百面機器學習——python實現二分類邏輯迴歸

後面註釋的是一個py文件寫好分裝的LogisticRegression函數，而不是直接調用sklearn

import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from LogisticRegression import LogisticRegression
# 取鳶尾花數據集
iris = datasets.load_iris()
X = iris.data
y = iris.target
# 篩選特徵
X = X[y < 2, :2]
y = y[y < 2]

y

array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])

# 繪製出圖像
plt.scatter(X[y == 0, 0], X[y == 0, 1], color="red")
plt.scatter(X[y == 1, 0], X[y == 1, 1], color="blue")
plt.show()

# 切分數據集
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)
# 調用我們自己的邏輯迴歸函數
log_reg = LogisticRegression()
log_reg.fit(X_train, y_train)
print("final score is :%s" % log_reg.score(X_test, y_test))
print("actual prob is :")
print(log_reg.predict_proba(X_test))

final score is :1.0
actual prob is :
[0.93292947 0.98717455 0.15541379 0.18370292 0.03909442 0.01972689
 0.05214631 0.99683149 0.98092348 0.75469962 0.0473811  0.00362352
 0.27122595 0.03909442 0.84902103 0.80627393 0.83574223 0.33477608
 0.06921637 0.21582553 0.0240109  0.1836441  0.98092348 0.98947619
 0.08342411]

# import numpy as np
# from sklearn.metrics import accuracy_score
# class LogisticRegression(object):

#     def __init__(self):
#         """初始化Logistic Regression模型"""
#         self.coef = None
#         self.intercept = None
#         self._theta = None

#     def sigmoid(self, t):
#         return 1. / (1. + np.exp(-t))

#     def fit(self, X_train, y_train, eta=0.01, n_iters=1e4):
#         """使用梯度下降法訓練logistic Regression模型"""
#         assert X_train.shape[0] == y_train.shape[0], \
#             "the size of X_train must be equal to the size of y_train"

#         def J(theta, X_b, y):
#             y_hat = self.sigmoid(X_b.dot(theta))
#             try:
#                 return -np.sum(y * np.log(y_hat) + (1 - y) * np.log(1 - y_hat)) / len(y)
#             except:
#                 return float('inf')

#         def dJ(theta, X_b, y):
#             # 向量化後的公式
#             return X_b.T.dot(self.sigmoid(X_b.dot(theta)) - y) / len(y)

#         def gradient_descent(X_b, y, initial_theta, eta, n_iters=1e4, epsilon=1e-8):
#             theta = initial_theta
#             cur_iter = 0

#             while cur_iter < n_iters:
#                 gradient = dJ(theta, X_b, y)
#                 last_theta = theta
#                 theta = theta - eta * gradient
#                 if abs(J(theta, X_b, y) - J(last_theta, X_b, y)) < epsilon:
#                     break

#                 cur_iter += 1

#             return theta

#         X_b = np.hstack([np.ones((len(X_train), 1)), X_train])
#         initial_theta = np.zeros(X_b.shape[1])
#         self._theta = gradient_descent(X_b, y_train, initial_theta, eta, n_iters)

#         # 截距
#         self.intercept = self._theta[0]
#         # x_i前的參數
#         self.coef = self._theta[1:]

#         return self

#     def predict_proba(self, X_predict):
#         """給定待預測數據集X_predict，返回表示X_predict的結果概率向量"""
#         assert self.intercept is not None and self.coef is not None, \
#             "must fit before predict"
#         assert X_predict.shape[1] == len(self.coef), \
#             "the feature number of X_predict must be equal to X_train"

#         X_b = np.hstack([np.ones((len(X_predict), 1)), X_predict])
#         return self.sigmoid(X_b.dot(self._theta))

#     def predict(self, X_predict):
#         """給定待預測數據集X_predict，返回表示X_predict的結果向量"""
#         assert self.intercept is not None and self.coef is not None, \
#             "must fit before predict!"
#         assert X_predict.shape[1] == len(self.coef), \
#             "the feature number of X_predict must be equal to X_train"
#         prob = self.predict_proba(X_predict)
#         return np.array(prob >= 0.5, dtype='int')

#     def score(self, X_test, y_test):
#         """根據測試數據集X_test和y_test確定當前模型的準確度"""

#         y_predict = self.predict(X_test)
#         return accuracy_score(y_test, y_predict)

#     def __repr__(self):
#         return "LogisticRegression()"