From fe4fc91100026af790d49f4b5e589469501d8aa4 Mon Sep 17 00:00:00 2001
From: Yoonjin Im <skewondr@ajou.ac.kr>
Date: Sun, 26 Apr 2020 18:31:26 +0900
Subject: [PATCH] week 5
---
.DS_Store | Bin 6148 -> 8196 bytes
week5/BPE_Embedding.ipynb | 1107 +++++++++++++++++++++++++++++++++++++
week5/note5.pdf | Bin 0 -> 587694 bytes
3 files changed, 1107 insertions(+)
create mode 100644 week5/BPE_Embedding.ipynb
create mode 100644 week5/note5.pdf
diff --git a/.DS_Store b/.DS_Store
index bf5eac4fefc027a85675313a9116935e921aa7e5..6414c4e4af3b847ee80101a4b9bf9c996ce2239d 100644
GIT binary patch
delta 549
zcmZoMXmOBWU|?W$DortDU;r^W7$CF&_ryZ6iF%5R5*q_{GjCSl_`|%Ior7ay0}G?X
zW*!zJCO!*O9R(u`<60esYIAdg$v$jXxLFy>8B!Ti8L}BnCa+_C#Aq^kBAaZzNp8N2
zOHxjL5(5JRho)kLo~ha)M+OE4B-Oe3DFjp$WEch~=jRqMAOMp7vf!e;ocz3WkY7+t
zHccv@yn(fs-`K(k<XR(yYh4&kL9R<SMRwhVX;bbT)k1TfDYEOZtHA3z)5(JD-!?X|
zF$yw6952Dmz`)?jz#zcAS&-v9^JIPz&&mEg92|@c44~j=m>kbDbuu%X)nITnGa~@?
CTzw+|
delta 112
zcmZp1XfcprU|?W$DortDU=UznVBlbY&@5aN3&ketDKheG4A{-g$g`P8z=&z`ZsDNG
z6NDyAmJ)lun4N<|keLAt1h^R(7+e_`ShzM8erKM{FXIU^ih&7Y9LNTS&G9^Qm;rZG
B6IcKM
diff --git a/week5/BPE_Embedding.ipynb b/week5/BPE_Embedding.ipynb
new file mode 100644
index 0000000..1a1d23f
--- /dev/null
+++ b/week5/BPE_Embedding.ipynb
@@ -0,0 +1,1107 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "import pandas as pd \n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "yelp = open('yelp_academic_dataset_review.json')\n",
+ "\n",
+ "#read the first 150k entries\n",
+ "stop = 150000 \n",
+ "all_data=list()\n",
+ "for i, line in enumerate(yelp):\n",
+ " if i%10000==0: print(i)\n",
+ " if i==stop: break\n",
+ " data=json.loads(line)\n",
+ " text=data['text']\n",
+ " stars=data['stars']\n",
+ " all_data.append([stars, text])\n",
+ " \n",
+ "df= pd.DataFrame(all_data, columns=['stars','text'])\n",
+ "print(df)\n",
+ "\n",
+ "yelp.close()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1.0, 2.0, 3.0, 4.0, 5.0]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'number')"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "stars=df['stars']\n",
+ "print(sorted(stars.unique())) #고유값 나열\n",
+ "plt.hist(stars, bins=5, rwidth=0.85)\n",
+ "plt.title('Distribution of stars')\n",
+ "plt.xlabel('stars')\n",
+ "plt.ylabel('number')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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BncBdwAJgP2CxpNlkyebUdPwaSYuB+9LxZ6XkBnAmcAUwgmyRvakX2s3MmlG1ieTH6TUgIuIzwGdKwjvIRifljp8PzC8T7wSOGqh2mZlZ31WVSCJioaQRwLiIWFfjNpmZWROp6qotSScBdwM3pvdHS1pSy4aZmVlzqPby3/OBY4DHACLibrLLd83MbIirNpHsjIjHS2JlP7NhZmZDS7WL7aslvRMYJmkS2b2yflW7ZpmZWbOodkRyNtlNE3cAVwN/Aj5Sq0aZmVnzqPaqraeA8yRdmL2N7bVtlpmZNYtqr9p6raRVwL1kH0y8R9KU2jbNzMyaQbVrJJcBH4iI2wAkHQt8G3hlrRpmvqW8mTWHatdItncnEYCI+CXg6S0zM+t5RCLpNWlzhaRvki20B3Aa2YOnzMxsiOttausrJe/z98fy50jMzKznRBIRf1evhpiZWXOqarFd0kFkz/uYkC/Tn9vIm5lZa6n2qq2fAMuBVcBztWuOmZk1m2oTyT4RcU5NW2JmZk2p2st/r5T0fkmjJY3sftW0ZWZm1hSqHZE8A3wJOI/dV2sFcHgtGmVmZs2j2kRyDnBERDxcy8aYmVnzqXZqaw3wVC0bYmZmzanaEcku4G5JvyC7lTzgy3/NzKz6RPKf6WVmZvYC1T6PZGGtG2JmZs2p2ueRbJD0QOmrvyeVdJCkayX9RtJaSa9LlxQvlXR/+npw7vh5ktZLWifphFx8iqRVad/FktTfNpmZWf9Uu9g+FXhter0BuBi4qsB5LwJujIi/Al4FrAXmAssiYhKwLL1H0mSgg+xRvzOASyQNS/VcCswBJqXXjAJtMjOzfqh2auuRktDXJf0S+HRfTyjpAOCNwHtS3c8Az0iaCRyXDltIdpv6c4GZwDURsQPYIGk9cIykjcABEXF7qncRcDJwQ1/b1Az8kCszG6yqvWnja3JvX0Q2Qtm/n+c8HNgGfFvSq4CVwIeBQyNiC0BEbJF0SDq+new+X926UuzZtF0aL9f+OWQjF8aNG9fPZpuZWTnVXrX1FXZ/on0nsBE4tcA5XwOcHRF3SLqINI1VQbl1j+ghvmcwYgGwAGDq1Kl+joqZ2QCqdo3kRLLnti8D/gv4A9m6RX90AV0RcUd6fy1ZYnlI0miA9HVr7vixufJjgM0pPqZM3