BERTopic/my_topic_modeling.py

import os # Miscellaneous operating system interfaces
os.environ["TOKENIZERS_PARALLELISM"] = "false"

from os.path import join # path joining
from pathlib import Path # path joining

import pandas as pd
import numpy as np
import sklearn as sk
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
import regex as re
from scipy.cluster import hierarchy as sch

import datetime
import time
import timeit
import json
import pickle

import copy
import random
from itertools import chain

import logging
import sys
import argparse
import nltk
nltk.download('wordnet')
nltk.download('punkt')


import textblob
from textblob import TextBlob
from textblob.wordnet import Synset
from textblob import Word
from textblob.wordnet import VERB

from bertopic import BERTopic
from bertopic.vectorizers import ClassTfidfTransformer
from bertopic.representation import KeyBERTInspired, MaximalMarginalRelevance
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from sentence_transformers import SentenceTransformer
# from cuml.manifold import UMAP
# from umap import UMAP
# from hdbscan import HDBSCAN
from cuml.cluster import HDBSCAN
from cuml.manifold import UMAP

import gensim.corpora as corpora
from gensim.models.coherencemodel import CoherenceModel


# Get working directory
working_dir =  os.path.abspath(os.path.join("/notebooks", "TopicModelingRepo"))
data_dir = os.path.join(working_dir, 'data')
lib_dir = os.path.join(working_dir, 'libs')
outer_output_dir = os.path.join(working_dir, 'outputs')

output_dir_name = time.strftime('%Y_%m_%d')
# output_dir_name = time.strftime(args.datetime)

output_dir = os.path.join(outer_output_dir, output_dir_name)
if not os.path.exists(output_dir):
    os.makedirs(output_dir)
    
stopwords_path = os.path.join(data_dir, 'vietnamese_stopwords_dash.txt')
        
    # Setting variables
doc_time = '2024_Jan_15'
doc_type = 'reviews'
doc_level = 'sentence'
target_col = 'normalized_content'



def create_logger_file_and_console():
    # create logger for "Sample App"
    logger = logging.getLogger('automated_testing')
    logger.setLevel(logging.DEBUG)

    # create file handler which logs even debug messages
    fileh = logging.FileHandler('info.log', mode='a')
    fileh.setLevel(logging.DEBUG)

    # create console handler with a higher log level
    consoleh = logging.StreamHandler(stream=sys.stdout)
    consoleh.setLevel(logging.INFO)

    # create formatter and add it to the handlers
    formatter = logging.Formatter('[%(asctime)s] %(levelname)8s --- %(message)s ',datefmt='%H:%M:%S')
    fileh.setFormatter(formatter)
    consoleh.setFormatter(formatter)

    # add the handlers to the logger
    # logger.addHandler(consoleh)
    logger.addHandler(fileh)

    return logger

def create_logger_file():
    # create logger for "Sample App"
    logger = logging.getLogger('automated_testing')
    logger.setLevel(logging.INFO)

    # create file handler which logs even debug messages
    fileh = logging.FileHandler('info.log', mode='a')
    fileh.setLevel(logging.INFO)

    # create formatter and add it to the handlers
    formatter = logging.Formatter('[%(asctime)s] %(levelname)8s --- %(message)s ',datefmt='%H:%M:%S')
    fileh.setFormatter(formatter)

    # add the handlers to the logger
    logger.addHandler(fileh)

    return logger

def create_logger_console():
     # create logger for "Sample App"
    logger = logging.getLogger('automated_testing')
    logger.setLevel(logging.INFO)


    # create console handler with a higher log level
    consoleh = logging.StreamHandler(stream=sys.stdout)
    consoleh.setLevel(logging.INFO)

    # create formatter and add it to the handlers
    formatter = logging.Formatter('[%(asctime)s] %(levelname)8s --- %(message)s ',datefmt='%H:%M:%S')
    consoleh.setFormatter(formatter)

