, 654
diff --git a/configs/experiment/s2bms_experiment_2.yaml b/configs/experiment/s2bms_experiment_2.yaml
index 0d9a84d..0a1eea9 100644
--- a/configs/experiment/s2bms_experiment_2.yaml
+++ b/configs/experiment/s2bms_experiment_2.yaml
@@ -38,7 +38,7 @@ data:
   pin_memory: true
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_3.yaml b/configs/experiment/s2bms_experiment_3.yaml
index e4d789f..7f8ce34 100644
--- a/configs/experiment/s2bms_experiment_3.yaml
+++ b/configs/experiment/s2bms_experiment_3.yaml
@@ -39,7 +39,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_4.yaml b/configs/experiment/s2bms_experiment_4.yaml
index 68dd9f4..3d6d510 100644
--- a/configs/experiment/s2bms_experiment_4.yaml
+++ b/configs/experiment/s2bms_experiment_4.yaml
@@ -25,6 +25,7 @@ model:
   trainable_modules: [prediction_head]
   prediction_head:
     _target_: src.models.components.pred_heads.linear_pred_head.LinearPredictionHead
+    hidden_dim: 512
 
 data:
   dataset:
@@ -39,7 +40,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_5.yaml b/configs/experiment/s2bms_experiment_5.yaml
index 366bfed..2b6f2c2 100644
--- a/configs/experiment/s2bms_experiment_5.yaml
+++ b/configs/experiment/s2bms_experiment_5.yaml
@@ -40,7 +40,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_6.yaml b/configs/experiment/s2bms_experiment_6.yaml
index 2cd63c8..66d0cf0 100644
--- a/configs/experiment/s2bms_experiment_6.yaml
+++ b/configs/experiment/s2bms_experiment_6.yaml
@@ -34,7 +34,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_7.yaml b/configs/experiment/s2bms_experiment_7.yaml
index ec4f230..c0ec25f 100644
--- a/configs/experiment/s2bms_experiment_7.yaml
+++ b/configs/experiment/s2bms_experiment_7.yaml
@@ -34,7 +34,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_8.yaml b/configs/experiment/s2bms_experiment_8.yaml
index 4af78f0..50f9d63 100644
--- a/configs/experiment/s2bms_experiment_8.yaml
+++ b/configs/experiment/s2bms_experiment_8.yaml
@@ -36,7 +36,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_9.yaml b/configs/experiment/s2bms_experiment_9.yaml
index 4bd5def..d5bb66d 100644
--- a/configs/experiment/s2bms_experiment_9.yaml
+++ b/configs/experiment/s2bms_experiment_9.yaml
@@ -36,7 +36,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
deleted file mode 100644
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GIT binary patch
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diff --git a/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth b/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth
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HcmV?d00001


From 0fa94b73057ed340886e0ed3192c5d83f41b941b Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 27 May 2026 13:26:54 +0200
Subject: [PATCH 090/173] Add validation run of the best ckpt

---
 configs/train.yaml |  2 ++
 src/train.py       | 18 ++++++++++++++++++
 2 files changed, 20 insertions(+)

diff --git a/configs/train.yaml b/configs/train.yaml
index 894cfa0..5faeeca 100644
--- a/configs/train.yaml
+++ b/configs/train.yaml
@@ -43,6 +43,8 @@ train: True
 # lightning chooses best weights based on the metric specified in checkpoint callback
 test: True
 
+validate: True
+
 # simply provide checkpoint path to resume training
 ckpt_path: null
 
diff --git a/src/train.py b/src/train.py
index 949e21b..92207bd 100644
--- a/src/train.py
+++ b/src/train.py
@@ -135,6 +135,24 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
 
     train_metrics = trainer.callback_metrics
 
+    if cfg.get("validate"):
+        # Run validation with the best ckpt
+        log.info("Validating the best ckpt!")
+        ckpt_path = trainer.checkpoint_callback.best_model_path
+        if ckpt_path == "":
+            log.warning("Best ckpt not found! Using current weights for testing...")
+            ckpt_path = None
+
+        trainer.validate(
+            model=model,
+            datamodule=datamodule,
+            ckpt_path=ckpt_path,
+            weights_only=False,
+        )
+
+        val_metrics = trainer.callback_metrics
+        wandb_logger.log({f"best_val_{k}": v for k, v in val_metrics.items()})
+
     if cfg.get("test"):
         log.info("Starting testing!")
         ckpt_path = trainer.checkpoint_callback.best_model_path

From ecabcce52b813e5bd6e05be30cff8c7ac9463c26 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:01:54 +0200
Subject: [PATCH 091/173] Add avr embedding modalities

# Conflicts:
#	src/data/base_dataset.py
---
 src/data/base_dataset.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 64ccd7b..5e6d2fc 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -9,7 +9,9 @@
 import torch
 from torch.utils.data import Dataset
 
+from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError
 from src.utils.data_utils import center_crop_npy
+from src.utils.errors import MissingDataError
 
 TORCH_DTYPES = {
     "float32": torch.float32,
@@ -86,6 +88,9 @@ def __init__(
                 m_dtype = dtype
                 self.modalities[mod] = {"dtype": m_dtype}
 
+        # More precise dataset name (with modalities)
+        self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
+
         # Set data attributes
         self.registry_path = os.path.join(data_dir, "registry.txt")
         if type(dataset_name) is str:
@@ -156,6 +161,16 @@ def get_records(self) -> dict[str, Any]:
         for modality, params in self.modalities.items():
             if modality == "coords":
                 columns.extend(["lat", "lon"])
+            elif modality in ["aef_avr", "tessera_avr"]:
+                df = pd.read_csv(params.get("path", KeyError))
+                df["name_loc"] = df["name_loc"].astype(str)
+
+                self.df["name_loc"] = self.df["name_loc"].astype(str)
+
+                self.df = self.df.merge(df, on="name_loc", how="left")
+
+                max_no = 128 if modality == "tessera_avr" else 64
+                columns.extend([f"avr_{i}" for i in range(0, max_no)])
             else:
                 # Add paths
                 self.add_modality_paths_to_df(
@@ -246,6 +261,7 @@ def setup_tessera(self) -> None:
             tessera_from_df,
         )
 
+
         print("\n\nSetting up Tessera data...\n\n")
         download_missing_tiles = False
 
@@ -306,7 +322,7 @@ def setup_tessera(self) -> None:
                                 tessera_con=gt,
                             )
                             continue
-                        except Exception as e:
+                        except NoTileError or PartialTileError as e:
                             print(f"Tile for {fname} could not be retrieved. Error: {e}")
                     else:
                         self.records.pop(i)

From 5ac35e9da7e1f0997c58fc32ca062e8c0d6122d3 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:02:24 +0200
Subject: [PATCH 092/173] Make search in power of 2, plus add max batch size

---
 configs/callbacks/batch_size_finder.yaml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/configs/callbacks/batch_size_finder.yaml b/configs/callbacks/batch_size_finder.yaml
index 55533d3..d9c117b 100644
--- a/configs/callbacks/batch_size_finder.yaml
+++ b/configs/callbacks/batch_size_finder.yaml
@@ -1,5 +1,6 @@
 # https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.callbacks.BatchSizeFinder.html
 
 _target_: lightning.pytorch.callbacks.BatchSizeFinder
-mode: 'binsearch'
-init_val: 16
\ No newline at end of file
+mode: "power"
+init_val: 16
+max_val: 1024

From 9c7ac990ee8b180c9b9ab7ca86303013332d1734 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:02:59 +0200
Subject: [PATCH 093/173] Add average embedding modalities

---
 src/data/butterfly_dataset.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index aa6f342..91ef4e8 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -54,7 +54,7 @@ def __init__(
             dataset_name=dataset_name,
             seed=seed,
             cache_dir=cache_dir,
-            implemented_mod={"s2", "tessera", "coords", "aef"},
+            implemented_mod={"s2", "tessera", "coords", "aef", "aef_avr", "tessera_avr"},
             mock=mock,
             dtype=dtype,
         )
@@ -192,6 +192,16 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 formatted_row["eo"][modality] = self.load_tessera(row["tessera_path"])
             elif modality == "aef":
                 formatted_row["eo"][modality] = self.load_aef(row["aef_path"])
+            elif modality == "aef_avr":
+                formatted_row["eo"][modality] = torch.tensor(
+                    [row[f"avr_{i}"] for i in range(64)],
+                    dtype=getattr(torch, self.modalities["aef_avr"].get("dtype")),
+                )
+            elif modality == "tessera_avr":
+                formatted_row["eo"][modality] = torch.tensor(
+                    [row[f"avr_{i}"] for i in range(128)],
+                    dtype=getattr(torch, self.modalities["tessera_avr"].get("dtype")),
+                )
 
         if self.use_target_data:
             formatted_row["target"] = torch.tensor(

From ba07d3cda85c162c7168e828f35b8fc35b70704c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:03:55 +0200
Subject: [PATCH 094/173] Add no gradient computation option if model is not
 trained

---
 src/models/components/text_encoders/clip_text_encoder.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 5052ca5..234ea4f 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -31,6 +31,7 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
 
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
+        """Forward pass through the text encoder."""
         # Get text inputs
         text_input = batch.get("text")
 
@@ -50,7 +51,11 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
             device = next(self.model.parameters()).device
             text_tokens = {k: v.to(device) for k, v in text_tokens.items()}
 
-            text_embeds = self.model.get_text_features(**text_tokens)
+            if any(p.requires_grad for p in self.model.parameters()):
+                text_embeds = self.model.get_text_features(**text_tokens)
+            else:
+                with torch.inference_mode():
+                    text_embeds = self.model.get_text_features(**text_tokens)
 
             # Project
             if self.projector is not None:

From e4c4485d29f16f8f6fb3417b1f0059b41dc9ad22 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:04:24 +0200
Subject: [PATCH 095/173] Allow identity encoder for the average embeddings

---
 src/models/components/geo_encoders/encoder_wrapper.py | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/src/models/components/geo_encoders/encoder_wrapper.py b/src/models/components/geo_encoders/encoder_wrapper.py
index a44a3fa..d8d467d 100644
--- a/src/models/components/geo_encoders/encoder_wrapper.py
+++ b/src/models/components/geo_encoders/encoder_wrapper.py
@@ -4,9 +4,7 @@
 import torch.nn as nn
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
-from src.models.components.geo_encoders.identity_encoder import IdentityEncoder
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
-from src.utils.errors import IllegalArgumentCombination
 
 
 class EncoderWrapper(BaseGeoEncoder):
@@ -98,10 +96,6 @@ def _setup(self) -> List[str]:
 
             # Configure adapter/projector if requested
             if "projector" in branch:
-                if isinstance(encoder, IdentityEncoder):
-                    raise IllegalArgumentCombination(
-                        "Identity encoder cannot have linear projector"
-                    )
                 projector = branch["projector"]
 
                 projector.set_input_dim(input_dim=branch_dim)

From dcd17d5b124947a3f847c2e11225eb00e56feb5f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:05:38 +0200
Subject: [PATCH 096/173] Compute average embeddings into csv

---
 src/data_preprocessing/get_avr_embeds.py | 61 ++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 src/data_preprocessing/get_avr_embeds.py

diff --git a/src/data_preprocessing/get_avr_embeds.py b/src/data_preprocessing/get_avr_embeds.py
new file mode 100644
index 0000000..9eee19a
--- /dev/null
+++ b/src/data_preprocessing/get_avr_embeds.py
@@ -0,0 +1,61 @@
+import glob
+
+import numpy as np
+import pandas as pd
+import rasterio
+import torch
+
+from src.utils.data_utils import center_crop_npy
+
+
+def _load_tiff(tiff_file_path: str, dtype: np.dtype) -> np.ndarray:
+    """Load tiff files as desired dtype np arrays."""
+    with rasterio.open(tiff_file_path) as f:
+        im = f.read()
+        assert isinstance(im, np.ndarray)
+        if im.dtype != np.dtype(dtype):
+            im = im.astype(dtype=dtype, copy=False)
+    return im
+
+
+def _load_npy(filepath: str, dtype: np.dtype) -> np.ndarray:
+    """Load npy files as desired dtype np arrays."""
+    arr = np.load(filepath).transpose(2, 0, 1)
+    if arr.dtype != np.dtype(dtype):
+        arr = arr.astype(dtype=dtype, copy=False)
+    return arr
+
+
+def main():
+    """Compute average embeddings into csv file."""
+    size = 128
+    mod = "aef"
+    format = "tif"
+
+    # save aef averages
+    paths = glob.glob(f"data/s2bms/eo/{mod}/*.{format}")
+
+    rows = []
+    for p in paths:
+        if format == "npy":
+            im = _load_npy(p, np.dtype("float32"))
+        else:
+            im = _load_tiff(p, np.dtype("float32"))
+        im = center_crop_npy(im, (64 if mod == "aef" else 128, size, size))
+        tile = torch.from_numpy(im)
+        tile = tile.nanmean(dim=(-2, -1))  # shape [64]
+        row = {f"avr_{i}": v.item() for i, v in enumerate(tile)}
+        row["name_loc"] = f"UKBMS_loc-{p.split('-')[-1].split('.')[0]}"
+        rows.append(row)
+
+    df = pd.DataFrame(rows)
+    df.to_csv(f"data/s2bms/eo/{mod}_avr_{size}.csv", index=False)
+    print(f"Saved to data/s2bms/eo/{mod}_avr_{size}.csv")
+
+
+if __name__ == "__main__":
+    import os
+
+    os.chdir("../..")
+    print(os.getcwd())
+    main()

From c4506bbe5f821cb67e17dcfbffe1e08a2d203f7a Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:06:35 +0200
Subject: [PATCH 097/173] Adapt identity encoder for average embeddings

---
 .../geo_encoders/identity_encoder.py          | 32 +++++++++++++++----
 1 file changed, 25 insertions(+), 7 deletions(-)

diff --git a/src/models/components/geo_encoders/identity_encoder.py b/src/models/components/geo_encoders/identity_encoder.py
index 29f9bfe..19fbf4a 100644
--- a/src/models/components/geo_encoders/identity_encoder.py
+++ b/src/models/components/geo_encoders/identity_encoder.py
@@ -11,24 +11,25 @@ def __init__(
         self,
         geo_data_name="aef",
     ) -> None:
-        """Encoder to avreage tile values into a 1D vector.
+        """Encoder to pass through the data with no changes.
 
         :param geo_data_name: modality name
         """
         super().__init__()
 
-        self.dict_n_bands_default = {"s2": 4, "aef": 64, "tessera": 128}
+        self.dict_n_bands_default = {"aef": 64, "tessera": 128, "aef_avr": 64, "tessera_avr": 128}
         self.allowed_geo_data_names: list[str] = list(self.dict_n_bands_default.keys())
         assert (
             geo_data_name in self.allowed_geo_data_names
         ), f"geo_data_name must be one of {self.allowed_geo_data_names}, got {geo_data_name}"
         self.geo_data_name = geo_data_name
 
+        self.output_dim = self.dict_n_bands_default[geo_data_name]
+
     @override
     def _setup(self) -> List[str]:
         """Configures modules and returns newly initialised, trainable module names."""
 
-        self.output_dim = None
         self.geo_encoder = nn.Identity()
         return []
 
@@ -37,9 +38,6 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Data forward pass through the encoder."""
         tile = batch.get("eo", {}).get(self.geo_data_name)
 
-        if self.output_dim is None:
-            self.output_dim = tile.shape
-
         feats = self.geo_encoder(tile)
         if self.extra_projector:
             feats = self.extra_projector(feats)
@@ -47,4 +45,24 @@ def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
 
     @property
     def device(self):
-        return
+        if self.extra_projector:
+            devices = {p.device for p in self.extra_projector.parameters()}
+            if len(devices) > 1:
+                raise RuntimeError("Extra encoder is on multiple devices")
+            elif len(devices) == 0:
+                return None
+            return devices.pop()
+        else:
+            return None
+
+    @property
+    def dtype(self):
+        if self.extra_projector:
+            dtypes = {p.dtype for p in self.extra_projector.parameters()}
+            if len(dtypes) > 1:
+                raise RuntimeError("Extra encoder has multiple dtypes")
+            elif len(dtypes) == 0:
+                return None
+            return dtypes.pop()
+        else:
+            return None

From eb85a3c70c4c699b3cb815ec795ba4c1c810ccb6 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Thu, 28 May 2026 16:07:05 +0200
Subject: [PATCH 098/173] Avoid gradient computation where not needed

---
 src/models/text_alignment_model.py | 36 +++++++++++++++++++++---------
 1 file changed, 26 insertions(+), 10 deletions(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 7a67cde..02ead9a 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -1,6 +1,5 @@
 from typing import Dict, Tuple, override
 
-import numpy as np
 import torch
 import torch.nn.functional as F
 
@@ -124,8 +123,9 @@ def setup_retrieval_evaluation(
                 return
 
         # Encode concepts if text branch is frozen
-        with torch.no_grad():
+        with torch.inference_mode():
             self.concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            self.concept_embeds = F.normalize(self.concept_embeds, dim=1)
 
     @override
     def forward(
@@ -185,10 +185,12 @@ def _step(self, batch: Dict[str, torch.Tensor], mode: str = "train"):
         self.log_dict(metrics, batch_size=local_batch_size, **self.log_kwargs)
 
         if mode in ["val", "test"]:
+            aux = batch.get("aux", {}).get("aux")
             self.outputs_epoch_memory.append(
                 {
-                    "geo_feats": geo_feats.detach(),
-                    "aux_vals": batch.get("aux", {}).get("aux").detach(),
+                    # Store on CPU to avoid holding the whole epoch on GPU.
+                    "geo_feats": geo_feats.detach().cpu(),
+                    "aux_vals": aux.detach().cpu() if aux is not None else None,
                 }
             )
 
@@ -198,8 +200,11 @@ def _on_epoch_end(self, mode: str, verbose=0):
 
         # Combine batches
         geo_feats = torch.cat([x["geo_feats"] for x in self.outputs_epoch_memory], dim=0)
+        geo_feats = geo_feats.to(self.device, non_blocking=True)
 
-        aux_vals = torch.cat([x["aux_vals"] for x in self.outputs_epoch_memory], dim=0)
+        aux_vals = torch.cat([x["aux_vals"] for x in self.outputs_epoch_memory], dim=0).to(
+            self.device, non_blocking=True
+        )
 
         # Rank on similarity
         similarity = self.concept_similarities(geo_feats)
@@ -247,23 +252,34 @@ def on_test_epoch_end(self):
         return self._on_epoch_end("test")
 
     def concept_similarities(self, geo_embeds, concept=None) -> torch.Tensor:
+        device_type = geo_embeds.device.type
+        is_bf16 = self.trainer.precision == "bf16-mixed" and device_type == "cuda"
+
         # Get concept embeddings
         if concept is not None:
             # If only one concept is provided
             if isinstance(concept, str):
                 concept = [concept]
-            with torch.no_grad():
-                concept_embeds = self.text_encoder({"text": concept}, mode="train")
+
+            with torch.inference_mode():
+                with torch.autocast(
+                    device_type=device_type, dtype=torch.bfloat16, enabled=is_bf16
+                ):
+                    concept_embeds = self.text_encoder({"text": concept}, mode="train")
+            concept_embeds = F.normalize(concept_embeds, dim=1)
 
         elif self.concept_embeds is not None:
             concept_embeds = self.concept_embeds
         else:
-            with torch.no_grad():
-                concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            with torch.inference_mode():
+                with torch.autocast(
+                    device_type=device_type, dtype=torch.bfloat16, enabled=is_bf16
+                ):
+                    concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
+            concept_embeds = F.normalize(concept_embeds, dim=1)
 
         # Similarity
         geo_embeds = F.normalize(geo_embeds, dim=1)
-        concept_embeds = F.normalize(concept_embeds, dim=1)
         similarity_matrix = concept_embeds @ geo_embeds.T
 
         return similarity_matrix

From d49525865460e056f3b0291bb379edb8d0021f18 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 29 May 2026 18:05:25 +0200
Subject: [PATCH 099/173] Inference -> no_grad

---
 src/models/components/text_encoders/clip_text_encoder.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 234ea4f..764af9a 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -54,7 +54,7 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
             if any(p.requires_grad for p in self.model.parameters()):
                 text_embeds = self.model.get_text_features(**text_tokens)
             else:
-                with torch.inference_mode():
+                with torch.no_grad():
                     text_embeds = self.model.get_text_features(**text_tokens)
 
             # Project

From 6658debfab837703caa6b457205d92f380600043 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:07:55 +0200
Subject: [PATCH 100/173] Add ability to load unlabelled data

# Conflicts:
#	src/data/butterfly_dataset.py
---
 configs/data/butterfly_coords_text_unlabelled.yaml | 2 +-
 src/data/base_datamodule.py                        | 2 ++
 src/data/base_dataset.py                           | 2 --
 src/data/butterfly_dataset.py                      | 2 +-
 4 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/configs/data/butterfly_coords_text_unlabelled.yaml b/configs/data/butterfly_coords_text_unlabelled.yaml
index 9ac97d0..e8399d3 100644
--- a/configs/data/butterfly_coords_text_unlabelled.yaml
+++ b/configs/data/butterfly_coords_text_unlabelled.yaml
@@ -24,5 +24,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 7f969cf..ac8bcf8 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -67,6 +67,8 @@ def __init__(
             self.caption_builder.sync_with_dataset(self.dataset)
             self.concept_configs = caption_builder.concepts
 
+        self.split_data()
+
     @property
     def tabular_dim(self):
         return self.dataset.tabular_dim
diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 5e6d2fc..97160c8 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -11,7 +11,6 @@
 
 from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError
 from src.utils.data_utils import center_crop_npy
-from src.utils.errors import MissingDataError
 
 TORCH_DTYPES = {
     "float32": torch.float32,
@@ -261,7 +260,6 @@ def setup_tessera(self) -> None:
             tessera_from_df,
         )
 
-
         print("\n\nSetting up Tessera data...\n\n")
         download_missing_tiles = False
 
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index 91ef4e8..d21376c 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -1,5 +1,5 @@
 import os
-from typing import Any, Dict, List, override
+from typing import Any, Dict, override
 
 import numpy as np
 import pooch

From 87c0dda40f2a38025de26d99344cf79acc7f9995 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Mon, 1 Jun 2026 10:14:23 +0200
Subject: [PATCH 101/173] Add avr embedding modalities

# Conflicts:
#	src/data/base_dataset.py
---
 src/data/base_dataset.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 97160c8..0d5dbc0 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -87,9 +87,6 @@ def __init__(
                 m_dtype = dtype
                 self.modalities[mod] = {"dtype": m_dtype}
 
-        # More precise dataset name (with modalities)
-        self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
-
         # Set data attributes
         self.registry_path = os.path.join(data_dir, "registry.txt")
         if type(dataset_name) is str:

From 2df820ae5878d8d8a30ff19e0b9e1d3845bd55e0 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 08:49:12 +0200
Subject: [PATCH 102/173] Add setup flag

---
 src/data/base_datamodule.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index ac8bcf8..4c3ffce 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -67,7 +67,7 @@ def __init__(
             self.caption_builder.sync_with_dataset(self.dataset)
             self.concept_configs = caption_builder.concepts
 
-        self.split_data()
+        self._setup_flag = False
 
     @property
     def tabular_dim(self):
@@ -83,10 +83,11 @@ def setup(self, stage: str = "fit") -> None:
 
         Called by model trainer (trainer.fit()).
         """
-
-        # Set up the dataset (download requested modalities)
-        self.dataset.setup()
-        self.split_data()
+        if not self._setup_flag:
+            # Set up the dataset (download requested modalities)
+            self.dataset.setup()
+            self.split_data()
+            self._setup_flag = True
 
     @property
     def batch_size_per_device(self) -> None:

From 44365a663b93d2dc5489251b7fc4c9e64502d8cd Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 09:19:31 +0200
Subject: [PATCH 103/173] Re do unlabelled split

---
 ...ms-unlabelled-20260602_2026-06-02-0918.pth | Bin 0 -> 294641 bytes
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 152 +++++++++---------
 2 files changed, 72 insertions(+), 80 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
new file mode 100644
index 0000000000000000000000000000000000000000..429e3b22f30eea24bd0c4487cf24133330d28780
GIT binary patch
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diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 020dcf6..a615779 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -1,11 +1,10 @@
 {
  "cells": [
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import glob\n",
     "import os\n",
@@ -15,14 +14,14 @@
     "\n",
     "os.chdir(\"..\")\n",
     "os.getcwd()"
-   ]
+   ],
+   "id": "939196e9008db348"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Check how do aef and tessera files overlap\n",
     "paths = glob.glob(\"data/s2bms/eo/aef/*.npy\")\n",
@@ -46,26 +45,26 @@
     "\n",
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
-   ]
+   ],
+   "id": "c2cb4fa1631de632"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Load in original datasplit file\n",
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
-   ]
+   ],
+   "id": "2bb456727dac5b56"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Print data split sizes\n",
     "s = 0\n",
@@ -74,14 +73,14 @@
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "e0c511a6a42c7e04"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Filter out remaining aef but missing tessera tiles form val and test splits\n",
     "\n",
@@ -107,14 +106,14 @@
     "        split_indices,\n",
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
-   ]
+   ],
+   "id": "fc1df825496acd14"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Visualise data split\n",
     "df = pd.read_csv(\"data/s2bms/model_ready_s2bms.csv\")\n",
@@ -127,14 +126,14 @@
     "\n",
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
-   ]
+   ],
+   "id": "2662586411a249e3"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "import folium\n",
     "\n",
@@ -153,35 +152,33 @@
     "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"red\", fill=True).add_to(m)\n",
     "\n",
     "m"
-   ]
+   ],
+   "id": "3166c3b7a65343e"
   },
   {
-   "cell_type": "markdown",
-   "id": "7",
    "metadata": {},
-   "source": [
-    "# Re-do Unlabelled split"
-   ]
+   "cell_type": "markdown",
+   "source": "# Re-do Unlabelled split",
+   "id": "7e00edf2aff7fec7"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "df_unlabelled = pd.read_csv(\"data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\")\n",
     "df_unlabelled = df_unlabelled[df_unlabelled.split == \"train\"]\n",
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
-   ]
+   ],
+   "id": "241e4a149b6e0fd9"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "pth = \"data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260529_2026-05-29-1438.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)\n",
@@ -192,39 +189,39 @@
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "65d0974da931eab6"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "split_indices[\"train_indices\"] = pd.Series(\n",
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
-   ]
+   ],
+   "id": "a7a955dde50a359e"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "11",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "val_test_indices = torch.load(\n",
     "    \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    weights_only=False,\n",
     ")"
-   ]
+   ],
+   "id": "21c97f95f864dc00"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "12",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "split_indices[\"val_indices\"] = pd.Series(\n",
     "    [i for i in list(val_test_indices[\"val_indices\"]) if \"UKBMS\" in i]\n",
@@ -232,14 +229,14 @@
     "split_indices[\"test_indices\"] = pd.Series(\n",
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
-   ]
+   ],
+   "id": "3548150c4bcce3ad"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "13",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "print(\"After test/val/train:\")\n",
     "s = 0\n",
@@ -247,14 +244,14 @@
     "    if not k == \"clusters\":\n",
     "        print(k, len(v))\n",
     "        s += len(v)"
-   ]
+   ],
+   "id": "2548543906de6fb6"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "14",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "if not os.path.exists(\"data/s2bms/splits/s2bms_unlabelled_union_val_test.pth\"):\n",
     "    torch.save(\n",
@@ -263,14 +260,14 @@
     "    )\n",
     "else:\n",
     "    print(\"Already saved\")"
-   ]
+   ],
+   "id": "874e57d465ea2dda"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "15",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "# Visualise data split\n",
     "df_merged = pd.concat([df, df_unlabelled])\n",
@@ -283,14 +280,14 @@
     "\n",
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
-   ]
+   ],
+   "id": "3288b5089e24add4"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "16",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
+   "execution_count": null,
    "source": [
     "m = folium.Map(location=[df.lat.mean(), df.lon.mean()], zoom_start=3)\n",
     "\n",
@@ -307,24 +304,19 @@
     "    folium.CircleMarker(location=[row.lat, row.lon], radius=2, color=\"red\", fill=True).add_to(m)\n",
     "\n",
     "m"
-   ]
+   ],
+   "id": "41ffc003f4be593"
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "17",
    "metadata": {},
+   "cell_type": "code",
    "outputs": [],
-   "source": []
+   "execution_count": null,
+   "source": "",
+   "id": "30e84ed4b82195fe"
   }
  ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  }
- },
+ "metadata": {},
  "nbformat": 4,
  "nbformat_minor": 5
 }

From c3ac46915ba85972aee99b26345b4067aa67e73c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:52:18 +0200
Subject: [PATCH 104/173] Add size print statements

---
 src/data/base_datamodule.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 4c3ffce..64ef14b 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -275,10 +275,13 @@ def split_data(self) -> None:
 
         if split_data_from_inds:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
+            print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
+            print(f"Validate dataset split size: {len(self.data_val)}")
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)
+                print(f"Test dataset split size: {len(self.data_test)}")
             else:
                 self.data_test = None
 

From 0a584554a6cc56a9a0e5d85d79e13241e840431c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:56:08 +0200
Subject: [PATCH 105/173] Make format hook changes

---
 .../yield_africa_tessera_preprocess.py        | 42 +++++++++++++------
 1 file changed, 30 insertions(+), 12 deletions(-)

diff --git a/src/data_preprocessing/yield_africa_tessera_preprocess.py b/src/data_preprocessing/yield_africa_tessera_preprocess.py
index 3a1e770..cea8a85 100644
--- a/src/data_preprocessing/yield_africa_tessera_preprocess.py
+++ b/src/data_preprocessing/yield_africa_tessera_preprocess.py
@@ -36,8 +36,8 @@
 import multiprocessing
 import os
 import socket
-import time
 import sys
+import time
 from pathlib import Path
 
 # Ensure the project root is on sys.path when the script is run directly.
@@ -45,7 +45,11 @@
 
 import pandas as pd
 
-from src.data_preprocessing.tessera_embeds import NoTileError, PartialTileError, get_tessera_embeds
+from src.data_preprocessing.tessera_embeds import (
+    NoTileError,
+    PartialTileError,
+    get_tessera_embeds,
+)
 
 log = logging.getLogger(__name__)
 
@@ -65,6 +69,7 @@
 def _init_worker(cache_dir: str, use_local_registry: bool, registry_dir: str) -> None:
     """Pool initializer: runs once per worker process to set up GeoTessera."""
     from geotessera import GeoTessera
+
     global _process_gt
     socket.setdefaulttimeout(60)
     embeddings_dir = str(Path(cache_dir) / "raw")
@@ -78,13 +83,18 @@ def _init_worker(cache_dir: str, use_local_registry: bool, registry_dir: str) ->
 def _worker_fetch(args: tuple) -> str:
     """Multiprocessing worker — reuses the per-process GeoTessera instance.
 
