diff --git a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
index 9d423f14a5152b64ff77c08bf19ed99ba94234c6..acc6a8fef00f2990b837d4f3fba11943826d4ef4 100644
--- a/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
+++ b/multiview_platform/tests/test_mono_view/test_ExecClassifMonoView.py
@@ -128,19 +128,19 @@ class Test_getHPs(unittest.TestCase):
os.rmdir(tmp_path)
def test_simple(self):
- kwargs, test_folds_predictions = exec_classif_mono_view.getHPs(self.classifierModule,
- self.hyper_param_search,
- self.n_iter,
- self.classifier_name,
- self.classifier_class_name,
- self.X,
- self.y,
- self.random_state,
- self.output_file_name,
- self.cv,
- self.nb_cores,
- self.metrics,
- self.kwargs)
+ kwargs = exec_classif_mono_view.getHPs(self.classifierModule,
+ self.hyper_param_search,
+ self.n_iter,
+ self.classifier_name,
+ self.classifier_class_name,
+ self.X,
+ self.y,
+ self.random_state,
+ self.output_file_name,
+ self.cv,
+ self.nb_cores,
+ self.metrics,
+ self.kwargs)
# class Test_getKWARGS(unittest.TestCase):
#
diff --git a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
index 2257f14a4842ddea064e131bbb4f236b2f1a1d22..e1f6db063d3fe6fea97b957583db7796ae8610c8 100644
--- a/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
+++ b/multiview_platform/tests/test_utils/test_hyper_parameter_search.py
@@ -4,7 +4,7 @@ import unittest
import h5py
import numpy as np
from sklearn.model_selection import StratifiedKFold
-from multiview_platform.tests.utils import rm_tmp, tmp_path
+from multiview_platform.tests.utils import rm_tmp, tmp_path, test_dataset
from multiview_platform.mono_multi_view_classifiers.utils.dataset import HDF5Dataset
@@ -55,13 +55,116 @@ class Test_randomized_search(unittest.TestCase):
def test_simple(self):
- best_params, test_folds_preds = hyper_parameter_search.randomized_search(
+ best_params, _ = hyper_parameter_search.randomized_search(
self.dataset, self.labels[()], "multiview", self.random_state, tmp_path,
weighted_linear_early_fusion, "WeightedLinearEarlyFusion", self.k_folds,
1, ["accuracy_score", None], 2, {}, learning_indices=self.learning_indices)
+ self.assertIsInstance(best_params, dict)
+from sklearn.base import BaseEstimator
-if __name__ == '__main__':
- # unittest.main()
- suite = unittest.TestLoader().loadTestsFromTestCase(Test_randomized_search)
- unittest.TextTestRunner(verbosity=2).run(suite)
\ No newline at end of file
+class FakeEstim(BaseEstimator):
+ def __init__(self, param1=None, param2=None):
+ self.param1 = param1
+ self.param2 = param2
+
+ def fit(self, X, y,):
+ return self
+
+ def predict(self, X):
+ return np.zeros(X.shape[0])
+
+class FakeEstimMV(BaseEstimator):
+ def __init__(self, param1=None, param2=None):
+ self.param1 = param1
+ self.param2 = param2
+
+ def fit(self, X, y,train_indices=None, view_indices=None):
+ return self
+
+ def predict(self, X, example_indices=None, view_indices=None):
+ return np.zeros(example_indices.shape[0])
+
+from sklearn.metrics import accuracy_score, make_scorer
+from sklearn.model_selection import StratifiedKFold
+
+class Test_MultiviewCompatibleRandomizedSearchCV(unittest.TestCase):
+
+ @classmethod
+ def setUpClass(cls):
+ n_splits=2
+ cls.estimator = FakeEstim()
+ cls.param_distributions = {"param1":[10,100], "param2":[11, 101]}
+ cls.n_iter = 4
+ cls.refit = True
+ cls.n_jobs = 1
+ cls.scoring = make_scorer(accuracy_score, )
+ cls.cv = StratifiedKFold(n_splits=n_splits, )
+ cls.random_state = np.random.RandomState(42)
+ cls.learning_indices = np.array([0,1,2])
+ cls.view_indices = None
+ cls.framework = "monoview"
+ cls.equivalent_draws = False
+ cls.X = cls.random_state.randint(0,100, (5,11))
+ cls.y = cls.random_state.randint(0,1, 5)
+
+ def test_simple(self):
+ hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV(
+ self.estimator, self.param_distributions, n_iter=self.n_iter,
+ refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring, cv=self.cv,
+ random_state=self.random_state,
+ learning_indices=self.learning_indices, view_indices=self.view_indices,
+ framework=self.framework,
+ equivalent_draws=self.equivalent_draws
+ )
+
+ def test_fit(self):
+ RSCV = hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV(
+ self.estimator, self.param_distributions, n_iter=self.n_iter,
+ refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
+ cv=self.cv,
+ random_state=self.random_state,
+ learning_indices=self.learning_indices,
+ view_indices=self.view_indices,
+ framework=self.framework,
+ equivalent_draws=self.equivalent_draws
+ )
+ RSCV.fit(self.X, self.y, )
+ tested_param1 = np.ma.masked_array(data=[10,10,100,100],
+ mask=[False, False, False, False])
+ np.testing.assert_array_equal(RSCV.cv_results_['param_param1'],
+ tested_param1)
+
+ def test_fit_multiview(self):
+ RSCV = hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV(
+ FakeEstimMV(), self.param_distributions, n_iter=self.n_iter,
+ refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
+ cv=self.cv,
+ random_state=self.random_state,
+ learning_indices=self.learning_indices,
+ view_indices=self.view_indices,
+ framework="multiview",
+ equivalent_draws=self.equivalent_draws
+ )
+ RSCV.fit(test_dataset, self.y, )
+ self.assertEqual(RSCV.n_iter, self.n_iter)
+
+ def test_fit_multiview_equiv(self):
+ RSCV = hyper_parameter_search.MultiviewCompatibleRandomizedSearchCV(
+ FakeEstimMV(), self.param_distributions, n_iter=self.n_iter,
+ refit=self.refit, n_jobs=self.n_jobs, scoring=self.scoring,
+ cv=self.cv,
+ random_state=self.random_state,
+ learning_indices=self.learning_indices,
+ view_indices=self.view_indices,
+ framework="multiview",
+ equivalent_draws=True
+ )
+ RSCV.fit(test_dataset, self.y, )
+ self.assertEqual(RSCV.n_iter, self.n_iter*test_dataset.nb_view)
+
+
+# if __name__ == '__main__':
+# # unittest.main()
+# suite = unittest.TestLoader().loadTestsFromTestCase(Test_randomized_search)
+# unittest.TextTestRunner(verbosity=2).run(suite)
\ No newline at end of file