APINumPyDocExtractor

class ragger_duck.scraping.APINumPyDocExtractor

Extract text from the API documentation using numpydoc.

To discover the classes and functions, one should provide the path containing the HTML autogenerated pages. Usually, only public API is present in the documentation. For scikit-learn, the documentation is available in the folder modules/generated.

We leverage the structured information provided by numpydoc to create meaningful chunk of information. Notably, every chunk contains the associate class or function import name.

Read more in the User Guide.

Methods

fit([X, y])	No-op operation, only validate parameters.
fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X)	Extract text from the API documentation.

fit(X=None, y=None)

No-op operation, only validate parameters.

Parameters:

XNone

This parameter is ignored.

yNone

This parameter is ignored.

Returns:

self

The fitted estimator.

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_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

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 Pipeline). 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:

selfestimator instance

Estimator instance.

transform(X)

Extract text from the API documentation.

Parameters:

Xpathlib.Path

The path to the API documentation folder.

Returns:

outputlist

A list of dictionaries containing the source and text of the API documentation.

Examples using ragger_duck.scraping.APINumPyDocExtractor

Documentation scraping strategies

Documentation scraping strategies