-    Must be a top-level function so it is picklable across processes.
-    Returning normally means success; raising means error (logged by caller).
+    Must be a top-level function so it is picklable across processes. Returning normally means
+    success; raising means error (logged by caller).
     """
     lon, lat, name_loc, year, save_dir, tile_size = args
     get_tessera_embeds(
-        lon=lon, lat=lat, name_loc=name_loc, year=year,
-        save_dir=save_dir, tile_size=tile_size, tessera_con=_process_gt,
+        lon=lon,
+        lat=lat,
+        name_loc=name_loc,
+        year=year,
+        save_dir=save_dir,
+        tile_size=tile_size,
+        tessera_con=_process_gt,
     )
     return name_loc
 
@@ -160,8 +170,8 @@ def fetch_tessera_tiles(
     # Per-task timeout: when a worker process exceeds this, it is killed and
     # the record added to stuck.txt.  Multiprocessing (unlike threading) allows
     # true process termination, so stuck downloads cannot block forward progress.
-    HEARTBEAT = 15      # seconds between "still fetching" log lines
-    TILE_TIMEOUT = 60   # seconds per record before the worker process is killed
+    HEARTBEAT = 15  # seconds between "still fetching" log lines
+    TILE_TIMEOUT = 60  # seconds per record before the worker process is killed
     # Note: stuck workers spin at 100% CPU and leak ~55 MB/s of rasterio
     # MemoryFile objects inside GeoTessera's fetch loop.  60s caps peak memory
     # at ~3 GB per stuck record and recovers 3x faster than the old 180s limit.
@@ -177,13 +187,17 @@ def fetch_tessera_tiles(
         else:
             stuck_records = set(stuck_file.read_text().splitlines())
             if stuck_records:
-                print(f"  Skipping {len(stuck_records)} previously-stuck record(s): {sorted(stuck_records)}")
+                print(
+                    f"  Skipping {len(stuck_records)} previously-stuck record(s): {sorted(stuck_records)}"
+                )
 
     rows = [row for _, row in df.iterrows() if row.name_loc not in stuck_records]
     done = 0
 
     _pool_initargs = (cache_dir, _use_local_registry, str(_default_registry_dir))
-    pool = multiprocessing.Pool(processes=workers, initializer=_init_worker, initargs=_pool_initargs)
+    pool = multiprocessing.Pool(
+        processes=workers, initializer=_init_worker, initargs=_pool_initargs
+    )
     try:
         for row in rows:
             args = (row.lon, row.lat, row.name_loc, int(row.year), str(save_dir), tile_size)
@@ -204,7 +218,9 @@ def fetch_tessera_tiles(
                     print(f"  Skipped {row.name_loc}: no TESSERA data for this location/year")
                     break
                 except PartialTileError:
-                    print(f"  Skipped {row.name_loc}: tile too close to mosaic edge, not enough context")
+                    print(
+                        f"  Skipped {row.name_loc}: tile too close to mosaic edge, not enough context"
+                    )
                     break
                 except Exception as exc:
                     print(f"  ERROR fetching {row.name_loc}: {exc}")
@@ -213,7 +229,9 @@ def fetch_tessera_tiles(
             if timed_out:
                 pool.terminate()
                 pool.join()
-                pool = multiprocessing.Pool(processes=workers, initializer=_init_worker, initargs=_pool_initargs)
+                pool = multiprocessing.Pool(
+                    processes=workers, initializer=_init_worker, initargs=_pool_initargs
+                )
                 with open(stuck_file, "a") as fh:
                     fh.write(row.name_loc + "\n")
                 print(

From 7a9494872b874c7dc4c800656b1fd6bb839b8207 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:57:51 +0200
Subject: [PATCH 106/173] Remove full model name printing

---
 src/models/base_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index d0f92e8..350ae31 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -78,7 +78,7 @@ def setup(self, stage: str) -> None:
         """Updates model based data-bound configurations (through datamodule), This method is
         called after trainer is initialized and datamodule is available."""
         if self.setup_flag:
-            print(f"Model {self.__str__()} is already set up!")
+            print("Model is already set up!")
             return
 
         # If trainer is attached get num_classes and tabular_dim from datamodule (data-dependent)

From be5562394dc8099fec1afffb5020120d00fc7be5 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:09:52 +0200
Subject: [PATCH 107/173] Hook security

---
 src/utils/utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/utils/utils.py b/src/utils/utils.py
index f1771a2..a34d208 100644
--- a/src/utils/utils.py
+++ b/src/utils/utils.py
@@ -119,8 +119,9 @@ def wrap(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     if torch.cuda.is_available():
                         torch.cuda.empty_cache()
                         torch.cuda.ipc_collect()
-            except Exception:
+            except Exception as e:
                 # cleanup should never mask the original exception
+                print(e)
                 pass
 
         return metric_dict, object_dict

From 547140a0abda64c92bba5da0bbaf3fdad825773d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:23:28 +0200
Subject: [PATCH 108/173] Fix logging

---
 src/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index 92207bd..69d7c09 100644
--- a/src/train.py
+++ b/src/train.py
@@ -151,7 +151,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         )
 
         val_metrics = trainer.callback_metrics
-        wandb_logger.log({f"best_val_{k}": v for k, v in val_metrics.items()})
+        wandb_logger.log_metrics({f"best_val_{k}": v for k, v in val_metrics.items()})
 
     if cfg.get("test"):
         log.info("Starting testing!")

From b85a0a12df6d9b516ad5758e7fa9e4b802c688f6 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:23:53 +0200
Subject: [PATCH 109/173] Fix: getting size

---
 src/train.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index 69d7c09..c7b729a 100644
--- a/src/train.py
+++ b/src/train.py
@@ -118,7 +118,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     else:
                         data_name = "satclip"
                 else:
-                    data_name = f"{k}_{data_dict[k]['size']}"
+                    data_name = f"{k}_{data_dict[k].get('size', '')}"
             else:
                 ks = list(data_dict.keys())
                 data_name = str("".join([f'{k}_{data_dict[k].get("size", "")}' for k in ks]))

From 65f659bd3bbd5e3712e20edc996aded90e68ffb2 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:26:02 +0200
Subject: [PATCH 110/173] Hook fixes

---
 src/data/base_caption_builder.py              | 25 ++++++-------
 .../geo_encoders/average_encoder.py           |  2 +-
 .../components/geo_encoders/cnn_encoder.py    | 35 ++++++++++++-------
 src/models/components/geo_encoders/geoclip.py |  1 -
 .../components/pred_heads/base_pred_head.py   |  2 +-
 src/models/predictive_model.py                |  4 ++-
 6 files changed, 40 insertions(+), 29 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 79de05b..875cabc 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -169,24 +169,25 @@ def get_adjective_for_percentage(value: float) -> str:
     else:
         return "almost entirely"
 
+
 def sample_adjective_for_percentage(percent: float) -> str:
     """Convert a percentage (0-100) to a descriptive adjective, randomly sampled from synonyms."""
     if not 0 <= percent <= 100:
         raise ValueError(f"Percentage must be between 0 and 100, got {percent}")
 
     synonyms = {
-        "none":         ["none", "zero", "absent", "nonexistent"],
-        "negligible":   ["negligible", "trivial", "trace", "barely any", "scarcely any"],
-        "minimal":      ["minimal", "tiny", "very little", "marginal", "meager"],
-        "slight":       ["slight", "small", "modest", "limited", "faint"],
-        "some":         ["some", "a bit of", "a portion of", "partial", "a measure of"],
-        "moderate":     ["moderate", "fair", "reasonable", "middling", "decent"],
+        "none": ["none", "zero", "absent", "nonexistent"],
+        "negligible": ["negligible", "trivial", "trace", "barely any", "scarcely any"],
+        "minimal": ["minimal", "tiny", "very little", "marginal", "meager"],
+        "slight": ["slight", "small", "modest", "limited", "faint"],
+        "some": ["some", "a bit of", "a portion of", "partial", "a measure of"],
+        "moderate": ["moderate", "fair", "reasonable", "middling", "decent"],
         "considerable": ["considerable", "notable", "meaningful", "appreciable", "marked"],
-        "substantial":  ["substantial", "solid", "sizable", "hefty", "goodly"],
-        "significant":  ["significant", "large", "strong", "pronounced", "prominent"],
-        "major":        ["major", "great", "high", "intense", "serious"],
-        "extensive":    ["extensive", "vast", "sweeping", "far-reaching", "immense"],
-        "complete":     ["complete", "total", "full", "entire", "absolute"],
+        "substantial": ["substantial", "solid", "sizable", "hefty", "goodly"],
+        "significant": ["significant", "large", "strong", "pronounced", "prominent"],
+        "major": ["major", "great", "high", "intense", "serious"],
+        "extensive": ["extensive", "vast", "sweeping", "far-reaching", "immense"],
+        "complete": ["complete", "total", "full", "entire", "absolute"],
     }
 
     if percent == 0:
@@ -214,4 +215,4 @@ def sample_adjective_for_percentage(percent: float) -> str:
     else:
         key = "complete"
 
-    return random.choice(synonyms[key])
\ No newline at end of file
+    return random.choice(synonyms[key])
diff --git a/src/models/components/geo_encoders/average_encoder.py b/src/models/components/geo_encoders/average_encoder.py
index f3152bd..46258d5 100644
--- a/src/models/components/geo_encoders/average_encoder.py
+++ b/src/models/components/geo_encoders/average_encoder.py
@@ -36,7 +36,7 @@ def _setup(self) -> List[str]:
     def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         """Data forward pass through the encoder."""
         tile = batch.get("eo", {}).get(self.geo_data_name)
-        
+
         # NaNs vs 0s are specified in dataset configs and returned from BaseDataset class
         feats = self.geo_encoder(tile.nanmean(dim=(-2, -1)))
         if self.extra_projector:
diff --git a/src/models/components/geo_encoders/cnn_encoder.py b/src/models/components/geo_encoders/cnn_encoder.py
index 918b755..75c80c5 100644
--- a/src/models/components/geo_encoders/cnn_encoder.py
+++ b/src/models/components/geo_encoders/cnn_encoder.py
@@ -3,16 +3,13 @@
 import torch
 import torchvision.models as models
 from torch import nn
-from torchgeo.models import resnet50, ResNet50_Weights, ResNet18_Weights, resnet18
+from torchgeo.models import ResNet18_Weights, ResNet50_Weights, resnet18, resnet50
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
 from src.utils.errors import IllegalArgumentCombination
 
-RN_DIM = {
-    18 : 512,
-    34: 512,
-    50: 2048
-}
+RN_DIM = {18: 512, 34: 512, 50: 2048}
+
 
 class CNNEncoder(BaseGeoEncoder):
     """Convolutional neural network EO encoder. Adapted from PECL.
@@ -41,7 +38,12 @@ def __init__(
             assert resnet_version in [18, 34, 50], f"Unsupported resnet version: {resnet_version}"
             self.resnet_version = resnet_version
 
-            assert pretrained_cnn in ["imagenet", "IMAGENET1K_V1", 'SSL4EO_RGB_MOCO', None], f"Unsupported pretrained_cnn: {pretrained_cnn}"
+            assert pretrained_cnn in [
+                "imagenet",
+                "IMAGENET1K_V1",
+                "SSL4EO_RGB_MOCO",
+                None,
+            ], f"Unsupported pretrained_cnn: {pretrained_cnn}"
             self.pretrained_cnn = pretrained_cnn
 
             self.output_dim = RN_DIM[resnet_version]
@@ -52,7 +54,9 @@ def __init__(
         self.geo_data_name = geo_data_name
 
         self.set_n_input_bands(input_n_bands)
-        assert (self.input_n_bands >= 3 and type(self.input_n_bands) is int), f"input_n_bands must be int >=3, got {self.input_n_bands}"
+        assert (
+            self.input_n_bands >= 3 and type(self.input_n_bands) is int
+        ), f"input_n_bands must be int >=3, got {self.input_n_bands}"
 
     def set_n_input_bands(self, n_bands: int | None = None) -> None:
         """Sets number of input bands based on geo_data_name if n_bands is None.
@@ -77,6 +81,7 @@ def set_n_input_bands(self, n_bands: int | None = None) -> None:
     @override
     def _setup(self) -> List[str]:
         """Gets backbone model given configuration stored in self.
+
         :return: backbone model
         """
         trainable_modules = []
@@ -87,7 +92,9 @@ def _setup(self) -> List[str]:
                 if self.resnet_version == 18:
                     self.geo_encoder = resnet18(weights=ResNet18_Weights.SENTINEL2_RGB_MOCO)
                 elif self.resnet_version == 34:
-                    raise IllegalArgumentCombination('SSL4EO_RGB_MOCO weights are not available for RN-34')
+                    raise IllegalArgumentCombination(
+                        "SSL4EO_RGB_MOCO weights are not available for RN-34"
+                    )
                 else:
                     self.geo_encoder = resnet50(weights=ResNet50_Weights.SENTINEL2_RGB_MOCO)
             # Imagenet
@@ -123,7 +130,7 @@ def _setup(self) -> List[str]:
                             self.geo_encoder.conv1.weight[:, i, :, :] = weight[:, i % 3, :, :]
 
                 # Ensure replaced layer is not frozen
-                trainable_modules.append('geo_encoder.conv1')
+                trainable_modules.append("geo_encoder.conv1")
 
             # I think for features fc often is replaced with identity?
             self.geo_encoder.fc = nn.Identity()
@@ -145,8 +152,10 @@ def forward(
         :param batch: input batch
         :return: extracted features
         """
-        eo_data = batch.get("eo", KeyError(f"Batch must contain batch['eo']"))
-        eo_data = eo_data.get(self.geo_data_name,  KeyError(f"Batch must contain batch['eo']['{self.geo_data_name}']"))
+        eo_data = batch.get("eo", KeyError("Batch must contain batch['eo']"))
+        eo_data = eo_data.get(
+            self.geo_data_name, KeyError(f"Batch must contain batch['eo']['{self.geo_data_name}']")
+        )
         dtype = self.dtype
 
         if eo_data.dtype != dtype:
@@ -156,4 +165,4 @@ def forward(
         if self.extra_projector:
             feats = self.extra_projector(feats)
 
-        return feats
\ No newline at end of file
+        return feats
diff --git a/src/models/components/geo_encoders/geoclip.py b/src/models/components/geo_encoders/geoclip.py
index 9224d60..85eca81 100644
--- a/src/models/components/geo_encoders/geoclip.py
+++ b/src/models/components/geo_encoders/geoclip.py
@@ -2,7 +2,6 @@
 
 import torch
 from geoclip import LocationEncoder
-from torch.nn import functional as F
 
 from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
 
diff --git a/src/models/components/pred_heads/base_pred_head.py b/src/models/components/pred_heads/base_pred_head.py
index 02e2af6..29f19ae 100644
--- a/src/models/components/pred_heads/base_pred_head.py
+++ b/src/models/components/pred_heads/base_pred_head.py
@@ -76,4 +76,4 @@ def dtype(self) -> torch.dtype | None:
             raise RuntimeError("Prediction head has multiple dtypes")
         elif len(dtypes) == 0:
             return None
-        return dtypes.pop()
\ No newline at end of file
+        return dtypes.pop()
diff --git a/src/models/predictive_model.py b/src/models/predictive_model.py
index 9e3c28a..a956eb0 100644
--- a/src/models/predictive_model.py
+++ b/src/models/predictive_model.py
@@ -112,7 +112,9 @@ def _setup_encoders_adapters(self):
         self.trainable_modules.extend(new_modules)
 
         if self.normalize_features:
-            self.normalizer = nn.LayerNorm(self.geo_encoder.output_dim, dtype=self.geo_encoder.dtype)
+            self.normalizer = nn.LayerNorm(
+                self.geo_encoder.output_dim, dtype=self.geo_encoder.dtype
+            )
             self.trainable_modules.append("normalizer")
             print("Model set up to normalise geo_encoder features.")
 

From 4a8ef1d7df43082840db054fba2786781fb35c96 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:20:02 +0200
Subject: [PATCH 111/173] Save average embeddings to csv

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index a615779..678d957 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "939196e9008db348"
+   "id": "fcec66ea10d99e0"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "c2cb4fa1631de632"
+   "id": "f0d8c57a1cc02e44"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "2bb456727dac5b56"
+   "id": "a84b9f43dbb2e283"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "e0c511a6a42c7e04"
+   "id": "eddedae95604c90a"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "fc1df825496acd14"
+   "id": "2c2f42a97b08c33f"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "2662586411a249e3"
+   "id": "a221ff72f85b5fe1"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "3166c3b7a65343e"
+   "id": "e7ca73cd872e09c7"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "7e00edf2aff7fec7"
+   "id": "459956b76d1cd74b"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "241e4a149b6e0fd9"
+   "id": "9422dd90cd08b1db"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "65d0974da931eab6"
+   "id": "8b15b52938e0e434"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "a7a955dde50a359e"
+   "id": "9c8da08b231eb880"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "21c97f95f864dc00"
+   "id": "dabb072862be4ce9"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "3548150c4bcce3ad"
+   "id": "e21d65dd36e76b1f"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "2548543906de6fb6"
+   "id": "b8cf7b8bd9c27b3c"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "874e57d465ea2dda"
+   "id": "8c5c67abed35a3bd"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "3288b5089e24add4"
+   "id": "13393b3b78d487cb"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "41ffc003f4be593"
+   "id": "27c8bc482bc897fa"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "30e84ed4b82195fe"
+   "id": "8ba8a99d5501cf06"
   }
  ],
  "metadata": {},

From fe3272ce3d4693f0c2a35206f47567b36f559695 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:26:54 +0200
Subject: [PATCH 112/173] emb_ reading for csv of avr embeds

---
 src/data/base_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 0d5dbc0..b749b09 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -166,7 +166,7 @@ def get_records(self) -> dict[str, Any]:
                 self.df = self.df.merge(df, on="name_loc", how="left")
 
                 max_no = 128 if modality == "tessera_avr" else 64
-                columns.extend([f"avr_{i}" for i in range(0, max_no)])
+                columns.extend([f"emb_{i}" for i in range(0, max_no)])
             else:
                 # Add paths
                 self.add_modality_paths_to_df(

From f021b8ac4d26ac9f06b6a34d37e96476bd23dba5 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:27:40 +0200
Subject: [PATCH 113/173] Return name_loc parameter in dataset for tracing back
 data

---
 src/data/base_dataset.py      | 2 ++
 src/data/butterfly_dataset.py | 5 +++++
 2 files changed, 7 insertions(+)

diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index b749b09..0012e39 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -37,6 +37,7 @@ def __init__(
         use_features: bool = True,
         csv_name: str = None,
         dtype: str = "float32",
+        return_name_loc: bool = False,
     ) -> None:
         """Interface for any use case dataset.
 
@@ -144,6 +145,7 @@ def __init__(
         self.dataset_name: str = dataset_name + "_" + "_".join(modalities)
         self.mode: str = mode  # 'train', 'val', 'test'
         self.records: dict[str, Any] = self.get_records()
+        self.return_name_loc: bool = return_name_loc
 
     @final
     def get_records(self) -> dict[str, Any]:
diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index d21376c..5eb9a61 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -23,6 +23,7 @@ def __init__(
         cache_dir: str = None,
         mock: bool = False,
         dtype: str = "float32",
+        return_name_loc: bool = False,
     ) -> None:
         """A dataset implementation for the Butterfly diversity use case.
 