    # add the handlers to the logger
    logger.addHandler(consoleh)

    return logger


def init_args():
    parser = argparse.ArgumentParser()
    # basic settings
    parser.add_argument(
        "--n_topics",
        type=int,
        default=10,
        required=True,
        help="Number of topics for topic modeling.",
    )
    
    parser.add_argument(
        "--name_dataset",
        default="booking",
        type=str,
        help="The name of the dataset, selected from: [booking, tripadvisor]",
    )
    
    parser.add_argument(
        "--train_both",
        default="yes",
        type=str,
        required=True,
        help="Train both booking and tripadvisor or only one.",
    )
    
    parser.add_argument(
        "--only_coherence_score",
        default="yes",
        type=str,
        required=True,
        help="Only train both models for calculating coherence score.",
    )
    
    args = parser.parse_args()


    return args
    
def check_valid(list_topics):
    count = 0
    for topic in list_topics:
        if topic[0] != '':
            count += 1
    
    return True if count > 2 else False


def prepare_data(doc_source, doc_type, type_framework = 'pandas'):
    name_file = doc_source.split('.')[0]
    out_dir = os.path.join(output_dir, name_file)
    if not os.path.exists(out_dir):
        os.makedirs(out_dir)

    date_col = 'Date'
    df_reviews_path = os.path.join(data_dir, doc_source)

    if type_framework == 'pandas':
        df_reviews = pd.read_csv(df_reviews_path, lineterminator='\n', encoding='utf-8') # Pandas
        df_reviews = df_reviews.loc[df_reviews['year']>0] # Pandas
        df_reviews = df_reviews.loc[df_reviews['language'] == 'English'] # Pandas

        if doc_type == 'reviews':
            df_doc = df_reviews
            df_doc['dates'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \
                    dt.to_period('M'). \
                    dt.strftime('%Y-%m-%d') # pandas


        #             timestamps = df_doc['dates'].to_list()
        #             df_doc = df_doc.loc[(df_doc['dates']>='2020-04-01') & (df_doc['dates']<'2022-01-01')]

            df_doc['dates_yearly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \
                dt.to_period('Y'). \
                dt.strftime('%Y') # pandas


            df_doc['dates_quarterly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \
                dt.to_period('d'). \
                dt.strftime('%YQ%q') # pandas


            df_doc['dates_monthly'] = pd.to_datetime(df_doc[date_col],dayfirst=False,errors='coerce'). \
                dt.to_period('M'). \
                dt.strftime('%Y-%m')

    elif type_framework == 'polars':
        df_reviews = pl.read_csv(df_reviews_path, separator='\n') # Polars
        df_reviews = df_reviews.filter(pl.col("year")>0) # Polars
        df_reviews = df_reviews.filter(pl.col('language') == 'English') # Polars

        if doc_type == 'reviews':
            df_doc = df_reviews

            df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \
                            to_period('M'). \
                            strftime('%Y-%m-%d').alias('dates')) # polars

            df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \
                            to_period('Y'). \
                            strftime('%Y').alias('dates_yearly')) # polars


            df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \
                            to_period('d'). \
                            strftime('%YQ%q').alias('dates_quarterly')) # polars


            df_doc = df_doc.with_column(pl.col(date_col).str_to_datetime(dayfirst=False, errors='coerce'). \
                            to_period('M'). \
                            strftime('%Y-%m').alias('dates_monthly')) # polars


    timestamps_dict = dict()
    timestamps_dict['yearly'] = df_doc['dates_yearly'].to_list()
    timestamps_dict['quarterly'] = df_doc['dates_quarterly'].to_list()
    timestamps_dict['monthly'] = df_doc['dates_monthly'].to_list()
    timestamps_dict['date'] = df_doc['dates'].to_list()

    target_col = 'normalized_content'
    df_documents = df_doc[target_col]

    return (timestamps_dict, df_doc, df_documents, df_reviews)    
    
def flatten_comprehension(matrix):
    return [item for row in matrix for item in row]

def processing_data(df_doc, df_documents, timestamps_dict, doc_level, target_col):

    if doc_level == 'sentence':
    #     num_sent = [len(TextBlob(row).sentences) for row in df_doc[target_col]]
    #     df_documents = pd.Series(flatten_comprehension([[str(sentence) for sentence in TextBlob(row).sentences] for row in df_documents]))

