Updated config functions

multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py

 from sklearn.model_selection import RandomizedSearchCV
 from scipy.stats import uniform, randint
-from sklearn.pipeline import Pipeline
 import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FuncFormatter
+import pickle
 
 from .. import Metrics
 from ..utils import HyperParameterSearch
@@ -113,11 +115,39 @@ class BaseMonoviewClassifier(object):
         else:
             return str(self.get_params()[param_name])
 
+    def getFeatureImportance(self, directory, nb_considered_feats=50):
+        """Used to generate a graph and a pickle dictionary representing feature importances"""
+        featureImportances = self.feature_importances_
+        sortedArgs = np.argsort(-featureImportances)
+        featureImportancesSorted = featureImportances[sortedArgs][:nb_considered_feats]
+        featureIndicesSorted = sortedArgs[:nb_considered_feats]
+        fig, ax = plt.subplots()
+        x = np.arange(len(featureIndicesSorted))
+        formatter = FuncFormatter(percent)
+        ax.yaxis.set_major_formatter(formatter)
+        plt.bar(x, featureImportancesSorted)
+        plt.title("Importance depending on feature")
+        fig.savefig(directory + "feature_importances.png")
+        plt.close()
+        featuresImportancesDict = dict((featureIndex, featureImportance)
+                                       for featureIndex, featureImportance in enumerate(featureImportances)
+                                       if featureImportance != 0)
+        with open(directory + 'feature_importances.pickle', 'wb') as handle:
+            pickle.dump(featuresImportancesDict, handle)
+        interpretString = "Feature importances : \n"
+        for featureIndex, featureImportance in zip(featureIndicesSorted, featureImportancesSorted):
+            if featureImportance > 0:
+                interpretString += "- Feature index : " + str(featureIndex) + \
+                                   ", feature importance : " + str(featureImportance) + "\n"
+        return interpretString
+
 
 def get_names(classed_list):
     return np.array([object_.__class__.__name__ for object_ in classed_list])
 
-
+def percent(x, pos):
+    """Used to print percentage of importance on the y axis"""
+    return '%1.1f %%' % (x * 100)
 
 
 # def isUseful(labelSupports, index, CLASS_LABELS, labelDict):

multiview_platform/MonoMultiViewClassifiers/Monoview/analyzeResult.py

@@ -18,12 +18,12 @@ def getDBConfigString(name, feat, classificationIndices, shape, classLabelsNames
 
 def getClassifierConfigString(gridSearch, nbCores, nIter, clKWARGS, classifier, directory):
     classifierConfigString = "Classifier configuration : \n"
-    classifierConfigString += "\t- " + classifier.getConfig(clKWARGS)[5:] + "\n"
+    classifierConfigString += "\t- " + classifier.getConfig()[5:] + "\n"
     classifierConfigString += "\t- Executed on " + str(nbCores) + " core(s) \n"
     if gridSearch:
         classifierConfigString += "\t- Got configuration using randomized search with " + str(nIter) + " iterations \n"
     classifierConfigString += "\n\n"
-    classifierInterpretString = classifier.getInterpret(classifier, directory)
+    classifierInterpretString = classifier.getInterpret(directory)
     return classifierConfigString, classifierInterpretString
 
 

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py

 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.tree import DecisionTreeClassifier
 
-from ..utils.Interpret import getFeatureImportance
 from ..Monoview.MonoviewUtils import CustomRandint, BaseMonoviewClassifier
 
 # Author-Info
@@ -35,28 +34,9 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
                               "base_estimator": DecisionTreeClassifier()})
         return paramsSet
 
-    # def genPipeline(self):
-    #     return Pipeline([('classifier', AdaBoostClassifier())])
-
-    # def genDistribs(self,):
-    #     return {"classifier__n_estimators": CustomRandint(low=1, high=500),
-    #             "classifier__base_estimator": [DecisionTreeClassifier()]}
-
-    # def genParamsFromDetector(self, detector):
-    #     nIter = len(detector.cv_results_['param_classifier__n_estimators'])
-    #     return [("baseEstimators", np.array(["DecisionTree" for _ in range(nIter)])),
-    #             ("nEstimators", np.array(detector.cv_results_['param_classifier__n_estimators']))]
-
-    def getConfig(self, config):
-        if type(config) is not dict:  # Used in late fusion when config is a classifier
-            return "\n\t\t- Adaboost with num_esimators : " + str(config.n_estimators) + ", base_estimators : " + str(
-                config.base_estimator)
-        else:
-            return "\n\t\t- Adaboost with n_estimators : " + str(config["n_estimators"]) + ", base_estimator : " + str(
-                   config["base_estimator"])
-
-    def getInterpret(self, classifier, directory):
-        interpretString = getFeatureImportance(classifier, directory)
+    def getInterpret(self, directory):
+        interpretString = ""
+        interpretString += self.getFeatureImportance(directory)
         return interpretString
 
 

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/DecisionTree.py

@@ -8,7 +8,6 @@ import numpy as np
 
 from .. import Metrics
 from ..utils.HyperParameterSearch import genHeatMaps
-from ..utils.Interpret import getFeatureImportance
 
 # Author-Info
 __author__ = "Baptiste Bauvin"

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/RandomForest.py

@@ -3,11 +3,6 @@ from sklearn.pipeline import Pipeline
 from sklearn.model_selection import RandomizedSearchCV
 from scipy.stats import randint
 import numpy as np
-# import cPickle
-
-from .. import Metrics
-from ..utils.HyperParameterSearch import genHeatMaps
-from ..utils.Interpret import getFeatureImportance
 
 # Author-Info
 __author__ = "Baptiste Bauvin"

multiview_platform/MonoMultiViewClassifiers/utils/Interpret.py

-import numpy as np
-import matplotlib.pyplot as plt
-from matplotlib.ticker import FuncFormatter
-import pickle
-
-
-def percent(x, pos):
-    """Used to print percentage of importance on the y axis"""
-    return '%1.1f %%' % (x * 100)
-
-
-def getFeatureImportance(classifier, directory, interpretString=""):
-    """Used to generate a graph and a pickle dictionary representing feature importances"""
-    featureImportances = classifier.feature_importances_
-    sortedArgs = np.argsort(-featureImportances)
-    featureImportancesSorted = featureImportances[sortedArgs][:50]
-    featureIndicesSorted = sortedArgs[:50]
-    fig, ax = plt.subplots()
-    x = np.arange(len(featureIndicesSorted))
-    formatter = FuncFormatter(percent)
-    ax.yaxis.set_major_formatter(formatter)
-    plt.bar(x, featureImportancesSorted)
-    plt.title("Importance depending on feature")
-    fig.savefig(directory + "feature_importances.png")
-    plt.close()
-    featuresImportancesDict = dict((featureIndex, featureImportance)
-                                   for featureIndex, featureImportance in enumerate(featureImportances)
-                                   if featureImportance != 0)
-    with open(directory+'feature_importances.pickle', 'wb') as handle:
-        pickle.dump(featuresImportancesDict, handle)
-    interpretString += "Feature importances : \n"
-    for featureIndex, featureImportance in zip(featureIndicesSorted, featureImportancesSorted):
-        if featureImportance>0:
-            interpretString+="- Feature index : "+str(featureIndex)+\
-                             ", feature importance : "+str(featureImportance)+"\n"
-    return interpretString
\ No newline at end of file

multiview_platform/MonoMultiViewClassifiers/utils/__init__.py

-from . import Dataset, execution, HyperParameterSearch, Transformations, Interpret
+from . import Dataset, execution, HyperParameterSearch, Transformations