@@ -57,6 +58,7 @@ def __init__(
             implemented_mod={"s2", "tessera", "coords", "aef", "aef_avr", "tessera_avr"},
             mock=mock,
             dtype=dtype,
+            return_name_loc=return_name_loc,
         )
 
     def setup(self):
@@ -218,6 +220,9 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 else:
                     formatted_row["aux"][aux_cat] = [row[v] for v in vals]
 
+        if self.return_name_loc:
+            formatted_row["name_loc"] = row["name_loc"]
+
         return formatted_row
 
 

From 3dee98da5aac0fe408707a89c8352c994a71b888 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:28:50 +0200
Subject: [PATCH 114/173] Freezing statement

---
 src/models/base_model.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 350ae31..9a3259c 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -104,8 +104,11 @@ def _setup(self, stage: str) -> None:
     @final
     def full_freezer(self):
         """Freeze the whole network."""
+        print("--------Frozen--------")
         for name, param in self.named_parameters():
             param.requires_grad = False
+        print("Full model")
+        print("------------------------")
 
         for name, module in self.named_modules():
             module.eval()

From 7d6a75b7e7e90c7111f64be2fd8fd2c6aa8f6b43 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:29:51 +0200
Subject: [PATCH 115/173] Add dtype conversion

---
 src/models/components/loss_fns/bce_loss.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/models/components/loss_fns/bce_loss.py b/src/models/components/loss_fns/bce_loss.py
index b4f6663..d3ddab1 100644
--- a/src/models/components/loss_fns/bce_loss.py
+++ b/src/models/components/loss_fns/bce_loss.py
@@ -21,9 +21,10 @@ def forward(
         mode: str | None = None,
         **kwargs,
     ) -> torch.Tensor or Dict[str, torch.Tensor]:
+        """Forward pass to get BCE loss."""
 
         labels = labels if labels is not None else batch.get("target")
-        loss = self.criterion(pred, labels)
+        loss = self.criterion(pred, labels.to(pred.dtype))
 
         if "return_label" in kwargs:
             return {f"{mode}_{self.name}": loss}

From 8585dd58b202ee8aa339d8ac40400fe08419709a Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:30:17 +0200
Subject: [PATCH 116/173] Make geoclip projector as optional argument

---
 src/models/components/text_encoders/clip_text_encoder.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/models/components/text_encoders/clip_text_encoder.py b/src/models/components/text_encoders/clip_text_encoder.py
index 764af9a..33254f0 100644
--- a/src/models/components/text_encoders/clip_text_encoder.py
+++ b/src/models/components/text_encoders/clip_text_encoder.py
@@ -10,7 +10,7 @@
 
 
 class ClipTextEncoder(BaseTextEncoder):
-    def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -> None:
+    def __init__(self, hf_cache_dir: str = "../.cache", use_geoclip_projector=True) -> None:
         super().__init__()
         self.processor = CLIPProcessor.from_pretrained(
             "openai/clip-vit-large-patch14",
@@ -22,12 +22,13 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
             cache_dir=hf_cache_dir,
         )
 
-        self.projector = GeoCLIP().image_encoder.mlp
+        if use_geoclip_projector:
+            self.projector = GeoCLIP().image_encoder.mlp
 
         self.model.vision_model = None
         self.model.visual_projection = None
 
-        self.output_dim = 512
+        self.output_dim = 512 if use_geoclip_projector else 768
 
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:

From 1a645812ddcee82cb1c6a2eefe4fd21852e02f84 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 5 Jun 2026 15:30:53 +0200
Subject: [PATCH 117/173] Fix dataset name creation for multi-modal encoders

---
 src/train.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/train.py b/src/train.py
index c7b729a..3e170c8 100644
--- a/src/train.py
+++ b/src/train.py
@@ -121,7 +121,11 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
                     data_name = f"{k}_{data_dict[k].get('size', '')}"
             else:
                 ks = list(data_dict.keys())
-                data_name = str("".join([f'{k}_{data_dict[k].get("size", "")}' for k in ks]))
+                ks_new = [
+                    f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
+                    for k in ks
+                ]
+                data_name = "-".join(map(str, ks_new))
 
             # Log details to wandb
             wandb_logger.log_metrics(

From 63233996038eed03a7e94c045e7af89bc0811776 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:52:56 +0200
Subject: [PATCH 118/173] Data split and encoding merging for aef_256 averages

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index 678d957..db04de0 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "fcec66ea10d99e0"
+   "id": "65de5fc3c6bf5ae7"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "f0d8c57a1cc02e44"
+   "id": "dd6fbfcf6721b59d"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "a84b9f43dbb2e283"
+   "id": "92bf611a8161c2b0"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "eddedae95604c90a"
+   "id": "fdd45b910e1ce370"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "2c2f42a97b08c33f"
+   "id": "e2471b2f6e31a921"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "a221ff72f85b5fe1"
+   "id": "2e44c05c43494127"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "e7ca73cd872e09c7"
+   "id": "94309214afa86e4c"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "459956b76d1cd74b"
+   "id": "e5ba50459738958"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "9422dd90cd08b1db"
+   "id": "8434a4c727d8abb9"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "8b15b52938e0e434"
+   "id": "58591df65d72755b"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "9c8da08b231eb880"
+   "id": "19f514f8a3308e4e"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "dabb072862be4ce9"
+   "id": "14f4e1d50a7f5ee9"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "e21d65dd36e76b1f"
+   "id": "b96a748f20033142"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "b8cf7b8bd9c27b3c"
+   "id": "c1367ece43cc444d"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "8c5c67abed35a3bd"
+   "id": "f99f150f387d2e38"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "13393b3b78d487cb"
+   "id": "ed413850060b05a8"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "27c8bc482bc897fa"
+   "id": "360f3309d8cf2ccf"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "8ba8a99d5501cf06"
+   "id": "1e1bed9c4e61a6d7"
   }
  ],
  "metadata": {},

From 548419321d27a9f1fe255691c6445b9345720911 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:54:53 +0200
Subject: [PATCH 119/173] Fix embs from avr

---
 src/data/butterfly_dataset.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index 5eb9a61..aaa53f3 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -196,12 +196,12 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                 formatted_row["eo"][modality] = self.load_aef(row["aef_path"])
             elif modality == "aef_avr":
                 formatted_row["eo"][modality] = torch.tensor(
-                    [row[f"avr_{i}"] for i in range(64)],
+                    [row[f"emb_{i}"] for i in range(64)],
                     dtype=getattr(torch, self.modalities["aef_avr"].get("dtype")),
                 )
             elif modality == "tessera_avr":
                 formatted_row["eo"][modality] = torch.tensor(
-                    [row[f"avr_{i}"] for i in range(128)],
+                    [row[f"emb_{i}"] for i in range(128)],
                     dtype=getattr(torch, self.modalities["tessera_avr"].get("dtype")),
                 )
 

From ca5a8a3063ee75b79fec0755058c174a120f4eb1 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:55:19 +0200
Subject: [PATCH 120/173] Add name_loc extraction in collate function

---
 src/data/collate_fns.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 2c001b4..1a008ab 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -36,6 +36,9 @@ def collate_fn(
     if batch[0].get("target") is not None:
         batch_collected["target"] = smart_stack([item["target"] for item in batch])
 
+    if batch[0].get("name_loc") is not None:
+        batch_collected["name_loc"] = smart_stack([item["name_loc"] for item in batch])
+
     # convert aux into captions
     if mode == "train":
         batch_collected["text"] = caption_builder.random(batch_collected["aux"])

From 25153cc35192dafc0d74bb625a0c5f042c70a043 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 10:55:27 +0200
Subject: [PATCH 121/173] Verbose early stopping

---
 configs/callbacks/early_stopping.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/callbacks/early_stopping.yaml b/configs/callbacks/early_stopping.yaml
index c826c8d..0ab2df1 100644
--- a/configs/callbacks/early_stopping.yaml
+++ b/configs/callbacks/early_stopping.yaml
@@ -5,7 +5,7 @@ early_stopping:
   monitor: ??? # quantity to be monitored, must be specified !!!
   min_delta: 0. # minimum change in the monitored quantity to qualify as an improvement
   patience: 3 # number of checks with no improvement after which training will be stopped
-  verbose: False # verbosity mode
+  verbose: true # verbosity mode
   mode: "min" # "max" means higher metric value is better, can be also "min"
   strict: True # whether to crash the training if monitor is not found in the validation metrics
   check_finite: True # when set True, stops training when the monitor becomes NaN or infinite

From a9c447d6732b82ba397f90fc504c89be1cdbe2d8 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:06:42 +0200
Subject: [PATCH 122/173] Temp fix to get inference model working

---
 configs/inference.yaml        | 37 +++++++++++++--
 src/models/inference_model.py | 86 +++++++++++++++++++++++++----------
 2 files changed, 93 insertions(+), 30 deletions(-)

diff --git a/configs/inference.yaml b/configs/inference.yaml
index 45f5bcc..ef9fdd0 100644
--- a/configs/inference.yaml
+++ b/configs/inference.yaml
@@ -1,12 +1,14 @@
 defaults:
-  - _self_
   - paths: ${oc.env:STORAGE_MODE,local}
   - extras: default
   - hydra: default
+  - data: s2bms_prediction
+  - _self_
 
 # task name, determines output directory path
 task_name: "inference"
 tags: ["dev"]
+seed: 12345
 
 # If set, inference.py will load this merged checkpoint directly.
 #inference_ckpt_path: ${paths.log_dir}${task_name}/2026-04-07_12-56-16/inference_model.ckpt
@@ -14,12 +16,37 @@ tags: ["dev"]
 # If `inference_ckpt_path` is not set, stitch the inference model from:
 # - predictive ckpt (provides prediction_head weights)
 # - alignment ckpt (provides text_encoder weights + geo_encoder)
-predictive_ckpt_path: ${paths.log_dir}train/runs/2026-04-02_15-54-53/checkpoints/epoch_000.ckpt
-alignment_ckpt_path: ${paths.log_dir}train/runs/2026-04-02_15-40-03/checkpoints/epoch_000.ckpt
+predictive_ckpt_path: ${paths.checkpoint_root}/s2bms_prediction_ckpt/1cti8qah_epoch_037.ckpt
+alignment_ckpt_path: ${paths.checkpoint_root}/s2bms_alignment_ckpt/dmts2wpm_epoch_097.ckpt
 
 # If set, inference.py will save a merged inference checkpoint you can reload with
 # `inference_ckpt_path`.
-save_inference_ckpt_path: ${paths.log_dir}${task_name}/${now:%Y-%m-%d}_${now:%H-%M-%S}/inference_model.ckpt
+#save_inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/${now:%Y-%m-%d}_${now:%H-%M-%S}.ckpt
+inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_12-05-05.ckpt
 #save_inference_ckpt_path: null
+save_ckpt: true
+
+training_order: ["prediction_model", "alignment_model"]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
 
-training_order: ["alignment_model", "prediction_model"]
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+  num_workers: 8
+  persistent_workers: false
+  pin_memory: false
+  batch_size: 64
+  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
diff --git a/src/models/inference_model.py b/src/models/inference_model.py
index 5c0023e..717d51d 100644
--- a/src/models/inference_model.py
+++ b/src/models/inference_model.py
@@ -23,6 +23,7 @@ class InferenceModel(BaseModel):
     def __init__(
         self,
         geo_encoder: BaseGeoEncoder | None,
+        geo_encoder_prediction: BaseGeoEncoder | None,
         text_encoder: BaseTextEncoder | None,
         prediction_head: BasePredictionHead | None,
         num_classes: int | None,
@@ -56,6 +57,9 @@ def __init__(
             tabular_dim=None,
         )
 
+        if geo_encoder_prediction:
+            self.geo_encoder_prediction = geo_encoder_prediction
+
         # Params from alignment model
         self.match_to_geo = match_to_geo
         self.ks = ks
@@ -72,6 +76,8 @@ def _setup(self, stage: str) -> None:
             self.geo_encoder.setup()
         if hasattr(self, "text_encoder"):
             self.text_encoder.setup()
+        if hasattr(self, "geo_encoder_prediction"):
+            self.geo_encoder_prediction.setup()
 
         if hasattr(self, "text_encoder") and hasattr(self, "geo_encoder"):
             # Configure optional extra projection so text embeddings match geo embeddings.
@@ -86,9 +92,11 @@ def _setup(self, stage: str) -> None:
                 raise ValueError(
                     "InferenceModel requires `num_classes` to build the prediction head."
                 )
-            self.prediction_head.set_dim(
-                input_dim=self.geo_encoder.output_dim, output_dim=self.num_classes
-            )
+            if hasattr(self, "geo_encoder_prediction"):
+                input_dim = self.geo_encoder_prediction.output_dim
+            else:
+                self.geo_encoder.output_dim
+            self.prediction_head.set_dim(input_dim=input_dim, output_dim=self.num_classes)
             self.prediction_head.setup()
         print("------------------------")
 
@@ -101,21 +109,31 @@ def _step(
         """Step forward computation of the model."""
         pass
 
+    def forward_text(self, text: list[str]) -> torch.Tensor:
+        batch = {"text": text}
+        return self.text_encoder(batch, "train")
+
+    def forward_geo(self, batched_eo) -> Tuple[torch.Tensor, torch.Tensor | None]:
+        geo_feats = self.geo_encoder(batched_eo)
+        pred = None
+        if hasattr(self, "prediction_head"):
+            if hasattr(self, "geo_encoder_prediction"):
+                geo_feats_pr = self.geo_encoder_prediction(batched_eo)
+                pred = self.prediction_head(geo_feats_pr)
+            else:
+                pred = self.prediction_head(geo_feats)
+        return geo_feats, pred
+
     @override
     def forward(
         self,
         batch: Dict[str, torch.Tensor],
-        mode: str = "train",
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """Model forward logic."""
-
-        # Embed modalities
+    ) -> Tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
         if hasattr(self, "geo_encoder"):
-            geo_feats = self.geo_encoder(batch)
+            geo_feats, pred = self.forward_geo(batch)
+
         if hasattr(self, "text_encoder"):
-            text_feats = self.text_encoder(batch, mode)
-        if hasattr(self, "prediction_head"):
-            pred_feats = self.prediction_head(geo_feats)
+            text_feats = self.text_encoder(batch, "test")
 
         # Change dtype of geo data if it does not match text dtype
         if (
@@ -125,7 +143,7 @@ def forward(
         ):
             geo_feats = geo_feats.to(text_feats.dtype)
 
-            return pred_feats, geo_feats, text_feats
+            return pred, geo_feats, text_feats
 
     def concept_similarities(self, geo_embeds, concept=None) -> torch.Tensor:
         # Get concept embeddings
@@ -188,16 +206,20 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
     pred_ckpt = torch.load(pred_ckpt_path, map_location="cpu", weights_only=False)
     align_ckpt = torch.load(align_ckpt_path, map_location="cpu", weights_only=False)
 
+    inference_hparams = {}
+    geo_encoder_prediction_flag = False
     # Sanity check: ensure geo encoder configs match.
-    align_ckpt["hyper_parameters"]["geo_encoder"] = pred_ckpt["hyper_parameters"].get(
-        "geo_encoder"
-    )
     if pred_ckpt["hyper_parameters"].get("geo_encoder") != align_ckpt["hyper_parameters"].get(
         "geo_encoder"
     ):
         log.warning("Geo encoder configs differ between checkpoints; results may be invalid.")
-        if input("Do you want to proceed? y/n").lower() == "n":
+        if input("Do you want to proceed? y/n").lower() != "y":
             return None
+        if input("Use separate geo_encoders for prediction and alignment? y/n").lower() == "y":
+            inference_hparams["geo_encoder_prediction"] = pred_ckpt["hyper_parameters"].get(
+                "geo_encoder"
+            )
+            geo_encoder_prediction_flag = True
 
     pred_trainable_modules = pred_ckpt["hyper_parameters"].get("trainable_modules", [])
     align_trainable_modules = align_ckpt["hyper_parameters"].get("trainable_modules", [])
@@ -209,7 +231,7 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
         raise ValueError("Models are not aligned: both checkpoints trained geo_encoder.")
 
     # Instantiate InferenceModel via hydra, using alignment encoder configs with prediction model head configs
-    inference_hparams = align_ckpt["hyper_parameters"]
+    inference_hparams.update(align_ckpt["hyper_parameters"])
     inference_hparams.update(
         {
             "_target_": "src.models.inference_model.InferenceModel",
@@ -217,6 +239,7 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
             "num_classes": pred_ckpt["hyper_parameters"].get("num_classes"),
         }
     )
+    inference_hparams["trainable_modules"] = None
     inference_hparams["text_encoder"]["hf_cache_dir"] = os.path.join(
         cfg.paths.cache_dir, "huggingface"
     )
@@ -228,10 +251,21 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
     text_state = {
         k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("text_encoder.")
     }
-    res = model.load_state_dict(text_state, strict=False)
-    log_model_loading("text_encoder", res)
+    if text_state:
+        res = model.load_state_dict(text_state, strict=False)
+        log_model_loading("text_encoder", res)
 
-    if cfg.training_order[0] == "prediction_model" and not geo_align_encoder_trained:
+    if geo_encoder_prediction_flag:
+        geo_state = {
+            k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
+        }
+        geo_pred_state = {
+            k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
+        }
+        if geo_pred_state:
+            res = model.load_state_dict(geo_pred_state, strict=False)
+            log_model_loading("geo_encoder_pred", res)
+    elif cfg.training_order[0] == "prediction_model" and not geo_align_encoder_trained:
         geo_state = {
             k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
         }
@@ -239,15 +273,17 @@ def merge_inference_model(cfg, save_ckpt=False) -> InferenceModel | None:
         geo_state = {
             k: v for k, v in align_ckpt["state_dict"].items() if k.startswith("geo_encoder.")
         }
-    res = model.load_state_dict(geo_state, strict=False)
-    log_model_loading("geo_encoder", res)
+    if geo_state:
+        res = model.load_state_dict(geo_state, strict=False)
+        log_model_loading("geo_encoder", res)
 
     # Load prediction head weights from predictive ckpt.
     head_state = {
         k: v for k, v in pred_ckpt["state_dict"].items() if k.startswith("prediction_head.")
     }
-    res = model.load_state_dict(head_state, strict=False)
-    log_model_loading("Predictive_head", res)
+    if head_state:
+        res = model.load_state_dict(head_state, strict=False)
+        log_model_loading("Predictive_head", res)
 
     # Save model
     if save_ckpt:

From cbb972e54a69c0d197cf4ff62c9b4ed04f1c0ec7 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:09:55 +0200
Subject: [PATCH 123/173]  Temp fix to get inference model working

---
 src/inference.py | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/src/inference.py b/src/inference.py
index dc15b50..aafbce6 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -36,8 +36,21 @@ def main(cfg: DictConfig) -> Optional[float]:
     # Otherwise merge model from two checkpoints
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
-
+    model.to("mps")
     # TODO: do what you need with the inference model
+    if cfg.data:
+        datamodule = hydra.utils.instantiate(cfg.get("data"))
+        datamodule.setup()
+
+        concepts = [
+            c["concept_caption"] for c in datamodule.caption_builder.__dict__["concepts"]
+        ]  # or other source of concepts
+        text_embeds = model.forward_text(concepts)
+
+        # per location (batching uses location text generation)
+        for d in datamodule.data_train:
+            geo_embeds, pred = model.forward_geo(d)
+            # TODO: do what you need with the inference model
 
     return
 

From 10c5d0b2cc8c7d4d8725484e5dc5166444fff12e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 13:21:17 +0200
Subject: [PATCH 124/173]  Temp fix to get inference model working

---
 configs/inference.yaml | 2 +-
 src/inference.py       | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/configs/inference.yaml b/configs/inference.yaml
index ef9fdd0..47ad5e4 100644
--- a/configs/inference.yaml
+++ b/configs/inference.yaml
@@ -22,7 +22,7 @@ alignment_ckpt_path: ${paths.checkpoint_root}/s2bms_alignment_ckpt/dmts2wpm_epoc
 # If set, inference.py will save a merged inference checkpoint you can reload with
 # `inference_ckpt_path`.
 #save_inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/${now:%Y-%m-%d}_${now:%H-%M-%S}.ckpt
-inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_12-05-05.ckpt
+inference_ckpt_path: ${paths.checkpoint_root}/s2bms_inference_model/2026-06-09_13-17-38.ckpt
 #save_inference_ckpt_path: null
 save_ckpt: true
 
diff --git a/src/inference.py b/src/inference.py
index aafbce6..f082056 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -37,6 +37,7 @@ def main(cfg: DictConfig) -> Optional[float]:
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
     model.to("mps")
+
     # TODO: do what you need with the inference model
     if cfg.data:
         datamodule = hydra.utils.instantiate(cfg.get("data"))
@@ -49,7 +50,8 @@ def main(cfg: DictConfig) -> Optional[float]:
 
         # per location (batching uses location text generation)
         for d in datamodule.data_train:
-            geo_embeds, pred = model.forward_geo(d)
+            b = {"eo": {"aef_avr": d["eo"]["aef_avr"].unsqueeze(0).to("mps")}}
+            geo_embeds, pred = model.forward_geo(b)
             # TODO: do what you need with the inference model
 
     return

From f20e950bc784c10429b0f69ee491be66fba099ba Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 14:15:08 +0200
Subject: [PATCH 125/173] Fix cache dir

---
 src/inference.py              | 2 +-
 src/models/inference_model.py | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/src/inference.py b/src/inference.py
index f082056..6de9d6c 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -32,7 +32,7 @@ def main(cfg: DictConfig) -> Optional[float]:
     # If a merged inference ckpt is provided, just load it.
     inference_ckpt_path = cfg.get("inference_ckpt_path")
     if inference_ckpt_path:
-        model = load_inference_model(inference_ckpt_path)
+        model = load_inference_model(inference_ckpt_path, cfg.paths.cache_dir)
     # Otherwise merge model from two checkpoints
     else:
         model = merge_inference_model(cfg, save_ckpt=True)
diff --git a/src/models/inference_model.py b/src/models/inference_model.py
index 717d51d..f07f839 100644
--- a/src/models/inference_model.py
+++ b/src/models/inference_model.py
@@ -173,13 +173,15 @@ def _is_prefix_trained(trainable_modules: list[str], prefix: str) -> bool:
     return any(m.split(".")[0] == prefix for m in trainable_modules)
 
 
-def load_inference_model(inference_ckpt_path: str) -> InferenceModel:
+def load_inference_model(inference_ckpt_path: str, cache_path: str | None) -> InferenceModel:
     """Loads inference model from a merged checkpoint.
 
     :param inference_ckpt_path: path to inference model weights
     :return: an InferenceModel with pre-trained weights
     """
     inference_ckpt = torch.load(inference_ckpt_path, map_location="cpu", weights_only=False)
+    if cache_path:
+        inference_ckpt["hyper_parameters"]["text_encoder"]["hf_cache_dir"] = cache_path
     model = hydra.utils.instantiate(inference_ckpt["hyper_parameters"])
     model.setup("inference")
     res = model.load_state_dict(inference_ckpt["state_dict"], strict=False)

From cd05ecdf70286b788f0a0dea86dd18e976bd3453 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 14:25:06 +0200
Subject: [PATCH 126/173] Add split file

---
 ...6-06-09-09-45-48_union_aef_tsr_val_test.pth | Bin 0 -> 385207 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth

diff --git a/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth b/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth
new file mode 100644
index 0000000000000000000000000000000000000000..d80085f84b33681b67c69031c6b33ad7c294bcb9
GIT binary patch
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z`3%S(J4#(caznC(sYjwP`3jUjT9kO@%AZO;E`_Q4UX*+d&lV-0RoSBCq_ah-$2#>p
zGJjt3{uL#ELuZSU+mk<)oLEuv?<BIt$(_%?P$uV4oVrKF$^Fb95lZ~d%AZQkwm5m5
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qph1JB|L(;<Q_Fwf^M7?U?tgo6`o-__#?LR_B>#)2{_-y_`+ozKYSWbf

literal 0
HcmV?d00001


From 467b56c0fa95b4c98fd8375766b335150bfcc45f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:35:57 +0200
Subject: [PATCH 127/173] Fix adopt encoder

---
 .../components/geo_encoders/adopt_encoder.py   | 18 +++++++++++++-----
 1 file changed, 13 insertions(+), 5 deletions(-)

diff --git a/src/models/components/geo_encoders/adopt_encoder.py b/src/models/components/geo_encoders/adopt_encoder.py
index 2a5bd88..969be07 100644
--- a/src/models/components/geo_encoders/adopt_encoder.py
+++ b/src/models/components/geo_encoders/adopt_encoder.py
@@ -1,5 +1,3 @@
-from typing import Dict, List, override
-
 import hydra
 import torch
 
@@ -19,18 +17,28 @@ def adopt_encoder(ckpt_path: str) -> BaseGeoEncoder:
     # Get skeleton
     geo_config = ckpt["hyper_parameters"].get("geo_encoder")
     encoder: BaseGeoEncoder = hydra.utils.instantiate(geo_config)
+
     print("---Adopted encoder------")
+    encoder.adopted = True
     encoder.setup()
     encoder.cfg_dict = geo_config
-    print("------------------------")
+    encoder.cfg_dict.update(
+        {
+            "adopted": True,
+            "save_weights": bool(
+                "geo_encoder" in "".join(ckpt["hyper_parameters"].get("trainable_modules", []))
+            ),
+        }
+    )
 
     # Load in the weights
     state_dict = {
-        k.replace("geo_encoder.", ""): v
+        k.replace("geo_encoder.", "", 1): v
         for k, v in ckpt["state_dict"].items()
         if "geo_encoder." in k
     }
     res = encoder.load_state_dict(state_dict, strict=False)
-    log_model_loading("geo_encoder_ckpt", res)
+    log_model_loading("geo_encoder_ckpt", res, True)
+    print("------------------------")
 
     return encoder

From f59c407766db35ee52f0e6d4fc2000171adf5977 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:36:49 +0200
Subject: [PATCH 128/173] If adopted, do not train

---
 src/models/components/geo_encoders/base_geo_encoder.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/models/components/geo_encoders/base_geo_encoder.py b/src/models/components/geo_encoders/base_geo_encoder.py
index 8711140..2b6062b 100644
--- a/src/models/components/geo_encoders/base_geo_encoder.py
+++ b/src/models/components/geo_encoders/base_geo_encoder.py
@@ -19,7 +19,9 @@ def __init__(self) -> None:
 
         self.allowed_geo_data_names: list[str] | None = None
         self.geo_data_name: str | None = None
+        self.adopted: bool = False
 
+    @final
     def update_configs(self, cfg):
         if len(self.cfg_dict) == 0:
             self.cfg_dict = cfg
@@ -41,6 +43,8 @@ def setup(self, verbose=1) -> List[str]:
             if verbose > 0:
                 print(f"Model set up with {self.__str__()}")
             self.setup_flag = True
+            if self.adopted:
+                trainable_modules = []
             return trainable_modules
 
     @abstractmethod

From 3627b2d571c1d89d7cf3ebe40d9f6b21c37a145d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:38:13 +0200
Subject: [PATCH 129/173] Move projector to new group of encoders: projectors
 and adapters

---
 .../projectors_adapters/base_encoder.py       | 65 +++++++++++++++++++
 .../mlp_projector.py                          |  7 +-
 tests/test_dynamic_gate_fusion_encoder.py     |  3 +-
 tests/test_gated_fusion_encoder.py            |  3 +-
 tests/test_geo_encoders.py                    |  2 +-
 5 files changed, 72 insertions(+), 8 deletions(-)
 create mode 100644 src/models/components/projectors_adapters/base_encoder.py
 rename src/models/components/{geo_encoders => projectors_adapters}/mlp_projector.py (86%)

diff --git a/src/models/components/projectors_adapters/base_encoder.py b/src/models/components/projectors_adapters/base_encoder.py
new file mode 100644
index 0000000..cfa7da1
--- /dev/null
+++ b/src/models/components/projectors_adapters/base_encoder.py
@@ -0,0 +1,65 @@
+from abc import ABC, abstractmethod
+from typing import Dict, List, final
+
+import torch
+from torch import nn
+
+
+class BaseEncoder(nn.Module, ABC):
+    def __init__(self) -> None:
+        super().__init__()
+
+        # Modules
+        self.output_dim: int | None = None
+        self.setup_flag: bool = False
+        self.cfg_dict: Dict = {}
+        self.input_dim: int | None = None
+        self.cfg_dict: Dict = {}
+
+    @final
+    def set_input_dim(self, input_dim: int) -> None:
+        self.input_dim = input_dim
+
+    @final
+    def setup(self, verbose=1) -> List[str]:
+        """Configures modules.
+
+        Gets called in model.setup() method. Returns names of any new module configured to be added
+        to the trainable modules list.
+        """
+        if self.setup_flag:
+            print(f"Module {self.__str__()} is already set up.")
+            return []
+        else:
+            trainable_modules = self._setup()
+            if verbose > 0:
+                print(f"Model set up with {self.__str__()}")
+            self.setup_flag = True
+            return trainable_modules
+
+    @abstractmethod
+    def _setup(self) -> List[str]:
+        """Configures modules and returns newly initialised, trainable module names."""
+        pass
+
+    @abstractmethod
+    def forward(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
+        pass
+
+    @property
+    def device(self) -> torch.device | None:
+        devices = {p.device for p in self.parameters()}
+        if len(devices) > 1:
+            raise RuntimeError("Encoder is on multiple devices")
+        elif len(devices) == 0:
+            return None
+        return devices.pop()
+
+    @property
+    def dtype(self) -> torch.dtype | None:
+        dtypes = {p.dtype for p in self.parameters()}
+        if len(dtypes) > 1:
+            raise RuntimeError("Encoder has multiple dtypes")
+        elif len(dtypes) == 0:
+            return None
+        return dtypes.pop()
diff --git a/src/models/components/geo_encoders/mlp_projector.py b/src/models/components/projectors_adapters/mlp_projector.py
similarity index 86%
rename from src/models/components/geo_encoders/mlp_projector.py
rename to src/models/components/projectors_adapters/mlp_projector.py
index a43316e..7517508 100644
--- a/src/models/components/geo_encoders/mlp_projector.py
+++ b/src/models/components/projectors_adapters/mlp_projector.py
@@ -3,10 +3,10 @@
 import torch
 from torch import nn
 
-from src.models.components.geo_encoders.base_geo_encoder import BaseGeoEncoder
+from src.models.components.projectors_adapters.base_encoder import BaseEncoder
 
 
-class MLPProjector(BaseGeoEncoder):
+class MLPProjector(BaseEncoder):
     def __init__(
         self,
         output_dim: int,
@@ -29,9 +29,6 @@ def _setup(self) -> List[str]:
         self.configure_nn()
         return ["net"]
 
