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@sharanry
Created November 18, 2018 16:24
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import nltk\n",
"nltk.download('stopwords')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to /home/sharan/nltk_data...\n",
"[nltk_data] Unzipping corpora/stopwords.zip.\n"
]
}
],
"source": [
"from nltk.collocations import BigramCollocationFinder, TrigramCollocationFinder\n",
"from nltk.metrics import BigramAssocMeasures, TrigramAssocMeasures\n",
"from nltk.corpus import stopwords\n",
"stop_words = set(stopwords.words('english'))\n",
"import nltk\n",
"import csv\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"with open('data/travel/quora.txt') as f:\n",
" words_quora = [word for line in f for word in line.split()]\n",
"\n",
"with open('data/travel/wikihow.txt') as f:\n",
" words_wiki = [re.sub(r'[^\\w\\s]', '', word) for line in f for word in line.split()]\n",
"\n",
"with open('data/travel/stackexchange.txt') as f:\n",
" words_stackexchange = [re.sub(r'[^\\w\\s]', '', word) for line in f for word in line.split()]\n",
"\n",
"with open('data/travel/reddit.txt') as f:\n",
" words_reddit = [re.sub(r'[^\\w\\s]', '', word) for line in f for word in line.split()]\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"wiki_sentence = []\n",
"for w in words_wiki:\n",
" if w.lower() not in stop_words:\n",
" wiki_sentence.append(w.lower())\n",
"\n",
"quora_sentence = []\n",
"for w in words_quora:\n",
" if w.lower() not in stop_words:\n",
" quora_sentence.append(w.lower())\n",
"\n",
"stackexchange_sentence = []\n",
"for w in words_stackexchange:\n",
" if w.lower() not in stop_words:\n",
" stackexchange_sentence.append(w.lower())\n",
"\n",
"reddit_sentence = []\n",
"for w in words_reddit:\n",
" if w.lower() not in stop_words:\n",
" reddit_sentence.append(w.lower())"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['act', 'white', 'house', 'tour', 'arrange'],\n",
" ['best', 'travel', 'website', 'spain', 'ever'],\n",
" ['traveling', 'us', 'via', 'lhr', 'duplicate'],\n",
" ['data', 'visualization', 'competition', 'rdataisbeautiful', 'rtravel'])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wiki_sentence[:5], quora_sentence[:5],stackexchange_sentence[:5], reddit_sentence[:5]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"finder = TrigramCollocationFinder.from_words(wiki_sentence)\n",
"trigram_wiki = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)\n",
"finder = TrigramCollocationFinder.from_words(quora_sentence)\n",
"trigram_quora = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)\n",
"\n",
"finder = BigramCollocationFinder.from_words(wiki_sentence)\n",
"bigram_wiki = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)\n",
"finder = BigramCollocationFinder.from_words(quora_sentence)\n",
"bigram_quora = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)\n",
"\n",
"finder = BigramCollocationFinder.from_words(reddit_sentence)\n",
"bigram_reddit = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)\n",
"finder = BigramCollocationFinder.from_words(stackexchange_sentence)\n",
"bigram_stackexchange = finder.nbest(BigramAssocMeasures.likelihood_ratio, 20)\n",
"\n",
"finder = TrigramCollocationFinder.from_words(reddit_sentence)\n",
"trigram_reddit = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)\n",
"finder = TrigramCollocationFinder.from_words(stackexchange_sentence)\n",
"trigram_stackexchange = finder.nbest(TrigramAssocMeasures.likelihood_ratio, 20)\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"([('new', 'york', 'city'),\n",
" ('inexpensively', 'new', 'york'),\n",
" ('wander', 'new', 'york'),\n",
" ('visit', 'new', 'york'),\n",
" ('la', 'new', 'york')],\n",
" [('new', 'york'),\n",
" ('san', 'francisco'),\n",
" ('road', 'trip'),\n",
" ('united', 'states'),\n",
" ('washington', 'dc')])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"trigram_wiki[:5], bigram_wiki[:5]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"universal = trigram_wiki + bigram_wiki + trigram_quora + bigram_quora + trigram_stackexchange + bigram_stackexchange + trigram_reddit + bigram_reddit"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import spacy\n",
