upgrade

engines/tbats.py

@@ -0,0 +1,134 @@
+
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+from keras.models import Sequential
+
+import math
+#import helpers as h
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import mean_squared_error,mean_absolute_error
+
+from tbats import BATS, TBATS
+
+
+from numpy.random import seed
+seed(69)
+from math import sqrt
+from numpy import concatenate
+import matplotlib.pyplot as plt
+from pandas import DataFrame
+from pandas import concat
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import mean_squared_error
+import pickle
+
+#import multiprocessing
+from . BBDD import new_model, get_best_model
+from . helpers import create_train_test,seasonal_options
+
+
+
+
+
+
+
+
+
+def anomaly_uni_TBATS(lista_datos,num_forecast=10,desv_mse=2,train='True',name='test'):
+
+    lista_puntos = np.arange(0, len(lista_datos),1)
+
+    df, df_train, df_test = create_train_test(lista_puntos, lista_datos)
+
+    engine_output={}
+
+    actual_model=''
+
+    if (train):
+
+        ##########################################################################################
+        #############################################################################################3
+        periods = seasonal_options(df.valores)
+        estimator = TBATS(seasonal_periods= periods[:2])
+        # Fit model
+        print("Starting Anomaly Model Fitted")
+
+        fitted_model = estimator.fit(df_train['valores'])
+        print("Anomaly Model Fitted")
+
+        # Forecast 14 steps ahead
+        anomaly_forecasted = fitted_model.forecast(steps=len(df_test['valores']))
+
+
+        mae = mean_absolute_error(anomaly_forecasted,df_test['valores'].values)
+
+
+
+        #mae = mean_absolute_error(y_forecasted,df_test['valores'].values)
+
+        df_aler = pd.DataFrame(anomaly_forecasted,index = df_test.index,columns=['expected value'])
+        df_aler['step'] = df['puntos']
+        df_aler['real_value'] = df_test['valores']
+        df_aler['mae'] = mean_absolute_error(anomaly_forecasted, df_test['valores'].values)
+        df_aler['anomaly_score'] = abs(df_aler['expected value'] - df_aler['real_value']) / df_aler['mae']
+        df_aler_ult = df_aler[:5]
+        df_aler_ult = df_aler_ult[(df_aler_ult.index==df_aler.index.max())|(df_aler_ult.index==((df_aler.index.max())-1))
+                             |(df_aler_ult.index==((df_aler.index.max())-2))|(df_aler_ult.index==((df_aler.index.max())-3))
+                             |(df_aler_ult.index==((df_aler.index.max())-4))]
+        if len(df_aler_ult) == 0:
+          exists_anom_last_5 = 'FALSE'
+        else:
+          exists_anom_last_5 = 'TRUE'
+
+        df_aler = df_aler[(df_aler['anomaly_score']> 2)]
+        max = df_aler['anomaly_score'].max()
+        min = df_aler['anomaly_score'].min()
+
+        df_aler['anomaly_score']= ( df_aler['anomaly_score'] - min ) /(max - min)
+
+        max = df_aler_ult['anomaly_score'].max()
+        min = df_aler_ult['anomaly_score'].min()
+
+        df_aler_ult['anomaly_score']= ( df_aler_ult['anomaly_score'] - min ) /(max - min)
+
+        # Fit model
+        fitted_model = estimator.fit(df['valores'])
+        print("Forecast Model Fitted")
+
+        # Forecast num_forecast steps ahead
+        y_forecasted = fitted_model.forecast(steps=num_forecast)
+
+        df_future= pd.DataFrame(y_forecasted,columns=['value'])
+        df_future['value']=df_future.value.astype("float32")
+        df_future['step']= np.arange( len(lista_datos),len(lista_datos)+num_forecast,1)
+
+
+
+        #engine_output['rmse'] = rmse
+        #engine_output['mse'] = mse
+        engine_output['mae'] = mae
+        engine_output['present_status']=exists_anom_last_5
+        engine_output['present_alerts']=df_aler_ult.fillna(0).to_dict(orient='record')
+        engine_output['past']=df_aler.fillna(0).to_dict(orient='record')
+        engine_output['engine']='TBATS'
+        print ("Only for future")
+
+
+        engine_output['future'] = df_future.to_dict(orient='record')
+        test_values = pd.DataFrame(anomaly_forecasted,index = df_test.index,columns=['expected value'])
+
+        test_values['step'] = test_values.index
+        #print ("debug de Holtwinters")
+        #print (test_values)
+        engine_output['debug'] = test_values.to_dict(orient='record')
+
+        #print ("la prediccion es")
+        #print (df_future)
+
+        return engine_output