ml-finance-python

linear_regression.py

(1610B)

 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from sklearn.datasets import make_regression
 
 from mlfromscratch.utils import train_test_split, polynomial_features
 from mlfromscratch.utils import mean_squared_error, Plot
 from mlfromscratch.supervised_learning import LinearRegression
 
 def main():
 
     X, y = make_regression(n_samples=100, n_features=1, noise=20)
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
 
     n_samples, n_features = np.shape(X)
 
     model = LinearRegression(n_iterations=100)
 
     model.fit(X_train, y_train)
     
     # Training error plot
     n = len(model.training_errors)
     training, = plt.plot(range(n), model.training_errors, label="Training Error")
     plt.legend(handles=[training])
     plt.title("Error Plot")
     plt.ylabel('Mean Squared Error')
     plt.xlabel('Iterations')
     plt.show()
 
     y_pred = model.predict(X_test)
     mse = mean_squared_error(y_test, y_pred)
     print ("Mean squared error: %s" % (mse))
 
     y_pred_line = model.predict(X)
 
     # Color map
     cmap = plt.get_cmap('viridis')
 
     # Plot the results
     m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
     m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
     plt.plot(366 * X, y_pred_line, color='black', linewidth=2, label="Prediction")
     plt.suptitle("Linear Regression")
     plt.title("MSE: %.2f" % mse, fontsize=10)
     plt.xlabel('Day')
     plt.ylabel('Temperature in Celcius')
     plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
     plt.show()
 
 if __name__ == "__main__":
     main()