def train(
load: Path = typer.Argument(
...,
help="Path to a file containing documents for training.",
exists=True),
save: Path = typer.Argument(...,
help="Output path for the model.",
callback=check_parent_exists),
embedding_model: Model = typer.Option(
Model.DOC2VEC,
help=
"Which model is used to generate the document and word embeddings.",
),
training_speed: Speed = typer.Option(
Speed.LEARN, help="How fast the model takes to train"),
workers: int = typer.Option(
128,
help="The amount of worker threads to be used in training the model"),
min_count: int = typer.Option(
50, help="Ignores all words with total frequency lower than this"),
noun_phrases: bool = typer.Option(True,
help="Use noun-phrases for training."),
):
"""Train Top2Vec algorithm."""
typer.echo("Loading data...")
docs = load_json(load) if load.suffix == ".json" else load_csv(
load, noun_phrases)
typer.echo(f"Loaded {len(docs)} documents")
speed = training_speed.value
model = embedding_model.value
tokenizer = tokenize if noun_phrases else None
if model == "doc2vec":
typer.echo(
f"Training the model with following parameters: {model=}, {speed=}, {workers=}, {min_count=}, {noun_phrases=}"
)
t2v = Top2Vec(
documents=docs,
embedding_model=model,
speed=speed,
workers=workers,
tokenizer=tokenizer,
min_count=min_count,
)
else:
typer.echo(
f"Training the model with following parameters{model=}, {noun_phrases=}"
)
t2v = Top2Vec(
documents=docs,
embedding_model=model,
tokenizer=tokenizer,
min_count=min_count,
)
typer.echo(f"Saving the model to {save}")
t2v.save(save)
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 top2vec_(df):
import re
import string
def custom_preprocessing(x):
x = x.lower()
x = re.sub(
r"""(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|\(([^\s()<>]+|(\([^\s()<>]+\)))*\))+(?:\(([^\s()<>]+|(\([^\s()<>]+\)))*\)|[^\s`!()\[\]{};:'".,<>?«»“”‘’]))""",
"",
x,
)
for p in list(string.punctuation) + list(string.digits):
x = x.replace(p, "")
x = x.replace(r'\xa0', '')
x = [
a.encode('ascii', 'ignore').decode('ascii') for a in x.split() if a
]
return x
docs = df["tweet"].apply(custom_preprocessing).values
model = Top2Vec(docs, speed="deep-learn", workers=4)
model.save("./top2vec.model")
topic_sizes, topic_nums = model.get_topic_sizes()
print(f'vocab learned: {len(model.model.wv.vocab.keys())}')
print(topic_sizes)
print(topic_nums)
def run_yearly():
"""
Creates yearly topic models and saves it to disk.
"""
print(f'Reading files from {Config.risk_dir()}')
corpus = get_corpus()
yearly_doc_ids = defaultdict(list)
for k in corpus.keys():
yearly_doc_ids[ReportInfo.from_doc_id(k).start_date.year].append(k)
print(f'Read {len(corpus)} files.')
base_dir = os.path.join(Config.top2vec_models_dir(), 'yearly_models')
create_dir_if_not_exists(base_dir)
print(f'Storing yearly models to {base_dir}.')
