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devashishd12/s_one_pre_example_usage.ipynb

Created May 27, 2016 17:03
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Example usage for S_One_Pre segmentation
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s_one_pre_example_usage.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from gensim.corpora import Dictionary, MmCorpus\n",
"from gensim.models.ldamodel import LdaModel\n",
"from gensim.segmentation import S_One_Pre\n",
"from gensim.matutils import argsort"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"texts = [['human', 'interface', 'computer'],\n",
" ['survey', 'user', 'computer', 'system', 'response', 'time'],\n",
" ['eps', 'user', 'interface', 'system'],\n",
" ['system', 'human', 'system', 'eps'],\n",
" ['user', 'response', 'time'],\n",
" ['trees'],\n",
" ['graph', 'trees'],\n",
" ['graph', 'minors', 'trees'],\n",
" ['graph', 'minors', 'survey']]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dictionary = Dictionary(texts)\n",
"corpus = [dictionary.doc2bow(text) for text in texts]\n",
"MmCorpus.serialize('/tmp/deerwester.mm', corpus)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"topics = []\n",
"str_topics = []\n",
"lm = LdaModel(corpus=corpus)\n",
"for topic in lm.state.get_lambda():\n",
" topic = topic / topic.sum()\n",
" bestn = argsort(topic, topn=3, reverse=True)\n",
" topics.append(bestn)\n",
" beststr = [(topic[id], lm.id2word[id]) for id in bestn]\n",
" str_topics.append(beststr)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 9 10 7]\n"
]
}
],
"source": [
"print topics[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{0: [(10, 9), (7, 9), (7, 10)]}\n"
]
}
],
"source": [
"print S_One_Pre([topics[0]])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0: [(10, 9), (7, 9), (7, 10)],\n",
" 1: [(10, 9), (7, 9), (7, 10)],\n",
" 2: [(10, 9), (7, 9), (7, 10)],\n",
" 3: [(10, 9), (7, 9), (7, 10)],\n",
" 4: [(10, 9), (7, 9), (7, 10)],\n",
" 5: [(10, 9), (7, 9), (7, 10)],\n",
" 6: [(10, 9), (7, 9), (7, 10)],\n",
" 7: [(10, 9), (7, 9), (7, 10)],\n",
" 8: [(10, 9), (7, 9), (7, 10)],\n",
" 9: [(10, 9), (7, 9), (7, 10)],\n",
" 10: [(10, 9), (7, 9), (7, 10)],\n",
" 11: [(10, 9), (7, 9), (7, 10)],\n",
" 12: [(10, 9), (7, 9), (7, 10)],\n",
" 13: [(10, 9), (7, 9), (7, 10)],\n",
" 14: [(10, 9), (7, 9), (7, 10)],\n",
" 15: [(10, 9), (7, 9), (7, 10)],\n",
" 16: [(10, 9), (7, 9), (7, 10)],\n",
" 17: [(10, 9), (7, 9), (7, 10)],\n",
" 18: [(10, 9), (7, 9), (7, 10)],\n",
" 19: [(10, 9), (7, 9), (7, 10)],\n",
" 20: [(10, 9), (7, 9), (7, 10)],\n",
" 21: [(7, 4), (6, 4), (6, 7)],\n",
" 22: [(10, 9), (7, 9), (7, 10)],\n",
" 23: [(10, 9), (7, 9), (7, 10)],\n",
" 24: [(10, 9), (7, 9), (7, 10)],\n",
" 25: [(10, 9), (7, 9), (7, 10)],\n",
" 26: [(1, 2), (0, 2), (0, 1)],\n",
" 27: [(10, 9), (7, 9), (7, 10)],\n",
" 28: [(10, 9), (7, 9), (7, 10)],\n",
