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label_generation.py
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label_generation.py
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# coding: utf-8
import argparse
import pickle
import re
from os.path import join, exists
from time import time
import pandas as pd
from numpy import dot, float32 as REAL, sqrt, newaxis
from gensim import matutils
from gensim.models import Word2Vec, Doc2Vec
from topic_reranking import METRICS
from utils import init_logging, log_args, load, tprint
from constants import ETL_PATH, PARAMS, NBTOPICS, LDA_PATH, EMB_PATH, DSETS
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=UserWarning)
LOGG = print
def get_word(word):
if type(word) != str:
return word
inst = re.search(r"_\(([A-Za-z0-9_]+)\)", word)
if inst is None:
return word
else:
word = re.sub(r"_\(.+\)", "", word)
return word
def get_labels(
topic, nb_labels, d2v_docvecs, d2v_wv, w2v_wv, w2v_indexed, d_indices, w_indices
):
LOGG(f"Generating labels for {topic.name}")
valdoc2vec = 0.0
valword2vec = 0.0
store_indices = []
topic_len = len(topic)
for item in topic:
try:
# The word2vec value of topic word from doc2vec trained model
tempdoc2vec = d2v_wv.syn0norm[d2v_wv.vocab[item].index]
except:
pass
else:
meandoc2vec = matutils.unitvec(tempdoc2vec).astype(
REAL
) # Getting the unit vector
# The dot product of all labels in doc2vec with the unit vector of topic word
distsdoc2vec = dot(d2v_docvecs.doctag_syn0norm, meandoc2vec)
valdoc2vec = valdoc2vec + distsdoc2vec
try:
# The word2vec value of topic word from word2vec trained model
tempword2vec = w2v_wv.syn0norm[w2v_wv.vocab[item].index]
except:
pass
else:
# Unit vector
meanword2vec = matutils.unitvec(tempword2vec).astype(REAL)
# dot product of all possible labels in word2vec vocab with the unit vector of topic word
distsword2vec = dot(w2v_indexed, meanword2vec)
"""
This next section of code checks if the topic word is also a potential label in trained
word2vec model. If that is the case, it is important the dot product of label with that
topic word is not taken into account.Hence we make that zero and further down the code
also exclude it in taking average of that label over all topic words.
"""
if w2v_wv.vocab[item].index in w_indices:
i_val = w_indices.index(w2v_wv.vocab[item].index)
store_indices.append(i_val)
distsword2vec[i_val] = 0.0
valword2vec = valword2vec + distsword2vec
avgdoc2vec = valdoc2vec / float(
topic_len
) # Give the average vector over all topic words
avgword2vec = valword2vec / float(
topic_len
) # Average of word2vec vector over all topic words
# argsort and get top 100 doc2vec label indices
bestdoc2vec = matutils.argsort(avgdoc2vec, topn=100, reverse=True)
resultdoc2vec = []
# Get the doc2vec labels from indices
for elem in bestdoc2vec:
ind = d_indices[elem]
temp = d2v_docvecs.index_to_doctag(ind)
resultdoc2vec.append((temp, float(avgdoc2vec[elem])))
