Prophet#
AimCallback for Prophet is designed to enhance your experiment logging and monitoring. It thoroughly records essential information, including hyperparameters, training, validation, and test time metrics like loss and accuracy. Moreover, it offers comprehensive system usage tracking, keeping an eye on CPU and GPU memory utilization.
Aim provides an AimLogger object designed to track Prophet hyperparameters and metrics. It takes three steps to integrate Aim into your Prophet script.
Step 1: Explicitly import the AimLogger.
from aimstack.experiment_tracker.prophet import Logger as AimLogger
Step 2: After initializing a Prophet model, instantiate the AimLogger with your Prophet model.
model = Prophet()
logger = AimLogger(prophet_model=model, experiment="test_experiment")
Step 3 (optional): pass any metrics you want after fitting the Prophet model.
metrics = {"backtest_mse": backtest_mse, "backtest_mape": backtest_mape}
logger.track_metrics(metrics)
Note that the metrics are assumed to be validation metrics by default. Alternatively, you can pass a context argument to the track_metrics method.
metrics = {"train_mse": backtest_mse, "train_mape": backtest_mape}
logger.track_metrics(metrics, context={"subset": "train"})