Documentation Index
Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
Use this file to discover all available pages before exploring further.
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
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
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 |
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);
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,
)
fcst.ts._uids = fcst.ts.uids
fcst.ts._idxs = None
fcst.ts._static_features = fcst.ts.static_features_
fcst.ts._ga = copy.copy(fcst.ts.ga)
fcst.ts._predict_setup()
for attr in ('head', 'tail'):
new_x = fcst.ts._get_features_for_next_step(None)
original_preds = fcst.models_['model'].predict(new_x)
expected = 1.1 * original_preds
actual = getattr(scaled_preds.groupby('unique_id')['model'], attr)(1).values
np.testing.assert_equal(expected, actual)
fcst.ts._update_y(actual)
