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.
Access and interpret the models after fitting
Data setup
from mlforecast.utils import generate_daily_series
series = generate_daily_series(10)
series.head()
| unique_id | ds | y |
|---|
| 0 | id_0 | 2000-01-01 | 0.322947 |
| 1 | id_0 | 2000-01-02 | 1.218794 |
| 2 | id_0 | 2000-01-03 | 2.445887 |
| 3 | id_0 | 2000-01-04 | 3.481831 |
| 4 | id_0 | 2000-01-05 | 4.191721 |
Training
Suppose that you want to train a linear regression model using the day
of the week and lag1 as features.
from sklearn.linear_model import LinearRegression
from mlforecast import MLForecast
fcst = MLForecast(
freq='D',
models={'lr': LinearRegression()},
lags=[1],
date_features=['dayofweek'],
)
MLForecast(models=[lr], freq=<Day>, lag_features=['lag1'], date_features=['dayofweek'], num_threads=1)
MLForecast.fit does is save the required data for the predict
step and also train the models (in this case the linear regression). The
trained models are available in the MLForecast.models_ attribute,
which is a dictionary where the keys are the model names and the values
are the model themselves.
{'lr': LinearRegression()}
Inspect parameters
We can access the linear regression coefficients in the following way:
fcst.models_['lr'].intercept_, fcst.models_['lr'].coef_
(3.2476337167384415, array([ 0.19896416, -0.21441331]))
SHAP
Training set
If you need to generate the training data you can use
MLForecast.preprocess.
prep = fcst.preprocess(series)
prep.head()
| unique_id | ds | y | lag1 | dayofweek |
|---|
| 1 | id_0 | 2000-01-02 | 1.218794 | 0.322947 | 6 |
| 2 | id_0 | 2000-01-03 | 2.445887 | 1.218794 | 0 |
| 3 | id_0 | 2000-01-04 | 3.481831 | 2.445887 | 1 |
| 4 | id_0 | 2000-01-05 | 4.191721 | 3.481831 | 2 |
| 5 | id_0 | 2000-01-06 | 5.395863 | 4.191721 | 3 |
X = prep.drop(columns=['unique_id', 'ds', 'y'])
X.head()
| lag1 | dayofweek |
|---|
| 1 | 0.322947 | 6 |
| 2 | 1.218794 | 0 |
| 3 | 2.445887 | 1 |
| 4 | 3.481831 | 2 |
| 5 | 4.191721 | 3 |
X100 = shap.utils.sample(X, 100)
explainer = shap.Explainer(fcst.models_['lr'].predict, X100)
shap_values = explainer(X)
shap.plots.beeswarm(shap_values)
Predictions
Sometimes you want to determine why the model gave a specific
prediction. In order to do this you need the input features, which
aren’t returned by default, but you can retrieve them using a callback.
from mlforecast.callbacks import SaveFeatures
save_feats = SaveFeatures()
preds = fcst.predict(1, before_predict_callback=save_feats)
preds.head()
| unique_id | ds | lr |
|---|
| 0 | id_0 | 2000-08-10 | 3.468643 |
| 1 | id_1 | 2000-04-07 | 3.016877 |
| 2 | id_2 | 2000-06-16 | 2.815249 |
| 3 | id_3 | 2000-08-30 | 4.048894 |
| 4 | id_4 | 2001-01-08 | 3.524532 |
SaveFeatures.get_features
features = save_feats.get_features()
features.head()
| lag1 | dayofweek |
|---|
| 0 | 4.343744 | 3 |
| 1 | 3.150799 | 4 |
| 2 | 2.137412 | 4 |
| 3 | 6.182456 | 2 |
| 4 | 1.391698 | 0 |
shap_values_predictions = explainer(features)
'id_4'.
round(preds.loc[4, 'lr'], 3)
shap.plots.waterfall(shap_values_predictions[4])
