diff --git a/multiview_platform/tests/test_ExecClassif.py b/multiview_platform/tests/test_ExecClassif.py
index 5348634a6ef1760ab04df2b71497d84ca1f488c3..71f53c9948fe44f0ca550b35dbdaad7e7d584ea3 100644
--- a/multiview_platform/tests/test_ExecClassif.py
+++ b/multiview_platform/tests/test_ExecClassif.py
@@ -232,16 +232,9 @@ class Test_execBenchmark(unittest.TestCase):
@classmethod
def setUpClass(cls):
rm_tmp()
-<<<<<<< HEAD
- os.mkdir("./tests/tmp_tests")
- cls.Dataset = h5py.File(
- tmp_path+"test_file.hdf5", "w")
- cls.labels = cls.Dataset.create_dataset("Labels",
- data=np.array([0, 1, 2]))
-=======
+
os.mkdir(tmp_path)
cls.Dataset = test_dataset
->>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
cls.argument_dictionaries = [{"a": 4, "args": {}}]
cls.args = {
"Base":{"name": "chicken_is_heaven", "type": "type", "pathf": "pathF"},
diff --git a/multiview_platform/tests/test_utils/test_GetMultiviewDB.py b/multiview_platform/tests/test_utils/test_GetMultiviewDB.py
index 7706331636fae346ef4b2298d8a4c69c60869fe6..91c283aa2e3d156edeff4ea5428579428ab60a07 100644
--- a/multiview_platform/tests/test_utils/test_GetMultiviewDB.py
+++ b/multiview_platform/tests/test_utils/test_GetMultiviewDB.py
@@ -5,102 +5,102 @@ import h5py
import numpy as np
from ...mono_multi_view_classifiers.utils import get_multiview_db
-<<<<<<< HEAD
-from ..utils import rm_tmp, tmp_path
-
-
-class Test_copyhdf5Dataset(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.random_state = np.random.RandomState(42)
- if not os.path.exists("multiview_platform/tests/tmp_tests"):
- os.mkdir("multiview_platform/tests/tmp_tests")
- cls.dataset_file = h5py.File(
- tmp_path+"test_copy.hdf5", "w")
- cls.dataset = cls.dataset_file.create_dataset("test",
- data=cls.random_state.randint(
- 0, 100, (10, 20)))
- cls.dataset.attrs["test_arg"] = "Am I copied"
-
- def test_simple_copy(self):
- get_multiview_db.copyhdf5_dataset(self.dataset_file, self.dataset_file,
- "test", "test_copy_1", np.arange(10))
- np.testing.assert_array_equal(self.dataset_file.get("test").value,
- self.dataset_file.get("test_copy_1").value)
- self.assertEqual("Am I copied",
- self.dataset_file.get("test_copy_1").attrs["test_arg"])
-
- def test_copy_only_some_indices(self):
- usedIndices = self.random_state.choice(10, 6, replace=False)
- get_multiview_db.copyhdf5_dataset(self.dataset_file, self.dataset_file,
- "test", "test_copy", usedIndices)
- np.testing.assert_array_equal(
- self.dataset_file.get("test").value[usedIndices, :],
- self.dataset_file.get("test_copy").value)
- self.assertEqual("Am I copied",
- self.dataset_file.get("test_copy").attrs["test_arg"])
-
- @classmethod
- def tearDownClass(cls):
- os.remove(tmp_path+"test_copy.hdf5")
- os.rmdir("multiview_platform/tests/tmp_tests")
-
-
-class Test_filterViews(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- rm_tmp()
- cls.random_state = np.random.RandomState(42)
- cls.views = ["test_view_1", "test_view_2"]
- if not os.path.exists("multiview_platform/tests/tmp_tests"):
- os.mkdir("multiview_platform/tests/tmp_tests")
- cls.dataset_file = h5py.File(
- tmp_path+"test_copy.hdf5", "w")
- cls.metadata_group = cls.dataset_file.create_group("Metadata")
- cls.metadata_group.attrs["nbView"] = 4
-
- for i in range(4):
- cls.dataset = cls.dataset_file.create_dataset("View" + str(i),
- data=cls.random_state.randint(
- 0, 100, (10, 20)))
- cls.dataset.attrs["name"] = "test_view_" + str(i)
-
- def test_simple_filter(self):
- self.temp_dataset_file = h5py.File(
- tmp_path+"test_copy_temp.hdf5", "w")
- self.dataset_file.copy("Metadata", self.temp_dataset_file)
- get_multiview_db.filter_views(self.dataset_file, self.temp_dataset_file,
- self.views, np.arange(10))
- self.assertEqual(self.dataset_file.get("View1").attrs["name"],
- self.temp_dataset_file.get("View0").attrs["name"])
- np.testing.assert_array_equal(self.dataset_file.get("View2").value,
- self.temp_dataset_file.get("View1").value)
- self.assertEqual(self.temp_dataset_file.get("Metadata").attrs["nbView"],
- 2)
-
- def test_filter_view_and_examples(self):
- self.temp_dataset_file = h5py.File(
- tmp_path+"test_copy_temp.hdf5", "w")
- self.dataset_file.copy("Metadata", self.temp_dataset_file)
- usedIndices = self.random_state.choice(10, 6, replace=False)
- get_multiview_db.filter_views(self.dataset_file, self.temp_dataset_file,
- self.views, usedIndices)
- np.testing.assert_array_equal(
- self.dataset_file.get("View1").value[usedIndices, :],
- self.temp_dataset_file.get("View0").value)
- self.temp_dataset_file.close()
-
- @classmethod
- def tearDownClass(cls):
- os.remove(tmp_path+"test_copy.hdf5")
- os.remove(tmp_path+"test_copy_temp.hdf5")
- os.rmdir("multiview_platform/tests/tmp_tests")
-=======
+# <<<<<<< HEAD
+# from ..utils import rm_tmp, tmp_path
+#
+#
+# class Test_copyhdf5Dataset(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# rm_tmp()
