logit模型应用实例_逻辑斯谛回归模型

import numpy as np
import time
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
 
plt.rcParams['font.sans-serif'] = ['SimHei']  # 正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False  # 显示正负号
 
class LogisticRegression:
    def __init__(self, learn_rate=0.1, max_iter=10000, tol=1e-2):
        self.learn_rate = learn_rate  # 学习率
        self.max_iter = max_iter   # 迭代次数
        self.tol = tol  # 迭代停止阈值
        self.w = None   # 权重
 
    def preprocessing(self, X):
        """ 将原始X末尾加上一列，该列数值全部为1 """
        row = X.shape[0]
        y = np.ones(row).reshape(row, 1)
        X_prepro = np.hstack((X, y))
        return X_prepro
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
 
    def fit(self, X_train, y_train):
        """ 训练模型 """
        # 处理数据
        X = self.preprocessing(X_train)
        y = y_train.T
        # 初始化权重
        self.w = np.array([[0] * X.shape[1]], dtype=np.float)
        k = 0
        for loop in range(self.max_iter):
            # 计算梯度
            z = np.dot(X, self.w.T)
            grad = X * (y - self.sigmoid(z))
            grad = grad.sum(axis=0)
            # 利用梯度的绝对值作为迭代中止的条件
            if (np.abs(grad) <= self.tol).all():
                break
            else:
                # 更新权重w，梯度上升求极大值
                self.w += self.learn_rate * grad
                k += 1
        print("迭代次数： {}次".format(k))
        print("最终梯度： {}".format(grad))
        print("最终权重： {}".format(self.w[0]))
 
    def predict(self, x):
        p = self.sigmoid(np.dot(self.preprocessing(x), self.w.T))
        #print("Y=1的概率估计为： {:.2%}".format(p[0][0]))
        p[np.where(p > 0.5)] = 1
        p[np.where(p < 0.5)] = 0
        return p
 
    def score(self, X, y):
        y_c = self.predict(X)
        # 错误率
        error_rate = np.sum(np.abs(y_c - y.T)) / y_c.shape[0]
        return 1 - error_rate
 
    def draw(self, X, y):
        # 分离正负实例点
        y = y[0]
        X_po = X[np.where(y == 1)]
        X_ne = X[np.where(y == 0)]
        # 绘制数据集散点图
        ax = plt.axes(projection='3d')
        x_1 = X_po[0, :]
        y_1 = X_po[1, :]
        z_1 = X_po[2, :]
        x_2 = X_ne[0, :]
        y_2 = X_ne[1, :]
        z_2 = X_ne[2, :]
        # 画图
        ax.scatter(x_1, y_1, z_1, c='r', label="正实例")
        ax.scatter(x_2, y_2, z_2, c='b', label="负实例")
        ax.legend(loc='best')
        # 绘制p=0.5的区分平面
        x = np.linspace(-3, 3, 3)
        y = np.linspace(-3, 3, 3)
        x_3, y_3 = np.meshgrid(x, y)
        a, b, c, d = self.w[0]
        z_3 = -(a*x_3+b*y_3+d) / c
        ax.plot_surface(x_3, y_3, z_3, alpha=0.5)  # 调节透明度
        plt.show()
def main():
    # 训练数据集
    X_train = np.array([
        [3, 3, 3],
        [4, 3, 2],
        [2, 1, 2],
        [1, 1, 1],
        [-1, 0, 1],
        [2, -2, 1],
    ])
    y_train = np.array([[1, 1, 1, 0, 0, 0]])
    # 实例化模型，进行训练
    clf = LogisticRegression()
    clf.fit(X_train, y_train)
    # 预测新数据
    X_new = np.array([[1, 2, -2]])
    y_predict = clf.predict(X_new)
    print("{}被分类为： {}".format(X_new[0], y_predict[0]))
    # 展示数据
    clf.draw(X_train, y_train)
    # 评价模型
    X_test = X_train
    y_test = y_train
    correct_rate = clf.score(X_test, y_test)
    print("共测试{}组数据，正确率： {:.2%}".format(X_test.shape[0], correct_rate))
 
if __name__ == '__main__':
    main()

from sklearn.linear_model import LogisticRegression
import numpy as np
 
# 训练数据集
X_train = np.array([
    [3, 3, 3],
    [4, 3, 2],
    [2, 1, 2],
    [1, 1, 1],
    [-1, 0, 1],
    [2, -2, 1],
])
y_train = np.array([1, 1, 1, 0, 0, 0])
# 选择不同的求解器，实例化模型，进行训练和测试
methods = ['liblinear', 'newton-cg', 'lbfgs', 'sag', 'saga']
res = []
X_new = np.array([[1, 2, -2]])
for method in methods:
    # 实例化模型
    clf = LogisticRegression(solver=method, intercept_scaling=2, max_iter=1000)
    # 训练
    clf.fit(X_train, y_train)
    # 预测
    y_predict = clf.predict(X_new)
    # 利用训练数据，评价模型
    X_test = X_train
    y_test = y_train
    correct_rate = clf.score(X_test, y_test)
    # 保存结果
    res.append((y_predict, correct_rate))
 
# 格式化输出
methodes = ["liblinear", "newton-cg", "lbfgs    ", "sag      ", "saga      "]
print("solver选择：          {}".format("  ".join(method for method in methodes)))
print("{}被分类为：  {}".format(X_new[0], "        ".join(str(re[0]) for re in res)))
print("测试{}组数据，正确率： {}".format(X_train.shape[0], "        ".join(str(round(re[1], 1)) for re in res)))