for year, doc_ids in tqdm(yearly_doc_ids.items(),
total=len(yearly_doc_ids)):
yearly_corpus = [corpus[d] for d in doc_ids]
try:
model = Top2Vec(documents=yearly_corpus,
document_ids=doc_ids,
tokenizer=RiskSectionCleaner(),
keep_documents=False,
speed='learn',
workers=24)
model.save(os.path.join(base_dir, f'{year}_topics'))
except:
print(f'Could not create topic model for year: {year}')
continue
def create_topic(self):
corpus = self.get_corpus()
doc_ids, docs = list(zip(*corpus.items()))
return Top2Vec(docs,
document_ids=doc_ids,
speed='learn',
tokenizer=RiskSectionCleaner(),
workers=16)
def main():
st.set_option('deprecation.showfileUploaderEncoding', False)
@st.cache(suppress_st_warning=True)
def load_data(uploaded_file):
df = pd.read_csv(uploaded_file)
return df
uploaded_file = st.file_uploader('Upload CSV file to begin', type='csv')
#if upload then show left bar
if uploaded_file is not None:
df = load_data(uploaded_file)
st.sidebar.subheader("Text column to analyse")
st_ms = st.sidebar.selectbox("Select Text Columns To Analyse",
(df.columns.tolist()))
import nltk
import top2vec
from top2vec import Top2Vec
#INITIALIZE AN EMPTY DATAFRAME, CONVERT THE TEXT INTO STRING AND APPEND INTO THE NEW COLUMN
d1 = pd.DataFrame()
d1['text'] = ""
d1['text'] = df[st_ms]
d1['text'] = d1['text'].astype(str)
for x in range(len(d1)):
d1.text.iloc[x] = d1.text.iloc[x].lower() #to lower case
d1.text.iloc[x] = re.sub(r"@\S+", "",
d1.text.iloc[x]) #remove mentions
d1.text.iloc[x] = re.sub(r"http\S+", "",
d1.text.iloc[x]) #remove hyperlinks
d1.text.iloc[x] = ''.join([
word for word in d1.text.iloc[x] if not word.isdigit()
]) #remove numbers
d1.text.iloc[x] = nltk.word_tokenize(d1.text.iloc[x]) #tokenising
d1.text.iloc[x] = [
i for i in d1.text.iloc[x] if not i in english_stop_words
] #remove stop words
d1.text.iloc[x] = [
i for i in d1.text.iloc[x] if not i in malay_stop_words
]
d1.text.iloc[x] = [i for i in d1.text.iloc[x]
if len(i) > 2] #too short potong
print('Completed line : ', x)
#INITIALIZE THE TOP2VEC MODEL AND FIT THE TEXT
#model.build_vocab(df_list, update=False)
model = Top2Vec(documents=d1['text'], speed="learn", workers=10)
topic_sizes, topic_nums = model.get_topic_sizes()
for topic in topic_nums:
st.pyplot(model.generate_topic_wordcloud(topic))
def top2vec_model():
newsgroups_train = fetch_20newsgroups(subset='all',
remove=('headers', 'footers',
'quotes'))
top2vec = Top2Vec(newsgroups_train.data[0:1000],
speed="fast-learn",
workers=8)
return top2vec
def main():
if not sys.argv[1:]:
print("need to give dataset path!")
exit(1)
data_file = sys.argv[1:][0]
print(data_file)
save_file = "top2vec.model"
docs, doc_ids = parser(data_file)
model = Top2Vec(documents=docs, document_ids=doc_ids, min_count=20, keep_documents=False,
use_corpus_file=True, workers=multiprocessing.cpu_count(),
verbose=True)
model.save(save_file)
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
def train_top2vec(**kwargs):
print('Building corpus')
t2v_corpus = build_top2vec_corpus(
build_spans(sentences, keywords, **kwargs))
print(f'corpus length: {len(t2v_corpus)}')
print('Training...')
t2v = Top2Vec(
documents=list(t2v_corpus.values()),
document_ids=list(t2v_corpus.keys()),
min_count=20,
speed='learn',
workers=multiprocessing.cpu_count(),
embedding_model_path='../data/models/word2vec_270422/keyedvectors.txt')
num_topics = t2v.get_num_topics()
print(f'Found {num_topics} topics')
return t2v
def run_all():
"""
Creates a topic model for the entire corpus and saves it to disk.
"""
print(f'Reading files from {Config.risk_dir()}')
corpus = get_corpus()
print(f'Read {len(corpus)} files.')
