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from modules.module_ann import Ann
from memory_profiler import profile
from sklearn.neighbors import NearestNeighbors
from sklearn.decomposition import PCA
from gensim.models import KeyedVectors
from typing import List
import os
import operator
import pandas as pd
import numpy as np
from numpy import dot
from gensim import matutils
class Embedding:
@profile
def __init__(self,
path: str,
binary: bool,
limit: int=None,
randomizedPCA: bool=False,
max_neighbors: int=20
) -> None:
# Embedding vars
self.path = path
self.limit = limit
self.randomizedPCA = randomizedPCA
self.binary = binary
self.max_neighbors = max_neighbors
# Full embedding dataset
self.ds = None
# Estimate NearestNeighbors
self.ann = None # Aproximate with Annoy method
self.neigh = None # Exact with Sklearn method
# Load embedding and pca dataset
self.__load()
def __load(
self,
) -> None:
print(f"Preparing {os.path.basename(self.path)} embeddings...")
# --- Prepare dataset ---
self.ds = self.__preparate(
self.path, self.binary, self.limit, self.randomizedPCA
)
# --- Estimate Nearest Neighbors
# Method A: Througth annoy using forest tree
self.ann = Ann(
words=self.ds['word'],
vectors=self.ds['embedding'],
coord=self.ds['pca']
)
self.ann.init(
n_trees=20, metric='dot', n_jobs=-1
)
# Method B: Througth Sklearn method
self.neigh = NearestNeighbors(
n_neighbors=self.max_neighbors
)
self.neigh.fit(
X=self.ds['embedding'].to_list()
)
def __preparate(
self,
path: str,
binary: bool,
limit: int,
randomizedPCA: bool
) -> pd.DataFrame:
if randomizedPCA:
pca = PCA(
n_components=2,
copy=False,
whiten=False,
svd_solver='randomized',
iterated_power='auto'
)
else:
pca = PCA(
n_components=2
)
print("--------> PATH:", path)
model = KeyedVectors.load_word2vec_format(
fname=path,
binary=binary,
limit=limit
)
# Cased Vocab
cased_words = model.index_to_key
cased_emb = model.get_normed_vectors()
cased_pca = pca.fit_transform(cased_emb)
df_cased = pd.DataFrame(
zip(
cased_words,
cased_emb,
cased_pca
),
columns=['word', 'embedding', 'pca']
)
df_cased['word'] = df_cased.word.apply(lambda w: w.lower())
df_uncased = df_cased.drop_duplicates(subset='word')
return df_uncased
def __getValue(
self,
word: str,
feature: str
):
word_id, value = None, None
if word in self:
word_id = self.ds['word'].to_list().index(word)
if word_id != None:
value = self.ds[feature].to_list()[word_id]
return value
def getEmbedding(
self,
word: str
):
return self.__getValue(word, 'embedding')
def getPCA(
self,
word: str
):
return self.__getValue(word, 'pca')
def getNearestNeighbors(
self,
word: str,
n_neighbors: int=10,
nn_method: str='sklearn'
) -> List[str]:
assert(n_neighbors <= self.max_neighbors), f"Error: The value of the parameter 'n_neighbors:{n_neighbors}' must less than or equal to {self.max_neighbors}!."
if nn_method == 'ann':
words = self.ann.get(word, n_neighbors)
elif nn_method == 'sklearn':
word_emb = self.getEmbedding(word).reshape(1,-1)
_, nn_ids = self.neigh.kneighbors(word_emb, n_neighbors)
words = operator.itemgetter(*nn_ids[0])(self.ds['word'].to_list())
else:
words = []
return words
def __contains__(
self,
word: str
) -> bool:
return word in self.ds['word'].to_list()
# ToDo: Revisar estos dos métodos usados en la pestaña sesgoEnPalabras
# ya que ahora los embedding vienen normalizados
def cosineSimilarities(self, vector_1, vectors_all):
norm = np.linalg.norm(vector_1)
all_norms = np.linalg.norm(vectors_all, axis=1)
dot_products = dot(vectors_all, vector_1)
similarities = dot_products / (norm * all_norms)
return similarities
def getCosineSimilarities(self, w1, w2):
return dot(
matutils.unitvec(self.getEmbedding(w1)),
matutils.unitvec(self.getEmbedding(w2))
) |