def load_vectors():
model = Top2Vec.load("./models/top2vec.model")
topic_vectors = model.topic_vectors
tweet_vectors = model.model.docvecs.vectors_docs
pca_tweet_vec = pca.fit_transform(tweet_vectors)
pca_topic_vec = pca.fit_transform(topic_vectors)
return pca_tweet_vec, pca_topic_vec, model
def _test_topics():
def _get_noun_phrases(text):
pass
model_path = os.path.join(Config.top2vec_models_dir(),
'top2vec_model_with_doc_ids')
model = Top2Vec.load(model_path)
print('Creating topics.')
for topic_size, topic_num in zip(*model.get_topic_sizes()):
if topic_num < 200:
continue
_, doc_ids = model.search_documents_by_topic(topic_num,
num_docs=topic_size,
return_documents=False)
topic_words = model.topic_words[topic_num]
word_scores = model.topic_word_scores[topic_num]
topic = Topic(topic_words, word_scores, topic_num, doc_ids)
neg_words = topic.get_negative_terms()
a = 1
data = data.dropna(subset=["pubdate"])
#data = data.loc[(data.pubdate >= 2010) & (data.pubdate <=2020)]
data = data.sort_values("pubdate")
data = data.reset_index()
print("TOTAL RECORDS:" + str(len(data)))
documents = data["combined"].tolist()
import time
start = time.perf_counter()
model = Top2Vec(documents, speed="deep-learn", workers=8)
stop = time.perf_counter()
print(f"Runtime {start - stop:0.4f} seconds")
model.save("models/top2vec_d2v")
model = Top2Vec.load("models/top2vec_d2v")
print("Number of Topics Identified:" + str(model.get_num_topics()))
model.model.init_sims()
data = model.model.docvecs.vectors_docs
umap_args = {'n_neighbors': 15, 'n_components': 5, 'metric': 'cosine'}
umap_model = umap.UMAP(**umap_args).fit(model.model.docvecs.vectors_docs)
# find dense areas of document vectors
hdbscan_args = {
'min_cluster_size': 15,
'metric': 'euclidean',
'cluster_selection_method': 'eom'
for file in transcriptsLocation.rglob("*.txt"):
all_txt_files.append(file.name)
all_txt_files.sort()
all_docs = []
# adds each transcript to a list in string format for processing
for txt_file in all_txt_files:
with open(transcriptsLocation / txt_file, encoding="utf-8") as f:
txt_file_as_string = f.read()
all_docs.append(txt_file_as_string)
model = Top2Vec(all_docs, speed="fast-learn")
model.save("modelTwo")
model = Top2Vec.load("modelTwo")
def printMetaData():
# get total number of model topics
print("\nThe total number of model topics are: " +
str(model.get_num_topics()))
# This will return the number of documents most similar to each topic.
topic_sizes, topic_nums = model.get_topic_sizes()
print("\nThe number of documents most similar to each topic are: " +
str(topic_sizes))
print("The unique indexes of each topic are: " + str(topic_nums))
def printModelTopics():
# {
# "paperTitle1": "paperContent1",
# "paperTitle2": "paperContent2",
# ...
# }
paper_content = {}
# a two dimensional dictionary
# paper_similarity["paperTitle1"]["paperTitle2"] gives the similarity
# between two papers
paper_similarity = {}
# model.documents --> content
# model.document_ids --> title
model = Top2Vec.load('model.thelibrarian')
model_lock = Lock()
for title in model.document_ids:
paper_similarity[title] = {}
doc_scores, doc_ids = model.search_documents_by_documents(
doc_ids=[title],
num_docs=len(model.documents) - 1,
return_documents=False)
for score, doc_id in zip(doc_scores, doc_ids):
paper_similarity[title][doc_id] = score
app = Flask(__name__)
CORS(app)
paper_positions = None
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import List
from top2vec import Top2Vec
app = FastAPI(title="Top2Vec API",
description="Speak REST to a Top2Vec trained model.",
version="1.0.0", )
top2vec = Top2Vec.load("top2vec_model/top2vec_20newsgroups")
@app.exception_handler(ValueError)
async def value_error_handler(request: Request, exc: ValueError):
return JSONResponse(
status_code=404,
content={"message": str(exc)},
)
class NumTopics(BaseModel):
num_topics: int
class TopicSizes(BaseModel):
topic_nums: List[int]
topic_sizes: List[int]
class Topic(BaseModel):
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel, BaseSettings
from typing import List
from top2vec import Top2Vec
import numpy as np
class Settings(BaseSettings):
model_name: str = "Top2Vec API"
model_path: str
settings = Settings()
top2vec = Top2Vec.load(settings.model_path)
app = FastAPI(
title=settings.model_name,
description="RESTful Top2Vec API",
version="1.0.0",
)
@app.exception_handler(ValueError)
async def value_error_handler(request: Request, exc: ValueError):
return JSONResponse(
status_code=404,
content={"message": str(exc)},
)
import sys, multiprocessing
from top2vec import Top2Vec
import umap, hdbscan
import logging
from joblib import dump, load
logger = logging.getLogger('gensim')
logger.setLevel(logging.INFO)
sh = logging.StreamHandler(sys.stderr)
sh.setFormatter(
logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logger.addHandler(sh)
model_file = 'top2vec.model'
model = Top2Vec.load(model_file)
wvs = model.model.wv.vectors
docvecs = model._get_document_vectors()
dims = [5, 3, 2]
metrics = ['cosine', 'euclidean']
for dim in dims:
for metric in metrics:
logger.info('Creating ' + str(dim) +
' dimension embedding of documents with ' + metric)
umap_model = umap.UMAP(n_neighbors=15, n_components=dim,
metric=metric).fit(docvecs)
#logger.info('Finding dense areas of documents')
import os
from top2vec import Top2Vec
from config import Config
from risk_detection.preprocessing.report_parser import (
report_info_from_risk_path)
from risk_detection.utils import (get_company_industry_mapping,
get_sik_industry_name_mapping,
get_risk_filenames)
def _get_noun_phrases(txt):
pass
model_path = os.path.join(Config.top2vec_models_dir(),
'top2vec_model_with_doc_ids')
model = Top2Vec.load(model_path)
cik_sic_df = get_company_industry_mapping()
sic_name_df = get_sik_industry_name_mapping()
risk_files = get_risk_filenames()
# TODO: Add diversification detection code
for risk_file in risk_files:
report_info = report_info_from_risk_path(risk_file)
doc_id = report_info.get_document_id()
topic = model.get_documents_topics(doc_ids=(doc_id, ))
def __post_init__(self):
self.model = Top2Vec.load(self.model_path)
self.vectors = None
self.topic_labels = None
def load_model():
return Top2Vec.load("data/doc2vec_production")
def test_similar_words_index(top2vec_model):
temp = tempfile.NamedTemporaryFile(mode='w+b')
top2vec_model.save(temp.name)
Top2Vec.load(temp.name)
temp.close()
