from read_data import *

from sklearn.ensemble import RandomForestClassifier

from sklearn.datasets import make_classification

from sklearn.model_selection import train_test_split

from sklearn.mixture import GaussianMixture

if __name__ == '__main__':

    data = execute()

    print(data.shape)

    X = data[:, :16]  # 16 features

    y = data[:, 16]

    print(X.shape)

    print(y.shape)

    print(y)

    train_features, test_features, train_labels, test_labels = train_test_split(X, y,

                                                                                test_size=0.25)

    print('Training Features Shape:', train_features.shape)

    print('Training Labels Shape:', train_labels.shape)

    print('Testing Features Shape:', test_features.shape)

    print('Testing Labels Shape:', test_labels.shape)

    clf = RandomForestClassifier(n_estimators=100, max_depth=5, oob_score=True)

    clf.fit(X, y)

    print(clf.feature_importances_)

    # print(clf.oob_decision_function_)

    print(clf.oob_score_)

    predictions = clf.predict(test_features)

    errors = abs(predictions - test_labels)

    print("M A E: ", np.mean(errors))

    print(np.count_nonzero(errors), len(test_labels))

    print("Accuracy:", np.count_nonzero(errors)/len(test_labels))