MzM6qjaRPKfwElk00lPpNeT/TlhRPw38KCkl6fQdOA+YAkwK8VmAden7SVAh6ThkiaSLaqvSNNg2yVNS1drnZErY2ZmdVLt1NaYiBjIK6LOBr4j6cXAA8B7yZLaYkmzgU2kqbOIWCNpMVmy2QmcFRG7Uj1nAlcAI8gW2Vtyod3MbDCrNpH8StJfR8SqgThpRNxNtmBfanqF4+cD88vEO4GjBqJNZmbWP9UmkmOB90jaQHavLQEREa+sWcusIl8KbGaDSbWJ5MSatsLMzJpWtR9I/H2tG2JmZs2p2qu2zMzMynIiMTOzQpxIzMysECcSMzMrxInEzMwKcSIxM7NCnEjMzKwQJxIzMyvEicTMzAqp9hYpNkj5vltm1mgekZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmJlZIU4kZmZWiC//7af8ZbdmZkOZE0kL8WdKzKwRPLVlZmaFOJGYmVkhDUskkoZJukvSj9L7kZKWSro/fT04d+w8SeslrZN0Qi4+RdKqtO9iSWpEX8zMhrJGjkg+DKzNvZ8LLIuIScCy9B5Jk4EO4EhgBnCJpGGpzKXAHGBSes2oT9PNzKxbQxKJpDHAW4Fv5cIzgYVpeyFwci5+TUTsiIgNwHrgGEmjgQMi4vaICGBRroyZmdVJo67a+jrwcWD/XOzQiNgCEBFbJB2S4u3A8txxXSn2bNouje9B0hyykQvjxo0biPYPer6Cy8zqpe4jEklvA7ZGxMpqi5SJRQ/xPYMRCyJiakRMbWtrq/K0ZmZWjUaMSF4PvF3SW4B9gAMkXQU8JGl0Go2MBram47uAsbnyY4DNKT6mTNzMzOqo7iOSiJgXEWMiYgLZIvrPI+J0YAkwKx02C7g+bS8BOiQNlzSRbFF9RZoG2y5pWrpa64xcGTMzq5PB9Mn2C4DFkmYDm4BTASJijaTFwH3ATuCsiNiVypwJXAGMAG5ILzMzq6OGJpKIuBm4OW0/AkyvcNx8YH6ZeCdwVO1aaGZmvfEn283MrJDBNLVlNeJLgc2sljwiMTOzQpxIzMysECcSMzMrxInEzMwKcSIxM7NCnEjMzKwQJxIzMyvEicTMzArxBxKHGH840cwGmkckZmZWiBOJmZkV4kRiZmaFeI1kCPN6iZkNBI9IzMysECcSMzMrxInEzMwK8RqJAV4vMbP+84jEzMwKcSIxM7NCPLVle/A0l5n1hUckZmZWSN0TiaSxkn4haa2kNZI+nOIjJS2VdH/6enCuzDxJ6yWtk3RCLj5F0qq072JJqnd/zMyGukaMSHYCH4uIVwDTgLMkTQbmAssiYhKwLL0n7esAjgRmAJdIGpbquhSYA0xKrxn17IiZmTUgkUTEloi4M21vB9YC7cBMYGE6bCFwctqeCVwTETsiYgOwHjhG0mjggIi4PSICWJQrY2ZmddLQxXZJE4BXA3cAh0bEFsiSjaRD0mHtwPJcsa4UezZtl8bLnWcO2ciFcePGDVwHhgAvvJtZbxq22C5pP+D7wEci4k89HVomFj3E9wxGLIiIqRExta2tre+NNTOzihqSSCTtTZZEvhMRP0jhh9J0Fenr1hTvAsbmio8BNqf4mDJxMzOro0ZctSXgMmBtRHw1t2sJMCttzwKuz8U7JA2XNJFsUX1FmgbbLmlaqvOMXBkzM6uTRqyRvB54N7BK0t0p9gngAmCxpNnAJuBUgIhYI2kxcB/ZFV9nRcSuVO5M4ApgBHBDelmNeL3EzMqpeyKJiF9Sfn0DYHqFMvOB+WXincBRA9c6MzPrK3+y3czMCvG9tqxfPM1lZt08IjEzs0I8IrHCPDoxG9qcSGxAOamYDT2e2jIzs0KcSMzMrBBPbVnNeJrLbGjwiMTMzArxiMTqwqMTs9blRGJ1l08q4MRi1uycSKzhPFoxa25eIzEzs0I8IrFBxaMTs+bjRGKDlpOKWXNwIrGm4KRiNnh5jcTMzArxiMSajkcnZoOLE4k1tdLPpJTjZGNWW04k1vI8gjGrLScSG1IqjWDyCcaJx6xvnEjMeuCkYtY7RUSj21BXU6dOjc7Ozn6VrWY+3oY2JxtrVZJWRsTUcvuafkQiaQZwETAM+FZEXNDgJtkQ5sV/G4qaOpFIGgb8G/A/gS7g15KWRMR9jW2ZWWVFR7aV1nMqHWNWa02dSIBjgPUR8QCApGuAmYATibWsahLRUJmGrSapVlO21dT7D4xmTyTtwIO5913A35QeJGkOMCe9fULSun6ebxTwcD/LNiv3eWhoyj7rwkJlm7LPRRTs8/hKO5o9kahMbI+rByJiAbCg8MmkzkqLTa3KfR4a3OehoVZ9bvZ7bXUBY3PvxwCbG9QWM7MhqdkTya+BSZImSnox0AEsaXCbzMyGlKae2oqInZI+CPyU7PLfyyNiTQ1PWXh6rAm5z0OD+zw01KTPQ+4DiWZmNrCafWrLzMwazInEzMwKcSKpkqQZktZJWi9pbqPb01+SLpe0VdLqXGykpKWS7k9fD87tm5f6vE7SCbn4FEmr0r6LJZW7FHtQkDRW0i8krZW0RtKHU7xl+y1pH0krJN2T+vzZFG/ZPkN2twtJd0n6UXrf0v0FkLQxtfduSZ0pVt9+R4RfvbzIFvJ/BxwOvBi4B5jc6Hb1sy9vBF4DrM7FvgjMTdtzgQvT9uTU1+HAxPQ9GJb2rQBeR/ZZnhuAExvdtx76PBp4TdreH/ht6lvL9ju1b7+0vTdwBzCtlfuc2noO8F3gR0Ph33Zq70ZgVEmsrv32iKQ6z9+KJSKeAbpvxdJ0IuJW4NGS8ExgYdpeCJyci18TETsiYgOwHjhG0mjggIi4PbJ/gYtyZQadiNgSEXem7e3AWrK7IrRsvyPzRHq7d3oFLdxnSWOAtwLfyoVbtr+9qGu/nUiqU+5WLO0NakstHBoRWyD7pQsckuKV+t2etkvjg56kCcCryf5Cb+l+p2meu4GtwNKIaPU+fx34OPBcLtbK/e0WwE2SVqbbQUGd+93UnyOpo6puxdKCKvW7Kb8fkvYDvg98JCL+1MMUcEv0OyJ2AUdLOgi4TtJRPRze1H2W9DZga0SslHRcNUXKxJqmvyVeHxGbJR0CLJX0mx6OrUm/PSKpTqvfiuWhNLQlfd2a4pX63ZW2S+ODlqS9yZLIdyLiBync8v0GiIjHgJuBGbRun18PvF3SRrKp5/8h6Spat7/Pi4jN6etW4Dqyqfi69tuJpDqtfiuWJcCstD0LuD4X75A0XNJEYBKwIg2Vt0ualq7sOCNXZtBJbbwMWBsRX83tatl+S2pLIxEkjQCOB35Di/Y5IuZFxJiImED2//PnEXE6LdrfbpJeImn/7m3gzcBq6t3vRl9x0Cwv4C1kV/v8Djiv0e0p0I+rgS3As2R/hcwGXgosA+5PX0fmjj8v9Xkduas4gKnpH+zvgG+Q7pIwGF/AsWTD9HuBu9PrLa3cb+CVwF2pz6uBT6d4y/Y5197j2H3VVkv3l+xK0nvSa03376Z699u3SDEzs0I8tWVmZoU4kZiZWSFOJGZmVogTiZmZFeJEYmZmhTiRmBUk6TBJ1w5wnRsljRrgOidIemfu/XskfWMgz2FDkxOJWY4yffp/ERGbI+KUWrVpAE0A3tnbQWZ95URiQ176S32tpEuAO4Gxkv5V0q8l3avdz/K4UNIHcuXOl/SxVH51ig2T9KVc2X9K8UskvT1tXyfp8rQ9W9Lne2nf6cqeLXK3pG9KGpbiT0iar+yZI8slHZrif5ne/1rS5yR13wX4AuANqZ6Ppthhkm5U9tyKLw7Qt9SGGCcSs8zLgUUR8eq0PYnsnkVHA1MkvZHsHk6n5cr8A/AfJfXMBh6PiNcCrwXen25FcSvwhnRMO9lzISD71P1tlRol6RXpnK+PiKOBXcC70u6XAMsj4lWp/ven+EXARakN+fslzQVui4ijI+JrKXZ0qv+vgdMk5e/DZFYVJxKzzO8jYnnafnN63UU2QvkrYFJE3AUcktZEXgX8MSI2ldTzZuCMdPv2O8huVTGJLFm8QdJk4D5231TvdcCvemjXdGAK8OtU53Sy22IAPAP8KG2vJJu6ItXZneC+20u/l0XE4xHxdGrX+F6ON9uDbyNvlnkyty3gCxHxzTLHXQucAvwF2QillICzI+Kne+zIHnc6g2z0MJJsRPNEZA/bqkTAwoiYV2bfs7H7Hke76N//5x257f7WYUOcRyRme/op8L70/BIktadnPUCWPDrIkkm5K7V+CpyZbluPpJelu7IC3A58hCyR3Ab8Cz1MayXLgFO6z6/sWdy9jRqWA3+ftjty8e1kjxo2G1BOJGYlIuImsimh2yWtIksY+6d9a9L2HyI9ga7Et8imiO5MC/DfZPdf+bcBe0XEerIps5H0kkgi4j7gk2RPwLsXWEr2DPqefAQ4R9KKdOzjKX4vsDMtzn+0YmmzPvLdf81ajKR9gT9HREjqAN4RETMb3S5rXZ4PNWs9U4BvpAcUPQa8r8HtsRbnEYmZmRXiNRIzMyvEicTMzApxIjEzs0KcSMzMrBAnEjMzK+T/A8gFgm7MbZU9AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "리뷰의 최대 길이 : 5000\n",
+ "리뷰의 평균 길이 : 575.17\n",
+ "리뷰의 중앙값 길이 : 406.00\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "plt.clf() #clear previous figure\n",
+ "plt.hist(df['text'].str.len(),bins=100)\n",
+ "plt.title('Distribution of review length')\n",
+ "plt.xlabel('review length')\n",
+ "plt.ylabel('number')\n",
+ "plt.show()\n",
+ "\n",
+ "len_result = [len(s) for s in df['text']]\n",
+ "print('리뷰의 최대 길이 : {}'.format(np.max(len_result)))\n",
+ "print('리뷰의 평균 길이 : {:.2f}'.format(np.mean(len_result)))\n",
+ "print('리뷰의 중앙값 길이 : {:.2f}'.format(np.median(len_result)))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>stars</th>\n",
+ " <th>text</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>38202</th>\n",
+ " <td>1.0</td>\n",
+ " <td>This is not hyperbole, March 2nd I had the abs...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>52164</th>\n",
+ " <td>1.0</td>\n",
+ " <td>Went to Zipps last night 4/29/2017 to celebrat...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>68710</th>\n",
+ " <td>4.0</td>\n",
+ " <td>Restaurant LOV is the perfect haven for vegeta...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>77793</th>\n",
+ " <td>3.0</td>\n",
+ " <td>I was set to try a burrito and all the flavore...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>92661</th>\n",
+ " <td>3.0</td>\n",
+ " <td>ALMOST perfect...\\n\\nWhile I usually post revi...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>110868</th>\n",
+ " <td>4.0</td>\n",
+ " <td>Finally got to try this place out. Granted it ...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>113043</th>\n",
+ " <td>3.0</td>\n",
+ " <td>Even though this is my first review of Rentals...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>142641</th>\n",
+ " <td>1.0</td>\n",
+ " <td>On July 14, 2015 I called the University of Ph...</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " stars text\n",
+ "38202 1.0 This is not hyperbole, March 2nd I had the abs...\n",
+ "52164 1.0 Went to Zipps last night 4/29/2017 to celebrat...\n",
+ "68710 4.0 Restaurant LOV is the perfect haven for vegeta...\n",
+ "77793 3.0 I was set to try a burrito and all the flavore...\n",
+ "92661 3.0 ALMOST perfect...\\n\\nWhile I usually post revi...\n",
+ "110868 4.0 Finally got to try this place out. Granted it ...\n",
+ "113043 3.0 Even though this is my first review of Rentals...\n",
+ "142641 1.0 On July 14, 2015 I called the University of Ph..."
