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#                               4. Classifiers                                #
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# Create list of tuples with classifier label and classifier object
classifiers = {}
classifiers.update({"LDA": LinearDiscriminantAnalysis()})
classifiers.update({"QDA": QuadraticDiscriminantAnalysis()})
classifiers.update({"AdaBoost": AdaBoostClassifier()})
classifiers.update({"Bagging": BaggingClassifier()})
classifiers.update({"Extra Trees Ensemble": ExtraTreesClassifier()})
classifiers.update({"Gradient Boosting": GradientBoostingClassifier()})
classifiers.update({"Random Forest": RandomForestClassifier()})
classifiers.update({"Ridge": RidgeClassifier()})
classifiers.update({"SGD": SGDClassifier()})
classifiers.update({"BNB": BernoulliNB()})
classifiers.update({"GNB": GaussianNB()})
classifiers.update({"KNN": KNeighborsClassifier()})
classifiers.update({"MLP": MLPClassifier()})
classifiers.update({"LSVC": LinearSVC()})
classifiers.update({"NuSVC": NuSVC()})
classifiers.update({"SVC": SVC()})
classifiers.update({"DTC": DecisionTreeClassifier()})
classifiers.update({"ETC": ExtraTreeClassifier()})

# Create dict of decision function labels
DECISION_FUNCTIONS = {"Ridge", "SGD", "LSVC", "NuSVC", "SVC"}

# Create dict for classifiers with feature_importances_ attribute
FEATURE_IMPORTANCE = {"Gradient Boosting", "Extra Trees Ensemble", "Random Forest"}