gbdt_classifier_example.py

from __future__ import division, print_function
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt

# Import helper functions
from utils import train_test_split, accuracy_score
from utils.loss_functions import CrossEntropy
from utils import Plot
from gradient_boosting_decision_tree.gbdt_model import GBDTClassifier

def main():

    print ("-- Gradient Boosting Classification --")

    data = datasets.load_iris()
    X = data.data
    y = data.target

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
    print(y_train)

    clf = GBDTClassifier()
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)

    accuracy = accuracy_score(y_test, y_pred)

    print ("Accuracy:", accuracy)


    Plot().plot_in_2d(X_test, y_pred,
        title="Gradient Boosting",
        accuracy=accuracy,
        legend_labels=data.target_names)



if __name__ == "__main__":
    main()