回归算法实例五：葡萄酒质量预测

青旅半醒 2023-07-13 10:50 18阅读 0赞

import numpy as np
    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import pandas as pd
    import warnings
    import sklearn
    from sklearn.linear_model import LinearRegression, LassoCV, RidgeCV, ElasticNetCV
    from sklearn.preprocessing import PolynomialFeatures
    from sklearn.pipeline import Pipeline
    from sklearn.linear_model.coordinate_descent import ConvergenceWarning
    from sklearn.model_selection import train_test_split
    
    # 设置字符集，防止中文乱码
    mpl.rcParams['font.sans-serif']=[u'simHei']
    mpl.rcParams['axes.unicode_minus']=False
    # 拦截异常
    warnings.filterwarnings(action = 'ignore', category=ConvergenceWarning)
    warnings.filterwarnings(action = 'ignore', category=UserWarning)
    
    # 读取数据
    path1 = "datas/winequality-red.csv"
    df1 = pd.read_csv(path1, sep=";")
    # 设置数据类型为红葡萄酒
    df1['type'] = 1
    
    path2 = "datas/winequality-white.csv"
    df2 = pd.read_csv(path2, sep=";")
    # 设置数据类型为白葡萄酒
    df2['type'] = 2
    
    # 合并两个df
    df = pd.concat([df1,df2], axis=0)
    
    # 自变量名称
    names = ["fixed acidity","volatile acidity","citric acid",
             "residual sugar","chlorides","free sulfur dioxide",
             "total sulfur dioxide","density","pH","sulphates",
             "alcohol", "type"]
    
    # 因变量名称
    quality = "quality"
    
    names1=[]
    for i in list(df):
        names1.append(i)
    
    # 异常数据处理
    new_df = df.replace('?', np.nan)
    # 只要有列为空，就进行删除操作
    datas = new_df.dropna(how = 'any')
    
    X = datas[names]
    Y = datas[quality]
    Y.ravel()

![20200311174621276.png][]

# 创建模型列表
    models = [
        Pipeline([
                ('Poly', PolynomialFeatures()),
                ('Linear', LinearRegression())
            ]),
        Pipeline([
                ('Poly', PolynomialFeatures()),
                ('Linear', RidgeCV(alphas=np.logspace(-4, 2, 20)))
            ]),
        Pipeline([
                ('Poly', PolynomialFeatures()),
                ('Linear', LassoCV(alphas=np.logspace(-4, 2, 20)))
            ]),
        Pipeline([
                ('Poly', PolynomialFeatures()),
                ('Linear', ElasticNetCV(alphas=np.logspace(-4,2, 20), l1_ratio=np.linspace(0, 1, 5)))
            ])
    ]
    
    plt.figure(figsize=(16,8), facecolor='w')
    titles = u'线性回归预测', u'Ridge回归预测', u'Lasso回归预测', u'ElasticNet预测'
    
    # 将数据分为训练数据和测试数据
    X_train,X_test,Y_train,Y_test = train_test_split(X, Y, test_size=0.01, random_state=0)
    ln_x_test = range(len(X_test))
    
    # 给定阶以及颜色
    d_pool = np.arange(1,4,1) # 1 2 3 阶
    m = len(d_pool)
    # 颜色
    clrs = []
    for c in np.linspace(5570560, 255, m):
        clrs.append('#%06x' % int(c))
    
    for t in range(4):
        plt.subplot(2, 2, t + 1)
        model = models[t]
        plt.plot(ln_x_test, Y_test, c='r', lw=2, alpha=0.75, zorder=10, label=u'真实值')
        for i,d in enumerate(d_pool):
            # 设置参数
            model.set_params(Poly__degree=d)
            # 模型训练
            model.fit(X_train, Y_train)
            # 模型预测及计算R^2
            Y_pre = model.predict(X_test)
            R = model.score(X_train, Y_train)
            # 输出信息
            lin = model.get_params('Linear')['Linear']
            output = u"%s:%d阶, 截距:%d, 系数:" % (titles[t], d, lin.intercept_)
            print(output, lin.coef_)
            # 图形展示
            plt.plot(ln_x_test, Y_pre, c=clrs[i], lw=2,alpha=0.75, zorder=i, label=u'%d阶预测值,$R^2$=%.3f' % (d,R))
        plt.legend(loc = 'upper left')
        plt.grid(True)
        plt.title(titles[t], fontsize=18)
        plt.xlabel('X', fontsize=16)
        plt.ylabel('Y', fontsize=16)
    plt.suptitle(u'葡萄酒质量预测', fontsize=22)
    plt.show()

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[20200311174621276.png]: https://img-blog.csdnimg.cn/20200311174621276.png
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