folie.functions.sklearnWrapper

class folie.functions.sklearnWrapper(estimator, domain, output_shape=(), expose_params=[])

Wraps sklearn predictor as functions. Allow to use non parametric estimator for fit.

expose_params is a list of key for the parameters of the estimator to be exposed for optimisation

copy()

Makes a deep copy of this function.

Returns:

copy

A new copy of this model.

fit(x, y=None, **kwargs)

Fit coefficients of the function using linear regression. Use as features the derivative of the function with respect to the coefficients

Parameters:

X{array-like} of shape (n_samples, dim)

Point of evaluation of the training data

yarray-like of shape (n_samples,) or (n_samples, n_targets)

Target values. Will be cast to X’s dtype if necessary.

estimator: sklearn compatible estimator

Defaut to sklearn.linear_model.LinearRegression(copy_X=False, fit_intercept=False) but any compatible estimator can be used.

Estimator should have a coef_ attibutes after fitting

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_params(deep=False)

Get the parameters.

Returns:

paramsmapping of string to any

Parameter names mapped to their values.

grad_coeffs(x, *args, **kwargs)

Gradient of the function with respect to the coefficients.

Parameters:

xarray_like

Input data.

Returns:

transformedarray_like

The gradient

resize(new_shape)

Change the output shape of the function.

Parameters:

new_shapetuple, array-like

The new output shape of the function

set_output(*, transform=None)

Set output container.

See Introducing the set_output API for an example on how to use the API.

Parameters:

transform{“default”, “pandas”, “polars”}, default=None

Configure output of transform and fit_transform.

• “default”: Default output format of a transformer

• “pandas”: DataFrame output

• “polars”: Polars output

• None: Transform configuration is unchanged

Added in version 1.4: “polars” option was added.

Returns:

selfestimator instance

Estimator instance.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfobject

Estimator instance.

transform(X, *args, **kwargs)

Transforms the input data.

transform_d2x(x, *args, **kwargs)

Hessian of the function with respect to input data. Implemented by finite difference.

transform_dx(x, **kwargs)

Gradient of the function with respect to input data. Implemented by finite difference.

property coefficients

Access the coefficients