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update

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Newnius 2020-07-01 11:42:52 +08:00
parent 4fe3b29cda
commit 4fcc033267
2 changed files with 40 additions and 40 deletions
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25
.idea/workspace.xml
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55
serve.py
View File

@ -34,35 +34,38 @@ def train_models(job):
if job not in models or 'features' not in models[job]: if job not in models or 'features' not in models[job]:
return return
models[job]['lock'].acquire() models[job]['lock'].acquire()
try:
for label in models[job]['labels']:
trainfile = './data/' + job + '_' + label + '.csv'
traindata = pd.read_csv(trainfile)
feature_data = traindata.iloc[:, 1:-1]
label_data = traindata.iloc[:, -1]
for label in models[job]['labels']: X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
trainfile = './data/' + job + '_' + label + '.csv' params = {
traindata = pd.read_csv(trainfile) 'n_estimators': 70,
feature_data = traindata.iloc[:, 1:-1] 'max_depth': 13,
label_data = traindata.iloc[:, -1] 'min_samples_split': 10,
'min_samples_leaf': 5, # 10
'max_features': len(models[job]['features']) - 1 # 7
}
# print(params)
model = RandomForestRegressor(**params)
model.fit(X_train, y_train)
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01) # save the model to disk
params = { modelname = './data/' + job + '_' + label + '.sav'
'n_estimators': 70, pickle.dump(model, open(modelname, 'wb'))
'max_depth': 13,
'min_samples_split': 10,
'min_samples_leaf': 5, # 10
'max_features': len(models[job]['features']) - 1 # 7
}
# print(params)
model = RandomForestRegressor(**params)
model.fit(X_train, y_train)
# save the model to disk # 对测试集进行预测
modelname = './data/' + job + '_' + label + '.sav' y_pred = model.predict(X_test)
pickle.dump(model, open(modelname, 'wb')) # 计算准确率
MSE = mean_squared_error(y_test, y_pred)
# 对测试集进行预测 RMSE = np.sqrt(MSE)
y_pred = model.predict(X_test) print('RMSE of {}:{} is {}'.format(job, label, str(RMSE)))
# 计算准确率 except Exception as e:
MSE = mean_squared_error(y_test, y_pred) print(traceback.format_exc())
RMSE = np.sqrt(MSE) print(str(e))
print('RMSE of {}:{} is {}'.format(job, label, str(RMSE)))
models[job]['lock'].release() models[job]['lock'].release()