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Commit 8b9094c9 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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Removed warnings

parent cbef2800
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Pipeline #3575 passed
...@@ -37,12 +37,14 @@ class SGD(SGDClassifier, BaseMonoviewClassifier): ...@@ -37,12 +37,14 @@ class SGD(SGDClassifier, BaseMonoviewClassifier):
""" """
def __init__(self, random_state=None, loss='hinge', def __init__(self, random_state=None, loss='hinge',
penalty='l2', alpha=0.0001, **kwargs): penalty='l2', alpha=0.0001, max_iter=5, tol=None, **kwargs):
super(SGD, self).__init__( super(SGD, self).__init__(
loss=loss, loss=loss,
penalty=penalty, penalty=penalty,
alpha=alpha, alpha=alpha,
max_iter=5,
tol=None,
random_state=random_state random_state=random_state
) )
self.param_names = ["loss", "penalty", "alpha", "random_state"] self.param_names = ["loss", "penalty", "alpha", "random_state"]
......
...@@ -84,7 +84,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier): ...@@ -84,7 +84,7 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
example_indices, self.view_indices = get_examples_views_indices(dataset, example_indices, self.view_indices = get_examples_views_indices(dataset,
example_indices, example_indices,
view_indices) view_indices)
if self.view_weights is None or self.view_weights=="None": if self.view_weights is None:
self.view_weights = np.ones(len(self.view_indices), dtype=float) self.view_weights = np.ones(len(self.view_indices), dtype=float)
else: else:
self.view_weights = np.array(self.view_weights) self.view_weights = np.array(self.view_weights)
......
...@@ -190,7 +190,7 @@ class Dataset(): ...@@ -190,7 +190,7 @@ class Dataset():
example_indices = example_indices[sorted_indices] example_indices = example_indices[sorted_indices]
if not self.dataset["View" + str(view_index)].attrs["sparse"]: if not self.dataset["View" + str(view_index)].attrs["sparse"]:
return self.dataset["View" + str(view_index)][example_indices, :][ return self.dataset["View" + str(view_index)][()][example_indices, :][
np.argsort(sorted_indices), :] np.argsort(sorted_indices), :]
else: else:
sparse_mat = sparse.csr_matrix( sparse_mat = sparse.csr_matrix(
...@@ -208,11 +208,11 @@ class Dataset(): ...@@ -208,11 +208,11 @@ class Dataset():
def get_nb_class(self, example_indices=None): def get_nb_class(self, example_indices=None):
example_indices = self.init_example_indces(example_indices) example_indices = self.init_example_indces(example_indices)
return len(np.unique(self.dataset["Labels"][example_indices])) return len(np.unique(self.dataset["Labels"][()][example_indices]))
def get_labels(self, example_indices=None): def get_labels(self, example_indices=None):
example_indices = self.init_example_indces(example_indices) example_indices = self.init_example_indces(example_indices)
return self.dataset["Labels"][example_indices] return self.dataset["Labels"][()][example_indices]
def copy_view(self, target_dataset=None, source_view_name=None, def copy_view(self, target_dataset=None, source_view_name=None,
target_view_index=None, example_indices=None): target_view_index=None, example_indices=None):
...@@ -273,7 +273,7 @@ class Dataset(): ...@@ -273,7 +273,7 @@ class Dataset():
target_view_index=view_index) target_view_index=view_index)
for view_index in range(noisy_dataset["Metadata"].attrs["nbView"]): for view_index in range(noisy_dataset["Metadata"].attrs["nbView"]):
view_key = "View" + str(view_index) view_key = "View" + str(view_index)
view_dset = noisy_dataset.get[view_key] view_dset = noisy_dataset[view_key]
try: try:
view_limits = self.dataset[ view_limits = self.dataset[
"Metadata/View" + str(view_index) + "_limits"][()] "Metadata/View" + str(view_index) + "_limits"][()]
......
...@@ -21,7 +21,7 @@ class Test_get_classic_db_hdf5(unittest.TestCase): ...@@ -21,7 +21,7 @@ class Test_get_classic_db_hdf5(unittest.TestCase):
self.views = [self.rs.randint(0, 10, size=(self.nb_examples, 7)) self.views = [self.rs.randint(0, 10, size=(self.nb_examples, 7))
for _ in range(self.nb_view)] for _ in range(self.nb_view)]
self.labels = self.rs.randint(0, self.nb_class, self.nb_examples) self.labels = self.rs.randint(0, self.nb_class, self.nb_examples)
self.dataset_file = h5py.File(os.path.join(tmp_path, self.file_name)) self.dataset_file = h5py.File(os.path.join(tmp_path, self.file_name), 'w')
self.view_names = ["ViewN" + str(index) for index in self.view_names = ["ViewN" + str(index) for index in
range(len(self.views))] range(len(self.views))]
self.are_sparse = [False for _ in self.views] self.are_sparse = [False for _ in self.views]
......
