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Commit 93c22046 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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multiview_platform/tests/test_ExecClassif.py
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...@@ -499,308 +499,6 @@ class Test_get_path_dict(unittest.TestCase): ...@@ -499,308 +499,6 @@ class Test_get_path_dict(unittest.TestCase):
self.assertEqual(path_dict, {"a.b.c.d.e":1, "a.b.c.d.f":[1]}) self.assertEqual(path_dict, {"a.b.c.d.e":1, "a.b.c.d.f":[1]})
#
# class Test_analyzeMulticlass(unittest.TestCase):
#
# @classmethod
# def setUpClass(cls):
# cls.flags = [[0, [0,1]], [0, [0,2]], [0, [0,3]], [0, [1,2]], [0, [1,3]], [0, [2,3]],
# [1, [0,1]], [1, [0,2]], [1, [0,3]], [1, [1,2]], [1, [1,3]], [1, [2,3]]]
# cls.preds = [np.array([1, 0, 1, 1, 1]), np.array([1,0,0,1,1]), np.array([1,0,0,0,1]), np.array([1,1,0,1,1]),
# np.array([1,1,0,0,1]), np.array([1,1,1,0,1])] + \
# [np.array([0 in range(5)]) for i in range(6)]
# cls.preds2 = [np.array([0 in range(5)]) for i in range(6)] + \
# [np.array([1, 0, 1, 1, 1]), np.array([1,0,0,1,1]),
# np.array([1,0,0,0,1]), np.array([1,1,0,1,1]), np.array([1,1,0,0,1]), np.array([1,1,1,0,1])]
# cls.classifiers_names = ["chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven",
# "chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven",
# "chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven", "chicken_is_heaven",]
# cls.classifiersNames2 = ["cheese_is_no_disease", "cheese_is_no_disease", "cheese_is_no_disease",
# "cheese_is_no_disease", "cheese_is_no_disease", "cheese_is_no_disease",
# "cheese_is_no_disease", "cheese_is_no_disease", "cheese_is_no_disease",
# "cheese_is_no_disease", "cheese_is_no_disease", "cheese_is_no_disease"]
# cls.results = [[flag, [["", [name, "", "", pred]], ["", [name1, "", "", pred1]]], ["", ""]]
# for flag, name, pred, name1, pred1 in zip(cls.flags, cls.classifiers_names, cls.preds,
# cls.classifiersNames2, cls.preds2)]
# # cls.results = [[flag, ["", ["", name, "", pred]], ""] for flag, pred, name in
# # zip(cls.flags, cls.preds, cls.classifiers_names)]
# cls.statsIter = 2
# cls.nbExample = 5
# cls.nbLabels = 4
# cls.true_labels = np.array([0,1,2,3,0])
# cls.metrics = [["accuracy_score"]]
#
# def test_simple(cls):
# multiclassResults = ExecClassif.analyzeMulticlass(cls.results, cls.statsIter, cls.nbExample, cls.nbLabels, cls.true_labels, [["accuracy_score"]])
# np.testing.assert_array_equal(multiclassResults[1]["chicken_is_heaven"]["labels"], cls.true_labels)
#
# class Test_genMetricsScores(unittest.TestCase):
#
# @classmethod
# def setUpClass(cls):
# cls.multiclass_labels = np.array([0,1,2,3,4,5,2,1,3])
# cls.wrong_labels = np.array([1,3,3,4,5,0,2,4,3])
# cls.multiclassResults = [{"chicken_is_heaven":
# {"labels": cls.multiclass_labels}}]
# cls.true_labels = np.array([0,2,2,3,4,5,1,3,2])
# cls.metrics = [["accuracy_score"]]
# cls.score_to_get = accuracy_score(cls.true_labels, cls.multiclass_labels)
#
# def test_simple(cls):
# multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
# cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
#
# def test_multiple_clf(cls):
# cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
# "cheese_is_no_disease": {"labels": cls.wrong_labels}},
# ]
# multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
# cls.assertEqual(0, multiclassResults[0]["cheese_is_no_disease"]["metricsScores"]["accuracy_score"])
# cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
#
# def test_multiple_metrics(cls):
# from sklearn.metrics import f1_score
# cls.score_to_get_f1 = f1_score(cls.true_labels, cls.multiclass_labels, average="micro")
# cls.metrics = [["accuracy_score"], ["f1_score"]]
# multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
# cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
# cls.assertEqual(cls.score_to_get_f1, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["f1_score"])
#
# def test_multiple_iterations(cls):
# cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels}},
# {"chicken_is_heaven": {"labels": cls.wrong_labels}},
# ]
# multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
# cls.assertEqual(0, multiclassResults[1]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
# cls.assertEqual(cls.score_to_get, multiclassResults[0]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
