diff --git a/tutorials/gensim_topic_transformations.ipynb b/tutorials/gensim_topic_transformations.ipynb new file mode 100644 index 0000000..6140d4b --- /dev/null +++ b/tutorials/gensim_topic_transformations.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO )" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-23 18:15:06,938 : INFO : adding document #0 to Dictionary<0 unique tokens: []>\n", + "2022-05-23 18:15:06,939 : INFO : built Dictionary<12 unique tokens: ['computer', 'human', 'interface', 'response', 'survey']...> from 9 documents (total 29 corpus positions)\n", + "2022-05-23 18:15:06,939 : INFO : Dictionary lifecycle event {'msg': \"built Dictionary<12 unique tokens: ['computer', 'human', 'interface', 'response', 'survey']...> from 9 documents (total 29 corpus positions)\", 'datetime': '2022-05-23T18:15:06.939467', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'created'}\n" + ] + } + ], + "source": [ + "from collections import defaultdict\n", + "from gensim import corpora\n", + "\n", + "documents = [\n", + " \"Human machine interface for lab abc computer applications\",\n", + " \"A survey of user opinion of computer system response time\",\n", + " \"The EPS user interface management system\",\n", + " \"System and human system engineering testing of EPS\",\n", + " \"Relation of user perceived response time to error measurement\",\n", + " \"The generation of random binary unordered trees\",\n", + " \"The intersection graph of paths in trees\",\n", + " \"Graph minors IV Widths of trees and well quasi ordering\",\n", + " \"Graph minors A survey\",\n", + "]\n", + "\n", + "# remove common words and tokenize\n", + "stoplist = set('for a of the and to in'.split())\n", + "texts = [\n", + " [word for word in document.lower().split() if word not in stoplist]\n", + " for document in documents\n", + "]\n", + "\n", + "# remove words that appear only once\n", + "frequency = defaultdict(int)\n", + "for text in texts:\n", + " for token in text:\n", + " frequency[token] += 1\n", + "\n", + "texts = [\n", + " [token for token in text if frequency[token] > 1]\n", + " for text in texts\n", + "]\n", + "\n", + "dictionary = corpora.Dictionary(texts)\n", + "corpus = [dictionary.doc2bow(text) for text in texts]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-23 18:16:10,608 : INFO : collecting document frequencies\n", + "2022-05-23 18:16:10,609 : INFO : PROGRESS: processing document #0\n", + "2022-05-23 18:16:10,609 : INFO : TfidfModel lifecycle event {'msg': 'calculated IDF weights for 9 documents and 12 features (28 matrix non-zeros)', 'datetime': '2022-05-23T18:16:10.609938', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'initialize'}\n" + ] + } + ], + "source": [ + "from gensim import models\n", + "\n", + "tfidf = models.TfidfModel(corpus) # 1. initialize the model" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0, 0.7071067811865476), (1, 0.7071067811865476)]\n" + ] + } + ], + "source": [ + "doc_bow = [(0,1), (1,1)]\n", + "print(tfidf[doc_bow]) # 2. use the model to transform vectors" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[(0, 0.5773502691896257), (1, 0.5773502691896257), (2, 0.5773502691896257)]\n", + "[(0, 0.44424552527467476), (3, 0.44424552527467476), (4, 0.44424552527467476), (5, 0.3244870206138555), (6, 0.44424552527467476), (7, 0.3244870206138555)]\n", + "[(2, 0.5710059809418182), (5, 0.4170757362022777), (7, 0.4170757362022777), (8, 0.5710059809418182)]\n", + "[(1, 0.49182558987264147), (5, 0.7184811607083769), (8, 0.49182558987264147)]\n", + "[(3, 0.6282580468670046), (6, 0.6282580468670046), (7, 0.45889394536615247)]\n", + "[(9, 1.0)]\n", + "[(9, 0.7071067811865475), (10, 0.7071067811865475)]\n", + "[(9, 0.5080429008916749), (10, 0.5080429008916749), (11, 0.695546419520037)]\n", + "[(4, 0.6282580468670046), (10, 0.45889394536615247), (11, 0.6282580468670046)]\n" + ] + } + ], + "source": [ + "corpus_tfidf = tfidf[corpus]\n", + "for doc in corpus_tfidf:\n", + " print(doc)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2022-05-23 18:40:30,354 : INFO : using serial LSI version on this node\n", + "2022-05-23 18:40:30,354 : INFO : updating model with new documents\n", + "2022-05-23 18:40:30,355 : INFO : preparing a new chunk of documents\n", + "2022-05-23 18:40:30,358 : INFO : using 100 extra samples and 2 power iterations\n", + "2022-05-23 18:40:30,359 : INFO : 1st phase: constructing (12, 102) action matrix\n", + "2022-05-23 18:40:30,360 : INFO : orthonormalizing (12, 102) action matrix\n", + "2022-05-23 18:40:30,362 : INFO : 2nd phase: running dense svd on (12, 9) matrix\n", + "2022-05-23 18:40:30,363 : INFO : computing the final decomposition\n", + "2022-05-23 18:40:30,364 : INFO : keeping 2 factors (discarding 47.565% of energy spectrum)\n", + "2022-05-23 18:40:30,365 : INFO : processed documents up to #9\n", + "2022-05-23 18:40:30,365 : INFO : topic #0(1.594): 0.703*\"trees\" + 0.538*\"graph\" + 0.402*\"minors\" + 0.187*\"survey\" + 0.061*\"system\" + 0.060*\"time\" + 0.060*\"response\" + 0.058*\"user\" + 0.049*\"computer\" + 0.035*\"interface\"\n", + "2022-05-23 18:40:30,365 : INFO : topic #1(1.476): 0.460*\"system\" + 0.373*\"user\" + 0.332*\"eps\" + 0.328*\"interface\" + 0.320*\"response\" + 0.320*\"time\" + 0.293*\"computer\" + 0.280*\"human\" + 0.171*\"survey\" + -0.161*\"trees\"\n", + "2022-05-23 18:40:30,365 : INFO : LsiModel lifecycle event {'msg': 'trained LsiModel in 0.01s', 'datetime': '2022-05-23T18:40:30.365269', 'gensim': '4.2.0', 'python': '3.10.2 (tags/v3.10.2:a58ebcc, Jan 17 2022, 14:12:15) [MSC v.1929 64 bit (AMD64)]', 'platform': 'Windows-10-10.0.22000-SP0', 'event': 'created'}\n" + ] + } + ], + "source": [ + "lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2) # initialize an LSI transf\n", + "corpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "''" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lsi_model.print_topic(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "a991b7e5a58af45663279ce1606e861d35361e78ec04a120e3cc987f7e474d97" + }, + "kernelspec": { + "display_name": "Python 3.10.2 ('venv': venv)", + "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": "3.10.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}