Sklearn Voting ensemble with models using different features and testing with k-fold cross validation

问题

I have a data frame with 4 different groups of features.

I need to create 4 different models with these four different feature groups and combine them with the ensemble voting classifier. Furthermore, I need to test the classifier using k-fold cross validation.

However, I am finding it difficult to combine different feature sets, voting classifier and k-fold cross validation with functionality available in sklearn. Following is the code that I have so far.

y = df1.index
x = preprocessing.scale(df1)

SVM = svm.SVC(kernel='rbf', C=1)
rf=RandomForestClassifier(n_estimators=200)
ann = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(25, 2), random_state=1)
neigh = KNeighborsClassifier(n_neighbors=10)

models = list()
models.append(('facial', SVM))
models.append(('posture', rf))
models.append(('computer', ann))
models.append(('physio', neigh))

ens = VotingClassifier(estimators=models)

cv = KFold(n_splits=10, random_state=None, shuffle=True)
scores = cross_val_score(ens, x, y, cv=cv, scoring='accuracy')

As you can see, this program uses same features for all 4 models. How can I improve this program to achieve my objective?

回答1:

I did manage to achieve this using Pipelines,

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))

Please refer sklearn Pipeline: argument of type 'ColumnTransformer' is not iterable for if you are receiving an TypeError when using ColumnTransforms.

来源：https://stackoverflow.com/questions/62065365/sklearn-voting-ensemble-with-models-using-different-features-and-testing-with-k

标签

scikit-learn

classification

voting

ensemble-learning

k-fold