Documentation Index
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Leverage scikit-learn’s composability to define pipelines as models
mlforecast takes scikit-learn estimators as models, which means you can
provide scikit-learn’s
pipelines
as models in order to further apply transformations to the data before
passing it to the model.
Data setup
from mlforecast.utils import generate_daily_series
series = generate_daily_series(5)
series.head()
| unique_id | ds | y |
|---|
| 0 | id_0 | 2000-01-01 | 0.428973 |
| 1 | id_0 | 2000-01-02 | 1.423626 |
| 2 | id_0 | 2000-01-03 | 2.311782 |
| 3 | id_0 | 2000-01-04 | 3.192191 |
| 4 | id_0 | 2000-01-05 | 4.148767 |
Pipelines definition
Suppose that you want to use a linear regression model with the lag1 and
the day of the week as features. mlforecast returns the day of the week
as a single column, however, that’s not the optimal format for a linear
regression model, which benefits more from having indicator columns for
each day of the week (removing one to avoid colinearity). We can achieve
this by using scikit-learn’s
OneHotEncoder
and then fitting our linear regression model, which we can do in the
following way:
from mlforecast import MLForecast
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import OneHotEncoder
fcst = MLForecast(
models=[],
freq='D',
lags=[1],
date_features=['dayofweek']
)
X, y = fcst.preprocess(series, return_X_y=True)
X.head()
| lag1 | dayofweek |
|---|
| 1 | 0.428973 | 6 |
| 2 | 1.423626 | 0 |
| 3 | 2.311782 | 1 |
| 4 | 3.192191 | 2 |
| 5 | 4.148767 | 3 |
dayofweek column and perform one hot encoding, leaving the lag1
column untouched. We can achieve that with the following:
ohe = ColumnTransformer(
transformers=[
('encoder', OneHotEncoder(drop='first'), ['dayofweek'])
],
remainder='passthrough',
)
X_transformed = ohe.fit_transform(X)
X_transformed.shape
ohe.get_feature_names_out()
array(['encoder__dayofweek_1', 'encoder__dayofweek_2',
'encoder__dayofweek_3', 'encoder__dayofweek_4',
'encoder__dayofweek_5', 'encoder__dayofweek_6', 'remainder__lag1'],
dtype=object)
Training
We can now build a pipeline that does this and then passes it to our
linear regression model.
model = make_pipeline(ohe, LinearRegression())
fcst = MLForecast(
models={'ohe_lr': model},
freq='D',
lags=[1],
date_features=['dayofweek']
)
fcst.fit(series)
MLForecast(models=[ohe_lr], freq=<Day>, lag_features=['lag1'], date_features=['dayofweek'], num_threads=1)
Forecasting
Finally, we compute the forecasts.
| unique_id | ds | ohe_lr |
|---|
| 0 | id_0 | 2000-08-10 | 4.312748 |
| 1 | id_1 | 2000-04-07 | 4.537019 |
| 2 | id_2 | 2000-06-16 | 4.160505 |
| 3 | id_3 | 2000-08-30 | 3.777040 |
| 4 | id_4 | 2001-01-08 | 2.676933 |
Summary
You can provide complex scikit-learn pipelines as models to mlforecast,
which allows you to perform different transformations depending on the
model and use any of scikit-learn’s compatible estimators.