doc_ids, docs = list(zip(*corpus.items()))
model = Top2Vec(docs,
document_ids=doc_ids,
tokenizer=RiskSectionCleaner(),
keep_documents=False,
speed='learn',
workers=24)
model_path = os.path.join(Config.top2vec_models_dir(),
'top2vec_model_phrases')
model.save(model_path)
print(f'Saved model to {model_path}')
import pytest
from top2vec import Top2Vec
from sklearn.datasets import fetch_20newsgroups
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
# get 20 newsgroups data
newsgroups_train = fetch_20newsgroups(subset='all',
remove=('headers', 'footers', 'quotes'))
newsgroups_documents = newsgroups_train.data[0:2000]
# train top2vec model without doc_ids provided
top2vec = Top2Vec(documents=newsgroups_documents,
speed="fast-learn",
workers=8)
# train top2vec model with doc_ids provided
doc_ids = [str(num) for num in range(0, len(newsgroups_documents))]
top2vec_docids = Top2Vec(documents=newsgroups_documents,
document_ids=doc_ids,
speed="fast-learn",
workers=8)
# train top2vec model without saving documents
top2vec_no_docs = Top2Vec(documents=newsgroups_documents,
keep_documents=False,
speed="fast-learn",
workers=8)
# train top2vec model with corpus_file
top2vec_corpus_file = Top2Vec(documents=newsgroups_documents,
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):
count_by_customer = customers_reviews.groupby(['externalHandle'])['channel'].count().reset_index(name = 'count')
plt.figure(figsize=(8,4))
x1 = count_by_customer['count']
plt.hist(x1, color='#7F0442', label='Customers', bins = 30)
plt.title("Distribution Number of Reviews per Customer", fontweight='bold', fontsize = 14, family = 'monospace')
plt.xlabel("Number of Reviews")
plt.legend()
plt.savefig('images/distribution_per_customer.png')
plt.show()
customers_reviews['comment'][0]
## Train the model
customers_reviews_list = list(customers_reviews["comment"])
model = Top2Vec(customers_reviews_list,speed="deep-learn", workers=8 )
"""
documents: Input corpus, should be a list of strings.
speed: This parameter will determine how fast the model takes to train. The 'fast-learn' option is the fastest and will generate the lowest quality vectors. The 'learn' option will learn better quality vectors but take a longer time to train. The 'deep-learn' option will learn the best quality vectors but will take significant time to train.
workers: The amount of worker threads to be used in training the model. Larger amount will lead to faster training.
"""
## Get the number of topics
model.get_num_topics()
## Get the topics
topic_words, word_scores, topic_nums = model.get_topics(117)
## View the topics
topic_words[1]
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 pytest
from top2vec import Top2Vec
from sklearn.datasets import fetch_20newsgroups
import numpy as np
# get 20 newsgroups data
newsgroups_train = fetch_20newsgroups(subset='all',
remove=('headers', 'footers', 'quotes'))
newsgroups_documents = newsgroups_train.data[0:2000]
# train top2vec model without doc_ids provided
top2vec = Top2Vec(documents=newsgroups_documents,
speed="fast-learn",
workers=8)
# train top2vec model with doc_ids provided
doc_ids = [str(num) for num in range(0, len(newsgroups_documents))]
top2vec_docids = Top2Vec(documents=newsgroups_documents,
document_ids=doc_ids,
speed="fast-learn",
workers=8)
# train top2vec model without saving documents
top2vec_no_docs = Top2Vec(documents=newsgroups_documents,
keep_documents=False,
speed="fast-learn",
workers=8)
# train top2vec model with corpus_file
top2vec_corpus_file = Top2Vec(documents=newsgroups_documents,
use_corpus_file=True,
def load_model():
return Top2Vec.load("data/doc2vec_production")
with open('sentences.bin', 'rb') as f:
sentences = pickle.load(f)
# Commented out IPython magic to ensure Python compatibility.
# import tensorflow as tf
import tensorflow_datasets as tfds
