PMD-Arima Logging

You can explore logging other models here.

Import necessary packages

from katonic.ml import MyClient
import os
import numpy as np
import pmdarima as pm
from pmdarima.datasets import load_wineind

Create client object using MyClient

myclient = MyClient()
mlflow = myclient.mlflow
client = myclient.client

Implement model training and logging steps

wineind = load_wineind().astype(np.float64)
exp_name = "mlflow-test-PMDArima"
mlflow.set_experiment(exp_name)
exp_details = mlflow.get_experiment_by_name(exp_name)
params = {
    'start_p':1, 
    'start_q':1, 
    'max_p':3, 
    'max_q':3, 
    'm':12,
    'start_P':0, 
    'seasonal':True, 
    'd':1, 
    'D':1, 
    'trace':True,
    'error_action':'ignore', 
    'suppress_warnings':True,
    'stepwise':True
}
with mlflow.start_run(run_name=exp_name):
    stepwise_fit = pm.auto_arima(wineind, **params)  # set to stepwise
    mlflow.log_params(params)
    model_info = mlflow.pmdarima.log_model(stepwise_fit, artifact_path="model")