随机森林算法python实现

墨蓝 2022-01-27 07:45 301阅读 0赞

### 随机森林算法python实现 ###

*  瞎BB
 *  代码
 *   *  导入数据
     *  切分训练集测试集
     *  找到最有用的几个属性
     *  根据上面的代码更改属性
     *  参数组合遍历找最优
     *  随机森林
 *  样本数据

# 瞎BB #

1.实现根据样本数据（用眼距离distance、最长持续用眼时长duration、总用眼时长total\_time、户外运动时长outdoor、用眼角度angle、健康环境光照用眼比例proportion）判别是否需要近视预警  
2.样本实在太少，结果还行，原理都是一样的

# 代码 #

## 导入数据 ##

import pandas
    patients = pandas.read_csv("data.csv")
    patients.head(5)

## 切分训练集测试集 ##

from sklearn.model_selection import train_test_split
    patients_data=patients.loc[:,'distance':'proportion']
    patients_target=patients.loc[:,'warning']
    data_train,data_test,target_train,target_test=train_test_split(patients_data,patients_target,test_size=0.1,random_state=42)

## 找到最有用的几个属性 ##

import numpy as np
    from sklearn.feature_selection import SelectKBest, f_classif
    import matplotlib.pyplot as plt
    predictors = ["distance", "duration", "total_time", "outdoor", "angle", "proportion"]
    
    selector = SelectKBest(f_classif, k=5)
    selector.fit(data_train, target_train)
    
    scores = -np.log10(selector.pvalues_)
    
    plt.bar(range(len(predictors)), scores)
    plt.xticks(range(len(predictors)), predictors, rotation='vertical')
    plt.show()

## 根据上面的代码更改属性 ##

predictors_best = ["distance", "total_time", "angle", "proportion"]
    data_train = data_train[predictors_best]
    data_test = data_test[predictors_best]

## 参数组合遍历找最优 ##

from sklearn.model_selection import GridSearchCV
    tree_param_grid = {  'min_samples_split': list((2,3,4)),'n_estimators':list((3,5,10,15,20,25,30,35,40,45,50))}
    grid = GridSearchCV(RandomForestClassifier(),param_grid=tree_param_grid, cv=kf)#(算法，调节参数（用字典形式），交叉验证次数)
    grid.fit(data_train, target_train)#训练集
    grid.cv_results_ , grid.best_params_, grid.best_score_#得分，最优参数，最优得分

## 随机森林 ##

from sklearn import model_selection
    from sklearn.ensemble import RandomForestClassifier
    rf = RandomForestClassifier(random_state=1, n_estimators=35, min_samples_split=2, min_samples_leaf=2)
    #交叉验证
    kf = model_selection.KFold(n_splits=3)
    scores = model_selection.cross_val_score(rf, data_train, target_train, cv=kf)
    print(scores.mean())

