逻辑回归-机器学习

# coding=utf-8
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

def logistic():
    """
    逻辑回归做二分类进行癌症预测 (根据细胞的属性特征)
    :return:
    """

    # 构造列明

    column = ['sample code number', 'clump thickness', 'uniformity of cell size', 'yniformity of cell shape', 'marginal adhesion', 'single epithelial cell size', 'bare nuclei', 'bland chromatin', 'normal nucleoli', 'mitoses', 'class']

    # 读取数据
    data = pd.read_csv("./breast-cancer-wisconsin.data", names=column)
    print(len(data))
    print(data.head(10))

    # 缺失值处理
    data = data.replace(to_replace='?', value=np.nan)
    # 删除nan值的数据
    data = data.dropna()



    # 数据分割
    x_train, x_test, y_train, y_test = train_test_split(data[column[1:10]], data[column[10]], test_size=0.25)
    print("-"*100)



    # 标准化处理
    std = StandardScaler()
    x_train = std.fit_transform(x_train)
    x_test =std.fit_transform(x_test)

    # print(x_train)
    # print(x_test)


    # 逻辑回归机器学习
    log = LogisticRegression(C=1.0)
    log.fit(x_train, y_train)


    print(log.coef_)
    y_predict = log.predict(x_test)

    print("预测值: ", log.score(x_test, y_test))


    print("召回率:" , classification_report(y_test, y_predict, labels=[2, 4], target_names=["良性", "恶性"]))



    return None

if __name__ == "__main__":
    logistic()