Commit 9feebfba authored by Jean-Didier's avatar Jean-Didier
Browse files

min max scaler improvement

parent f587389c
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......@@ -15,7 +15,7 @@ orchestrator_queue_name = os.environ.get("ORCHESTRATOR_QUEUE_NAME","/topic/orche
#/////////////////////////////////////////////////////////////////////////////////
activemq_username = os.environ.get("ACTIVEMQ_USER","morphemic")
activemq_password = os.environ.get("ACTIVEMQ_PASSWORD","morphemic")
activemq_hostname = os.environ.get("ACTIVEMQ_HOST","34.204.67.207")
activemq_hostname = os.environ.get("ACTIVEMQ_HOST","34.205.156.81")
activemq_port = int(os.environ.get("ACTIVEMQ_PORT","61610"))
#/////////////////////////////////////////////////////////////////////////////////
datasets_path = os.environ.get("DATASET_PATH","./datasets")
......@@ -25,7 +25,7 @@ forecasting_method_name = os.environ.get("FORECASTING_METHOD_NAME","cnn")
#/////////////////////////////////////////////////////////////////////////////////
steps = int(os.environ.get("BACKWARD_STEPS","8"))
#/////////////////////////////////////////////////////////////////////////////////
influxdb_hostname = os.environ.get("INFLUXDB_HOSTNAME","34.204.67.207") #persistent_storage_hostname
influxdb_hostname = os.environ.get("INFLUXDB_HOSTNAME","34.205.156.81") #persistent_storage_hostname
influxdb_port = int(os.environ.get("INFLUXDB_PORT","8086"))
influxdb_username = os.environ.get("INFLUXDB_USERNAME","morphemic")
influxdb_password = os.environ.get("INFLUXDB_PASSWORD","password")
......@@ -34,10 +34,10 @@ influxdb_org = os.environ.get("INFLUXDB_ORG","morphemic")
start_forecasting_queue = os.environ.get("START_FORECASTING","/topic/start_forecasting.cnn")
metric_to_predict_queue = os.environ.get("METRIC_TO_PREDICT","/topic/metrics_to_predict")
#//////////////////////////////////////////////////////////////////////////////////
_time_column_name = os.environ.get("TIME_COLUMN","time")
_column_to_remove_str = os.environ.get("COLUMNS_TO_REMOVE","ems_time,level,name,application")
_time_column_name = os.environ.get("TIME_COLUMN","ems_time")
_column_to_remove_str = os.environ.get("COLUMNS_TO_REMOVE","time,level")
_column_to_remove = _column_to_remove_str.split(",")
retrain_interval = int(os.environ.get("RETRAIN_INTERVAL","5")) #in minute
retrain_interval = int(os.environ.get("RETRAIN_INTERVAL","2")) #in minute
retrain_interval *= 60
_new_epoch = False
......
time,
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from pre_processing.preprocessing import load_data, percent_missing, datetime_conversion
from pre_processing.preprocessing import important_data, resample, resample_median, missing_data_handling, resample_quantile
from pre_processing.preprocessing import important_data, resample, missing_values_imputer, resample_median, missing_data_handling, resample_quantile
from pre_processing.Data_transformation import reshape_data_single_lag, series_to_supervised, \
prediction_and_score_for_CNN, missing_values_imputer
from models.ML_models import LSTM_model, CNN_model, CNN_model_multi_steps
......@@ -77,29 +77,59 @@ class Predictor():
if col in important_feature:
important_feature.remove(col)
data.replace('None', np.nan, inplace=True)
data = missing_values_imputer(data)
data = important_data(data, important_feature)
#print(important_feature)
data, scaler = Min_max_scal(data)
#print(data)
data = data[~np.isnan(data).any(axis=1)]
#data = data.values
new_sample = data[-self.steps:]
#new_sample = np.array(self.feature_dict)
new_sample = new_sample.reshape((1, self.steps, len(important_feature)))
#new_sample = list()
#new_sample.append(data)
#new_sample = np.array(new_sample
predictor = keras.models.load_model(path)
#[lower_bound, y_predict, upper_bound] = prediction_interval(predictor, x_train, y_train, new_sample, alpha=0.05)
y_predict = predictor.predict(new_sample, verbose=0)
y_predict = Min_max_scal_inverse(scaler, y_predict)
data = pd.DataFrame()
for y in y_predict[0].astype('float'):
row = {}
for col in important_feature:
if col == self.target:
continue
row[col] = 0
row[self.target] = y
data = data.append(row, ignore_index=True)
df_y_predict = Min_max_scal_inverse(scaler, data)
returned_data = []
for y_predict in df_y_predict:
y_predict = y_predict.astype('float')
v = y_predict[-1]
returned_data.append([v, 0.8, [v-3,v+3]])
return returned_data
"""
y_predict = y_predict[0].astype('float')
data = pd.DataFrame()
for y in y_predict:
row = {}
row[self.target] = y
for col in important_feature:
row[col] = 0.1
data.append(row, ignore_index=True)
returned_data = []
for v in y_predict:
interval = [v-3,v+3] #to change
prob = 0.8 #to change
returned_data.append([v, prob, interval])
return returned_data
"""
#y_predict = np.repeat(y_predict, len(important_features)).reshape((-1, len(important_features)))
#return Min_max_scal_inverse(scaler, y_predict)[-1][-1] #the target is in the last position
......@@ -327,11 +357,12 @@ class Train():
###########
_start = time.time()
data = data.round(decimals=2)
data.replace('None', np.nan, inplace=True)
data.fillna(method='ffill', inplace=True)
data = data.dropna()
#data = data[~np.isnan(data).any(axis=1)]
#data = missing_data_handling(data, drop_all_nan=True)
data = missing_values_imputer(data)
#data = missing_data_handling(data, drop_all_nan=True, fill_w_forward=True, rolling_median=True)
print(data)
data = datetime_conversion(data, self.time_column_name)
data = important_data(data, self.features)
sampling_rate = '{0}S'.format(self.prediction_horizon)
......@@ -341,7 +372,8 @@ class Train():
try:
data, scaler = Min_max_scal(data)
except Exception as e:
return {"status": False, "message": "Cannot scale data", "data": None}
return {"status": False, "message": "Cannot scale data => {0}".format(e), "data": None}
data = data[~np.isnan(data).any(axis=1)]
#X_train, y_train, X_test,y_test = split_sequences(data, n_steps=steps)
#X_train, y_train, X_test,y_test = split_sequences_univariate(data, n_steps=self.number_of_foreward_forecating)
X_train, y_train, X_test,y_test = split_sequences_multi_steps(data, n_steps_in=self.steps, n_steps_out=self.number_of_foreward_forecating)
......
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