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Add gensim.models.BaseKeyedVectors.add_entity method for fill KeyedVectors in manual way. Fix #1942 #1957

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persiyanov
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Pull request related to this issue.

With these changes, it would be possible to add new word vectors into a KeyedVectors object.

@menshikh-iv please, take a look. I will write tests on it after addressing your comments.

Moreover, I have some questions/doubts:

  1. Method name: is it okay right now or maybe it should be named like add_entity/add_word?
  2. Weights parameter: should we check the type and shape of weights and raise an exception in a bad case?
  3. self.vectors contiguity: here vectors list is casted to C-contiguous array. Does numpy preserve C-contiguity after operations such as np.vstack? If so, I think I should add numpy.ascontiguousarray cast in my code.

@menshikh-iv
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Hello @persiyanov

  1. I think both (add_word & add_entity) is OK for this case
  2. I don't think so (because you have no predefined length if you'll create the empty class). Anyway, this needs only for some special cases.

@menshikh-iv menshikh-iv changed the title [Fixes #1942]: Introduce BaseKeyedVectors.add(...) method Add BaseKeyedVectors.add for fill kv in manual way. Fix #1942 Mar 7, 2018
@@ -154,6 +154,19 @@ def get_vector(self, entity):
else:
raise KeyError("'%s' not in vocabulary" % entity)

def add(self, entity, weights):
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What's about re-using this function in (this is duplication right now from https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/utils_any2vec.py#L182)?

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Also, need to add tests to check this functionality:

  • load some kv, add more vectors in a manual way and check that this added fine
  • create empty kv, fill it manually and check that all fine

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@persiyanov persiyanov Mar 7, 2018

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@menshikh-iv

About reusing this function:

It's a bit difficult because in this function vstack is used to append new word vector to self.vectors, while add_word in utils_any2vec creates vectors array at first (https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/utils_any2vec.py#L180) and then it just inserts vectors into it (https://github.com/RaRe-Technologies/gensim/blob/develop/gensim/models/utils_any2vec.py#L197).

While it's possible to follow DRY here, the interface of BaseKeyedVectors.add() method will be more complicated (or I can change the logic in utils_any2vec -- not to create vectors = np.zeros(...) but append each word to the array, but it could decrease the performance of load_word2vec_format function).

If some of these two options is okay, I'll implement it.

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Aha, thanks for suggestion, let's stay it as is.

@@ -153,6 +153,27 @@ def test_wv_property(self):
"""Test that the deprecated `wv` property returns `self`. To be removed in v4.0.0."""
self.assertTrue(self.vectors is self.vectors.wv)

def test_add_word(self):
"""Test that adding word in a manual way works correctly."""
from numpy.random import randn
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can you remove import and use np.random.randn

@@ -154,6 +154,28 @@ def get_vector(self, entity):
else:
raise KeyError("'%s' not in vocabulary" % entity)

def add_entity(self, entity, weights):
"""Accept an entity specified by string tag and vector weights as 1D numpy array with shape (`vector_size`,).
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Please use numpy-style docstrings

self.vocab[entity] = Vocab(index=entity_id, count=1)
self.index2entity.append(entity)

def add_word(self, entity, weights):
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Maybe this doesn't need?

@menshikh-iv
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CC: @gojomo wdyt?

@gojomo
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gojomo commented Mar 8, 2018

This sort of functionality is a natural and useful addition; ideally it'd also be joined with other new APIs to assist initial building of a KeyedVectors from external data sources (rather than the just the direct property-tampering of current usage).

There should be a bulk addition option: otherwise doing lots of single-adds in a loop requires many wasteful reallocations of 1-vector-larger-arrays.

Should perhaps use __setitem__() rather than a named method, or at least have that as an idiomatic option.

Longer-term, a KeyedVectors that actually splits its contents into multiple segments of smaller arrays for more efficient add/delete grow/shrinks would also make sense. But, that'd require more hiding of internal implementation details in a way that could break lots of the current direct-property-accesses by other code.

re: @persiyanov your questions - (1) I'd avoid 'word' in any method names, to stay loyal to intent that this class accepts keys other than just words. (2) if the vstack() exception which results from attempting a mismatched shape is already sufficiently descriptive, no need for extra checking here... but if it's unclear/confusing, then a compat-shape-check would make sense. (3) I don't know but that's worth checking.

@menshikh-iv menshikh-iv changed the title Add BaseKeyedVectors.add for fill kv in manual way. Fix #1942 Add gensim.models.BaseKeyedVectors.add_entity method for fill KeyedVectors in manual way. Fix #1942 Mar 9, 2018
@persiyanov
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persiyanov commented Mar 9, 2018

@menshikh-iv @gojomo please, take a look.

I've implemented add_entities method and its alias __setitem__. Also, I've added bool flag replace which specifies vectors replacement strategy for those entities which are already in vocabulary.

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Ping @gojomo @menshikh-iv

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Looks good to me @persiyanov: +1:
I have only several nitpicks about docstrings + not sure about add_entity.

----------
entities : list of str
Entities specified by string tags.
weights: list of np.array or np.array
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{list of numpy.ndarray, numpy.ndarray}

@@ -154,6 +154,75 @@ def get_vector(self, entity):
else:
raise KeyError("'%s' not in vocabulary" % entity)

def add_entity(self, entity, weights, replace=False):
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maybe remove this method (add_entities looks enough, wdyt @gojomo?)