#
# def test_all(cls):
# cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
# "cheese_is_no_disease": {"labels": cls.wrong_labels}},
# {"chicken_is_heaven": {"labels": cls.wrong_labels},
# "cheese_is_no_disease": {"labels": cls.multiclass_labels}},
# ]
# cls.metrics = [["accuracy_score"], ["f1_score"]]
# from sklearn.metrics import f1_score
# cls.score_to_get_f1 = f1_score(cls.true_labels, cls.multiclass_labels, average="micro")
# multiclassResults = ExecClassif.genMetricsScores(cls.multiclassResults, cls.true_labels, cls.metrics)
# cls.assertEqual(0, multiclassResults[1]["chicken_is_heaven"]["metricsScores"]["accuracy_score"])
# cls.assertEqual(cls.score_to_get_f1, multiclassResults[1]["cheese_is_no_disease"]["metricsScores"]["f1_score"])
#
#
# class Test_getErrorOnLabels(unittest.TestCase):
#
# @classmethod
# def setUpClass(cls):
# cls.multiclass_labels = np.array([0,1,2,3,4,5,2,1,3])
# cls.wrong_labels = np.array([1,3,3,4,5,0,2,4,3])
# cls.multiclassResults = [{"chicken_is_heaven":
# {"labels": cls.multiclass_labels}}]
# cls.true_labels = np.array([0,2,2,3,4,5,1,3,2])
#
# def test_simple(cls):
# multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
# np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
# multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
#
# def test_full(cls):
# cls.multiclassResults = [{"chicken_is_heaven": {"labels": cls.multiclass_labels},
# "cheese_is_no_disease": {"labels": cls.wrong_labels}},
# {"chicken_is_heaven": {"labels": cls.wrong_labels},
# "cheese_is_no_disease": {"labels": cls.wrong_labels}},
# ]
# multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
# np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
# multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
# np.testing.assert_array_equal(np.array([0, 0, 0, 0, 0, 0, 0, 0, 0]),
# multiclassResults[1]["cheese_is_no_disease"]["errorOnExample"])
#
# def test_type(cls):
# multiclassResults = ExecClassif.getErrorOnLabels(cls.multiclassResults, cls.true_labels)
# cls.assertEqual(type(multiclassResults[0]["chicken_is_heaven"]["errorOnExample"][0]), np.int64)
# np.testing.assert_array_equal(np.array([1, 0, 1, 1, 1, 1, 0, 0, 0]),
# multiclassResults[0]["chicken_is_heaven"]["errorOnExample"])
#
# class Essai(unittest.TestCase):
#
# def setUp(self):
# parser = argparse.ArgumentParser(
# description='This file is used to benchmark the scores fo multiple classification algorithm on multiview data.',
# formatter_class=argparse.ArgumentDefaultsHelpFormatter)
#
# groupStandard = parser.add_argument_group('Standard arguments')
# groupStandard.add_argument('-log', action='store_true', help='Use option to activate Logging to Console')
# groupStandard.add_argument('--name', metavar='STRING', action='store', help='Name of Database (default: %(default)s)',
# default='Plausible')
# groupStandard.add_argument('--type', metavar='STRING', action='store',
# help='Type of database : .hdf5 or .csv (default: %(default)s)',
# default='.hdf5')
# groupStandard.add_argument('--views', metavar='STRING', action='store', nargs="+",
# help='Name of the views selected for learning (default: %(default)s)',
# default=[''])
# groupStandard.add_argument('--pathF', metavar='STRING', action='store', help='Path to the views (default: %(default)s)',
# default='/home/bbauvin/Documents/data/Data_multi_omics/')
# groupStandard.add_argument('--nice', metavar='INT', action='store', type=int,
# help='Niceness for the process', default=0)
# groupStandard.add_argument('--random_state', metavar='STRING', action='store',
# help="The random state seed to use or a file where we can find it's get_state", default=None)
#
# groupClass = parser.add_argument_group('Classification arguments')
# groupClass.add_argument('--CL_split', metavar='FLOAT', action='store',
# help='Determine the split between learning and validation sets', type=float,
# default=0.2)
# groupClass.add_argument('--CL_nbFolds', metavar='INT', action='store', help='Number of folds in cross validation',
# type=int, default=2)
# groupClass.add_argument('--CL_nb_class', metavar='INT', action='store', help='Number of classes, -1 for all', type=int,
# default=2)
# groupClass.add_argument('--CL_classes', metavar='STRING', action='store', nargs="+",
# help='Classes used in the dataset (names of the folders) if not