diff --git a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
index 8c73ab9aadb2c8da300010435788e8d82bcc7ef7..fb3c7605d8938b7bc27c439cdc484f8ec35206c0 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
@@ -558,12 +558,7 @@ def execBenchmark(nbCores, statsIter, nbMulticlass,
for arguments in benchmarkArgumentsDictionaries:
results += [execOneBenchmarkMonoCore(DATASET=DATASET, **arguments)]
logging.debug("Done:\t Executing all the needed biclass benchmarks")
- if nbCores > 1:
- logging.debug("Start:\t Deleting " + str(
- nbCores) + " temporary datasets for multiprocessing")
- args = benchmarkArgumentsDictionaries[0]["args"]
- datasetFiles = delete(args.pathF, args.name, nbCores)
- logging.debug("Start:\t Deleting datasets for multiprocessing")
+
# Do everything with flagging
nbExamples = len(classificationIndices[0][0]) + len(
classificationIndices[0][1])
@@ -573,10 +568,7 @@ def execBenchmark(nbCores, statsIter, nbMulticlass,
multiclassGroundTruth, metrics, classificationIndices,
directories, directory, labelsDictionary, nbExamples, nbLabels)
logging.debug("Done:\t Analyzing predictions")
- filename = DATASET.filename
- DATASET.close()
- if "_temp_" in filename:
- os.remove(filename)
+ delete(benchmarkArgumentsDictionaries, nbCores, DATASET)
return results_mean_stds
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py b/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
index 52a9c21c4adc9a9094d5362fdb60edbbe2c308b1..c0e1a6b0e30d30d70cb6108c6f5a146e935a9d2a 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/GetMultiviewDb.py
@@ -1,5 +1,6 @@
import errno
import os
+import logging
import h5py
import numpy as np
@@ -58,10 +59,20 @@ def datasetsAlreadyExist(pathF, name, nbCores):
return allDatasetExist
-def deleteHDF5(pathF, name, nbCores):
+def deleteHDF5(benchmarkArgumentsDictionaries, nbCores, DATASET):
"""Used to delete temporary copies at the end of the benchmark"""
- for coreIndex in range(nbCores):
- os.remove(pathF + name + str(coreIndex) + ".hdf5")
+ if nbCores > 1:
+ logging.debug("Start:\t Deleting " + str(
+ nbCores) + " temporary datasets for multiprocessing")
+ args = benchmarkArgumentsDictionaries[0]["args"]
+ logging.debug("Start:\t Deleting datasets for multiprocessing")
+
+ for coreIndex in range(nbCores):
+ os.remove(args.pathF + args.name + str(coreIndex) + ".hdf5")
+ filename = DATASET.filename
+ DATASET.close()
+ if "_temp_" in filename:
+ os.remove(filename)
def makeMeNoisy(viewData, randomState, percentage=15):
@@ -484,11 +495,11 @@ def getClassicDBcsv(views, pathF, nameDB, NB_CLASS, askedLabelsNames,
metaDataGrp.attrs["nbClass"] = len(labelsNames)
metaDataGrp.attrs["datasetLength"] = len(labels)
datasetFile.close()
- datasetFile, labelsDictionary = getClassicDBhdf5(views, pathF, nameDB,
+ datasetFile, labelsDictionary, dataset_name = getClassicDBhdf5(views, pathF, nameDB,
NB_CLASS, askedLabelsNames,
randomState, full)
- return datasetFile, labelsDictionary
+ return datasetFile, labelsDictionary, dataset_name
# def getLabelSupports(CLASS_LABELS):
# """Used to get the number of example for each label"""
diff --git a/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DisagreeFusion/test_DisagreeFusionModule.py b/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DisagreeFusion/test_DisagreeFusionModule.py
index 1a32682b29f54a4de95651d56891f4b8918ebebf..16b809f78e39ca987e65b945f04789965d628fe9 100644
--- a/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DisagreeFusion/test_DisagreeFusionModule.py
+++ b/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DisagreeFusion/test_DisagreeFusionModule.py
@@ -6,150 +6,50 @@ from multiview_platform.MonoMultiViewClassifiers.Multiview.Additions import \
diversity_utils
from ....MonoMultiViewClassifiers.MultiviewClassifiers.DisagreeFusion import \
DisagreeFusionModule
-