# This modifies the average word2vec vector for cases
# in which the word2vec label was same as topic word.
for element in store_indices:
avgword2vec[element] = (avgword2vec[element] * topic_len) / (
float(topic_len - 1)
)
# argsort and get top 100 word2vec label indices
bestword2vec = matutils.argsort(avgword2vec, topn=100, reverse=True)
# Get the word2vec labels from indices
resultword2vec = []
for element in bestword2vec:
ind = w_indices[element]
temp = w2v_wv.index2word[ind]
resultword2vec.append((temp, float(avgword2vec[element])))
# Get the combined set of both doc2vec labels and word2vec labels
comb_labels = sorted(
set([i[0] for i in resultdoc2vec] + [i[0] for i in resultword2vec])
)
newlist_doc2vec = []
newlist_word2vec = []
# Get indices from combined labels
for elem in comb_labels:
try:
newlist_doc2vec.append(d_indices.index(d2v_docvecs.doctags[elem].offset))
temp = get_word(elem)
newlist_word2vec.append(w_indices.index(w2v_wv.vocab[temp].index))
except:
pass
newlist_doc2vec = sorted(set(newlist_doc2vec))
newlist_word2vec = sorted(set(newlist_word2vec))
# Finally again get the labels from indices. We searched for the score from both d2v and w2v models
resultlist_doc2vecnew = [
(d2v_docvecs.index_to_doctag(d_indices[elem]), float(avgdoc2vec[elem]))
for elem in newlist_doc2vec
]
resultlist_word2vecnew = [
(w2v_wv.index2word[w_indices[elem]], float(avgword2vec[elem]))
for elem in newlist_word2vec
]
# Finally get the combined score with the label. The label used will be of doc2vec not of word2vec.
new_score = []
for item in resultlist_word2vecnew:
k, v = item
for elem in resultlist_doc2vecnew:
k2, v2 = elem
k3 = get_word(k2)
if k == k3:
v3 = v + v2
new_score.append((k2, v3))
resultlist_doc2vecnew = sorted(
resultlist_doc2vecnew, key=lambda x: x[1], reverse=True
)[:nb_labels]
resultlist_word2vecnew = sorted(
resultlist_word2vecnew, key=lambda x: x[1], reverse=True
)[:nb_labels]
new_score = sorted(new_score, key=lambda x: x[1], reverse=True)[:nb_labels]
return [resultlist_doc2vecnew, resultlist_word2vecnew, new_score]
def load_embeddings(d2v_path, w2v_path, use_ftx=False):
LOGG(f"Doc2Vec loading {d2v_path}")
d2v = Doc2Vec.load(d2v_path)
d2v.delete_temporary_training_data()
LOGG(f"vocab size: {len(d2v.wv.vocab)}")
LOGG(f"docvecs size: {len(d2v.docvecs.vectors_docs)}")
LOGG(f"Word2Vec loading {w2v_path}")
w2v = Word2Vec.load(w2v_path)
if not use_ftx:
w2v.delete_temporary_training_data()
LOGG(f"vocab size: {len(w2v.wv.vocab)}")
return d2v.docvecs, d2v.wv, w2v.wv
def get_phrases(max_title_length, min_doc_length, lemmatized_only=True):
dewiki_phrases_lemmatized = "dewiki_phrases_lemmatized.pickle"
phrases = pd.read_pickle(join(ETL_PATH, dewiki_phrases_lemmatized))
# creating a list containing original and lemmatized phrases
phrases = phrases.query(
f"doc_len >= {min_doc_length} and title_len <= {max_title_length}"
)
if lemmatized_only:
phrases = phrases.token.unique()
else:
phrases = phrases.token.append(phrases.text).unique()
pat = re.compile(r"^[a-zA-ZÄÖÜäöü]+.*")
phrases = filter(lambda x: pat.match(x), phrases)
return phrases
def get_indices(d2v_docvecs, w2v_wv, max_title_length=4, min_doc_length=41):
phrases = get_phrases(
max_title_length=max_title_length, min_doc_length=min_doc_length
)
d2v_indices = []
w2v_indices = []
dout = wout = 0
for label in phrases:
try:
idx = d2v_docvecs.doctags[label].offset
d2v_indices.append(idx)
except:
dout += 1
try:
idx = w2v_wv.vocab[label].index
w2v_indices.append(idx)
except:
wout += 1
return d2v_indices, w2v_indices
def index_embeddings(d2v_docvecs, d2v_wv, w2v_wv, d2v_indices, w2v_indices):
"""
Modifies the argument models. Normalizes the d2v und w2v vectors.
Also reduces the number of d2v docvecs.
