mlinsights.search_rank.SearchEngineVectors (self, pknn)
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
mlinsights.search_rank.SearchEnginePredictions (self, fct, fct_params = None, knn)
mlinsights.search_rank.SearchEnginePredictionImages (self, fct, fct_params = None, knn)
SearchEnginePredictions. Vectors are coming from images. The metadata must contains information about path names. We assume all images can hold in memory. An example can found in notebook Search images with deep learning (keras) or Search images with deep learning (torch). Another example can be found there: search_images_dogcat.py.