Predict callbacks
Get access to the input features and predictions in each forecasting horizon
If you want to do something to the input before predicting or something to the output before it gets used to update the target (and thus the next features that rely on lags), you can pass a function to run at any of these times.
Here are a couple of examples:
import copy
import lightgbm as lgb
import numpy as np
from IPython.display import display
from mlforecast import MLForecast
from mlforecast.utils import generate_daily_series
series = generate_daily_series(1)
Before predicting
Inspecting the input
We can define a function that displays our input dataframe before predicting.
def inspect_input(new_x):
"""Displays the model inputs to inspect them"""
display(new_x)
return new_x
And now we can pass this function to the before_predict_callback
argument of
MLForecast.predict
.
fcst = MLForecast(lgb.LGBMRegressor(verbosity=-1), freq='D', lags=[1, 2])
fcst.fit(series, static_features=['unique_id'])
preds = fcst.predict(2, before_predict_callback=inspect_input)
preds
unique_id | lag1 | lag2 | |
---|---|---|---|
0 | id_0 | 4.15593 | 3.000028 |
unique_id | lag1 | lag2 | |
---|---|---|---|
0 | id_0 | 5.250205 | 4.15593 |
unique_id | ds | LGBMRegressor | |
---|---|---|---|
0 | id_0 | 2000-08-10 | 5.250205 |
1 | id_0 | 2000-08-11 | 6.241739 |
Saving the input features
Saving the features that are sent as input to the model in each
timestamp can be helpful, for example to estimate SHAP values. This can
be easily achieved with the
SaveFeatures
callback.
from mlforecast.callbacks import SaveFeatures
fcst = MLForecast(lgb.LGBMRegressor(verbosity=-1), freq='D', lags=[1])
fcst.fit(series, static_features=['unique_id'])
save_features_cbk = SaveFeatures()
fcst.predict(2, before_predict_callback=save_features_cbk);
Once we’ve called predict we can just retrieve the features.
save_features_cbk.get_features()
unique_id | lag1 | |
---|---|---|
0 | id_0 | 4.155930 |
1 | id_0 | 5.281643 |
After predicting
When predicting with the recursive strategy (the default) the
predictions for each timestamp are used to update the target and
recompute the features. If you want to do something to these predictions
before that happens you can use the after_predict_callback
argument of
MLForecast.predict
.
Increasing predictions values
Suppose we know that our model always underestimates and we want to prevent that from happening by making our predictions 10% higher. We can achieve that with the following:
def increase_predictions(predictions):
"""Increases all predictions by 10%"""
return 1.1 * predictions
fcst = MLForecast(
{'model': lgb.LGBMRegressor(verbosity=-1)},
freq='D',
date_features=['dayofweek'],
)
fcst.fit(series)
original_preds = fcst.predict(2)
scaled_preds = fcst.predict(2, after_predict_callback=increase_predictions)
np.testing.assert_array_less(
original_preds['model'].values,
scaled_preds['model'].values,
)