laywerrobot/lib/python3.6/site-packages/gensim/models/tfidfmodel.py
2020-08-27 21:55:39 +02:00

455 lines
17 KiB
Python

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2012 Radim Rehurek <radimrehurek@seznam.cz>
# Copyright (C) 2017 Mohit Rathore <mrmohitrathoremr@gmail.com>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
"""This module implements functionality related to the `Term Frequency - Inverse Document Frequency
<https://en.wikipedia.org/wiki/Tf%E2%80%93idf>` vector space bag-of-words models.
For a more in-depth exposition of TF-IDF and its various SMART variants (normalization, weighting schemes),
see the blog post at https://rare-technologies.com/pivoted-document-length-normalisation/
"""
import logging
from functools import partial
from gensim import interfaces, matutils, utils
from six import iteritems
import numpy as np
logger = logging.getLogger(__name__)
def resolve_weights(smartirs):
"""Check the validity of `smartirs` parameters.
Parameters
----------
smartirs : str
`smartirs` or SMART (System for the Mechanical Analysis and Retrieval of Text)
Information Retrieval System, a mnemonic scheme for denoting tf-idf weighting
variants in the vector space model. The mnemonic for representing a combination
of weights takes the form ddd, where the letters represents the term weighting of the document vector.
for more information visit `SMART Information Retrieval System
<https://en.wikipedia.org/wiki/SMART_Information_Retrieval_System>`_.
Returns
-------
3-tuple (local_letter, global_letter, normalization_letter)
local_letter : str
Term frequency weighing, one of:
* `n` - natural,
* `l` - logarithm,
* `a` - augmented,
* `b` - boolean,
* `L` - log average.
global_letter : str
Document frequency weighting, one of:
* `n` - none,
* `t` - idf,
* `p` - prob idf.
normalization_letter : str
Document normalization, one of:
* `n` - none,
* `c` - cosine.
Raises
------
ValueError
If `smartirs` is not a string of length 3 or one of the decomposed value
doesn't fit the list of permissible values.
"""
if not isinstance(smartirs, str) or len(smartirs) != 3:
raise ValueError("Expected a string of length 3 except got " + smartirs)
w_tf, w_df, w_n = smartirs
if w_tf not in 'nlabL':
raise ValueError("Expected term frequency weight to be one of 'nlabL', except got {}".format(w_tf))
if w_df not in 'ntp':
raise ValueError("Expected inverse document frequency weight to be one of 'ntp', except got {}".format(w_df))
if w_n not in 'nc':
raise ValueError("Expected normalization weight to be one of 'ncb', except got {}".format(w_n))
return w_tf, w_df, w_n
def df2idf(docfreq, totaldocs, log_base=2.0, add=0.0):
"""Compute inverse-document-frequency for a term with the given document frequency `docfreq`:
:math:`idf = add + log_{log\_base} \\frac{totaldocs}{docfreq}`
Parameters
----------
docfreq : {int, float}
Document frequency.
totaldocs : int
Total number of documents.
log_base : float, optional
Base of logarithm.
add : float, optional
Offset.
Returns
-------
float
Inverse document frequency.
"""
return add + np.log(float(totaldocs) / docfreq) / np.log(log_base)
def precompute_idfs(wglobal, dfs, total_docs):
"""Pre-compute the inverse document frequency mapping for all terms.
Parameters
----------
wglobal : function
Custom function for calculating the "global" weighting function.
See for example the SMART alternatives under :func:`~gensim.models.tfidfmodel.smartirs_wglobal`.
dfs : dict
Dictionary mapping `term_id` into how many documents did that term appear in.
total_docs : int
Total number of documents.
Returns
-------
dict of (int, float)
Inverse document frequencies in the format `{term_id_1: idfs_1, term_id_2: idfs_2, ...}`.
"""
# not strictly necessary and could be computed on the fly in TfidfModel__getitem__.
# this method is here just to speed things up a little.
return {termid: wglobal(df, total_docs) for termid, df in iteritems(dfs)}
def smartirs_wlocal(tf, local_scheme):
"""Calculate local term weight for a term using the weighting scheme specified in `local_scheme`.
Parameters
----------
tf : int
Term frequency.
local : {'n', 'l', 'a', 'b', 'L'}
Local transformation scheme.
Returns
-------
float
Calculated local weight.
"""
if local_scheme == "n":
return tf
elif local_scheme == "l":
return 1 + np.log2(tf)
elif local_scheme == "a":
return 0.5 + (0.5 * tf / tf.max(axis=0))
elif local_scheme == "b":
return tf.astype('bool').astype('int')
elif local_scheme == "L":
return (1 + np.log2(tf)) / (1 + np.log2(tf.mean(axis=0)))
def smartirs_wglobal(docfreq, totaldocs, global_scheme):
"""Calculate global document weight based on the weighting scheme specified in `global_scheme`.
Parameters
----------
docfreq : int
Document frequency.
totaldocs : int
Total number of documents.
global_scheme : {'n', 't', 'p'}
Global transformation scheme.
Returns
-------
float
Calculated global weight.
