Example submission

.github/workflows/submit.yaml → .github/workflows/submit_example.yaml

@@ -16,7 +16,7 @@ jobs:
     strategy:
       matrix:
         python-version: ['3.13']
-        submission: []
+        submission: ['example']
 
     steps:
       - uses: actions/checkout@v3

requirements_example.txt

@@ -0,0 +1,2 @@
+pz-rail-base
+pz-rail-sklearn

tests/test_example.py

@@ -0,0 +1,250 @@
+import os
+from pathlib import Path
+import pytest
+
+from rail.core.data import TableHandle
+from rail.estimation.algos import sklearn_neurnet
+from rail.utils import catalog_utils
+
+from pz_data_challenge.taskset_1 import run_taskset_1
+from pz_data_challenge.taskset_2 import run_taskset_2
+
+from pz_data_challenge import submit_utils
+
+SUBMISSION_NAME: str = "example"
+SUBMISSION_URL: str = "https://s3df.slac.stanford.edu/people/echarles/example.tgz"
+
+# don't change these
+SUBMIT_DIR: str = f"submissions/{SUBMISSION_NAME}"
+PUBLIC_AREA: str = "tests/public"
+
+
+@pytest.fixture(name="setup_submit_area", scope="module")
+def setup_submit_area(request: pytest.FixtureRequest) -> int:
+
+    if not os.path.exists(SUBMIT_DIR):
+        submit_utils.download_and_extract_tar(SUBMISSION_URL, SUBMIT_DIR)
+
+    def teardown_submit_area() -> None:
+        if not os.environ.get("NO_TEARDOWN"):
+            os.system(f"\\rm -rf {SUBMIT_DIR}")
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_2"))
+    except Exception:
+        pass
+
+    try:
+        os.makedirs(os.path.join(SUBMIT_DIR, "outputs_3"))
+    except Exception:
+        pass
+
+    request.addfinalizer(teardown_submit_area)
+
+    catalog_utils.load_yaml("tests/catalogs.yaml")
+    catalog_utils.apply("cardinal_roman_rubin")
+
+    return 0
+
+
+def run_taskset_1_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = sklearn_neurnet.SklNeurNetEstimator.make_stage(
+        name="estimate",
+        model=model_file,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_1_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    train_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be trained
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = sklearn_neurnet.SklNeurNetInformer.make_stage(
+        name="inform",
+    )
+    model = informer.inform(train_data)
+
+    estimator = sklearn_neurnet.SklNeurNetEstimator.make_stage(
+        name="estimate",
+        model=model,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_estimation_only(
+    model_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run estimation for task set 1
+
+    This function should use a model stored in model_file, which
+    is downloaded as part of the submission tar file.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    model_file:
+        Path to the model.  This should be part of the submission
+        tar file.
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    test_data = TableHandle("test", path=test_file)
+    estimator = sklearn_neurnet.SklNeurNetEstimator.make_stage(
+        name="estimate",
+        model=model_file,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def run_taskset_2_training_and_estimation(
+    train_file: str | Path,
+    test_file: str | Path,
+    output_file: str | Path,
+) -> None:
+    """
+    User supplied function to run training and estimation for task set 1
+
+    This function should train a model and use it.
+
+    This function should write output data to output_file in qp
+    format.
+
+    Parameters
+    ----------
+    test_file:
+        Path to the test file contains the photometric test data on
+        which the PZ estimation will be run
+    output_file:
+        Path to write the output data to.  The output data should
+        be written in qp format.
+    """
+    train_data = TableHandle("train", path=train_file)
+    test_data = TableHandle("test", path=test_file)
+
+    informer = sklearn_neurnet.SklNeurNetInformer.make_stage(
+        name="inform",
+    )
+    model = informer.inform(train_data)
+
+    estimator = sklearn_neurnet.SklNeurNetEstimator.make_stage(
+        name="estimate",
+        model=model,
+        output_mode="return",
+    )
+    pz_out = estimator.estimate(test_data)
+    pz_out.data.ancil["object_id"] = test_data()["object_id"].astype(int)
+    pz_out.path = output_file
+    pz_out.write()
+
+
+def test_example_taskset_1(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 1
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_1(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_1_estimation_only,
+        run_taskset_1_training_and_estimation,
+    )
+
+
+def test_example_taskset_2(
+    setup_public_area: int,
+    setup_submit_area: int,
+) -> None:
+    """
+    Test fuction to validate a submisson for Taskset 1
+
+    You should not need to change this function
+    """
+
+    assert setup_public_area == 0
+    assert setup_submit_area == 0
+
+    run_taskset_2(
+        PUBLIC_AREA,
+        SUBMISSION_NAME,
+        run_taskset_2_estimation_only,
+        run_taskset_2_training_and_estimation,
+    )