sklearn：随机森林_回归树_波士顿房价_填补缺失值

• 分类树和回归树参数差别：

1. criterion
   • 分类：使用信息增益，
   • 回归：
     • 均方误差MSE，使用均值。mse是父节点与叶子节点之间的均方误差，用来选择特征。同时也是用于衡量模型质量的指标。均方误差是正的，但是sklearn中的均方误差是负数。
     • 绝对误差mae，使用中值。
     • 注意：回归树的接口score默认返回的是R方（负无穷到1，越接近1越好），不是mse

from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor

boston = load_boston()

import sklearn
sorted(sklearn.metrics.SCORERS.keys())

['accuracy',
 'adjusted_mutual_info_score',
 'adjusted_rand_score',
 'average_precision',
 'balanced_accuracy',
 'brier_score_loss',
 'completeness_score',
 'explained_variance',
 'f1',
 'f1_macro',
 'f1_micro',
 'f1_samples',
 'f1_weighted',
 'fowlkes_mallows_score',
 'homogeneity_score',
 'jaccard',
 'jaccard_macro',
 'jaccard_micro',
 'jaccard_samples',
 'jaccard_weighted',
 'max_error',
 'mutual_info_score',
 'neg_log_loss',
 'neg_mean_absolute_error',
 'neg_mean_squared_error',
 'neg_mean_squared_log_error',
 'neg_median_absolute_error',
 'normalized_mutual_info_score',
 'precision',
 'precision_macro',
 'precision_micro',
 'precision_samples',
 'precision_weighted',
 'r2',
 'recall',
 'recall_macro',
 'recall_micro',
 'recall_samples',
 'recall_weighted',
 'roc_auc',
 'v_measure_score']

regresor = RandomForestRegressor(n_estimators=100, random_state=0)
cross_val_score(regresor, boston.data, boston.target, cv=10
               , scoring="neg_mean_squared_error"  # 可以通过 sklearn.metrics.SCORERS.keys() 查看scoring对应的参数，默认是R方
               )
# 返回10次交叉验证的衡量指标结果

array([-10.72900447,  -5.36049859,  -4.74614178, -20.84946337,
       -12.23497347, -17.99274635,  -6.8952756 , -93.78884428,
       -29.80411702, -15.25776814])

用随机森林回归填补缺失值

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score

dataset = load_boston()
dataset.data.shape

(506, 13)

x_full, y_full = dataset.data, dataset.target  # 保存完整的数据
n_samples = x_full.shape[0]
n_features = x_full.shape[1]
n_samples, n_features

(506, 13)

# 首先确定希望放入的缺失值数据的比例。
rng = np.random.RandomState(0)
missing_rate = 0.5
n_missing_samples = int(np.floor(n_samples * n_features * missing_rate))
n_missing_samples

3289

# 构建缺失数据

missing_features = rng.randint(0, n_features, n_missing_samples)  # 生成从0-n之间的n_missing_samples个数据
missing_samples = rng.randint(0, n_samples, n_missing_samples)

x_missing = x_full.copy()
y_missing = y_full.copy()

x_missing[missing_samples, missing_features] = np.nan
x_missing = pd.DataFrame(x_missing)
x_missing

	0	1	2	3	4	5	6	7	8	9	10	11	12
0	NaN	18.0	NaN	NaN	0.538	NaN	65.2	4.0900	1.0	296.0	NaN	NaN	4.98
1	0.02731	0.0	NaN	0.0	0.469	NaN	78.9	4.9671	2.0	NaN	NaN	396.90	9.14
2	0.02729	NaN	7.07	0.0	NaN	7.185	61.1	NaN	2.0	242.0	NaN	NaN	NaN
3	NaN	NaN	NaN	0.0	0.458	NaN	45.8	NaN	NaN	222.0	18.7	NaN	NaN
4	NaN	0.0	2.18	0.0	NaN	7.147	NaN	NaN	NaN	NaN	18.7	NaN	5.33
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	NaN	NaN	NaN	0.0	0.573	NaN	69.1	NaN	1.0	NaN	21.0	NaN	9.67
502	0.04527	0.0	11.93	0.0	0.573	6.120	76.7	2.2875	1.0	273.0	NaN	396.90	9.08
503	NaN	NaN	11.93	NaN	0.573	6.976	91.0	NaN	NaN	NaN	21.0	NaN	5.64
504	0.10959	0.0	11.93	NaN	0.573	NaN	89.3	NaN	1.0	NaN	21.0	393.45	6.48
505	0.04741	0.0	11.93	0.0	0.573	6.030	NaN	NaN	1.0	NaN	NaN	396.90	7.88

506 rows × 13 columns

from sklearn.impute import SimpleImputer  # 专门用于填补缺失值的类

# 使用均值填充
imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
x_missing_mean = imp_mean.fit_transform(x_missing)
x_missing_mean = pd.DataFrame(x_missing_mean)
x_missing_mean