+# cls.random_state = np.random.RandomState(42)
+# if not os.path.exists("multiview_platform/tests/tmp_tests"):
+# os.mkdir("multiview_platform/tests/tmp_tests")
+# cls.dataset_file = h5py.File(
+# tmp_path+"test_copy.hdf5", "w")
+# cls.dataset = cls.dataset_file.create_dataset("test",
+# data=cls.random_state.randint(
+# 0, 100, (10, 20)))
+# cls.dataset.attrs["test_arg"] = "Am I copied"
+#
+# def test_simple_copy(self):
+# get_multiview_db.copyhdf5_dataset(self.dataset_file, self.dataset_file,
+# "test", "test_copy_1", np.arange(10))
+# np.testing.assert_array_equal(self.dataset_file.get("test").value,
+# self.dataset_file.get("test_copy_1").value)
+# self.assertEqual("Am I copied",
+# self.dataset_file.get("test_copy_1").attrs["test_arg"])
+#
+# def test_copy_only_some_indices(self):
+# usedIndices = self.random_state.choice(10, 6, replace=False)
+# get_multiview_db.copyhdf5_dataset(self.dataset_file, self.dataset_file,
+# "test", "test_copy", usedIndices)
+# np.testing.assert_array_equal(
+# self.dataset_file.get("test").value[usedIndices, :],
+# self.dataset_file.get("test_copy").value)
+# self.assertEqual("Am I copied",
+# self.dataset_file.get("test_copy").attrs["test_arg"])
+#
+# @classmethod
+# def tearDownClass(cls):
+# os.remove(tmp_path+"test_copy.hdf5")
+# os.rmdir("multiview_platform/tests/tmp_tests")
+#
+#
+# class Test_filterViews(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# rm_tmp()
+# cls.random_state = np.random.RandomState(42)
+# cls.views = ["test_view_1", "test_view_2"]
+# if not os.path.exists("multiview_platform/tests/tmp_tests"):
+# os.mkdir("multiview_platform/tests/tmp_tests")
+# cls.dataset_file = h5py.File(
+# tmp_path+"test_copy.hdf5", "w")
+# cls.metadata_group = cls.dataset_file.create_group("Metadata")
+# cls.metadata_group.attrs["nbView"] = 4
+#
+# for i in range(4):
+# cls.dataset = cls.dataset_file.create_dataset("View" + str(i),
+# data=cls.random_state.randint(
+# 0, 100, (10, 20)))
+# cls.dataset.attrs["name"] = "test_view_" + str(i)
+#
+# def test_simple_filter(self):
+# self.temp_dataset_file = h5py.File(
+# tmp_path+"test_copy_temp.hdf5", "w")
+# self.dataset_file.copy("Metadata", self.temp_dataset_file)
+# get_multiview_db.filter_views(self.dataset_file, self.temp_dataset_file,
+# self.views, np.arange(10))
+# self.assertEqual(self.dataset_file.get("View1").attrs["name"],
+# self.temp_dataset_file.get("View0").attrs["name"])
+# np.testing.assert_array_equal(self.dataset_file.get("View2").value,
+# self.temp_dataset_file.get("View1").value)
+# self.assertEqual(self.temp_dataset_file.get("Metadata").attrs["nbView"],
+# 2)
+#
+# def test_filter_view_and_examples(self):
+# self.temp_dataset_file = h5py.File(
+# tmp_path+"test_copy_temp.hdf5", "w")
+# self.dataset_file.copy("Metadata", self.temp_dataset_file)
+# usedIndices = self.random_state.choice(10, 6, replace=False)
+# get_multiview_db.filter_views(self.dataset_file, self.temp_dataset_file,
+# self.views, usedIndices)
+# np.testing.assert_array_equal(
+# self.dataset_file.get("View1").value[usedIndices, :],
+# self.temp_dataset_file.get("View0").value)
+# self.temp_dataset_file.close()
+#
+# @classmethod
+# def tearDownClass(cls):
+# os.remove(tmp_path+"test_copy.hdf5")
+# os.remove(tmp_path+"test_copy_temp.hdf5")
+# os.rmdir("multiview_platform/tests/tmp_tests")
+# =======
from ..utils import rm_tmp, tmp_path, test_dataset
->>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
+# >>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
# class Test_copyhdf5Dataset(unittest.TestCase):
@@ -429,356 +429,356 @@ from ..utils import rm_tmp, tmp_path, test_dataset
# exception = catcher.exception
# # cls.assertTrue("Dataset must have at least two different labels" in exception)
#
-<<<<<<< HEAD
-class Test_filterLabels(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.labelsSet = set(range(4))
- cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
- cls.fullLabels = cls.random_state.randint(0, 4, 10)
- cls.availableLabelsNames = ["test_label_0", "test_label_1",
- "test_label_2", "test_label_3"]
- cls.askedLabelsNames = ["test_label_1", "test_label_3"]
-
- def test_simple(self):
- newLabels, \
- newLabelsNames, \
- usedIndices = get_multiview_db.filter_labels(self.labelsSet,
- self.askedLabelsNamesSet,
- self.fullLabels,
- self.availableLabelsNames,
- self.askedLabelsNames)
- self.assertEqual(["test_label_1", "test_label_3"], newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.array([1, 5, 9]))
- np.testing.assert_array_equal(newLabels, np.array([1, 1, 0]))
-
- def test_biclasse(self):
- self.labelsSet = {0, 1}
- self.fullLabels = self.random_state.randint(0, 2, 10)
- self.availableLabelsNames = ["test_label_0", "test_label_1"]
- newLabels, \
- newLabelsNames, \
- usedIndices = get_multiview_db.filter_labels(self.labelsSet,
- self.askedLabelsNamesSet,
- self.fullLabels,
- self.availableLabelsNames,
