def predict():
json_input = request.json
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
ip_address = request.headers.get("X-Forwarded-For", request.remote_addr)
logger.info(
f'{current_datatime} request from {ip_address}: {request.json}')
start_prediction = time()
ID = json_input['ID']
hf = process_input(json_input)
prediction_Poisson = model_glm_poisson.predict(hf)
value_Poisson = prediction_Poisson.as_data_frame()['predict'][0]
prediction_Gamma = model_glm_gamma.predict(hf)
value_Gamma = prediction_Gamma.as_data_frame()['predict'][0]
value_BurningCost = value_Poisson * value_Gamma
result = {
'ID': ID,
'value_Poisson': value_Poisson,
'value_Gamma': value_Gamma,
'value_BurningCost': value_BurningCost
}
end_prediction = time()
duration = round(end_prediction - start_prediction, 6)
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
logger.info(
f'{current_datatime} predicted for {duration} msec: {result}\n')
return jsonify(result)
def predict():
json_input = request.get_json(force=True)
# Request logging
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
ip_address = request.headers.get("X-Forwarded-For", request.remote_addr)
logger.info(
f'{current_datatime} request from {ip_address}: {request.json}')
start_prediction = time()
id = json_input['ID']
user_data = process_input(json_input)
print(user_data)
prediction_ClaimInd = xgb_ClaimInd_model.predict(user_data)
ClaimInd = int(prediction_ClaimInd[0])
print('prediction:', ClaimInd)
result = {'ID': id, 'ClaimInd': ClaimInd}
# Response logging
end_prediction = time()
duration = round(end_prediction - start_prediction, 6)
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
logger.info(
f'{current_datatime} predicted for {duration} msec: {result}\n')
return jsonify(result)
def predict():
json_input = request.json
# Request logging
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
ip_address = request.headers.get("X-Forwarded-For", request.remote_addr)
logger.info(
f'{current_datatime} request from {ip_address}: {request.json}')
start_prediction = time()
id = json_input['ID']
dmatrix = process_input(json_input)
value_xgb = xgb_model.predict(dmatrix)[0]
result = {'ID': id, 'ClaimsCount': int(value_xgb)}
# Response logging
end_prediction = time()
duration = round(end_prediction - start_prediction, 6)
current_datatime = strftime('[%Y-%b-%d %H:%M:%S]')
logger.info(
f'{current_datatime} predicted for {duration} msec: {result}\n')
return jsonify(result)
def predict():
json_input = request.json
# Request logging
current_datetime = strftime('[%Y-%b-%d %H:%M:%S]')
ip_address = request.headers.get("X-Forwarded-For", request.remote_addr)
logger.info(f'{current_datetime} request from {ip_address}: {request.json}')
start_prediction = time()
# id запроса
id = json_input['ID']
# подготовка входных данных
hf = process_input(json_input)
# предсказание модели
prediction = model.predict(hf).as_data_frame()
# вероятности классов
prob_0 = prediction['p0'][0]
prob_1 = prediction['p1'][0]
THRESHOLD = 0.58 # порог вероятности для класса 1
claim_ind = 1 if prob_1 > THRESHOLD else 0
claim_ind_front = 'Страховой случай НАСТУПИТ' if bool(claim_ind) else 'Страховой случай НЕ наступит'
result = {
'ID': id,
'prob_0': prob_0,
'prob_1': prob_1,
'threshold_prob': THRESHOLD,
'claim_ind': claim_ind,
'claim_ind_front': claim_ind_front
}
# Response logging
end_prediction = time()
duration = round(end_prediction - start_prediction, 6)
current_datetime = strftime('[%Y-%b-%d %H:%M:%S]')
logger.info(f'{current_datetime} predicted for {duration} msec: {result}\n')
return jsonify(result)
def predict():
json_input = request.json
id = json_input['ID']
hf = process_input(json_input)
prediction_Poisson = model_glm_poisson.predict(hf)
value_Poisson = prediction_Poisson.as_data_frame()['predict'][0]
prediction_Gamma = model_glm_gamma.predict(hf)
value_Gamma = prediction_Gamma.as_data_frame()['predict'][0]
value_BurningCost = value_Poisson * value_Gamma
result = {
'ID': id,
'value_Poisson': value_Poisson,
'value_Gamma': value_Gamma,
'value_BurningCost': value_BurningCost
}
return jsonify(result)
def predict():
json_input = request.json
# Request logging
current_datetime = strftime('[%Y-%b-%d %H:%M:%S]')
ip_address = request.headers.get("X-Forwarded-For", request.remote_addr)
logger.info(
f'{current_datetime} request from {ip_address}: {request.json}')
start_prediction = time()
_id = json_input['ID']
df = process_input(json_input)
prediction_freq = xgb_freq.predict(xgb.DMatrix(df.values)) - 1
value_freq = prediction_freq[0]
prediction_avgclaim = xgb_avgclaim.predict(xgb.DMatrix(df.values))
value_avgclaim = prediction_avgclaim[0]
value_burningcost = value_freq * value_avgclaim
result = {
'ID': str(_id),
'value_freq': str(round(value_freq, 2)),
'value_avgclaim': str(round(value_avgclaim, 2)),
'value_burningcost': str(round(value_burningcost, 2))
}
# Response logging
end_prediction = time()
duration = round(end_prediction - start_prediction, 6)
current_datetime = strftime('[%Y-%b-%d %H:%M:%S]')
logger.info(
f'{current_datetime} predicted for {duration} msec: {result}\n')
return jsonify(result)