y_pred_rfc_moe = rfc_moe.predict(X_test)
print 'MOE RFC Metrics: '
print 'Best n_estimators: ', n_estimators
print 'Best max_features: ', max_features
print 'Best max_depth: ', max_depth
print 'Best min_samples_leaf: ' min_samples_leaf
rfc_moe_acc, rfc_moe_prec, rfc_moe_rec, rfc_moe_cm, rfc_moe_cm_full = get_scores(y_test,y_pred_rfc_moe)
# Get MOE best ABC Model & it's metrics
abc_moe_param, abc_moe_accuracy = do_abc_MOE(num_points_to_sample, X_train, y_train)
print 'Best ABC train param: ', abc_moe_param
print 'Best ABC train Accuracy: ', abc_moe_accuracy
n_estimators = abc_moe_param[0]
learning_rate = abc_moe_param[1]
abc = AdaBoostClassifier((DecisionTreeClassifier(max_depth=2)),n_estimators=n_estimators, learning_rate=learning_rate)
abc_moe.fit(X_train, y_train)
y_pred_abc_moe = abc_moe.predict(X_test)
print 'MOE ABC Metrics: '
print 'Best n_estimators: ', n_estimators
print 'Best learning_rate: ', learning_rate
abc_moe_acc, abc_moe_prec, abc_moe_rec, abc_moe_cm, abc_moe_cm_full = get_scores(y_test,y_pred_abc_moe)
# Boosting SVC, RFC & ABC
y_all = np.clumn_stack((y_pred_svc_linear_moe, y_pred_svc_rbf_moe, y_pred_rfc_moe, y_pred_abc_moe))
y_pred_all = (stats.mode(y_all)[0][0]).astype(int)
print 'MOE All Metrics: '
all_moe_acc, all_moe_prec, all_moe_rec, all_moe_cm, all_moe_cm_full = get_scores(y_test,y_pred_all)
svc_parameters = {'kernel':('linear', 'rbf'), 'C':C_range, 'gamma': gamma_range}
y_pred_svc = do_svc(X_train, y_train, X_test, svc_parameters)
print 'GridSearch SVC Metrics:'
svc_acc, svc_prec, svc_rec, svc_cm, svc_cm_full = get_scores(y_test,y_pred_svc)
'''
# Get GridSearch best RFC Model & it's metrics
n_estimators_range = [100, 500, 1000]
max_features_range = [9, 24, 561] # log2(nfeatures=562) = 24, sqrt(561) = 9
max_depth_range = [None, 6, 8]
min_samples_leaf_range = [1, 4, 6]
rfc_parameters = {'criterion':('gini', 'entropy'), 'n_estimators': n_estimators_range, 'max_features': max_features_range,
'max_depth': max_depth_range, 'min_samples_leaf': min_samples_leaf_range}
y_pred_rfc = do_rfc(X_train, y_train, X_test, rfc_parameters)
print 'GridSearch RFC Metrics: '
rfc_acc, rfc_prec, rfc_rec, rfc_cm, rfc_cm_full = get_scores(y_test,y_pred_rfc)
# Get GridSearch best ABC Model & it's metrics
n_estimators_range = [5, 10, 25, 50, 100, 500, 1000]
learning_rate_range = [0.1, 0.3, 0.5, 0.7, 0.9, 1]
abc_parameters = {'n_estimators': n_estimators_range, 'learning_rate': learning_rate_range}
y_pred_abc = do_abc(X_train, y_train, X_test, abc_parameters)
print 'GridSearch ABC Metrics: '
abc_acc, abc_prec, abc_rec, abc_cm, abc_cm_full = get_scores(y_test,y_pred_abc)
# Boosting SVC, RFC & ABC
y_all = np.clumn_stack((y_pred_svc, y_pred_rfc, y_pred_abc))
y_pred_all = (stats.mode(y_all)[0][0]).astype(int)
print 'All Metrics: '
all_acc, all_prec, all_rec, all_cm, all_cm_full = get_scores(y_test,y_pred_all)