from tensorboard.plugins import projector
#load tensorboards with magics
# %tensorflow_version 2.x
# %load_ext tensorboard
documents = random.choices(sentences,k = 100000)
documents = [d.lower() for d in documents]
from top2vec import Top2Vec
model = Top2Vec(documents, embedding_model='universal-sentence-encoder', workers=4)
model.hierarchical_topic_reduction(100)
# model.get_topic_hierarchy()
# topic_sizes, topic_nums = model.get_topic_sizes()
print("Original Number of topics: ", model.get_num_topics())
print("Reduced number of topics: ", model.get_num_topics(reduced=True))
#word similarity
topic_words, word_scores, topic_scores, topic_nums = model.search_topics(keywords=["swimming"], num_topics=3, reduced=False) #swimming, cleanliness #keywords_neg=stop,
for topic in topic_nums:
model.generate_topic_wordcloud(topic)
#reduce the number of topics and do hierarchical clustering
# topic_words, word_scores, topic_nums = model.get_topics(reduced=True)
# topic_nums = random.choices(topic_nums, k=20)
def main():
st.set_option('deprecation.showfileUploaderEncoding', False)
st.title("HATI.AI")
image = Image.open('macroview.jpg')
#st.image(image, use_column_width=False)
st.sidebar.image(image)
st.sidebar.title("Hati.Ai Web App")
menu = ["Login","SignUp"]
choice = st.sidebar.selectbox("Menu",menu)
if choice == "Login":
st.subheader("Login Section")
username = st.sidebar.text_input("User Name")
password = st.sidebar.text_input("Password",type='password')
if st.sidebar.checkbox("Login"):
# if password == '12345':
create_usertable()
hashed_pswd = make_hashes(password)
result = login_user(username,check_hashes(password,hashed_pswd))
if result:
st.success("Logged In as {}".format(username))
def process_text(text):
processed_data = []
# Make all the strings lowercase and remove non alphabetic characters
#text = re.sub('[^A-Za-z]', ' ', text.lower())
# Tokenize the text; this is, separate every sentence into a list of words
# Since the text is already split into sentences you don't have to call sent_tokenize
tokenized_text = word_tokenize(text)
#append the result into a new list called processed_data
processed_data.append(tokenized_text)
# Remember, this final output is a list of words
return processed_data
@st.cache(suppress_st_warning=True)
def load_data(uploaded_file):
df = pd.read_csv(uploaded_file)
return df
st.sidebar.subheader("Choose What Do You Want To Do")
classifier = st.sidebar.selectbox(" ", ("Find new topics automatically", "POWER BI Dashboard", "Interact with our chatbot"))
if classifier == 'POWER BI Dashboard':
import streamlit.components.v1 as components
from urllib.request import urlopen
html = urlopen("https://app.powerbi.com/view?r=eyJrIjoiZTA4NWU4MjYtOTk3Yi00N2ZhLTgwZWQtZWFhMzNkNDk1Zjk3IiwidCI6Ijk5NmQwYTI3LWUwOGQtNDU1Ny05OWJlLTY3ZmQ2Yjk3OTA0NCIsImMiOjEwfQ%3D%3D&pageName=ReportSection06db5928b6af61b2868f").read()
#components.html(html, width=None, height=600, scrolling=True)
st.markdown("""
<iframe width="900" height="606" src="https://app.powerbi.com/view?r=eyJrIjoiZTA4NWU4MjYtOTk3Yi00N2ZhLTgwZWQtZWFhMzNkNDk1Zjk3IiwidCI6Ijk5NmQwYTI3LWUwOGQtNDU1Ny05OWJlLTY3ZmQ2Yjk3OTA0NCIsImMiOjEwfQ%3D%3D&pageName=ReportSection06db5928b6af61b2868f" frameborder="0" style="border:0" allowfullscreen></iframe>
""", unsafe_allow_html=True)
if classifier == 'Interact with our chatbot':
import pickle
with open('tnb_topic_classifier_svm', 'rb') as training_model:
topic_model = pickle.load(training_model)
import malaya
model = malaya.sentiment.transformer(model = 'albert', size = 'base')
#from src import model
#malay_bert = model.BertModel()
# eng_flair = model.Flair()
# eng_vader = model.Vader()
test = pd.DataFrame()
test['Positive'] = ''
test['Neutral'] = ''
test['Negative'] = ''
st.title("Sentiment Analyzer")
message = st.text_area("Enter Text","Type Here ..")