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df.loc[ df['text'].str.len() == df['text'].str.len().max() ] "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:19: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
+ ]
+ }
+ ],
+ "source": [
+ "#불용어 제거 \n",
+ "\n",
+ "import nltk\n",
+ "from nltk.tokenize import word_tokenize\n",
+ "#nltk.download('punkt')\n",
+ "#nltk.download('stopwords')\n",
+ "from nltk.corpus import stopwords\n",
+ "stop_words=set(stopwords.words('english'))\n",
+ "\n",
+ "df2=df.copy()\n",
+ "for i, line in enumerate(df2['text']): #index와 value 동시에 접근\n",
+ " word_tokens = word_tokenize(line)\n",
+ " result=[]\n",
+ " for w in word_tokens:\n",
+ " w_lower=w.lower()\n",
+ " if w_lower not in stop_words:\n",
+ " result.append(w_lower)\n",
+ " line_f=' '.join(result)\n",
+ " df2['text'][i]=line_f\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 As someone who has worked with many museums, I...\n",
+ "1 I am actually horrified this place is still in...\n",
+ "2 I love Deagan's. I do. I really do. The atmosp...\n",
+ "3 Dismal, lukewarm, defrosted-tasting \"TexMex\" g...\n",
+ "4 Oh happy day, finally have a Canes near my cas...\n",
+ "5 This is definitely my favorite fast food sub s...\n",
+ "6 Really good place with simple decor, amazing f...\n",
+ "7 Awesome office and staff, very professional an...\n",
+ "8 Most delicious authentic Italian I've had in t...\n",
+ "9 I have been here twice. Very nice and laid bac...\n",
+ "10 Maria is VERY good at what she does with great...\n",
+ "11 ORDER In (Delivery) Review\\n\\nI discovered thi...\n",
+ "12 We purchased new office furniture from this lo...\n",
+ "13 Everything that my husband and I tried today w...\n",
+ "14 Called for a 5:15 morning reservation. The dri...\n",
+ "Name: text, dtype: object\n",
+ "0 someone worked many museums , eager visit gall...\n",
+ "1 actually horrified place still business . 3 ye...\n",
+ "2 love deagan 's . . really . atmosphere cozy fe...\n",
+ "3 dismal , lukewarm , defrosted-tasting `` texme...\n",
+ "4 oh happy day , finally canes near casa . yes o...\n",
+ "5 definitely favorite fast food sub shop . ingre...\n",
+ "6 really good place simple decor , amazing food ...\n",
+ "7 awesome office staff , professional friendly ....\n",
+ "8 delicious authentic italian 've us . would n't...\n",
+ "9 twice . nice laid back . tried weekend souther...\n",
+ "10 maria good great prices , ! 've always hard ti...\n",
+ "11 order ( delivery ) review discovered place fro...\n",
+ "12 purchased new office furniture location . advi...\n",
+ "13 everything husband tried today amazing . glad ...\n",
+ "14 called 5:15 morning reservation . driver 5 n't...\n",
+ "Name: text, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df['text'][:15]) \n",
+ "print(df2['text'][:15])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#txt 파일로 저장\n",
+ "with open('yelp_review.txt', 'w') as f:\n",
+ " for item in df2['text']:\n",
+ " f.write(\"%s\\n\" % item)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "93832\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>key</th>\n",
+ " <th>value</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>n't</td>\n",
+ " <td>106559</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>'s</td>\n",
+ " <td>89808</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>food</td>\n",
+ " <td>79192</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>good</td>\n",
+ " <td>75147</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>place</td>\n",
+ " <td>73940</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93827</th>\n",
+ " <td>femove</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93828</th>\n",
+ " <td>eldoradocleaners</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93829</th>\n",
+ " <td>easternlocation</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93830</th>\n",
+ " <td>justa</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>93831</th>\n",
+ " <td>80'</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>93832 rows × 2 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " key value\n",
+ "0 n't 106559\n",
+ "1 's 89808\n",
+ "2 food 79192\n",
+ "3 good 75147\n",
+ "4 place 73940\n",
+ "... ... ...\n",
+ "93827 femove 1\n",
+ "93828 eldoradocleaners 1\n",
+ "93829 easternlocation 1\n",
+ "93830 justa 1\n",
+ "93831 80' 1\n",
+ "\n",
+ "[93832 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X=[]\n",