+from multiview_platform.MonoMultiViewClassifiers.Multiview.MultiviewUtils import MultiviewResult
class Test_disagreement(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.randomState = np.random.RandomState(42)
- cls.allClassifiersNames = ["SCM", "SVM", "DT"]
- cls.directory = ""
+ cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
cls.viewsIndices = np.array([0, 1])
- cls.resultsMonoview = np.array([[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]]
- ])
+ cls.classifiersDecisions = np.zeros((cls.viewsIndices.shape[0], len(cls.allClassifiersNames), 3, 6),
+ dtype=int)
+ for classifer_index, classifier in enumerate(cls.allClassifiersNames):
+ for view_index, view in enumerate(cls.viewsIndices):
+ cls.classifiersDecisions[view_index, classifer_index] = np.array([
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6)])
+ cls.folds_ground_truth = np.array([np.array([1,1,1,0,0,0]) for _ in range(3)])
cls.classificationIndices = np.array([])
def test_simple(cls):
bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices, cls.resultsMonoview,
- DisagreeFusionModule.disagree)
+ cls.allClassifiersNames, cls.classifiersDecisions, DisagreeFusionModule.disagree, cls.folds_ground_truth)
cls.assertAlmostEqual(disagreement, 0.666666666667)
cls.assertEqual(len(bestCombi), 2)
- def test_viewsIndices(cls):
- cls.viewsIndices = np.array([0, 6])
- cls.resultsMonoview =np.array( [[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]]
- ])
- bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices,
- cls.resultsMonoview, DisagreeFusionModule.disagree)
- cls.assertAlmostEqual(disagreement, 0.611111111111)
- cls.assertEqual(len(bestCombi), 2)
-
def test_multipleViews(cls):
cls.viewsIndices = np.array([0, 6, 18])
- cls.resultsMonoview = np.array([[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [18, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]],
- [18, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]],
- [18, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]]
- ])
+ cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
+ cls.classifiersDecisions = np.zeros(
+ (cls.viewsIndices.shape[0], len(cls.allClassifiersNames), 3, 6),
+ dtype=int)
+ for classifer_index, classifier in enumerate(cls.allClassifiersNames):
+ for view_index, view in enumerate(cls.viewsIndices):
+ cls.classifiersDecisions[
+ view_index, classifer_index] = np.array([
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6)])
+
bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices,
- cls.resultsMonoview, DisagreeFusionModule.disagree)
- cls.assertAlmostEqual(disagreement, 0.592592592593)
+ cls.allClassifiersNames, cls.classifiersDecisions,
+ DisagreeFusionModule.disagree, cls.folds_ground_truth)
+ cls.assertAlmostEqual(disagreement, 0.55555555555555)
cls.assertEqual(len(bestCombi), 3)
diff --git a/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DoubleFaultFusion/test_DoubleFaultFusionModule.py b/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DoubleFaultFusion/test_DoubleFaultFusionModule.py
index 7267f3087a0c40e54f31e9c045644750e17ab2ed..ab7b9d31a43334cb24db5a186ff850f22f062b14 100644
--- a/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DoubleFaultFusion/test_DoubleFaultFusionModule.py
+++ b/multiview_platform/Tests/Test_MultiviewClassifiers/Test_DoubleFaultFusion/test_DoubleFaultFusionModule.py
@@ -13,148 +13,45 @@ class Test_doubleFaultRatio(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.randomState = np.random.RandomState(42)
- cls.allClassifiersNames = ["SCM", "SVM", "DT"]
+ cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
cls.directory = ""
- cls.viewsIndices = [0, 1]
- cls.resultsMonoview = [[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]]
- ]
- cls.classificationIndices = []
- cls.ground_truth = np.array([1, 1, 1, 0, 0, 0])
+ cls.viewsIndices = np.array([0, 1])
