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update
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@ -1,11 +1,7 @@
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@ -96,8 +92,8 @@
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@ -435,8 +432,8 @@
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55
serve.py
55
serve.py
@ -34,35 +34,38 @@ def train_models(job):
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if job not in models or 'features' not in models[job]:
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return
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models[job]['lock'].acquire()
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try:
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for label in models[job]['labels']:
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trainfile = './data/' + job + '_' + label + '.csv'
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traindata = pd.read_csv(trainfile)
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feature_data = traindata.iloc[:, 1:-1]
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label_data = traindata.iloc[:, -1]
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for label in models[job]['labels']:
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trainfile = './data/' + job + '_' + label + '.csv'
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traindata = pd.read_csv(trainfile)
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feature_data = traindata.iloc[:, 1:-1]
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label_data = traindata.iloc[:, -1]
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X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
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params = {
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'n_estimators': 70,
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'max_depth': 13,
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'min_samples_split': 10,
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'min_samples_leaf': 5, # 10
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'max_features': len(models[job]['features']) - 1 # 7
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}
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# print(params)
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model = RandomForestRegressor(**params)
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model.fit(X_train, y_train)
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X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
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params = {
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'n_estimators': 70,
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'max_depth': 13,
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'min_samples_split': 10,
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'min_samples_leaf': 5, # 10
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'max_features': len(models[job]['features']) - 1 # 7
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}
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# print(params)
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model = RandomForestRegressor(**params)
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model.fit(X_train, y_train)
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# save the model to disk
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modelname = './data/' + job + '_' + label + '.sav'
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pickle.dump(model, open(modelname, 'wb'))
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# save the model to disk
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modelname = './data/' + job + '_' + label + '.sav'
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pickle.dump(model, open(modelname, 'wb'))
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# 对测试集进行预测
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y_pred = model.predict(X_test)
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# 计算准确率
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MSE = mean_squared_error(y_test, y_pred)
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RMSE = np.sqrt(MSE)
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print('RMSE of {}:{} is {}'.format(job, label, str(RMSE)))
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# 对测试集进行预测
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y_pred = model.predict(X_test)
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# 计算准确率
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MSE = mean_squared_error(y_test, y_pred)
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RMSE = np.sqrt(MSE)
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print('RMSE of {}:{} is {}'.format(job, label, str(RMSE)))
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except Exception as e:
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print(traceback.format_exc())
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print(str(e))
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models[job]['lock'].release()
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