xdc_git_python_20210511/机器学习/day02_machine.py

import pandas as pd
import numpy as np

# 1、读取数据
path = "https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"
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(path, names=column_name)
data.head()
# 2、缺失值处理
# 1）替换-》np.nan
data = data.replace(to_replace="?", value=np.nan)
# 2）删除缺失样本
data.dropna(inplace=True)
data.isnull().any() # 不存在缺失值
# 3、划分数据集
from sklearn.model_selection import train_test_split
data.head()
# 筛选特征值和目标值
x = data.iloc[:, 1:-1]
y = data["Class"]
x.head()
y.head()
x_train, x_test, y_train, y_test = train_test_split(x, y)
x_train.head()

# 4、标准化
from sklearn.preprocessing import StandardScaler
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
x_train
from sklearn.linear_model import LogisticRegression
# 5、预估器流程
estimator = LogisticRegression()
estimator.fit(x_train, y_train)
# 逻辑回归的模型参数：回归系数和偏置
estimator.coef_
estimator.intercept_
# 6、模型评估
# 方法1：直接比对真实值和预测值
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("直接比对真实值和预测值:\n", y_test == y_predict)

# 方法2：计算准确率
score = estimator.score(x_test, y_test)
print("准确率为：\n", score)
# 查看精确率、召回率、F1-score
from sklearn.metrics import classification_report

report = classification_report(y_test, y_predict, labels=[2, 4], target_names=["良性", "恶性"])
print(report)
y_test.head()
# y_true：每个样本的真实类别，必须为0(反例),1(正例)标记
# 将y_test 转换成 0 1
y_true = np.where(y_test > 3, 1, 0)
from sklearn.metrics import roc_auc_score
roc_auc_score(y_true, y_predict)
#Output 0.9562646028037384