利用train.csv中的数据，通过H2O框架中的随机森林算法构建分类模型，然后利用模型对test.csv中的数据进行预测，并计算分类的准确度进而评价模型的分类效果；通过调节参数，观察分类准确度的变化情况。注：准确度＝预测正确的数与样本总数的比【注：可以做一些特征选择的工作，来提高准确度】

import  h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator 
from h2o.grid.grid_search import H2OGridSearch

h2o.init()

Checking whether there is an H2O instance running at http://localhost:54321 . connected.

train=h2o.import_file(path ="C:\\Users\\zzh\\Desktop\\dataMiningExperment\\data4\\train.csv")
test=h2o.import_file(path = "C:\\Users\\zzh\\Desktop\\dataMiningExperment\\data4\\test.csv")

Parse progress: |█████████████████████████████████████████████████████████| 100%
Parse progress: |█████████████████████████████████████████████████████████| 100%

train.head(5)

train.csv为训练数据集，该数据集是驾驶员行为识别聚类结果经处理后的数据。其中driver，trip这2列在构建模型时没有用

train=train[2:]# 删除driver trip 两个无用列
test=test[2:]# 删除driver trip 两个无用列

train.head(5)

1、直接建立模型，参数全部默认

准确率：0.8666666666666667

model1 = H2ORandomForestEstimator()  # 初始化（建立）模型
model1.train(x = train.names[0:-1],y = 'Catrgory',training_frame = train)  # 训练模型 train.names[0:-1]去除最后一列

drf Model Build progress: |███████████████████████████████████████████████| 100%

predict=H2ORandomForestEstimator.predict(model1 ,test[test.names[0:-1]]) # 对测试集进行预测  test[test.names[0:-1]]删除最后一列
predict.head(5)

drf prediction progress: |████████████████████████████████████████████████| 100%

注：准确度＝预测正确的数与样本总数的比

tmp = predict[predict['predict'] == test['Catrgory']].nrow 
accuracy = tmp/test.nrow
accuracy

0.8666666666666667

查看模型深层信息，以获取对预测结果产生比较重要影响的特征

model1.deepfeatures

Model Details
=============
H2ORandomForestEstimator :  Distributed Random Forest
Model Key:  DRF_model_python_1577882615850_1


Model Summary: 

ModelMetricsMultinomial: drf
** Reported on train data. **

MSE: 0.048564890251647425
RMSE: 0.22037443193720868
LogLoss: 0.16320718635092735
Mean Per-Class Error: 0.07050700819826967

Confusion Matrix: Row labels: Actual class; Column labels: Predicted class

Top-3 Hit Ratios: 

Scoring History: 

See the whole table with table.as_data_frame()

Variable Importances: 

<bound method ModelBase.deepfeatures of >

2、进行特征选择后建立模型，参数全部默认

挑选影响最大的八个特征对数据进行处理，按影响程度从大到小是

[[‘Average_speed’,‘r_a’, ‘r_b’,‘Average_RPM’,‘v_a’,‘v_d’,‘Variance_speed’,‘v_c’,‘Catrgory’]]

准确率：0.8666666666666667 没有变

train_features= train[['Average_speed','r_a', 'r_b','Average_RPM','v_a','v_d','Variance_speed','v_c','Catrgory']]
test_features= test[['Average_speed','r_a', 'r_b','Average_RPM','v_a','v_d','Variance_speed','v_c','Catrgory']]

### 进行特征选择后建立模型，参数默认
### 准确率：

model2 = H2ORandomForestEstimator()
model2.train(x = train_features.names[0:-1],y = 'Catrgory',training_frame = train_features)

drf Model Build progress: |███████████████████████████████████████████████| 100%

predict=H2ORandomForestEstimator.predict(model2 ,test_features[test_features.names[0:-1]]) 

drf prediction progress: |████████████████████████████████████████████████| 100%

tmp = predict[predict['predict'] == test_features['Catrgory']].nrow 
accuracy = tmp/test_features.nrow
accuracy

0.8666666666666667

3、通过调节参数，观察分类准确度的变化情况。

3.1、for循环调节参数（ntrees和max_depth）,得到最大准确率,寻找最佳参数

最大准确率：0.894

ntrees: 5

max_depth : 9

这部分太大，没有展示，从这里求得最优参数（ntrees和max_depth）

max_accuracy=0
ntrees=0
max_depth=0
for i in range(1,20):
    for j in range(1,20):
        model3=H2ORandomForestEstimator(ntrees=i,max_depth =j)
        model3.train(x=train.names[0:-1],y='Catrgory',training_frame=train)
        predict=H2ORandomForestEstimator.predict(model3 ,test[test.names[0:-1]])
        tmp = predict[predict['predict'] == test['Catrgory']].nrow 
        accuracy = tmp/test.nrow
        accuracy
        print("now acc is:", accuracy, "--- max acc is :",max_accuracy)
        if max_accuracy<accuracy:
            max_accuracy=accuracy
            ntrees=i
            max_depth=j

print("最大acc:",max_accuracy)
print("最优ntrees :",ntrees)
print("最优max_depth :",max_depth)

model3 = H2ORandomForestEstimator(ntrees=3,max_depth=6)
model3.train(x = train.names[0:-1],y = 'Catrgory',training_frame = train)

drf Model Build progress: |███████████████████████████████████████████████| 100%

predict=H2ORandomForestEstimator.predict(model3,test[test.names[0:-1]]) 

drf prediction progress: |████████████████████████████████████████████████| 100%

tmp = predict[predict['predict'] == test['Catrgory']].nrow 
accuracy = tmp/test.nrow
accuracy

test数据与预测结果合并后的数据集，命名为predict.csv

out = test.concat(predict['predict'])
h2o.download_csv(out,"predict.csv")

'C:\\Users\\zzh\\Desktop\\dataMiningExperment\\exp4\\predict.csv'

3.2、Grid Search寻找最佳参数

准确率：0.8708333333333333

ntrees: 10

max_depth : 10

rf_params = {'ntrees': [x for x in range(30,60,1)],
                'max_depth': [x for x in range(10,20,1)]
               }
 
rf_grid = H2OGridSearch(model = H2ORandomForestEstimator,
                        hyper_params=rf_params)

rf_grid.train(x = train.names[0:-1],
               y = 'Catrgory',
               training_frame = train)

这部分太大，没有展示，从这里求得最优参数（ntrees和max_depth）

rfm_grid.show()

model4 = H2ORandomForestEstimator(ntrees=3,max_depth=6)
model4.train(x = train.names[0:-1],y = 'Catrgory',training_frame = train)

predict=H2ORandomForestEstimator.predict(model4,test[test.names[0:-1]]) 

tmp = predict[predict['predict'] == test['Catrgory']].nrow 
accuracy = tmp/test.nrow
accuracy