List of 1D np.array vectors or 2D np.array of vectors.
replace: bool, optional
Boolean flag indicating whether to replace vectors for entities which are already in the vocabulary.
Default, False, means that old vectors for those entities are keeped.
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No need to duplicate "default" value for trivial case in docstring, maybe better to write something like

Flag indicating whether to replace vectors for entities which are already in the vocabulary, if True - replace vectors, otherwise - keep old vectors.

self.vectors[in_vocab_idxs] = weights[in_vocab_mask]

def __setitem__(self, entities, weights):
"""Idiomatic way to call `add_entities` with `replace=True`.
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better to write full docstring

@menshikh-iv
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@gojomo please have a look (for me LGTM, except add_entity method, because I don't see any reason for this alias).

@persiyanov
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ping @gojomo

in_vocab_idxs = []
out_vocab_entities = []

for idx, entity in zip(range(len(entities)), entities):
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enumerate() more idiomatic.

if len(self.vectors) == 0:
self.vectors = weights[~in_vocab_mask]
else:
self.vectors = vstack((self.vectors, weights[~in_vocab_mask]))
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Might this line work even in the case where len(self.vectors)==0, making the check/branch unnecessary?

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I think it's not obvious how to do that, because when empty KeyedVectors object is created, self.vectors = [] is true. In that case, we can't use vstack(([], weights[~in_vocab_mask])) and ValueError: all the input array dimensions except for the concatenation axis must match exactly is raised.

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Is it possible for an empty KeyedVectors to have a self.vectors that is already a proper-dimensioned (0, vector_size) empty ndarray? (Not sure myself, but would simplify things in later places like this.)


in_vocab_mask = np.zeros(len(entities), dtype=np.bool)
in_vocab_idxs = []
out_vocab_entities = []
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This method might be simpler without separate in_vocab_idxs and out_vocab_entities – just driving those ops from the mask, using options like where() or nonzero().

@gojomo
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gojomo commented Mar 14, 2018

Functionality seems good.

I think class has a preexisting terminology issue with the use of word 'entity' where often 'key' would be more-consistent/more-specific. Also, as the 'items' inside this are definitionally 'vectors', generally vector/vectors better terms than weights . While I'm not sure this PR can/should fix all of that, I'd prefer add_entity() & add_entities() be replaced with a single add(keys, vectors, replace=True) (that could also for convenience tolerate a single key/vector).

@persiyanov
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persiyanov commented Mar 16, 2018

@gojomo @menshikh-iv please, take a look.

I've removed add_entities & add_entity but kept add method which can be used for all cases. Also, I got rid of out_vocab_entities variable, doing several np.nonzero(...) calls.

I also think that operating with keys/vectors instead of entities/weights is better, but it's not related to this task and better to be done in another PR.

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Looks good to me, great work @persiyanov 👍

replace: bool, optional
Flag indicating whether to replace vectors for entities which are already in the vocabulary,
if True - replace vectors, otherwise - keep old vectors.
"""
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nitpick: multiline docstring should ends with empty line, i.e.

"""
...
last text

"""

"""
if isinstance(entities, string_types):
entities = [entities]
weights = weights.reshape(1, -1)
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probably, should be weights = np.array(weights).reshape(1, -1) for case if weights, for example, list of floats

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@menshikh-iv Fixed

@@ -73,7 +73,7 @@
PYEMD_EXT = False

from numpy import dot, zeros, float32 as REAL, empty, memmap as np_memmap, \
double, array, vstack, sqrt, newaxis, integer, \
double, array, zeros, vstack, sqrt, newaxis, integer, \
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zeros imported twice

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@gojomo yeah, i've fixed it

@menshikh-iv
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Congratz with first time contribution @persiyanov 👍 🥇

@menshikh-iv menshikh-iv merged commit 58d560b into piskvorky:develop Mar 20, 2018
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gojomo commented Mar 20, 2018

@persiyanov Yes, and thanks for your patience through all the subtle refinements!

@persiyanov persiyanov deleted the feature/add-word-method-to-keyed-vectors branch March 21, 2018 11:12
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piskvorky commented Mar 27, 2018

Is the word entity still in? I'm strongly -1 on that, we should not merge that.

"Entity" has an established meaning in NLP, it comes with certain connotations. Introducing a new concept into gensim like this muddles the waters. Especially since the concept is not really introduced at all in the PR AFAICS—what is an "entity" here? I only skimmed the docstrings and they only mention the word, never explain it. This is confusing and inconsistent with our other docs.

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gojomo commented Mar 27, 2018

The widespread use of 'entity' was introduced in #1777; this PR is just following the code's existing practice. The code desperately needs a refactoring for consistency/clarity, even moreso since the #1777 attempt!

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@piskvorky this modification for BaseKeyedVectors class (that doesn't say anything about words, only abstract "entities" that can be "words" in the subclasses).

Check out other code from this class, for example

https://github.com/RaRe-Technologies/gensim/blob/2e08f4d3b218c9675d4f842f724af40a4f4ec1ee/gensim/models/keyedvectors.py#L124-L144

BaseKeyedVectors used as base class for PoincareKeyedVectors (where we have no "words")
https://github.com/RaRe-Technologies/gensim/blob/2e08f4d3b218c9675d4f842f724af40a4f4ec1ee/gensim/models/poincare.py#L772

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piskvorky commented Mar 29, 2018

I agree with @gojomo , the code desperately needs a proper refactoring. The current post-#1777 situation seems untenable. @gojomo any chance you could take this up?

@menshikh-iv your snippet doesn't explain entity in any way, so not sure how that's helpful. I'm strongly -1 on introducing new fundamental concepts in this way.

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menshikh-iv commented Mar 29, 2018

@piskvorky I don't explain it, I just show that the current code mimics the already existing and doesn't introduce any new concepts.

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Understood. It's an issue with #1777 , not this PR.

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4 participants