Ejemplo n.º 1
0
def TrainAllModels(splitData):
    for i in range(5):
        print("Current i: ", i)
        if splitData:
            X_train, X_test, y_train, y_test = getData(useImbalancer=True,
                                                       useStratify=True)
        else:
            X_train, y_train = getData(splitData=splitData,
                                       useImbalancer=False,
                                       useStratify=True)
            X_test, y_test = None, None

        AdaBoostModel(splitData=splitData,
                      X_train=X_train,
                      X_test=X_test,
                      y_train=y_train,
                      y_test=y_test)
        LogisticRegressionModel(splitData=splitData,
                                X_train=X_train,
                                X_test=X_test,
                                y_train=y_train,
                                y_test=y_test)
        NeuralNetworkModel(splitData=splitData,
                           X_train=X_train,
                           X_test=X_test,
                           y_train=y_train,
                           y_test=y_test)
        RandomForestModel(splitData=splitData,
                          X_train=X_train,
                          X_test=X_test,
                          y_train=y_train,
                          y_test=y_test)
        SVMModel(splitData=splitData,
                 X_train=X_train,
                 X_test=X_test,
                 y_train=y_train,
                 y_test=y_test)
        XGBClassifierModel(splitData=splitData,
                           X_train=X_train,
                           X_test=X_test,
                           y_train=y_train,
                           y_test=y_test)
Ejemplo n.º 2
0
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.svm import SVC

from Utility import printMetrics, getMetrics, logAndSave, logAndSaveV2, getAnnealingData, getData

splitData = False
if splitData:
	X_train, X_test, y_train, y_test = getData(useImbalancer=True, useStratify=True)
else:
	X_train, y_train = getData(splitData=splitData, useImbalancer=True, useStratify=True)
	X_test, y_test = None, None

X_train, X_test, y_train, y_test = getAnnealingData()


def AdaBoostModel(splitData, X_train, X_test, y_train, y_test):
	svc = SVC()
	clf = AdaBoostClassifier(base_estimator=svc, n_estimators=100, algorithm='SAMME')
	clf.fit(X_train, y_train.ravel())

	if splitData:
		y_preds = clf.predict(X_test)
		printMetrics(y_test, y_preds)
		val_acc, val_pre, val_recall, val_auc, val_f1 = getMetrics(y_test, y_preds)
	else:
		val_acc, val_pre, val_recall, val_auc, val_f1 = 0, 0, 0, 0, 0

	y_preds = clf.predict(X_train).reshape(-1, 1)
	acc, pre, recall, auc, f1 = getMetrics(y_train, y_preds)
	val_metrics = (val_acc, val_pre, val_recall, val_auc, val_f1)
	metrics = (acc, pre, recall, auc, f1)