module mlbatch.pipeline_cache

Inheritance diagram of mlinsights.mlbatch.pipeline_cache

Short summary

module mlinsights.mlbatch.pipeline_cache

Caches training.

source on GitHub

Classes

class

truncated documentation

PipelineCache

Same as sklearn.pipeline.Pipeline but it can skip training if it detects a step was already trained the …

Properties

property

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_estimator_type

_final_estimator

_pairwise

classes_

inverse_transform

Apply inverse transformations in reverse order All estimators in the pipeline must support inverse_transform. …

named_steps

transform

Apply transforms, and transform with the final estimator This also works where final estimator is None: all …

Methods

method

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__init__

_fit

_get_fit_params_steps

Documentation

Caches training.

source on GitHub

class mlinsights.mlbatch.pipeline_cache.PipelineCache(steps, cache_name=None, verbose=False)[source]

Bases: sklearn.pipeline.Pipeline

Same as sklearn.pipeline.Pipeline but it can skip training if it detects a step was already trained the model was already trained accross even in a different pipeline.

Parameters
  • steps (list) – List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator.

  • cache_name (name of the cache, if None, a new name is created) –

  • verbose (boolean, optional) – If True, the time elapsed while fitting each step will be printed as it is completed.

named_steps

Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.

Type

bunch object, a dictionary with attribute access

source on GitHub

__abstractmethods__ = frozenset({})
__init__(steps, cache_name=None, verbose=False)[source]

Initialize self. See help(type(self)) for accurate signature.

_abc_impl = <_abc_data object>
_fit(X, y=None, **fit_params)[source]
_get_fit_params_steps(fit_params)[source]