filled, random classes will be '
# 'selected ex. walrus mole leopard', default=["yes", "no"])
# groupClass.add_argument('--CL_type', metavar='STRING', action='store', nargs="+",
# help='Determine whether to use multiview and/or monoview, or Benchmark',
# default=['Benchmark'])
# groupClass.add_argument('--CL_algos_monoview', metavar='STRING', action='store', nargs="+",
# help='Determine which monoview classifier to use if empty, considering all',
# default=[''])
# groupClass.add_argument('--CL_algos_multiview', metavar='STRING', action='store', nargs="+",
# help='Determine which multiview classifier to use if empty, considering all',
# default=[''])
# groupClass.add_argument('--CL_cores', metavar='INT', action='store', help='Number of cores, -1 for all', type=int,
# default=2)
# groupClass.add_argument('--CL_statsiter', metavar='INT', action='store',
# help="Number of iteration for each algorithm to mean preds if using multiple cores, it's highly recommended to use statsiter mod(nbCores) = 0",
# type=int,
# default=2)
# groupClass.add_argument('--CL_metrics', metavar='STRING', action='store', nargs="+",
# help='Determine which metrics to use, separate metric and configuration with ":".'
# ' If multiple, separate with space. If no metric is specified, '
# 'considering all with accuracy for classification '
# , default=[''])
# groupClass.add_argument('--CL_metric_princ', metavar='STRING', action='store',
# help='Determine which metric to use for randomSearch and optimization', default="f1_score")
# groupClass.add_argument('--CL_GS_iter', metavar='INT', action='store',
# help='Determine how many Randomized grid search tests to do', type=int, default=2)
# groupClass.add_argument('--CL_HPS_type', metavar='STRING', action='store',
# help='Determine which hyperparamter search function use', default="randomizedSearch")
#
# groupRF = parser.add_argument_group('Random Forest arguments')
# groupRF.add_argument('--CL_RandomForest_trees', metavar='INT', type=int, action='store', help='Number max trees',
# default=25)
# groupRF.add_argument('--CL_RandomForest_max_depth', metavar='INT', type=int, action='store',
# help='Max depth for the trees',
# default=5)
# groupRF.add_argument('--CL_RandomForest_criterion', metavar='STRING', action='store', help='Criterion for the trees',
# default="entropy")
#
# groupSVMLinear = parser.add_argument_group('Linear SVM arguments')
# groupSVMLinear.add_argument('--CL_SVMLinear_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
# default=1)
#
# groupSVMRBF = parser.add_argument_group('SVW-RBF arguments')
# groupSVMRBF.add_argument('--CL_SVMRBF_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
# default=1)
#
# groupSVMPoly = parser.add_argument_group('Poly SVM arguments')
# groupSVMPoly.add_argument('--CL_SVMPoly_C', metavar='INT', type=int, action='store', help='Penalty parameter used',
# default=1)
# groupSVMPoly.add_argument('--CL_SVMPoly_deg', metavar='INT', type=int, action='store', help='Degree parameter used',
# default=2)
#
# groupAdaboost = parser.add_argument_group('Adaboost arguments')
# groupAdaboost.add_argument('--CL_Adaboost_n_est', metavar='INT', type=int, action='store', help='Number of estimators',
# default=2)
# groupAdaboost.add_argument('--CL_Adaboost_b_est', metavar='STRING', action='store', help='Estimators',
# default='DecisionTreeClassifier')
#
# groupDT = parser.add_argument_group('Decision Trees arguments')
# groupDT.add_argument('--CL_DecisionTree_depth', metavar='INT', type=int, action='store',
# help='Determine max depth for Decision Trees', default=3)
# groupDT.add_argument('--CL_DecisionTree_criterion', metavar='STRING', action='store',
# help='Determine max depth for Decision Trees', default="entropy")
# groupDT.add_argument('--CL_DecisionTree_splitter', metavar='STRING', action='store',
# help='Determine criterion for Decision Trees', default="random")
#
# groupSGD = parser.add_argument_group('SGD arguments')
# groupSGD.add_argument('--CL_SGD_alpha', metavar='FLOAT', type=float, action='store',
# help='Determine alpha for SGDClassifier', default=0.1)
# groupSGD.add_argument('--CL_SGD_loss', metavar='STRING', action='store',
# help='Determine loss for SGDClassifier', default='log')
# groupSGD.add_argument('--CL_SGD_penalty', metavar='STRING', action='store',
# help='Determine penalty for SGDClassifier', default='l2')
