random_forest.py

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
from sklearn.ensemble import RandomForestClassifier

X = np.load('X_train.npy')
y = np.load('y_train.npy')
X_new = np.load('X_test.npy')
y_new = np.load('y_test.npy')


def grid_search(X, y):
    clf = RandomForestClassifier()
    params = {'max_depth': [2, 4, 6, None],
                'max_features': ['sqrt', 'log2', None],
                'class_weight': ['balanced'],
                'min_samples_split': [2, 4],
                'min_samples_leaf': [1, 2, 4],
                'bootstrap': [True, False],
                'n_estimators': [10, 20, 40, 80],
                'random_state': [1]}

    gs = GridSearchCV(estimator=clf, param_grid=params, n_jobs=-1, verbose=True)
    gs.fit(X, y)
    best_model = gs.best_estimator_
    return best_model


def rf_train_predict():
    seed = 34
    test_size = 0.20

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed)

    # model = grid_search(X_train, y_train)
    model = RandomForestClassifier(bootstrap=True, class_weight='balanced', criterion='gini', max_depth=2, max_features='sqrt', max_leaf_nodes=None, min_impurity_split=1e-07, min_samples_leaf=4, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=80, n_jobs=1, oob_score=False, random_state=1, verbose=0, warm_start=False)

    rf_clf = model.fit(X_train, y_train)

    # some cross validation
    print("Cross validation:")
    accuracy_scores = cross_val_score(rf_clf, X_train, y_train, cv=5, scoring='accuracy')
    print("Accuracy: %.2f%%" % (np.mean(accuracy_scores) * 100.0))
    f1_scores = cross_val_score(rf_clf, X_train, y_train, cv=5, scoring='f1')
    print("f1: %.2f%%" % (np.mean(f1_scores) * 100.0))
    print("\n")

    # make predictions using training data
    y_pred = rf_clf.predict(X_test)
    predictions = [round(value) for value in y_pred]

    # evaluate predictions
    print("Model evaluation on training data:")
    accuracy = accuracy_score(y_test, predictions)
    print("Accuracy: %.2f%%" % (accuracy * 100.0))
    recall = recall_score(y_test, predictions)
    print("Recall: %.2f%%" % (recall * 100.0))
    precision = precision_score(y_test, predictions)
    print("Precision: %.2f%%" % (precision * 100.0))
    f1 = f1_score(y_test, predictions)
    print("f1: %.2f%%" % (f1 * 100.0))
    print("\n")

    # make predictions for new unseen data
    y_pred_new = rf_clf.predict(X_new)
    pred_new = [round(value) for value in y_pred_new]

    # evaluate unseen data predictions
    print("Model evaluation on unseen data:")
    accuracy = accuracy_score(y_new, pred_new)
    print("Accuracy: %.2f%%" % (accuracy * 100.0))
    recall = recall_score(y_new, pred_new)
    print("Recall: %.2f%%" % (recall * 100.0))
    precision = precision_score(y_new, pred_new)
    print("Precision: %.2f%%" % (precision * 100.0))
    f1 = f1_score(y_new, pred_new)
    print("f1: %.2f%%" % (f1 * 100.0))


if __name__ == '__main__':
    rf_train_predict()