- self.askedLabelsNames)
- self.assertEqual(self.availableLabelsNames, newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.arange(10))
- np.testing.assert_array_equal(newLabels, self.fullLabels)
-
- def test_asked_too_many_labels(self):
- self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
- "test_label_2", "test_label_3",
- "chicken_is_heaven"}
- with self.assertRaises(get_multiview_db.DatasetError) as catcher:
- get_multiview_db.filter_labels(self.labelsSet,
- self.askedLabelsNamesSet,
- self.fullLabels,
- self.availableLabelsNames,
- self.askedLabelsNames)
- exception = catcher.exception
-
- def test_asked_all_labels(self):
- self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
- "test_label_2", "test_label_3"}
- self.askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- newLabels, \
- newLabelsNames, \
- usedIndices = get_multiview_db.filter_labels(self.labelsSet,
- self.askedLabelsNamesSet,
- self.fullLabels,
- self.availableLabelsNames,
- self.askedLabelsNames)
- self.assertEqual(self.availableLabelsNames, newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.arange(10))
- np.testing.assert_array_equal(newLabels, self.fullLabels)
-
-
-class Test_selectAskedLabels(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
- cls.fullLabels = cls.random_state.randint(0, 4, 10)
- cls.availableLabelsNames = ["test_label_0", "test_label_1",
- "test_label_2", "test_label_3"]
- cls.askedLabelsNames = ["test_label_1", "test_label_3"]
-
- def test_simple(self):
- newLabels, \
- newLabelsNames, \
- usedIndices = get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
- self.availableLabelsNames,
- self.askedLabelsNames,
- self.fullLabels)
- self.assertEqual(["test_label_1", "test_label_3"], newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.array([1, 5, 9]))
- np.testing.assert_array_equal(newLabels, np.array([1, 1, 0]))
-
- def test_asked_all_labels(self):
- self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
- "test_label_2", "test_label_3"}
- self.askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- newLabels, \
- newLabelsNames, \
- usedIndices = get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
- self.availableLabelsNames,
- self.askedLabelsNames,
- self.fullLabels)
- self.assertEqual(self.availableLabelsNames, newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.arange(10))
- np.testing.assert_array_equal(newLabels, self.fullLabels)
-
- def test_asked_unavailable_labels(self):
- self.askedLabelsNamesSet = {"test_label_1", "test_label_3",
- "chicken_is_heaven"}
- with self.assertRaises(get_multiview_db.DatasetError) as catcher:
- get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
- self.availableLabelsNames,
- self.askedLabelsNames,
- self.fullLabels)
- exception = catcher.exception
- # self.assertTrue("Asked labels are not all available in the dataset" in exception)
-
-
-class Test_getAllLabels(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
- cls.fullLabels = cls.random_state.randint(0, 4, 10)
- cls.availableLabelsNames = ["test_label_0", "test_label_1",
- "test_label_2", "test_label_3"]
-
- def test_simple(self):
- newLabels, newLabelsNames, usedIndices = get_multiview_db.get_all_labels(
- self.fullLabels, self.availableLabelsNames)
- self.assertEqual(self.availableLabelsNames, newLabelsNames)
- np.testing.assert_array_equal(usedIndices, np.arange(10))
- np.testing.assert_array_equal(newLabels, self.fullLabels)
-
-
-class Test_fillLabelNames(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.NB_CLASS = 2
- cls.askedLabelsNames = ["test_label_1", "test_label_3"]
- cls.random_state = np.random.RandomState(42)
- cls.availableLabelsNames = ["test_label_" + str(_) for _ in range(40)]
-
- def test_simple(self):
- askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
- self.NB_CLASS,
- self.askedLabelsNames,
- self.random_state,
- self.availableLabelsNames)
- self.assertEqual(askedLabelsNames, self.askedLabelsNames)
- self.assertEqual(askedLabelsNamesSet, set(self.askedLabelsNames))
-
- def test_missing_labels_names(self):
- self.NB_CLASS = 39
- askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
- self.NB_CLASS,
- self.askedLabelsNames,
- self.random_state,
- self.availableLabelsNames)
-
- self.assertEqual(askedLabelsNames,
- ['test_label_1', 'test_label_3', 'test_label_35',
- 'test_label_38', 'test_label_6', 'test_label_15',
- 'test_label_32', 'test_label_28', 'test_label_8',
- 'test_label_29', 'test_label_26', 'test_label_17',
- 'test_label_19', 'test_label_10', 'test_label_18',
- 'test_label_14', 'test_label_21', 'test_label_11',
- 'test_label_34', 'test_label_0', 'test_label_27',
- 'test_label_7', 'test_label_13', 'test_label_2',
- 'test_label_39', 'test_label_23', 'test_label_4',
- 'test_label_31', 'test_label_37', 'test_label_5',
- 'test_label_36', 'test_label_25', 'test_label_33',
- 'test_label_12', 'test_label_24', 'test_label_20',
