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Version: 4.5

Prophet Logging

Import necessary packages​

from katonic.ml import MyClient
import numpy as np
from prophet import Prophet
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
import pandas as pd

Create client object using MyClient​

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

Implement model training and logging steps​

exp_name = "mlflow-test-prophet"
mlflow.set_experiment(exp_name)
exp_details = mlflow.get_experiment_by_name(exp_name)
with mlflow.start_run(run_name=exp_name):
    data = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv')
    
    model = Prophet()
    model.fit(data)
    
    future = model.make_future_dataframe(periods=365)
    forecast = model.predict(future)
    
    model_info = mlflow.prophet.log_model(pr_model=model, artifact_path="model")