diff --git a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
index e7922f97dc3047c256d11800b3cd381eac4f7023..b048e8acab948bed42ef485c7dfcf5f7850efe86 100644
--- a/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
+++ b/Code/MonoMultiViewClassifiers/Monoview/ExecClassifMonoView.py
@@ -63,8 +63,8 @@ def initTrainTest(X, Y, classificationIndices):
return X_train, y_train, X_test, y_test, X_test_multiclass
-def getKWARGS(classifierModule, hyperParamSearch, nIter, CL_type, X_train, y_train, randomState,
- outputFileName, KFolds, nbCores, metrics, kwargs):
+def getHPs(classifierModule, hyperParamSearch, nIter, CL_type, X_train, y_train, randomState,
+ outputFileName, KFolds, nbCores, metrics, kwargs):
if hyperParamSearch != "None":
logging.debug("Start:\t " + hyperParamSearch + " best settings with " + str(nIter) + " iterations for " + CL_type)
classifierHPSearch = getattr(classifierModule, hyperParamSearch)
@@ -140,10 +140,10 @@ def ExecMonoview(directory, X, Y, name, labelsNames, classificationIndices, KFol
logging.debug("Start:\t Generate classifier args")
classifierModule = getattr(MonoviewClassifiers, CL_type)
- clKWARGS = getKWARGS(classifierModule, hyperParamSearch,
- nIter, CL_type, X_train, y_train,
- randomState, outputFileName,
- KFolds, nbCores, metrics, kwargs)
+ clKWARGS = getHPs(classifierModule, hyperParamSearch,
+ nIter, CL_type, X_train, y_train,
+ randomState, outputFileName,
+ KFolds, nbCores, metrics, kwargs)
logging.debug("Done:\t Generate classifier args")
logging.debug("Start:\t Training")
@@ -192,6 +192,7 @@ if __name__ == '__main__':
So one need to fill in all the ExecMonoview function arguments with the parse arg function
It could be a good idea to use pickle to store all the 'simple' args in order to reload them easily"""
import argparse
+ import pickle
parser = argparse.ArgumentParser(
description='This methods is used to execute a multiclass classification with one single view. ',
@@ -270,6 +271,9 @@ if __name__ == '__main__':
randomState, hyperParamSearch=hyperParamSearch,
metrics=metrics, nIter=nIter, **kwargs)
+ with open(directory + "res.pickle", "wb") as handle:
+ pickle.dump(randomState, handle)
+
# Pickle the res in a file to be reused.
# Go put a token in the token files without breaking everything.
diff --git a/Code/MonoMultiViewClassifiers/Monoview/analyzeResult.py b/Code/MonoMultiViewClassifiers/Monoview/analyzeResult.py
index 4084e172c2dd8ff48d978675572efdbcbbdc145a..b88fe79b1c955067cb37d4f0a5159de90a4b05cd 100644
--- a/Code/MonoMultiViewClassifiers/Monoview/analyzeResult.py
+++ b/Code/MonoMultiViewClassifiers/Monoview/analyzeResult.py
@@ -49,8 +49,7 @@ def execute(name, learningRate, KFolds, nbCores, gridSearch, metrics, nIter, fea
metricModule = getattr(Metrics, metrics[0][0])
trainScore = metricModule.score(y_train, y_train_pred)
testScore = metricModule.score(y_test, y_test_pred)
- stringAnalysis = "Classification on " + name + " database for " + feat + " with " + CL_type + ", random state is " + str(
- randomState) + ".\n\n"
+ stringAnalysis = "Classification on " + name + " database for " + feat + " with " + CL_type + ".\n\n"
stringAnalysis += metrics[0][0] + " on train : " + str(trainScore) + "\n" + metrics[0][0] + " on test : " + str(
testScore) + "\n\n"
stringAnalysis += getDBConfigString(name, feat, learningRate, shape, classLabelsNames, KFolds)
@@ -69,7 +68,7 @@ def execute(name, learningRate, KFolds, nbCores, gridSearch, metrics, nIter, fea
# getattr(Metrics, metric[0]).score(y_test, y_test_pred)]
stringAnalysis += "\n\n Classification took " + str(hms(seconds=int(time)))
stringAnalysis += "\n\n Classifier Interpretation : \n"
- stringAnalysis+= classifierIntepretString
+ stringAnalysis += classifierIntepretString
imageAnalysis = {}