        # Split sentence which "."
        ll_sent = [[str(sent) for sent in nltk.sent_tokenize(row,language='english')] for row in df_doc[target_col]]

        # Count number sentence for each comment
        num_sent = [len(x) for x in ll_sent]

        # Flat m' sentence in N comment to m'*N comment
        df_documents = pd.Series(flatten_comprehension([x for x in ll_sent]))

    #     timestamps = list(chain.from_iterable(n*[item] for item, n in zip(timestamps, num_sent)))

        # Copy timestamp features to number sentence times for each comment and flatten them adopt with new m'*N comment
        for key in timestamps_dict.keys():
            timestamps_dict[key] = list(chain.from_iterable(n*[item] for item, n in zip(timestamps_dict[key], num_sent)))
    #     time_slice = df_doc['year'].value_counts().sort_index().tolist()
    #     time_slice = np.diff([np.cumsum(num_sent)[n-1] for n in np.cumsum(time_slice)],prepend=0).tolist()
    # elif doc_level == 'whole':
    #     df_documents

    # Copy id features to number sentence times for each comment and flatten them adopt with new m'*N comment
    sent_id_ll = [[j]*num_sent[i] for i,j in enumerate(df_doc.index)]
    sent_id = flatten_comprehension(sent_id_ll)

    # Define a new data frame with new m'*N comment
    df_doc_out = pd.DataFrame({
                'sentence':df_documents, 'review_id':sent_id,
                'date':timestamps_dict['date'],
                'monthly':timestamps_dict['monthly'],
                'quarterly':timestamps_dict['quarterly'],
                'yearly':timestamps_dict['yearly']})


    return df_documents, timestamps_dict, sent_id, df_doc_out

def create_model_bertopic_booking(n_topics: int = 10):
    sentence_model = SentenceTransformer("thenlper/gte-small")

    # Get 50 neighbor datapoints and 10 dimensional with metric distance: euclidean
    umap_model = UMAP(n_neighbors=50, n_components=10,
                      min_dist=0.0, metric='euclidean',
                      low_memory=True,
                      random_state=1)


    cluster_model = HDBSCAN(min_cluster_size=50, metric='euclidean',
                            cluster_selection_method='leaf',
    #                         cluster_selection_method='eom',
                            prediction_data=True,
                            leaf_size=20,
                            min_samples=10)


    # cluster_model = AgglomerativeClustering(n_clusters=11)
    vectorizer_model = CountVectorizer(min_df=1,ngram_range=(1, 1),stop_words="english")
    ctfidf_model = ClassTfidfTransformer()
    # representation_model = KeyBERTInspired()

    # Diversity param is lambda in equation of Maximal Marginal Relevance
    representation_model = MaximalMarginalRelevance(diversity=0.7,top_n_words=10)


    # Create model
    topic_model = BERTopic(embedding_model=sentence_model,
                       umap_model=umap_model,
                       hdbscan_model=cluster_model,
                       vectorizer_model=vectorizer_model,
                       ctfidf_model=ctfidf_model,
                       representation_model=representation_model,
                       # zeroshot_topic_list=zeroshot_topic_list,
                       # zeroshot_min_similarity=0.7,
                       nr_topics = n_topics,
                       top_n_words = 10,
                       low_memory=True,
                        verbose=True)

    return topic_model

def create_model_bertopic_tripadvisor(n_topics: int = 10):
    sentence_model = SentenceTransformer("thenlper/gte-small")

    # Get 50 neighbor datapoints and 10 dimensional with metric distance: euclidean
    umap_model = UMAP(n_neighbors=200, n_components=10,
                  min_dist=0.0, metric='euclidean',
                  low_memory=True,
                  random_state=1)


    cluster_model = HDBSCAN(min_cluster_size=500, metric='euclidean',
                        cluster_selection_method='leaf',
                        prediction_data=True,
                        leaf_size=100,
                        min_samples=10)


    # cluster_model = AgglomerativeClustering(n_clusters=11)
    vectorizer_model = CountVectorizer(min_df=10,ngram_range=(1, 1),stop_words="english")
    ctfidf_model = ClassTfidfTransformer()
    # representation_model = KeyBERTInspired()