"nlp = spacy.load('en_core_web_lg')\n",
"import math\n",
"from subprocess import Popen, PIPE"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"def caterr(nue):\n",
" try:\n",
" return int(math.ceil(((1/nue)-1)))\n",
" except ZeroDivisionError:\n",
" return 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"calculating Similarity matrix\")\n",
"similarity_mat = [[nlp(' '.join(k)).similarity(nlp(' '.join(i)))\n",
" for k in universal] for i in universal]\n",
"distance_mat = [[caterr(k) for k in i] for i in similarity_mat]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"with open(\"data/ddcrp/Universal_Pool.csv\", \"w\") as f:\n",
" writer = csv.writer(f)\n",
" writer.writerows(universal)\n",
"\n",
"with open('data/ddcrp/Distance_Matrix.csv', 'w') as f:\n",
" writer = csv.writer(f)\n",
" writer.writerows(distance_mat)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"160"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(universal)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DDCRP starting\n",
"b'Loading required package: NLP\\n'\n"
]
}
],
"source": [
"print(\"DDCRP starting\")\n",
"process = Popen(['Rscript', 'ddcrp/ddcrp.R'], stdout=PIPE, stderr=PIPE)\n",
"stdout, stderr = process.communicate()\n",
"print(stderr)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\"idx\" \"cluster\" \"customer\"\r\n",
"1 1 1\r\n",
"2 20 20\r\n",
"3 1 129\r\n",
"4 4 4\r\n",
"5 48 15\r\n",
"6 4 18\r\n",
"7 54 151\r\n",
"8 20 2\r\n",
"9 107 66\r\n"
]
}
],
"source": [
"!head cluster.txt"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"with open(\"cluster.txt\", \"r\") as f:\n",
" ddcrp_clust = f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"ddcrp_clust = ddcrp_clust[1:]\n",
"op = {}\n",
"for i in ddcrp_clust:\n",
" line = i.split(' ')\n",
" if line[1] not in op:\n",
" op[line[1]] = []\n",
" op[line[1]].append(universal[int(line[2])-1])\n",
"\n",
"for key, value in op.items():\n",
" val = list(set(value))\n",
" op[key] = val"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing output\n"
]
}
],
"source": [
"print(\"Writing output\")\n",
"opfile = open(\"output.txt\", \"w\")\n",
"for key, value in op.items():\n",
" quora_score = 0\n",
" wiki_score = 0\n",
" reddit_score = 0\n",
" stackexchange_score = 0\n",
" val_list = []\n",
" for i in value:\n",
" val_list.append(i)\n",
" if i in bigram_quora or i in trigram_quora:\n",
" quora_score += 1\n",
" if i in bigram_wiki or i in trigram_wiki:\n",
" wiki_score += 1\n",
" if i in bigram_reddit or i in trigram_reddit:\n",
" reddit_score += 1\n",
" if i in bigram_stackexchange or i in trigram_stackexchange:\n",
" stackexchange_score += 1\n",
"\n",
" total_score = quora_score + wiki_score + reddit_score + stackexchange_score\n",
" score_list = {\"Q\": str(round(quora_score/total_score, 2)), \"W\": str(round(wiki_score/total_score, 2)), \"R\": str(round(reddit_score/total_score, 2)), \"S\": str(round(stackexchange_score/total_score, 2))}\n",
" opfile.write(\"Score = \" + str(score_list) + \" --- Cluster \" + str(val_list) + \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cluster starting\n",
"Writing output\n"
]
}
],
"source": [
"print(\"Cluster starting\")\n",
"try:\n",
" with open(\"cluster.txt\", \"r\") as f:\n",
" ddcrp_clust = f.readlines()\n",
"\n",
" ddcrp_clust = ddcrp_clust[1:]\n",
" op = {}\n",
" for i in ddcrp_clust:\n",
" line = i.split(' ')\n",
" if line[1] not in op:\n",
" op[line[1]] = []\n",
" op[line[1]].append(universal[int(line[2])-1])\n",
"\n",
" for key, value in op.items():\n",
" val = list(set(value))\n",
" op[key] = val\n",
"\n",
" print(\"Writing output\")\n",
" opfile = open(\"output.txt\", \"w\")\n",
" for key, value in op.items():\n",
" quora_score = 0\n",
" wiki_score = 0\n",
" reddit_score = 0\n",
" stackexchange_score = 0\n",
" val_list = []\n",
" for i in value:\n",
" val_list.append(i)\n",
" if i in bigram_quora or i in trigram_quora:\n",
" quora_score += 1\n",
" if i in bigram_wiki or i in trigram_wiki:\n",
" wiki_score += 1\n",
" if i in bigram_reddit or i in trigram_reddit:\n",
" reddit_score += 1\n",
" if i in bigram_stackexchange or i in trigram_stackexchange:\n",
