ml-finance-python

python scripts for finance machine learning

git clone https://9o.is/git/ml-finance-python.git

bayesian_regression.py

(2472B)

      1 import numpy as np
      2 import pandas as pd
      3 import matplotlib.pyplot as plt
      4 
      5 # Import helper functions
      6 from mlfromscratch.utils.data_operation import mean_squared_error
      7 from mlfromscratch.utils.data_manipulation import train_test_split, polynomial_features
      8 from mlfromscratch.supervised_learning import BayesianRegression
      9 
     10 def main():
     11 
     12     # Load temperature data
     13     data = pd.read_csv('mlfromscratch/data/TempLinkoping2016.txt', sep="\t")
     14 
     15     time = np.atleast_2d(data["time"].values).T
     16     temp = np.atleast_2d(data["temp"].values).T
     17 
     18     X = time # fraction of the year [0, 1]
     19     y = temp
     20 
     21     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
     22 
     23     n_samples, n_features = np.shape(X)
     24 
     25     # Prior parameters
     26     # - Weights are assumed distr. according to a Normal distribution
     27     # - The variance of the weights are assumed distributed according to 
     28     #   a scaled inverse chi-squared distribution.
     29     # High prior uncertainty!
     30     # Normal
     31     mu0 = np.array([0] * n_features)
     32     omega0 = np.diag([.0001] * n_features)
     33     # Scaled inverse chi-squared
     34     nu0 = 1
     35     sigma_sq0 = 100
     36 
     37     # The credible interval
     38     cred_int = 10
     39 
     40     clf = BayesianRegression(n_draws=2000, 
     41         poly_degree=4, 
     42         mu0=mu0, 
     43         omega0=omega0, 
     44         nu0=nu0, 
     45         sigma_sq0=sigma_sq0,
     46         cred_int=cred_int)
     47     clf.fit(X_train, y_train)
     48     y_pred = clf.predict(X_test)
     49 
     50     mse = mean_squared_error(y_test, y_pred)
     51 
     52     # Get prediction line
     53     y_pred_, y_lower_, y_upper_ = clf.predict(X=X, eti=True)
     54 
     55     # Print the mean squared error
     56     print ("Mean Squared Error:", mse)
     57 
     58     # Color map
     59     cmap = plt.get_cmap('viridis')
     60 
     61     # Plot the results
     62     m1 = plt.scatter(366 * X_train, y_train, color=cmap(0.9), s=10)
     63     m2 = plt.scatter(366 * X_test, y_test, color=cmap(0.5), s=10)
     64     p1 = plt.plot(366 * X, y_pred_, color="black", linewidth=2, label="Prediction")
     65     p2 = plt.plot(366 * X, y_lower_, color="gray", linewidth=2, label="{0}% Credible Interval".format(cred_int))
     66     p3 = plt.plot(366 * X, y_upper_, color="gray", linewidth=2)
     67     plt.axis((0, 366, -20, 25))
     68     plt.suptitle("Bayesian Regression")
     69     plt.title("MSE: %.2f" % mse, fontsize=10)
     70     plt.xlabel('Day')
     71     plt.ylabel('Temperature in Celcius')
     72     plt.legend(loc='lower right')
     73     # plt.legend((m1, m2), ("Training data", "Test data"), loc='lower right')
     74     plt.legend(loc='lower right')
     75 
     76     plt.show()
     77 
     78 if __name__ == "__main__":
     79     main()