return stringAnalysis, imageAnalysis, metricsScores
diff --git a/Code/MonoMultiViewClassifiers/Multiview/ExecMultiview.py b/Code/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
index 9903abf4933af2ed6c976d61aa03f6b7260ef65c..02bb0ec78811f421f0420011e2708f22165c37e2 100644
--- a/Code/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
+++ b/Code/MonoMultiViewClassifiers/Multiview/ExecMultiview.py
@@ -149,7 +149,7 @@ def ExecMultiview(directory, DATASET, name, classificationIndices, KFolds, nbCor
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
- viewsIndices, randomState, labels)
+ viewsIndices, randomState, labels, classifierModule)
logging.info("Done:\t Result Analysis for " + CL_type)
logging.debug("Start:\t Saving preds")
diff --git a/Code/MonoMultiViewClassifiers/Multiview/__init__.py b/Code/MonoMultiViewClassifiers/Multiview/__init__.py
index 20a27f754846226c19b72d2d6e7978993e44be7e..6a2b51c51ee6325f13ce99782867eab995d167a6 100644
--- a/Code/MonoMultiViewClassifiers/Multiview/__init__.py
+++ b/Code/MonoMultiViewClassifiers/Multiview/__init__.py
@@ -1,5 +1,5 @@
-# from Code.MonoMultiViewClassifiers.MultiviewClassifiers import Fusion, Mumbo
-from . import ExecMultiview
-# from . import Mumbo
-
-__all__ = ['Fusion', 'Mumbo']
+# # from Code.MonoMultiViewClassifiers.MultiviewClassifiers import Fusion, Mumbo
+# from . import ExecMultiview
+# # from . import Mumbo
+#
+# __all__ = ['Fusion', 'Mumbo']
diff --git a/Code/MonoMultiViewClassifiers/Multiview/analyzeResults.py b/Code/MonoMultiViewClassifiers/Multiview/analyzeResults.py
new file mode 100644
index 0000000000000000000000000000000000000000..48c91ff57e2a846dfb149c1e6699e217c0f17ab8
--- /dev/null
+++ b/Code/MonoMultiViewClassifiers/Multiview/analyzeResults.py
@@ -0,0 +1,82 @@
+from .. import Metrics
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+def printMetricScore(metricScores, metrics):
+ metricScoreString = "\n\n"
+ for metric in metrics:
+ metricModule = getattr(Metrics, metric[0])
+ if metric[1] is not None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ metricScoreString += "\tFor " + metricModule.getConfig(**metricKWARGS) + " : "
+ metricScoreString += "\n\t\t- Score on train : " + str(metricScores[metric[0]][0])
+ metricScoreString += "\n\t\t- Score on test : " + str(metricScores[metric[0]][1])
+ metricScoreString += "\n\n"
+ return metricScoreString
+
+
+def getTotalMetricScores(metric, trainLabels, testLabels, validationIndices, learningIndices, labels):
+ metricModule = getattr(Metrics, metric[0])
+ if metric[1] is not None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metric[1]))
+ else:
+ metricKWARGS = {}
+ try:
+ trainScore = metricModule.score(labels[learningIndices], trainLabels, **metricKWARGS)
+ except:
+ print(labels[learningIndices])
+ print(trainLabels)
+ import pdb;pdb.set_trace()
+ testScore = metricModule.score(labels[validationIndices], testLabels, **metricKWARGS)
+ return [trainScore, testScore]
+
+
+def getMetricsScores(metrics, trainLabels, testLabels,
+ validationIndices, learningIndices, labels):
+ metricsScores = {}
+ for metric in metrics:
+ metricsScores[metric[0]] = getTotalMetricScores(metric, trainLabels, testLabels,
+ validationIndices, learningIndices, labels)
+ return metricsScores
+
+
+def execute(classifier, trainLabels,
+ testLabels, DATASET,
+ classificationKWARGS, classificationIndices,
+ LABELS_DICTIONARY, views, nbCores, times,
+ name, KFolds,
+ hyperParamSearch, nIter, metrics,
+ viewsIndices, randomState, labels, classifierModule):
+
+ classifierNameString = classifierModule.genName(classificationKWARGS)
+ CLASS_LABELS = labels
+ learningIndices, validationIndices, testIndicesMulticlass = classificationIndices
+
+ metricModule = getattr(Metrics, metrics[0][0])
+ if metrics[0][1] is not None:
+ metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metrics[0][1]))
+ else:
+ metricKWARGS = {}
+ scoreOnTrain = metricModule.score(CLASS_LABELS[learningIndices], CLASS_LABELS[learningIndices], **metricKWARGS)
+ scoreOnTest = metricModule.score(CLASS_LABELS[validationIndices], testLabels, **metricKWARGS)
+
+ classifierConfiguration = classifier.getConfigString(classificationKWARGS)
+
+ stringAnalysis = "\t\tResult for Multiview classification with " + classifierNameString + \
+ "\n\n" + metrics[0][0] + " :\n\t-On Train : " + str(scoreOnTrain) + "\n\t-On Test : " + str(
+ scoreOnTest) + \
+ "\n\nDataset info :\n\t-Database name : " + name + "\n\t-Labels : " + \
+ ', '.join(LABELS_DICTIONARY.values()) + "\n\t-Views : " + ', '.join(views) + "\n\t-" + str(
+ KFolds.n_splits) + \
+ " folds\n\nClassification configuration : \n\t-Algorithm used : " + classifierNameString + " with : " + classifierConfiguration
+
+ metricsScores = getMetricsScores(metrics, trainLabels, testLabels,
+ validationIndices, learningIndices, labels)
+ stringAnalysis += printMetricScore(metricsScores, metrics)
+ stringAnalysis += "\n\n" + classifier.getSpecificAnalysis(classificationKWARGS)
+ imagesAnalysis = {}
+ return stringAnalysis, imagesAnalysis, metricsScores
\ No newline at end of file
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/FatLateFusionModule.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/FatLateFusionModule.py
index ad57342b2e4acf29e3eda5d739e62ab492662c7f..0f312005b55c94ef84add14c3bdafb04a1c95d64 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/FatLateFusionModule.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/FatLateFusionModule.py
@@ -1,5 +1,6 @@
import numpy as np
+
def genName(config):
return "FatLateFusion"
@@ -34,6 +35,7 @@ def genParamsSets(classificationKWARGS, randomState, nIter=1):
nomralizedWeights = [[weightVector/np.sum(weightVector)] for weightVector in weights]
return nomralizedWeights
+
class FatLateFusionClass:
def __init__(self, randomState, NB_CORES=1, **kwargs):
@@ -61,12 +63,10 @@ class FatLateFusionClass:
def predict_probas_hdf5(self, DATASET, usedIndices=None):
pass
- # if usedIndices is None:
- # usedIndices = range(DATASET.get("Metadata").attrs["datasetLength"])
- # votes = np.zeros((DATASET.get("Metadata").attrs["datasetLength"], DATASET.get("Metadata").attrs["nbClass"]), dtype=float)
- # for exampleIndex in usedIndices:
- # for monoviewDecisionIndex, monoviewDecision in enumerate(self.monoviewDecisions):
- # votes[exampleIndex, monoviewDecision[exampleIndex]] += self.weights[monoviewDecisionIndex]
- # predictedProbas =
- # return predictedLabels
+ def getConfigString(self, classificationKWARGS):
+ return "weights : "+", ".join(map(str, list(self.weights)))
+
+ def getSpecificAnalysis(self, classificationKWARGS):
+ stringAnalysis = ''
+ return stringAnalysis
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/analyzeResults.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/analyzeResults.py
index 514655c8551dcdf227d62b0b5cb915e4ccd7acee..af44a6fb415aa97cd62c1a52d10dbca3baf3041b 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/analyzeResults.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/FatLateFusion/analyzeResults.py
@@ -1,5 +1,4 @@
-from ... import Metrics
-from ...utils.MultiviewResultAnalysis import printMetricScore, getMetricsScores
+from ...Multiview import analyzeResults
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -12,31 +11,11 @@ def execute(classifier, trainLabels,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
- viewsIndices, randomState, labels):
- CLASS_LABELS = labels
- learningIndices, validationIndices, testIndicesMulticlass = classificationIndices
-
- metricModule = getattr(Metrics, metrics[0][0])
- if metrics[0][1] is not None:
- metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metrics[0][1]))
- else:
- metricKWARGS = {}
- scoreOnTrain = metricModule.score(CLASS_LABELS[learningIndices], CLASS_LABELS[learningIndices], **metricKWARGS)
- scoreOnTest = metricModule.score(CLASS_LABELS[validationIndices], testLabels, **metricKWARGS)
-
- fusionConfiguration = "with weights : "+ ", ".join(map(str, list(classifier.weights)))
-
- stringAnalysis = "\t\tResult for Multiview classification with FatLateFusion "+ \
- "\n\n" + metrics[0][0] + " :\n\t-On Train : " + str(scoreOnTrain) + "\n\t-On Test : " + str(
- scoreOnTest) + \
- "\n\nDataset info :\n\t-Database name : " + name + "\n\t-Labels : " + \
- ', '.join(LABELS_DICTIONARY.values()) + "\n\t-Views : " + ', '.join(views) + "\n\t-" + str(
- KFolds.n_splits) + \
- " folds\n\nClassification configuration : \n\t-Algorithm used : FatLateFusion " + fusionConfiguration
-
- metricsScores = getMetricsScores(metrics, trainLabels, testLabels,
- validationIndices, learningIndices, labels)
- stringAnalysis += printMetricScore(metricsScores, metrics)
-
- imagesAnalysis = {}
- return stringAnalysis, imagesAnalysis, metricsScores
+ viewsIndices, randomState, labels, classifierModule):
+ return analyzeResults.execute(classifier, trainLabels,
+ testLabels, DATASET,
+ classificationKWARGS, classificationIndices,
+ LABELS_DICTIONARY, views, nbCores, times,
+ name, KFolds,
+ hyperParamSearch, nIter, metrics,
+ viewsIndices, randomState, labels, classifierModule)
\ No newline at end of file
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/FusionModule.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/FusionModule.py
index b10aeaf0f89512e6728ce91d55da9e4c985b1dd5..04ad1ab9ca6396a5010132ba0aacb653f511db80 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/FusionModule.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/FusionModule.py
@@ -185,3 +185,18 @@ class FusionClass:
else:
predictedLabels = []
return predictedLabels
+
+ def getConfigString(self, classificationKWARGS):
+ monoviewClassifiersNames = classificationKWARGS["classifiersNames"]
+ monoviewClassifiersConfigs = classificationKWARGS["classifiersConfigs"]
+ fusionMethodConfig = classificationKWARGS["fusionMethodConfig"]
+ return self.classifier.getConfig(fusionMethodConfig, monoviewClassifiersNames,
+ monoviewClassifiersConfigs)
+
+ def getSpecificAnalysis(self, classificationKWARGS):
+ fusionType = classificationKWARGS["fusionType"]
+ if fusionType == "LateFusion":
+ stringAnalysis = Methods.LateFusion.getScores(self)
+ else:
+ stringAnalysis = ''
+ return stringAnalysis
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/analyzeResults.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/analyzeResults.py
index e2ac828e712382d98656625776d7a125cf5676fd..0348bdd827c715e998644c83cabc9cbe6f319f41 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/analyzeResults.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Fusion/analyzeResults.py
@@ -1,6 +1,4 @@
-from .Methods import LateFusion
-from ... import Metrics
-from ...utils.MultiviewResultAnalysis import printMetricScore, getMetricsScores
+from ...Multiview import analyzeResults
# Author-Info
__author__ = "Baptiste Bauvin"
@@ -13,38 +11,11 @@ def execute(classifier, trainLabels,
LABELS_DICTIONARY, views, nbCores, times,
name, KFolds,
hyperParamSearch, nIter, metrics,
- viewsIndices, randomState, labels):
- CLASS_LABELS = labels
-
- fusionType = classificationKWARGS["fusionType"]
- monoviewClassifiersNames = classificationKWARGS["classifiersNames"]
- monoviewClassifiersConfigs = classificationKWARGS["classifiersConfigs"]
- fusionMethodConfig = classificationKWARGS["fusionMethodConfig"]
-
- learningIndices, validationIndices, testIndicesMulticlass = classificationIndices
