Source code for eli5.sklearn.unhashing

# -*- coding: utf-8 -*-
Utilities to reverse transformation done by FeatureHasher or HashingVectorizer.
from __future__ import absolute_import
from collections import defaultdict, Counter
from itertools import chain
from typing import List, Iterable, Any, Dict, Tuple, Union

import numpy as np
import six
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.feature_extraction.text import (
from sklearn.pipeline import FeatureUnion

from eli5._feature_names import FeatureNames

[docs]class InvertableHashingVectorizer(BaseEstimator, TransformerMixin): """ A wrapper for HashingVectorizer which allows to get meaningful feature names. Create it with an existing HashingVectorizer instance as an argument:: vec = InvertableHashingVectorizer(my_hashing_vectorizer) Unlike HashingVectorizer it can be fit. During fitting :class:`~.InvertableHashingVectorizer` learns which input terms map to which feature columns/signs; this allows to provide more meaningful :meth:`get_feature_names`. The cost is that it is no longer stateless. You can fit :class:`~.InvertableHashingVectorizer` on a random sample of documents (not necessarily on the whole training and testing data), and use it to inspect an existing HashingVectorizer instance. If several features hash to the same value, they are ordered by their frequency in documents that were used to fit the vectorizer. :meth:`transform` works the same as HashingVectorizer.transform. """ def __init__(self, vec, unkn_template="FEATURE[%d]"): # type: (HashingVectorizer, str) -> None self.vec = vec self.unkn_template = unkn_template self.unhasher = FeatureUnhasher( hasher=vec._get_hasher(), unkn_template=unkn_template, ) self.n_features = vec.n_features # type: int
[docs] def fit(self, X, y=None): """ Extract possible terms from documents """ return self
def partial_fit(self, X): self.unhasher.partial_fit(self._get_terms_iter(X)) return self def transform(self, X): return self.vec.transform(X)
[docs] def get_feature_names(self, always_signed=True): # type: (bool) -> FeatureNames """ Return feature names. This is a best-effort function which tries to reconstruct feature names based on what it has seen so far. HashingVectorizer uses a signed hash function. If always_signed is True, each term in feature names is prepended with its sign. If it is False, signs are only shown in case of possible collisions of different sign. You probably want always_signed=True if you're checking unprocessed classifier coefficients, and always_signed=False if you've taken care of :attr:`column_signs_`. """ return self.unhasher.get_feature_names( always_signed=always_signed, always_positive=self._always_positive(), )
def _get_terms_iter(self, X): analyze = self.vec.build_analyzer() return chain.from_iterable(analyze(doc) for doc in X) @property def column_signs_(self): """ Return a numpy array with expected signs of features. Values are * +1 when all known terms which map to the column have positive sign; * -1 when all known terms which map to the column have negative sign; * ``nan`` when there are both positive and negative known terms for this column, or when there is no known term which maps to this column. """ if self._always_positive(): return np.ones(self.n_features) self.unhasher.recalculate_attributes() return self.unhasher.column_signs_ def _always_positive(self): # type: () -> bool return ( self.vec.binary or getattr(self.vec, 'non_negative', False) or not getattr(self.vec, 'alternate_sign', True) )
[docs]class FeatureUnhasher(BaseEstimator): """ Class for recovering a mapping used by FeatureHasher. """ def __init__(self, hasher, unkn_template="FEATURE[%d]"): # type: (FeatureHasher, str) -> None if hasher.input_type != 'string': raise ValueError("FeatureUnhasher only supports hashers with " "input_type 'string', got %r." % hasher.input_type) self.hasher = hasher self.n_features = self.hasher.n_features # type: int self.unkn_template = unkn_template self._attributes_dirty = True self._term_counts = Counter() # type: Counter def fit(self, X, y=None): # type: (Iterable[str], Any) -> FeatureUnhasher self._term_counts.clear() self.partial_fit(X, y) self.recalculate_attributes(force=True) return self def partial_fit(self, X, y=None): # type: (Iterable[str], Any) -> FeatureUnhasher self._term_counts.update(X) self._attributes_dirty = True return self def get_feature_names(self, always_signed=True, always_positive=False): # type: (bool, bool) -> FeatureNames self.recalculate_attributes() # lists of names with signs of known features column_ids, term_names, term_signs = self._get_collision_info() feature_names = {} for col_id, names, signs in zip(column_ids, term_names, term_signs): if always_positive: feature_names[col_id] = [{'name': name, 'sign': 1} for name in names] else: if not always_signed and _invert_signs(signs): signs = [-sign for sign in signs] feature_names[col_id] = [{'name': name, 'sign': sign} for name, sign in zip(names, signs)] return FeatureNames( feature_names, n_features=self.n_features, unkn_template=self.unkn_template)
