回归算法实例四：波士顿房屋租赁价格预测

桃扇骨 2023-07-13 10:45 39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
    from sklearn.preprocessing import StandardScaler
    from sklearn.grid_search import GridSearchCV
    from sklearn import metrics

def notEmpty(s):
        return s != ''
    
    # 加载数据
    names = ['CRIM','ZN', 'INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT']
    path = "datas/boston_housing.data"
    # 由于数据文件格式不统一，所以读取的时候，先按照一行一个字段属性读取数据，然后再按照每行数据进行处理
    fd = pd.read_csv(path,header=None)
    fd.head()

![20200311162736558.png][]

# 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)
    
    # 加载数据
    names = ['CRIM','ZN', 'INDUS','CHAS','NOX','RM','AGE','DIS','RAD','TAX','PTRATIO','B','LSTAT']
    path = "datas/boston_housing.data"
    # 由于数据文件格式不统一，所以读取的时候，先按照一行一个字段属性读取数据，然后再按照每行数据进行处理
    fd = pd.read_csv(path,header=None)
    print(fd.shape)
    data = np.empty((len(fd), 14))
    # enumerate生成一列索引i，d为其元素
    for i, d in enumerate(fd.values):
        d = map(float, filter(notEmpty, d[0].split(' ')))
        # 根据函数结果是否为真来过滤list中的项
        data[i] = list(d)
        
    # 分割数据
    x, y = np.split(data, (13,), axis=1)
    print (x[0:5])
    # 转换格式，拉直操作
    y = y.ravel()
    print (y[0:5])
    ly=len(y)
    print(y.shape)
    print ("样本数据量:%d, 特征个数：%d" % x.shape)
    print ("target样本数据量:%d" % y.shape[0])

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dvbmd4aWZhY2FpX2JlbGlldmU_size_16_color_FFFFFF_t_70][]

# Pipeline常用于并行调参
    models = [
        Pipeline([
                ('ss', StandardScaler()),
                ('poly', PolynomialFeatures()),
                ('linear', RidgeCV(alphas=np.logspace(-3,1,20)))
            ]),
        Pipeline([
                ('ss', StandardScaler()),
                ('poly', PolynomialFeatures()),
                ('linear', LassoCV(alphas=np.logspace(-3,1,20)))
            ])
    ] 
    
    # 参数字典，字典中的key是属性的名称，value是可选的参数列表
    parameters = {
        "poly__degree": [3,2,1], 
        # 是否产生交互项，如X1*X2 
        "poly__interaction_only": [True, False],
        # 多项式幂为零的特征作为线性模型中的截距
        "poly__include_bias": [True, False],
        "linear__fit_intercept": [True, False]
    }

rf=PolynomialFeatures(2,interaction_only=True)
    a=pd.DataFrame({
        'name':[1,2,3,4,5],
        'score':[2,3,4,4,5]
    })
    b=rf.fit_transform(a)
    b

![20200311163110717.png][]

# 数据分割
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
    
    # Lasso和Ridge模型比较运行图表展示
    titles = ['Ridge', 'Lasso']
    colors = ['g-', 'b-']
    plt.figure(figsize=(16,8), facecolor='w')
    ln_x_test = range(len(x_test))
    
    plt.plot(ln_x_test, y_test, 'r-', lw=2, label=u'真实值')
    for t in range(2):
        # 获取模型并设置参数
        # GridSearchCV: 进行交叉验证，选择出最优的参数值出来
        # models[t]：进行参数选择的模型
        # param_grid： 用于进行模型选择的参数字段，要求是字典类型
        # cv：进行几折交叉验证，下式进行五折交叉验证
        model = GridSearchCV(models[t], param_grid=parameters,cv=5, n_jobs=1)
        # 模型训练-网格搜索
        model.fit(x_train, y_train)
        # 模型效果值获取（最优参数）
        print ("%s算法:最优参数:" % titles[t], model.best_params_)
        print ("%s算法:R值=%.3f" % (titles[t], model.best_score_))
        # 模型预测
        y_predict = model.predict(x_test)
        # 画图
        plt.plot(ln_x_test, y_predict, colors[t], lw = t + 3, label=u'%s算法估计值,$R^2$=%.3f' % (titles[t], model.best_score_))
    
    # 图形显示
    plt.legend(loc = 'upper left')
    plt.grid(True)
    plt.title(u"波士顿房屋价格预测")
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dvbmd4aWZhY2FpX2JlbGlldmU_size_16_color_FFFFFF_t_70 1][]

# 单个Lasso模型训练（一阶特征选择，给定一阶情况的最优参数）
    model = Pipeline([
                ('ss', StandardScaler()),
                ('poly', PolynomialFeatures(degree=1, include_bias=True, interaction_only=True)),
                ('linear', LassoCV(alphas=np.logspace(-3,1,20), fit_intercept=False))
            ])
    # 模型训练
    model.fit(x_train, y_train)
    
    # 模型评测和数据输出
    print ("参数:", list(zip(names,model.get_params('linear')['linear'].coef_)))
    print ("截距:", model.get_params('linear')['linear'].intercept_)

![20200311163409560.png][]  
L1-norm是可以做特征选择的。主要原因在于，通过Lasso模型训练后，有的参数是有可能出现为0的或者接近0的情况。针对于这样的特征，可以使用函数df.drop(xx)进行特征的删除操作。一般情况下，特征属性参数的绝对值小于 1 0 − 6 10^\{-6\} 10−6时会被删除。

[20200311162736558.png]: https://img-blog.csdnimg.cn/20200311162736558.png
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[20200311163110717.png]: https://img-blog.csdnimg.cn/20200311163110717.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dvbmd4aWZhY2FpX2JlbGlldmU_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20200311163249961.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2dvbmd4aWZhY2FpX2JlbGlldmU=,size_16,color_FFFFFF,t_70
[20200311163409560.png]: https://img-blog.csdnimg.cn/20200311163409560.png