    # Diversity param is lambda in equation of Maximal Marginal Relevance
    representation_model = MaximalMarginalRelevance(diversity=0.7,top_n_words=10)


    # Create model
    topic_model = BERTopic(embedding_model=sentence_model,
                       umap_model=umap_model,
                       hdbscan_model=cluster_model,
                       vectorizer_model=vectorizer_model,
                       ctfidf_model=ctfidf_model,
                       representation_model=representation_model,
                       # zeroshot_topic_list=zeroshot_topic_list,
                       # zeroshot_min_similarity=0.7,
                       nr_topics = n_topics,
                       top_n_words = 10,
                       low_memory=True,
                        verbose=True)

    return topic_model


def coherence_score(topic_model, df_documents):
    cleaned_docs = topic_model._preprocess_text(df_documents)
    vectorizer = topic_model.vectorizer_model
    analyzer = vectorizer.build_analyzer()
    tokens = [analyzer(doc) for doc in cleaned_docs]
    dictionary = corpora.Dictionary(tokens)
    corpus = [dictionary.doc2bow(token) for token in tokens]
    topics = topic_model.get_topics()
    
    topic_words = [
        [word for word, _ in topic_model.get_topic(topic) if word != ""] for topic in topics if check_valid(topic_model.get_topic(topic))
    ]
    
    coherence_model = CoherenceModel(topics=topic_words, 
                              texts=tokens, 
                              corpus=corpus,
                              dictionary=dictionary, 
                              coherence='c_npmi')
    coherence = coherence_model.get_coherence()
    return coherence

def working(args: argparse.Namespace, name_dataset: str):
    
    
    ############# Create logger##################################
    fandc_logger = create_logger_file_and_console()
    file_logger = create_logger_file()
    console_logger = create_logger_console() 
    ##############################################################
    
    ######### Create dataframe for dataset booking and tripadvisor #####
    fandc_logger.log(logging.INFO, f'STARTING WITH TOPIC MODEL FOR {name_dataset} dataset')
    fandc_logger.log(logging.INFO, f'Get data from {name_dataset}')
    doc_source = f'en_{name_dataset}.csv'
    list_tmp = prepare_data(doc_source, doc_type, type_framework = 'pandas')

    (timestamps_dict, df_doc,
     df_documents, df_reviews) = list_tmp

    fandc_logger.log(logging.INFO, f'Get data from {name_dataset} successfully!')
    #################################################################### 
    
    ######### Processing data for booking and tripadvisor dataset #########
    fandc_logger.log(logging.INFO, f'Processing data for {name_dataset} dataset')
    (df_documents, timestamps_dict,
     sent_id, df_doc_out) = processing_data(df_doc, df_documents, timestamps_dict, doc_level, target_col)
    fandc_logger.log(logging.INFO, f'Processing data for {name_dataset} dataset successfully!')
    #######################################################################
    
    source = f'en_{name_dataset}'
    output_subdir_name = source + f'/bertopic2_non_zeroshot_{args.n_topics}topic_'+doc_type+'_'+doc_level+'_'+doc_time
    output_subdir = os.path.join(output_dir, output_subdir_name)
    if not os.path.exists(output_subdir):
        os.makedirs(output_subdir)
    
    
    # Create model
    fandc_logger.log(logging.INFO, f'Create model for {name_dataset} dataset')
    topic_model = create_model_bertopic_booking(args.n_topics) if name_dataset == 'booking' else create_model_bertopic_tripadvisor(args.n_topics)
    
    # Fitting model
    fandc_logger.log(logging.INFO, f'Training model for {name_dataset} dataset')
    fandc_logger.log(logging.INFO, f'Fitting model processing...')
    t_start = time.time()
    t = time.process_time()
    topic_model = topic_model.fit(df_documents)
    elapsed_time = time.process_time() - t
    t_end = time.time()
    fandc_logger.log(logging.INFO, f'Time working for fitting process: {t_end - t_start}\t --- \t Time model processing:{elapsed_time}')
    console_logger.log(logging.INFO, 'End of fitting process')
    
    topics_save_dir = os.path.join(output_subdir, 'topics_bertopic_'+doc_type+'_'+doc_level+'_'+doc_time)
    topic_model.save(topics_save_dir, serialization="safetensors", save_ctfidf=True, save_embedding_model=True)
    fandc_logger.log(logging.INFO, f'Save fitting model for {name_dataset} dataset successfully!')
    