+ "for line in df2['text']:\n",
+ " X.append(line)\n",
+ "\n",
+ "from keras.preprocessing.text import Tokenizer\n",
+ "\n",
+ "t= Tokenizer()\n",
+ "t.fit_on_texts(X)\n",
+ "count=t.word_counts\n",
+ "print(len(count))\n",
+ "\n",
+ "sorted_count=sorted(count.items(), key=lambda item: item[1], reverse=True)\n",
+ "word_data=pd.DataFrame(sorted_count)\n",
+ "word_data= word_data.rename(columns={0: \"key\", 1:\"value\"})\n",
+ "x=word_data[0:50]['key']\n",
+ "y=word_data[0:50]['value']\n",
+ "word_data\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#단어 빈도수 분포 \n",
+ "y2_l=len(word_data)\n",
+ "list1=[]\n",
+ "list2=[]\n",
+ "index=0\n",
+ "for i in range(106559): \n",
+ " cnt=0\n",
+ " while y2_l-index-1>=0 and i+1==word_data['value'][y2_l-index-1]:\n",
+ " cnt+=1\n",
+ " #print(y2_l-index-1,y2[y2_l-index-1],cnt)\n",
+ " index+=1\n",
+ " if(cnt!=0):\n",
+ " list1.append(i+1)\n",
+ " list2.append(cnt) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>count</th>\n",
+ " <th>ammount</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>41987</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>2</td>\n",
+ " <td>11675</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>3</td>\n",
+ " <td>5963</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>4</td>\n",
+ " <td>3848</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>5</td>\n",
+ " <td>2854</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>95</th>\n",
+ " <td>96</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>96</th>\n",
+ " <td>97</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>97</th>\n",
+ " <td>98</td>\n",
+ " <td>32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>98</th>\n",
+ " <td>99</td>\n",
+ " <td>38</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>99</th>\n",
+ " <td>100</td>\n",
+ " <td>28</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>100 rows × 2 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " count ammount\n",
+ "0 1 41987\n",
+ "1 2 11675\n",
+ "2 3 5963\n",
+ "3 4 3848\n",
+ "4 5 2854\n",
+ ".. ... ...\n",
+ "95 96 38\n",
+ "96 97 38\n",
+ "97 98 32\n",
+ "98 99 38\n",
+ "99 100 28\n",
+ "\n",
+ "[100 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "word_count_data = pd.DataFrame(list(zip(list1, list2)), \n",
+ " columns =['count', 'ammount']) \n",
+ "word_count_data[:100]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "87553 93.30825304800068\n"
+ ]
+ }
+ ],
+ "source": [
+ "total=0\n",
+ "for i in range(100):\n",
+ " total+=word_count_data['ammount'][i]\n",
+ "print(total,total/93832*100)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting sentencepiece\n",
+ " Downloading sentencepiece-0.1.85-cp37-cp37m-manylinux1_x86_64.whl (1.0 MB)\n",
+ "\u001b[K |████████████████████████████████| 1.0 MB 5.2 MB/s eta 0:00:01\n",
+ "\u001b[?25hInstalling collected packages: sentencepiece\n",
+ "Successfully installed sentencepiece-0.1.85\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install sentencepiece"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sentencepiece as spm\n",
+ "spm.SentencePieceTrainer.train('--input=yelp_review.txt --model_prefix=m --vocab_size=5000')\n",
+ "sp = spm.SentencePieceProcessor()\n",
+ "sp.load('m.model')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 71,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0= ⁇ 1= 2= 3=. 4=, 5=s 6=! 7=' 8=t 9=n 10=' 11=ed 12= 13=food 14=ing 15=place 16=good 17=great 18=) 19=d 20=ly 21=( 22=service 23=- 24=time 25=like 26=one 27=get 28=would 29=... 30=y 31=back 32=m 33=go 34=really 35=ve 36=e 37=also 38=$ 39=us 40=- 41=er 42=re 43=got 44=nice 45=even 46=? 47=love 48=well 49=could 50=`` 51=best 52='' 53=ordered 54=order 55=staff 56=came 57=: 58=restaurant 59=always 60=friendly 61=first 62=chicken 63=come 64=try 65=went 66=never 67=little 68=definitely 69=delicious 70=menu 71=much 72=amazing 73=people 74=experience 75=/ 76=2 77=r 78=make 79=made 80=. 81=o 82=pretty 83=new 84=know 85=n 86=ll 87=better 88=way 89=day 90=bar 91=two 92=going 93=a 94=recommend 95=said 96=ca 97=want 98=fresh 99=wait "
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(100):\n",
