Scikit-learn for OHLC data sample with bitbank.cc API

Scikit-learn for OHLC data

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score

X_train, X_test, y_train, y_test = train_test_split(X_, y_, test_size=0.33, random_state=42)
print('X_train shape: {}'.format(X_train.shape))
print('X_test shape: {}'.format(X_test.shape))
print('y_train shape: {}'.format(y_train.shape))
print('y_test shape: {}'.format(y_test.shape))

pipe_knn = Pipeline([('scl', StandardScaler()), ('est', KNeighborsClassifier(n_neighbors=3))])
pipe_logistic = Pipeline([('scl', StandardScaler()), ('est', LogisticRegression(solver='lbfgs', multi_class='multinomial', random_state=39))])
pipe_rf = Pipeline([('scl', StandardScaler()), ('est', RandomForestClassifier(random_state=39))])
pipe_gb = Pipeline([('scl', StandardScaler()), ('est', GradientBoostingClassifier(random_state=39))])

pipe_names = ['KNN','Logistic','RandomForest','GradientBoosting']
pipe_lines = [pipe_knn, pipe_logistic, pipe_rf, pipe_gb]

for (i, pipe) in enumerate(pipe_lines):
    pipe.fit(X_train, y_train.values.ravel())
    print('%s: %.3f' % (pipe_names[i] + ' Train Accuracy', accuracy_score(y_train.values.ravel(), pipe.predict(X_train))))
    print('%s: %.3f' % (pipe_names[i] + ' Test Accuracy', accuracy_score(y_test.values.ravel(), pipe.predict(X_test))))
    print('%s: %.3f' % (pipe_names[i] + ' Train F1 Score', f1_score(y_train.values.ravel(), pipe.predict(X_train), average='weighted')))
    print('%s: %.3f' % (pipe_names[i] + ' Test F1 Score', f1_score(y_test.values.ravel(), pipe.predict(X_test), average='weighted')))