...@@ -22,7 +22,7 @@ class Test_Dataset(unittest.TestCase): ...@@ -22,7 +22,7 @@ class Test_Dataset(unittest.TestCase):
cls.views = [cls.rs.randint(0, 10, size=(cls.nb_examples, cls.nb_attr)) cls.views = [cls.rs.randint(0, 10, size=(cls.nb_examples, cls.nb_attr))
for _ in range(cls.nb_view)] for _ in range(cls.nb_view)]
cls.labels = cls.rs.randint(0, cls.nb_class, cls.nb_examples) cls.labels = cls.rs.randint(0, cls.nb_class, cls.nb_examples)
cls.dataset_file = h5py.File(os.path.join(tmp_path, cls.file_name)) cls.dataset_file = h5py.File(os.path.join(tmp_path, cls.file_name), "w")
cls.view_names = ["ViewN" + str(index) for index in range(len(cls.views))] cls.view_names = ["ViewN" + str(index) for index in range(len(cls.views))]
cls.are_sparse = [False for _ in cls.views] cls.are_sparse = [False for _ in cls.views]
for view_index, (view_name, view, is_sparse) in enumerate( for view_index, (view_name, view, is_sparse) in enumerate(
...@@ -50,7 +50,7 @@ class Test_Dataset(unittest.TestCase): ...@@ -50,7 +50,7 @@ class Test_Dataset(unittest.TestCase):
def test_filter(self): def test_filter(self):
"""Had to create a new dataset to aviod playing with the class one""" """Had to create a new dataset to aviod playing with the class one"""
file_name = "test_filter.hdf5" file_name = "test_filter.hdf5"
dataset_file_filter = h5py.File(os.path.join(tmp_path, file_name)) dataset_file_filter = h5py.File(os.path.join(tmp_path, file_name), "w")
for view_index, (view_name, view, is_sparse) in enumerate( for view_index, (view_name, view, is_sparse) in enumerate(
zip(self.view_names, self.views, self.are_sparse)): zip(self.view_names, self.views, self.are_sparse)):
view_dataset = dataset_file_filter.create_dataset( view_dataset = dataset_file_filter.create_dataset(
...@@ -155,7 +155,7 @@ class Test_Dataset(unittest.TestCase): ...@@ -155,7 +155,7 @@ class Test_Dataset(unittest.TestCase):
source_view_name="ViewN0", source_view_name="ViewN0",
target_view_index=1) target_view_index=1)
self.assertIn("View1", list(new_dataset.keys())) self.assertIn("View1", list(new_dataset.keys()))
np.testing.assert_array_equal(dataset_object.get_v(0), new_dataset["View1"].value) np.testing.assert_array_equal(dataset_object.get_v(0), new_dataset["View1"][()])
self.assertEqual(new_dataset["View1"].attrs["name"], "ViewN0") self.assertEqual(new_dataset["View1"].attrs["name"], "ViewN0")
new_dataset.close() new_dataset.close()
os.remove(os.path.join(tmp_path, "test_copy.hdf5")) os.remove(os.path.join(tmp_path, "test_copy.hdf5"))
...@@ -180,7 +180,7 @@ class Test_Dataset(unittest.TestCase): ...@@ -180,7 +180,7 @@ class Test_Dataset(unittest.TestCase):
def test_select_views_and_labels(self): def test_select_views_and_labels(self):
file_name = "test_filter.hdf5" file_name = "test_filter.hdf5"
dataset_file_select = h5py.File(os.path.join(tmp_path, file_name)) dataset_file_select = h5py.File(os.path.join(tmp_path, file_name), "w")
for view_index, (view_name, view, is_sparse) in enumerate( for view_index, (view_name, view, is_sparse) in enumerate(
zip(self.view_names, self.views, self.are_sparse)): zip(self.view_names, self.views, self.are_sparse)):
view_dataset = dataset_file_select.create_dataset( view_dataset = dataset_file_select.create_dataset(
...@@ -208,7 +208,7 @@ class Test_Dataset(unittest.TestCase): ...@@ -208,7 +208,7 @@ class Test_Dataset(unittest.TestCase):
def test_add_gaussian_noise(self): def test_add_gaussian_noise(self):
file_name = "test_noise.hdf5" file_name = "test_noise.hdf5"
dataset_file_select = h5py.File(os.path.join(tmp_path, file_name)) dataset_file_select = h5py.File(os.path.join(tmp_path, file_name), "w")
limits = np.zeros((self.nb_attr, 2)) limits = np.zeros((self.nb_attr, 2))
limits[:, 1] += 100 limits[:, 1] += 100
meta_data_grp = dataset_file_select.create_group("Metadata") meta_data_grp = dataset_file_select.create_group("Metadata")
......
...@@ -55,7 +55,7 @@ class Test_randomized_search(unittest.TestCase): ...@@ -55,7 +55,7 @@ class Test_randomized_search(unittest.TestCase):
def test_simple(self): def test_simple(self):
best_params, test_folds_preds = hyper_parameter_search.randomized_search( best_params, test_folds_preds = hyper_parameter_search.randomized_search(
self.dataset, self.labels.value, "multiview", self.random_state, tmp_path, self.dataset, self.labels[()], "multiview", self.random_state, tmp_path,
weighted_linear_early_fusion, "WeightedLinearEarlyFusion", self.k_folds, weighted_linear_early_fusion, "WeightedLinearEarlyFusion", self.k_folds,
1, ["accuracy_score", None], 2, {}, learning_indices=self.learning_indices) 1, ["accuracy_score", None], 2, {}, learning_indices=self.learning_indices)
......
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