"""
# Models normalised in unit vectord from the indices given above in pickle files.
d2v_wv.syn0norm = (
d2v_wv.syn0 / sqrt((d2v_wv.syn0 ** 2).sum(-1))[..., newaxis]
).astype(REAL)
d2v_docvecs.vectors_docs_norm = (
d2v_docvecs.doctag_syn0
/ sqrt((d2v_docvecs.doctag_syn0 ** 2).sum(-1))[..., newaxis]
).astype(REAL)[d2v_indices]
LOGG("doc2vec normalized")
w2v_wv.syn0norm = (
w2v_wv.syn0 / sqrt((w2v_wv.syn0 ** 2).sum(-1))[..., newaxis]
).astype(REAL)
w2v_indexed = w2v_wv.syn0norm[w2v_indices]
LOGG("word2vec normalized")
return w2v_indexed
def load_topics(topics_path, metrics, params, nbtopics, print_sample=False):
LOGG(f"Loading topics {topics_path}")
topics = pd.read_csv(topics_path, index_col=None)
if metrics and "metric" in topics.columns:
topics = topics[topics.metric.isin(metrics)]
if params and "param_id" in topics.columns:
topics = topics[topics.param_id.isin(params)]
if nbtopics and "nb_topics" in topics.columns:
topics = topics[topics.nb_topics.isin(nbtopics)]
for key in [
"dataset",
"metric",
"param_id",
"nb_topics",
"topic_idx",
"topic_id",
"domain",
]:
if key in topics.columns:
topics = topics.set_index(key, append=True)
if print_sample:
LOGG(f"\n{topics.head(10)}")
LOGG(f"number of topics {len(topics)}")
return topics
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--topics_file", type=str, required=False, default=None)
parser.add_argument("--labels_file", type=str, required=False, default=None)
parser.add_argument("--d2v_indices", type=str, required=False, default=None)
parser.add_argument("--w2v_indices", type=str, required=False, default=None)
parser.add_argument(
"--d2v_path", type=str, required=False, default=join(EMB_PATH, "d2v", "d2v")
)
parser.add_argument("--w2v_path", type=str, required=False, default=None)
parser.add_argument(
"--fasttext", dest="use_ftx", action="store_true", required=False
)
parser.add_argument(
"--no-fasttext", dest="use_ftx", action="store_false", required=False
)
parser.set_defaults(use_ftx=False)
parser.add_argument("--dataset", type=str, required=False)
parser.add_argument("--version", type=str, required=False, default="noun")
parser.add_argument("--tfidf", dest="tfidf", action="store_true", required=False)
parser.add_argument(
"--no-tfidf", dest="tfidf", action="store_false", required=False
)
parser.set_defaults(tfidf=False)
parser.add_argument(
"--metrics", nargs="*", type=str, required=False, default=["ref"]
)
parser.add_argument("--rerank", dest="rerank", action="store_true", required=False)
parser.add_argument(
"--no-rerank", dest="rerank", action="store_false", required=False
)
parser.set_defaults(rerank=False)
parser.add_argument(
"--params", nargs="*", type=str, required=False, default=["e42"]
)
parser.add_argument(
"--nbtopics", nargs="*", type=int, required=False, default=[100]
)
parser.add_argument("--total_num_topics", type=int, required=False, default=None)
parser.add_argument("--nblabels", type=int, required=False, default=20)
parser.add_argument("--max_title_length", type=int, required=False, default=4)
parser.add_argument("--min_doc_length", type=int, required=False, default=41)
args = parser.parse_args()
if "all" in args.metrics:
args.metrics = METRICS
if "all" in args.params:
args.params = PARAMS
if -1 in args.nbtopics:
args.nbtopics = NBTOPICS
args.dataset = DSETS.get(args.dataset, args.dataset)
corpus_type = "tfidf" if args.tfidf else "bow"