[docs] def recalculate_attributes(self, force=False): # type: (bool) -> None """ Update all computed attributes. It is only needed if you need to access computed attributes after :meth:`patrial_fit` was called. """ if not self._attributes_dirty and not force: return terms = [term for term, _ in self._term_counts.most_common()] if six.PY2: terms = np.array(terms, dtype=np.object) else: terms = np.array(terms) if len(terms): indices, signs = _get_indices_and_signs(self.hasher, terms) else: indices, signs = np.array([]), np.array([]) self.terms_ = terms # type: np.ndarray self.term_columns_ = indices self.term_signs_ = signs self.collisions_ = _get_collisions(indices) self.column_signs_ = self._get_column_signs() self._attributes_dirty = False
def _get_column_signs(self): colums_signs = np.ones(self.n_features) * np.nan for hash_id, term_ids in self.collisions_.items(): term_signs = self.term_signs_[term_ids] if _invert_signs(term_signs): colums_signs[hash_id] = -1 elif (term_signs > 0).all(): colums_signs[hash_id] = 1 return colums_signs def _get_collision_info(self): # type: () -> Tuple[List[int], List[np.ndarray], List[np.ndarray]] column_ids, term_names, term_signs = [], [], [] for column_id, _term_ids in self.collisions_.items(): column_ids.append(column_id) term_names.append(self.terms_[_term_ids]) term_signs.append(self.term_signs_[_term_ids]) return column_ids, term_names, term_signs
def _get_collisions(indices): # type: (...) -> Dict[int, List[int]] """ Return a dict ``{column_id: [possible term ids]}`` with collision information. """ collisions = defaultdict(list) # type: Dict[int, List[int]] for term_id, hash_id in enumerate(indices): collisions[hash_id].append(term_id) return dict(collisions) def _get_indices_and_signs(hasher, terms): """ For each term from ``terms`` return its column index and sign, as assigned by FeatureHasher ``hasher``. """ X = _transform_terms(hasher, terms) indices = X.nonzero()[1] signs = X.sum(axis=1).A.ravel() return indices, signs def _transform_terms(hasher, terms): return hasher.transform(np.array(terms).reshape(-1, 1)) def _invert_signs(signs): """ Shall we invert signs? Invert if first (most probable) term is negative. """ return signs[0] < 0 def is_invhashing(vec): return isinstance(vec, InvertableHashingVectorizer)
[docs]def handle_hashing_vec(vec, feature_names, coef_scale, with_coef_scale=True): """ Return feature_names and coef_scale (if with_coef_scale is True), calling .get_feature_names for invhashing vectorizers. """ needs_coef_scale = with_coef_scale and coef_scale is None if is_invhashing(vec): if feature_names is None: feature_names = vec.get_feature_names(always_signed=False) if needs_coef_scale: coef_scale = vec.column_signs_ elif (isinstance(vec, FeatureUnion) and any(is_invhashing(v) for _, v in vec.transformer_list) and (needs_coef_scale or feature_names is None)): _feature_names, _coef_scale = _invhashing_union_feature_names_scale(vec) if feature_names is None: feature_names = _feature_names if needs_coef_scale: coef_scale = _coef_scale return (feature_names, coef_scale) if with_coef_scale else feature_names
def _invhashing_union_feature_names_scale(vec_union): # type: (FeatureUnion) -> Tuple[FeatureNames, np.ndarray] feature_names_store = {} # type: Dict[int, Union[str, List]] unkn_template = None shift = 0 coef_scale_values = [] for vec_name, vec in vec_union.transformer_list: if isinstance(vec, InvertableHashingVectorizer): vec_feature_names = vec.get_feature_names(always_signed=False) unkn_template = vec_feature_names.unkn_template for idx, fs in vec_feature_names.feature_names.items(): new_fs = [] for f in fs: new_f = dict(f) new_f['name'] = '{}__{}'.format(vec_name, f['name']) new_fs.append(new_f) feature_names_store[idx + shift] = new_fs coef_scale_values.append((shift, vec.column_signs_)) shift += vec_feature_names.n_features else: vec_feature_names = vec.get_feature_names() feature_names_store.update( (shift + idx, '{}__{}'.format(vec_name, fname)) for idx, fname in enumerate(vec_feature_names)) shift += len(vec_feature_names) n_features = shift feature_names = FeatureNames( feature_names=feature_names_store, n_features=n_features, unkn_template=unkn_template) coef_scale = np.ones(n_features) * np.nan for idx, values in coef_scale_values: coef_scale[idx: idx + len(values)] = values return feature_names, coef_scale
[docs]def invert_hashing_and_fit( vec, # type: Union[FeatureUnion, HashingVectorizer] docs ): # type: (...) -> Union[FeatureUnion, InvertableHashingVectorizer] """ Create an :class:`~.InvertableHashingVectorizer` from hashing vectorizer vec and fit it on docs. If vec is a FeatureUnion, do it for all hashing vectorizers in the union. Return an :class:`~.InvertableHashingVectorizer`, or a FeatureUnion, or an unchanged vectorizer. """ if isinstance(vec, HashingVectorizer): vec = InvertableHashingVectorizer(vec) elif (isinstance(vec, FeatureUnion) and any(isinstance(v, HashingVectorizer) for _, v in vec.transformer_list)): vec = _fit_invhashing_union(vec, docs) return vec
def _fit_invhashing_union(vec_union, docs): # type: (FeatureUnion, Any) -> FeatureUnion """ Fit InvertableHashingVectorizer on doc inside a FeatureUnion. """ return FeatureUnion( [(name, invert_hashing_and_fit(v, docs)) for name, v in vec_union.transformer_list], transformer_weights=vec_union.transformer_weights, n_jobs=vec_union.n_jobs)