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Start refactoring of classifiers

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Alinson S. Xavier
2021-01-22 11:35:29 -06:00
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docs/customization.md
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@@ -152,25 +152,18 @@ dtype: float64
### Using customized ML classifiers and regressors
By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then
selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component
should use through the `classifier` or `regressor` contructor parameters. The provided classifiers and regressors must
follow the sklearn API. In particular, classifiers must provide the methods `fit`, `predict_proba` and `predict`,
while regressors must provide the methods `fit` and `predict`
By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component should use through the `classifier` or `regressor` contructor parameters. Scikit-learn classifiers and regressors are currently supported. A future version of the package will add compatibility with Keras models.
!!! danger
MIPLearn must be able to generate a copy of any custom ML classifiers and regressors through
the standard `copy.deepcopy` method. This currently makes it incompatible with Keras and TensorFlow
predictors. This is a known limitation, which will be addressed in a future version.
The example below shows how to construct a `PrimalSolutionComponent` which internally uses
sklearn's `KNeighborsClassifiers`. Any other sklearn classifier or pipeline can be used.
The example below shows how to construct a `PrimalSolutionComponent` which internally uses scikit-learn's `KNeighborsClassifiers`. Any other scikit-learn classifier or pipeline can be used. The classifier needs to be provided as a lambda function because the component may need to create multiple copies of it. It needs to be wrapped in `ScikitLearnClassifier` to ensure that all the proper data transformations are applied.
```python
from miplearn import PrimalSolutionComponent
from miplearn import PrimalSolutionComponent, ScikitLearnClassifier
from sklearn.neighbors import KNeighborsClassifier
comp = PrimalSolutionComponent(classifier=KNeighborsClassifier(n_neighbors=5))
comp = PrimalSolutionComponent(
classifier=lambda: ScikitLearnClassifier(
KNeighborsClassifier(n_neighbors=5),
),
)
comp.fit(train_instances)
```