Created
May 28, 2020 17:14
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y = df1.index | |
x = preprocessing.scale(df1) | |
phy_features = ['A', 'B', 'C'] | |
phy_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) | |
phy_processer = ColumnTransformer(transformers=[('phy', phy_transformer, phy_features)]) | |
fa_features = ['D', 'E', 'F'] | |
fa_transformer = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())]) | |
fa_processer = ColumnTransformer(transformers=[('fa', fa_transformer, fa_features)]) | |
pipe_phy = Pipeline(steps=[('preprocessor', phy_processer ),('classifier', SVM)]) | |
pipe_fa = Pipeline(steps=[('preprocessor', fa_processer ),('classifier', SVM)]) | |
ens = VotingClassifier(estimators=[pipe_phy, pipe_fa]) | |
cv = KFold(n_splits=10, random_state=None, shuffle=True) | |
for train_index, test_index in cv.split(x): | |
x_train, x_test = x[train_index], x[test_index] | |
y_train, y_test = y[train_index], y[test_index] | |
ens.fit(x_train,y_train) | |
print(ens.score(x_test, y_test)) |
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