if args.labels_file is None:
if args.topics_file is not None:
args.labels_file = args.topics_file.strip(".csv") + "_label-candidates"
else:
args.labels_file = join(
LDA_PATH,
args.version,
corpus_type,
"topics",
f"{args.dataset}_{args.version}_{corpus_type}_label-candidates",
)
if args.d2v_indices and args.w2v_indices:
args.max_title_length = None
args.min_doc_length = None
if args.w2v_path is None:
if args.use_ftx:
args.w2v_path = join(EMB_PATH, "ftx", "ftx")
args.labels_file += "_ftx"
else:
args.w2v_path = join(EMB_PATH, "w2v", "w2v")
print_sample = False
return (
args.topics_file,
args.labels_file,
args.d2v_indices,
args.w2v_indices,
args.d2v_path,
args.w2v_path,
args.use_ftx,
args.dataset,
args.version,
corpus_type,
args.rerank,
args.metrics,
args.params,
args.nbtopics,
args.total_num_topics,
args.max_title_length,
args.min_doc_length,
args.nblabels,
print_sample,
args,
)
def main():
global LOGG
(
topics_file,
labels_file,
d2v_indices_file,
w2v_indices_file,
d2v_path,
w2v_path,
use_ftx,
dataset,
version,
corpus_type,
rerank,
metrics,
params,
nbtopics,
total_num_topics,
max_title_length,
min_doc_length,
nb_labels,
print_sample,
args,
) = parse_args()
logger = init_logging(
name=f"Labeling_{dataset}", basic=False, to_stdout=True, to_file=False
)
log_args(logger, args)
LOGG = logger.info
if topics_file is not None:
topics = load_topics(
topics_path=topics_file,
metrics=metrics,
params=params,
nbtopics=nbtopics,
print_sample=print_sample,
)
else:
if rerank:
topics = load("rerank", dataset, version, *params, *nbtopics, logger=logger)
topics = topics.query("metric in @metrics")
print(topics)
else:
topics = load(
"topics",
dataset,
version,
corpus_type,
*params,
*nbtopics,
logger=logger,
)
d2v_docvecs, d2v_wv, w2v_wv = load_embeddings(
d2v_path=d2v_path,
w2v_path=w2v_path,
use_ftx=use_ftx,
)
if d2v_indices_file and w2v_indices_file:
with open(d2v_indices_file, "rb") as fp:
LOGG(f"Loading {d2v_indices_file}")
d2v_indices = pickle.load(fp)
with open(w2v_indices_file, "rb") as fp:
LOGG(f"Loading {w2v_indices_file}")
w2v_indices = pickle.load(fp)
else:
d2v_indices, w2v_indices = get_indices(
d2v_docvecs=d2v_docvecs,
w2v_wv=w2v_wv,
max_title_length=max_title_length,
min_doc_length=min_doc_length,
)
d2v_indices = sorted(set(d2v_indices))
w2v_indices = sorted(set(w2v_indices))
w2v_indexed = index_embeddings(
d2v_docvecs=d2v_docvecs,
d2v_wv=d2v_wv,
w2v_wv=w2v_wv,
d2v_indices=d2v_indices,
w2v_indices=w2v_indices,
)
t0 = time()
labels = topics[:total_num_topics].apply(
lambda row: get_labels(
topic=row,
nb_labels=nb_labels,
d2v_docvecs=d2v_docvecs,
d2v_wv=d2v_wv,
w2v_wv=w2v_wv,
w2v_indexed=w2v_indexed,
d_indices=d2v_indices,
w_indices=w2v_indices,
),
axis=1,
)
t1 = int(time() - t0)
LOGG(f"done in {t1//3600:02d}:{(t1//60) % 60:02d}:{t1 % 60:02d}")
if print_sample:
LOGG(f"\n{labels.head(10)}")
# reformatting output files
col2 = "ftx" if use_ftx else "w2v"
col3 = "comb_ftx" if use_ftx else "comb"
labels = (
labels.apply(pd.Series)
.rename(columns={0: "d2v", 1: col2, 2: col3})
.stack()
.apply(pd.Series)
.rename(columns=lambda x: f"label{x}")
)
if print_sample:
LOGG(f"\n{labels.head(10)}")
if exists(labels_file + ".csv"):
labels_file = labels_file + "_" + str(time()) + ".csv"
else:
labels_file += ".csv"
LOGG(f"Writing labels to {labels_file}")
labels.to_csv(labels_file)
if __name__ == "__main__":
main()