- 'test_label_22', 'test_label_9', 'test_label_16'])
- self.assertEqual(askedLabelsNamesSet, set(
- ["test_label_" + str(_) for _ in range(30)] + [
- "test_label_" + str(31 + _) for _ in range(9)]))
-
- def test_too_many_label_names(self):
- self.NB_CLASS = 2
- self.askedLabelsNames = ["test_label_1", "test_label_3", "test_label_4",
- "test_label_6"]
- askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
- self.NB_CLASS,
- self.askedLabelsNames,
- self.random_state,
- self.availableLabelsNames)
- self.assertEqual(askedLabelsNames, ["test_label_3", "test_label_6"])
- self.assertEqual(askedLabelsNamesSet, {"test_label_3", "test_label_6"})
-
-
-class Test_allAskedLabelsAreAvailable(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
- cls.availableLabelsNames = ["test_label_0", "test_label_1",
- "test_label_2", "test_label_3"]
-
- def test_asked_available_labels(self):
- self.assertTrue(
- get_multiview_db.all_asked_labels_are_available(self.askedLabelsNamesSet,
- self.availableLabelsNames))
-
- def test_asked_unavailable_label(self):
- self.askedLabelsNamesSet = {"test_label_1", "test_label_3",
- "chicken_is_heaven"}
- self.assertFalse(
- get_multiview_db.all_asked_labels_are_available(self.askedLabelsNamesSet,
- self.availableLabelsNames))
-
-
-class Test_getClasses(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.random_state = np.random.RandomState(42)
-
- def test_multiclass(self):
- labelsSet = get_multiview_db.get_classes(
- self.random_state.randint(0, 5, 30))
- self.assertEqual(labelsSet, {0, 1, 2, 3, 4})
-
- def test_biclass(self):
- labelsSet = get_multiview_db.get_classes(
- self.random_state.randint(0, 2, 30))
- self.assertEqual(labelsSet, {0, 1})
-
- def test_one_class(self):
- with self.assertRaises(get_multiview_db.DatasetError) as catcher:
- get_multiview_db.get_classes(np.zeros(30, dtype=int))
- exception = catcher.exception
- # self.assertTrue("Dataset must have at least two different labels" in exception)
-
-=======
->>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
+# <<<<<<< HEAD
+# class Test_filterLabels(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.random_state = np.random.RandomState(42)
+# cls.labelsSet = set(range(4))
+# cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
+# cls.fullLabels = cls.random_state.randint(0, 4, 10)
+# cls.availableLabelsNames = ["test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"]
+# cls.askedLabelsNames = ["test_label_1", "test_label_3"]
+#
+# def test_simple(self):
+# newLabels, \
+# newLabelsNames, \
+# usedIndices = get_multiview_db.filter_labels(self.labelsSet,
+# self.askedLabelsNamesSet,
+# self.fullLabels,
+# self.availableLabelsNames,
+# self.askedLabelsNames)
+# self.assertEqual(["test_label_1", "test_label_3"], newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.array([1, 5, 9]))
+# np.testing.assert_array_equal(newLabels, np.array([1, 1, 0]))
+#
+# def test_biclasse(self):
+# self.labelsSet = {0, 1}
+# self.fullLabels = self.random_state.randint(0, 2, 10)
+# self.availableLabelsNames = ["test_label_0", "test_label_1"]
+# newLabels, \
+# newLabelsNames, \
+# usedIndices = get_multiview_db.filter_labels(self.labelsSet,
+# self.askedLabelsNamesSet,
+# self.fullLabels,
+# self.availableLabelsNames,
+# self.askedLabelsNames)
+# self.assertEqual(self.availableLabelsNames, newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.arange(10))
+# np.testing.assert_array_equal(newLabels, self.fullLabels)
+#
+# def test_asked_too_many_labels(self):
+# self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
+# "test_label_2", "test_label_3",
+# "chicken_is_heaven"}
+# with self.assertRaises(get_multiview_db.DatasetError) as catcher:
+# get_multiview_db.filter_labels(self.labelsSet,
+# self.askedLabelsNamesSet,
+# self.fullLabels,
+# self.availableLabelsNames,
+# self.askedLabelsNames)
+# exception = catcher.exception
+#
+# def test_asked_all_labels(self):
+# self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"}
+# self.askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# newLabels, \
+# newLabelsNames, \
+# usedIndices = get_multiview_db.filter_labels(self.labelsSet,
+# self.askedLabelsNamesSet,
+# self.fullLabels,
+# self.availableLabelsNames,
+# self.askedLabelsNames)
+# self.assertEqual(self.availableLabelsNames, newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.arange(10))
+# np.testing.assert_array_equal(newLabels, self.fullLabels)
+#
+#
+# class Test_selectAskedLabels(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.random_state = np.random.RandomState(42)
+# cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
+# cls.fullLabels = cls.random_state.randint(0, 4, 10)
+# cls.availableLabelsNames = ["test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"]
+# cls.askedLabelsNames = ["test_label_1", "test_label_3"]
+#
+# def test_simple(self):
+# newLabels, \
+# newLabelsNames, \
