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Topic Modeling using Latent Dirichlet Allocation. Best viewed here: https://nbviewer.jupyter.org/gist/petermchale/29014b456089a871555d8c5cecd16dcf#topic=1&lambda=0.11&term=
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Latent Dirichlet Allocation.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import data \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>year</th>\n",
" <th>title</th>\n",
" <th>event_type</th>\n",
" <th>pdf_name</th>\n",
" <th>abstract</th>\n",
" <th>paper_text</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1987</td>\n",
" <td>Self-Organization of Associative Database and ...</td>\n",
" <td>NaN</td>\n",
" <td>1-self-organization-of-associative-database-an...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10</td>\n",
" <td>1987</td>\n",
" <td>A Mean Field Theory of Layer IV of Visual Cort...</td>\n",
" <td>NaN</td>\n",
" <td>10-a-mean-field-theory-of-layer-iv-of-visual-c...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>100</td>\n",
" <td>1988</td>\n",
" <td>Storing Covariance by the Associative Long-Ter...</td>\n",
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" <td>Abstract Missing</td>\n",
" <td>394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000</td>\n",
" <td>1994</td>\n",
" <td>Bayesian Query Construction for Neural Network...</td>\n",
" <td>NaN</td>\n",
" <td>1000-bayesian-query-construction-for-neural-ne...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Bayesian Query Construction for Neural\\nNetwor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1001</td>\n",
" <td>1994</td>\n",
" <td>Neural Network Ensembles, Cross Validation, an...</td>\n",
" <td>NaN</td>\n",
" <td>1001-neural-network-ensembles-cross-validation...</td>\n",
" <td>Abstract Missing</td>\n",
" <td>Neural Network Ensembles, Cross\\nValidation, a...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id year title event_type \\\n",
"0 1 1987 Self-Organization of Associative Database and ... NaN \n",
"1 10 1987 A Mean Field Theory of Layer IV of Visual Cort... NaN \n",
"2 100 1988 Storing Covariance by the Associative Long-Ter... NaN \n",
"3 1000 1994 Bayesian Query Construction for Neural Network... NaN \n",
"4 1001 1994 Neural Network Ensembles, Cross Validation, an... NaN \n",
"\n",
" pdf_name abstract \\\n",
"0 1-self-organization-of-associative-database-an... Abstract Missing \n",
"1 10-a-mean-field-theory-of-layer-iv-of-visual-c... Abstract Missing \n",
"2 100-storing-covariance-by-the-associative-long... Abstract Missing \n",
"3 1000-bayesian-query-construction-for-neural-ne... Abstract Missing \n",
"4 1001-neural-network-ensembles-cross-validation... Abstract Missing \n",
"\n",
" paper_text \n",
"0 767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... \n",
"1 683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... \n",
"2 394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... \n",
"3 Bayesian Query Construction for Neural\\nNetwor... \n",
"4 Neural Network Ensembles, Cross\\nValidation, a... "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"from IPython.display import display\n",
"\n",
"def import_papers():\n",
" return pd.read_csv('NIPS Papers/papers.csv')\n",
" \n",
"display(import_papers().head())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clean data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>paper_text</th>\n",
" <th>paper_text_processed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA...</td>\n",
" <td>767\\n\\nself-organization of associative databa...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU...</td>\n",
" <td>683\\n\\na mean field theory of layer iv of visu...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n...</td>\n",
