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logistic regression

Author: louwill

logistic regression/lr_class.py

@@ -0,0 +1,131 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2018/9/3 15:30
+# @Author  : louwill
+# @File    : lr_class.py
+# @mail: [email protected]
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets.samples_generator import make_classification
+
+class logistic_regression():
+    def __init__(self):
+        pass
+
+    def sigmoid(self, x):
+        z = 1 / (1 + np.exp(-x))
+        return z
+
+    def initialize_params(self, dims):
+        W = np.zeros((dims, 1))
+        b = 0
+        return W, b
+
+    def logistic(self, X, y, W, b):
+        num_train = X.shape[0]
+        num_feature = X.shape[1]
+
+        a = self.sigmoid(np.dot(X, W) + b)
+        cost = -1 / num_train * np.sum(y * np.log(a) + (1 - y) * np.log(1 - a))
+
+        dW = np.dot(X.T, (a - y)) / num_train
+        db = np.sum(a - y) / num_train
+        cost = np.squeeze(cost)
+
+        return a, cost, dW, db
+
+    def logistic_train(self, X, y, learning_rate, epochs):
+        W, b = self.initialize_params(X.shape[1])
+        cost_list = []
+
+        for i in range(epochs):
+            a, cost, dW, db = self.logistic(X, y, W, b)
+            W = W - learning_rate * dW
+            b = b - learning_rate * db
+
+            if i % 100 == 0:
+                cost_list.append(cost)
+            if i % 100 == 0:
+                print('epoch %d cost %f' % (i, cost))
+
+        params = {
+            'W': W,
+            'b': b
+        }
+        grads = {
+            'dW': dW,
+            'db': db
+        }
+
+        return cost_list, params, grads
+
+    def predict(self, X, params):
+        y_prediction = self.sigmoid(np.dot(X, params['W']) + params['b'])
+
+        for i in range(len(y_prediction)):
+            if y_prediction[i] > 0.5:
+                y_prediction[i] = 1
+            else:
+                y_prediction[i] = 0
+
+        return y_prediction
+
+    def accuracy(self, y_test, y_pred):
+        correct_count = 0
+        for i in range(len(y_test)):
+            for j in range(len(y_pred)):
+                if y_test[i] == y_pred[j] and i == j:
+                    correct_count += 1
+
+        accuracy_score = correct_count / len(y_test)
+        return accuracy_score
+
+    def create_data(self):
+        X, labels = make_classification(n_samples=100, n_features=2, n_redundant=0, n_informative=2,
+                                        random_state=1, n_clusters_per_class=2)
+        labels = labels.reshape((-1, 1))
+        offset = int(X.shape[0] * 0.9)
+        X_train, y_train = X[:offset], labels[:offset]
+        X_test, y_test = X[offset:], labels[offset:]
+        return X_train, y_train, X_test, y_test
+
+    def plot_logistic(self, X_train, y_train, params):
+        n = X_train.shape[0]
+        xcord1 = []
+        ycord1 = []
+        xcord2 = []
+        ycord2 = []
+        for i in range(n):
+            if y_train[i] == 1:
+                xcord1.append(X_train[i][0])
+                ycord1.append(X_train[i][1])
+            else:
+                xcord2.append(X_train[i][0])
+                ycord2.append(X_train[i][1])
+        fig = plt.figure()
+        ax = fig.add_subplot(111)
+        ax.scatter(xcord1, ycord1, s=32, c='red')
+        ax.scatter(xcord2, ycord2, s=32, c='green')
+        x = np.arange(-1.5, 3, 0.1)
+        y = (-params['b'] - params['W'][0] * x) / params['W'][1]
+        ax.plot(x, y)
+        plt.xlabel('X1')
+        plt.ylabel('X2')
+        plt.show()
+
+
+if __name__ == "__main__":
+    model = logistic_regression()
+    X_train, y_train, X_test, y_test = model.create_data()
+    print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
+    cost_list, params, grads = model.logistic_train(X_train, y_train, 0.01, 1000)
+    print(params)
+    y_train_pred = model.predict(X_train, params)
+    accuracy_score_train = model.accuracy(y_train, y_train_pred)
+    print('train accuracy is:', accuracy_score_train)
+    y_test_pred = model.predict(X_test, params)
+    accuracy_score_test = model.accuracy(y_test, y_test_pred)
+    print('test accuracy is:', accuracy_score_test)
+    model.plot_logistic(X_train, y_train, params)