Debugging

Code/MonoMutliViewClassifiers/MonoviewClassifiers/SCM.py

@@ -71,7 +71,7 @@ def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIt
             if fold != range(len(y_train)):
                 fold.sort()
                 trainIndices = [index for index in range(len(y_train)) if (index not in fold)]
-                attributeClassification, binaryAttributes, dsetFile = transformData(X_train[trainIndices])
+                attributeClassification, binaryAttributes, dsetFile, name = transformData(X_train[trainIndices])
                 try:
                     classifier.fit(binaryAttributes, y_train[trainIndices], X=None, attribute_classifications=attributeClassification, iteration_callback=None)
 
@@ -81,6 +81,7 @@ def gridSearch(X_train, y_train, nbFolds=4, metric=["accuracy_score", None], nIt
                 except:
                     pass
                 dsetFile.close()
+                os.remove(name)
         if scores==[]:
             score = baseScore
         else:

Code/MonoMutliViewClassifiers/Multiview/Mumbo/Classifiers/Kover.py

-from sklearn.metrics import precision_recall_fscore_support
-from sklearn.cross_validation import StratifiedShuffleSplit as split
-import numpy as np
-# from sklearn.multiclass import OneVsRestClassifier
-from ModifiedMulticlass import OneVsRestClassifier
-
-# Add weights
-
-pass
+# from sklearn.metrics import precision_recall_fscore_support
+# from sklearn.cross_validation import StratifiedShuffleSplit as split
+# import numpy as np
+# # from sklearn.multiclass import OneVsRestClassifier
+# from ModifiedMulticlass import OneVsRestClassifier
+#
+# from sklearn import tree
+# from sklearn.metrics import accuracy_score
+# import numpy as np
+# from ModifiedMulticlass import OneVsRestClassifier
+# from SubSampling import subSample
+# import logging
+# # Add weights
+#
+# def DecisionTree(data, labels, arg, weights):
+#     depth = int(arg[0])
+#     subSampling = float(arg[1])
+#     if subSampling != 1.0:
+#         subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, subSampling, weights=weights)
+#     else:
+#         subSampledData, subSampledLabels, subSampledWeights = data, labels, weights
+#     isBad = False
+#     classifier = tree.DecisionTreeClassifier(max_depth=depth)
+#     #classifier = OneVsRestClassifier(tree.DecisionTreeClassifier(max_depth=depth))
+#     classifier.fit(subSampledData, subSampledLabels, subSampledWeights)
+#     prediction = classifier.predict(data)
+#     accuracy = accuracy_score(labels, prediction)
+#     if accuracy < 0.5:
+#         isBad = True
+#
+#     return classifier, prediction, isBad, accuracy
+#
+#
+# def getConfig(classifierConfig):
+#     depth = classifierConfig[0]
+#     subSampling = classifierConfig[1]
+#     return 'with depth ' + str(depth) + ', ' + ' sub-sampled at ' + str(subSampling) + ' '
+#
+#
+# def gridSearch(data, labels, metric="accuracy_score"):
+#     minSubSampling = 1.0/(len(labels)/2)
+#     bestSettings = []
+#     bestResults = []
+#     classifier = tree.DecisionTreeClassifier(max_depth=1)
+#     preliminary_accuracies = np.zeros(50)
+#     for i in range(50):
+#         subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, 0.05)
+#         classifier.fit(subSampledData, subSampledLabels)
+#         prediction = classifier.predict(data)
+#         preliminary_accuracies[i] = accuracy_score(labels, prediction)
+#     preliminary_accuracy = np.mean(preliminary_accuracies)
+#     if preliminary_accuracy < 0.50:
+#         for max_depth in np.arange(10)+1:
+#             for subSampling in sorted(np.arange(20, dtype=float)+1/20, reverse=True):
+#                 if subSampling > minSubSampling:
+#                     accuracies = np.zeros(50)
+#                     for i in range(50):
+#                         if subSampling != 1.0:
+#                             subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, subSampling)
+#                         else:
+#                             subSampledData, subSampledLabels, = data, labels
+#                         classifier = tree.DecisionTreeClassifier(max_depth=max_depth)
+#                         classifier.fit(subSampledData, subSampledLabels)
+#                         prediction = classifier.predict(data)
+#                         accuracies[i] = accuracy_score(labels, prediction)
+#                     accuracy = np.mean(accuracies)
+#                     if 0.5 < accuracy < 0.60:
+#                         bestSettings.append([max_depth, subSampling])
+#                         bestResults.append(accuracy)
+#     else:
+#         preliminary_accuracies = np.zeros(50)
+#         if minSubSampling < 0.01:
+#             for i in range(50):
+#                 subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, 0.01)
+#                 classifier.fit(subSampledData, subSampledLabels)
+#                 prediction = classifier.predict(data)
+#                 preliminary_accuracies[i] = accuracy_score(labels, prediction)
+#         preliminary_accuracy = np.mean(preliminary_accuracies)
+#         if preliminary_accuracy < 0.50:
+#             for subSampling in sorted((np.arange(19, dtype=float)+1)/200, reverse=True):
+#                 if minSubSampling < subSampling:
+#                     accuracies = np.zeros(50)
+#                     for i in range(50):
+#                         subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, subSampling)
+#                         classifier = tree.DecisionTreeClassifier(max_depth=1)
+#                         classifier.fit(subSampledData, subSampledLabels)
+#                         prediction = classifier.predict(data)
+#                         accuracies[i] = accuracy_score(labels, prediction)
+#                     accuracy = np.mean(accuracies)
+#                     if 0.5 < accuracy < 0.60:
+#                         bestSettings.append([1, subSampling])
+#                         bestResults.append(accuracy)
+#         else:
+#             for subSampling in sorted((np.arange(19, dtype=float)+1)/2000, reverse=True):
+#                 accuracies = np.zeros(50)
+#                 for i in range(50):
+#                     subSampledData, subSampledLabels, subSampledWeights = subSample(data, labels, subSampling)
+#                     if minSubSampling < subSampling:
+#                         classifier1 = tree.DecisionTreeClassifier(max_depth=1)
+#                         classifier1.fit(subSampledData, subSampledLabels)
+#                         prediction = classifier1.predict(data)
+#                         accuracies[i] = accuracy_score(labels, prediction)
+#                 accuracy = np.mean(accuracies)
+#                 if 0.5 < accuracy < 0.60:
+#                     bestSettings.append([1, subSampling])
+#                     bestResults.append(accuracy)
+#
+#     assert bestResults!=[], "No good settings found for Decision Tree!"
+#
+#     return getBestSetting(bestSettings, bestResults)
+#
+#
+# def getBestSetting(bestSettings, bestResults):
+#     diffTo52 = 100.0
+#     bestSettingsIndex = 0
+#     for resultIndex, result in enumerate(bestResults):
+#         if abs(0.55-result) < diffTo52:
+#             diffTo52 = abs(0.55-result)
+#             bestResult = result
+#             bestSettingsIndex = resultIndex
+#     logging.debug("\t\tInfo:\t Best Reslut : "+str(result))
+#
+#     return map(lambda p: round(p, 4), bestSettings[bestSettingsIndex])
+#     #    return map(round(,4), bestSettings[bestSettingsIndex])
\ No newline at end of file