" 29: [(10, 9), (7, 9), (7, 10)],\n",
" 30: [(10, 9), (7, 9), (7, 10)],\n",
" 31: [(10, 9), (7, 9), (7, 10)],\n",
" 32: [(10, 9), (7, 9), (7, 10)],\n",
" 33: [(10, 9), (7, 9), (7, 10)],\n",
" 34: [(10, 9), (7, 9), (7, 10)],\n",
" 35: [(10, 9), (7, 9), (7, 10)],\n",
" 36: [(10, 9), (7, 9), (7, 10)],\n",
" 37: [(10, 9), (7, 9), (7, 10)],\n",
" 38: [(10, 9), (7, 9), (7, 10)],\n",
" 39: [(10, 9), (7, 9), (7, 10)],\n",
" 40: [(10, 9), (7, 9), (7, 10)],\n",
" 41: [(10, 9), (7, 9), (7, 10)],\n",
" 42: [(10, 9), (7, 9), (7, 10)],\n",
" 43: [(10, 9), (7, 9), (7, 10)],\n",
" 44: [(10, 9), (7, 9), (7, 10)],\n",
" 45: [(10, 9), (7, 9), (7, 10)],\n",
" 46: [(10, 9), (7, 9), (7, 10)],\n",
" 47: [(11, 5), (10, 5), (10, 11)],\n",
" 48: [(10, 9), (7, 9), (7, 10)],\n",
" 49: [(10, 9), (7, 9), (7, 10)],\n",
" 50: [(10, 9), (7, 9), (7, 10)],\n",
" 51: [(10, 9), (7, 9), (7, 10)],\n",
" 52: [(10, 9), (7, 9), (7, 10)],\n",
" 53: [(10, 9), (7, 9), (7, 10)],\n",
" 54: [(10, 9), (7, 9), (7, 10)],\n",
" 55: [(10, 9), (7, 9), (7, 10)],\n",
" 56: [(10, 9), (7, 9), (7, 10)],\n",
" 57: [(10, 9), (7, 9), (7, 10)],\n",
" 58: [(10, 9), (7, 9), (7, 10)],\n",
" 59: [(10, 9), (7, 9), (7, 10)],\n",
" 60: [(10, 9), (7, 9), (7, 10)],\n",
" 61: [(10, 9), (7, 9), (7, 10)],\n",
" 62: [(10, 9), (7, 9), (7, 10)],\n",
" 63: [(10, 9), (7, 9), (7, 10)],\n",
" 64: [(10, 9), (7, 9), (7, 10)],\n",
" 65: [(10, 9), (11, 9), (11, 10)],\n",
" 66: [(10, 9), (7, 9), (7, 10)],\n",
" 67: [(10, 9), (7, 9), (7, 10)],\n",
" 68: [(10, 9), (7, 9), (7, 10)],\n",
" 69: [(10, 9), (7, 9), (7, 10)],\n",
" 70: [(10, 9), (7, 9), (7, 10)],\n",
" 71: [(3, 4), (7, 4), (7, 3)],\n",
" 72: [(10, 9), (7, 9), (7, 10)],\n",
" 73: [(10, 9), (7, 9), (7, 10)],\n",
" 74: [(10, 9), (7, 9), (7, 10)],\n",
" 75: [(2, 6), (8, 6), (8, 2)],\n",
" 76: [(6, 8), (0, 8), (0, 6)],\n",
" 77: [(10, 9), (7, 9), (7, 10)],\n",
" 78: [(10, 9), (7, 9), (7, 10)],\n",
" 79: [(10, 9), (7, 9), (7, 10)],\n",
" 80: [(10, 9), (7, 9), (7, 10)],\n",
" 81: [(10, 9), (7, 9), (7, 10)],\n",
" 82: [(10, 9), (7, 9), (7, 10)],\n",
" 83: [(10, 9), (7, 9), (7, 10)],\n",
" 84: [(10, 9), (7, 9), (7, 10)],\n",
" 85: [(10, 9), (7, 9), (7, 10)],\n",
" 86: [(10, 9), (7, 9), (7, 10)],\n",
" 87: [(10, 9), (7, 9), (7, 10)],\n",
" 88: [(10, 9), (7, 9), (7, 10)],\n",
" 89: [(10, 9), (7, 9), (7, 10)],\n",
" 90: [(10, 9), (7, 9), (7, 10)],\n",
" 91: [(10, 9), (7, 9), (7, 10)],\n",
" 92: [(10, 9), (7, 9), (7, 10)],\n",
" 93: [(10, 9), (7, 9), (7, 10)],\n",
" 94: [(10, 9), (7, 9), (7, 10)],\n",
" 95: [(10, 9), (7, 9), (7, 10)],\n",
" 96: [(10, 9), (7, 9), (7, 10)],\n",
" 97: [(10, 9), (7, 9), (7, 10)],\n",
" 98: [(10, 9), (7, 9), (7, 10)],\n",
" 99: [(10, 9), (7, 9), (7, 10)]}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"S_One_Pre(topics)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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