{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Topic-Term shape: (20, 14567)\n", "Doc-Topic shape: (2000, 20)\n" ] } ], "source": [ "import json\n", "import numpy as np\n", "\n", "def load_R_model(filename):\n", " with open(filename, 'r') as j:\n", " data_input = json.load(j)\n", " data = {'topic_term_dists': data_input['phi'], \n", " 'doc_topic_dists': data_input['theta'],\n", " 'doc_lengths': data_input['doc.length'],\n", " 'vocab': data_input['vocab'],\n", " 'term_frequency': data_input['term.frequency']}\n", " return data\n", "\n", "movies_model_data = load_R_model('movie_reviews_input.json')\n", "\n", "print('Topic-Term shape: %s' % str(np.array(movies_model_data['topic_term_dists']).shape))\n", "print('Doc-Topic shape: %s' % str(np.array(movies_model_data['doc_topic_dists']).shape))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import pyLDAvis\n", "movies_vis_data = pyLDAvis.prepare(**movies_model_data)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "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_el6810651331412648403513710\"></div>\n", "<script type=\"text/javascript\">\n", "\n", "var ldavis_el6810651331412648403513710_data = {\"mdsDat\": {\"x\": [-0.04505004308194396, 0.007062462533571069, -0.07095073901597508, -0.16214597732416247, 0.05272342955287187, 0.26109374707176347, -0.09880276069863358, 0.10611533980731977, 0.13358488271014907, -0.17788516772845905, 0.16081575059452508, 0.19681622777074032, -0.1658126988683471, 0.08265893067047525, 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load library \" + url);};\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", "}\n", "\n", "if(typeof(LDAvis) !== \"undefined\"){\n", " // already loaded: just create the visualization\n", " !function(LDAvis){\n", " new LDAvis(\"#\" + \"ldavis_el6810651331412648403513710\", ldavis_el6810651331412648403513710_data);\n", " }(LDAvis);\n", "}else if(typeof define === \"function\" && define.amd){\n", " // require.js is available: use it to load d3/LDAvis\n", " require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n", " require([\"d3\"], function(d3){\n", " window.d3 = d3;\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_el6810651331412648403513710\", ldavis_el6810651331412648403513710_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_el6810651331412648403513710\", ldavis_el6810651331412648403513710_data);\n", " })\n", " });\n", "}\n", "</script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyLDAvis.display(movies_vis_data)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }