from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor, LinearRegression, LogisticRegression
from sklearn.metrics import mean_squared_error, classification_report
import numpy as np
def linearreg():
#obtain data
data = load_boston()
#split
x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.25)
# print(x_train.shape)
#target 特征值和目标值矩阵形状不一样,创建两个
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train.reshape(-1,1))
x_test = std_x.fit_transform(x_test.reshape(-1, 1))
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train.reshape(-1,1))
y_test = std_y.fit_transform(y_test.reshape(-1,1))
#转换器、estimator要求数据必须为二维形状,因此使用reshape更改形状
#reshape(a, b) a行b列, reshape(c,-1) 只能矩阵/数组, c行,d列(-1表示自动计算)
#
#
#estimator
#正规方程
lr = LinearRegression()
print(x_train.shape, y_train.shape, x_test.shape)
lr.fit(x_train.reshape(379,13), y_train)
print(lr.coef_)
y_predict = std_y.inverse_transform(lr.predict(x_test.reshape(-1,13)))
print(y_predict)
#梯度下降
sgd = SGDRegressor()
sgd.fit(x_train.reshape(379,13), y_train)
print(sgd.coef_)
y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test.reshape(-1,13)))
print(y_sgd_predict)
print(y_predict.shape, y_sgd_predict.shape)
error1 = mean_squared_error(std_y.inverse_transform(y_test), y_predict)
error2 = mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict)
print(error1, error2
return None
import pandas as pd
import numpy as np
def mylogistic():
#读取数据
column = ['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("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data", names=column)
print(data)
#缺失值处理
print(data.isnull().sum())
data = data.replace(to_replace='?', value=np.nan)
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)
#标准化处理
std = StandardScaler()
x_test = std.fit_transform(x_test)
#模型拟合
lr = LogisticRegression(C=1.0)
lr.fit(x_train, y_train)
print(lr.coef_)
print(lr.score())
print(classification_report(y_train, y_test, labels=[2, 4], target_names=["良性", "恶性"]))
return None
if __name__ == "__main__":
# linearreg()
mylogistic()