+ cls.classifiersDecisions = np.zeros(
+ (cls.viewsIndices.shape[0], len(cls.allClassifiersNames), 3, 6),
+ dtype=int)
+ for classifer_index, classifier in enumerate(cls.allClassifiersNames):
+ for view_index, view in enumerate(cls.viewsIndices):
+ cls.classifiersDecisions[
+ view_index, classifer_index] = np.array([
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6)])
+ cls.folds_ground_truth = np.array([np.array([1,1,1,0,0,0]) for _ in range(3)])
def test_simple(cls):
bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices,
- cls.resultsMonoview,
- DoubleFaultFusionModule.doubleFault,
- cls.ground_truth)
- cls.assertAlmostEqual(disagreement, 0.55555555555555547)
- cls.assertEqual(len(bestCombi), 2)
-
- def test_viewsIndices(cls):
- cls.viewsIndices = [0, 6]
- cls.resultsMonoview = [[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]]
- ]
- bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices,
- cls.resultsMonoview, DoubleFaultFusionModule.doubleFault,
- cls.ground_truth)
- cls.assertAlmostEqual(disagreement, 0.33333333333333331)
+ cls.allClassifiersNames,cls.classifiersDecisions,
+ DoubleFaultFusionModule.doubleFault, cls.folds_ground_truth)
+ cls.assertAlmostEqual(disagreement, 0.3888888888888)
cls.assertEqual(len(bestCombi), 2)
def test_multipleViews(cls):
- cls.viewsIndices = [0, 6, 18]
- cls.resultsMonoview = [[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [6, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [18, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]],
- [18, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]],
- [18, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2,
- 6)])
- ]]
- ]
+ cls.viewsIndices = np.array([0, 6, 18])
+ cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
+ cls.classifiersDecisions = np.zeros(
+ (cls.viewsIndices.shape[0], len(cls.allClassifiersNames), 3, 6),
+ dtype=int)
+ for classifer_index, classifier in enumerate(cls.allClassifiersNames):
+ for view_index, view in enumerate(cls.viewsIndices):
+ cls.classifiersDecisions[
+ view_index, classifer_index] = np.array([
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6)])
bestCombi, disagreement = diversity_utils.couple_div_measure(
- cls.allClassifiersNames, cls.viewsIndices,
- cls.resultsMonoview, DoubleFaultFusionModule.doubleFault,
- cls.ground_truth)
- cls.assertAlmostEqual(disagreement, 0.31481481481481483)
+ cls.allClassifiersNames, cls.classifiersDecisions,
+ DoubleFaultFusionModule.doubleFault, cls.folds_ground_truth)
+ cls.assertAlmostEqual(disagreement, 0.3333333333)
cls.assertEqual(len(bestCombi), 3)
diff --git a/multiview_platform/Tests/Test_MultiviewClassifiers/test_diversity_utils.py b/multiview_platform/Tests/Test_MultiviewClassifiers/test_diversity_utils.py
index a8e8149c4665b6b74cb266910e18485a46eab1d8..ea1c0bd88608d0e06a4fe2adc98b77bb20c9931f 100644
--- a/multiview_platform/Tests/Test_MultiviewClassifiers/test_diversity_utils.py
+++ b/multiview_platform/Tests/Test_MultiviewClassifiers/test_diversity_utils.py
@@ -14,50 +14,29 @@ class Test_global_div_measure(unittest.TestCase):
@classmethod
def setUpClass(cls):
- cls.allClassifersNames = ["SCM", "DT", "SVM"]
- cls.viewsIndices = [0, 1]
cls.randomState = np.random.RandomState(42)
- cls.resultsMonoview = np.array([[0, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [0, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SCM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["SVM", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]],
- [1, ["DT", "", "", "", "", "",
- np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
- ]]
- ])
+ cls.allClassifiersNames = [["SCM", "SVM", "DT"], ["SCM", "SVM", "DT"]]
+ cls.viewsIndices = np.array([0, 1])
+ cls.classifiersDecisions = np.zeros(
+ (cls.viewsIndices.shape[0], len(cls.allClassifiersNames), 3, 6),
+ dtype=int)
