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0102_GENSIM_LDA_FINE_TUNING.py
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0102_GENSIM_LDA_FINE_TUNING.py
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# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.2
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
import pandas as pd
import numpy as np
df_1 = pd.read_csv('data/articles1.csv')
df_1
# ### LOAD DICTIONARY AND ALL LISTS
# +
import pickle
with open("lists/bow_corpus.txt", "rb") as fp: # Unpickling
bow_corpus = pickle.load(fp)
with open("lists/norm_corpus_bigrams.txt", "rb") as fp: # Unpickling
norm_corpus_bigrams = pickle.load(fp)
with open("lists/norm_papers.txt", "rb") as fp: # Unpickling
norm_papers = pickle.load(fp)
with open("lists/pre_papers.txt", "rb") as fp: # Unpickling
pre_papers = pickle.load(fp)
with open("lists/pre_titles.txt", "rb") as fp: # Unpickling
pre_titles = pickle.load(fp)
# +
import nltk
import gensim
dictionary = gensim.corpora.Dictionary.load('models/dictionary.gensim')
# -
# ### LDA TUNING; FINDING THE OPTIMAL NUMBER OF TOPICS (SAVE ALL MODELS & COHERENCE SCORES)
#
# Finding the optimal number of topics in a topic model is tough, given that it is like a model hyperparameter that you always have to set before training the model. We can use an iterative approach and build several models with differing numbers of topics and select the one that has the highest coherence score. To implement this method, we build the following function.
from tqdm import tqdm
def topic_model_coherence_generator(corpus, texts, dictionary,
start_topic_count=2, end_topic_count=10, step=1,
cpus=1):
models = []
coherence_scores = []
for topic_nums in tqdm(range(start_topic_count, end_topic_count+1, step)):
gensim_lda_model = gensim.models.LdaModel(
corpus=corpus,
num_topics=topic_nums,
id2word=dictionary,
chunksize=1740,
iterations=500,
alpha="auto",
eta="auto",
passes=20
)
cv_coherence_model_gensim_lda = gensim.models.CoherenceModel(model=gensim_lda_model,
corpus=corpus,
texts=texts,
dictionary=dictionary,
coherence='c_v')
coherence_score = cv_coherence_model_gensim_lda.get_coherence()
coherence_scores.append(coherence_score)
models.append(gensim_lda_model)
### saving each model
gensim_lda_model.save('models/gensim/model_'+str(topic_nums)+'.gensim')
return models, coherence_scores
lda_models, coherence_scores = topic_model_coherence_generator(corpus=bow_corpus,
texts=norm_corpus_bigrams,
dictionary=dictionary,
start_topic_count=2,
end_topic_count=30, step=1,
cpus=16)
# +
coherence_df = pd.DataFrame({'Number of Topics': range(2, 31, 1),
'Coherence Score': np.round(coherence_scores, 4)})
coherence_df.sort_values(by=['Coherence Score'], ascending=False).head(10)
# +
# save coherence score df and coherence score list
coherence_df.to_csv('models/gensim_scores/coherence_df.csv', index=False)
with open("models/gensim_scores/coherence_scores.txt", "wb") as fp: #Pickling
pickle.dump(coherence_scores, fp)
# -
# ### VISUALIZE COHERENCE SCORES
# +
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
# %matplotlib inline
x_ax = range(2, 31, 1)
y_ax = coherence_scores
plt.figure(figsize=(12, 6))
plt.plot(x_ax, y_ax, c="r")
plt.axhline(y=0.535, c="k", linestyle="--", linewidth=2)
plt.rcParams['figure.facecolor'] = 'white'
xl = plt.xlabel('Number of Topics')
yl = plt.ylabel('Coherence Score')
# -