+# usedIndices = get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
+# self.availableLabelsNames,
+# self.askedLabelsNames,
+# self.fullLabels)
+# self.assertEqual(["test_label_1", "test_label_3"], newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.array([1, 5, 9]))
+# np.testing.assert_array_equal(newLabels, np.array([1, 1, 0]))
+#
+# def test_asked_all_labels(self):
+# self.askedLabelsNamesSet = {"test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"}
+# self.askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# newLabels, \
+# newLabelsNames, \
+# usedIndices = get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
+# self.availableLabelsNames,
+# self.askedLabelsNames,
+# self.fullLabels)
+# self.assertEqual(self.availableLabelsNames, newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.arange(10))
+# np.testing.assert_array_equal(newLabels, self.fullLabels)
+#
+# def test_asked_unavailable_labels(self):
+# self.askedLabelsNamesSet = {"test_label_1", "test_label_3",
+# "chicken_is_heaven"}
+# with self.assertRaises(get_multiview_db.DatasetError) as catcher:
+# get_multiview_db.select_asked_labels(self.askedLabelsNamesSet,
+# self.availableLabelsNames,
+# self.askedLabelsNames,
+# self.fullLabels)
+# exception = catcher.exception
+# # self.assertTrue("Asked labels are not all available in the dataset" in exception)
+#
+#
+# class Test_getAllLabels(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.random_state = np.random.RandomState(42)
+# cls.fullLabels = cls.random_state.randint(0, 4, 10)
+# cls.availableLabelsNames = ["test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"]
+#
+# def test_simple(self):
+# newLabels, newLabelsNames, usedIndices = get_multiview_db.get_all_labels(
+# self.fullLabels, self.availableLabelsNames)
+# self.assertEqual(self.availableLabelsNames, newLabelsNames)
+# np.testing.assert_array_equal(usedIndices, np.arange(10))
+# np.testing.assert_array_equal(newLabels, self.fullLabels)
+#
+#
+# class Test_fillLabelNames(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.NB_CLASS = 2
+# cls.askedLabelsNames = ["test_label_1", "test_label_3"]
+# cls.random_state = np.random.RandomState(42)
+# cls.availableLabelsNames = ["test_label_" + str(_) for _ in range(40)]
+#
+# def test_simple(self):
+# askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
+# self.NB_CLASS,
+# self.askedLabelsNames,
+# self.random_state,
+# self.availableLabelsNames)
+# self.assertEqual(askedLabelsNames, self.askedLabelsNames)
+# self.assertEqual(askedLabelsNamesSet, set(self.askedLabelsNames))
+#
+# def test_missing_labels_names(self):
+# self.NB_CLASS = 39
+# askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
+# self.NB_CLASS,
+# self.askedLabelsNames,
+# self.random_state,
+# self.availableLabelsNames)
+#
+# self.assertEqual(askedLabelsNames,
+# ['test_label_1', 'test_label_3', 'test_label_35',
+# 'test_label_38', 'test_label_6', 'test_label_15',
+# 'test_label_32', 'test_label_28', 'test_label_8',
+# 'test_label_29', 'test_label_26', 'test_label_17',
+# 'test_label_19', 'test_label_10', 'test_label_18',
+# 'test_label_14', 'test_label_21', 'test_label_11',
+# 'test_label_34', 'test_label_0', 'test_label_27',
+# 'test_label_7', 'test_label_13', 'test_label_2',
+# 'test_label_39', 'test_label_23', 'test_label_4',
+# 'test_label_31', 'test_label_37', 'test_label_5',
+# 'test_label_36', 'test_label_25', 'test_label_33',
+# 'test_label_12', 'test_label_24', 'test_label_20',
+# 'test_label_22', 'test_label_9', 'test_label_16'])
+# self.assertEqual(askedLabelsNamesSet, set(
+# ["test_label_" + str(_) for _ in range(30)] + [
+# "test_label_" + str(31 + _) for _ in range(9)]))
+#
+# def test_too_many_label_names(self):
+# self.NB_CLASS = 2
+# self.askedLabelsNames = ["test_label_1", "test_label_3", "test_label_4",
+# "test_label_6"]
+# askedLabelsNames, askedLabelsNamesSet = get_multiview_db.fill_label_names(
+# self.NB_CLASS,
+# self.askedLabelsNames,
+# self.random_state,
+# self.availableLabelsNames)
+# self.assertEqual(askedLabelsNames, ["test_label_3", "test_label_6"])
+# self.assertEqual(askedLabelsNamesSet, {"test_label_3", "test_label_6"})
+#
+#
+# class Test_allAskedLabelsAreAvailable(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.askedLabelsNamesSet = {"test_label_1", "test_label_3"}
+# cls.availableLabelsNames = ["test_label_0", "test_label_1",
+# "test_label_2", "test_label_3"]
+#
+# def test_asked_available_labels(self):
+# self.assertTrue(
+# get_multiview_db.all_asked_labels_are_available(self.askedLabelsNamesSet,
+# self.availableLabelsNames))
+#
+# def test_asked_unavailable_label(self):
+# self.askedLabelsNamesSet = {"test_label_1", "test_label_3",
+# "chicken_is_heaven"}
+# self.assertFalse(
+# get_multiview_db.all_asked_labels_are_available(self.askedLabelsNamesSet,
+# self.availableLabelsNames))
+#
+#
+# class Test_getClasses(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.random_state = np.random.RandomState(42)
+#
+# def test_multiclass(self):
+# labelsSet = get_multiview_db.get_classes(
+# self.random_state.randint(0, 5, 30))
+# self.assertEqual(labelsSet, {0, 1, 2, 3, 4})
+#
+# def test_biclass(self):
+# labelsSet = get_multiview_db.get_classes(
+# self.random_state.randint(0, 2, 30))
+# self.assertEqual(labelsSet, {0, 1})
+#
+# def test_one_class(self):
+# with self.assertRaises(get_multiview_db.DatasetError) as catcher:
+# get_multiview_db.get_classes(np.zeros(30, dtype=int))
+# exception = catcher.exception
+# # self.assertTrue("Dataset must have at least two different labels" in exception)
+#
+# =======
+# >>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
class Test_get_classic_db_hdf5(unittest.TestCase):
def setUp(self):
rm_tmp()
-<<<<<<< HEAD
- if not os.path.exists("multiview_platform/tests/tmp_tests"):
- os.mkdir("multiview_platform/tests/tmp_tests")
- cls.dataset_file = h5py.File(
- tmp_path+"test_dataset.hdf5", "w")
- cls.pathF = tmp_path
- cls.nameDB = "test_dataset"
- cls.NB_CLASS = 2
- cls.askedLabelsNames = ["test_label_1", "test_label_3"]
- cls.random_state = np.random.RandomState(42)
- cls.views = ["test_view_1", "test_view_3"]
- cls.metadata_group = cls.dataset_file.create_group("Metadata")
- cls.metadata_group.attrs["nbView"] = 4
- cls.labels_dataset = cls.dataset_file.create_dataset("Labels",
- data=cls.random_state.randint(
- 0, 4, 10))
- cls.labels_dataset.attrs["names"] = ["test_label_0".encode(),
- "test_label_1".encode(),
- "test_label_2".encode(),
- "test_label_3".encode()]
-
- for i in range(4):
- cls.dataset = cls.dataset_file.create_dataset("View" + str(i),
- data=cls.random_state.randint(
- 0, 100, (10, 20)))
- cls.dataset.attrs["name"] = "test_view_" + str(i)
-
- def test_simple(self):
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
- self.views, self.pathF, self.nameDB,
- self.NB_CLASS, self.askedLabelsNames,
- self.random_state)
- self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
- self.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
- np.testing.assert_array_equal(dataset_file.get("View0").value,
- self.dataset_file.get("View1").value[
- np.array([1, 5, 9]), :])
-
- def test_all_labels_asked(self):
- askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- NB_CLASS = 4
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
- self.views, self.pathF, self.nameDB,
- NB_CLASS, askedLabelsNames,
- self.random_state)
- self.assertEqual(dataset_name, 'test_dataset_temp_view_label_select')
- self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
- self.assertEqual(labels_dictionary,
- {0: "test_label_0", 1: "test_label_1",
- 2: "test_label_2", 3: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
- np.testing.assert_array_equal(dataset_file.get("View0").value,
- self.dataset_file.get("View1").value)
-
- def test_all_views_asked(self):
- views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(views,
- self.pathF,
- self.nameDB,
- self.NB_CLASS,
- self.askedLabelsNames,
- self.random_state)
- for viewIndex in range(4):
- np.testing.assert_array_equal(
- dataset_file.get("View" + str(viewIndex)).value,
- self.dataset_file.get("View" + str(viewIndex)).value[
- np.array([1, 5, 9]), :])
- self.assertEqual(
- dataset_file.get("View" + str(viewIndex)).attrs["name"],
- "test_view_" + str(viewIndex))
- self.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
-
- def test_asked_the_whole_dataset(self):
- askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- NB_CLASS = 4
- views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(views,
- self.pathF,
- self.nameDB,
- NB_CLASS,
- askedLabelsNames,
- self.random_state)
- for viewIndex in range(4):
- np.testing.assert_array_equal(
- dataset_file.get("View" + str(viewIndex)).value,
- self.dataset_file.get("View" + str(viewIndex)))
- self.assertEqual(
- dataset_file.get("View" + str(viewIndex)).attrs["name"],
- "test_view_" + str(viewIndex))
- self.assertEqual(labels_dictionary,
- {0: "test_label_0", 1: "test_label_1",
- 2: "test_label_2", 3: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
-=======
+# <<<<<<< HEAD
+# if not os.path.exists("multiview_platform/tests/tmp_tests"):
+# os.mkdir("multiview_platform/tests/tmp_tests")
+# cls.dataset_file = h5py.File(
+# tmp_path+"test_dataset.hdf5", "w")
+# cls.pathF = tmp_path
+# cls.nameDB = "test_dataset"
+# cls.NB_CLASS = 2
+# cls.askedLabelsNames = ["test_label_1", "test_label_3"]
+# cls.random_state = np.random.RandomState(42)
+# cls.views = ["test_view_1", "test_view_3"]
+# cls.metadata_group = cls.dataset_file.create_group("Metadata")
+# cls.metadata_group.attrs["nbView"] = 4
+# cls.labels_dataset = cls.dataset_file.create_dataset("Labels",
+# data=cls.random_state.randint(
+# 0, 4, 10))
+# cls.labels_dataset.attrs["names"] = ["test_label_0".encode(),
