Feature engineering | MLForecast

Compute transformations on exogenous regressors

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

Setup

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()

	ds	price	unique_id	price2
0	2000-10-05	0.548814	0	0.345011
1	2000-10-06	0.715189	0	0.445598
2	2000-10-07	0.602763	0	0.165147
3	2000-10-08	0.544883	0	0.041373
4	2000-10-09	0.423655	0	0.391577

---

transform_exog

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

Compute lag features for dynamic exogenous regressors.

Parameters:

Name	Type	Description	Default
df	pandas or polars DataFrame	Dataframe with ids, times and values for the exogenous regressors.	required
lags	list of int	Lags of the target to use as features. Defaults to None.	None
lag_transforms	dict of int to list of functions	Mapping of target lags to their transformations. Defaults to None.	None
id_col	str	Column that identifies each serie. Defaults to ‘unique_id’.	‘unique_id’
time_col	str	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	‘ds’
num_threads	int	Number of threads to use when computing the features. Use -1 to use all available CPU cores. Defaults to 1.	1

Returns:

Type	Description
pandas or polars DataFrame	Original DataFrame with the computed features

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

	ds	price	unique_id	price2	price_lag1	price_lag2	price_expanding_mean_lag1	price2_lag1	price2_lag2	price2_expanding_mean_lag1
0	2000-10-05	0.548814	0	0.345011	NaN	NaN	NaN	NaN	NaN	NaN
1	2000-10-06	0.715189	0	0.445598	0.548814	NaN	0.548814	0.345011	NaN	0.345011
2	2000-10-07	0.602763	0	0.165147	0.715189	0.548814	0.632001	0.445598	0.345011	0.395304
3	2000-10-08	0.544883	0	0.041373	0.602763	0.715189	0.622255	0.165147	0.445598	0.318585
4	2000-10-09	0.423655	0	0.391577	0.544883	0.602763	0.602912	0.041373	0.165147	0.249282

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()

ds	price	unique_id	price2	price_lag1	price_lag2	price_expanding_mean_lag1	price2_lag1	price2_lag2	price2_expanding_mean_lag1
datetime[ns]	f64	i64	f64	f64	f64	f64	f64	f64	f64
2000-10-05 00:00:00	0.548814	0	0.345011	NaN	NaN	NaN	NaN	NaN	NaN
2000-10-06 00:00:00	0.715189	0	0.445598	0.548814	NaN	0.548814	0.345011	NaN	0.345011
2000-10-07 00:00:00	0.602763	0	0.165147	0.715189	0.548814	0.632001	0.445598	0.345011	0.395304
2000-10-08 00:00:00	0.544883	0	0.041373	0.602763	0.715189	0.622255	0.165147	0.445598	0.318585
2000-10-09 00:00:00	0.423655	0	0.391577	0.544883	0.602763	0.602912	0.041373	0.165147	0.249282