	0	1	2	3	4	5	6	7	8	9	10	11	12
0	3.627579	18.000000	11.163464	0.066007	0.538000	6.305921	65.2	4.090000	1.000000	296.000000	18.521192	352.741952	4.980000
1	0.027310	0.000000	11.163464	0.000000	0.469000	6.305921	78.9	4.967100	2.000000	405.935275	18.521192	396.900000	9.140000
2	0.027290	10.722951	7.070000	0.000000	0.564128	7.185000	61.1	3.856371	2.000000	242.000000	18.521192	352.741952	12.991767
3	3.627579	10.722951	11.163464	0.000000	0.458000	6.305921	45.8	3.856371	9.383871	222.000000	18.700000	352.741952	12.991767
4	3.627579	0.000000	2.180000	0.000000	0.564128	7.147000	67.4	3.856371	9.383871	405.935275	18.700000	352.741952	5.330000
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	3.627579	10.722951	11.163464	0.000000	0.573000	6.305921	69.1	3.856371	1.000000	405.935275	21.000000	352.741952	9.670000
502	0.045270	0.000000	11.930000	0.000000	0.573000	6.120000	76.7	2.287500	1.000000	273.000000	18.521192	396.900000	9.080000
503	3.627579	10.722951	11.930000	0.066007	0.573000	6.976000	91.0	3.856371	9.383871	405.935275	21.000000	352.741952	5.640000
504	0.109590	0.000000	11.930000	0.066007	0.573000	6.305921	89.3	3.856371	1.000000	405.935275	21.000000	393.450000	6.480000
505	0.047410	0.000000	11.930000	0.000000	0.573000	6.030000	67.4	3.856371	1.000000	405.935275	18.521192	396.900000	7.880000

506 rows × 13 columns

# 使用 0填充缺失值
imp_0 = SimpleImputer(missing_values=np.nan, strategy='constant', fill_value=0)
x_missing_0 = imp_0.fit_transform(x_missing)
x_missing_0 = pd.DataFrame(x_missing_0)
x_missing_0

	0	1	2	3	4	5	6	7	8	9	10	11	12
0	0.00000	18.0	0.00	0.0	0.538	0.000	65.2	4.0900	1.0	296.0	0.0	0.00	4.98
1	0.02731	0.0	0.00	0.0	0.469	0.000	78.9	4.9671	2.0	0.0	0.0	396.90	9.14
2	0.02729	0.0	7.07	0.0	0.000	7.185	61.1	0.0000	2.0	242.0	0.0	0.00	0.00
3	0.00000	0.0	0.00	0.0	0.458	0.000	45.8	0.0000	0.0	222.0	18.7	0.00	0.00
4	0.00000	0.0	2.18	0.0	0.000	7.147	0.0	0.0000	0.0	0.0	18.7	0.00	5.33
...	...	...	...	...	...	...	...	...	...	...	...	...	...
501	0.00000	0.0	0.00	0.0	0.573	0.000	69.1	0.0000	1.0	0.0	21.0	0.00	9.67
502	0.04527	0.0	11.93	0.0	0.573	6.120	76.7	2.2875	1.0	273.0	0.0	396.90	9.08
503	0.00000	0.0	11.93	0.0	0.573	6.976	91.0	0.0000	0.0	0.0	21.0	0.00	5.64
504	0.10959	0.0	11.93	0.0	0.573	0.000	89.3	0.0000	1.0	0.0	21.0	393.45	6.48
505	0.04741	0.0	11.93	0.0	0.573	6.030	0.0	0.0000	1.0	0.0	0.0	396.90	7.88

506 rows × 13 columns

# 使用 随机森林 填充缺失值
# 通过已有的 特征数据 和 标签信息来 回归预测 缺失的数据
# 先填充缺失较少的特征数据

x_missing_reg = x_missing.copy()
sortindex = np.argsort(x_missing_reg.isnull().sum(axis=0)).values  # 计算出特征空值数据，然后排序返回对应列的索引
sortindex

array([ 6, 12,  8,  7,  9,  0,  2,  1,  5,  4,  3, 10, 11], dtype=int64)

# 遍历，填补空值
for i in sortindex:
    df = x_missing_reg
    fillc = df.iloc[:, i]
    df = pd.concat([df.drop(i, axis=1), pd.DataFrame(y_full)], axis=1)
    
    df_0 = SimpleImputer(missing_values=np.nan
                        , strategy='constant'
                        , fill_value=0
                        ).fit_transform(df)
    
    y_train = fillc[fillc.notnull()]
    y_test = fillc[fillc.isnull()]
    x_train = df_0[y_train.index, :]
    x_test = df_0[y_test.index, :]
    
    rfc = RandomForestRegressor(n_estimators=100)
    rfc = rfc.fit(x_train, y_train)
    y_predict = rfc.predict(x_test)
    
    x_missing_reg.loc[x_missing_reg.loc[:, i].isnull(), i] = y_predict

# 对填补好的数据进行建模

X = [x_full, x_missing_mean, x_missing_0, x_missing_reg]

mse = []
std = []
for x in X:
    estimator = RandomForestRegressor(random_state=0, n_estimators=100)
    scores = cross_val_score(estimator, x, y_full, scoring='neg_mean_squared_error', cv=5).mean()
    mse.append(scores * -1)

# 用所得的结果画出条形图

x_labels = ['Full data'
            , 'Zero Imputation'
            , 'Mean Imputation'
            , 'Regressor Imputation'
           ]
colors = ['r', 'g', 'b', 'orange']

plt.figure(figsize=(12, 6))
ax = plt.subplot(111)
for i in range(len(mse)):
    ax.barh(i, mse[i], color=colors[i], alpha=0.6, align='center')
    
ax.set_title('Imputation Techniques with Boston Data')
ax.set_xlim(left=np.min(mse) * 0.9,
            right=np.max(mse) * 1.1
           )
ax.set_yticks(range(len(mse)))
ax.set_xlabel('MSE')
ax.set_yticklabels(x_labels)
plt.show()