" <td>394\\n\\nstoring covariance by the associative\\n...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Bayesian Query Construction for Neural\\nNetwor...</td>\n",
" <td>bayesian query construction for neural\\nnetwor...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Neural Network Ensembles, Cross\\nValidation, a...</td>\n",
" <td>neural network ensembles cross\\nvalidation and...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" paper_text \\\n",
"0 767\\n\\nSELF-ORGANIZATION OF ASSOCIATIVE DATABA... \n",
"1 683\\n\\nA MEAN FIELD THEORY OF LAYER IV OF VISU... \n",
"2 394\\n\\nSTORING COVARIANCE BY THE ASSOCIATIVE\\n... \n",
"3 Bayesian Query Construction for Neural\\nNetwor... \n",
"4 Neural Network Ensembles, Cross\\nValidation, a... \n",
"\n",
" paper_text_processed \n",
"0 767\\n\\nself-organization of associative databa... \n",
"1 683\\n\\na mean field theory of layer iv of visu... \n",
"2 394\\n\\nstoring covariance by the associative\\n... \n",
"3 bayesian query construction for neural\\nnetwor... \n",
"4 neural network ensembles cross\\nvalidation and... "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import re\n",
"\n",
"def clean():\n",
" papers = import_papers()\n",
" # remove punctuation\n",
" papers['paper_text_processed'] = papers['paper_text'].map(lambda text: re.sub('[,.!?]', '', text))\n",
" # make lower-case\n",
" papers['paper_text_processed'] = papers['paper_text_processed'].map(lambda text: text.lower())\n",
" display(papers[['paper_text', 'paper_text_processed']].head())\n",
" return papers['paper_text_processed'].values.tolist()\n",
" \n",
"texts = clean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare documents for LDA training"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n"
]
},
{
"data": {
"text/plain": [
"['mean',\n",
" 'field',\n",
" 'theory',\n",
" 'of',\n",
" 'layer',\n",
" 'iv',\n",
" 'of',\n",
" 'visual',\n",
" 'cortex',\n",
" 'and']"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from gensim.utils import simple_preprocess\n",
"\n",
"# tokenize texts\n",
"# \"deacc=True\" removes accent marks from tokens\n",
"tokenized_texts = [simple_preprocess(text, deacc=True, min_len=2, max_len=15) for text in texts]\n",
"tokenized_texts[1][:10] "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] /Users/petermchale/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"['mean',\n",
" 'field',\n",
" 'theory',\n",
" 'layer',\n",
" 'iv',\n",
" 'visual',\n",
" 'cortex',\n",
" 'application',\n",
" 'artificial',\n",
" 'neural']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('stopwords')\n",
"from nltk.corpus import stopwords\n",
"\n",
"# create stop words\n",
"stop_words = stopwords.words('english')\n",
"stop_words.extend(['from', 'subject', 're', 'edu', 'use'])\n",
"\n",
"# remove stopwords from documents\n",
"texts_without_stops = []\n",
"for tokenized_text in tokenized_texts: \n",
" text_without_stops = [word for word in tokenized_text if word not in stop_words]\n",
" texts_without_stops.append(text_without_stops)\n",
" \n",
"texts_without_stops[1][:10]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'catches'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from gensim.corpora import Dictionary\n",
"\n",
"# map words to unique IDs\n",
"id2word = Dictionary(texts_without_stops)\n",
"id2word[100]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 1)\n"
]
}
],
"source": [
"# Convert document (a list of words) into the bag-of-words format: a list of (token_id, token_count) tuples\n",
"id2counts = [id2word.doc2bow(text_without_stops) for text_without_stops in texts_without_stops]\n",
"\n",
"print(id2counts[0][100])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Specify the LDA model \n",
