module `search_rank.search_engine_vectors`#

Short summary#

module mlinsights.search_rank.search_engine_vectors

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

Classes#

class	truncated documentation
`SearchEngineVectors`	Implements a kind of local search engine which looks for similar results assuming they are vectors. The class is …

Static Methods#

staticmethod	truncated documentation
`read_zip`	Restore the features, the metadata to a `SearchEngineVectors`.

Methods#

method	truncated documentation
`__init__`
`__repr__`	usual
`_first_pass`	Finds the closest n_neighbors.
`_fit_knn`	Fits the nearest neighbors.
`_is_iterable`	Tells if an objet is an iterator or not.
`_prepare_fit`	Stores data in the class itself.
`_second_pass`	Reorders the closest n_neighbors.
`fit`	Every vector comes with a list of metadata.
`kneighbors`	Searches for neighbors close to X.
`to_zip`	Saves the features and the metadata into a zipfile. The function does not save the k-nn.

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_vectors.SearchEngineVectors(**pknn)#

Bases: object

Implements a kind of local search engine which looks for similar results assuming they are vectors. The class is using sklearn.neighborsNearestNeighbors to find the nearest neighbors of a vector and follows the same API. The class populates members:

features_: vectors used to compute the neighbors
knn_: parameters for the sklearn.neighborsNearestNeighbors
metadata_: metadata, can be None

source on GitHub

Parameters: pknn – list of parameters, see sklearn.neighborsNearestNeighbors

source on GitHub

__init__(**pknn)#

Parameters: pknn – list of parameters, see sklearn.neighborsNearestNeighbors

source on GitHub

__repr__()#

usual

source on GitHub

_first_pass(X, n_neighbors=None)#

Finds the closest n_neighbors.

Parameters

X – features
n_neighbors – number of neighbors to get (default is the value passed to the constructor)

Returns

dist, ind

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

source on GitHub

_fit_knn()#

Fits the nearest neighbors.

source on GitHub

_is_iterable(data)#

Tells if an objet is an iterator or not.

source on GitHub

_prepare_fit(data=None, features=None, metadata=None, transform=None)#

Stores data in the class itself.

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
transform – transform each vector before using it

transform is a function whose signature:

def transform(vec, many):
    # Many tells is the functions receives many vectors
    # or just one (many=False).

Function transform is applied only if data is not None.

source on GitHub

_second_pass(X, dist, ind)#

Reorders the closest n_neighbors.

Parameters

X – features
dist – array representing the lengths to points
ind – indices of the nearest points in the population matrix

Returns

score, ind

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

source on GitHub

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

source on GitHub

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

source on GitHub

static read_zip(zipfilename, **kwargs)#

Restore the features, the metadata to a SearchEngineVectors.

Parameters