if st.button("Analyze"):
with st.spinner("Analyzing the text …"):
result = model.predict_proba([message])
#result = malay_bert.predict(message)
message = [message]
topic = topic_model.predict(message)
#output = "Result is: Positive:" + str(result[0]) + "Neutral:" + str(result[1]) + "Negative:" + str(result[2]) + "topic is: " + str(topic)
output = "result is:" + str(result) + "topic is: " + str(topic)
st.write(output)
else:
st.warning("Not sure! Try to add some more words")
from stop_words import get_stop_words
if classifier == 'Find new topics automatically':
uploaded_file = st.file_uploader('Upload CSV file to begin', type='csv')
#if upload then show left bar
if uploaded_file is not None:
df = load_data(uploaded_file)
if st.sidebar.checkbox("Show raw data", False):
st.subheader("Uploaded Data Set")
st.write(df)
st.sidebar.subheader("Text column to analyse")
st_ms = st.sidebar.selectbox("Select Text Columns To Analyse", (df.columns.tolist()))
df_list = list(df)
import top2vec
from top2vec import Top2Vec
#INITIALIZE AN EMPTY DATAFRAME, CONVERT THE TEXT INTO STRING AND APPEND INTO THE NEW COLUMN
d1 = pd.DataFrame()
d1['text'] = ""
d1['text'] = df[st_ms]
d1['text'] = d1['text'].astype(str)
#INITIALIZE THE TOP2VEC MODEL AND FIT THE TEXT
#model.build_vocab(df_list, update=False)
model = Top2Vec(documents=d1['text'], speed="learn", workers=10)
topic_sizes, topic_nums = model.get_topic_sizes()
for topic in topic_nums:
st.pyplot(model.generate_topic_wordcloud(topic))
# Display the generated image:
else:
st.warning("Incorrect Username/Password")
elif choice == "SignUp":
st.subheader("Create New Account")
new_user = st.text_input("Username")
new_password = st.text_input("Password",type='password')
if st.button("Signup"):
create_usertable()
add_userdata(new_user,make_hashes(new_password))
st.success("You have successfully created a valid Account")
st.info("Go to Login Menu to login")
all_txt_files = []
# adds each transcript to a list
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))
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
from top2vec import Top2Vec
content = pd.read_csv('../data/content-clean.csv')
documents = content['text'].values.tolist()
model = Top2Vec(documents, speed='learn', workers=8)
model.save("content-model.bin")
def __init__(self, *args, **kwargs):
"""Initialize Top2Vec with the given args"""
self.__model__ = Top2Vec(*args, **kwargs)
from sklearn.datasets import fetch_20newsgroups
from top2vec import Top2Vec
import numpy as np
news_groups_train = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'))
# news_groups_documents = news_groups_train[]
model = Top2Vec(documents=news_groups_train.data[0:2000], use_corpus_file=True, speed='fast-learn', workers=8)
print("Number of Topics:- ", model.get_num_topics())
# Get Topic Sizes
topic_sizes, topic_nums = model.get_topic_sizes()
for i in range(len(topic_nums)):
print(str(topic_nums[i]) + ":- " + str(topic_sizes[i]))
# Get Topics
topic_words, word_scores, topic_nums = model.get_topics()
for i in range(len(topic_nums)):
print(str(topic_nums[i]) + ": " + str(topic_sizes[i]) + ": " + str(topic_words[i]))
# topic_words, word_scores, topic_scores, topic_nums = model.search_topics(keywords=["medicine"], num_topics=5)
# for i in range(topic_nums):
# print
print("Done!!!!")
import pandas as pd
from top2vec import Top2Vec
June= pd.read_excel('./dataset/NewsResult_20200601-20200630.xlsx')
July= pd.read_excel('./dataset/NewsResult_20200701-20200731.xlsx')
August= pd.read_excel('./dataset/NewsResult_20200801-20200831.xlsx')
September= pd.read_excel('./dataset/NewsResult_20200901-20200930.xlsx')
October= pd.read_excel('./dataset/NewsResult_20201001-20201029.xlsx')
daylist_June= June[['일자','키워드']].groupby('일자').apply(lambda d: ",".join(d['키워드']))
daylist_July= July[['일자','키워드']].groupby('일자').apply(lambda d: ",".join(d['키워드']))
daylist_August= August[['일자','키워드']].groupby('일자').apply(lambda d: ",".join(d['키워드']))
daylist_September= September[['일자','키워드']].groupby('일자').apply(lambda d: ",".join(d['키워드']))
daylist_October= October[['일자','키워드']].groupby('일자').apply(lambda d: ",".join(d['키워드']))
model = Top2Vec(documents=daylist_June[20200601].split(','), speed="learn", workers=5)
topic_sizes, topic_nums = model.get_topic_sizes()
print(topic_sizes)
print(topic_nums)
model.get_topics()[0]
model.generate_topic_wordcloud(0)
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
# {
# "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
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')
data["pubdate"] = data["pubdate"].astype(str).str[0:4]
data = data[pd.to_numeric(data["pubdate"], errors="coerce").notnull()]
data = data.dropna(subset=["pubdate"])
data["pubdate"] = data["pubdate"].astype(float)
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
def test_similar_words_index(top2vec_model):
temp = tempfile.NamedTemporaryFile(mode='w+b')
top2vec_model.save(temp.name)
Top2Vec.load(temp.name)
temp.close()
