module search_rank.search_engine_predictions#

Inheritance diagram of mlinsights.search_rank.search_engine_predictions

Short summary#

module mlinsights.search_rank.search_engine_predictions

Implements a way to get close examples based on the output of a machine learned model.

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Classes#

class

truncated documentation

SearchEnginePredictions

Extends class SearchEngineVectors by looking for neighbors to a vector X by looking neighbors to f(X) …

Methods#

method

truncated documentation

__init__

__repr__

usual

fit

Every vector comes with a list of metadata.

kneighbors

Searches for neighbors close to X.

Documentation#

Implements a way to get close examples based on the output of a machine learned model.

source on GitHub

class mlinsights.search_rank.search_engine_predictions.SearchEnginePredictions(fct, fct_params=None, **knn)#

Bases: SearchEngineVectors

Extends class SearchEngineVectors by looking for neighbors to a vector X by looking neighbors to f(X) and not X. f can be any function which converts a vector into another one or a machine learned model. In that case, f will be set to a default behavior. See function model_featurizer.

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Parameters:

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__init__(fct, fct_params=None, **knn)#
Parameters:

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__repr__()#

usual

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fit(data=None, features=None, metadata=None)#

Every vector comes with a list of metadata.

Parameters:

  • data – a dataframe or None if the the features and the metadata are specified with an array and a dictionary

  • features – features columns or an array

  • metadata – data

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kneighbors(X, n_neighbors=None)#

Searches for neighbors close to X.

Parameters:

X – features

Returns:

score, ind, meta

score is an array representing the lengths to points, ind contains the indices of the nearest points in the population matrix, meta is the metadata.

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