-    def set_input_dim(self, input_dim: int) -> None:
-        self.input_dim = input_dim
-
     def configure_nn(self) -> None:
         """Configure the MLP network."""
         assert self.input_dim is not None, "input_dim must be defined"
diff --git a/tests/test_dynamic_gate_fusion_encoder.py b/tests/test_dynamic_gate_fusion_encoder.py
index 6dff786..30e4893 100644
--- a/tests/test_dynamic_gate_fusion_encoder.py
+++ b/tests/test_dynamic_gate_fusion_encoder.py
@@ -17,6 +17,7 @@
 from src.models.components.geo_encoders.encoder_wrapper import EncoderWrapper
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
 
+
 # ---------------------------------------------------------------------------
 # Minimal stub encoder for testing — outputs a fixed-size embedding.
 # ---------------------------------------------------------------------------
@@ -169,7 +170,7 @@ def test_with_tabular_encoder():
 
     Both branches projected to the same output_dim (32) via per-branch projectors.
     """
-    from src.models.components.geo_encoders.mlp_projector import MLPProjector
+    from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
     tabular_dim = 23
     tab_enc = TabularEncoder(output_dim=32)
diff --git a/tests/test_gated_fusion_encoder.py b/tests/test_gated_fusion_encoder.py
index 9fc2169..4593d0d 100644
--- a/tests/test_gated_fusion_encoder.py
+++ b/tests/test_gated_fusion_encoder.py
@@ -15,6 +15,7 @@
 from src.models.components.geo_encoders.encoder_wrapper import EncoderWrapper
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
 
+
 # ---------------------------------------------------------------------------
 # Minimal stub encoder for testing — outputs a fixed-size embedding.
 # ---------------------------------------------------------------------------
@@ -137,7 +138,7 @@ def test_with_tabular_encoder():
 
     Both branches projected to the same output_dim (32) via per-branch projectors.
     """
-    from src.models.components.geo_encoders.mlp_projector import MLPProjector
+    from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
     tabular_dim = 23
     tab_enc = TabularEncoder(output_dim=32)
diff --git a/tests/test_geo_encoders.py b/tests/test_geo_encoders.py
index 4f062fb..d4f9add 100644
--- a/tests/test_geo_encoders.py
+++ b/tests/test_geo_encoders.py
@@ -3,8 +3,8 @@
 from src.models.components.geo_encoders.average_encoder import AverageEncoder
 from src.models.components.geo_encoders.cnn_encoder import CNNEncoder
 from src.models.components.geo_encoders.geoclip import GeoClipCoordinateEncoder
-from src.models.components.geo_encoders.mlp_projector import MLPProjector
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
+from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
 
 # @pytest.mark.slow

From ea84d00b0a1dbd1cc7a2076ec4200b2914973b47 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:38:59 +0200
Subject: [PATCH 130/173] Introduce adapters to the alignment model

---
 src/models/base_model.py                      | 15 +++++--
 .../components/pred_heads/base_pred_head.py   |  7 ++++
 src/models/text_alignment_model.py            | 41 ++++++++++++++++---
 3 files changed, 55 insertions(+), 8 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 9a3259c..b7b6f47 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -218,12 +218,16 @@ def update_configs(self, cfg):
         """Update hyper-parameters from the model."""
         if hasattr(self, "geo_encoder"):
             self.geo_encoder.update_configs(cfg["geo_encoder"])
+        if hasattr(self, "geo_adapter"):
+            self.geo_adapter.cfg_dict = cfg["geo_adapter"]
 
         if hasattr(self, "text_encoder"):
             self.text_encoder.cfg_dict = cfg["text_encoder"]
+        if hasattr(self, "text_adapter"):
+            self.text_adapter.cfg_dict = cfg["text_adapter"]
 
-        if hasattr(self, "prediction_head"):
-            self.prediction_head.cfg_dict = cfg["prediction_head"]
+        if self.prediction_head and cfg.get("prediction_head"):
+            self.prediction_head.update_configs(cfg["prediction_head"])
 
     def on_save_checkpoint(self, checkpoint):
         """Save checkpoint.
@@ -263,11 +267,16 @@ def on_save_checkpoint(self, checkpoint):
 
         if hasattr(self, "geo_encoder"):
             checkpoint["hyper_parameters"]["geo_encoder"] = self.geo_encoder.cfg_dict
+        if hasattr(self, "geo_adapter"):
+            checkpoint["hyper_parameters"]["geo_adapter"] = self.geo_adapter.cfg_dict
+
         if hasattr(self, "prediction_head"):
             checkpoint["hyper_parameters"]["prediction_head"] = self.prediction_head.cfg_dict
+
         if hasattr(self, "text_encoder"):
             checkpoint["hyper_parameters"]["text_encoder"] = self.text_encoder.cfg_dict
-
+        if hasattr(self, "text_adapter"):
+            checkpoint["hyper_parameters"]["text_adapter"] = self.text_adapter.cfg_dict
         return
 
     def on_load_checkpoint(self, checkpoint):
diff --git a/src/models/components/pred_heads/base_pred_head.py b/src/models/components/pred_heads/base_pred_head.py
index 29f19ae..60d493b 100644
--- a/src/models/components/pred_heads/base_pred_head.py
+++ b/src/models/components/pred_heads/base_pred_head.py
@@ -77,3 +77,10 @@ def dtype(self) -> torch.dtype | None:
         elif len(dtypes) == 0:
             return None
         return dtypes.pop()
+
+    @final
+    def update_configs(self, cfg):
+        if len(self.cfg_dict) == 0:
+            self.cfg_dict = cfg
+        else:
+            print("Configs for prediction head not updated")
diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 02ead9a..0e14d7c 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -10,9 +10,8 @@
     RetrievalContrastiveValidation,
 )
 from src.models.components.metrics.metrics_wrapper import MetricsWrapper
-from src.models.components.text_encoders.base_text_encoder import (
-    BaseTextEncoder,
-)
+from src.models.components.projectors_adapters.base_encoder import BaseEncoder
+from src.models.components.text_encoders.base_text_encoder import BaseTextEncoder
 
 
 class TextAlignmentModel(BaseModel):
@@ -25,6 +24,8 @@ def __init__(
         scheduler: torch.optim.lr_scheduler,
         loss_fn: BaseLossFn,
         metrics: MetricsWrapper,
+        geo_adapter: BaseEncoder | None = None,
+        text_adapter: BaseEncoder | None = None,
         num_classes: int | None = None,
         tabular_dim: int | None = None,
         ks: list[int] | None = [5, 10, 15],
@@ -45,6 +46,7 @@ def __init__(
         :param match_to_geo: whether to match dimensions of text encoder to geo_encoder or visa-
             versa
         """
+
         super().__init__(
             trainable_modules=trainable_modules,
             geo_encoder=geo_encoder,
@@ -58,6 +60,8 @@ def __init__(
             tabular_dim=tabular_dim,
         )
 
+        self.geo_adapter = geo_adapter
+        self.text_adapter = text_adapter
         # Metrics
         self.ks = ks
         self.log_kwargs = dict(on_step=False, on_epoch=True, prog_bar=True, sync_dist=True)
@@ -75,12 +79,35 @@ def _setup(self, stage: str = "fit") -> None:
         # Set up encoders and missing adapters/projectors
         print("-------Model------------")
         new_modules = [f"geo_encoder.{i}" for i in self.geo_encoder.setup() or []]
+
+        if self.geo_adapter:
+            self.geo_adapter.set_input_dim(self.geo_encoder.output_dim)
+            new_modules.extend([f"geo_adapter.{i}" for i in self.geo_adapter.setup() or []])
+
         new_modules.extend([f"text_encoder.{i}" for i in self.text_encoder.setup() or []])
+        if self.text_adapter:
+            self.text_adapter.set_input_dim(self.text_encoder.input_dim)
+            new_modules.extend([f"text_adapter.{i}" for i in self.text_adapter.setup() or []])
+
         self.trainable_modules.extend(new_modules)
 
         # Extra projector for text encoder if eo and text dim not match
-        if self.geo_encoder.output_dim != self.text_encoder.output_dim:
-            if self.match_to_geo:
+        geo_branch_dim = (
+            self.geo_adapter.output_dim if self.geo_adapter else self.geo_encoder.output_dim
+        )
+        text_branch_dim = (
+            self.text_adapter.output_dim if self.text_adapter else self.text_encoder.output_dim
+        )
+
+        if geo_branch_dim != text_branch_dim:
+            if self.geo_adapter or self.text_adapter:
+                print(
+                    f"You opted to use: {'geo' if self.geo_adapter else '' and 'text' if self.text_adapter else ''} adapter(s)",
+                    "but you miss-configured output dimensions:"
+                    f"geo:{geo_branch_dim} vs text: {text_branch_dim}",
+                    "Please try again.",
+                )
+            elif self.match_to_geo:
                 self.text_encoder.add_projector(projected_dim=self.geo_encoder.output_dim)
                 self.trainable_modules.append("text_encoder.extra_projector")
             else:
@@ -137,7 +164,11 @@ def forward(
 
         # Embed modalities
         geo_feats = self.geo_encoder(batch)
+        if self.geo_adapter:
+            geo_feats = self.geo_adapter(geo_feats)
         text_feats = self.text_encoder(batch, mode)
+        if self.text_adapter:
+            text_feats = self.text_adapter(text_feats)
 
         # Change dtype of geo data if it does not match text dtype
         if geo_feats.dtype != text_feats.dtype:

From 6a2f5f860826081d361048d2165ebf5afb836970 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:39:32 +0200
Subject: [PATCH 131/173] Print not log when adopting encoder

---
 src/utils/logging_utils.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/src/utils/logging_utils.py b/src/utils/logging_utils.py
index f23f4df..18efa73 100644
--- a/src/utils/logging_utils.py
+++ b/src/utils/logging_utils.py
@@ -70,16 +70,25 @@ def _group_keys(keys: list[str]) -> dict[str, list[str]]:
     return grouped
 
 
-def log_model_loading(tag: str, result) -> None:
+def log_model_loading(tag: str, result, printing=False) -> None:
     """Log missing/unexpected keys from `load_state_dict`."""
     missing_keys, unexpected_keys = result
     if missing_keys:
         grouped = _group_keys(list(missing_keys))
         summary = {k: len(v) for k, v in grouped.items()}
-        log.warning(f"[{tag}] Missing keys: {summary}")
+        if printing:
+            print(f"[{tag}] Missing keys: {summary}")
+        else:
+            log.warning(f"[{tag}] Missing keys: {summary}")
     if unexpected_keys:
         grouped = _group_keys(list(unexpected_keys))
         summary = {k: len(v) for k, v in grouped.items()}
-        log.warning(f"[{tag}] Unexpected keys: {summary}")
+        if printing:
+            print(f"[{tag}] Unexpected keys: {summary}")
+        else:
+            log.warning(f"[{tag}] Unexpected keys: {summary}")
     if not missing_keys and not unexpected_keys:
-        log.info(f"[{tag}] Module weights loaded successfully")
+        if printing:
+            print(f"[{tag}] Module weights loaded successfully")
+        else:
+            log.info(f"[{tag}] Module weights loaded successfully")

From f0497c31dfd29eb9cd6b93553972a43945d922dd Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:40:00 +0200
Subject: [PATCH 132/173] wandb logging updates

---
 src/utils/experiment_tracking.py | 28 +++++++++++++++++++++++-----
 1 file changed, 23 insertions(+), 5 deletions(-)

diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index a19ccc2..2654758 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -1,6 +1,5 @@
 import os
 import re
-import shutil
 from pathlib import Path
 
 import pandas as pd
@@ -8,11 +7,25 @@
 from omegaconf import DictConfig
 
 # Which wandb projects to log runs from
-PROJECTS = ["s2bms_prediction", "s2bms_alignment"]  # Important to keep updating!
+PROJECTS = ["s2bms_prediction"]  # , "s2bms_alignment"]  # Important to keep updating!
 EXPERIMENT_TRACKER_NAME = "experiment_tracker.csv"
 ENTITY = "aether_xai"
 
 
+def update_wandb_run_id(run_id, project_name, updates_dict):
+    api = wandb.Api()
+    entity = ENTITY
+
+    run = api.run(f"{entity}/{project_name}/{run_id}")
+
+    # Update the config
+    for key, value in updates_dict.items():
+        run.config[key] = value
+
+    run.config.save()
+    print(f"Successfully updated run {run_id}")
+
+
 def experiment_check(cfg: DictConfig):
     """Check if requested through configs experiments has already been run."""
     # Get experiments from wandb (cross-device sync)
@@ -102,7 +115,12 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                         else:
                             data_name = "satclip"
                     else:
-                        data_name = f"{k}_{data_dict[k]['size']}"
+                        ks = list(data_dict.keys())
+                        ks_new = [
+                            f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
+                            for k in ks
+                        ]
+                        data_name = "-".join(map(str, ks_new))
                     run.summary.update({"data_used": data_name})
 
                 # Add missing experiments to the table
@@ -209,6 +227,6 @@ def clean_local_ckpts(cfg: DictConfig, df: pd.DataFrame) -> None:
         if str(local_ckpt) not in keep:
             print(f"Do you want to remove {local_ckpt}? (y/n)")
             answer = input()
-            if answer != "y":
-                shutil.rmtree(local_ckpt)
+            if answer == "y":
+                os.remove(str(local_ckpt))
                 print(f"Removed {local_ckpt}.")

From 8d11ce234395132d9e6c61332aa6064fcbad42a4 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 9 Jun 2026 16:40:26 +0200
Subject: [PATCH 133/173] projectors adapters importable

---
 src/models/components/projectors_adapters/__init__.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 src/models/components/projectors_adapters/__init__.py

diff --git a/src/models/components/projectors_adapters/__init__.py b/src/models/components/projectors_adapters/__init__.py
new file mode 100644
index 0000000..e69de29

From 0765c6bf72926b9518cc636b99479d63159723f9 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:38:22 +0200
Subject: [PATCH 134/173] Validation loss based on random selection of
 templates

---
 src/data/base_caption_builder.py      | 30 +++++++++++++++++++++++----
 src/data/butterfly_caption_builder.py | 14 +++++++++----
 src/data/collate_fns.py               |  2 +-
 3 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 875cabc..a985c9b 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -8,18 +8,26 @@
 import torch
 
 from src.data.base_dataset import BaseDataset
+from src.utils.errors import IllegalArgumentCombination
 
 
 class BaseCaptionBuilder(ABC):
     def __init__(
-        self, templates_fname: str, concepts_fname: str, data_dir: str, seed: int
+        self,
+        templates_fname: str,
+        concepts_fname: str,
+        data_dir: str,
+        seed: int,
+        n_captions_for_validation: int | str = "all",
     ) -> None:
         """Interface of caption builder class for converting numerical auxiliary data values into
         textual descriptions from provided caption templates.
 
         :param templates_fname: path to a json file with caption templates.
+        :param concepts_fname: path to a json file with concepts.
         :param data_dir: directory where data is stored.
         :param seed: random seed.
+        :param n_captions_for_validation: number of captions to randomly sample for validation
         """
 
         self.data_dir = data_dir
@@ -39,6 +47,15 @@ def __init__(
         self.seed = seed
         random.seed(self.seed)
 
+        if n_captions_for_validation == "all":
+            self.n = self.__len__
+        elif n_captions_for_validation > len(self):
+            raise IllegalArgumentCombination(
+                f"Requested {n_captions_for_validation} captions exceeds template dictionary size"
+            )
+        else:
+            self.n = n_captions_for_validation
+
     @final
     def __len__(self):
         """Number of caption templates."""
@@ -111,15 +128,20 @@ def random(self, aux_values) -> List[str]:
 
         return formatted_rows
 
-    def all(self, aux_values) -> List[str]:
-        """Return a list of captions from all available templates."""
+    def sample_multiple_or_all(self, aux_values):
+        """Return self.n captions from randomly sampled templates for each data point."""
         formatted_rows = []
         for i in range(0, len(aux_values["aux"])):
             descriptions = []
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
 
-            for template_idx in range(0, len(self)):
+            template_ids = random.choices(
+                range(len(self.templates)),
+                k=self.n,
+            )
+
+            for template_idx in template_ids:
                 descriptions.append(
                     self._build_from_template(template_idx, aux=row_aux, top=row_top)
                 )
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index af23298..786a6fd 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -1,12 +1,11 @@
 import os
-from typing import Any, List, override
+from typing import List, override
 
 import pandas as pd
 import torch
 
 from src.data.base_caption_builder import (
     BaseCaptionBuilder,
-    get_adjective_for_percentage,
     sample_adjective_for_percentage,
 )
 from src.data.base_dataset import BaseDataset
@@ -18,9 +17,16 @@
 
 class ButterflyCaptionBuilder(BaseCaptionBuilder):
     def __init__(
-        self, templates_fname: str, concepts_fname: str, data_dir: str, seed: int
+        self,
+        templates_fname: str,
+        concepts_fname: str,
+        data_dir: str,
+        seed: int,
+        n_captions_for_validation: int | str = "all",
     ) -> None:
-        super().__init__(templates_fname, concepts_fname, data_dir, seed)
+        super().__init__(
+            templates_fname, concepts_fname, data_dir, seed, n_captions_for_validation
+        )
 
     @override
     def sync_with_dataset(self, dataset: BaseDataset) -> None:
diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 1a008ab..9cfe811 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -43,6 +43,6 @@ def collate_fn(
     if mode == "train":
         batch_collected["text"] = caption_builder.random(batch_collected["aux"])
     else:
-        batch_collected["text"] = caption_builder.all(batch_collected["aux"])
+        batch_collected["text"] = caption_builder.sample_multiple_or_all(batch_collected["aux"])
 
     return batch_collected

From c29972fb629412a7db84dcf08f8d714792564a11 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:40:13 +0200
Subject: [PATCH 135/173] fix config saving bug for missing parts

---
 src/models/base_model.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index b7b6f47..170ea6d 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -218,15 +218,19 @@ def update_configs(self, cfg):
         """Update hyper-parameters from the model."""
         if hasattr(self, "geo_encoder"):
             self.geo_encoder.update_configs(cfg["geo_encoder"])
-        if hasattr(self, "geo_adapter"):
+        if hasattr(self, "geo_adapter") and self.geo_adapter is not None:
             self.geo_adapter.cfg_dict = cfg["geo_adapter"]
 
         if hasattr(self, "text_encoder"):
             self.text_encoder.cfg_dict = cfg["text_encoder"]
-        if hasattr(self, "text_adapter"):
+        if hasattr(self, "text_adapter") and self.text_adapter is not None:
             self.text_adapter.cfg_dict = cfg["text_adapter"]
 
-        if self.prediction_head and cfg.get("prediction_head"):
+        if (
+            hasattr(self, "prediction_head")
+            and self.prediction_head
+            and cfg.get("prediction_head")
+        ):
             self.prediction_head.update_configs(cfg["prediction_head"])
 
     def on_save_checkpoint(self, checkpoint):

From 6b78516661cd3b0c954f1e47df5b96e44d95e154 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:41:03 +0200
Subject: [PATCH 136/173] Fix wandb interactions with alignment project

---
 src/utils/experiment_tracking.py | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index 2654758..8623a2f 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -7,7 +7,7 @@
 from omegaconf import DictConfig
 
 # Which wandb projects to log runs from
-PROJECTS = ["s2bms_prediction"]  # , "s2bms_alignment"]  # Important to keep updating!
+PROJECTS = ["s2bms_prediction", "s2bms_alignment"]  # Important to keep updating!
 EXPERIMENT_TRACKER_NAME = "experiment_tracker.csv"
 ENTITY = "aether_xai"
 
@@ -53,6 +53,7 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
     # Get existing experiment table
     df, df_path = open_experiment_df(cfg)
     ids = list(df.run_id) if len(df) > 0 else []
+    df_len = len(df)
 
     # Connect to wandb api
     api = wandb.Api()
@@ -92,9 +93,10 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                 if best_val_loss is None:
                     if len(results) > 0:
                         loss_per_epoch = results.groupby("epoch")["val_loss"].mean()
-                        if best_epoch != loss_per_epoch.idxmin():
-                            raise ValueError()
-                        best_val_loss = loss_per_epoch.min().item()
+                        if best_epoch:
+                            best_val_loss = loss_per_epoch[best_epoch]
+                        else:
+                            best_val_loss = loss_per_epoch.min().item()
                         run.summary.update({"best_val_loss": best_val_loss})
                 if project == "s2bms_prediction" and best_val_mse_loss is None:
                     mse_loss_per_epoch = results.groupby("epoch")["val_mse_loss"].mean()
@@ -137,9 +139,12 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                 )
 
     # Return experiment df
-    if len(df) > 0:
+    if df_len - len(df) > 0:
         print(f"Saved {len(runs_list)} runs to {df_path}.")
         return df
+    if len(df) > 0:
+        print("No runs updated.")
+        return df
     else:
         print("No runs found.")
         return None

From 274948a70e63e568704daebbc54c996ad514a8c6 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 10 Jun 2026 10:42:46 +0200
Subject: [PATCH 137/173] Remove extra projector for adapters/projectors

---
 src/models/components/projectors_adapters/mlp_projector.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/src/models/components/projectors_adapters/mlp_projector.py b/src/models/components/projectors_adapters/mlp_projector.py
index 7517508..1822038 100644
--- a/src/models/components/projectors_adapters/mlp_projector.py
+++ b/src/models/components/projectors_adapters/mlp_projector.py
@@ -46,6 +46,4 @@ def configure_nn(self) -> None:
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         feats = self.net(x)
-        if self.extra_projector:
-            feats = self.extra_projector(feats)
         return feats

From 20de6fe5a17ec254ef7ef0c13acf9c38f8b98418 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Mon, 15 Jun 2026 08:54:42 +0200
Subject: [PATCH 138/173] Embed concepts once model is put on correct device

---
 src/models/text_alignment_model.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 0e14d7c..9c9b792 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -114,9 +114,12 @@ def _setup(self, stage: str = "fit") -> None:
                 self.geo_encoder.add_projector(projected_dim=self.text_encoder.output_dim)
                 self.trainable_modules.append("geo_encoder.extra_projector")
 
+        print("------------------------")
+
+    def on_fit_start(self):
         # Configure contrastive retrieval evaluation
         self.setup_retrieval_evaluation(verbose=0)
-        print("------------------------")
+        print("Retrieval evaluation configured")
 
     def setup_retrieval_evaluation(
         self,
@@ -153,6 +156,7 @@ def setup_retrieval_evaluation(
         with torch.inference_mode():
             self.concept_embeds = self.text_encoder({"text": self.concepts}, mode="train")
             self.concept_embeds = F.normalize(self.concept_embeds, dim=1)
+            self.concept_embeds = self.concept_embeds.to(self.device)
 
     @override
     def forward(

From a9220f360fa2156c207559179d16bf4cd25487dc Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:10:37 +0200
Subject: [PATCH 139/173] New data splits and their explanations

---
 ...2bms+s2bms-unlabelled-2026-06-02-0918.pth} | Bin 294641 -> 294459 bytes
 ...06-11-15-23-51_union_aef_tsr_val_test.pth} | Bin 385207 -> 385437 bytes
 ...ms-unlabelled-20260529_2026-05-29-1433.pth | Bin 543245 -> 0 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 rename data/s2bms/splits/{split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth => split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth} (99%)
 rename data/s2bms/splits/{split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth => split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth} (99%)
 delete mode 100644 data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
similarity index 99%
rename from data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
rename to data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
index 429e3b22f30eea24bd0c4487cf24133330d28780..b1f1cdf1466eff9f3790b2cca8cd82ee3c3b6e80 100644
GIT binary patch
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diff --git a/data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
similarity index 99%
rename from data/s2bms/splits/split_indices_s2bms_2026-06-09-09-45-48_union_aef_tsr_val_test.pth
rename to data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
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diff --git a/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth b/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth
deleted file mode 100644
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OpXssTe--ib-TwmqV*51!


From 58a179d161140714d34d92be00764bafd981a44d Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:12:12 +0200
Subject: [PATCH 140/173] Seaborn for visualisation

---
 pyproject.toml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/pyproject.toml b/pyproject.toml
index 4e39772..44eed0a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,6 +50,7 @@ create-data = [
 
 visualisation = [
     "folium>=0.20.0",
+    "seaborn>=0.13.2",
 ]
 
 [tool.pytest.ini_options]

From 706e4e899cf33848a7137f5ab5e800270ad084e5 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:12:49 +0200
Subject: [PATCH 141/173] Fix cache dir to be in the data folder

---
 configs/paths/local.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/configs/paths/local.yaml b/configs/paths/local.yaml
index c3683f3..c7e598e 100644
--- a/configs/paths/local.yaml
+++ b/configs/paths/local.yaml
@@ -23,4 +23,4 @@ work_dir: ${hydra:runtime.cwd}
 
 # huggingface cache directory
 # can be overridden via HF_HOME environment variable
-huggingface_cache: ${oc.env:HF_HOME,${paths.root_dir}/.cache/huggingface}
+huggingface_cache: ${oc.env:HF_HOME,${paths.cache_dir}/.cache/huggingface}

From 57d0f95a88c6efaae51838c850f6c7262c9ba21f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:13:27 +0200
Subject: [PATCH 142/173] Return standardised aux values if requested

---
 src/data/collate_fns.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 9cfe811..06675b5 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -32,6 +32,10 @@ def collate_fn(
         batch_collected["aux"] = {
             k: smart_stack([item["aux"][k] for item in batch]) for k in batch[0]["aux"].keys()
         }
+        if caption_builder.stats is not None:
+            batch_collected["aux"]["aux_standardized"] = (
+                batch_collected["aux"]["aux"] - caption_builder.means
+            ) / caption_builder.stds
 
     if batch[0].get("target") is not None:
         batch_collected["target"] = smart_stack([item["target"] for item in batch])

From a57918417202dc9712b3f0a584e58f6ea2a94839 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:13:50 +0200
Subject: [PATCH 143/173] Return column ids used in the location template

---
 src/data/collate_fns.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index 06675b5..bf05aca 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -49,4 +49,9 @@ def collate_fn(
     else:
         batch_collected["text"] = caption_builder.sample_multiple_or_all(batch_collected["aux"])
 
+    # If requested to return aux_ids, recompile stacks
+    if caption_builder.return_aux_ids:
+        batch_collected["text_aux_ids"] = batch_collected["text"][1]
+        batch_collected["text"] = batch_collected["text"][0]
+
     return batch_collected

From 40623f0a5291827245c231621b67cd68852f6642 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:28:18 +0200
Subject: [PATCH 144/173] Add statistics and return aux ids for caption

---
 src/data/base_caption_builder.py      | 109 ++++++++++++++++++--------
 src/data/butterfly_caption_builder.py |  31 +++++++-
 2 files changed, 108 insertions(+), 32 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index a985c9b..50c4afd 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -3,7 +3,7 @@
 import random
 import re
 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, final
+from typing import Any, Dict, List, Tuple, final
 
 import torch
 
@@ -19,6 +19,8 @@ def __init__(
         data_dir: str,
         seed: int,
         n_captions_for_validation: int | str = "all",
+        return_aux_ids: bool = False,
+        stats_file: str | None = None,
     ) -> None:
         """Interface of caption builder class for converting numerical auxiliary data values into
         textual descriptions from provided caption templates.
@@ -28,6 +30,8 @@ def __init__(
         :param data_dir: directory where data is stored.
         :param seed: random seed.
         :param n_captions_for_validation: number of captions to randomly sample for validation
+        :param return_aux_ids: whether to return auxiliary column ids.
+        :param stats_file: path to a json file with statistics of the aux cols (train split).
         """
 
         self.data_dir = data_dir
@@ -56,6 +60,11 @@ def __init__(
         else:
             self.n = n_captions_for_validation
 
+        self.return_aux_ids = return_aux_ids
+
+        if stats_file:
+            self.stats = json.load(open(stats_file))
+
     @final
     def __len__(self):
         """Number of caption templates."""
@@ -106,48 +115,86 @@ def _build_from_template(
         """Build caption text from template and row of auxiliary data."""
         pass
 
-    def random(self, aux_values) -> List[str]:
-        """Return a caption from a randomly sampled template for each data point."""
-        formatted_rows = []
+    def random(self, aux_values) -> Tuple[List[str], List[int] | None]:
+        """Return a caption per location from a randomly sampled template for each data point.
 