# 样本数据 #

<table> 
 <thead> 
  <tr> 
   <th>sample</th> 
   <th>distance</th> 
   <th>duration</th> 
   <th>total_time</th> 
   <th>outdoor</th> 
   <th>angle</th> 
   <th>proportion</th> 
   <th>warning(1 yes 0 no)</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>1</td> 
   <td>20</td> 
   <td>72</td> 
   <td>344</td> 
   <td>148</td> 
   <td>11</td> 
   <td>81</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>2</td> 
   <td>34</td> 
   <td>68</td> 
   <td>263</td> 
   <td>135</td> 
   <td>7</td> 
   <td>50</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>3</td> 
   <td>25</td> 
   <td>98</td> 
   <td>357</td> 
   <td>32</td> 
   <td>12</td> 
   <td>64</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>4</td> 
   <td>37</td> 
   <td>65</td> 
   <td>291</td> 
   <td>157</td> 
   <td>8</td> 
   <td>89</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>5</td> 
   <td>34</td> 
   <td>151</td> 
   <td>162</td> 
   <td>169</td> 
   <td>18</td> 
   <td>63</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>6</td> 
   <td>30</td> 
   <td>178</td> 
   <td>259</td> 
   <td>146</td> 
   <td>32</td> 
   <td>50</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>7</td> 
   <td>20</td> 
   <td>35</td> 
   <td>134</td> 
   <td>37</td> 
   <td>23</td> 
   <td>68</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>8</td> 
   <td>39</td> 
   <td>111</td> 
   <td>169</td> 
   <td>87</td> 
   <td>4</td> 
   <td>52</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>9</td> 
   <td>22</td> 
   <td>44</td> 
   <td>265</td> 
   <td>136</td> 
   <td>14</td> 
   <td>76</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>10</td> 
   <td>39</td> 
   <td>151</td> 
   <td>219</td> 
   <td>140</td> 
   <td>2</td> 
   <td>55</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>11</td> 
   <td>21</td> 
   <td>179</td> 
   <td>184</td> 
   <td>64</td> 
   <td>18</td> 
   <td>60</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>12</td> 
   <td>25</td> 
   <td>41</td> 
   <td>241</td> 
   <td>71</td> 
   <td>16</td> 
   <td>72</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>13</td> 
   <td>18</td> 
   <td>171</td> 
   <td>286</td> 
   <td>131</td> 
   <td>35</td> 
   <td>89</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>14</td> 
   <td>32</td> 
   <td>33</td> 
   <td>236</td> 
   <td>102</td> 
   <td>29</td> 
   <td>50</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>15</td> 
   <td>20</td> 
   <td>133</td> 
   <td>226</td> 
   <td>124</td> 
   <td>17</td> 
   <td>81</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>16</td> 
   <td>17</td> 
   <td>148</td> 
   <td>236</td> 
   <td>66</td> 
   <td>32</td> 
   <td>75</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>17</td> 
   <td>34</td> 
   <td>111</td> 
   <td>214</td> 
   <td>57</td> 
   <td>5</td> 
   <td>88</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>18</td> 
   <td>24</td> 
   <td>85</td> 
   <td>163</td> 
   <td>155</td> 
   <td>14</td> 
   <td>32</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>19</td> 
   <td>32</td> 
   <td>165</td> 
   <td>276</td> 
   <td>146</td> 
   <td>33</td> 
   <td>52</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>20</td> 
   <td>25</td> 
   <td>124</td> 
   <td>359</td> 
   <td>171</td> 
   <td>33</td> 
   <td>70</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>21</td> 
   <td>31</td> 
   <td>51</td> 
   <td>167</td> 
   <td>47</td> 
   <td>25</td> 
   <td>47</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>22</td> 
   <td>31</td> 
   <td>63</td> 
   <td>352</td> 
   <td>58</td> 
   <td>22</td> 
   <td>44</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>23</td> 
   <td>16</td> 
   <td>58</td> 
   <td>164</td> 
   <td>45</td> 
   <td>13</td> 
   <td>73</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>24</td> 
   <td>29</td> 
   <td>37</td> 
   <td>326</td> 
   <td>104</td> 
   <td>33</td> 
   <td>68</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>25</td> 
   <td>34</td> 
   <td>47</td> 
   <td>197</td> 
   <td>59</td> 
   <td>5</td> 
   <td>66</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>26</td> 
   <td>36</td> 
   <td>123</td> 
   <td>185</td> 
   <td>165</td> 
   <td>26</td> 
   <td>70</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>27</td> 
   <td>25</td> 
   <td>126</td> 
   <td>171</td> 
   <td>45</td> 
   <td>23</td> 
   <td>33</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>28</td> 
   <td>31</td> 
   <td>84</td> 
   <td>98</td> 
   <td>37</td> 
   <td>30</td> 
   <td>51</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <td>29</td> 
   <td>30</td> 
   <td>92</td> 
   <td>153</td> 
   <td>114</td> 
   <td>14</td> 
   <td>48</td> 
   <td>0</td> 
  </tr> 
  <tr> 
   <td>30</td> 
   <td>29</td> 
   <td>178</td> 
   <td>278</td> 
   <td>146</td> 
   <td>27</td> 
   <td>45</td> 
   <td>1</td> 
  </tr> 
 </tbody> 
</table>