+ " print('{}={}'.format(i,sp.decode_ids([i])), end=' ')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[364, 919, 152, 2717, 5, 4, 3927, 188, 4367, 1974, 488, 523, 142, 3, 491, 28, 3139, 2607, 379, 816, 664, 286, 1577, 1270, 792, 4675, 598, 2717, 489, 945, 5, 21, 729, 32, 1475, 18, 4, 739, 33, 6, 3271, 212, 719, 1538, 550, 287, 93, 2206, 4, 4367, 82, 71, 1593, 757, 3, 7, 5, 451, 2200, 550, 3918, 5, 28, 277, 50, 1611, 52, 50, 3600, 52, 40, 1080, 426, 844, 1112, 775, 5, 32, 140, 3, 95, 4, 112, 115, 346, 945, 4367, 487, 4, 91, 932, 5, 237, 46, 230, 244, 57, 932, 1533, 4, 2108, 20, 2791, 523, 142, 3793, 323, 4, 686, 216, 3, 638, 519, 945, 1141, 4, 132, 71, 3, 932, 7, 5, 1335, 60, 3, 817, 4, 9, 10, 8, 343, 3, 932, 1995, 2043, 11, 2199, 220, 3, 1419, 1939, 1245, 1142, 5, 2544, 385, 858, 3030, 3659, 1793, 4, 4471, 3389, 3, 500, 5, 188, 2002, 757, 945, 782, 645, 1831, 1412, 4, 1664, 1010, 4883, 153, 2738, 334, 667, 8, 329, 153, 623, 1473, 340, 1442, 21, 3948, 81, 2957, 5, 337, 4407, 11, 18, 3, 459, 4367, 49, 244, 342, 4, 1995, 782, 20, 339, 3, 133, 50, 226, 118, 52, 50, 1209, 3, 52, 932, 252, 755, 1777, 2609, 8, 268, 234, 8, 323, 4, 146, 841, 2692, 2426, 945, 764, 1684, 14, 3], [264, 677, 77, 77, 2732, 15, 112, 297, 3, 101, 410, 258, 605, 366, 1954, 545, 1079, 1175, 42, 38, 464, 419, 394, 623, 5, 43, 405, 3, 99, 126, 1841, 5, 495, 73, 3, 477, 15, 431, 3430, 3, 4969, 3, 4226, 24, 321, 195, 93, 1010, 4226, 2182, 1807, 5, 3, 605, 891, 12, 759, 5, 4842, 14, 537, 1879, 3, 1967, 414, 3, 264, 4, 443, 11, 118, 414, 12, 955, 3496, 9, 10, 8, 3, 1904, 693, 2966, 1841, 823, 138, 1954, 7, 5, 443, 3, 244, 97, 12, 0, 76, 4181, 1211, 118, 31, 1087, 570, 394, 216, 877, 1166, 5, 394, 3, 34, 2405, 14, 702, 4, 100, 46, 417, 6, 672, 279, 4181, 1211, 5, 33, 264, 394, 360, 4, 65, 3, 1256, 1495, 2037, 1609, 9, 10, 8, 394, 360, 4, 3299, 5, 3, 518, 1787, 2063, 11, 408, 5, 566, 428, 2291, 30, 3672, 84, 279, 548, 2405, 14, 4181, 1211, 5, 46, 46, 46, 1981, 349, 795, 867, 3, 867, 3, 186, 126, 1564, 411, 2934, 4, 95, 50, 84, 46, 7, 5, 489, 3, 50, 4, 585, 214, 3, 176, 27, 1131, 748, 1497, 1076, 103, 605, 451, 1954, 15, 3, 1262, 304, 57, 33, 3, 542, 88, 6, 6, 6, 6, 6], [47, 213, 956, 249, 7, 5, 3, 3, 34, 3, 209, 1611, 606, 380, 546, 3, 338, 400, 286, 191, 866, 1991, 5, 3, 191, 794, 16, 794, 17, 4, 328, 2338, 123, 63, 1139, 3, 269, 564, 72, 1224, 17, 3, 192, 1976, 23, 195, 35, 196, 4, 7, 5, 528, 2132, 33, 137, 311, 4, 672, 904, 3, 64, 3, 294, 9, 10, 8, 314, 6], [671, 32, 153, 4, 4375, 4, 1604, 462, 5, 8, 11, 23, 530, 5, 609, 50, 1522, 369, 4141, 52, 1401, 813, 253, 1138, 1463, 20, 4, 2875, 1660, 956, 11, 456, 253, 2992, 635, 260, 4, 1399, 39, 1450, 9, 111, 1638, 1313, 1177, 1000, 668, 81, 35, 19, 895, 3564, 4, 420, 11, 39, 547, 252, 544, 105, 428, 287, 20, 3233, 784, 253, 7, 42, 3614, 2142, 1936, 253, 4, 1855, 335, 1814, 8, 457, 5, 644, 3958, 130, 4, 4064, 323, 3, 66, 4, 66, 4, 66, 21, 31, 3593, 286, 451, 13, 18], [475, 167, 89, 4, 382, 2016, 138, 719, 96, 5, 93, 3, 525, 714, 936, 111, 1345, 425, 1682, 846, 25, 2016, 138, 131, 25, 33, 476, 296, 62, 3, 1308, 5, 573, 309, 13, 3067, 510, 1765, 4076, 62, 3, 150, 2294, 12, 2400, 1731, 118, 467, 131, 2418, 7, 5, 337, 48, 646, 3, 4614, 26, 1960, 93, 445, 360, 512, 1572, 5, 1391, 1024, 3276, 1868, 540, 611, 5, 5, 11, 2805, 5, 231, 121, 356, 33, 144, 3, 208, 2381, 11, 3165, 50, 2074, 208, 46, 52, 2131, 7, 5, 391, 290, 565, 67, 121, 2249, 5, 1030, 715, 88, 3, 95, 112, 25, 3958, 599, 7, 5, 382, 96, 503, 7, 5, 351, 3], [68, 190, 305, 13, 1027, 326, 3, 783, 106, 4, 106, 115, 181, 244, 5, 7, 42, 1256, 216, 23, 2075, 907, 98, 783, 3, 1723, 266, 273, 20, 190, 29, 7, 5, 26, 403, 59, 27, 5, 3, 2052, 4, 608, 3830, 36, 118, 70, 64, 185, 219, 3, 68, 1411, 2940, 4, 2283, 452, 800, 5, 4, 4542, 2085, 5, 4, 614, 1135, 3, 362, 4, 3869, 15, 2108, 547, 543, 546, 1113, 320, 40, 793, 295, 1760, 11, 3], [34, 16, 15, 702, 477, 4, 72, 13, 17, 3881, 3, 562, 134, 551, 3, 497, 22, 124, 67, 370, 24, 1797, 127, 276, 83, 297, 3, 486, 815, 62, 16, 100, 1012, 2554, 145, 3, 68, 94, 15, 714, 3], [157, 613, 55, 4, 532, 60, 3, 491, 1426, 379, 3363, 1008, 774, 23, 105, 3525, 4, 60, 4, 532, 4, 339, 3, 271, 94, 12, 1424, 227, 85, 1529, 213, 77, 471, 428, 2291, 30, 6, 68, 324, 2234, 3], [69, 701, 833, 7, 35, 39, 3, 28, 9, 10, 8, 463, 15, 3968, 3, 1857, 1611, 223, 241, 729, 19, 4, 3925, 546, 710, 516, 453, 4, 204, 724, 3, 7, 86, 78, 174, 25, 7, 42, 4244, 3], [628, 3, 44, 2430, 31, 3, 162, 662, 2763, 70, 69, 6, 4495, 19, 2535, 4, 753, 102, 135, 4, 1146, 4, 572, 62, 266, 3, 4564, 334, 479, 8, 8, 11, 281, 3477, 6, 6, 6, 6, 1753, 292, 5, 1700, 3809, 4073, 386, 2319, 2234, 3, 363, 24, 225, 4, 136, 266, 3, 136, 72, 21, 3367, 46, 1805, 46, 4, 1266, 18, 4148, 3, 266, 16, 4, 218, 314, 225, 3, 761, 347, 50, 997, 30, 52, 3, 559, 70, 608, 292, 4630, 26, 3, 96, 9, 10, 8, 99, 33, 31, 64, 3, 1190, 44, 634, 1111, 48, 3]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "X2=[]\n",
