Feature engineering

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

---

source

transform_exog

 transform_exog (df:~DFType, lags:Optional[Iterable[int]]=None, lag_transf
                 orms:Optional[Dict[int,List[Union[Callable,Tuple[Callable
                 ,Any]]]]]=None, id_col:str='unique_id',
                 time_col:str='ds', num_threads:int=1)

Compute lag features for dynamic exogenous regressors.

	Type	Default	Details
df	DFType		Dataframe with ids, times and values for the exogenous regressors.
lags	Optional	None	Lags of the target to use as features.
lag_transforms	Optional	None	Mapping of target lags to their transformations.
id_col	str	unique_id	Column that identifies each serie.
time_col	str	ds	Column that identifies each timestep, its values can be timestamps or integers.
num_threads	int	1	Number of threads to use when computing the features.
Returns	DFType		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