"\n",
"https://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the LDA model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"from pprint import pprint\n",
"from gensim.models import LdaMulticore\n",
"\n",
"# number of topics\n",
"num_topics = 10\n",
"\n",
"# build and train the LDA model\n",
"lda_model = LdaMulticore(corpus=id2counts,\n",
" id2word=id2word,\n",
" num_topics=num_topics)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Infer the top ten most probable words from each of the topics"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[(0,\n",
" '0.006*\"learning\" + 0.005*\"data\" + 0.005*\"network\" + 0.005*\"algorithm\" + '\n",
" '0.005*\"set\" + 0.005*\"using\" + 0.004*\"function\" + 0.004*\"model\" + '\n",
" '0.004*\"problem\" + 0.004*\"training\"'),\n",
" (1,\n",
" '0.006*\"model\" + 0.006*\"learning\" + 0.005*\"data\" + 0.005*\"one\" + '\n",
" '0.005*\"network\" + 0.004*\"time\" + 0.004*\"algorithm\" + 0.004*\"neural\" + '\n",
" '0.004*\"figure\" + 0.004*\"two\"'),\n",
" (2,\n",
" '0.006*\"learning\" + 0.006*\"model\" + 0.006*\"function\" + 0.005*\"algorithm\" + '\n",
" '0.005*\"set\" + 0.004*\"data\" + 0.004*\"one\" + 0.004*\"using\" + 0.003*\"models\" + '\n",
" '0.003*\"training\"'),\n",
" (3,\n",
" '0.008*\"data\" + 0.006*\"model\" + 0.006*\"learning\" + 0.004*\"one\" + '\n",
" '0.004*\"network\" + 0.004*\"set\" + 0.004*\"two\" + 0.004*\"algorithm\" + '\n",
" '0.004*\"using\" + 0.004*\"figure\"'),\n",
" (4,\n",
" '0.007*\"learning\" + 0.007*\"model\" + 0.005*\"algorithm\" + 0.005*\"set\" + '\n",
" '0.004*\"network\" + 0.004*\"data\" + 0.004*\"using\" + 0.004*\"function\" + '\n",
" '0.004*\"time\" + 0.004*\"neural\"'),\n",
" (5,\n",
" '0.006*\"model\" + 0.005*\"data\" + 0.005*\"learning\" + 0.004*\"algorithm\" + '\n",
" '0.004*\"neural\" + 0.004*\"one\" + 0.004*\"function\" + 0.003*\"results\" + '\n",
" '0.003*\"using\" + 0.003*\"number\"'),\n",
" (6,\n",
" '0.007*\"learning\" + 0.006*\"model\" + 0.005*\"data\" + 0.005*\"time\" + '\n",
" '0.005*\"algorithm\" + 0.004*\"figure\" + 0.004*\"network\" + 0.004*\"using\" + '\n",
" '0.004*\"neural\" + 0.004*\"training\"'),\n",
" (7,\n",
" '0.006*\"model\" + 0.005*\"data\" + 0.005*\"learning\" + 0.005*\"set\" + '\n",
" '0.004*\"algorithm\" + 0.004*\"function\" + 0.004*\"network\" + 0.004*\"one\" + '\n",
" '0.003*\"error\" + 0.003*\"figure\"'),\n",
" (8,\n",
" '0.007*\"learning\" + 0.006*\"function\" + 0.005*\"set\" + 0.005*\"model\" + '\n",
" '0.004*\"data\" + 0.004*\"algorithm\" + 0.004*\"number\" + 0.004*\"two\" + '\n",
" '0.004*\"one\" + 0.004*\"network\"'),\n",
" (9,\n",
" '0.006*\"learning\" + 0.005*\"model\" + 0.005*\"set\" + 0.004*\"using\" + '\n",
" '0.004*\"algorithm\" + 0.004*\"two\" + 0.004*\"data\" + 0.004*\"time\" + '\n",
" '0.004*\"function\" + 0.003*\"one\"')]\n"
]
}
],
"source": [
"pprint(lda_model.show_topics())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use pyLDAvis to interpret the inferred topics \n",
"\n",
"The chart on the left plots each topic along their first two principal components (each topic is characterized by a probability vector over the available words), thereby gauging how different or similar the inferred topics are. The areas of the circles (in their default mode) are proportional to the estimated number of tokens that were generated by the corresponding topic across the entire corpus. \n",
"\n",
"In the chart on the right, the\n",
"widths of the blue bars represent the corpus-wide\n",
"frequencies of each term, and the widths of the\n",
"red bars represent the topic-specific frequencies of\n",
"each term. \n",
"\n",