+ for classifer_index, classifier in enumerate(cls.allClassifiersNames):
+ for view_index, view in enumerate(cls.viewsIndices):
+ cls.classifiersDecisions[
+ view_index, classifer_index] = np.array([
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6),
+ cls.randomState.randint(0, 2, 6)])
+ cls.folds_ground_truth = np.array(
+ [np.array([1, 1, 1, 0, 0, 0]) for _ in range(3)])
+ cls.classificationIndices = np.array([])
cls.measurement = fake_measure
- cls.foldsGroudTruth = np.array([cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6),
- cls.randomState.randint(0, 2, 6)])
def test_simple(cls):
clf_names, diversity_measure = diversity_utils.global_div_measure(
- cls.allClassifersNames,
- cls.resultsMonoview,
+ cls.allClassifiersNames,
+ cls.classifiersDecisions,
cls.measurement,
- cls.foldsGroudTruth)#cls.viewsIndices,
+ cls.folds_ground_truth)
cls.assertEqual(len(clf_names), 2)
cls.assertEqual(diversity_measure, 42)
diff --git a/multiview_platform/Tests/Test_utils/test_GetMultiviewDB.py b/multiview_platform/Tests/Test_utils/test_GetMultiviewDB.py
index 111cb76273ff616f5b678cb00500c13eb68d4ca6..4f344d0ad3ae3baf04e1c128127482aa0f82c0be 100644
--- a/multiview_platform/Tests/Test_utils/test_GetMultiviewDB.py
+++ b/multiview_platform/Tests/Test_utils/test_GetMultiviewDB.py
@@ -363,7 +363,7 @@ class Test_getClassicDBhdf5(unittest.TestCase):
cls.dataset.attrs["name"] = "test_view_" + str(i)
def test_simple(cls):
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBhdf5(
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBhdf5(
cls.views, cls.pathF, cls.nameDB,
cls.NB_CLASS, cls.askedLabelsNames,
cls.random_state)
@@ -381,10 +381,11 @@ class Test_getClassicDBhdf5(unittest.TestCase):
askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
"test_label_3"]
NB_CLASS = 4
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBhdf5(
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBhdf5(
cls.views, cls.pathF, cls.nameDB,
NB_CLASS, askedLabelsNames,
cls.random_state)
+ cls.assertEqual(dataset_name, 'test_dataset_temp_view_label_select')
cls.assertEqual(dataset_file.get("View1").attrs["name"], "test_view_3")
cls.assertEqual(labels_dictionary,
{0: "test_label_0", 1: "test_label_1",
@@ -397,7 +398,7 @@ class Test_getClassicDBhdf5(unittest.TestCase):
def test_all_views_asked(cls):
views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBhdf5(views,
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBhdf5(views,
cls.pathF,
cls.nameDB,
cls.NB_CLASS,
@@ -422,7 +423,7 @@ class Test_getClassicDBhdf5(unittest.TestCase):
"test_label_3"]
NB_CLASS = 4
views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBhdf5(views,
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBhdf5(views,
cls.pathF,
cls.nameDB,
NB_CLASS,
@@ -480,7 +481,7 @@ class Test_getClassicDBcsv(unittest.TestCase):
cls.datas.append(data)
def test_simple(cls):
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBcsv(
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBcsv(
cls.views, cls.pathF, cls.nameDB,
cls.NB_CLASS, cls.askedLabelsNames,
cls.random_state, delimiter=",")
@@ -496,7 +497,7 @@ class Test_getClassicDBcsv(unittest.TestCase):
def test_all_views_asked(cls):
views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBcsv(views,
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBcsv(views,
cls.pathF,
cls.nameDB,
cls.NB_CLASS,
@@ -508,6 +509,7 @@ class Test_getClassicDBcsv(unittest.TestCase):
cls.assertEqual(dataset_file.get("Metadata").attrs["datasetLength"], 3)
cls.assertEqual(dataset_file.get("Metadata").attrs["nbView"], 4)
cls.assertEqual(dataset_file.get("Metadata").attrs["nbClass"], 2)
+ cls.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,
@@ -520,7 +522,7 @@ class Test_getClassicDBcsv(unittest.TestCase):
askedLabelsNames = ["test_label_0", "test_label_1", "test_label_2",
"test_label_3"]
NB_CLASS = 4
