Installing dependencies
To install Neuralforecast refer to
Installation.
To install mlflow: pip install mlflow
Imports
import logging
import warnings
import matplotlib.pyplot as plt
import mlflow
import mlflow.data
import numpy as np
import pandas as pd
from mlflow.client import MlflowClient
from mlflow.data.pandas_dataset import PandasDataset
from utilsforecast.plotting import plot_series
from neuralforecast.core import NeuralForecast
from neuralforecast.models import NBEATSx
from neuralforecast.utils import AirPassengersDF
from neuralforecast.losses.pytorch import MAE
logging.getLogger("mlflow").setLevel(logging.ERROR)
logging.getLogger("pytorch_lightning").setLevel(logging.ERROR)
warnings.filterwarnings("ignore")
Splitting the data
# Split data and declare panel dataset
Y_df = AirPassengersDF
Y_train_df = Y_df[Y_df.ds<='1959-12-31'] # 132 train
Y_test_df = Y_df[Y_df.ds>'1959-12-31'] # 12 test
Y_df.tail()
| unique_id | ds | y |
---|
139 | 1.0 | 1960-08-31 | 606.0 |
140 | 1.0 | 1960-09-30 | 508.0 |
141 | 1.0 | 1960-10-31 | 461.0 |
142 | 1.0 | 1960-11-30 | 390.0 |
143 | 1.0 | 1960-12-31 | 432.0 |
MLflow UI
Run the following command from the terminal to start the UI:
mlflow ui
. You can then go to the printed URL to visualize the
experiments.
Model training
mlflow.pytorch.autolog(checkpoint=False)
with mlflow.start_run() as run:
# Log the dataset to the MLflow Run. Specify the "training" context to indicate that the
# dataset is used for model training
dataset: PandasDataset = mlflow.data.from_pandas(Y_df, source="AirPassengersDF")
mlflow.log_input(dataset, context="training")
# Define and log parameters
horizon = len(Y_test_df)
model_params = dict(
input_size=1 * horizon,
h=horizon,
max_steps=300,
loss=MAE(),
valid_loss=MAE(),
activation='ReLU',
scaler_type='robust',
random_seed=42,
enable_progress_bar=False,
)
mlflow.log_params(model_params)
# Fit NBEATSx model
models = [NBEATSx(**model_params)]
nf = NeuralForecast(models=models, freq='M')
train = nf.fit(df=Y_train_df, val_size=horizon)
# Save conda environment used to run the model
mlflow.pytorch.get_default_conda_env()
# Save pip requirements
mlflow.pytorch.get_default_pip_requirements()
mlflow.pytorch.autolog(disable=True)
# Save the neural forecast model
nf.save(path='./checkpoints/test_run_1/',
model_index=None,
overwrite=True,
save_dataset=True)
Forecasting the future
Y_hat_df = nf.predict(futr_df=Y_test_df)
plot_series(Y_train_df, Y_hat_df, palette='tab20b')