asterism.core.ntm

Module contents

class asterism.core.ntm.ATLAS(min_topics=1, *, channels=(128, 32), doc_size=16, topic_rate=8.0, kld_scale=0.1, optim='adam', desc='ATLAS', seed=None)

Bases: Asterism, Module

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

fit_predict(x, locs, y=None, *args, **kwargs)

Perform clustering on X and returns cluster labels.

Parameters

Xarray-like of shape (n_samples, n_features)

Input data.

yIgnored

Not used, present for API consistency by convention.

**kwargsdict

Arguments to be passed to fit.

Added in version 1.4.

Returns

labelsndarray of shape (n_samples,), dtype=np.int64

Cluster labels.

set_fit_request(*, locs: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$') → ATLAS

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit 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 fit.

• 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.

Parameters

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

Metadata routing for locs parameter in fit.

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

Metadata routing for x parameter in fit.

Returns

selfobject

The updated object.

class asterism.core.ntm.NTM(n_topics=5, *, channels=(128, 32), kld_scale=0.1, mode='softmax', optim='adam', desc='NTM', seed=None)

Bases: Asterism, Module

set_fit_request(*, display_rate: bool | None | str = '$UNCHANGED$', locs: bool | None | str = '$UNCHANGED$', n_steps: bool | None | str = '$UNCHANGED$', verbosity: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$') → NTM

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit 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 fit.

• 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.

Parameters

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

Metadata routing for display_rate parameter in fit.

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

Metadata routing for locs parameter in fit.

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

Metadata routing for n_steps parameter in fit.

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

Metadata routing for verbosity parameter in fit.

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

Metadata routing for x parameter in fit.

Returns

selfobject

The updated object.

class asterism.core.ntm.RSB(min_topics=1, *, channels=(128, 32), topic_rate=16.0, kld_scale=0.1, optim='adam', desc='RSB', seed=None)

Bases: Asterism, Module

set_fit_request(*, display_rate: bool | None | str = '$UNCHANGED$', locs: bool | None | str = '$UNCHANGED$', n_steps: bool | None | str = '$UNCHANGED$', verbosity: bool | None | str = '$UNCHANGED$', x: bool | None | str = '$UNCHANGED$') → RSB

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit 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 fit.

• 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.

Parameters

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

Metadata routing for display_rate parameter in fit.

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

Metadata routing for locs parameter in fit.

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

Metadata routing for n_steps parameter in fit.

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

Metadata routing for verbosity parameter in fit.

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

Metadata routing for x parameter in fit.

Returns

selfobject

The updated object.