+        :param aux_values: a batch of auxiliary values to use for random sampling.
+        :return: a batch of text captions and optionally aux col ids used for each of the caption.
+        """
         batch_size = len(aux_values["aux"])
 
-        template_ids = random.choices(
-            range(len(self.templates)),
-            k=batch_size,
-        )
-        for (
-            i,
-            template_idx,
-        ) in enumerate(template_ids):
+        # Location captions holders
+        formatted_location_captions = []
+
+        # Ids of used aux col ids per template (location)
+        if self.return_aux_ids:
+            ids = []
+
+        # Sample templates
+        template_ids = random.choices(range(len(self.templates)), k=batch_size)
+        for i, template_idx in enumerate(template_ids):
+            # Get aux and top values per location
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
-            formatted_rows.append(
-                self._build_from_template(template_idx, aux=row_aux, top=row_top)
-            )
 
-        return formatted_rows
+            # Get filled in template for location
+            if self.return_aux_ids:
+                filled_template, template_ids = self._build_from_template(
+                    template_idx, aux=row_aux, top=row_top
+                )
+                ids.append(template_ids)
+            else:
+                filled_template = self._build_from_template(template_idx, aux=row_aux, top=row_top)
+            formatted_location_captions.append(filled_template)
+
+        if self.return_aux_ids:
+            return formatted_location_captions, ids
+        return formatted_location_captions
+
+    def sample_multiple_or_all(self, aux_values) -> Tuple[List[str], List[int] | None]:
+        """Return self.n captions from randomly sampled templates for each data point.
 
-    def sample_multiple_or_all(self, aux_values):
-        """Return self.n captions from randomly sampled templates for each data point."""
-        formatted_rows = []
-        for i in range(0, len(aux_values["aux"])):
-            descriptions = []
+        :param aux_values: a batch of auxiliary values to use for random sampling.
+        :return: a batch of text captions and optionally aux col ids used for each of the caption.
+        """
+        batch_size = len(aux_values["aux"])
+
+        # Location captions holders
+        formatted_location_captions = []
+
+        # Ids of used aux col ids per template (location)
+        if self.return_aux_ids:
+            ids = []
+
+        for i in range(0, batch_size):
+            # Get aux and top values per location
             row_aux = aux_values["aux"][i]
             row_top = aux_values.get("top")[i] if aux_values.get("top") else None
 
-            template_ids = random.choices(
-                range(len(self.templates)),
-                k=self.n,
-            )
+            # Sample templates
+            template_ids = random.choices(range(len(self.templates)), k=self.n)
 
+            # Get filled in templates for location
+            filled_in_location_templates = []
+            ids_per_location = []
             for template_idx in template_ids:
-                descriptions.append(
-                    self._build_from_template(template_idx, aux=row_aux, top=row_top)
-                )
-            formatted_rows.append(descriptions)
-
-        return formatted_rows
+                if self.return_aux_ids:
+                    filled_template, template_ids = self._build_from_template(
+                        template_idx, aux=row_aux, top=row_top
+                    )
+                    ids_per_location.extend(filled_template)
+                else:
+                    filled_template = self._build_from_template(
+                        template_idx, aux=row_aux, top=row_top
+                    )
+                filled_in_location_templates.append(filled_template)
+
+            if self.return_aux_ids:
+                ids.append(template_ids)
+            formatted_location_captions.append(filled_in_location_templates)
+        if self.return_aux_ids:
+            return formatted_location_captions, ids
+        return formatted_location_captions
 
     def sync_concepts(self) -> List[str]:
         for concept in self.concepts:
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index 786a6fd..dd3ae3a 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -23,9 +23,17 @@ def __init__(
         data_dir: str,
         seed: int,
         n_captions_for_validation: int | str = "all",
+        return_aux_ids: bool = False,
+        stats_file: str | None = None,
     ) -> None:
         super().__init__(
-            templates_fname, concepts_fname, data_dir, seed, n_captions_for_validation
+            templates_fname,
+            concepts_fname,
+            data_dir,
+            seed,
+            n_captions_for_validation,
+            return_aux_ids,
+            stats_file,
         )
 
     @override
@@ -51,6 +59,20 @@ def sync_with_dataset(self, dataset: BaseDataset) -> None:
                     "units": units,
                 }
 
+        # If auxiliary value statistics are provided, make them into tensors
+        if self.stats:
+            max_id = len(aux_columns) - 1
+            means = torch.zeros(max_id + 1)
+            stds = torch.ones(max_id + 1)
+
+            for name, stats in self.stats.items():
+                idx = self.column_to_metadata_map["aux"][name]["id"]
+                means[idx] = stats["mean"]
+                stds[idx] = stats["std"]
+
+            self.means = means
+            self.stds = stds + 1e-8
+
         self.sync_concepts()
 
     def get_corine_column_keys(self):
@@ -119,6 +141,9 @@ def _build_from_template(
         template = self.templates[template_idx]
         tokens = self.tokens_in_template[template_idx]
         replacements = {}
+        if self.return_aux_ids:
+            ids = []
+
         for token in tokens:
             init_token = token
             if "top" in token:
@@ -132,6 +157,8 @@ def _build_from_template(
                 )
 
             idx = values_dict["id"]
+            if self.return_aux_ids:
+                ids.append(idx)
             value = aux[idx].item()
 
             formatted_desc = values_dict["description"].lower() or ""
@@ -149,6 +176,8 @@ def _build_from_template(
             replacements[init_token] = formatted_desc
 
         template = self._fill(template, replacements)
+        if self.return_aux_ids:
+            return template, ids
         return template
 
 

From 936f40347aea42dccd3729e30d077033b721cd46 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:37:16 +0200
Subject: [PATCH 145/173] make geo and text adapters not logged in hparams

---
 src/models/base_model.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/src/models/base_model.py b/src/models/base_model.py
index 170ea6d..44d65a4 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -45,7 +45,9 @@ def __init__(
         self.save_hyperparameters(
             ignore=[
                 "geo_encoder",
+                "geo_adapter",
                 "text_encoder",
+                "text_adapter",
                 "prediction_head",
                 "optimizer",
                 "scheduler",
@@ -269,17 +271,17 @@ def on_save_checkpoint(self, checkpoint):
             }
         )
 
-        if hasattr(self, "geo_encoder"):
+        if hasattr(self, "geo_encoder") and self.geo_encoder is not None:
             checkpoint["hyper_parameters"]["geo_encoder"] = self.geo_encoder.cfg_dict
-        if hasattr(self, "geo_adapter"):
+        if hasattr(self, "geo_adapter") and self.geo_adapter is not None:
             checkpoint["hyper_parameters"]["geo_adapter"] = self.geo_adapter.cfg_dict
 
-        if hasattr(self, "prediction_head"):
+        if hasattr(self, "prediction_head") and self.prediction_head is not None:
             checkpoint["hyper_parameters"]["prediction_head"] = self.prediction_head.cfg_dict
 
-        if hasattr(self, "text_encoder"):
+        if hasattr(self, "text_encoder") and self.text_encoder is not None:
             checkpoint["hyper_parameters"]["text_encoder"] = self.text_encoder.cfg_dict
-        if hasattr(self, "text_adapter"):
+        if hasattr(self, "text_adapter") and self.text_adapter is not None:
             checkpoint["hyper_parameters"]["text_adapter"] = self.text_adapter.cfg_dict
         return
 

From 8b905b87f8b91c8cb160010c9a55ff0afe32ed8e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:56:59 +0200
Subject: [PATCH 146/173] Inspect text encoders

---
 ...T-experimenting-with-text-embeddings.ipynb | 302 ++++++++++++++++++
 1 file changed, 302 insertions(+)
 create mode 100644 notebooks/11-GT-experimenting-with-text-embeddings.ipynb

diff --git a/notebooks/11-GT-experimenting-with-text-embeddings.ipynb b/notebooks/11-GT-experimenting-with-text-embeddings.ipynb
new file mode 100644
index 0000000..91e2aa8
--- /dev/null
+++ b/notebooks/11-GT-experimenting-with-text-embeddings.ipynb
@@ -0,0 +1,302 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "os.chdir(\"..\")\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import os\n",
+    "\n",
+    "import hydra\n",
+    "import lightning as pl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "from lightning import LightningDataModule, Trainer\n",
+    "\n",
+    "if os.environ.get(\"TOKENIZERS_PARALLELISM\") is None:\n",
+    "    os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from hydra import compose, initialize\n",
+    "from hydra.core.hydra_config import HydraConfig\n",
+    "\n",
+    "with initialize(version_base=\"1.3\", config_path=\"../configs\"):\n",
+    "    cfg = compose(\n",
+    "        config_name=\"train.yaml\", return_hydra_config=True, overrides=[\"experiment=pycharm\"]\n",
+    "    )\n",
+    "\n",
+    "HydraConfig.instance().set_config(cfg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)\n",
+    "datamodule.setup()\n",
+    "\n",
+    "trainer: Trainer = pl.Trainer(accelerator=\"mps\", max_epochs=1)\n",
+    "trainer.datamodule = datamodule\n",
+    "\n",
+    "# Get model\n",
+    "model = hydra.utils.instantiate(cfg.model)\n",
+    "model.to(\"mps\")\n",
+    "model.trainer = trainer\n",
+    "model.setup(\"test\")\n",
+    "\n",
+    "is_llm = \"LLM\" in cfg.model.text_encoder._target_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions = [c[\"concept_caption\"] for c in datamodule.caption_builder.__dict__[\"concepts\"]]\n",
+    "b = {\"text\": captions}\n",
+    "text_feats = model.text_encoder(b, \"train\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text_feats = F.normalize(text_feats)\n",
+    "cos_sim = text_feats @ text_feats.T\n",
+    "if is_llm:\n",
+    "    cos_sim = cos_sim.detach().cpu().to(torch.float32).numpy()\n",
+    "else:\n",
+    "    cos_sim = cos_sim.detach().cpu().detach().numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure(figsize=(16, 8))\n",
+    "gs = fig.add_gridspec(1, 2, width_ratios=[1.5, 1])\n",
+    "\n",
+    "ax1 = fig.add_subplot(gs[0])\n",
+    "short_labels = [f\"C{i}\" for i in range(len(captions))]\n",
+    "\n",
+    "sns.heatmap(\n",
+    "    cos_sim,\n",
+    "    xticklabels=short_labels,\n",
+    "    yticklabels=short_labels,\n",
+    "    ax=ax1,\n",
+    "    cmap=\"viridis\",\n",
+    "    # vmin=-1, vmax=1,\n",
+    "    cbar_kws={\"label\": \"Cosine Similarity\"},\n",
+    ")\n",
+    "\n",
+    "ax1.set_title(\n",
+    "    f\"Cosine Similarity Matrix For Concepts With {'LLM' if is_llm else 'CLIP'} Text Encoder\"\n",
+    ")\n",
+    "\n",
+    "ax2 = fig.add_subplot(gs[1])\n",
+    "ax2.axis(\"off\")\n",
+    "\n",
+    "legend_text = \"\\n\".join([f\"C{i}: {cap}\" for i, cap in enumerate(captions)])\n",
+    "ax2.text(0, 1, legend_text, fontsize=10, va=\"top\", ha=\"left\", wrap=True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataloader = datamodule.train_dataloader()\n",
+    "dataloader_cycle = itertools.cycle(dataloader)\n",
+    "\n",
+    "all_captions = []\n",
+    "all_embeddings = []\n",
+    "total_samples = 0\n",
+    "max_seq_len = 0\n",
+    "target_samples = 10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for batch in dataloader_cycle:\n",
+    "    with torch.no_grad():\n",
+    "        model.eval()\n",
+    "        if is_llm:\n",
+    "            text_input = batch.get(\"text\")\n",
+    "            text_feats = model.text_encoder(batch, \"train\")\n",
+    "        else:\n",
+    "            text_input = batch.get(\"text\")\n",
+    "        text_tokens = model.text_encoder.processor(\n",
+    "            text=text_input,\n",
+    "            return_tensors=\"pt\",\n",
+    "            padding=True,\n",
+    "            truncation=True,\n",
+    "            max_length=None,\n",
+    "        )\n",
+    "        if text_tokens.input_ids.shape[-1] <= 77:\n",
+    "            device = \"mps\"\n",
+    "            text_tokens = {k: v.to(device) for k, v in text_tokens.items()}\n",
+    "            text_embeds = model.text_encoder.model.get_text_features(**text_tokens)\n",
+    "            if model.text_encoder.projector is not None:\n",
+    "                text_embeds = model.text_encoder.projector(text_embeds)\n",
+    "            if model.text_encoder.extra_projector is not None:\n",
+    "                text_embeds = model.text_encoder.extra_projector(text_embeds)\n",
+    "        else:\n",
+    "            for i, t in enumerate(text_input):\n",
+    "                print(t, len(text_tokens[\"input_ids\"][i]))\n",
+    "\n",
+    "            if len(text_tokens[\"input_ids\"][i]) > max_seq_len:\n",
+    "                max_seq_len = len(text_tokens[\"input_ids\"][i])\n",
+    "            break\n",
+    "\n",
+    "    all_embeddings.append(text_feats.cpu())\n",
+    "    all_captions.extend(batch[\"text\"])\n",
+    "\n",
+    "    total_samples += text_feats.shape[0]\n",
+    "    print(total_samples)\n",
+    "\n",
+    "    # Break only when we have enough\n",
+    "    if total_samples >= target_samples:\n",
+    "        break\n",
+    "\n",
+    "# 3. Finalize\n",
+    "text_feats = torch.cat(all_embeddings, dim=0)[:target_samples]\n",
+    "captions = all_captions[:target_samples]\n",
+    "\n",
+    "text_feats = F.normalize(text_feats)\n",
+    "cos_sim = text_feats @ text_feats.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions_arr = np.array(captions)\n",
+    "identity_mask = captions_arr[:, None] == captions_arr[None, :]\n",
+    "lower_tri_mask = np.tril(np.ones(identity_mask.shape, dtype=bool), k=-1)\n",
+    "\n",
+    "final_mask = (~identity_mask) & lower_tri_mask\n",
+    "if is_llm:\n",
+    "    unique_sims = cos_sim.detach().cpu().to(torch.float32).numpy()[final_mask]\n",
+    "else:\n",
+    "    unique_sims = cos_sim.detach().cpu().numpy()[final_mask]\n",
+    "\n",
+    "row_idx, col_idx = np.where(final_mask)\n",
+    "\n",
+    "# Plot\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.histplot(unique_sims, bins=50, kde=True)\n",
+    "plt.title(\n",
+    "    f\"Distribution of Cosine Similarities \\n(for random 10k training location captions with {'LLM' if is_llm else 'CLIP'} text encoder)\"\n",
+    ")\n",
+    "plt.xlabel(\"Cosine Similarity\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the lower triangle\n",
+    "lower_tri_indices = np.tril_indices(cos_sim.shape[0], k=-1)\n",
+    "\n",
+    "# Get indices of the unique pairs\n",
+    "row_idx, col_idx = lower_tri_indices\n",
+    "\n",
+    "# Sort the unique similarities\n",
+    "sorted_indices = np.argsort(unique_sims)  # Ascending order\n",
+    "\n",
+    "print(\"--- Highest Similarity Pairs ---\")\n",
+    "for i in sorted_indices[-5:][::-1]:\n",
+    "    r, c = row_idx[i], col_idx[i]\n",
+    "    print(f\"Score: {unique_sims[i]:.4f}\")\n",
+    "    print(f\"  - '{captions[r]}'\")\n",
+    "    print(f\"  - '{captions[c]}'\\n\")\n",
+    "\n",
+    "print(\"--- Lowest Similarity Pairs ---\")\n",
+    "for i in sorted_indices[:5]:\n",
+    "    r, c = row_idx[i], col_idx[i]\n",
+    "    print(f\"Score: {unique_sims[i]:.4f}\")\n",
+    "    print(f\"  - '{captions[r]}'\")\n",
+    "    print(f\"  - '{captions[c]}'\\n\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From b256c387f890602be5e1d5b8e4ae70a146138e3e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:58:05 +0200
Subject: [PATCH 147/173] Fix LLM downloading, flatten captions for efficient
 forward pass

---
 .../text_encoders/llm2clip_text_encoder.py    | 84 +++++++++++--------
 1 file changed, 50 insertions(+), 34 deletions(-)

diff --git a/src/models/components/text_encoders/llm2clip_text_encoder.py b/src/models/components/text_encoders/llm2clip_text_encoder.py
index 0f9af97..eccd667 100644
--- a/src/models/components/text_encoders/llm2clip_text_encoder.py
+++ b/src/models/components/text_encoders/llm2clip_text_encoder.py
@@ -1,8 +1,8 @@
 from typing import Dict, override
 
 import torch
+from huggingface_hub import snapshot_download
 from llm2vec import LLM2Vec
-from torch.nn import functional as F
 from transformers import AutoConfig, AutoModel, AutoTokenizer
 
 from src.models.components.text_encoders.base_text_encoder import BaseTextEncoder
@@ -18,25 +18,26 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
         :param output_normalization: output normalization type
         """
         super().__init__()
+        self._download(hf_cache_dir)
 
-        # Adapter and image encoder
         self.projector = AutoModel.from_pretrained(
             "microsoft/LLM2CLIP-Openai-L-14-224",
             trust_remote_code=True,
             dtype=torch.bfloat16,
-            revision="50ed31c5248d8ff124893719e37829d59376be81",  # pin revision for full reproducibility
+            revision="50ed31c5248d8ff124893719e37829d59376be81",
             cache_dir=hf_cache_dir,
         ).eval()
 
-        # TODO: If we want to reuse the vision  part this is the fix place
         self.projector.vision_model = None
         self.projector.visual_projection = None
 
-        # The LLM sentence encoder
         llm_model_name = "microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned"
+        llm_revision = "main"
+
         config = AutoConfig.from_pretrained(
             llm_model_name,
             trust_remote_code=True,
+            revision=llm_revision,
             cache_dir=hf_cache_dir,
         )
         config._attn_implementation = "eager"
@@ -45,13 +46,17 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
             llm_model_name,
             config=config,
             dtype=torch.bfloat16,
-            trust_remote_code=False,  # local code
+            trust_remote_code=False,
+            revision=llm_revision,
             cache_dir=hf_cache_dir,
         )
-        llm_model.config._name_or_path = (
-            "meta-llama/Meta-Llama-3-8B-Instruct"  # Workaround for LLM2VEC
+        llm_model.config._name_or_path = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+        self.processor = AutoTokenizer.from_pretrained(
+            llm_model_name,
+            revision=llm_revision,
+            cache_dir=hf_cache_dir,
         )
-        self.processor = AutoTokenizer.from_pretrained(llm_model_name)
 
         # Caption to vector with the llama LLM
         self.model = LLM2Vec(
@@ -60,6 +65,20 @@ def __init__(self, hf_cache_dir: str = "../.cache", output_normalization="l2") -
 
         self.output_dim = 1280
 
+    @staticmethod
+    def _download(hf_cache_dir: str) -> None:
+        llm_revision = "main"
+        snapshot_download(
+            "microsoft/LLM2CLIP-Llama-3-8B-Instruct-CC-Finetuned",
+            revision=llm_revision,
+            cache_dir=hf_cache_dir,
+        )
+        snapshot_download(
+            "microsoft/LLM2CLIP-Openai-L-14-224",
+            revision="50ed31c5248d8ff124893719e37829d59376be81",
+            cache_dir=hf_cache_dir,
+        )
+
     @override
     def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
         """Forward pass through text encoder."""
@@ -67,32 +86,29 @@ def forward(self, batch: Dict[str, torch.Tensor], mode: str) -> torch.Tensor:
         text_input = batch.get("text")
 
         if mode == "train":
-            text_input = [text_input]
-        # Embed text and if not train average all templates
-        avr_embeds = []
-        for captions_per_row in text_input:
-            # LLM is frozen, no gradients needed
-            with torch.no_grad():
-                # Embed
-                text_embeds = self.model.encode(
-                    captions_per_row, convert_to_tensor=True, device=self.device
-                )
-
-                # Change dtype
-                text_embeds = text_embeds.to(
-                    dtype=self.projector.dtype, device=self.projector.device
-                )
-
-            # Project to align with ViT in LLM2CLIP
-            text_embeds = self.projector.get_text_features(text_embeds)
-
-            if self.extra_projector is not None:
-                text_embeds = self.extra_projector(text_embeds)
-
-            if mode != "train":
-                avr_embeds.append(text_embeds.mean(dim=0))
+            captions_flattened = text_input
+            num_per_location = 1
+        else:
+            num_per_location = len(text_input[0])
+            captions_flattened = [c for loc in text_input for c in loc]
+
+        # LLM is frozen, no gradients needed
+        with torch.no_grad():
+            # Embed
+            text_embeds = self.model.encode(
+                captions_flattened, convert_to_tensor=True, device=self.device
+            )
+            # Change dtype
+            text_embeds = text_embeds.to(dtype=self.projector.dtype, device=self.projector.device)
+
+        # Project to align with ViT in LLM2CLIP
+        text_embeds = self.projector.get_text_features(text_embeds)
+
+        if self.extra_projector is not None:
+            text_embeds = self.extra_projector(text_embeds)
 
         if mode != "train":
-            text_embeds = torch.stack(avr_embeds, dim=0)
+            text_embeds = text_embeds.reshape(-1, num_per_location, text_embeds.shape[-1])
+            text_embeds = text_embeds.mean(dim=1)
 
         return text_embeds

From 2f94ecc0b05c1c90a547fbe5738078ae5688c4b7 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:09:51 +0200
Subject: [PATCH 148/173] Add aux stats calculation

---
 data/s2bms/aux_stats/explanations             |   2 +
 data/s2bms/aux_stats/labelled.json            | 350 ++++++++++++++++++
 data/s2bms/aux_stats/unlabelled.json          | 350 ++++++++++++++++++
 ...9-GT-aef-tessera-datasplit-filtering.ipynb |  36 +-
 4 files changed, 720 insertions(+), 18 deletions(-)
 create mode 100644 data/s2bms/aux_stats/explanations
 create mode 100644 data/s2bms/aux_stats/labelled.json
 create mode 100644 data/s2bms/aux_stats/unlabelled.json

diff --git a/data/s2bms/aux_stats/explanations b/data/s2bms/aux_stats/explanations
new file mode 100644
index 0000000..7dcd42c
--- /dev/null
+++ b/data/s2bms/aux_stats/explanations
@@ -0,0 +1,2 @@
+labelled - s2bms aux statistical values, all splits (model ready csv used)
+unlabelled - model_ready_s2bms-unlabelled-20260529.csv aucx col stats.
diff --git a/data/s2bms/aux_stats/labelled.json b/data/s2bms/aux_stats/labelled.json
new file mode 100644
index 0000000..a7c4b12
--- /dev/null
+++ b/data/s2bms/aux_stats/labelled.json
@@ -0,0 +1,350 @@
+{
+    "aux_bioclim_01": {
+        "mean": 9.485303030303031,
+        "std": 0.6721779966099152
+    },
+    "aux_bioclim_02": {
+        "mean": 7.2680303030303035,
+        "std": 0.5689534430067483
+    },
+    "aux_bioclim_03": {
+        "mean": 35.41439393939394,
+        "std": 1.586124088724008
+    },
+    "aux_bioclim_04": {
+        "mean": 46.59897727272727,
+        "std": 2.687255141701679
+    },
+    "aux_bioclim_05": {
+        "mean": 20.717651515151516,
+        "std": 1.1752884954485494
+    },
+    "aux_bioclim_06": {
+        "mean": 0.4790151515151515,
+        "std": 0.7658998086098698
+    },
+    "aux_bioclim_07": {
+        "mean": 20.238636363636363,
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diff --git a/data/s2bms/aux_stats/unlabelled.json b/data/s2bms/aux_stats/unlabelled.json
new file mode 100644
index 0000000..fff3178
--- /dev/null
+++ b/data/s2bms/aux_stats/unlabelled.json
@@ -0,0 +1,350 @@
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+    },
+    "aux_corine_frac_31": {
+        "mean": 0.08315859345616691,
+        "std": 0.14797484710834283
+    },
+    "aux_corine_frac_32": {
+        "mean": 0.15094803130671286,
+        "std": 0.25041112285241357
+    },
+    "aux_corine_frac_33": {
+        "mean": 0.01337973803084801,
+        "std": 0.06768695209323439
+    },
+    "aux_corine_frac_41": {
+        "mean": 0.09884971802491883,
+        "std": 0.23626719477301794
+    },
+    "aux_corine_frac_42": {
+        "mean": 0.00551630986480931,
+        "std": 0.04218330504440695
+    },
+    "aux_corine_frac_51": {
+        "mean": 0.010279370518758095,
+        "std": 0.061858529662706774
+    },
+    "aux_corine_frac_52": {
+        "mean": 0.016437883155237348,
+        "std": 0.07527943347488333
+    },
+    "aux_total_population": {
+        "mean": 1384.9394092146385,
+        "std": 4642.19802237972
+    },
+    "aux_pop_density": {
+        "mean": 212.1150317572336,
+        "std": 712.2522496739724
+    },
+    "aux_maxdist_road": {
+        "mean": 2228.0529287226536,
+        "std": 1309.984693451521
+    },
+    "aux_meandist_road": {
+        "mean": 1230.6387740699668,
+        "std": 1292.7376322660916
+    }
+}
diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index db04de0..de03555 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "65de5fc3c6bf5ae7"
+   "id": "69c0aa19a87d28ee"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "dd6fbfcf6721b59d"
+   "id": "9e810e676222e0fd"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "92bf611a8161c2b0"
+   "id": "5b32c14111ab7ad2"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "fdd45b910e1ce370"
+   "id": "ba0b3e9a09136516"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "e2471b2f6e31a921"
+   "id": "3fe7d8ad5ddddf5b"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "2e44c05c43494127"
+   "id": "f9ae42b890402c6c"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "94309214afa86e4c"
+   "id": "46fdc0061d7961e4"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "e5ba50459738958"
+   "id": "8b527252fb457e6c"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "8434a4c727d8abb9"
+   "id": "65fcf117881ebcf4"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "58591df65d72755b"
+   "id": "cd38831d50115c87"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "19f514f8a3308e4e"
+   "id": "a1e3761eaa73917f"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "14f4e1d50a7f5ee9"
+   "id": "76a800a3f0e96e67"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "b96a748f20033142"
+   "id": "29457001980549c8"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "c1367ece43cc444d"
+   "id": "63506c515fd03a04"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "f99f150f387d2e38"
+   "id": "cc1eb430d2408dff"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "ed413850060b05a8"
+   "id": "e0a5d39b40e5bd47"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "360f3309d8cf2ccf"
+   "id": "801d40b78fd22a32"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "1e1bed9c4e61a6d7"
+   "id": "e271386e0bf680a6"
   }
  ],
  "metadata": {},

