-
Notifications
You must be signed in to change notification settings - Fork 0
/
预测-随机森林.py
178 lines (153 loc) · 5.29 KB
/
预测-随机森林.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from sklearn.model_selection import GridSearchCV,cross_val_score,StratifiedKFold,train_test_split
from sklearn.ensemble import RandomForestRegressor
import numpy as np
from math import sqrt
from sklearn.metrics import mean_squared_error, mean_absolute_error
from matplotlib import pyplot as plt
from sklearn.model_selection import GridSearchCV
plt.rcParams['font.family'] = 'SimHei'
# 载入训练集和测试集；
path = './data/'
Train_data = pd.read_csv(path+'模型构建所用数据-train.csv')
Test_data = pd.read_csv(path+'模型构建所用数据-test.csv', )
# Train_data = Train_data.fillna(-1)
# 使用时间空缺填充成平均值
Train_data['used_time']=Train_data['used_time'].fillna(Train_data['used_time'].mean())
# X_data=Train_data.drop(['SaleID','regDate','creatDate','regionCode'],axis=1)
# X_data=Train_data[['kilometer','v_0','v_1','v_2','v_3','v_4','v_5','v_6','v_7','used_time']]
X_data=Train_data.drop(['price','v_0','v_1','v_2','v_3','v_4','v_5','v_6','v_7','v_8','v_9','v_10','v_11','v_12','v_13','v_14'],axis=1)
Y_data=Train_data['price']
x_train,x_val,y_train,y_val = train_test_split(X_data,Y_data,test_size=0.3,random_state=33)
print(len(x_train),len(x_val),len(y_train))
# 随机森林分类器
model2 = RandomForestRegressor()
# n_estimators=190,max_depth=10
# 设置需要调试的参数
tuned_parameters = {'n_estimators': [190,290],'max_depth': [15,20]}
# 调用网格搜索函数
rf_clf = GridSearchCV(model2, tuned_parameters, cv=5, n_jobs=-1, scoring='r2')
rf_clf.fit(x_train, y_train)
print(rf_clf.best_params_)
print(rf_clf.best_score_)
# y_predict = model2.predict(x_val)
#
# score_train = model2.score(x_train,y_train)
# score_test = model2.score(x_val,y_val)
# print(score_train,score_test)
#
# # MSE
# mse_sklearn = mean_squared_error(y_val, y_predict)
#
# # RMSE
# rmse_sklearn = np.sqrt(mse_sklearn)
#
# # MAE
# mae_sklearn = mean_absolute_error(y_val, y_predict)
#
# print("MSE (scikit-learn):", mse_sklearn)
# print("RMSE (scikit-learn):", rmse_sklearn)
# print("MAE (scikit-learn):", mae_sklearn)
# # 定义存储分数的数组
# scores_train=[]
# scores_test=[]
# # 定义存储n_estimators取值的数组
# estimators=[]
#
# # 设置n_estimators在100-210中每隔20取一个数值
# for i in range(100,210,20):
# estimators.append(i)
# rf = RandomForestRegressor(n_estimators=i, random_state=12)
# rf.fit(x_train,y_train)
# y_predict = rf.predict(x_val)
# scores_test.append(rf.score(x_val,y_val))
#
# # 查看我们使用的n_estimators取值
# print("estimators =", estimators)
#
# # 查看以上模型中在测试集最好的评分
# print("best_scores_test =",max(scores_test))
#
# # 画出n_estimators与scores的图形
# fig,ax = plt.subplots()
#
# # 设置x y坐标名称
# ax.set_xlabel('estimators')
# ax.set_ylabel('决定系数分数')
# plt.plot(estimators,scores_test, label='测试集')
#
# #显示汉语标注
# plt.rcParams['font.sans-serif']=['SimHei']
# plt.rcParams['font.family']=['sans-serif']
#
# # 设置图例
# plt.legend(loc="lower right")
# plt.show()
# model2.fit(x_train,y_train)
#
#
# y_predict2 = model2.predict(x_val)
#
# print('预测数据长度',len(y_predict2))
#
# # 决定系数
# score_train = model2.score(x_train,y_train)
# score_test = model2.score(x_val,y_val)
# print("训练集score:"+str(score_train))
# print("测试集score:"+str(score_test))
#
# # MSE
# mse_sklearn = mean_squared_error(y_val, y_predict2)
#
# # RMSE
# rmse_sklearn = np.sqrt(mse_sklearn)
#
# # MAE
# mae_sklearn = mean_absolute_error(y_val, y_predict2)
#
# print("MSE (scikit-learn):", mse_sklearn)
# print("RMSE (scikit-learn):", rmse_sklearn)
# print("MAE (scikit-learn):", mae_sklearn)
#
#
#
# # 查看随机森林各项指标系数
# importance =model2.feature_importances_
#
#
# # 将特征重要性转换为DataFrame
# feature_importance = pd.DataFrame(importance, index=X_data.columns, columns=['Importance'])
# feature_importance = feature_importance.sort_values(by='Importance', ascending=False)
#
# # 打印特征重要性
# print("Feature Importances:")
# print(feature_importance)
#
#
# merged_importance = {}
# prefixes = ['brand_', 'bodyType_', 'gearbox_', 'power_bin_','fuelType','notRepairedDamage','Unnamed:0']
#
# for col in X_data.columns:
# added_to_merged = False
# for prefix in prefixes:
# if col.startswith(prefix):
# category_name = col.split('_')[0] # 获取分类变量名称
# merged_importance[category_name] = merged_importance.get(category_name, 0) + feature_importance.loc[col]
# added_to_merged = True
# break
# if not added_to_merged:
# merged_importance[col] = feature_importance.loc[col]
#
# # 将合并后的重要性转换为DataFrame并排序
# merged_importance_df = pd.DataFrame.from_dict(merged_importance, orient='index', columns=['Importance'])
# merged_importance_df = merged_importance_df.sort_values(by='Importance',ascending=True)
#
# # 使用条形图展示合并后的特征重要性
# plt.figure(figsize=(12, 6))
# # plt.barh(merged_importance_df.index, merged_importance_df['Importance'])
# merged_importance_df.plot(kind='barh', title='Feature Importances', figsize=(10, 6), legend=False)
# plt.title('随机森林模型指标重要性')
# plt.xlabel('Importance')
# plt.show()