"We interpret the topics by ranking words according to their \"relevance\", which represents a balance between (i) how often the word appears, conditioned upon being chosen from the topic in question and (ii) the enrichment of the word in the chosen topic relatative to the entire corpus. The balance can be tuned by the user by varying the $\\lambda$ slider. \n",
"\n",
"Hovering over a term in the chart on the right alters the circles in the chart on the left to reflect the \n",
"the conditional probability of the term given the chosen topic. \n",
"\n",
"More info:\n",
"<br>\n",
"https://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf\n",
"<br>\n",
"https://cran.r-project.org/web/packages/LDAvis/vignettes/details.pdf\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/pyLDAvis/_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
" default_term_info = default_term_info.sort_values(\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n",
"/Users/petermchale/anaconda/envs/LDA/lib/python3.9/site-packages/past/builtins/misc.py:45: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
" from imp import reload\n"
]
},
{
"data": {
"text/html": [
"\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
"\n",
"\n",
"<div id=\"ldavis_el2837869631454082563143802\"></div>\n",
"<script type=\"text/javascript\">\n",
"\n",
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" new LDAvis(\"#\" + \"ldavis_el2837869631454082563143802\", ldavis_el2837869631454082563143802_data);\n",
" });\n",
" });\n",
"}else{\n",
" // require.js not available: dynamically load d3 & LDAvis\n",
" LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
" LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
" new LDAvis(\"#\" + \"ldavis_el2837869631454082563143802\", ldavis_el2837869631454082563143802_data);\n",
" })\n",
" });\n",
"}\n",
"</script>"
],
"text/plain": [
"PreparedData(topic_coordinates= x y topics cluster Freq\n",
"topic \n",
"2 -0.005385 0.001843 1 1 15.988369\n",
"0 -0.002649 -0.000769 2 1 11.213842\n",
"8 -0.002355 -0.003914 3 1 11.072177\n",
"6 0.003698 0.001742 4 1 10.889077\n",
"5 -0.001911 0.001595 5 1 10.816704\n",
"3 0.002731 0.002924 6 1 10.192499\n",
"1 0.004146 -0.003182 7 1 7.765360\n",
"7 0.000459 -0.002540 8 1 7.572526\n",
"9 0.000217 0.000917 9 1 7.319893\n",
"4 0.001049 0.001385 10 1 7.169552, topic_info= Term Freq Total Category logprob loglift\n",
"479 model 86484.000000 86484.000000 Default 30.0000 30.0000\n",
"170 data 77512.000000 77512.000000 Default 29.0000 29.0000\n",
"499 network 56943.000000 56943.000000 Default 28.0000 28.0000\n",
"420 learning 91816.000000 91816.000000 Default 27.0000 27.0000\n",
"701 set 63787.000000 63787.000000 Default 26.0000 26.0000\n",
".. ... ... ... ... ... ...\n",
"527 one 3705.852870 57456.909895 Topic10 -5.6790 -0.1058\n",
"170 data 4345.754678 77512.523259 Topic10 -5.5198 -0.2459\n",
"471 method 2375.681995 30760.470659 Topic10 -6.1237 0.0744\n",
"325 given 2639.026960 37976.426566 Topic10 -6.0186 -0.0312\n",
"803 training 2828.885569 44796.506197 Topic10 -5.9491 -0.1269\n",
"\n",
"[927 rows x 6 columns], token_table= Topic Freq Term\n",
"term \n",
"2166 1 0.126758 accuracy\n",
"2166 2 0.102633 accuracy\n",
"2166 3 0.116605 accuracy\n",
"2166 4 0.120626 accuracy\n",
"2166 5 0.106854 accuracy\n",
"... ... ... ...\n",
"155931 5 0.051313 ztrn\n",
"155931 6 0.102626 ztrn\n",
"155931 7 0.051313 ztrn\n",
"155931 8 0.051313 ztrn\n",
"155931 9 0.051313 ztrn\n",
"\n",
"[5106 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 1, 9, 7, 6, 4, 2, 8, 10, 5])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pyLDAvis\n",
"import pyLDAvis.gensim_models\n",
"\n",
"pyLDAvis.enable_notebook()\n",
"pyLDAvis.gensim_models.prepare(lda_model, id2counts, id2word)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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