ml-finance-python

gradient_boosting_regressor.py

(1849B)

 from __future__ import division, print_function
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 import progressbar
 
 from mlfromscratch.utils import train_test_split, standardize, to_categorical
 from mlfromscratch.utils import mean_squared_error, accuracy_score, Plot
 from mlfromscratch.utils.loss_functions import SquareLoss
 from mlfromscratch.utils.misc import bar_widgets
 from mlfromscratch.supervised_learning import GradientBoostingRegressor
 
 
 def main():
     print ("-- Gradient Boosting Regression --")
 
     # Load temperature data
     data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
 
     time = np.atleast_2d(data["time"].values).T
     temp = np.atleast_2d(data["temp"].values).T
 
     X = time.reshape((-1, 1))               # Time. Fraction of the year [0, 1]
     X = np.insert(X, 0, values=1, axis=1)   # Insert bias term
     y = temp[:, 0]                          # Temperature. Reduce to one-dim
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
 
     model = GradientBoostingRegressor()
     model.fit(X_train, y_train)
     y_pred = model.predict(X_test)
 
     y_pred_line = model.predict(X)
 
     # Color map
     cmap = plt.get_cmap('viridis')
 
     mse = mean_squared_error(y_test, y_pred)
 
     print ("Mean Squared Error:", mse)
 
     # Plot the results
     m1 = plt.scatter(366 * X_train[:, 1], y_train, color=cmap(0.9), s=10)
     m2 = plt.scatter(366 * X_test[:, 1], y_test, color=cmap(0.5), s=10)
     m3 = plt.scatter(366 * X_test[:, 1], y_pred, color='black', s=10)
     plt.suptitle("Regression Tree")
     plt.title("MSE: %.2f" % mse, fontsize=10)
     plt.xlabel('Day')
     plt.ylabel('Temperature in Celcius')
     plt.legend((m1, m2, m3), ("Training data", "Test data", "Prediction"), loc='lower right')
     plt.show()
 
 
 if __name__ == "__main__":
     main()