tut 3
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import logging\n",
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"logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-05-23 18:15:06,938 : INFO : adding document #0 to Dictionary<0 unique tokens: []>\n",
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"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",
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"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"
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]
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}
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],
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"source": [
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"from collections import defaultdict\n",
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"from gensim import corpora\n",
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"\n",
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"documents = [\n",
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" \"Human machine interface for lab abc computer applications\",\n",
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" \"A survey of user opinion of computer system response time\",\n",
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" \"The EPS user interface management system\",\n",
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" \"System and human system engineering testing of EPS\",\n",
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" \"Relation of user perceived response time to error measurement\",\n",
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" \"The generation of random binary unordered trees\",\n",
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" \"The intersection graph of paths in trees\",\n",
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" \"Graph minors IV Widths of trees and well quasi ordering\",\n",
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" \"Graph minors A survey\",\n",
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"]\n",
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"\n",
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"# remove common words and tokenize\n",
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"stoplist = set('for a of the and to in'.split())\n",
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"texts = [\n",
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" [word for word in document.lower().split() if word not in stoplist]\n",
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" for document in documents\n",
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"]\n",
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"\n",
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"# remove words that appear only once\n",
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"frequency = defaultdict(int)\n",
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"for text in texts:\n",
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" for token in text:\n",
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" frequency[token] += 1\n",
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"\n",
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"texts = [\n",
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" [token for token in text if frequency[token] > 1]\n",
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" for text in texts\n",
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"]\n",
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"\n",
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"dictionary = corpora.Dictionary(texts)\n",
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"corpus = [dictionary.doc2bow(text) for text in texts]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-05-23 18:16:10,608 : INFO : collecting document frequencies\n",
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"2022-05-23 18:16:10,609 : INFO : PROGRESS: processing document #0\n",
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"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"
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]
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}
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],
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"source": [
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"from gensim import models\n",
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"\n",
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"tfidf = models.TfidfModel(corpus) # 1. initialize the model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(0, 0.7071067811865476), (1, 0.7071067811865476)]\n"
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]
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}
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],
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"source": [
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"doc_bow = [(0,1), (1,1)]\n",
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"print(tfidf[doc_bow]) # 2. use the model to transform vectors"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[(0, 0.5773502691896257), (1, 0.5773502691896257), (2, 0.5773502691896257)]\n",
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"[(0, 0.44424552527467476), (3, 0.44424552527467476), (4, 0.44424552527467476), (5, 0.3244870206138555), (6, 0.44424552527467476), (7, 0.3244870206138555)]\n",
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"[(2, 0.5710059809418182), (5, 0.4170757362022777), (7, 0.4170757362022777), (8, 0.5710059809418182)]\n",
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"[(1, 0.49182558987264147), (5, 0.7184811607083769), (8, 0.49182558987264147)]\n",
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"[(3, 0.6282580468670046), (6, 0.6282580468670046), (7, 0.45889394536615247)]\n",
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"[(9, 1.0)]\n",
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"[(9, 0.7071067811865475), (10, 0.7071067811865475)]\n",
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"[(9, 0.5080429008916749), (10, 0.5080429008916749), (11, 0.695546419520037)]\n",
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"[(4, 0.6282580468670046), (10, 0.45889394536615247), (11, 0.6282580468670046)]\n"
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]
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}
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],
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"source": [
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"corpus_tfidf = tfidf[corpus]\n",
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"for doc in corpus_tfidf:\n",
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" print(doc)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2022-05-23 18:40:30,354 : INFO : using serial LSI version on this node\n",
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"2022-05-23 18:40:30,354 : INFO : updating model with new documents\n",
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"2022-05-23 18:40:30,355 : INFO : preparing a new chunk of documents\n",
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"2022-05-23 18:40:30,358 : INFO : using 100 extra samples and 2 power iterations\n",
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"2022-05-23 18:40:30,359 : INFO : 1st phase: constructing (12, 102) action matrix\n",
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"2022-05-23 18:40:30,360 : INFO : orthonormalizing (12, 102) action matrix\n",
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"2022-05-23 18:40:30,362 : INFO : 2nd phase: running dense svd on (12, 9) matrix\n",
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"2022-05-23 18:40:30,363 : INFO : computing the final decomposition\n",
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"2022-05-23 18:40:30,364 : INFO : keeping 2 factors (discarding 47.565% of energy spectrum)\n",
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"2022-05-23 18:40:30,365 : INFO : processed documents up to #9\n",
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"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",
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"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",
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"2022-05-23 18:40:30,365 : INFO : LsiModel lifecycle event {'msg': 'trained LsiModel<num_terms=12, num_topics=2, decay=1.0, chunksize=20000> 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"
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]
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}
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],
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"source": [
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"lsi_model = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=2) # initialize an LSI transf\n",
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"corpus_lsi = lsi_model[corpus_tfidf] # create a double wrapper over the original"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"''"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lsi_model.print_topic(2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "a991b7e5a58af45663279ce1606e861d35361e78ec04a120e3cc987f7e474d97"
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},
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"kernelspec": {
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"display_name": "Python 3.10.2 ('venv': venv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.2"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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