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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, Any
import os
import pandas as pd
import numpy as np
from numpy import dot
from gensim import matutils
class Embedding:
def __init__(self,
path: str,
limit: int=None,
randomizedPCA: bool=False,
max_neighbors: int=20,
nn_method: str='sklearn'
) -> None:
# Embedding vars
self.path = path
self.limit = limit
self.randomizedPCA = randomizedPCA
self.max_neighbors = max_neighbors
self.availables_nn_methods = ['sklearn', 'ann']
self.nn_method = nn_method
# 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:
assert(self.nn_method in self.availables_nn_methods), f"Error: The value of the parameter 'nn method' can only be {self.availables_nn_methods}!"
print(f"Preparing {os.path.basename(self.path)} embeddings...")
# --- Prepare dataset ---
self.ds = self.__preparate(
self.path, self.limit, self.randomizedPCA
)
# --- Estimate Nearest Neighbors
if self.nn_method == 'sklearn':
# Method A: Througth Sklearn method
self.__init_sklearn_method(
max_neighbors=self.max_neighbors,
vectors=self.ds['embedding'].to_list()
)
elif self.nn_method == 'ann':
# Method B: Througth annoy using forest tree
self.__init_ann_method(
words=self.ds['word'].to_list(),
vectors=self.ds['embedding'].to_list(),
coord=self.ds['pca'].to_list()
)
def __preparate(
self,
path: str,
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
)
try:
model = KeyedVectors.load_word2vec_format(
fname=path,
binary=path.endswith('.bin'),
limit=limit,
unicode_errors='ignore'
)
except:
raise Exception(f"Can't load {path}. If it's a .bin extended file, only gensims c binary format are valid")
# 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 __init_ann_method(
self,
words: List[str],
vectors: List[float],
coord: List[float],
n_trees: int=20,
metric: str='dot'
) -> None:
print("Initializing Annoy method to search for nearby neighbors...")
self.ann = Ann(
words=words,
vectors=vectors,
coord=coord,
)
self.ann.init(
n_trees=n_trees,
metric=metric,
n_jobs=-1
)
def __init_sklearn_method(
self,
max_neighbors: int,
vectors: List[float]
) -> None:
print("Initializing sklearn method to search for nearby neighbors...")
self.neigh = NearestNeighbors(
n_neighbors=max_neighbors
)
self.neigh.fit(
X=vectors
)
def __getValue(
self,
word: str,
feature: str
) -> Any:
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]
else:
print(f"The word '{word}' does not exist")
return value
def getEmbedding(
self,
word: str
) -> np.ndarray:
return self.__getValue(word, 'embedding')
def getPCA(
self,
word: str
) -> np.ndarray:
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}!."
assert(nn_method in self.availables_nn_methods), f"Error: The value of the parameter 'nn method' can only be {self.availables_nn_methods}!"
neighbors_list = []
if word not in self:
print(f"The word '{word}' does not exist")
return neighbors_list
if nn_method == 'ann':
if self.ann is None:
self.__init_ann_method(
words=self.ds['word'].to_list(),
vectors=self.ds['embedding'].to_list(),
coord=self.ds['pca'].to_list()
)
neighbors_list = self.ann.get(word, n_neighbors)
elif nn_method == 'sklearn':
if self.neigh is None:
self.__init_sklearn_method(
max_neighbors=self.max_neighbors,
vectors=self.ds['embedding'].to_list()
)
word_emb = self.getEmbedding(word).reshape(1,-1)
_, nn_ids = self.neigh.kneighbors(word_emb, n_neighbors + 1)
neighbors_list = [self.ds['word'].to_list()[idx] for idx in nn_ids[0]][1:]
return neighbors_list
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))
)
def __contains__(
self,
word: str
) -> bool:
return word in self.ds['word'].to_list() |