Source code for mlinsights.search_rank.search_engine_predictions

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

from ..mlmodel import model_featurizer
from ..helpers.parameters import format_function_call
from .search_engine_vectors import SearchEngineVectors

[docs]class SearchEnginePredictions(SearchEngineVectors): """ Extends class :class:`SearchEngineVectors <mlinsights.search_rank.search_engine_vectors.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 :func:`model_featurizer <mlinsights.mlmodel.ml_featurizer.model_featurizer>`. :githublink:`%|py|20` """
[docs] def __init__(self, fct, fct_params=None, **knn): """ :param fct: function *f* applied before looking for neighbors, it can also be a machine learned model :param fct_params: parameters sent to function :func:`model_featurizer <mlinsights.mlmodel.ml_featurizer.model_featurizer>` :param pknn: list of parameters, see :epkg:`sklearn:neighborsNearestNeighbors` :githublink:`%|py|29` """ super().__init__(**knn) self._fct_params = fct_params self._fct_init = fct if callable(fct) and not hasattr(fct, 'predict') and \ not hasattr(fct, 'forward'): self.fct = fct else: if fct_params is None: fct_params = {} self.fct = model_featurizer(fct, **fct_params)
[docs] def __repr__(self): """ usual :githublink:`%|py|44` """ if self.pknn: pp = self.pknn.copy() else: pp = {} pp['fct'] = self._fct_init pp['fct_params'] = self._fct_params return format_function_call(self.__class__.__name__, pp)
[docs] def fit(self, data=None, features=None, metadata=None): """ Every vector comes with a list of metadata. :param data: a :epkg:`dataframe` or None if the the features and the metadata are specified with an array and a dictionary :param features: features columns or an array :param metadata: data :githublink:`%|py|63` """ iterate = self._is_iterable(data) if iterate: self._prepare_fit(data=data, features=features, metadata=metadata, transform=self.fct) else: self._prepare_fit(data=data, features=features, metadata=metadata) if isinstance(self.features_, list): raise TypeError( "features_ cannot be a list when training the model.") self.features_ = self.fct(self.features_, True) return self._fit_knn()
[docs] def kneighbors(self, X, n_neighbors=None): """ Searches for neighbors close to *X*. :param X: features :return: 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. :githublink:`%|py|86` """ xp = self.fct(X, False) if len(xp.shape) == 1: xp = xp.reshape((1, len(xp))) return super().kneighbors(xp, n_neighbors=n_neighbors)