08.多元线性回归模型

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets


"""
LinearRegression
"""

# 数据准备
boston = datasets.load_boston()
X = boston.data
y = boston.target

# 数据处理
X = X[y < 50.0]
y = y[y < 50.0]

# print(X.shape)

# 数据分割
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)

# 多元线性回归方程参数求解
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(X_train, y_train)

# 系数和截距
print(lin_reg.coef_, lin_reg.intercept_)

# R2
r_lin = lin_reg.score(X_test, y_test)
print(r_lin)


"""
kNN Regressor
"""

from sklearn.neighbors import KNeighborsRegressor

knn_reg = KNeighborsRegressor()
knn_reg.fit(X_train, y_train)
r_knn = knn_reg.score(X_test, y_test)
print(r_knn)

# 参数调节——网格搜索超参数
from sklearn.model_selection import GridSearchCV

param_grid = [
    {
        "weights":["uniform"],
        "n_neighbors":[i for i in range(1, 11)]
    },
    {
        "weights":["distance"],
        "n_neighbors":[i for i in range(1, 11)],
        "p":[i for i in range(1, 6)]
    }
]

knn_reg = KNeighborsRegressor()
grid_search = GridSearchCV(knn_reg, param_grid, n_jobs=-1, verbose=1)
grid_search.fit(X_train, y_train)

# 最好的超参数
best_p = grid_search.best_params_

# 此参数下的score
best_s = grid_search.score(X_test, y_test)

print(best_p)
print(best_s)

[-1.15625837e-01  3.13179564e-02 -4.35662825e-02 -9.73281610e-02
 -1.09500653e+01  3.49898935e+00 -1.41780625e-02 -1.06249020e+00
  2.46031503e-01 -1.23291876e-02 -8.79440522e-01  8.31653623e-03
 -3.98593455e-01] 32.59756158869959
0.8009390227581041
0.602674505080953
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.
Fitting 5 folds for each of 60 candidates, totalling 300 fits
[Parallel(n_jobs=-1)]: Done  41 tasks      | elapsed:    1.0s
[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:    1.3s finished
{‘n_neighbors‘: 6, ‘p‘: 1, ‘weights‘: ‘distance‘}
0.7353138117643773