#
# groupKNN = parser.add_argument_group('KNN arguments')
# groupKNN.add_argument('--CL_KNN_neigh', metavar='INT', type=int, action='store',
# help='Determine number of neighbors for KNN', default=1)
# groupKNN.add_argument('--CL_KNN_weights', metavar='STRING', action='store',
# help='Determine number of neighbors for KNN', default="distance")
# groupKNN.add_argument('--CL_KNN_algo', metavar='STRING', action='store',
# help='Determine number of neighbors for KNN', default="auto")
# groupKNN.add_argument('--CL_KNN_p', metavar='INT', type=int, action='store',
# help='Determine number of neighbors for KNN', default=1)
#
# groupSCM = parser.add_argument_group('SCM arguments')
# groupSCM.add_argument('--CL_SCM_max_rules', metavar='INT', type=int, action='store',
# help='Max number of rules for SCM', default=1)
# groupSCM.add_argument('--CL_SCM_p', metavar='FLOAT', type=float, action='store',
# help='Max number of rules for SCM', default=1.0)
# groupSCM.add_argument('--CL_SCM_model_type', metavar='STRING', action='store',
# help='Max number of rules for SCM', default="conjunction")
#
# groupMumbo = parser.add_argument_group('Mumbo arguments')
# groupMumbo.add_argument('--MU_types', metavar='STRING', action='store', nargs="+",
# help='Determine which monoview classifier to use with Mumbo',
# default=[''])
# groupMumbo.add_argument('--MU_config', metavar='STRING', action='store', nargs='+',
# help='Configuration for the monoview classifier in Mumbo separate each classifier with sapce and each argument with:',
# default=[''])
# groupMumbo.add_argument('--MU_iter', metavar='INT', action='store', nargs=3,
# help='Max number of iteration, min number of iteration, convergence threshold', type=float,
# default=[10, 1, 0.01])
# groupMumbo.add_argument('--MU_combination', action='store_true',
# help='Try all the monoview classifiers combinations for each view',
# default=False)
#
#
# groupFusion = parser.add_argument_group('fusion arguments')
# groupFusion.add_argument('--FU_types', metavar='STRING', action='store', nargs="+",
# help='Determine which type of fusion to use',
# default=[''])
# groupEarlyFusion = parser.add_argument_group('Early fusion arguments')
# groupEarlyFusion.add_argument('--FU_early_methods', metavar='STRING', action='store', nargs="+",
# help='Determine which early fusion method of fusion to use',
# default=[''])
# groupEarlyFusion.add_argument('--FU_E_method_configs', metavar='STRING', action='store', nargs='+',
# help='Configuration for the early fusion methods separate '
# 'method by space and values by :',
# default=[''])
# groupEarlyFusion.add_argument('--FU_E_cl_config', metavar='STRING', action='store', nargs='+',
# help='Configuration for the monoview classifiers used separate classifier by space '
# 'and configs must be of form argument1_name:value,argument2_name:value',
# default=[''])
# groupEarlyFusion.add_argument('--FU_E_cl_names', metavar='STRING', action='store', nargs='+',
# help='Name of the classifiers used for each early fusion method', default=[''])
#
# groupLateFusion = parser.add_argument_group('Late Early fusion arguments')
# groupLateFusion.add_argument('--FU_late_methods', metavar='STRING', action='store', nargs="+",
# help='Determine which late fusion method of fusion to use',
# default=[''])
# groupLateFusion.add_argument('--FU_L_method_config', metavar='STRING', action='store', nargs='+',
# help='Configuration for the fusion method', default=[''])
# groupLateFusion.add_argument('--FU_L_cl_config', metavar='STRING', action='store', nargs='+',
# help='Configuration for the monoview classifiers used', default=[''])
# groupLateFusion.add_argument('--FU_L_cl_names', metavar='STRING', action='store', nargs="+",
# help='Names of the classifier used for late fusion', default=[''])
# groupLateFusion.add_argument('--FU_L_select_monoview', metavar='STRING', action='store',
# help='Determine which method to use to select the monoview classifiers',
# default="intersect")
# self.args = parser.parse_args([])
# def suite():
# suite = unittest.TestSuite()
# suite.addTest(Test_initBenchmark('test_initKWARGSFunc_no_monoview'))
# # suite.addTest(WidgetTestCase('test_widget_resize'))
# return suite
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
\ No newline at end of file
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