From 0f10083d3fae2c4c9955fa5b7ea50748dd86a976 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:20:51 +0200
Subject: [PATCH 149/173] Create soft contrastive loss

---
 .../loss_fns/soft_contrastive_loss.py         | 95 +++++++++++++++++++
 1 file changed, 95 insertions(+)
 create mode 100644 src/models/components/loss_fns/soft_contrastive_loss.py

diff --git a/src/models/components/loss_fns/soft_contrastive_loss.py b/src/models/components/loss_fns/soft_contrastive_loss.py
new file mode 100644
index 0000000..2a6fa1f
--- /dev/null
+++ b/src/models/components/loss_fns/soft_contrastive_loss.py
@@ -0,0 +1,95 @@
+from typing import Dict, List, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing_extensions import override
+
+from src.models.components.loss_fns.base_loss_fn import BaseLossFn
+
+
+class SoftContrastiveLoss(BaseLossFn):
+    def __init__(self, temperature: float = 0.07, sigma: float = 1.0):
+        super().__init__()
+
+        self.log_temp = nn.Parameter(torch.log(torch.tensor(temperature)))
+        self.sigma = sigma
+
+        self.name = "SoftContrastiveLoss"
+
+    @override
+    def forward(
+        self,
+        eo_mod: torch.Tensor,
+        text_mod: torch.Tensor,
+        aux_values: torch.Tensor,
+        aux_ids_per_caption: List[List[int]],
+        mode: str | None = None,
+        **kwargs,
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        """Forward computation."""
+
+        # Get target matrix
+        T = self._get_soft_target_matrix(aux_ids_per_caption, aux_values)
+
+        # Normalize targets
+        T_eo2text = T / T.sum(dim=1, keepdim=True)
+        T_text2eo = T / T.sum(dim=0, keepdim=True)
+
+        # Normalise inputs
+        eo_mod = F.normalize(eo_mod, dim=-1)
+        text_mod = F.normalize(text_mod, dim=-1)
+
+        # Clip temperature to not exceed 100
+        temperature = torch.clamp(self.log_temp.exp(), max=100)
+
+        # Get cosine similarity
+        dot_product = (eo_mod @ text_mod.T) / temperature
+
+        # Cross entropy: sum(-target * log_prob)
+        loss_eo2text = -(T_eo2text * F.log_softmax(dot_product, dim=1)).sum(dim=1).mean()
+        loss_text2eo = -(T_text2eo * F.log_softmax(dot_product, dim=0)).sum(dim=0).mean()
+
+        loss = (loss_eo2text + loss_text2eo) / 2
+        if "return_label" in kwargs:
+            return {f"{mode}_{self.name}": loss}
+        else:
+            return loss
+
+    def _get_soft_target_matrix(
+        self,
+        aux_values: torch.Tensor,
+        aux_ids_per_caption: list[list[int]] | None,
+    ) -> torch.Tensor:
+        """Puts together a target matrix based on auxiliary value similarity (either all, or
+        specific per caption template).
+
+        :param aux_values: auxiliary column values (standardised).
+        :param aux_ids_per_caption: list of ids of auxiliary columns used per caption template.
+        :return: target matrix which tells how each row should be similar to column based on aux
+            values (all or specific ones per caption template).
+        """
+        batch_size, n_aux_cols = aux_values.shape
+        device = aux_values.device
+
+        # Soft targets based on the squared difference between location i and j for all aux values
+        diffs = (aux_values.unsqueeze(1) - aux_values.unsqueeze(0)) ** 2
+
+        # Create a mask based on aux_columns used per location caption
+        if aux_ids_per_caption:
+            # Create a mask
+            mask = torch.zeros((batch_size, n_aux_cols), dtype=aux_values.dtype, device=device)
+            for j, used_cols in enumerate(aux_ids_per_caption):
+                if used_cols:
+                    mask[j, used_cols] = 1.0
+            mask = mask.unsqueeze(0)
+
+            # Calculate the masked average distance for the selected aux cols
+            dist = (diffs * mask).sum(dim=-1) / (mask.sum(dim=-1) + 1e-8)
+        else:
+            # Distance based on all aux columns
+            dist = diffs.mean(dim=-1)
+
+        # Convert distances to similarities using a Gaussian kernel
+        T = torch.exp(-dist / (2 * self.sigma**2))  # (N, N)
+        return T

From 106d5bcb83581f8a02978bbb50c8e581472ff821 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:30:58 +0200
Subject: [PATCH 150/173] Implement loss pass

---
 src/models/text_alignment_model.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 9c9b792..0591a7e 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -195,7 +195,21 @@ def _step(self, batch: Dict[str, torch.Tensor], mode: str = "train"):
             geo_feats, text_feats = feats[0], feats[1]
 
         # Get loss
-        loss = self.loss_fn(geo_feats, text_feats)
+        aux_values = batch["aux"].get("aux_standardized")
+        aux_ids_per_caption = batch.get("text_aux_ids")
+
+        if geo_feats.isnan().any():
+            print(geo_feats)
+            print(batch["name_loc"])
+            exit()
+
+        loss = self.loss_fn(
+            geo_feats,
+            text_feats,
+            mode=mode,
+            aux_values=aux_values,
+            aux_ids_per_caption=aux_ids_per_caption,
+        )
 
         # Get similarities
         with torch.no_grad():

From af0cee393aae0482a842923fff5f630a0a29348e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 10:31:18 +0200
Subject: [PATCH 151/173] Dimension miss match warning

---
 src/models/text_alignment_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 0591a7e..88f54cd 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -102,9 +102,9 @@ def _setup(self, stage: str = "fit") -> None:
         if geo_branch_dim != text_branch_dim:
             if self.geo_adapter or self.text_adapter:
                 print(
-                    f"You opted to use: {'geo' if self.geo_adapter else '' and 'text' if self.text_adapter else ''} adapter(s)",
-                    "but you miss-configured output dimensions:"
-                    f"geo:{geo_branch_dim} vs text: {text_branch_dim}",
+                    f"You opted to use:{' geo' if self.geo_adapter else '' and ' text' if self.text_adapter else ''} adapter",
+                    "but you miss-configured output dimensions:\n"
+                    f"geo: {geo_branch_dim} vs text: {text_branch_dim}\n",
                     "Please try again.",
                 )
             elif self.match_to_geo:

From 7b6c0099c9c0ca4aeec9b94260c8cfe10b7e5a7e Mon Sep 17 00:00:00 2001
From: Thijs van der Plas <vdplasthijs@gmail.com>
Date: Fri, 29 May 2026 10:15:01 +0200
Subject: [PATCH 152/173] Add ability to load unlabelled data

# Conflicts:
#	configs/data/butterfly_coords_text_unlabelled.yaml
#	src/data/base_datamodule.py
#	src/data/base_dataset.py
#	src/data/butterfly_dataset.py
---
 src/data/butterfly_dataset.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/butterfly_dataset.py b/src/data/butterfly_dataset.py
index aaa53f3..ce49704 100644
--- a/src/data/butterfly_dataset.py
+++ b/src/data/butterfly_dataset.py
@@ -41,7 +41,7 @@ def __init__(
 
         assert not (
             use_unlabelled_data and use_target_data
-        ), "Joint use of unlabelled and target data is not supported."
+        ), "Joint use of unlabelled and target data is not supported yet."
         if use_unlabelled_data:
             dataset_name = ["s2bms", "s2bms-unlabelled-20260529"]
         else:

From 286a9755867bc78e702bf9100d9685aea444575f Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 13:13:58 +0200
Subject: [PATCH 153/173] New datasplits

---
 ...9-GT-aef-tessera-datasplit-filtering.ipynb | 36 +++++++++----------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
index de03555..61d713a 100644
--- a/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
+++ b/notebooks/09-GT-aef-tessera-datasplit-filtering.ipynb
@@ -15,7 +15,7 @@
     "os.chdir(\"..\")\n",
     "os.getcwd()"
    ],
-   "id": "69c0aa19a87d28ee"
+   "id": "e6b001bcbbcf1393"
   },
   {
    "metadata": {},
@@ -46,7 +46,7 @@
     "print(\"Tessera:\", tsr_left)\n",
     "print(\"AlphaEarth:\", aef_left)"
    ],
-   "id": "9e810e676222e0fd"
+   "id": "9e3ebf3cc288a22e"
   },
   {
    "metadata": {},
@@ -58,7 +58,7 @@
     "pth = \"data/s2bms/splits/split_indices_s2bms_2024-08-14-1459.pth\"\n",
     "split_indices = torch.load(pth, weights_only=False)"
    ],
-   "id": "5b32c14111ab7ad2"
+   "id": "8221726506e93827"
   },
   {
    "metadata": {},
@@ -74,7 +74,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "ba0b3e9a09136516"
+   "id": "5fae07c7d65654e3"
   },
   {
    "metadata": {},
@@ -107,7 +107,7 @@
     "        \"data/s2bms/splits/s2bms_union_val_test.pth\",\n",
     "    )"
    ],
-   "id": "3fe7d8ad5ddddf5b"
+   "id": "3136d7853d8e31ae"
   },
   {
    "metadata": {},
@@ -127,7 +127,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df[df[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "f9ae42b890402c6c"
+   "id": "5a1c5201ac4eb6a3"
   },
   {
    "metadata": {},
@@ -153,13 +153,13 @@
     "\n",
     "m"
    ],
-   "id": "46fdc0061d7961e4"
+   "id": "e8b5da285fa765f7"
   },
   {
    "metadata": {},
    "cell_type": "markdown",
    "source": "# Re-do Unlabelled split",
-   "id": "8b527252fb457e6c"
+   "id": "39a2419c83eb4ad6"
   },
   {
    "metadata": {},
@@ -172,7 +172,7 @@
     "\n",
     "train_unlabelled = list(df_unlabelled.name_loc)"
    ],
-   "id": "65fcf117881ebcf4"
+   "id": "95fcfd5175ce3714"
   },
   {
    "metadata": {},
@@ -190,7 +190,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "cd38831d50115c87"
+   "id": "70bd8ae3d91af6f8"
   },
   {
    "metadata": {},
@@ -202,7 +202,7 @@
     "    [i for i in list(split_indices[\"train_indices\"]) if i in train_unlabelled or \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "a1e3761eaa73917f"
+   "id": "185f140e10313ba6"
   },
   {
    "metadata": {},
@@ -215,7 +215,7 @@
     "    weights_only=False,\n",
     ")"
    ],
-   "id": "76a800a3f0e96e67"
+   "id": "6b5cc13055931dd0"
   },
   {
    "metadata": {},
@@ -230,7 +230,7 @@
     "    [i for i in list(val_test_indices[\"test_indices\"]) if \"UKBMS\" in i]\n",
     ")"
    ],
-   "id": "29457001980549c8"
+   "id": "fc1d88e4ad2f33c0"
   },
   {
    "metadata": {},
@@ -245,7 +245,7 @@
     "        print(k, len(v))\n",
     "        s += len(v)"
    ],
-   "id": "63506c515fd03a04"
+   "id": "3d73381d0e1951c0"
   },
   {
    "metadata": {},
@@ -261,7 +261,7 @@
     "else:\n",
     "    print(\"Already saved\")"
    ],
-   "id": "cc1eb430d2408dff"
+   "id": "5aa54d56f2a00083"
   },
   {
    "metadata": {},
@@ -281,7 +281,7 @@
     "test_idx = split_indices[\"test_indices\"]\n",
     "test_df = df_merged[df_merged[\"name_loc\"].isin(map(str, test_idx))]"
    ],
-   "id": "e0a5d39b40e5bd47"
+   "id": "a00999ae2f8b2c2f"
   },
   {
    "metadata": {},
@@ -305,7 +305,7 @@
     "\n",
     "m"
    ],
-   "id": "801d40b78fd22a32"
+   "id": "a5a2a3ca21c38542"
   },
   {
    "metadata": {},
@@ -313,7 +313,7 @@
    "outputs": [],
    "execution_count": null,
    "source": "",
-   "id": "e271386e0bf680a6"
+   "id": "c58d5db66584f599"
   }
  ],
  "metadata": {},

From 92e7d2ea69f1d3e069b5b8e48394c711de68f221 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 14:18:04 +0200
Subject: [PATCH 154/173] Split after getting missing data, in the setup() call

---
 src/data/base_datamodule.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 64ef14b..fe8dd8b 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -277,7 +277,7 @@ def split_data(self) -> None:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
             print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
-            print(f"Validate dataset split size: {len(self.data_val)}")
+            self.data_val.dataset.mode = "val"
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)

From b9081fde67fd587e9b7e879508821091f0aa3c80 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 15:07:55 +0200
Subject: [PATCH 155/173] Add ability to load unlabelled data

# Conflicts:
#	src/data/butterfly_dataset.py
---
 src/data/base_datamodule.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index fe8dd8b..5b96c0c 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -2,7 +2,7 @@
 import os
 import time
 from functools import partial
-from typing import Any, Tuple
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 import pandas as pd

From 88e2b5e88bd7482cd198ea0714bfba56c22f0eb4 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 09:19:31 +0200
Subject: [PATCH 156/173] Re do unlabelled split

---
 ...bms-unlabelled-20260602_2026-06-02-0918.pth | Bin 0 -> 294641 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
new file mode 100644
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From 9f5fe040575d1b1faa179934a86098393d550e07 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 2 Jun 2026 14:52:18 +0200
Subject: [PATCH 157/173] Add size print statements

---
 src/data/base_datamodule.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/data/base_datamodule.py b/src/data/base_datamodule.py
index 5b96c0c..04d6a69 100644
--- a/src/data/base_datamodule.py
+++ b/src/data/base_datamodule.py
@@ -66,7 +66,6 @@ def __init__(
             self.caption_builder = caption_builder
             self.caption_builder.sync_with_dataset(self.dataset)
             self.concept_configs = caption_builder.concepts
-
         self._setup_flag = False
 
     @property
@@ -83,6 +82,7 @@ def setup(self, stage: str = "fit") -> None:
 
         Called by model trainer (trainer.fit()).
         """
+
         if not self._setup_flag:
             # Set up the dataset (download requested modalities)
             self.dataset.setup()
@@ -277,7 +277,7 @@ def split_data(self) -> None:
             self.data_train = torch.utils.data.Subset(self.dataset, train_indices)
             print(f"Train dataset split size: {len(self.data_train)}")
             self.data_val = torch.utils.data.Subset(self.dataset, val_indices)
-            self.data_val.dataset.mode = "val"
+            print(f"Validate dataset split size: {len(self.data_val)}")
 
             if test_indices is not None:
                 self.data_test = torch.utils.data.Subset(self.dataset, test_indices)

From ea5fc18b55ccfd2f9230e0a2c6a0f54f0f026559 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 09:10:37 +0200
Subject: [PATCH 158/173] New data splits and their explanations

---
 ...bms-unlabelled-20260602_2026-06-02-0918.pth | Bin 294641 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth

diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth
deleted file mode 100644
index 429e3b22f30eea24bd0c4487cf24133330d28780..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From b51f91cf32178ba7324f69e48c103c1795239269 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Tue, 16 Jun 2026 16:00:58 +0200
Subject: [PATCH 159/173] fix the splits

---
 configs/data/butterfly_coords.yaml            |   2 +-
 configs/data/butterfly_coords_text.yaml       |   2 +-
 .../data/butterfly_full_param_example.yaml    |   2 +-
 configs/data/butterfly_s2_rgb.yaml            |   2 +-
 .../s2bms_align_avr_aef_clip_text.yaml        |  82 +++++++++++++++++
 .../s2bms_align_avr_aef_clip_text_1.yaml      |  81 +++++++++++++++++
 .../s2bms_align_avr_aef_clip_text_unlab.yaml  |  83 ++++++++++++++++++
 ...s2bms_align_avr_aef_clip_text_unlab_1.yaml |  82 +++++++++++++++++
 .../experiment/s2bms_align_avr_aef_llm.yaml   |  83 ++++++++++++++++++
 configs/experiment/s2bms_experiment_1.yaml    |   2 +-
 configs/experiment/s2bms_experiment_10.yaml   |   2 +-
 configs/experiment/s2bms_experiment_10_1.yaml |   2 +-
 configs/experiment/s2bms_experiment_11.yaml   |   2 +-
 configs/experiment/s2bms_experiment_12.yaml   |   2 +-
 configs/experiment/s2bms_experiment_13.yaml   |   2 +-
 configs/experiment/s2bms_experiment_14.yaml   |   2 +-
 configs/experiment/s2bms_experiment_15.yaml   |  71 +++++++++++++++
 configs/experiment/s2bms_experiment_2.yaml    |   2 +-
 configs/experiment/s2bms_experiment_3.yaml    |   2 +-
 configs/experiment/s2bms_experiment_4.yaml    |   3 +-
 configs/experiment/s2bms_experiment_5.yaml    |   2 +-
 configs/experiment/s2bms_experiment_6.yaml    |   2 +-
 configs/experiment/s2bms_experiment_7.yaml    |   2 +-
 configs/experiment/s2bms_experiment_8.yaml    |   2 +-
 configs/experiment/s2bms_experiment_9.yaml    |   2 +-
 ...s2bms+s2bms-unlabelled-2026-06-02-0918.pth | Bin 294459 -> 0 bytes
 ...-06-11-15-23-51_union_aef_tsr_val_test.pth | Bin 385437 -> 0 bytes
 ...ms-unlabelled-20260529_2026-05-29-1433.pth | Bin 0 -> 543245 bytes
 28 files changed, 502 insertions(+), 19 deletions(-)
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
 create mode 100644 configs/experiment/s2bms_align_avr_aef_llm.yaml
 create mode 100644 configs/experiment/s2bms_experiment_15.yaml
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
 delete mode 100644 data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-11-15-23-51_union_aef_tsr_val_test.pth
 create mode 100644 data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth

diff --git a/configs/data/butterfly_coords.yaml b/configs/data/butterfly_coords.yaml
index f4acb51..f0ad2d4 100644
--- a/configs/data/butterfly_coords.yaml
+++ b/configs/data/butterfly_coords.yaml
@@ -16,5 +16,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_coords_text.yaml b/configs/data/butterfly_coords_text.yaml
index 52cae6d..5fad06f 100644
--- a/configs/data/butterfly_coords_text.yaml
+++ b/configs/data/butterfly_coords_text.yaml
@@ -24,5 +24,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_full_param_example.yaml b/configs/data/butterfly_full_param_example.yaml
index 78923f5..63320dd 100644
--- a/configs/data/butterfly_full_param_example.yaml
+++ b/configs/data/butterfly_full_param_example.yaml
@@ -32,5 +32,5 @@ num_workers: 8
 pin_memory: true
 split_mode: "from_file"
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/data/butterfly_s2_rgb.yaml b/configs/data/butterfly_s2_rgb.yaml
index 1b4a627..4916c57 100644
--- a/configs/data/butterfly_s2_rgb.yaml
+++ b/configs/data/butterfly_s2_rgb.yaml
@@ -21,5 +21,5 @@ num_workers: 1
 pin_memory: false
 split_mode: "from_file" # default 'random'
 save_split: false
-saved_split_file_name: "split_indices_s2bms_2024-08-14-1459.pth"
+saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 seed: ${seed}
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text.yaml
new file mode 100644
index 0000000..f60723b
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text.yaml
@@ -0,0 +1,82 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/unlabelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
new file mode 100644
index 0000000..d5417f8
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_1.yaml
@@ -0,0 +1,81 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.clip_loss.ClipLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/unlabelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++data.caption_builder.return_aux_ids: false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
new file mode 100644
index 0000000..f1d13dd
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab.yaml
@@ -0,0 +1,83 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: unlab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_256_unlabelled.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    use_unlabelled_data: true
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_10k-unlabelled_coords_aef256.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
new file mode 100644
index 0000000..892897a
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_clip_text_unlab_1.yaml
@@ -0,0 +1,82 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: unlab_avr_aef_128_mlp_cliptext
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.clip_loss.ClipLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_256_unlabelled.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    use_unlabelled_data: true
+    dtype: float32
+    return_name_loc: true
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_10k-unlabelled_coords_aef256.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++data.caption_builder.return_aux_ids: false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_align_avr_aef_llm.yaml b/configs/experiment/s2bms_align_avr_aef_llm.yaml
new file mode 100644
index 0000000..e2674da
--- /dev/null
+++ b/configs/experiment/s2bms_align_avr_aef_llm.yaml
@@ -0,0 +1,83 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+# Avr aef 128 + geoclip
+
+defaults:
+  #  - /callbacks@callbacks.batch_size_finder: batch_size_finder
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
+
+tags:
+  - alignment
+  - aef
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: lab_avr_aef_128_mlp_llm
+projects: s2bms_alignment
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+    geo_data_name: aef_avr
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 1280
+  text_encoder:
+    _target_: src.models.components.text_encoders.llm2clip_text_encoder.LLM2CLIPTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+  loss_fn:
+    _target_: src.models.components.loss_fns.soft_contrastive_loss.SoftContrastiveLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+    stats_file: ${paths.data_dir}/s2bms/aux_stats/labelled.json
+
+  num_workers: 14
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 6
+  batch_size: 256
+  saved_split_file_name: "s2bms_aef_coords.pth"
+#    data.saved_split_file_name: "split_indices_s2bms+s2bms-unlabelled-20260602_2026-06-02-0918.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "alignment"
+    project: "s2bms_alignment"
+  aim:
+    experiment: "alignment"
+
+trainer.num_sanity_val_steps: 0
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345 #, 404, 654
+      ++model.loss_fn.sigma: 0.25, 0.5, 1
+      ++data.caption_builder.return_aux_ids: true, false
+      ++callbacks.early_stopping.patience: 20
diff --git a/configs/experiment/s2bms_experiment_1.yaml b/configs/experiment/s2bms_experiment_1.yaml
index 591b889..6c24802 100644
--- a/configs/experiment/s2bms_experiment_1.yaml
+++ b/configs/experiment/s2bms_experiment_1.yaml
@@ -38,7 +38,7 @@ data:
   pin_memory: true
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_10.yaml b/configs/experiment/s2bms_experiment_10.yaml
index 09ed4bf..3a69bfd 100644
--- a/configs/experiment/s2bms_experiment_10.yaml
+++ b/configs/experiment/s2bms_experiment_10.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 256
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_10_1.yaml b/configs/experiment/s2bms_experiment_10_1.yaml
index adbef0f..2ae5339 100644
--- a/configs/experiment/s2bms_experiment_10_1.yaml
+++ b/configs/experiment/s2bms_experiment_10_1.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 256
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_11.yaml b/configs/experiment/s2bms_experiment_11.yaml
index 4f0f986..550f2f3 100644
--- a/configs/experiment/s2bms_experiment_11.yaml
+++ b/configs/experiment/s2bms_experiment_11.yaml
@@ -41,7 +41,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_12.yaml b/configs/experiment/s2bms_experiment_12.yaml
index eb69c23..b208c7e 100644
--- a/configs/experiment/s2bms_experiment_12.yaml
+++ b/configs/experiment/s2bms_experiment_12.yaml
@@ -41,7 +41,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_13.yaml b/configs/experiment/s2bms_experiment_13.yaml
index c71624b..a6872a8 100644
--- a/configs/experiment/s2bms_experiment_13.yaml
+++ b/configs/experiment/s2bms_experiment_13.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_14.yaml b/configs/experiment/s2bms_experiment_14.yaml
index e5412c7..2de8821 100644
--- a/configs/experiment/s2bms_experiment_14.yaml
+++ b/configs/experiment/s2bms_experiment_14.yaml
@@ -43,7 +43,7 @@ data:
   batch_size: 128
   persistent_workers: true
   prefetch_factor: 2
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_15.yaml b/configs/experiment/s2bms_experiment_15.yaml
new file mode 100644
index 0000000..0be763a
--- /dev/null
+++ b/configs/experiment/s2bms_experiment_15.yaml
@@ -0,0 +1,71 @@
+# @package _global_
+
+# to execute this experiment run:
+# python train.py experiment=example
+
+#  Better toegther: AEF with average encoder and satclip, and MLP prediction head, for tile sizes 128, each with 3 random seeds
+
+defaults:
+  - override /model: s2bms_prediction
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_predictive
+
+tags:
+  - prediction
+  - aef_satclip
+  - aef_128
+  - ${str:${seed}}
+
+experiment_name: avr_aef_128_and_satclip_mlp
+
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.encoder_wrapper.EncoderWrapper
+
+    encoder_branches:
+      - encoder:
+          _target_: src.models.components.geo_encoders.identity_encoder.IdentityEncoder
+          geo_data_name: aef_avr
+      - encoder:
+          _target_: src.models.components.geo_encoders.satclip.SatClipCoordinateEncoder
+          geo_data_name: coords
+          hf_cache_dir: ${paths.huggingface_cache}
+    #            return_dtype: float32
+    normalise: false
+    fusion_strategy: "concat"
+
+  trainable_modules: [prediction_head]
+  prediction_head:
+    _target_: src.models.components.pred_heads.mlp_pred_head.MLPPredictionHead
+    hidden_dim: 512
+
+data:
+  dataset:
+    modalities:
+      coords:
+      aef_avr:
+        path: ${paths.data_dir}/s2bms/eo/avr_aef_128.csv
+        enable_nans: true
+    use_target_data: true
+    use_aux_data: false
+
+  persistent_workers: true
+  pin_memory: true
+  prefetch_factor: 4
+  num_workers: 7
+  batch_size: 128
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
+
+logger:
+  wandb:
+    tags: ${tags}
+    group: "predictive"
+    project: "s2bms_prediction"
+  aim:
+    experiment: "predictive"
+
+hydra:
+  mode: MULTIRUN
+  sweeper:
+    params:
+      seed: 12345, 404, 654
diff --git a/configs/experiment/s2bms_experiment_2.yaml b/configs/experiment/s2bms_experiment_2.yaml
index 0d9a84d..0a1eea9 100644
--- a/configs/experiment/s2bms_experiment_2.yaml
+++ b/configs/experiment/s2bms_experiment_2.yaml
@@ -38,7 +38,7 @@ data:
   pin_memory: true
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_3.yaml b/configs/experiment/s2bms_experiment_3.yaml
index e4d789f..7f8ce34 100644
--- a/configs/experiment/s2bms_experiment_3.yaml
+++ b/configs/experiment/s2bms_experiment_3.yaml
@@ -39,7 +39,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_4.yaml b/configs/experiment/s2bms_experiment_4.yaml
index 68dd9f4..3d6d510 100644
--- a/configs/experiment/s2bms_experiment_4.yaml
+++ b/configs/experiment/s2bms_experiment_4.yaml
@@ -25,6 +25,7 @@ model:
   trainable_modules: [prediction_head]
   prediction_head:
     _target_: src.models.components.pred_heads.linear_pred_head.LinearPredictionHead
+    hidden_dim: 512
 
 data:
   dataset:
@@ -39,7 +40,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_5.yaml b/configs/experiment/s2bms_experiment_5.yaml
index 366bfed..2b6f2c2 100644
--- a/configs/experiment/s2bms_experiment_5.yaml
+++ b/configs/experiment/s2bms_experiment_5.yaml
@@ -40,7 +40,7 @@ data:
   num_workers: 14
   batch_size: 128
   persistent_workers: false
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test_train.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_6.yaml b/configs/experiment/s2bms_experiment_6.yaml
index 2cd63c8..66d0cf0 100644
--- a/configs/experiment/s2bms_experiment_6.yaml
+++ b/configs/experiment/s2bms_experiment_6.yaml
@@ -34,7 +34,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_7.yaml b/configs/experiment/s2bms_experiment_7.yaml
index ec4f230..c0ec25f 100644
--- a/configs/experiment/s2bms_experiment_7.yaml
+++ b/configs/experiment/s2bms_experiment_7.yaml
@@ -34,7 +34,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_8.yaml b/configs/experiment/s2bms_experiment_8.yaml
index 4af78f0..50f9d63 100644
--- a/configs/experiment/s2bms_experiment_8.yaml
+++ b/configs/experiment/s2bms_experiment_8.yaml
@@ -36,7 +36,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/configs/experiment/s2bms_experiment_9.yaml b/configs/experiment/s2bms_experiment_9.yaml
index 4bd5def..d5bb66d 100644
--- a/configs/experiment/s2bms_experiment_9.yaml
+++ b/configs/experiment/s2bms_experiment_9.yaml
@@ -36,7 +36,7 @@ data:
   caption_builder:
   num_workers: 14
   batch_size: 128
-  saved_split_file_name: "split_indices_s2bms_2024-08-14-1459_union_aef_tsr_val_test.pth"
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
 
 logger:
   wandb:
diff --git a/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth b/data/s2bms/splits/split_indices_s2bms+s2bms-unlabelled-2026-06-02-0918.pth
deleted file mode 100644
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diff --git a/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth b/data/s2bms/splits/split_indices_s2bms-unlabelled-20260529_2026-05-29-1433.pth
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HcmV?d00001


