Scikit-Learn Models

  • Weighted Proportion

class causalpy.skl_models.WeightedProportion

Model which minimises sum squared error subject to:

  • All weights are bound between 0-1

  • Weights sum to 1.

Inspiration taken from this blog post


>>> import numpy as np
>>> from causalpy.skl_models import WeightedProportion
>>> rng = np.random.default_rng(seed=42)
>>> X = rng.normal(loc=0, scale=1, size=(20,2))
>>> y = rng.normal(loc=0, scale=1, size=(20,))
>>> wp = WeightedProportion()
>>>, y)
>>> wp.coef_
array([[0.36719946, 0.63280054]])
>>> X_new = rng.normal(loc=0, scale=1, size=(10,2))
>>> wp.predict(X_new)
fit(X, y)

Fit model on data X with predictor y

loss(W, X, y)

Compute root mean squared loss with data X, weights W, and predictor y


Predict results for data X

set_score_request(*, sample_weight='$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.


This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.


sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.



The updated object.

Return type: