def get_prediction():
loaded_model = joblib.load('data/decision_tree/model.pkl')
date_string = request.args.get('date')
date = datetime.strptime(date_string, '%Y-%m-%d')
product = products[request.args.get("item_nbr")]
data = {
"date": date_string,
"item_nbr": request.args.get("item_nbr"),
"family": product['family'],
"class": product['class'],
"perishable": product['perishable'],
"transactions": 1000,
"year": date.year,
"month": date.month,
"day": date.day,
"dayofweek": date.weekday(),
"days_til_end_of_data": 0,
"dayoff": date.weekday() >= 5
}
df = pd.DataFrame(data=data, index=['row1'])
df = decision_tree.encode_categorical_columns(df)
pred = loaded_model.predict(df)t
if FLUENTD_HOST:
logger = sender.FluentSender(TENANT, host=FLUENTD_HOST, port=int(FLUENTD_PORT))
log_payload = {'prediction': pred[0], **data}
print('logging {}'.format(log_payload))
if not logger.emit('prediction', log_payload):
print(logger.last_error)
logger.clear_last_error()
return "%d" % pred[0]
def get_prediction():
loaded_model = joblib.load('data/decision_tree/model9.pkl')
date_string = request.args.get('date')
date = datetime.strptime(date_string, '%Y-%m-%d')
data = {
"date": date_string,
"item_nbr": request.args.get("item_nbr"),
"family": request.args.get("family"),
"class": request.args.get("class"),
"perishable": request.args.get("perishable"),
"transactions": request.args.get("transactions"),
"year": date.year,
"month": date.month,
"day": date.day,
"dayofweek": date.weekday(),
"days_til_end_of_data": 0,
"dayoff": request.args.get("day_off")
}
df = pd.DataFrame(data=data, index=['row1'])
df = decision_tree.encode_categorical_columns(df)
pred = loaded_model.predict(df)
return "%d" % pred[0]
def get_prediction():
#loaded_model = joblib.load('data/decision_tree/model.pkl')
# mlflow.set_tracking_uri("http://35.247.183.209:5000")
# mlflow.create_experiment("sales_prediction")
tracking_uri = os.getenv('TRACKING_URI', "http://localhost:5000")
experiment_name = os.getenv('EXPERIMENT_NAME', "global_experiments")
mlflow.set_tracking_uri(tracking_uri)
mlflow.set_experiment(experiment_name)
artifact = mlflow.get_artifact_uri
print("Experiment URI " + artifact)
loaded_model = mlflow.sklearn.load_model(
"model", "937b7254b4834a72b085bc55cdfbf460")
date_string = request.args.get('date')
date = datetime.strptime(date_string, '%Y-%m-%d')
data = {
"date": date_string,
"item_nbr": request.args.get("item_nbr"),
"family": request.args.get("family"),
"class": request.args.get("class"),
"perishable": request.args.get("perishable"),
"transactions": request.args.get("transactions"),
"year": date.year,
"month": date.month,
"day": date.day,
"dayofweek": date.weekday(),
"days_til_end_of_data": 0,
"dayoff": request.args.get("day_off")
}
df = pd.DataFrame(data=data, index=['row1'])
df = decision_tree.encode_categorical_columns(df)
pred = loaded_model.predict(df)
return "%d (From the model version : %s)" % (pred[0], artifact)