"""
if global_scheme == "n":
return 1.
elif global_scheme == "t":
return np.log2(1.0 * totaldocs / docfreq)
elif global_scheme == "p":
return max(0, np.log2((1.0 * totaldocs - docfreq) / docfreq))
def smartirs_normalize(x, norm_scheme, return_norm=False):
"""Normalize a vector using the normalization scheme specified in `norm_scheme`.
Parameters
----------
x : numpy.ndarray
Input array
norm_scheme : {'n', 'c'}
Normalizing function to use:
`n`: no normalization
`c`: unit L2 norm (scale `x` to unit euclidean length)
return_norm : bool, optional
Return the length of `x` as well?
Returns
-------
numpy.ndarray
Normalized array.
float (only if return_norm is set)
L2 norm of `x`.
"""
if norm_scheme == "n":
if return_norm:
_, length = matutils.unitvec(x, return_norm=return_norm)
return x, length
else:
return x
elif norm_scheme == "c":
return matutils.unitvec(x, return_norm=return_norm)
class TfidfModel(interfaces.TransformationABC):
"""Objects of this class realize the transformation between word-document co-occurrence matrix (int)
into a locally/globally weighted TF-IDF matrix (positive floats).
Examples
--------
>>> import gensim.downloader as api
>>> from gensim.models import TfidfModel
>>> from gensim.corpora import Dictionary
>>>
>>> dataset = api.load("text8")
>>> dct = Dictionary(dataset) # fit dictionary
>>> corpus = [dct.doc2bow(line) for line in dataset] # convert corpus to BoW format
>>>
>>> model = TfidfModel(corpus) # fit model
>>> vector = model[corpus[0]] # apply model to the first corpus document
"""
def __init__(self, corpus=None, id2word=None, dictionary=None, wlocal=utils.identity,
wglobal=df2idf, normalize=True, smartirs=None, pivot=None, slope=0.65):
"""Compute TF-IDF by multiplying a local component (term frequency) with a global component
(inverse document frequency), and normalizing the resulting documents to unit length.
Formula for non-normalized weight of term :math:`i` in document :math:`j` in a corpus of :math:`D` documents
.. math:: weight_{i,j} = frequency_{i,j} * log_2 \\frac{D}{document\_freq_{i}}
or, more generally
.. math:: weight_{i,j} = wlocal(frequency_{i,j}) * wglobal(document\_freq_{i}, D)
so you can plug in your own custom :math:`wlocal` and :math:`wglobal` functions.
Parameters
----------
corpus : iterable of iterable of (int, int), optional
Input corpus
id2word : {dict, :class:`~gensim.corpora.Dictionary`}, optional
Mapping token - id, that was used for converting input data to bag of words format.
dictionary : :class:`~gensim.corpora.Dictionary`
If `dictionary` is specified, it must be a `corpora.Dictionary` object and it will be used.
to directly construct the inverse document frequency mapping (then `corpus`, if specified, is ignored).
wlocals : function, optional
Function for local weighting, default for `wlocal` is :func:`~gensim.utils.identity`
(other options: :func:`math.sqrt`, :func:`math.log1p`, etc).
wglobal : function, optional
Function for global weighting, default is :func:`~gensim.models.tfidfmodel.df2idf`.
normalize : bool, optional
Normalize document vectors to unit euclidean length? You can also inject your own function into `normalize`.
smartirs : str, optional
SMART (System for the Mechanical Analysis and Retrieval of Text) Information Retrieval System,
a mnemonic scheme for denoting tf-idf weighting variants in the vector space model.
The mnemonic for representing a combination of weights takes the form XYZ,
for example 'ntc', 'bpn' and so on, where the letters represents the term weighting of the document vector.
Term frequency weighing:
* `n` - natural,
* `l` - logarithm,
* `a` - augmented,
* `b` - boolean,
* `L` - log average.
Document frequency weighting:
* `n` - none,
* `t` - idf,
* `p` - prob idf.
Document normalization:
* `n` - none,
* `c` - cosine.
For more information visit `SMART Information Retrieval System
<https://en.wikipedia.org/wiki/SMART_Information_Retrieval_System>`_.
pivot : float, optional
See the blog post at https://rare-technologies.com/pivoted-document-length-normalisation/.
Pivot is the point around which the regular normalization curve is `tilted` to get the new pivoted
normalization curve. In the paper `Amit Singhal, Chris Buckley, Mandar Mitra:
"Pivoted Document Length Normalization" <http://singhal.info/pivoted-dln.pdf>`_ it is the point where the
retrieval and relevance curves intersect.
This parameter along with `slope` is used for pivoted document length normalization.
Only when `pivot` is not None will pivoted document length normalization be applied.
Otherwise, regular TfIdf is used.
slope : float, optional
Parameter required by pivoted document length normalization which determines the slope to which
the `old normalization` can be tilted. This parameter only works when pivot is defined.