+# "test_label_1".encode(),
+# "test_label_2".encode(),
+# "test_label_3".encode()]
+#
+# for i in range(4):
+# cls.dataset = cls.dataset_file.create_dataset("View" + str(i),
+# data=cls.random_state.randint(
+# 0, 100, (10, 20)))
+# cls.dataset.attrs["name"] = "test_view_" + str(i)
+#
+# def test_simple(self):
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
+# self.views, self.pathF, self.nameDB,
+# self.NB_CLASS, self.askedLabelsNames,
+# self.random_state)
+# self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_1", 1: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
+# np.testing.assert_array_equal(dataset_file.get("View0").value,
+# self.dataset_file.get("View1").value[
+# np.array([1, 5, 9]), :])
+#
+# def test_all_labels_asked(self):
+# askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# NB_CLASS = 4
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
+# self.views, self.pathF, self.nameDB,
+# NB_CLASS, askedLabelsNames,
+# self.random_state)
+# self.assertEqual(dataset_name, 'test_dataset_temp_view_label_select')
+# self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_0", 1: "test_label_1",
+# 2: "test_label_2", 3: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
+# np.testing.assert_array_equal(dataset_file.get("View0").value,
+# self.dataset_file.get("View1").value)
+#
+# def test_all_views_asked(self):
+# views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(views,
+# self.pathF,
+# self.nameDB,
+# self.NB_CLASS,
+# self.askedLabelsNames,
+# self.random_state)
+# for viewIndex in range(4):
+# np.testing.assert_array_equal(
+# dataset_file.get("View" + str(viewIndex)).value,
+# self.dataset_file.get("View" + str(viewIndex)).value[
+# np.array([1, 5, 9]), :])
+# self.assertEqual(
+# dataset_file.get("View" + str(viewIndex)).attrs["name"],
+# "test_view_" + str(viewIndex))
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_1", 1: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
+#
+# def test_asked_the_whole_dataset(self):
+# askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# NB_CLASS = 4
+# views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(views,
+# self.pathF,
+# self.nameDB,
+# NB_CLASS,
+# askedLabelsNames,
+# self.random_state)
+# for viewIndex in range(4):
+# np.testing.assert_array_equal(
+# dataset_file.get("View" + str(viewIndex)).value,
+# self.dataset_file.get("View" + str(viewIndex)))
+# self.assertEqual(
+# dataset_file.get("View" + str(viewIndex)).attrs["name"],
+# "test_view_" + str(viewIndex))
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_0", 1: "test_label_1",
+# 2: "test_label_2", 3: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
+# =======
os.mkdir(tmp_path)
self.rs = np.random.RandomState(42)
self.nb_view = 3
@@ -810,7 +810,7 @@ class Test_get_classic_db_hdf5(unittest.TestCase):
meta_data_grp.attrs["nbView"] = len(self.views)
meta_data_grp.attrs["nbClass"] = len(np.unique(self.labels))
meta_data_grp.attrs["datasetLength"] = len(self.labels)
->>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
+# >>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
def test_simple(self):
dataset , labels_dictionary, dataset_name = get_multiview_db.get_classic_db_hdf5(
@@ -868,102 +868,102 @@ class Test_get_classic_db_csv(unittest.TestCase):
data, delimiter=",")
self.datas.append(data)
-<<<<<<< HEAD
+# <<<<<<< HEAD
+# def test_simple(self):
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
+# self.views, self.pathF, self.nameDB,
+# self.NB_CLASS, self.askedLabelsNames,
+# self.random_state, delimiter=",")
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
+# self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
+# self.assertEqual(dataset_file.get("View0").attrs["name"], "test_view_1")
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_1", 1: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
+# np.testing.assert_array_equal(dataset_file.get("View0").value,
+# self.datas[1][np.array([1, 5, 9]), :])
+#
+# def test_all_views_asked(self):
+# views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(views,
+# self.pathF,
+# self.nameDB,
+# self.NB_CLASS,
+# self.askedLabelsNames,
+# self.random_state,
+# delimiter=",")
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_1", 1: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
+# self.assertEqual(dataset_name,'test_dataset_temp_view_label_select')
+# for viewIndex in range(4):
+# np.testing.assert_array_equal(
+# dataset_file.get("View" + str(viewIndex)).value,
+# self.datas[viewIndex][np.array([1, 5, 9]), :])
+# self.assertEqual(
+# dataset_file.get("View" + str(viewIndex)).attrs["name"],
+# "test_view_" + str(viewIndex))
+#
+# def test_all_labels_asked(self):
+# askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# NB_CLASS = 4
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
+# self.views, self.pathF, self.nameDB,
