ml-finance-python

logistic_regression.py

(1062B)

 from __future__ import print_function
 from sklearn import datasets
 import numpy as np
 import matplotlib.pyplot as plt
 
 # Import helper functions
 from mlfromscratch.utils import make_diagonal, normalize, train_test_split, accuracy_score
 from mlfromscratch.deep_learning.activation_functions import Sigmoid
 from mlfromscratch.utils import Plot
 from mlfromscratch.supervised_learning import LogisticRegression
 
 def main():
     # Load dataset
     data = datasets.load_iris()
     X = normalize(data.data[data.target != 0])
     y = data.target[data.target != 0]
     y[y == 1] = 0
     y[y == 2] = 1
 
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, seed=1)
 
     clf = LogisticRegression(gradient_descent=True)
     clf.fit(X_train, y_train)
     y_pred = clf.predict(X_test)
 
     accuracy = accuracy_score(y_test, y_pred)
     print ("Accuracy:", accuracy)
 
     # Reduce dimension to two using PCA and plot the results
     Plot().plot_in_2d(X_test, y_pred, title="Logistic Regression", accuracy=accuracy)
 
 if __name__ == "__main__":
     main()