From 1b323f0be771bc6fb0ccb1c2ae15657e9efd9200 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 09:50:13 +0200
Subject: [PATCH 160/173] Update stats based on train split only

---
 data/s2bms/aux_stats/labelled.json   | 304 +++++++++++++-------------
 data/s2bms/aux_stats/unlabelled.json | 316 +++++++++++++--------------
 2 files changed, 310 insertions(+), 310 deletions(-)

diff --git a/data/s2bms/aux_stats/labelled.json b/data/s2bms/aux_stats/labelled.json
index a7c4b12..60a2845 100644
--- a/data/s2bms/aux_stats/labelled.json
+++ b/data/s2bms/aux_stats/labelled.json
@@ -1,155 +1,155 @@
 {
     "aux_bioclim_01": {
-        "mean": 9.485303030303031,
-        "std": 0.6721779966099152
+        "mean": 9.478358208955223,
+        "std": 0.6813968574277965
     },
     "aux_bioclim_02": {
-        "mean": 7.2680303030303035,
-        "std": 0.5689534430067483
+        "mean": 7.261727078891258,
+        "std": 0.5743689424967182
     },
     "aux_bioclim_03": {
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diff --git a/data/s2bms/aux_stats/unlabelled.json b/data/s2bms/aux_stats/unlabelled.json
index fff3178..236fa13 100644
--- a/data/s2bms/aux_stats/unlabelled.json
+++ b/data/s2bms/aux_stats/unlabelled.json
@@ -1,127 +1,127 @@
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+        "std": 119.56237681535922
     },
     "aux_corine_frac_111": {
-        "mean": 0.0012381316493298737,
-        "std": 0.017396567488694732
+        "mean": 0.0013602792796026007,
+        "std": 0.019313170626270262
     },
     "aux_corine_frac_112": {
-        "mean": 0.05024036318228254,
-        "std": 0.13242626320000173
+        "mean": 0.050006013167217804,
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     "aux_corine_frac_121": {
-        "mean": 0.009470084490101288,
-        "std": 0.03977666167613134
+        "mean": 0.009250862708691302,
+        "std": 0.03881985995144867
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     "aux_corine_frac_122": {
-        "mean": 0.0008100089562997777,
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+        "mean": 0.0007880240503307102,
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     "aux_corine_frac_123": {
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-        "std": 0.008277578227576089
+        "mean": 0.00046706624328041413,
+        "std": 0.009420263901164894
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     "aux_corine_frac_124": {
-        "mean": 0.0011437095240403085,
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+        "mean": 0.0011089385169889571,
+        "std": 0.014364655530256759
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     "aux_corine_frac_131": {
-        "mean": 0.0024881257927702608,
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+        "mean": 0.002571445974733077,
+        "std": 0.02003256510611235
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     "aux_corine_frac_132": {
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-        "std": 0.0036346243381618244
+        "mean": 0.00017665416030145643,
+        "std": 0.0035253961655689545
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     "aux_corine_frac_133": {
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-        "std": 0.00739866412271175
+        "mean": 0.0005756500688074249,
+        "std": 0.007402470282847353
     },
     "aux_corine_frac_141": {
-        "mean": 0.0025903592606651414,
-        "std": 0.02008876942855595
+        "mean": 0.0028120972814816856,
+        "std": 0.021223127264056292
     },
     "aux_corine_frac_142": {
-        "mean": 0.011157809712526666,
-        "std": 0.03689460353806993
+        "mean": 0.011166278833193702,
+        "std": 0.038165164275206914
     },
     "aux_corine_frac_211": {
-        "mean": 0.2576389518683456,
-        "std": 0.3267167437405614
+        "mean": 0.2586450775682287,
+        "std": 0.32966642776729954
     },
     "aux_corine_frac_212": {
         "mean": 0.0,
@@ -136,215 +136,215 @@
         "std": 0.0
     },
     "aux_corine_frac_222": {
-        "mean": 0.0003715637297008934,
-        "std": 0.008382979498378294
+        "mean": 0.0003398871933901921,
+        "std": 0.008045773480885858
     },
     "aux_corine_frac_223": {
         "mean": 0.0,
         "std": 0.0
     },
     "aux_corine_frac_231": {
-        "mean": 0.27627695932709384,
-        "std": 0.2941670917165106
+        "mean": 0.27208956545103624,
+        "std": 0.29460130010474606
     },
     "aux_corine_frac_241": {
         "mean": 0.0,
         "std": 0.0
     },
     "aux_corine_frac_242": {
-        "mean": 0.0014760015337967132,
-        "std": 0.021106612715894486
+        "mean": 0.0015301132476283517,
+        "std": 0.022541814707966536
     },
     "aux_corine_frac_243": {
-        "mean": 0.005364096217631734,
-        "std": 0.02946542868616639
+        "mean": 0.0053086880969759696,
+        "std": 0.029445598088384678
     },
     "aux_corine_frac_244": {
-        "mean": 1.0015032939121665e-05,
-        "std": 0.0009974389585585735
+        "mean": 1.4017089279405644e-05,
+        "std": 0.0011800191515226413
     },
     "aux_corine_frac_311": {
-        "mean": 0.02141707060090821,
-        "std": 0.05388296072110032
+        "mean": 0.020470399162633234,
+        "std": 0.052253302577792045
     },
     "aux_corine_frac_312": {
-        "mean": 0.04905874518444355,
-        "std": 0.12687690033450122
+        "mean": 0.049353532759643035,
+        "std": 0.12652637234654923
     },
     "aux_corine_frac_313": {
-        "mean": 0.012682777670815144,
-        "std": 0.0457894393833769
+        "mean": 0.01244167875010827,
+        "std": 0.046049545976652805
     },
     "aux_corine_frac_321": {
-        "mean": 0.05954866447921192,
-        "std": 0.14481817436759822
+        "mean": 0.059258514947408365,
+        "std": 0.14397917118825002
     },
     "aux_corine_frac_322": {
-        "mean": 0.0748806007487766,
-        "std": 0.17334097442585797
+        "mean": 0.07682161065734752,
+        "std": 0.17283380584540975
     },
     "aux_corine_frac_323": {
         "mean": 0.0,
         "std": 0.0
     },
     "aux_corine_frac_324": {
-        "mean": 0.016518766078724344,
-        "std": 0.05970527218851874
+        "mean": 0.016625096426279753,
+        "std": 0.05992790760448416
     },
     "aux_corine_frac_331": {
-        "mean": 0.00037079828146080804,
-        "std": 0.010036436983798991
+        "mean": 0.0004443415806525385,
+        "std": 0.011574430529737981
     },
     "aux_corine_frac_332": {
-        "mean": 0.00072720624369643,
-        "std": 0.01157786569314129
+        "mean": 0.0006745155596987792,
+        "std": 0.011643913566303933
     },
     "aux_corine_frac_333": {
-        "mean": 0.012274641287433904,
-        "std": 0.06402570315588603
+        "mean": 0.011641901867519025,
+        "std": 0.06223229237384561
     },
     "aux_corine_frac_334": {
-        "mean": 7.092218256869019e-06,
-        "std": 0.0005991380211712938
+        "mean": 0.0,
+        "std": 0.0
     },
     "aux_corine_frac_335": {
         "mean": 0.0,
         "std": 0.0
     },
     "aux_corine_frac_411": {
-        "mean": 0.0011926928129083708,
-        "std": 0.0177629896973229
+        "mean": 0.0014524852178632711,
+        "std": 0.020316740577884202
     },
     "aux_corine_frac_412": {
-        "mean": 0.09765702521201046,
-        "std": 0.23604396908149422
+        "mean": 0.10249822833445148,
+        "std": 0.24051158401945827
     },
     "aux_corine_frac_421": {
-        "mean": 0.0015827466271050973,
-        "std": 0.01956554800791187
+        "mean": 0.001392197302331657,
+        "std": 0.01753513296020852
     },
     "aux_corine_frac_422": {
         "mean": 0.0,
         "std": 0.0
     },
     "aux_corine_frac_423": {
-        "mean": 0.003933563237704212,
-        "std": 0.032531498501610204
+        "mean": 0.004125761567925976,
+        "std": 0.03422460913306942
     },
     "aux_corine_frac_511": {
-        "mean": 0.000561918266933462,
-        "std": 0.009163339943300926
+        "mean": 0.0005947060170377448,
+        "std": 0.009810879261322713
     },
     "aux_corine_frac_512": {
-        "mean": 0.009717452251824635,
-        "std": 0.06122992674436525
+        "mean": 0.008423078073735008,
+        "std": 0.05239152569781716
     },
     "aux_corine_frac_521": {
-        "mean": 2.297982526156031e-05,
-        "std": 0.0014667058518795608
+        "mean": 2.05628405890116e-05,
+        "std": 0.0014344231921542916
     },
     "aux_corine_frac_522": {
-        "mean": 0.0021231797373174512,
-        "std": 0.024802238531671218
+        "mean": 0.002235854221870117,
+        "std": 0.02562336613443482
     },
     "aux_corine_frac_523": {
-        "mean": 0.014291723592658335,
-        "std": 0.0710510070030012
+        "mean": 0.013314875781737049,
+        "std": 0.06729427140720412
     },
     "aux_corine_frac_1": {
-        "mean": 0.0802927679330406,
-        "std": 0.18169086219409286
+        "mean": 0.08028331028462915,
+        "std": 0.1825027053945205
     },
     "aux_corine_frac_2": {
-        "mean": 0.5411375877095079,
-        "std": 0.38787876580612907
+        "mean": 0.5379273486465389,
+        "std": 0.3899391765334726
     },
     "aux_corine_frac_3": {
-        "mean": 0.2474863627937278,
-        "std": 0.31398304905390934
+        "mean": 0.24773159171129058,
+        "std": 0.31469171199310836
     },
     "aux_corine_frac_4": {
-        "mean": 0.10436602788972815,
-        "std": 0.2382482307321956
+        "mean": 0.1094686724225724,
+        "std": 0.24278980948174828
     },
     "aux_corine_frac_5": {
-        "mean": 0.026717253673995443,
-        "std": 0.09617029289776713
+        "mean": 0.024589076934968934,
+        "std": 0.08840739214247646
     },
     "aux_corine_frac_11": {
-        "mean": 0.05147849483161241,
-        "std": 0.13756143932672826
+        "mean": 0.0513662924468204,
+        "std": 0.13701592738232227
     },
     "aux_corine_frac_12": {
-        "mean": 0.011823189037622089,
-        "std": 0.04688613460990673
+        "mean": 0.011614891519291386,
+        "std": 0.0460258628072768
     },
     "aux_corine_frac_13": {
-        "mean": 0.0032429150906142703,
-        "std": 0.02152271396563153
+        "mean": 0.003323750203841959,
+        "std": 0.021796606691765877
     },
     "aux_corine_frac_14": {
-        "mean": 0.01374816897319181,
-        "std": 0.04341235709705624
+        "mean": 0.013978376114675386,
+        "std": 0.0452558663201929
     },
     "aux_corine_frac_21": {
-        "mean": 0.2576389518683456,
-        "std": 0.3267167437405614
+        "mean": 0.2586450775682287,
+        "std": 0.32966642776729954
     },
     "aux_corine_frac_22": {
-        "mean": 0.0003715637297008934,
-        "std": 0.008382979498378294
+        "mean": 0.0003398871933901921,
+        "std": 0.008045773480885858
     },
     "aux_corine_frac_23": {
-        "mean": 0.27627695932709384,
-        "std": 0.2941670917165106
+        "mean": 0.27208956545103624,
+        "std": 0.29460130010474606
     },
     "aux_corine_frac_24": {
-        "mean": 0.00685011278436757,
-        "std": 0.03645831010427902
+        "mean": 0.0068528184338837285,
+        "std": 0.03744219094823718
     },
     "aux_corine_frac_31": {
-        "mean": 0.08315859345616691,
-        "std": 0.14797484710834283
+        "mean": 0.08226561067238455,
+        "std": 0.14632419826435697
     },
     "aux_corine_frac_32": {
-        "mean": 0.15094803130671286,
-        "std": 0.25041112285241357
+        "mean": 0.1527052220310357,
+        "std": 0.2502187615368793
     },
     "aux_corine_frac_33": {
-        "mean": 0.01337973803084801,
-        "std": 0.06768695209323439
+        "mean": 0.012760759007870343,
+        "std": 0.06572354806621297
     },
     "aux_corine_frac_41": {
-        "mean": 0.09884971802491883,
-        "std": 0.23626719477301794
+        "mean": 0.10395071355231476,
+        "std": 0.24079581091787888
     },
     "aux_corine_frac_42": {
-        "mean": 0.00551630986480931,
-        "std": 0.04218330504440695
+        "mean": 0.005517958870257633,
+        "std": 0.042508099721360604
     },
     "aux_corine_frac_51": {
-        "mean": 0.010279370518758095,
-        "std": 0.061858529662706774
+        "mean": 0.009017784090772753,
+        "std": 0.05325796209639183
     },
     "aux_corine_frac_52": {
-        "mean": 0.016437883155237348,
-        "std": 0.07527943347488333
+        "mean": 0.01557129284419618,
+        "std": 0.07195454194364073
     },
     "aux_total_population": {
-        "mean": 1384.9394092146385,
-        "std": 4642.19802237972
+        "mean": 1394.7764921687597,
+        "std": 4643.2852761894
     },
     "aux_pop_density": {
-        "mean": 212.1150317572336,
-        "std": 712.2522496739724
+        "mean": 213.62537039650064,
+        "std": 712.4218778428469
     },
     "aux_maxdist_road": {
-        "mean": 2228.0529287226536,
-        "std": 1309.984693451521
+        "mean": 2232.251869620432,
+        "std": 1319.1743912122638
     },
     "aux_meandist_road": {
-        "mean": 1230.6387740699668,
-        "std": 1292.7376322660916
+        "mean": 1239.2566671370114,
+        "std": 1311.269053382563
     }
 }

From eb32ee06628a823f98b012cb10953eee96a686a4 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 09:51:45 +0200
Subject: [PATCH 161/173] Fix error of missing wandb logger

---
 src/train.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/train.py b/src/train.py
index 3e170c8..11b2b08 100644
--- a/src/train.py
+++ b/src/train.py
@@ -139,7 +139,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
 
     train_metrics = trainer.callback_metrics
 
-    if cfg.get("validate"):
+    if cfg.get("validate") and wandb_logger is not None:
         # Run validation with the best ckpt
         log.info("Validating the best ckpt!")
         ckpt_path = trainer.checkpoint_callback.best_model_path
@@ -157,7 +157,7 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         val_metrics = trainer.callback_metrics
         wandb_logger.log_metrics({f"best_val_{k}": v for k, v in val_metrics.items()})
 
-    if cfg.get("test"):
+    if cfg.get("test") and wandb_logger is not None:
         log.info("Starting testing!")
         ckpt_path = trainer.checkpoint_callback.best_model_path
         if ckpt_path == "":

From 83608057cb4da540ec16fc58b58606b7ffa01266 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 09:52:16 +0200
Subject: [PATCH 162/173] Fix argument order mix-up

---
 src/models/components/loss_fns/soft_contrastive_loss.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/models/components/loss_fns/soft_contrastive_loss.py b/src/models/components/loss_fns/soft_contrastive_loss.py
index 2a6fa1f..f97a4b8 100644
--- a/src/models/components/loss_fns/soft_contrastive_loss.py
+++ b/src/models/components/loss_fns/soft_contrastive_loss.py
@@ -30,7 +30,9 @@ def forward(
         """Forward computation."""
 
         # Get target matrix
-        T = self._get_soft_target_matrix(aux_ids_per_caption, aux_values)
+        T = self._get_soft_target_matrix(
+            aux_values=aux_values, aux_ids_per_caption=aux_ids_per_caption
+        )
 
         # Normalize targets
         T_eo2text = T / T.sum(dim=1, keepdim=True)

From a030e606ee694a13aef7e6d4123b634a89adc78e Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 09:53:06 +0200
Subject: [PATCH 163/173] Add aux stats computation notebook

---
 notebooks/12-GT-aux-stats.ipynb | 124 ++++++++++++++++++++++++++++++++
 1 file changed, 124 insertions(+)
 create mode 100644 notebooks/12-GT-aux-stats.ipynb

diff --git a/notebooks/12-GT-aux-stats.ipynb b/notebooks/12-GT-aux-stats.ipynb
new file mode 100644
index 0000000..d7bc8cf
--- /dev/null
+++ b/notebooks/12-GT-aux-stats.ipynb
@@ -0,0 +1,124 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import os\n",
+    "\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
+    "os.chdir(\"..\")\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labelled = True\n",
+    "\n",
+    "output_dir = \"/Users/gabriele/Desktop/aether/data/s2bms/aux_stats/\"\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "if labelled:\n",
+    "    df = pd.read_csv(\"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms.csv\")\n",
+    "    output_path = output_dir + \"labelled.json\"\n",
+    "else:\n",
+    "    df = pd.read_csv(\n",
+    "        \"/Users/gabriele/Desktop/aether/data/s2bms/model_ready_s2bms-unlabelled-20260529.csv\"\n",
+    "    )\n",
+    "    output_path = output_dir + \"unlabelled.json\"\n",
+    "\n",
+    "col = [i for i in df.columns if \"aux\" in i and \"top\" not in i]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load in original datasplit file\n",
+    "if labelled:\n",
+    "    pth = \"data/s2bms/splits/s2bms_union_val_test.pth\"\n",
+    "else:\n",
+    "    pth = \"data/s2bms/splits/s2bms_unlabelled_union_val_test.pth\"\n",
+    "\n",
+    "\n",
+    "split_indices = torch.load(pth, weights_only=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df[df[\"name_loc\"].isin(split_indices[\"train_indices\"])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if os.path.exists(output_path):\n",
+    "    print(f\"Output file already exists: {output_path}\")\n",
+    "else:\n",
+    "    stats = {}\n",
+    "    for col in col:\n",
+    "        stats[col] = {\n",
+    "            \"mean\": float(df[col].mean()),\n",
+    "            \"std\": float(df[col].std()),\n",
+    "        }\n",
+    "\n",
+    "        with open(output_path, \"w\") as f:\n",
+    "            json.dump(stats, f, indent=4)\n",
+    "\n",
+    "        print(f\"Saved global statistics for {len(stats)} variables to {output_path}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 120455be62ca21c2d830ef0508aeb718bf823d06 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 09:53:43 +0200
Subject: [PATCH 164/173] Add average embedding computation notebook

---
 notebooks/10-GT-avr_encoding.ipynb | 118 +++++++++++++++++++++++++++++
 1 file changed, 118 insertions(+)
 create mode 100644 notebooks/10-GT-avr_encoding.ipynb

diff --git a/notebooks/10-GT-avr_encoding.ipynb b/notebooks/10-GT-avr_encoding.ipynb
new file mode 100644
index 0000000..296c737
--- /dev/null
+++ b/notebooks/10-GT-avr_encoding.ipynb
@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "import os\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "\n",
+    "from src.utils.data_utils import center_crop_npy\n",
+    "\n",
+    "os.chdir(\"..\")\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1",
+   "metadata": {},
+   "source": [
+    "# Calculating from tiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mod = \"tessera\"\n",
+    "size = 128\n",
+    "\n",
+    "paths = glob.glob(f\"data/s2bms/eo/{mod}/*UKBMS*.npy\")\n",
+    "rows = []\n",
+    "for p in paths:\n",
+    "    arr = np.load(p).transpose(2, 0, 1)\n",
+    "    if arr.dtype != np.dtype(\"float32\"):\n",
+    "        arr = arr.astype(dtype=\"float32\", copy=False)\n",
+    "    arr = center_crop_npy(arr, (64 if mod == \"aef\" else 128, size, size))\n",
+    "    if np.isinf(arr).any():\n",
+    "        arr[np.isinf(arr)] = np.nan\n",
+    "    tensor = torch.from_numpy(arr)\n",
+    "    row = {f\"emb_{i}\": v.item() for i, v in enumerate(tensor.nanmean(dim=(-2, -1)))}\n",
+    "    row[\"name_loc\"] = p.split(\"/\")[-1].split(\".\")[0].replace(f\"{mod}_\", \"\")\n",
+    "    rows.append(row)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(rows)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if not os.path.exists(f\"data/s2bms/eo/avr_{mod}_{size}.csv\"):\n",
+    "    df.to_csv(f\"data/s2bms/eo/avr_{mod}_{size}.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5",
+   "metadata": {},
+   "source": [
+    "# Merging-in unlabelled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "size = 128\n",
+    "mod = \"aef\"\n",
+    "\n",
+    "df = pd.read_csv(f\"data/s2bms/eo/avr_{mod}_{size}.csv\")\n",
+    "df_unlabelled = pd.read_csv(f\"data/s2bms/eo/avr_{mod}_{size}_just_unlabelled.csv\")\n",
+    "\n",
+    "df_merged = pd.concat([df, df_unlabelled])\n",
+    "df_merged.name_loc\n",
+    "\n",
+    "if not os.path.exists(f\"data/s2bms/eo/avr_{mod}_{size}_unlabelled.csv\"):\n",
+    "    df.to_csv(f\"data/s2bms/eo/avr_{mod}_{size}_unlabelled.csv\")\n",
+    "else:\n",
+    "    print(\"Already saved\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0610a49024266302f340cc47564a5db1b57b6393 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 11:58:05 +0200
Subject: [PATCH 165/173] Fix config for the tests

---
 configs/experiment/alignment.yaml | 44 ++++++++++++++++++++++++++-----
 1 file changed, 38 insertions(+), 6 deletions(-)

diff --git a/configs/experiment/alignment.yaml b/configs/experiment/alignment.yaml
index d6281f8..4261a35 100644
--- a/configs/experiment/alignment.yaml
+++ b/configs/experiment/alignment.yaml
@@ -4,13 +4,48 @@
 # python train.py experiment=example
 
 defaults:
-  - override /model: geoclip_alignment
-  - override /data: butterfly_coords_text
-  - override /metrics: contrastive_similarities
+  - override /model: s2bms_alignment
+  - override /data: s2bms_prediction
+  - override /metrics: s2bms_alignment
 
 # all parameters below will be merged with parameters from default configurations set above
 # this allows you to overwrite only specified parameters
 
+model:
+  geo_encoder:
+    _target_: src.models.components.geo_encoders.geoclip.GeoClipCoordinateEncoder
+    geo_data_name: coords
+  geo_adapter:
+    _target_: src.models.components.projectors_adapters.mlp_projector.MLPProjector
+    nn_layers: 2
+    hidden_dim: 512
+    output_dim: 768
+  text_encoder:
+    _target_: src.models.components.text_encoders.clip_text_encoder.ClipTextEncoder
+    hf_cache_dir: ${paths.huggingface_cache}
+    use_geoclip_projector: false
+  loss_fn:
+    _target_: src.models.components.loss_fns.clip_loss.ClipLoss
+    temperature: 0.07
+  trainable_modules: [loss_fn.log_temp]
+
+data:
+  dataset:
+    modalities:
+      coords:
+    use_target_data: false
+    use_aux_data: "all"
+    dtype: float32
+  caption_builder:
+    _target_: src.data.butterfly_caption_builder.ButterflyCaptionBuilder
+    templates_fname: v5.json
+    concepts_fname: v4.json
+    data_dir: ${paths.data_dir}/s2bms
+    seed: ${seed}
+    n_captions_for_validation: 50
+  saved_split_file_name: "s2bms_unlabelled_union_val_test.pth"
+  batch_size: 32
+
 tags: ["alignment", "geoclip_coords", "geoclip_text"]
 
 seed: 12345
@@ -19,9 +54,6 @@ trainer:
   min_epochs: 10
   max_epochs: 100
 
-data:
-  batch_size: 64
-
 logger:
   wandb:
     tags: ${tags}

From ac9b5df7980e12521b85e01d66c245720ead48a5 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 11:58:49 +0200
Subject: [PATCH 166/173] Fix tests

---
 src/data/base_caption_builder.py | 3 +--
 tests/test_geo_encoders.py       | 6 +-----
 2 files changed, 2 insertions(+), 7 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index 50c4afd..ef99389 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -62,8 +62,7 @@ def __init__(
 
         self.return_aux_ids = return_aux_ids
 
-        if stats_file:
-            self.stats = json.load(open(stats_file))
+        self.stats = json.load(open(stats_file)) if stats_file else None
 
     @final
     def __len__(self):
diff --git a/tests/test_geo_encoders.py b/tests/test_geo_encoders.py
index d4f9add..5490eac 100644
--- a/tests/test_geo_encoders.py
+++ b/tests/test_geo_encoders.py
@@ -4,7 +4,6 @@
 from src.models.components.geo_encoders.cnn_encoder import CNNEncoder
 from src.models.components.geo_encoders.geoclip import GeoClipCoordinateEncoder
 from src.models.components.geo_encoders.tabular_encoder import TabularEncoder
-from src.models.components.projectors_adapters.mlp_projector import MLPProjector
 
 
 # @pytest.mark.slow
@@ -15,16 +14,13 @@ def test_geo_encoder_generic_properties(create_butterfly_dataset):
         "cnn": CNNEncoder,
         "average": AverageEncoder,
         "tabular": TabularEncoder,
-        "mlp_projector": MLPProjector,
     }
     ds, dm = create_butterfly_dataset
     dm.setup()
     batch = next(iter(dm.train_dataloader()))
 
     for geo_encoder_name, geo_encoder_class in dict_geo_encoders.items():
-        if geo_encoder_class is MLPProjector:
-            geo_encoder = geo_encoder_class(output_dim=64, input_dim=128)
-        elif geo_encoder_class is TabularEncoder:
+        if geo_encoder_class is TabularEncoder:
             geo_encoder = geo_encoder_class(output_dim=64, input_dim=128, hidden_dim=128)
         else:
             geo_encoder = geo_encoder_class()

From f7f352d29e219f6b143c2f649d7d58b3647927f7 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 12:03:47 +0200
Subject: [PATCH 167/173] Correct explanations

---
 data/s2bms/aux_stats/explanations | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/data/s2bms/aux_stats/explanations b/data/s2bms/aux_stats/explanations
index 7dcd42c..53dc9f6 100644
--- a/data/s2bms/aux_stats/explanations
+++ b/data/s2bms/aux_stats/explanations
@@ -1,2 +1,2 @@
-labelled - s2bms aux statistical values, all splits (model ready csv used)
-unlabelled - model_ready_s2bms-unlabelled-20260529.csv aucx col stats.
+labelled - s2bms aux statistical values, train split (from model_ready csv used)
+unlabelled - model_ready_s2bms-unlabelled-20260529.csv aux col stats for training data split.