+ "for line in df2['text']:\n",
+ " X2.append(sp.encode_as_ids(line))\n",
+ "\n",
+ "print(X2[:10]) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#df2.to_pickle(\"./df2.pkl\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Glove"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 90,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[['▁someone', '▁worked', '▁many', '▁museum', 's', '▁,', '▁eager', '▁visit', '▁gallery', '▁recent', '▁trip', '▁las', '▁vegas', '▁.', '▁saw', '▁would', '▁showing', '▁inf', 'am', 'ous', '▁eggs', '▁house', '▁fa', 'ber', 'ge', '▁virgin', 'ia', '▁museum', '▁fine', '▁art', 's', '▁(', '▁v', 'm', 'fa', '▁)', '▁,', '▁knew', '▁go', '▁!', '▁tucked', '▁away', '▁near', '▁gel', 'ate', 'ri', 'a', '▁garden', '▁,', '▁gallery', '▁pretty', '▁much', '▁hidden', '▁view', '▁.', \"▁'\", 's', '▁real', '▁est', 'ate', '▁agent', 's', '▁would', '▁call', '▁``', '▁cozy', \"▁''\", '▁``', '▁charming', \"▁''\", '▁-', '▁basically', '▁e', 'up', 'he', 'mi', 's', 'm', '▁small', '▁.', '▁said', '▁,', '▁still', '▁see', '▁wonderful', '▁art', '▁gallery', '▁size', '▁,', '▁two', '▁*', 's', '▁ask', '▁?', '▁let', '▁tell', '▁:', '▁*', '▁pricing', '▁,', '▁relative', 'ly', '▁inexpensive', '▁las', '▁vegas', '▁attract', 'ion', '▁,', '▁completely', '▁top', '▁.', '▁space', '▁amount', '▁art', '▁fit', '▁,', '▁bit', '▁much', '▁.', '▁*', \"▁'\", 's', '▁kid', '▁friendly', '▁.', '▁seriously', '▁,', '▁n', \"'\", 't', '▁bring', '▁.', '▁*', '▁security', '▁train', 'ed', '▁properly', '▁show', '▁.', '▁cur', 'ating', '▁design', '▁team', 's', '▁col', 'la', 'bo', 'rate', '▁exhibit', 'ions', '▁,', '▁definite', '▁flow', '▁.', '▁mean', 's', '▁visit', 'ors', '▁view', '▁art', '▁certain', '▁se', 'qu', 'ence', '▁,', '▁whe', 'ther', '▁historic', 'al', '▁period', '▁c', 'ul', 't', 'ur', 'al', '▁sign', 'if', 'ic', 'ance', '▁(', '▁audi', 'o', '▁guide', 's', '▁usually', '▁develop', 'ed', '▁)', '▁.', '▁arrived', '▁gallery', '▁could', '▁tell', '▁start', '▁,', '▁security', '▁certain', 'ly', '▁helpful', '▁.', '▁told', '▁``', '▁look', '▁around', \"▁''\", '▁``', '▁whatever', '▁.', \"▁''\", '▁*', 'f', 'ine', '*', '▁ins', 't', 'it', 'u', 't', 'ion', '▁,', '▁find', '▁lack', '▁knowledge', '▁respect', '▁art', '▁app', 'all', 'ing', '▁.'], ['▁actually', '▁ho', 'r', 'r', 'ified', '▁place', '▁still', '▁business', '▁.', '▁3', '▁year', '▁old', '▁son', '▁needed', '▁haircut', '▁past', '▁summer', '▁lu', 're', '▁$', '▁7', '▁kids', '▁cut', '▁sign', 's', '▁got', '▁door', '▁.', '▁wait', '▁minutes', '▁stylist', 's', '▁working', '▁people', '▁.', '▁decor', '▁place', '▁total', '▁garbage', '▁.', '▁tacky', '▁.', '▁sofa', '▁time', '▁p', 'le', 'a', 'ther', '▁sofa', '▁giant', '▁hole', 's', '▁.', '▁son', '▁noticed', '▁', 'ant', 's', '▁crawl', 'ing', '▁floor', '▁furniture', '▁.', '▁disgusting', '▁walked', '▁.', '▁actually', '▁,', '▁turn', 'ed', '▁around', '▁walked', '▁', 'upon', '▁entering', '▁n', \"'\", 't', '▁.', '▁older', '▁black', '▁male', '▁stylist', '▁finish', 'es', '▁haircut', \"▁'\", 's', '▁turn', '▁.', '▁tell', '▁want', '▁', '#', '▁2', '▁clip', 'per', '▁around', '▁back', '▁sides', '▁hand', '▁cut', '▁top', '▁standard', '▁boy', 's', '▁cut', '▁.', '▁really', '▁freak', 'ing', '▁simple', '▁,', '▁right', '▁?', '▁wrong', '▁!', '▁rather', '▁use', '▁clip', 'per', 's', '▁go', '▁actually', '▁cut', '▁hair', '▁,', '▁went', '▁.', '▁using', '▁moving', '▁down', 'ward', '▁n', \"'\", 't', '▁cut', '▁hair', '▁,', '▁rub', 's', '▁.', '▁man', '▁all', 'eg', 'ed', '▁co', 's', 'me', 'to', 'log', 'y', '▁license', '▁know', '▁use', '▁set', '▁freak', 'ing', '▁clip', 'per', 's', '▁?', '▁?', '▁?', '▁realized', '▁almost', '▁immediately', '▁idea', '▁.', '▁idea', '▁.', '▁10', '▁minutes', '▁watching', '▁guy', '▁stumble', '▁,', '▁said', '▁``', '▁know', '▁?', \"▁'\", 's', '▁fine', '▁.', '▁``', '▁,', '▁paid', '▁left', '▁.', '▁wanted', '▁get', '▁sc', 'um', 'my', '▁joint', '▁take', '▁son', '▁real', '▁haircut', '▁place', '▁.', '▁bottom', '▁line', '▁:', '▁go', '▁.', '▁run', '▁way', '▁!', '▁!', '▁!', '▁!', '▁!'], ['▁love', '▁de', 'ag', 'an', \"▁'\", 's', '▁.', '▁.', '▁really', '▁.', '▁atmosphere', '▁cozy', '▁f', 'est', 'ive', '▁.', '▁shrimp', '▁tacos', '▁house', '▁fries', '▁stand', 'by', 