May have done some shit

multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py

@@ -17,6 +17,62 @@ from ..utils.Interpret import getFeatureImportance
 __author__ = "Baptiste Bauvin"
 __status__ = "Prototype"  # Production, Development, Prototype
 
+class Adaboost(AdaBoostClassifier):
+
+    def __init__(self, random_state, **kwargs):
+        super(AdaBoostClassifier, self).__init__(
+            n_estimators=kwargs['n_estimators'],
+            base_estimator=kwargs['base_estimator'],
+            random_state=random_state)
+
+    def canProbas(self):
+        """Used to know if the classifier can return label probabilities"""
+        return True
+
+    def paramsToSrt(self, nIter=1):
+        """Used for weighted linear early fusion to generate random search sets"""
+        paramsSet = []
+        for _ in range(nIter):
+            paramsSet.append({"n_estimators": self.random_state.randint(1, 150),
+                              "base_estimator": DecisionTreeClassifier()})
+        return paramsSet
+
+    def getKWARGS(self, args):
+        """Used to format kwargs for the parsed args"""
+        kwargsDict = {}
+        kwargsDict['n_estimators'] = args.Ada_n_est
+        kwargsDict['base_estimator'] = DecisionTreeClassifier()  # args.Ada_b_est
+        return kwargsDict
+
+    def genPipeline(self):
+        return Pipeline([('classifier', AdaBoostClassifier())])
+
+    def genParamsDict(self, randomState):
+        return {"classifier__n_estimators": np.arange(150) + 1,
+                "classifier__base_estimator": [DecisionTreeClassifier()]}
+
+    def genBestParams(self, detector):
+        return {"n_estimators": detector.best_params_["classifier__n_estimators"],
+                "base_estimator": detector.best_params_["classifier__base_estimator"]}
+
+    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)
+        return interpretString
+
 
 def canProbas():
     return True