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Compute transformations on your exogenous features for MLForecast
The MLForecast class allows you to compute lag transformations on your
target, however, sometimes you want to also compute transformations on
your dynamic exogenous features. This guide shows you how to accomplish
that.
Data setup
from mlforecast.utils import generate_series, generate_prices_for_series
series = generate_series(10, equal_ends=True)
prices = generate_prices_for_series(series)
prices.head(2)
| ds | unique_id | price |
|---|
| 0 | 2000-10-05 | 0 | 0.548814 |
| 1 | 2000-10-06 | 0 | 0.715189 |
X_df in MLForecast.predict.
If you want to use not only the price but the lag7 of the price and the
expanding mean of the lag1 for example, you can compute them before
training, merge them with your series and then provide the future values
through X_df. Consider the following example.
from mlforecast.lag_transforms import ExpandingMean
from mlforecast.feature_engineering import transform_exog
transformed_prices = transform_exog(prices, lags=[7], lag_transforms={1: [ExpandingMean()]})
transformed_prices.head(10)
| ds | unique_id | price | price_lag7 | price_expanding_mean_lag1 |
|---|
| 0 | 2000-10-05 | 0 | 0.548814 | NaN | NaN |
| 1 | 2000-10-06 | 0 | 0.715189 | NaN | 0.548814 |
| 2 | 2000-10-07 | 0 | 0.602763 | NaN | 0.632001 |
| 3 | 2000-10-08 | 0 | 0.544883 | NaN | 0.622255 |
| 4 | 2000-10-09 | 0 | 0.423655 | NaN | 0.602912 |
| 5 | 2000-10-10 | 0 | 0.645894 | NaN | 0.567061 |
| 6 | 2000-10-11 | 0 | 0.437587 | NaN | 0.580200 |
| 7 | 2000-10-12 | 0 | 0.891773 | 0.548814 | 0.559827 |
| 8 | 2000-10-13 | 0 | 0.963663 | 0.715189 | 0.601320 |
| 9 | 2000-10-14 | 0 | 0.383442 | 0.602763 | 0.641580 |
series_with_prices = series.merge(transformed_prices, on=['unique_id', 'ds'])
series_with_prices.head(10)
| unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 |
|---|
| 0 | 0 | 2000-10-05 | 0.322947 | 0.548814 | NaN | NaN |
| 1 | 0 | 2000-10-06 | 1.218794 | 0.715189 | NaN | 0.548814 |
| 2 | 0 | 2000-10-07 | 2.445887 | 0.602763 | NaN | 0.632001 |
| 3 | 0 | 2000-10-08 | 3.481831 | 0.544883 | NaN | 0.622255 |
| 4 | 0 | 2000-10-09 | 4.191721 | 0.423655 | NaN | 0.602912 |
| 5 | 0 | 2000-10-10 | 5.395863 | 0.645894 | NaN | 0.567061 |
| 6 | 0 | 2000-10-11 | 6.264447 | 0.437587 | NaN | 0.580200 |
| 7 | 0 | 2000-10-12 | 0.284022 | 0.891773 | 0.548814 | 0.559827 |
| 8 | 0 | 2000-10-13 | 1.462798 | 0.963663 | 0.715189 | 0.601320 |
| 9 | 0 | 2000-10-14 | 2.035518 | 0.383442 | 0.602763 | 0.641580 |
from sklearn.linear_model import LinearRegression
from mlforecast import MLForecast
fcst = MLForecast(
models=[LinearRegression()],
freq='D',
lags=[1],
date_features=['dayofweek'],
)
fcst.preprocess(series_with_prices, static_features=[], dropna=True).head()
| unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 | lag1 | dayofweek |
|---|
| 1 | 0 | 2000-10-06 | 1.218794 | 0.715189 | NaN | 0.548814 | 0.322947 | 4 |
| 2 | 0 | 2000-10-07 | 2.445887 | 0.602763 | NaN | 0.632001 | 1.218794 | 5 |
| 3 | 0 | 2000-10-08 | 3.481831 | 0.544883 | NaN | 0.622255 | 2.445887 | 6 |
| 4 | 0 | 2000-10-09 | 4.191721 | 0.423655 | NaN | 0.602912 | 3.481831 | 0 |
| 5 | 0 | 2000-10-10 | 5.395863 | 0.645894 | NaN | 0.567061 | 4.191721 | 1 |
dropna argument only considers the
null values generated by the lag features based on the target. If you
want to drop all rows containing null values you have to do that in your
original series.
series_with_prices2 = series_with_prices.dropna()
fcst.preprocess(series_with_prices2, dropna=True, static_features=[]).head()
| unique_id | ds | y | price | price_lag7 | price_expanding_mean_lag1 | lag1 | dayofweek |
|---|
| 8 | 0 | 2000-10-13 | 1.462798 | 0.963663 | 0.715189 | 0.601320 | 0.284022 | 4 |
| 9 | 0 | 2000-10-14 | 2.035518 | 0.383442 | 0.602763 | 0.641580 | 1.462798 | 5 |
| 10 | 0 | 2000-10-15 | 3.043565 | 0.791725 | 0.544883 | 0.615766 | 2.035518 | 6 |
| 11 | 0 | 2000-10-16 | 4.010109 | 0.528895 | 0.423655 | 0.631763 | 3.043565 | 0 |
| 12 | 0 | 2000-10-17 | 5.416310 | 0.568045 | 0.645894 | 0.623190 | 4.010109 | 1 |
fcst.fit(series_with_prices2, static_features=[])
MLForecast(models=[LinearRegression], freq=D, lag_features=['lag1'], date_features=['dayofweek'], num_threads=1)
fcst.predict(1, X_df=transformed_prices).head()
| unique_id | ds | LinearRegression |
|---|
| 0 | 0 | 2001-05-15 | 3.803967 |
| 1 | 1 | 2001-05-15 | 3.512489 |
| 2 | 2 | 2001-05-15 | 3.170019 |
| 3 | 3 | 2001-05-15 | 4.307121 |
| 4 | 4 | 2001-05-15 | 3.018758 |
from fastcore.test import test_fail
test_fail(lambda: fcst.predict(8, X_df=transformed_prices), contains='Found missing inputs in X_df')