From 9549ddf8a4dff975d7c9caf30ab05ab53690af41 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 12:14:38 +0200
Subject: [PATCH 168/173] Remove duplicate

---
 src/data_preprocessing/get_avr_embeds.py | 61 ------------------------
 1 file changed, 61 deletions(-)
 delete mode 100644 src/data_preprocessing/get_avr_embeds.py

diff --git a/src/data_preprocessing/get_avr_embeds.py b/src/data_preprocessing/get_avr_embeds.py
deleted file mode 100644
index 9eee19a..0000000
--- a/src/data_preprocessing/get_avr_embeds.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import glob
-
-import numpy as np
-import pandas as pd
-import rasterio
-import torch
-
-from src.utils.data_utils import center_crop_npy
-
-
-def _load_tiff(tiff_file_path: str, dtype: np.dtype) -> np.ndarray:
-    """Load tiff files as desired dtype np arrays."""
-    with rasterio.open(tiff_file_path) as f:
-        im = f.read()
-        assert isinstance(im, np.ndarray)
-        if im.dtype != np.dtype(dtype):
-            im = im.astype(dtype=dtype, copy=False)
-    return im
-
-
-def _load_npy(filepath: str, dtype: np.dtype) -> np.ndarray:
-    """Load npy files as desired dtype np arrays."""
-    arr = np.load(filepath).transpose(2, 0, 1)
-    if arr.dtype != np.dtype(dtype):
-        arr = arr.astype(dtype=dtype, copy=False)
-    return arr
-
-
-def main():
-    """Compute average embeddings into csv file."""
-    size = 128
-    mod = "aef"
-    format = "tif"
-
-    # save aef averages
-    paths = glob.glob(f"data/s2bms/eo/{mod}/*.{format}")
-
-    rows = []
-    for p in paths:
-        if format == "npy":
-            im = _load_npy(p, np.dtype("float32"))
-        else:
-            im = _load_tiff(p, np.dtype("float32"))
-        im = center_crop_npy(im, (64 if mod == "aef" else 128, size, size))
-        tile = torch.from_numpy(im)
-        tile = tile.nanmean(dim=(-2, -1))  # shape [64]
-        row = {f"avr_{i}": v.item() for i, v in enumerate(tile)}
-        row["name_loc"] = f"UKBMS_loc-{p.split('-')[-1].split('.')[0]}"
-        rows.append(row)
-
-    df = pd.DataFrame(rows)
-    df.to_csv(f"data/s2bms/eo/{mod}_avr_{size}.csv", index=False)
-    print(f"Saved to data/s2bms/eo/{mod}_avr_{size}.csv")
-
-
-if __name__ == "__main__":
-    import os
-
-    os.chdir("../..")
-    print(os.getcwd())
-    main()

From 8ff3ce1b20d9e0ce444d2e4c1241a2ac045c2444 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 13:58:46 +0200
Subject: [PATCH 169/173] Automatic experiment name generation

---
 src/train.py                     |  4 +-
 src/utils/experiment_tracking.py | 82 +++++++++++++++++++++++++-------
 2 files changed, 69 insertions(+), 17 deletions(-)

diff --git a/src/train.py b/src/train.py
index 11b2b08..f29af2f 100644
--- a/src/train.py
+++ b/src/train.py
@@ -10,7 +10,7 @@
 from omegaconf import DictConfig, OmegaConf
 
 from src.data.base_datamodule import BaseDataModule
-from src.utils.experiment_tracking import experiment_check
+from src.utils.experiment_tracking import compose_experiment_name, experiment_check
 
 rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
 load_dotenv()
@@ -96,6 +96,8 @@ def train(cfg: DictConfig) -> Tuple[Dict[str, Any], Dict[str, Any]]:
         log.info("Logging hyperparameters!")
         log_hyperparameters(object_dict)
         group = cfg.get("experiment_name", "null")
+        if group == "null":
+            compose_experiment_name(cfg)
         wandb_logger.log_metrics({"experiment": group})
 
     if cfg.get("train"):
diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index 8623a2f..ccc782f 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -12,6 +12,71 @@
 ENTITY = "aether_xai"
 
 
+def parse_data_name(run=None, cfg=None):
+    if cfg is None:
+        assert run is not None
+        data_dict = run.config["data"]["dataset"]["modalities"]
+    else:
+        data_dict = cfg["data"]["dataset"]["modalities"]
+
+    if len(data_dict) == 1:
+        k = list(data_dict.keys())[0]
+        if k == "coords":
+            if "GeoClip" in run.config["model"]["geo_encoder"]["_target_"]:
+                data_name = "geoclip"
+            else:
+                data_name = "satclip"
+        else:
+            ks = list(data_dict.keys())
+            ks_new = [
+                f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
+                for k in ks
+            ]
+            data_name = "-".join(map(str, ks_new))
+
+        return data_name
+
+
+def compose_experiment_name(cfg: DictConfig) -> str:
+    """For alignment
+    (unlab_)(geo_encoder)_(eo_mod)_(geo_adapter)_(text_encoder)_(text_adapter)_(batch_size) # noqa:
+
+    RST306.
+    """
+
+    name = ""
+
+    # Unlabeled
+    if cfg.data.dataset.use_unlabelled_data:
+        name += "unlab_"  # noqa: RST306
+
+    # geo encoder
+    name += cfg.model.geo_encoder._target_.split(".")[-1]
+
+    # eo mod
+    name += "_" + parse_data_name(cfg=cfg)
+
+    # geo adapter
+    if cfg.model.get("geo_adapter") is not None:
+        name += "_" + cfg.model.geo_adapter._target_.split(".")[-1]
+
+    # text encoder
+    name += "_" + cfg.model.text_encoder._target_.split(".")[-1]
+
+    # text adapter
+    if cfg.model.get("text_adapter") is not None:
+        name += "_" + cfg.model.text_adapter._target_.split(".")[-1]
+
+    # batch size
+    name += f"_b-{cfg.data.batch_size}"
+
+    # extras for experiment identification
+    if cfg.get("experiment_name_extra"):
+        name += "_" + cfg.experiment_name_extra
+
+    return name
+
+
 def update_wandb_run_id(run_id, project_name, updates_dict):
     api = wandb.Api()
     entity = ENTITY
@@ -107,22 +172,7 @@ def get_experiments_from_wandb(cfg: DictConfig) -> pd.DataFrame | None:
                 if experiment is None:
                     experiment = run.config["experiment_name"]
                     run.summary.update({"experiment": experiment})
-
-                data_dict = run.config["data"]["dataset"]["modalities"]
-                if len(data_dict) == 1:
-                    k = list(data_dict.keys())[0]
-                    if k == "coords":
-                        if "GeoClip" in run.config["model"]["geo_encoder"]["_target_"]:
-                            data_name = "geoclip"
-                        else:
-                            data_name = "satclip"
-                    else:
-                        ks = list(data_dict.keys())
-                        ks_new = [
-                            f"{k}{f'_{k.get('size')}' if isinstance(k, dict) and k.get('size') else ''}"
-                            for k in ks
-                        ]
-                        data_name = "-".join(map(str, ks_new))
+                    data_name = parse_data_name(run=run)
                     run.summary.update({"data_used": data_name})
 
                 # Add missing experiments to the table

From 40f1a1808115d35fb47541e99f0747a67f12b8d3 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Wed, 17 Jun 2026 14:02:45 +0200
Subject: [PATCH 170/173] Automatic experiment name generation for prediction
 models

---
 src/utils/experiment_tracking.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/src/utils/experiment_tracking.py b/src/utils/experiment_tracking.py
index ccc782f..4f99ae0 100644
--- a/src/utils/experiment_tracking.py
+++ b/src/utils/experiment_tracking.py
@@ -56,19 +56,26 @@ def compose_experiment_name(cfg: DictConfig) -> str:
     # eo mod
     name += "_" + parse_data_name(cfg=cfg)
 
-    # geo adapter
-    if cfg.model.get("geo_adapter") is not None:
-        name += "_" + cfg.model.geo_adapter._target_.split(".")[-1]
+    # batch size
+    if cfg.model._target_ == "src.models.text_alignment_model.TextAlignmentModel":
+        # geo adapter
+        if cfg.model.get("geo_adapter") is not None:
+            name += "_" + cfg.model.geo_adapter._target_.split(".")[-1]
 
-    # text encoder
-    name += "_" + cfg.model.text_encoder._target_.split(".")[-1]
+        # text encoder
+        if cfg.model.get("text_adapter") is not None:
+            name += "_" + cfg.model.text_encoder._target_.split(".")[-1]
 
-    # text adapter
-    if cfg.model.get("text_adapter") is not None:
-        name += "_" + cfg.model.text_adapter._target_.split(".")[-1]
+        # text adapter
+        if cfg.model.get("text_adapter") is not None:
+            name += "_" + cfg.model.text_adapter._target_.split(".")[-1]
 
-    # batch size
-    name += f"_b-{cfg.data.batch_size}"
+        # batch size
+        name += f"_b-{cfg.data.batch_size}"
+    else:
+        # prediction head
+        if cfg.model.get("prediction_head") is not None:
+            name += "_" + cfg.model.prediction_head._target_.split(".")[-1]
 
     # extras for experiment identification
     if cfg.get("experiment_name_extra"):

From 0738579710ed978e0b7b55fbd4866c0bf9b7df1c Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 19 Jun 2026 11:57:05 +0200
Subject: [PATCH 171/173] Move aux col statistics from datamodule to loss
 function

---
 src/data/base_caption_builder.py              |  4 ---
 src/data/butterfly_caption_builder.py         | 17 ---------
 src/data/collate_fns.py                       |  4 ---
 src/models/base_model.py                      |  3 ++
 .../components/loss_fns/base_loss_fn.py       |  8 ++++-
 .../loss_fns/soft_contrastive_loss.py         | 36 +++++++++++++++++--
 src/models/text_alignment_model.py            |  2 +-
 7 files changed, 45 insertions(+), 29 deletions(-)

diff --git a/src/data/base_caption_builder.py b/src/data/base_caption_builder.py
index ef99389..feffef2 100644
--- a/src/data/base_caption_builder.py
+++ b/src/data/base_caption_builder.py
@@ -20,7 +20,6 @@ def __init__(
         seed: int,
         n_captions_for_validation: int | str = "all",
         return_aux_ids: bool = False,
-        stats_file: str | None = None,
     ) -> None:
         """Interface of caption builder class for converting numerical auxiliary data values into
         textual descriptions from provided caption templates.
@@ -31,7 +30,6 @@ def __init__(
         :param seed: random seed.
         :param n_captions_for_validation: number of captions to randomly sample for validation
         :param return_aux_ids: whether to return auxiliary column ids.
-        :param stats_file: path to a json file with statistics of the aux cols (train split).
         """
 
         self.data_dir = data_dir
@@ -62,8 +60,6 @@ def __init__(
 
         self.return_aux_ids = return_aux_ids
 
-        self.stats = json.load(open(stats_file)) if stats_file else None
-
     @final
     def __len__(self):
         """Number of caption templates."""
diff --git a/src/data/butterfly_caption_builder.py b/src/data/butterfly_caption_builder.py
index dd3ae3a..a4cc22e 100644
--- a/src/data/butterfly_caption_builder.py
+++ b/src/data/butterfly_caption_builder.py
@@ -24,7 +24,6 @@ def __init__(
         seed: int,
         n_captions_for_validation: int | str = "all",
         return_aux_ids: bool = False,
-        stats_file: str | None = None,
     ) -> None:
         super().__init__(
             templates_fname,
@@ -33,7 +32,6 @@ def __init__(
             seed,
             n_captions_for_validation,
             return_aux_ids,
-            stats_file,
         )
 
     @override
@@ -58,21 +56,6 @@ def sync_with_dataset(self, dataset: BaseDataset) -> None:
                     "description": description,
                     "units": units,
                 }
-
-        # If auxiliary value statistics are provided, make them into tensors
-        if self.stats:
-            max_id = len(aux_columns) - 1
-            means = torch.zeros(max_id + 1)
-            stds = torch.ones(max_id + 1)
-
-            for name, stats in self.stats.items():
-                idx = self.column_to_metadata_map["aux"][name]["id"]
-                means[idx] = stats["mean"]
-                stds[idx] = stats["std"]
-
-            self.means = means
-            self.stds = stds + 1e-8
-
         self.sync_concepts()
 
     def get_corine_column_keys(self):
diff --git a/src/data/collate_fns.py b/src/data/collate_fns.py
index bf05aca..b5f3739 100644
--- a/src/data/collate_fns.py
+++ b/src/data/collate_fns.py
@@ -32,10 +32,6 @@ def collate_fn(
         batch_collected["aux"] = {
             k: smart_stack([item["aux"][k] for item in batch]) for k in batch[0]["aux"].keys()
         }
-        if caption_builder.stats is not None:
-            batch_collected["aux"]["aux_standardized"] = (
-                batch_collected["aux"]["aux"] - caption_builder.means
-            ) / caption_builder.stds
 
     if batch[0].get("target") is not None:
         batch_collected["target"] = smart_stack([item["target"] for item in batch])
diff --git a/src/models/base_model.py b/src/models/base_model.py
index 44d65a4..19694cb 100644
--- a/src/models/base_model.py
+++ b/src/models/base_model.py
@@ -88,6 +88,9 @@ def setup(self, stage: str) -> None:
             self.num_classes = self.trainer.datamodule.num_classes
             self.tabular_dim = self.trainer.datamodule.tabular_dim
 
+        # set up loss if needed
+        self.loss_fn.setup(datamodule=self.trainer.datamodule, device=self.device)
+
         # Per model logic of setting up
         self._setup(stage)
         self.setup_flag = True
diff --git a/src/models/components/loss_fns/base_loss_fn.py b/src/models/components/loss_fns/base_loss_fn.py
index cfa0097..4c53253 100644
--- a/src/models/components/loss_fns/base_loss_fn.py
+++ b/src/models/components/loss_fns/base_loss_fn.py
@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Dict
+from typing import Any, Dict
 
 import torch
 from torch import nn
@@ -21,3 +21,9 @@ def forward(
         **kwargs,
     ) -> torch.Tensor:
         pass
+
+    @abstractmethod
+    def setup(self, **kwargs: Any) -> None:
+        """Setup method for losses in case they need some parameters from the dataset (e.g., for
+        standardisation in the soft contrastive loss."""
+        pass
diff --git a/src/models/components/loss_fns/soft_contrastive_loss.py b/src/models/components/loss_fns/soft_contrastive_loss.py
index f97a4b8..758ea83 100644
--- a/src/models/components/loss_fns/soft_contrastive_loss.py
+++ b/src/models/components/loss_fns/soft_contrastive_loss.py
@@ -1,3 +1,4 @@
+import json
 from typing import Dict, List, Union
 
 import torch
@@ -5,18 +6,46 @@
 import torch.nn.functional as F
 from typing_extensions import override
 
+from src.data.base_datamodule import BaseDataModule
 from src.models.components.loss_fns.base_loss_fn import BaseLossFn
 
 
 class SoftContrastiveLoss(BaseLossFn):
-    def __init__(self, temperature: float = 0.07, sigma: float = 1.0):
+    def __init__(self, stats_file: str, temperature: float = 0.07, sigma: float = 1.0):
+        """Soft-Contrastive Loss Function which allows environmentally similar locations to be
+        treated as soft positives.
+
+        :param temperature: Soft-Contrastive Loss Function Temperature
+        :param sigma: Soft-Contrastive Loss Function Strength
+        :param stats_file: path to a json file with statistics of the aux cols (train split).
+        """
         super().__init__()
 
         self.log_temp = nn.Parameter(torch.log(torch.tensor(temperature)))
         self.sigma = sigma
 
+        self.stats = json.load(open(stats_file))
+
         self.name = "SoftContrastiveLoss"
 
+    @override
+    def setup(self, datamodule: BaseDataModule, device: torch.device):
+        """Extract auxiliary value statistics into tensors with correct column id sequence."""
+        max_id = len(datamodule.caption_builder.column_to_metadata_map["aux"])
+        means = torch.zeros(max_id)
+        stds = torch.ones(max_id)
+
+        for name, stats in self.stats.items():
+            # Synchronise aux col names into proper col ids
+            idx = datamodule.caption_builder.column_to_metadata_map["aux"][name]["id"]
+            means[idx] = stats["mean"]
+            stds[idx] = stats["std"]
+
+        self.means = means
+        self.means = self.means.to(device)
+        self.stds = stds + 1e-8
+        self.stds = self.stds.to(device)
+
     @override
     def forward(
         self,
@@ -28,7 +57,6 @@ def forward(
         **kwargs,
     ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
         """Forward computation."""
-
         # Get target matrix
         T = self._get_soft_target_matrix(
             aux_values=aux_values, aux_ids_per_caption=aux_ids_per_caption
@@ -74,6 +102,10 @@ def _get_soft_target_matrix(
         batch_size, n_aux_cols = aux_values.shape
         device = aux_values.device
 
+        # Standardise aux values
+        if self.stats is not None:
+            aux_values = (aux_values - self.means) / self.stds
+
         # Soft targets based on the squared difference between location i and j for all aux values
         diffs = (aux_values.unsqueeze(1) - aux_values.unsqueeze(0)) ** 2
 
diff --git a/src/models/text_alignment_model.py b/src/models/text_alignment_model.py
index 88f54cd..e693446 100644
--- a/src/models/text_alignment_model.py
+++ b/src/models/text_alignment_model.py
@@ -195,7 +195,7 @@ def _step(self, batch: Dict[str, torch.Tensor], mode: str = "train"):
             geo_feats, text_feats = feats[0], feats[1]
 
         # Get loss
-        aux_values = batch["aux"].get("aux_standardized")
+        aux_values = batch["aux"].get("aux")
         aux_ids_per_caption = batch.get("text_aux_ids")
 
         if geo_feats.isnan().any():

From 507c67663b26444d9804ab403cad1820a1f4d1af Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 19 Jun 2026 12:17:28 +0200
Subject: [PATCH 172/173] Global setup method

---
 src/models/components/loss_fns/base_loss_fn.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/src/models/components/loss_fns/base_loss_fn.py b/src/models/components/loss_fns/base_loss_fn.py
index 4c53253..780bb09 100644
--- a/src/models/components/loss_fns/base_loss_fn.py
+++ b/src/models/components/loss_fns/base_loss_fn.py
@@ -22,7 +22,6 @@ def forward(
     ) -> torch.Tensor:
         pass
 
-    @abstractmethod
     def setup(self, **kwargs: Any) -> None:
         """Setup method for losses in case they need some parameters from the dataset (e.g., for
         standardisation in the soft contrastive loss."""

From 48d42e98ee50fa8b9cf64667618db7b5ef346111 Mon Sep 17 00:00:00 2001
From: GabrieleTi <gabriele.tijunaityte@gmail.com>
Date: Fri, 19 Jun 2026 15:17:32 +0200
Subject: [PATCH 173/173] Add version support for tessera v1.1.

---
 pyproject.toml                           |  4 +-
 src/data/base_dataset.py                 |  2 +
 src/data_preprocessing/tessera_embeds.py | 52 +++++++++++++++++++-----
 3 files changed, 46 insertions(+), 12 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 44eed0a..be5fd24 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -33,7 +33,7 @@ dependencies = [
     "peft>=0.18.1",
     "llm2vec",
     "setuptools<81",
-    "geotessera>=0.8.0",
+    "geotessera>=0.9.0",
     "ipykernel>=7.2.0",
     "xgboost>=3.2.0",
     "albumentations",
@@ -44,7 +44,7 @@ dependencies = [
 create-data = [
     "geemap>=0.36.6",
     "pipreqs>=0.5.0",
-    "earthaccess>=0.11.0",
+#    "earthaccess>=0.11.0", #updated geotessera is incompatible with this one
     "pyhdf>=0.11.4",
 ]
 
diff --git a/src/data/base_dataset.py b/src/data/base_dataset.py
index 0012e39..1a5d37e 100644
--- a/src/data/base_dataset.py
+++ b/src/data/base_dataset.py
@@ -271,6 +271,7 @@ def setup_tessera(self) -> None:
         size = self.modalities["tessera"].get(
             "size", KeyError('Missing parameter "size" for Tessera modality')
         )
+        version = self.modalities["tessera"].get("version") or "v1.0"
 
         # If data does not exist or is empty → full download
         if not os.path.exists(dst_dir) or len(os.listdir(dst_dir)) == 0:
@@ -283,6 +284,7 @@ def setup_tessera(self) -> None:
                     year=year,
                     tile_size=size,
                     cache_dir=self.cache_dir,
+                    version=version,
                 )
 
                 # TODO: if we compile the dataset and use zenodo (or sth else) then change to pooch downloading/loading
diff --git a/src/data_preprocessing/tessera_embeds.py b/src/data_preprocessing/tessera_embeds.py
index 1f9432a..427be38 100644
--- a/src/data_preprocessing/tessera_embeds.py
+++ b/src/data_preprocessing/tessera_embeds.py
@@ -130,6 +130,7 @@ def get_tessera_embeds(
     save_dir: str,
     tile_size: int,
     tessera_con: GeoTessera | None,
+    version: str,
     padding: int = 100,
     save_tif=False,
 ) -> None:
@@ -236,7 +237,15 @@ def _close_all():
         if padding > 500:
             raise NoTileError(f"Padding {padding} > 500")
         return get_tessera_embeds(
-            lon, lat, name_loc, year, save_dir, tile_size, tessera_con, padding=padding + 100
+            lon,
+            lat,
+            name_loc,
+            year,
+            save_dir,
+            tile_size,
+            tessera_con,
+            padding=padding + 100,
+            version=version,
         )
 
     crop = mosaic[row_min:row_max, col_min:col_max, :]
@@ -276,7 +285,14 @@ def _close_all():
 
     # Log its metadata
     meta_df = pd.DataFrame(
-        {"id": [name_loc], "year": [year], "lon": [lon], "lat": [lat], "crs": [utm_crs]}
+        {
+            "id": [name_loc],
+            "year": [year],
+            "lon": [lon],
+            "lat": [lat],
+            "crs": [utm_crs],
+            "version": [version],
+        },
     )
 
     meta_file = f"{save_dir}/meta.csv"
@@ -298,6 +314,7 @@ def tessera_from_df(
     tile_size: int = 256,
     cache_dir: str = "temp/",
     logs_dir: str = "logs",
+    version="v1",
 ) -> None:
     """Obtains Tessera embeddings from a CSV file for each (lon, lat).
 
@@ -306,19 +323,29 @@ def tessera_from_df(
     :param year: year for the embeddings
     :param tile_size: tile size in meters
     :param cache_dir: path to cache directory
+    :param version: version of the tessera dataset
     :return: None
     """
-
+    assert version in ["v1", "v1.1"]
+    dataset_var = {"v1.1": "cambridge", "1.1": "cambridge", "1.0": "vultr", "v1.0": "vultr"}[
+        version
+    ]
     # Tessera connection
     cache_dir = os.path.join(cache_dir, "tessera")
-    gt = GeoTessera(cache_dir=cache_dir, embeddings_dir=cache_dir, dataset_version="v1")
-
+    gt = GeoTessera(
+        cache_dir=cache_dir,
+        embeddings_dir=cache_dir,
+        dataset_version=version,
+        dataset_variant=dataset_var,
+    )
     # Iter each coord
     n = len(model_ready_df)
     for i, row in model_ready_df.iterrows():
         print(f"{i}/{n}")
         try:
-            get_tessera_embeds(row.lon, row.lat, row.name_loc, year, f"{data_dir}/", tile_size, gt)
+            get_tessera_embeds(
+                row.lon, row.lat, row.name_loc, year, f"{data_dir}/", tile_size, gt, version
+            )
         except Exception as e:
             if isinstance(e, NoTileError):
                 path = os.path.join(logs_dir, "tessera_skipped.txt")
@@ -389,24 +416,29 @@ def inspect_np_arr_as_tiff(
 
 
 if __name__ == "__main__":
-    os.chdir("../..")
+    # os.chdir("../..")
 
     print(os.getcwd())
 
     # df = pd.read_csv("data/heat_guatemala/model_ready_heat_guatemala.csv")
     # df = pd.read_csv("/lustre/backup/SHARED/AIN/aether/data/s2bms/model_ready_s2bms.csv")
     df = pd.read_csv("data/s2bms/model_ready_s2bms.csv")
-    # df.sort_values(by="name_loc", inplace=True, ascending=False)
+
+    # df = pd.read_csv("data/s2bms/model_ready_s2bms-unlabelled-20260529.csv")
+    # df = df[df.split == "train"]
+    df.sort_values(by="name_loc", inplace=True, ascending=False)
+    df.reset_index(drop=True, inplace=True)
+
     if os.path.exists("logs/tessera_skipped.txt"):
         with open(os.path.join("logs", "tessera_skipped.txt")) as f:
             skipped = set(f.read().splitlines())
         df = df[~df.name_loc.isin(skipped)]
-    # df.sort_values('name_loc', ascending=False, inplace=True)
 
     tessera_from_df(
         df,
         "data/s2bms/eo/tessera",
-        year=2024,
+        year=2019,
         tile_size=256,
         cache_dir="data/cache",
+        version="v1.1",
     )