Feature engineering | MLForecast

import numpy as np
import pandas as pd
from nbdev import show_doc

from mlforecast.lag_transforms import ExpandingMean
from mlforecast.utils import generate_daily_series

rng = np.random.RandomState(0)
series = generate_daily_series(100, equal_ends=True)
starts_ends = series.groupby(
    'unique_id', observed=True, as_index=False
)['ds'].agg(['min', 'max'])
prices = []
for r in starts_ends.itertuples():
    dates = pd.date_range(r.min, r.max + 14 * pd.offsets.Day())
    df = pd.DataFrame({'ds': dates, 'price': rng.rand(dates.size)})
    df['unique_id'] = r.Index
    prices.append(df)
prices = pd.concat(prices)
prices['price2'] = prices['price'] * rng.rand(prices.shape[0])
prices.head()

transform_exog(
    df,
    lags=None,
    lag_transforms=None,
    id_col="unique_id",
    time_col="ds",
    num_threads=1,
)

transformed = transform_exog(
    prices,
    lags=[1, 2],
    lag_transforms={1: [ExpandingMean()]}
)
transformed.head()

import polars as pl

prices_pl = pl.from_pandas(prices)
transformed_pl = transform_exog(
    prices_pl,
    lags=[1, 2],
    lag_transforms={1: [ExpandingMean()]},
    num_threads=2,
)
transformed_pl.head()