+# NB_CLASS, askedLabelsNames,
+# self.random_state, delimiter=",")
+# self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_0", 1: "test_label_1",
+# 2: "test_label_2", 3: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
+# np.testing.assert_array_equal(dataset_file.get("View0").value,
+# self.datas[1])
+#
+# def test_asked_the_whole_dataset(self):
+# askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
+# "test_label_3"]
+# NB_CLASS = 4
+# views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
+# dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(views,
+# self.pathF,
+# self.nameDB,
+# NB_CLASS,
+# askedLabelsNames,
+# self.random_state,
+# delimiter=",")
+# for viewIndex in range(4):
+# np.testing.assert_array_equal(
+# dataset_file.get("View" + str(viewIndex)).value,
+# self.datas[viewIndex])
+# self.assertEqual(
+# dataset_file.get("View" + str(viewIndex)).attrs["name"],
+# "test_view_" + str(viewIndex))
+# self.assertEqual(labels_dictionary,
+# {0: "test_label_0", 1: "test_label_1",
+# 2: "test_label_2", 3: "test_label_3"})
+# self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
+# self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
+# =======
def test_simple(self):
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
+ dataset, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
self.views, self.pathF, self.nameDB,
self.NB_CLASS, self.askedLabelsNames,
- self.random_state, delimiter=",")
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
- self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
- self.assertEqual(dataset_file.get("View0").attrs["name"], "test_view_1")
+ self.random_state, delimiter=",", path_for_new=tmp_path)
+ self.assertEqual(dataset.nb_view, 2)
+ self.assertEqual(dataset.get_view_dict(), {'test_view_1': 0, 'test_view_3': 1})
self.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
- np.testing.assert_array_equal(dataset_file.get("View0").value,
- self.datas[1][np.array([1, 5, 9]), :])
-
- def test_all_views_asked(self):
- views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(views,
- self.pathF,
- self.nameDB,
- self.NB_CLASS,
- self.askedLabelsNames,
- self.random_state,
- delimiter=",")
- self.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
- self.assertEqual(dataset_name,'test_dataset_temp_view_label_select')
- for viewIndex in range(4):
- np.testing.assert_array_equal(
- dataset_file.get("View" + str(viewIndex)).value,
- self.datas[viewIndex][np.array([1, 5, 9]), :])
- self.assertEqual(
- dataset_file.get("View" + str(viewIndex)).attrs["name"],
- "test_view_" + str(viewIndex))
-
- def test_all_labels_asked(self):
- askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- NB_CLASS = 4
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
- self.views, self.pathF, self.nameDB,
- NB_CLASS, askedLabelsNames,
- self.random_state, delimiter=",")
- self.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
- self.assertEqual(labels_dictionary,
- {0: "test_label_0", 1: "test_label_1",
- 2: "test_label_2", 3: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 2)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
- np.testing.assert_array_equal(dataset_file.get("View0").value,
- self.datas[1])
-
- def test_asked_the_whole_dataset(self):
- askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
- "test_label_3"]
- NB_CLASS = 4
- views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(views,
- self.pathF,
- self.nameDB,
- NB_CLASS,
- askedLabelsNames,
- self.random_state,
- delimiter=",")
- for viewIndex in range(4):
- np.testing.assert_array_equal(
- dataset_file.get("View" + str(viewIndex)).value,
- self.datas[viewIndex])
- self.assertEqual(
- dataset_file.get("View" + str(viewIndex)).attrs["name"],
- "test_view_" + str(viewIndex))
- self.assertEqual(labels_dictionary,
- {0: "test_label_0", 1: "test_label_1",
- 2: "test_label_2", 3: "test_label_3"})
- self.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 10)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
- self.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 4)
-=======
- def test_simple(cls):
- dataset, labels_dictionary, dataset_name = get_multiview_db.get_classic_db_csv(
- cls.views, cls.pathF, cls.nameDB,
- cls.NB_CLASS, cls.askedLabelsNames,
- cls.random_state, delimiter=",", path_for_new=tmp_path)
- cls.assertEqual(dataset.nb_view, 2)
- cls.assertEqual(dataset.get_view_dict(), {'test_view_1': 0, 'test_view_3': 1})
- cls.assertEqual(labels_dictionary,
- {0: "test_label_1", 1: "test_label_3"})
- cls.assertEqual(dataset.get_nb_examples(), 3)
- cls.assertEqual(dataset.get_nb_class(), 2)
+ {0: "test_label_1", 1: "test_label_3"})
+ self.assertEqual(dataset.get_nb_examples(), 3)
+ self.assertEqual(dataset.get_nb_class(), 2)
->>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
+# >>>>>>> 66129965ae7c38b4bdb4ae657369ab24357070cd
@classmethod
def tearDown(self):