Feature engineering

import numpy as np
import pandas as pd
from nbdev import show_doc
from window_ops.expanding import expanding_mean

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', 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()

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

import polars as pl

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