"""
self.id2word = id2word
self.wlocal, self.wglobal, self.normalize = wlocal, wglobal, normalize
self.num_docs, self.num_nnz, self.idfs = None, None, None
self.smartirs = smartirs
self.slope = slope
self.pivot = pivot
self.eps = 1e-12
# If smartirs is not None, override wlocal, wglobal and normalize
if smartirs is not None:
n_tf, n_df, n_n = resolve_weights(smartirs)
self.wlocal = partial(smartirs_wlocal, local_scheme=n_tf)
self.wglobal = partial(smartirs_wglobal, global_scheme=n_df)
# also return norm factor if pivot is not none
if self.pivot is None:
self.normalize = partial(smartirs_normalize, norm_scheme=n_n)
else:
self.normalize = partial(smartirs_normalize, norm_scheme=n_n, return_norm=True)
if dictionary is not None:
# user supplied a Dictionary object, which already contains all the
# statistics we need to construct the IDF mapping. we can skip the
# step that goes through the corpus (= an optimization).
if corpus is not None:
logger.warning(
"constructor received both corpus and explicit inverse document frequencies; ignoring the corpus"
)
self.num_docs, self.num_nnz = dictionary.num_docs, dictionary.num_nnz
self.dfs = dictionary.dfs.copy()
self.idfs = precompute_idfs(self.wglobal, self.dfs, self.num_docs)
if id2word is None:
self.id2word = dictionary
elif corpus is not None:
self.initialize(corpus)
else:
# NOTE: everything is left uninitialized; presumably the model will
# be initialized in some other way
pass
@classmethod
def load(cls, *args, **kwargs):
"""Load a previously saved TfidfModel class. Handles backwards compatibility from
older TfidfModel versions which did not use pivoted document normalization.
"""
model = super(TfidfModel, cls).load(*args, **kwargs)
if not hasattr(model, 'pivot'):
model.pivot = None
logger.info('older version of %s loaded without pivot arg', cls.__name__)
logger.info('Setting pivot to %s.', model.pivot)
if not hasattr(model, 'slope'):
model.slope = 0.65
logger.info('older version of %s loaded without slope arg', cls.__name__)
logger.info('Setting slope to %s.', model.slope)
return model
def __str__(self):
return "TfidfModel(num_docs=%s, num_nnz=%s)" % (self.num_docs, self.num_nnz)
def initialize(self, corpus):
"""Compute inverse document weights, which will be used to modify term frequencies for documents.
Parameters
----------
corpus : iterable of iterable of (int, int)
Input corpus.
"""
logger.info("collecting document frequencies")
dfs = {}
numnnz, docno = 0, -1
for docno, bow in enumerate(corpus):
if docno % 10000 == 0:
logger.info("PROGRESS: processing document #%i", docno)
numnnz += len(bow)
for termid, _ in bow:
dfs[termid] = dfs.get(termid, 0) + 1
# keep some stats about the training corpus
self.num_docs = docno + 1
self.num_nnz = numnnz
self.dfs = dfs
# and finally compute the idf weights
n_features = max(dfs) if dfs else 0
logger.info(
"calculating IDF weights for %i documents and %i features (%i matrix non-zeros)",
self.num_docs, n_features, self.num_nnz
)
self.idfs = precompute_idfs(self.wglobal, self.dfs, self.num_docs)
def __getitem__(self, bow, eps=1e-12):
"""Get the tf-idf representation of an input vector and/or corpus.
bow : {list of (int, int), iterable of iterable of (int, int)}
Input document in the `sparse Gensim bag-of-words format
<https://radimrehurek.com/gensim/intro.html#core-concepts>`_,
or a streamed corpus of such documents.
eps : float
Threshold value, will remove all position that have tfidf-value less than `eps`.
Returns
-------
vector : list of (int, float)
TfIdf vector, if `bow` is a single document
:class:`~gensim.interfaces.TransformedCorpus`
TfIdf corpus, if `bow` is a corpus.
"""
self.eps = eps
# if the input vector is in fact a corpus, return a transformed corpus as a result
is_corpus, bow = utils.is_corpus(bow)
if is_corpus:
return self._apply(bow)
# unknown (new) terms will be given zero weight (NOT infinity/huge weight,
# as strict application of the IDF formula would dictate)
termid_array, tf_array = [], []
for termid, tf in bow:
termid_array.append(termid)
tf_array.append(tf)
tf_array = self.wlocal(np.array(tf_array))
vector = [
(termid, tf * self.idfs.get(termid))
for termid, tf in zip(termid_array, tf_array) if abs(self.idfs.get(termid, 0.0)) > self.eps
]
if self.normalize is True:
self.normalize = matutils.unitvec
elif self.normalize is False:
self.normalize = utils.identity
# and finally, normalize the vector either to unit length, or use a
# user-defined normalization function
if self.pivot is None:
norm_vector = self.normalize(vector)
norm_vector = [(termid, weight) for termid, weight in norm_vector if abs(weight) > self.eps]
else:
_, old_norm = self.normalize(vector, return_norm=True)
pivoted_norm = (1 - self.slope) * self.pivot + self.slope * old_norm
norm_vector = [
(termid, weight / float(pivoted_norm))
for termid, weight in vector
if abs(weight / float(pivoted_norm)) > self.eps
]
return norm_vector