import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler


def logisticregression():
    """
    逻辑回归进行癌症预测
    :return: None
    """
    # 1、读取数据，处理缺失值以及标准化
    column_name = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 'Uniformity of Cell Shape',
                   'Marginal Adhesion', 'Single Epithelial Cell Size', 'Bare Nuclei', 'Bland Chromatin',
                   'Normal Nucleoli', 'Mitoses', 'Class']

    data = pd.read_csv("./cancer/breast-cancer-wisconsin.data", names=column_name)
    print(data)

    # 删除缺失值
    data = data.replace(to_replace='?', value=np.nan)

    data = data.dropna()

    # 取出特征值
    x = data[column_name[1:10]]

    y = data[column_name[10]]

    # 分割数据集
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)

    # 进行标准化
    std = StandardScaler()

    x_train = std.fit_transform(x_train)

    x_test = std.transform(x_test)

    # 使用逻辑回归
    lr = LogisticRegression()

    lr.fit(x_train, y_train)

    print("得出来的权重：", lr.coef_)

    # 预测类别
    print("预测的类别：", lr.predict(x_test))

    # 得出准确率
    print("预测的准确率:", lr.score(x_test, y_test))
    return None


if __name__ == "__main__":
    logisticregression()