    # Transform model
    t_start = time.time()
    t = time.process_time()
    topics, probs = topic_model.transform(df_documents)
    elapsed_time = time.process_time() - t
    t_end = time.time()
    fandc_logger.log(logging.INFO, f'Time working for transform process: {t_end - t_start}\t --- \t Time model processing:{elapsed_time}')
    console_logger.log(logging.INFO, 'End of transform process')
    
    topics_save_dir = os.path.join(output_subdir, 'topics_bertopic_transform_'+doc_type+'_'+doc_level+'_'+doc_time)
    topic_model.save(topics_save_dir, serialization="safetensors", save_ctfidf=True, save_embedding_model=True)
    fandc_logger.log(logging.INFO, f'Save transform model for {name_dataset} dataset successfully!')
    
    ############# Result ###############
    # ***** 1
    
    # Get coherence score
    fandc_logger.log(logging.INFO, f'Staring calculate coherence score for {name_dataset} dataset')
    coherence = coherence_score(topic_model, df_documents)
    fandc_logger.log(logging.INFO, f'Coherence score for {name_dataset} dataset: {coherence} with {args.n_topics} topics')
    
    if args.only_coherence_score == 'no':
        # Get topics 
        fandc_logger.log(logging.INFO, f'Get topics for {name_dataset} dataset')
        topic_info = topic_model.get_topic_info()
        topic_info_path_out = os.path.join(output_subdir, 'topic_info_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
        topic_info.to_csv(topic_info_path_out, encoding='utf-8')
        fandc_logger.log(logging.INFO, f'Save topic_info for {name_dataset} dataset successfully!')


        # Get weights for each topic
        fandc_logger.log(logging.INFO, f'Get weights for each topic')
        topic_keyword_weights = topic_model.get_topics(full=True)
        topic_keyword_weights_path_out = os.path.join(output_subdir, 'topic_keyword_weights_'+doc_type+'_'+doc_level+'_'+doc_time+'.json')
        with open(topic_keyword_weights_path_out, 'w', encoding="utf-8") as f:
            f.write(json.dumps(str(topic_keyword_weights),indent=4, ensure_ascii=False))
        fandc_logger.log(logging.INFO, f'Save weights for each topic successfully!')


        # Put data into dataframe
        df_topics = topic_model.get_document_info(df_documents)
        df_doc_out = pd.concat([df_topics, df_doc_out.loc[:,"review_id":]],axis=1)
        df_doc_out_path = os.path.join(output_subdir, 'df_documents_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
        df_doc_out.to_csv(df_doc_out_path, encoding='utf-8')
        fandc_logger.log(logging.INFO, f'Save df_doc_out for {name_dataset} dataset successfully!')

        df_doc_path = os.path.join(output_subdir, f'df_docs_{name_dataset}'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
        df_doc.to_csv(df_doc_path, encoding='utf-8')
        fandc_logger.log(logging.INFO, f'Save df_doc_{name_dataset} for {name_dataset} dataset successfully!')

        # Get params
        model_params = topic_model.get_params()
        model_params_path_txt_out = os.path.join(output_subdir, f'model_params_{name_dataset}'+doc_type+'_'+doc_level+'_'+doc_time+'.txt')
        with open(model_params_path_txt_out, 'w', encoding="utf-8") as f:
            f.write(json.dumps(str(model_params),indent=4, ensure_ascii=False))
        fandc_logger.log(logging.INFO, f'Save params of model for {name_dataset} dataset successfully!')

        # Get topics visualize
        fig = topic_model.visualize_topics()
        vis_save_dir = os.path.join(output_subdir, f'bertopic_vis_{name_dataset}'+doc_type+'_'+doc_level+'_'+doc_time+'.html')
        fig.write_html(vis_save_dir)
        fandc_logger.log(logging.INFO, f'Save visualize of topic for {name_dataset} dataset successfully!')

