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# -*- coding: utf-8 -*-
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"""
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Created on Sat May 24 20:16:34 2025
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@author: LENOVO
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"""
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error
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from sklearn.datasets import load_boston
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boston = load_boston()
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#print(boston.data.shape,boston.target.shape,boston.feature_names)
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df = pd.DataFrame(boston.data,columns=boston.feature_names)
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df.columns.insert(-1,'PRICE')
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df["PRICE"] = boston.target
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#print(df)
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print("各种回归模型预测波士顿房价及区间:")
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print("注：性能以均方误差MSE来衡量 (除logistic regression)")
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print()
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print("1.多元线性回归:")
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from sklearn.linear_model import LinearRegression
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lr = LinearRegression()
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x = df.drop('PRICE',axis = 1)
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y = df['PRICE']
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x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25,
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random_state=33)
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lr.fit(x_train, y_train)
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y_pred = lr.predict(x_test)
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#print(lr.intercept_,lr.coef_)
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print("R²: {}".format(lr.score(x_test,y_test)))
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print("MSE: {}".format(round(mean_squared_error(y_pred,y_test),3)))
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print()
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print("2.多项式回归:")
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from sklearn.preprocessing import PolynomialFeatures
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for i in range(1,4):
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poly_features = PolynomialFeatures(degree=i, include_bias=False)
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x_train_poly = poly_features.fit_transform(x_train)
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x_test_poly = poly_features.fit_transform(x_test)
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lr.fit(x_train_poly,y_train)
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y_pred = lr.predict(x_test_poly)
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#print(lr.intercept_,lr.coef_)
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print("R²: {}".format(lr.score(x_test_poly,y_test)))
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print("MSE: {}".format(round(mean_squared_error(y_pred,y_test),3)))
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print()
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print("3.逻辑回归:")
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import pandas as pd
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import accuracy_score
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y_train1 = pd.Series(y_train)
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y_test1 = pd.Series(y_test)
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y_train_binned = pd.cut(y_train1, bins=3, labels=['low', 'medium', 'high'])
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y_test_binned = pd.cut(y_test1, bins=3, labels=['low', 'medium', 'high'])
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log_reg = LogisticRegression(random_state=42)
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log_reg.fit(x_train, y_train_binned)
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y_pred_log = log_reg.predict(x_test)
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print("准确率:", round(accuracy_score(y_test_binned, y_pred_log),4))
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print()
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print("4.分位数回归:")
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import statsmodels.api as sm
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import statsmodels.formula.api as smf
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for q in [0.25,0.5,0.75]:
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model = smf.quantreg('PRICE ~ RM + LSTAT', data=df)
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result = model.fit(q=q)
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#print(result.summary())
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pred = result.predict(x_test)
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#print(f"预测分段{q}房价：", pred)
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print(f"预测分段{q}房价 MSE:",round(mean_squared_error(pred,y_test),3))
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print()
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print("5.XGboost:")
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import xgboost as xgb
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data_dmatrix = xgb.DMatrix(data=x,label=y)
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xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,max_depth = 5, alpha = 10, n_estimators = 10)
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xg_reg.fit(x_train,y_train)
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preds = xg_reg.predict(x_test)
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print("MSE: {}".format(round(mean_squared_error(preds,y_test),3)))
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