module `mlbatch.pipeline_cache`#

Short summary#

module mlinsights.mlbatch.pipeline_cache

Caches training.

Classes#

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

Functions#

function	truncated documentation
`isskl023`	Tells if scikit-learn is more recent than 0.23.

Properties#

property	truncated documentation
`_estimator_type`
`_final_estimator`
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …
`classes_`	The classes labels. Only exist if the last step is a classifier.
`feature_names_in_`	Names of features seen during first step fit method.
`n_features_in_`	Number of features seen during first step fit method.
`named_steps`	Access the steps by name. Read-only attribute to access any step by given name. Keys are steps names and …

Methods#

method	truncated documentation
`__init__`
`_fit`
`_get_fit_params_steps`

Documentation#

Caches training.

source on GitHub

class mlinsights.mlbatch.pipeline_cache.PipelineCache(steps, cache_name=None, verbose=False)#

Bases: 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.

Other attributes:

Parameters:: named_steps – bunch object, a dictionary with attribute access Read-only attribute to access any step parameter by user given name. Keys are step names and values are steps parameters.