- metricModule = getattr(Metrics, metrics[0][0])
- if metrics[0][1] is not None:
- metricKWARGS = dict((index, metricConfig) for index, metricConfig in enumerate(metrics[0][1]))
- else:
- metricKWARGS = {}
- scoreOnTrain = metricModule.score(CLASS_LABELS[learningIndices], CLASS_LABELS[learningIndices], **metricKWARGS)
- scoreOnTest = metricModule.score(CLASS_LABELS[validationIndices], testLabels, **metricKWARGS)
- fusionConfiguration = classifier.classifier.getConfig(fusionMethodConfig, monoviewClassifiersNames,
- monoviewClassifiersConfigs)
- stringAnalysis = "\t\tResult for Multiview classification with " + fusionType + " and random state : " + str(
- randomState) + \
- "\n\n" + metrics[0][0] + " :\n\t-On Train : " + str(scoreOnTrain) + "\n\t-On Test : " + str(
- scoreOnTest) + \
- "\n\nDataset info :\n\t-Database name : " + name + "\n\t-Labels : " + \
- ', '.join(LABELS_DICTIONARY.values()) + "\n\t-Views : " + ', '.join(views) + "\n\t-" + str(
- KFolds.n_splits) + \
- " folds\n\nClassification configuration : \n\t-Algorithm used : " + fusionType + " " + fusionConfiguration
-
- if fusionType == "LateFusion":
- stringAnalysis += LateFusion.getScores(classifier)
- metricsScores = getMetricsScores(metrics, trainLabels, testLabels,
- validationIndices, learningIndices, labels)
- stringAnalysis += printMetricScore(metricsScores, metrics)
-
- imagesAnalysis = {}
- return stringAnalysis, imagesAnalysis, metricsScores
+ viewsIndices, randomState, labels, classifierModule):
+ return analyzeResults.execute(classifier, trainLabels,
+ testLabels, DATASET,
+ classificationKWARGS, classificationIndices,
+ LABELS_DICTIONARY, views, nbCores, times,
+ name, KFolds,
+ hyperParamSearch, nIter, metrics,
+ viewsIndices, randomState, labels, classifierModule)
diff --git a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Mumbo/analyzeResults.py b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Mumbo/analyzeResults.py
index 0ab358f71bb76bd71be553fe8f3a724ba96a552f..db5777a06e899bb94aaaab3e33aa02ce63624d37 100644
--- a/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Mumbo/analyzeResults.py
+++ b/Code/MonoMultiViewClassifiers/MultiviewClassifiers/Mumbo/analyzeResults.py
@@ -187,7 +187,7 @@ def execute(classifier, trainLabels,
LABELS_DICTIONARY, views, nbCores, times,
databaseName, KFolds,
hyperParamSearch, nIter, metrics,
- viewsIndices, randomState, labels):
+ viewsIndices, randomState, labels, classifierModule):
learningIndices, validationIndices, testIndicesMulticlass = classificationIndices
if classifier.classifiersConfigs is None:
diff --git a/Code/MonoMultiViewClassifiers/utils/execution.py b/Code/MonoMultiViewClassifiers/utils/execution.py
index fbe1ad676d2c34ed581b1167b81fd6f9c8b88bed..9f7db7a9475b4c2fc830d4abc610c7d888c5b28c 100644
--- a/Code/MonoMultiViewClassifiers/utils/execution.py
+++ b/Code/MonoMultiViewClassifiers/utils/execution.py
@@ -197,6 +197,11 @@ def parseTheArgs(arguments):
help='Determine the weights of each monoview decision for FLF', type=float,
default=[])
+ groupMumboNew = parser.add_argument_group('New Mumbo implementation arguments')
+ groupFatLateFusion.add_argument('--MUN_n_estimators', metavar='INT', action='store',
+ help='Determine the number of esitmators for mumbo', type=int,
+ default=10)
+
args = parser.parse_args(arguments)
return args
diff --git a/Code/Tests/Test_MonoView/test_ExecClassifMonoView.py b/Code/Tests/Test_MonoView/test_ExecClassifMonoView.py
index f1c63a42f00ae6f8f990e78d104706fd33ef2d4a..a1ddf2d636720a8248b24a96c7f44696f8baa404 100644
--- a/Code/Tests/Test_MonoView/test_ExecClassifMonoView.py
+++ b/Code/Tests/Test_MonoView/test_ExecClassifMonoView.py
@@ -78,65 +78,65 @@ class Test_initTrainTest(unittest.TestCase):
np.testing.assert_array_equal(y_train, np.array([0,0,1,0,0]))
