Fix tests

.github/workflows/run_tests.yml

@@ -55,7 +55,7 @@ jobs:
     strategy:
       max-parallel: 4
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
       fail-fast: false
 
 

RELEASES.md

@@ -1,6 +1,10 @@
 # History of changes
 
-## 1.1.0 (Latest)
+## 1.1.1 (Latest)
+
++ Minor bug fix to ensure that all prediction calls can be carried.
+
+## 1.1.0
 
 + Updating documentation introduction of the package
 + Added the chi square divergence in GEMINIs: `gemclus.gemini.ChiSquareGEMINI`

gemclus/__init__.py

@@ -5,4 +5,4 @@
 __all__ = ['linear', 'mlp', 'sparse', 'data', 'nonparametric', 'gemini', 'tree', 'add_mlcl_constraint',
            '__version__', 'DiscriminativeModel']
 
-__version__ = '1.1.0'
+__version__ = '1.1.1'

gemclus/_base_gemini.py

@@ -8,9 +8,9 @@
 import numpy as np
 from sklearn.base import ClusterMixin, BaseEstimator
 from sklearn.neural_network._stochastic_optimizers import AdamOptimizer, SGDOptimizer
-from sklearn.utils import check_array, check_random_state
+from sklearn.utils import check_random_state
 from sklearn.utils._param_validation import Interval, StrOptions
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted, validate_data
 
 from gemclus.gemini import AVAILABLE_GEMINIS
 from .gemini._base_loss import _GEMINI
@@ -230,8 +230,7 @@ def fit(self, X, y=None):
         self._validate_params()
 
         # Check that X has the correct shape
-        X = check_array(X)
-        X = self._validate_data(X, accept_sparse=True, dtype=np.float64, ensure_min_samples=self.n_clusters)
+        X = validate_data(self, X, accept_sparse=False, dtype=np.float64, ensure_min_samples=self.n_clusters)
 
         # Fix the random seed
         random_state = check_random_state(self.random_state)
@@ -314,7 +313,7 @@ def predict_proba(self, X):
         check_is_fitted(self)
 
         # Input validation
-        X = check_array(X)
+        X = validate_data(self, X, accept_sparse=False, dtype=np.float64, reset=False)
 
         y_pred = self._infer(X, retain=False)
         return y_pred
@@ -337,7 +336,7 @@ def predict(self, X):
         check_is_fitted(self)
 
         # Input validation
-        X = check_array(X)
+        X = validate_data(self, X, accept_sparse=True, dtype=np.float64, reset=False)
 
         return np.argmax(self.predict_proba(X), axis=1)
 

gemclus/linear/_linear_geminis.py

@@ -7,7 +7,7 @@
 from sklearn.neural_network._stochastic_optimizers import AdamOptimizer, SGDOptimizer
 from sklearn.utils._param_validation import Interval, StrOptions
 from sklearn.utils.extmath import softmax
-from sklearn.utils.validation import check_is_fitted, check_array
+from sklearn.utils.validation import check_is_fitted, check_array, validate_data
 
 from .._base_gemini import DiscriminativeModel
 from ..gemini import MMDGEMINI, WassersteinGEMINI
@@ -579,5 +579,7 @@ def _compute_grads(self, X, y_pred, gradient):
 
 
     def predict_proba(self, X):
+        check_is_fitted(self)
+        X = validate_data(self, X, accept_sparse=False, reset=False)
         kernel = self._compute_kernel(X)
         return self._infer(kernel)

gemclus/sparse/_linear_sparse.py

@@ -5,6 +5,7 @@
 from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS
 from sklearn.neural_network._stochastic_optimizers import SGDOptimizer
 from sklearn.utils._param_validation import Interval, StrOptions
+from sklearn.utils.validation import validate_data
 
 from ._base_sparse import _path, check_groups
 from ._prox_grad import linear_prox_grad, group_linear_prox_grad
@@ -151,7 +152,7 @@ def _group_lasso_penalty(self):
         return np.linalg.norm(self.W_, axis=1, ord=2).sum()
 
     def fit(self, X, y=None):
-        self._validate_data(X)
+        X = validate_data(self, X, accept_sparse=False, dtype=np.float64)
         self.groups_ = check_groups(self.groups, X.shape[1])  # Intercept to check that group forms a partition
         return super().fit(X, y)
 

gemclus/sparse/_mlp_sparse.py

@@ -6,6 +6,7 @@
 from sklearn.neural_network._stochastic_optimizers import SGDOptimizer
 from sklearn.utils._param_validation import Interval, StrOptions
 from sklearn.utils.extmath import softmax
+from sklearn.utils.validation import validate_data
 
 from ._base_sparse import _path, check_groups
 from ._prox_grad import group_mlp_prox_grad, mlp_prox_grad
@@ -203,7 +204,7 @@ def _group_lasso_penalty(self):
         return np.linalg.norm(self.W_skip_, axis=1, ord=2).sum()
 
     def fit(self, X, y=None):
-        self._validate_data(X)
+        X = validate_data(self, X, accept_sparse=False, dtype=np.float64)
         self.groups_ = check_groups(self.groups, X.shape[1])  # Intercept to check that group forms a partition
         return super().fit(X, y)
 

gemclus/tree/kauri.py

@@ -9,7 +9,7 @@
 from sklearn.metrics.pairwise import PAIRWISE_KERNEL_FUNCTIONS, pairwise_kernels
 from sklearn.utils import check_array, check_random_state
 from sklearn.utils._param_validation import Interval, StrOptions
-from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.validation import check_is_fitted, validate_data
 
 from ._utils import find_best_split, gemini_objective, Split
 from .._constraints import constraint_params
@@ -187,8 +187,7 @@ def fit(self, X, y=None):
         self._validate_params()
 
         # Check that X has the correct shape
-        X = check_array(X)
-        X = self._validate_data(X, accept_sparse=True, dtype=np.float64, ensure_min_samples=self.min_samples_leaf)
+        X = validate_data(self, X, accept_sparse=False, dtype=np.float64, ensure_min_samples=self.min_samples_leaf)
 
         # Create the random state
         random_state = check_random_state(self.random_state)
@@ -352,7 +351,7 @@ def predict(self, X):
         check_is_fitted(self)
 
         # Input validation
-        X = check_array(X)
+        X = validate_data(self, X, accept_sparse=False, reset=False)
 
         return self.tree_.predict(X)
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "gemclus"
-version = "1.1.0"
+version = "1.1.1"
 authors = [
     {name = "Louis Ohl", email = "louis.ohl@liu.se"},
 ]