" stackexchange_score += 1\n",
"\n",
" total_score = quora_score + wiki_score + reddit_score + stackexchange_score\n",
" \n",
" score_list = {\"Q\": str(round(quora_score/total_score, 2)), \"W\": str(round(wiki_score/total_score, 2)), \"R\": str(round(reddit_score/total_score, 2)), \"S\": str(round(stackexchange_score/total_score, 2))}\n",
" opfile.write(\"Score = \" + str(score_list) + \" --- Cluster \" + str(val_list) + \"\\n\")\n",
"\n",
"except Exception as e:\n",
" print(e)\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f64a096aa20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.imshow(np.array(distance_mat), cmap='hot', interpolation='nearest')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import manifold\n"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('107',\n",
" [('foreign', 'visitor'),\n",
" ('residence', 'permit'),\n",
" ('schengen', 'visa', 'duplicate'),\n",
" ('visa', 'schengen', 'visa'),\n",
" ('schengen', 'visa', 'waiver'),\n",
" ('visa', 'application'),\n",
" ('uk', 'visa'),\n",
" ('visa', 'refusal'),\n",
" ('visitor', 'bring'),\n",
" ('schengen', 'visa', 'requirements'),\n",
" ('transit', 'visa')])"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(op.items())[5]\n"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"# list(op.values())"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"def get_index(op, i):\n",
" for ind, value in op.items():\n",
" if i in value: return int(ind)\n",
" return -1\n",
"# break"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"107"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_index(op, ('foreign', 'visitor') )"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"cluster_alloc = []\n",
"for i in universal:\n",
"# print(i)\n",
" cluster_alloc.append(get_index(op, i))"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f649a0a0f98>"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f649a44be48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"breakcities = [str(i) for i in universal]\n",
"dists = distance_mat\n",
"adist = np.array(dists)\n",
"amax = np.amax(adist)\n",
"adist = adist/ amax\n",
"\n",
"mds = manifold.MDS(n_components=2, dissimilarity=\"precomputed\", random_state=6)\n",
"results = mds.fit(adist)\n",
"\n",
"coords = results.embedding_\n",
"\n",
"plt.subplots_adjust(bottom = 0.1)\n",
"plt.scatter(\n",
" coords[:, 0], coords[:, 1], marker = '.', c=cluster_alloc\n",
" )\n",
"# for label, x, y in zip(cities, coords[:, 0], coords[:, 1]):\n",
"# plt.annotate(\n",
"# label,\n",
"# xy = (x, y), xytext = (-20, 20),\n",
"# textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
"# bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
"# arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))\n",
"\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6499f72e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(\n",
" coords[:, 0], coords[:, 1], marker = '.', c=cluster_alloc\n",
" )\n",
"for label, x, y in zip(cities, coords[:, 0], coords[:, 1]):\n",
" plt.annotate(\n",
" label,\n",
" xy = (x, y), xytext = (-20, 20),\n",
" textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
" bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
" arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f6499f842b0>"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6499fa64a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mpl_toolkits.mplot3d import Axes3D \n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"breakcities = [str(i) for i in universal]\n",
"dists = distance_mat\n",
"adist = np.array(dists)\n",
"amax = np.amax(adist)\n",
"adist = adist/ amax\n",
"\n",
"mds = manifold.MDS(n_components=3, dissimilarity=\"precomputed\", random_state=6)\n",
"results = mds.fit(adist)\n",
"\n",
"coords = results.embedding_\n",
"\n",
"fig.subplots_adjust(bottom = 0.1)\n",
"ax.scatter(\n",
" coords[:, 0], coords[:, 1], coords[:, 2], marker = '.', c=cluster_alloc\n",
" )\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (GPUReady)",
"language": "python",
"name": "gpuready"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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