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBcsv(
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBcsv(
cls.views, cls.pathF, cls.nameDB,
NB_CLASS, askedLabelsNames,
cls.random_state, delimiter=",")
@@ -539,7 +541,7 @@ class Test_getClassicDBcsv(unittest.TestCase):
"test_label_3"]
NB_CLASS = 4
views = ["test_view_0", "test_view_1", "test_view_2", "test_view_3"]
- dataset_file, labels_dictionary = GetMultiviewDb.getClassicDBcsv(views,
+ dataset_file, labels_dictionary, dataset_name = GetMultiviewDb.getClassicDBcsv(views,
cls.pathF,
cls.nameDB,
NB_CLASS,
diff --git a/multiview_platform/Tests/test_ExecClassif.py b/multiview_platform/Tests/test_ExecClassif.py
index 5d7bccb97c914dacba001129e849913ab341f20d..c9e6416adb0dd54ebb7794b3609ad90939d957d7 100644
--- a/multiview_platform/Tests/test_ExecClassif.py
+++ b/multiview_platform/Tests/test_ExecClassif.py
@@ -77,7 +77,7 @@ class Test_execBenchmark(unittest.TestCase):
execOneBenchmarkMonoCore=fakeBenchmarkExec_monocore,
getResults=fakegetResults,
delete=fakeDelete)
- cls.assertEqual(res, [[4]])
+ cls.assertEqual(res, 3)
def test_multiclass_no_iter(cls):
cls.argumentDictionaries = [{"a": 10, "args": FakeArg()},
@@ -90,7 +90,7 @@ class Test_execBenchmark(unittest.TestCase):
execOneBenchmarkMonoCore=fakeBenchmarkExec_monocore,
getResults=fakegetResults,
delete=fakeDelete)
- cls.assertEqual(res, [[0, 10], [1, 4]])
+ cls.assertEqual(res, 3)
def test_multiclass_and_iter(cls):
cls.argumentDictionaries = [{"a": 10, "args": FakeArg()},
@@ -105,7 +105,7 @@ class Test_execBenchmark(unittest.TestCase):
execOneBenchmarkMonoCore=fakeBenchmarkExec_monocore,
getResults=fakegetResults,
delete=fakeDelete)
- cls.assertEqual(res, [[0, 10], [1, 4], [0, 55], [1, 24]])
+ cls.assertEqual(res, 3)
def test_no_iter_biclass_multicore(cls):
res = ExecClassif.execBenchmark(2, 1, 1, cls.argumentDictionaries,
@@ -116,10 +116,11 @@ class Test_execBenchmark(unittest.TestCase):
execOneBenchmarkMonoCore=fakeBenchmarkExec_monocore,
getResults=fakegetResults,
delete=fakeDelete)
- cls.assertEqual(res, [[2, 4]])
+ cls.assertEqual(res, 3)
@classmethod
def tearDownClass(cls):
+ cls.Dataset.close()
os.remove("multiview_platform/Tests/tmp_tests/test_file.hdf5")
os.rmdir("multiview_platform/Tests/tmp_tests")
@@ -168,7 +169,7 @@ class Test_execOneBenchmark(unittest.TestCase):
os.mkdir("multiview_platform/Tests/tmp_tests")
def test_simple(cls):
- flag, resMono, resMulti = ExecClassif.execOneBenchmark(coreIndex=10,
+ flag, results = ExecClassif.execOneBenchmark(coreIndex=10,
LABELS_DICTIONARY={
0: "a",
1: "b"},
@@ -201,10 +202,9 @@ class Test_execOneBenchmark(unittest.TestCase):
initMultiviewArguments=fakeInitMulti)
cls.assertEqual(flag, None)
- cls.assertEqual(resMono,
- [["Mono", {"try": 0}], ["Mono", {"try2": 100}]])
- cls.assertEqual(resMulti,
- [["Multi", {"try3": 5}], ["Multi", {"try4": 10}]])
+ cls.assertEqual(results ,
+ [['Mono', {'try': 0}], ['Mono', {'try2': 100}],
+ ['Multi', {'try3': 5}], ['Multi', {'try4': 10}]])
@classmethod
def tearDownClass(cls):
@@ -225,7 +225,7 @@ class Test_execOneBenchmark_multicore(unittest.TestCase):
os.mkdir("multiview_platform/Tests/tmp_tests")
def test_simple(cls):
- flag, resMono, resMulti = ExecClassif.execOneBenchmark_multicore(
+ flag, results = ExecClassif.execOneBenchmark_multicore(
nbCores=2,
LABELS_DICTIONARY={0: "a", 1: "b"},
directory="multiview_platform/Tests/tmp_tests/",
@@ -246,10 +246,9 @@ class Test_execOneBenchmark_multicore(unittest.TestCase):
initMultiviewArguments=fakeInitMulti)
cls.assertEqual(flag, None)
- cls.assertEqual(resMono,
- [["Mono", {"try": 0}], ["Mono", {"try2": 100}]])
- cls.assertEqual(resMulti,
- [["Multi", {"try3": 5}], ["Multi", {"try4": 10}]])
+ cls.assertEqual(results ,
+ [['Mono', {'try': 0}], ['Mono', {'try2': 100}],
+ ['Multi', {'try3': 5}], ['Multi', {'try4': 10}]])
@classmethod
def tearDownClass(cls):