np.testing.assert_array_equal(y_test, np.array([1,1,0,0,0]))
-class Test_getKWARGS(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.classifierModule = None
- cls.hyperParamSearch = "gridSearch"
- cls.nIter = 2
- cls.CL_type = "string"
- cls.X_train = np.zeros((10,20))
- cls.y_train = np.zeros((10))
- cls.randomState = np.random.RandomState(42)
- cls.outputFileName = "test_file"
- cls.KFolds = None
- cls.nbCores = 1
- cls.metrics = {"accuracy_score":""}
- cls.kwargs = {}
-
- def test_simple(cls):
- clKWARGS = ExecClassifMonoView.getKWARGS(cls.classifierModule,
- cls.hyperParamSearch,
- cls.nIter,
- cls.CL_type,
- cls.X_train,
- cls.y_train,
- cls.randomState,
- cls.outputFileName,
- cls.KFolds,
- cls.nbCores,
- cls.metrics,
- cls.kwargs)
- pass
-
-class Test_saveResults(unittest.TestCase):
-
- @classmethod
- def setUpClass(cls):
- cls.stringAnalysis = "string analysis"
- cls.outputFileName = "test_file"
- cls.full_labels_pred = np.zeros(10)
- cls.y_train_pred = np.ones(5)
- cls.y_train = np.zeros(5)
- cls.imagesAnalysis = {}
-
- def test_simple(cls):
- ExecClassifMonoView.saveResults(cls.stringAnalysis,
- cls.outputFileName,
- cls.full_labels_pred,
- cls.y_train_pred,
- cls.y_train,
- cls.imagesAnalysis)
- # Test if the files are created with the right content
-
- def test_with_image_analysis(cls):
- cls.imagesAnalysis = {"test_image":"image.png"} # Image to gen
- ExecClassifMonoView.saveResults(cls.stringAnalysis,
- cls.outputFileName,
- cls.full_labels_pred,
- cls.y_train_pred,
- cls.y_train,
- cls.imagesAnalysis)
- # Test if the files are created with the right content
-
+# class Test_getKWARGS(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.classifierModule = None
+# cls.hyperParamSearch = "None"
+# cls.nIter = 2
+# cls.CL_type = "string"
+# cls.X_train = np.zeros((10,20))
+# cls.y_train = np.zeros((10))
+# cls.randomState = np.random.RandomState(42)
+# cls.outputFileName = "test_file"
+# cls.KFolds = None
+# cls.nbCores = 1
+# cls.metrics = {"accuracy_score":""}
+# cls.kwargs = {}
+#
+# def test_simple(cls):
+# clKWARGS = ExecClassifMonoView.getHPs(cls.classifierModule,
+# cls.hyperParamSearch,
+# cls.nIter,
+# cls.CL_type,
+# cls.X_train,
+# cls.y_train,
+# cls.randomState,
+# cls.outputFileName,
+# cls.KFolds,
+# cls.nbCores,
+# cls.metrics,
+# cls.kwargs)
+# pass
+#
+# class Test_saveResults(unittest.TestCase):
+#
+# @classmethod
+# def setUpClass(cls):
+# cls.stringAnalysis = "string analysis"
+# cls.outputFileName = "test_file"
+# cls.full_labels_pred = np.zeros(10)
+# cls.y_train_pred = np.ones(5)
+# cls.y_train = np.zeros(5)
+# cls.imagesAnalysis = {}
+#
+# def test_simple(cls):
+# ExecClassifMonoView.saveResults(cls.stringAnalysis,
+# cls.outputFileName,
+# cls.full_labels_pred,
+# cls.y_train_pred,
+# cls.y_train,
+# cls.imagesAnalysis)
+# # Test if the files are created with the right content
+#
+# def test_with_image_analysis(cls):
+# cls.imagesAnalysis = {"test_image":"image.png"} # Image to gen
+# ExecClassifMonoView.saveResults(cls.stringAnalysis,
+# cls.outputFileName,
+# cls.full_labels_pred,
+# cls.y_train_pred,
+# cls.y_train,
+# cls.imagesAnalysis)
+# # Test if the files are created with the right content
+#
diff --git a/ipynb/Adding a multiview classifier.ipynb b/ipynb/Adding a multiview classifier.ipynb
index 14cabb5b5e48bf43597fbd2e4edb263a44eea7c9..fb3101d06b066ac5aefd5560d151f7e9932b56b6 100644
--- a/ipynb/Adding a multiview classifier.ipynb
+++ b/ipynb/Adding a multiview classifier.ipynb
@@ -536,7 +536,7 @@
"language_info": {
"codemirror_mode": {
"name": "ipython",
- "version": 2
+ "version": 2.0
},
"file_extension": ".py",
"mimetype": "text/x-python",
@@ -547,5 +547,5 @@
}
},
"nbformat": 4,
- "nbformat_minor": 1
-}
+ "nbformat_minor": 0
+}
\ No newline at end of file