ml-finance-python

xgboost.py

(897B)

 from __future__ import division, print_function
 import numpy as np
 from sklearn import datasets
 import matplotlib.pyplot as plt
 import progressbar
 from mlfromscratch.utils import train_test_split, standardize, to_categorical, normalize
 from mlfromscratch.utils import mean_squared_error, accuracy_score, Plot
 from mlfromscratch.supervised_learning import XGBoost
 
 def main():
 
     print ("-- XGBoost --")
 
     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, seed=2)  
 
     clf = XGBoost()
     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="XGBoost", 
     accuracy=accuracy, 
     legend_labels=data.target_names)
 
 
 if __name__ == "__main__":
     main()