# Create pipeline
pipeline = Pipeline([('scaler', MinMaxScaler()), ('kbest', SelectKBest()), ('SVC', SVC())])
# grid search params
grid_search = GridSearchCV(pipeline, cv=3, param_grid={'kbest__k':[1,2,3,4,5,6,7,8,9,10],
                                                      'SVC__C': np.logspace(-3, 2, 6), 'SVC__gamma': np.logspace(-3, 2, 6)})
# fit the grid search
grid_search.fit(X_train,y_train)

print grid_search.best_params_
# assign optimal parameters' values to a variable
optimal_gamma = grid_search.best_params_.values()[0]
optimal_k = grid_search.best_params_.values()[1]
optimal_C =  grid_search.best_params_.values()[2]

# update pipeline with optimal parameter values
pipeline.set_params(SVC__gamma= optimal_gamma,kbest__k=optimal_k, SVC__C=optimal_C).fit(X_train, y_train)


features_list2 = ['salary', 'deferral_payments', 'total_payments',
       'exercised_stock_options', 'bonus', 'restricted_stock',
       'restricted_stock_deferred', 'total_stock_value', 'expenses',
       'loan_advances', 'other', 'director_fees', 'deferred_income',
       'long_term_incentive']

# get best features in indices
best_feats = pipeline.named_steps['kbest'].get_support(indices=True)
# print best features
for i in best_feats:
    print features_list2[i]

# predict training and testing features
y_pred_train = pipeline.predict(X_train)
y_pred_test = pipeline.predict(X_test)

# print training predictions' metrics
print confusion_matrix(y_train, y_pred_train)
print classification_report(y_train, y_pred_train)

# print testing predictions' metrics
print confusion_matrix(y_test, y_pred_test)
print classification_report(y_test, y_pred_test)


# OUTPUT:

{'SVC__gamma': 10.0, 'kbest__k': 1, 'SVC__C': 1.0}
bonus
[[87  0]
 [10  3]]
             precision    recall  f1-score   support

        0.0       0.90      1.00      0.95        87
        1.0       1.00      0.23      0.38        13

avg / total       0.91      0.90      0.87       100

[[38  1]
 [ 5  0]]
             precision    recall  f1-score   support

        0.0       0.88      0.97      0.93        39
        1.0       0.00      0.00      0.00         5

avg / total       0.78      0.86      0.82        44