def rossmann_predict():
test_json = request.get_json()
if test_json:
if isinstance(test_json, dict):
test_row = pd.DataFrame(test_json, index=[0])
else:
test_row = pd.DataFrame(test_json, columns=test_json[0].keys())
pipeline = Rossmann()
# data cleaning
df1 = pipeline.data_cleaning(test_row)
# feature engineering
df2 = pipeline.feature_engineering(df1)
# data preparation
df3 = pipeline.data_preparation(df2)
# prediction
df_response = pipeline.get_prediction(model, test_row, df3)
return df_response
else:
return Response('{}', status=200, mimetype='application/json')
def rossmann_predict(
): #function that is executed when an endpoint receives a POST request. This function works on the data received.
test_json = request.get_json() # retrieve the json data
if test_json: # test whether the data is there or not
if isinstance(
test_json,
dict): # if data is a dict, then we have only one line of data
test_raw = pd.DataFrame(test_json, index=[0])
else:
test_raw = pd.DataFrame(
test_json, columns=test_json[0].keys()
) # if data is not a dict, then it has multiple data. We need to name the columns.
# Instantiate Rossmann Class ("copy"/call Rossmann class)
pipeline = Rossmann()
# run Data Cleaning on raw data
df1 = pipeline.data_cleaning(test_raw)
# run feature engineering on df1
df2 = pipeline.feature_engineering(df1)
# run data preprocessing on df2
df3 = pipeline.data_preparation(df2)
# prediction
df_response = pipeline.get_prediction(
model, test_raw, df3
) #generate predictions with xgboost model, the data that the user sent, and the data to generate predictions on.
return df_response
else:
return Response(
'{}', status=200, mimetype='application/json'
) # if there's no data, return a response answer 200 (request was correct but execution failed)
def rossmann_predict():
# input data for prediction
print('===> getting the data from request')
test_json = request.get_json()
if test_json:
# convert json to DataFrame
if isinstance(test_json, dict): # an unique example of test
test_raw = pd.DataFrame(test_json, index=[0])
else: # multiple examples
test_raw = pd.DataFrame(test_json,
columns=list(test_json[0].keys()))
# class to prediction
pipeline = Rossmann()
# pre-process the test data to prediction
print('===> pre-process test data to prediction')
test_transformed = pipeline.transform(test_raw.copy())
# feature engineering
print('===> feature engineering')
data = pipeline.feature_engineering(test_transformed)
# make a prediction
print('===> prediction')
response_json = pipeline.get_prediction(model=model_rossmann,
test_original=test_raw,
test_data=data)
return response_json
else:
return Response("{}", status=201, mimetype='application/json')
def rossmann_predict():
test_json = request.get_json()
if test_json: # there is data
if isinstance(test_json, dict): # unique example
test_raw = pd.DataFrame(test_json, index=[0])
else: # multiple example
test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())
# Instantiate Rossmann class
pipeline = Rossmann()
# data cleaning
df1 = pipeline.data_cleaning(test_raw)
# feature engineering
df2 = pipeline.feature_engineering(df1)
# data preparation
df3 = pipeline.data_preparation(df2)
# prediction
df_response = pipeline.get_prediction(model, test_raw, df3)
return df_response
else:
return Response("{}", status=200, mimetype="application/json")
def rossmann_predict():
try:
test_json = request.get_json()
if test_json: #there is data
if isinstance(test_json, dict): #unique Example
test_raw = pd.DataFrame(test_json, index[0])
else: # multiple examples
test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())
# Instantiate Rossmann class
pipeline = Rossmann()
#data cleaning
df1 = pipeline.data_cleaning(test_raw)
#feature engineering
df2 = pipeline.feature_engineering(df1)
#data preparation
df3 = pipeline.data_preparation(df2)
#prediction
df_response = pipeline.get_prediction(model, test_raw, df3)
return df_response
else:
return Response('{}', status=404, mimetype='application/json')
except Exception as e:
print("/rossamann/predict/ error: ", e)
def rossmann_predcit():
# gets json that comes from API
test_json = request.get_json()
# checks if json exists
if test_json:
# unique example
if isinstance(test_json, dict):
test_raw = pd.DataFrame(test_json, index=[0])
#multiple examples
else:
test_raw = pd.DataFrame(test_json, columns=test_json[0].keys())
# instantiates Rossmann class
pipeline = Rossmann()
# data cleaning
df1 = pipeline.data_cleaning(test_raw)
# feature engineering
df2 = pipeline.feature_engineering(df1)
# data preparation
df3 = pipeline.data_preparation(df2)
# prediction
df_response = pipeline.get_prediction(model, test_raw, df3)
return df_response
else:
return Response('{}', status=200, mimetype='application/json')
def rossmanPredict():
test_JSON = request.get_json()
if test_JSON: #there is data
if isinstance(test_JSON, dict):
teste_raw = pd.DataFrame(test_JSON, index=[0]) #unique example
else:
teste_raw = pd.DataFrame(
test_JSON, columns=test_JSON[0].keys()) #multiple examples
# Instantiate
pipeline = Rossmann()
# Data Cleaning
df1 = pipeline.data_cleaning(teste_raw)
# Feature Engineering
df2 = pipeline.feature_engineering(df1)
# Data Preparation
df3 = pipeline.data_preparation(df2)
# Prediction
df_response = pipeline.get_prediction(model, teste_raw, df3)
return df_response
else:
return Response('{}', status=200, mimetype='application/json')
def rossmann_predict():
json_request = request.get_json()
if json_request: # there is data
if isinstance( json_request, dict ): # unique example
df_raw = pd.DataFrame( json_request, index=[0] )
else: # multiple example
df_raw = pd.DataFrame( json_request, columns=json_request[0].keys() )
pipeline = Rossmann()
df1 = pipeline.data_cleaning( df_raw )
df2 = pipeline.feature_engineering( df1 )
df3 = pipeline.data_preparation( df2 )
df_response = pipeline.get_prediction( model, df_raw, df3 )
return df_response
else:
return Reponse( '{}', status=200, mimetype='application/json' )