        # # Hierarchical topics
        # https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.linkage.html
        fandc_logger.log(logging.INFO, f'Staring hierarchical topics...')
        linkage_function = lambda x: sch.linkage(x, 'average', optimal_ordering=True)
        hierarchical_topics = topic_model.hierarchical_topics(df_documents, linkage_function=linkage_function)
        hierarchical_topics_path_out = os.path.join(output_subdir, f'hierarchical_topics_path_out_{name_dataset}'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
        hierarchical_topics.to_csv(hierarchical_topics_path_out, encoding='utf-8')
        fandc_logger.log(logging.INFO, f'Save hierarchical topics table for {name_dataset} dataset successfully!')

        fig = topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)
        vis_save_dir = os.path.join(output_subdir, f'bertopic_hierarchy_vis_{name_dataset}'+doc_type+'_'+doc_level+'_'+doc_time+'.html')
        fig.write_html(vis_save_dir)
        fandc_logger.log(logging.INFO, f'Save visualize of hierarchical topics for {name_dataset} dataset successfully!')

        # Get dynamic topic modeling
        fandc_logger.log(logging.INFO, f'Staring dynamic topic modeling over timestamp...')
        for key in timestamps_dict.keys():
            topics_over_time = topic_model.topics_over_time(df_documents, timestamps_dict[key])
            fig = topic_model.visualize_topics_over_time(topics_over_time, top_n_topics=10, title=f"Topics over time following {key}")
            fig.show()
            vis_save_dir = os.path.join(output_subdir, f'bertopic_dtm_vis_{name_dataset}'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')
            fig.write_html(vis_save_dir)

            topic_dtm_path_out = os.path.join(output_subdir, f'topics_dtm_{name_dataset}'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
            topics_over_time.to_csv(topic_dtm_path_out, encoding='utf-8')
        fandc_logger.log(logging.INFO, f'Save topics over time for {name_dataset} dataset successfully!')

        # ****** 2
        # Get reduce topics and topic over time for each n_topics
        fandc_logger.log(logging.INFO, f'Staring reduce topics and topic over time from 10 to 50...')
        for n_topics in [10,20,30,40,50]:
            topic_model_copy = copy.deepcopy(topic_model)
            topic_model_copy.reduce_topics(df_documents, nr_topics=n_topics)
            fig = topic_model_copy.visualize_topics(title=f"Intertopic Distance Map: {n_topics} topics")
            fig.show()
            vis_save_dir = os.path.join(output_subdir, f'bertopic_reduce_vis_{name_dataset}'+str(n_topics)+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')
            fig.write_html(vis_save_dir)

            topic_info = topic_model_copy.get_topic_info()
            topic_info_path_out = os.path.join(output_subdir, f'topic_reduce_info_{name_dataset}'+str(n_topics)+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
            topic_info.to_csv(topic_info_path_out, encoding='utf-8')

            for key in timestamps_dict.keys():
                topics_over_time_ = topic_model_copy.topics_over_time(df_documents, timestamps_dict[key])
                fig = topic_model_copy.visualize_topics_over_time(topics_over_time_, top_n_topics=10, title=f"Topics over time following {key}")
                fig.show()
                vis_save_dir = os.path.join(output_subdir, f'bertopic_reduce_dtm_vis_{name_dataset}'+str(n_topics)+'_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.html')
                fig.write_html(vis_save_dir)

                topic_dtm_path_out = os.path.join(output_subdir, f'topics_reduce_dtm_{name_dataset}'+str(n_topics)+'_'+key+'_'+doc_type+'_'+doc_level+'_'+doc_time+'.csv')
                topics_over_time_.to_csv(topic_dtm_path_out, encoding='utf-8')

        fandc_logger.log(logging.INFO, f'Save topics reduce and topic over time for {name_dataset} dataset successfully!')
    ###################################
    fandc_logger.log(logging.INFO, f'ENDING TRAINING TOPIC MODELING {name_dataset} dataset\n')


    
    

if __name__ == "__main__":
    args = init_args()
    if args.train_both == 'yes':
        working(args, 'booking')
        working(args, 'tripadvisor')
    else:
        working(args, args.name_dataset)