's', '▁.', '▁fries', '▁sometimes', '▁good', '▁sometimes', '▁great', '▁,', '▁spicy', '▁dipping', '▁sauce', '▁come', '▁die', '▁.', '▁beer', '▁list', '▁amazing', '▁cocktails', '▁great', '▁.', '▁prices', '▁mid', '-', 'le', 've', 'l', '▁,', \"▁'\", 's', '▁cheap', '▁dive', '▁go', '▁every', '▁week', '▁,', '▁rather', '▁treat', '▁.', '▁try', '▁.', '▁wo', '▁n', \"'\", 't', '▁disappointed', '▁!'], ['▁dis', 'm', 'al', '▁,', '▁lukewarm', '▁,', '▁def', 'ro', 's', 't', 'ed', '-', 'ta', 's', 'ting', '▁``', '▁te', 'x', 'mex', \"▁''\", '▁gl', 'op', '▁;', '▁mu', 'mb', 'ly', '▁,', '▁une', 'ng', 'ag', 'ed', '▁waiter', '▁;', '▁clue', 'less', '▁manager', '▁,', '▁seeing', '▁us', '▁barely', '▁n', 'i', 'bb', 'led', '▁entrees', '▁plates', '▁sh', 'o', 've', 'd', '▁forward', '▁pickup', '▁,', '▁thank', 'ed', '▁us', '▁per', 'f', 'un', 'c', 'to', 'ri', 'ly', '▁patron', 'age', '▁;', \"▁'\", 're', '▁texas', '▁hill', '▁country', '▁;', '▁,', '▁ja', 'il', '▁cri', 't', 'ter', 's', '▁serve', '▁grub', '▁bad', '▁,', '▁protect', 'ion', '▁.', '▁never', '▁,', '▁never', '▁,', '▁never', '▁(', '▁back', '▁yard', '▁house', '▁real', '▁food', '▁)'], ['▁oh', '▁happy', '▁day', '▁,', '▁finally', '▁can', 'es', '▁near', '▁ca', 's', 'a', '▁.', '▁yes', '▁others', '▁gr', 'i', 'ping', '▁drive', '▁thru', '▁packed', '▁like', '▁can', 'es', '▁area', '▁like', '▁go', '▁sit', '▁enjoy', '▁chicken', '▁.', '▁cashier', 's', '▁pleasant', '▁far', '▁food', '▁wise', '▁yet', '▁receive', '▁funky', '▁chicken', '▁.', '▁clean', '▁crew', '▁', 'zi', 'ps', '▁around', '▁dining', '▁area', '▁constantly', \"▁'\", 's', '▁usually', '▁well', '▁kept', '▁.', '▁gripe', '▁one', '▁fell', 'a', '▁red', '▁hair', '▁makes', '▁round', 's', '▁cleaning', '▁smile', '▁personality', '▁nights', '▁ago', '▁to', 's', 's', 'ed', '▁napkin', 's', '▁put', '▁table', '▁help', '▁go', '▁meal', '▁.', '▁done', '▁reach', 'ed', '▁tray', '▁``', '▁excuse', '▁done', '▁?', \"▁''\", '▁realize', \"▁'\", 's', '▁trying', '▁job', '▁quickly', '▁little', '▁table', '▁manner', 's', '▁goes', '▁along', '▁way', '▁.', '▁said', '▁still', '▁like', '▁grub', '▁glad', \"▁'\", 's', '▁finally', '▁ca', 'ne', \"▁'\", 's', '▁close', '▁.']]\n"
+ ]
+ }
+ ],
+ "source": [
+ "X3=[]\n",
+ "for line in df2['text']:\n",
+ " X3.append(sp.encode_as_pieces(line))\n",
+ "\n",
+ "print(X3[:5]) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 95,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting https://github.com/JonathanRaiman/glove/archive/master.zip\n",
+ " Downloading https://github.com/JonathanRaiman/glove/archive/master.zip\n",
+ "\u001b[K \\ 5.7 kB 6.8 MB/s\n",
+ "\u001b[?25hRequirement already satisfied: cython in /opt/conda/lib/python3.7/site-packages (from glove==1.0.1) (0.29.16)\n",
+ "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from glove==1.0.1) (1.18.2)\n",
+ "Building wheels for collected packages: glove\n",
+ " Building wheel for glove (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for glove: filename=glove-1.0.1-py3-none-any.whl size=4998 sha256=d775ed0b08248304e87de1e2d4f074dcc8e2650c5e2f1bf00f20f90b3a9f1ba2\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-9r05loxz/wheels/e9/94/1b/0e5924a72f76c112ecd16fe80ee3731b4fe82c024056cf9da5\n",
+ "Successfully built glove\n",
+ "Installing collected packages: glove\n",
+ "Successfully installed glove-1.0.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip3 install https://github.com/JonathanRaiman/glove/archive/master.zip\n",
+ "# 참조 https://github.com/maciejkula/glove-python/blob/master/readme.md"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#!pip install glove_python"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 99,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'glove.corpus'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m<ipython-input-99-be31983bc1c1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mglove\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;31m#from glove import Glove\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m#Creating a corpus object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCorpus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'glove.corpus'"
+ ]
+ }
+ ],
+ "source": [
+ "from glove import Corpus\n",
+ "\n",
+ "#Creating a corpus object\n",
+ "corpus = Corpus() \n",
+ "\n",
+ "#Training the corpus to generate the co occurence matrix which is used in GloVe\n",
+ "corpus.fit(X3, window=10)\n",
+ "\n",
+ "glove = Glove(no_components=5, learning_rate=0.05) \n",
+ "glove.fit(corpus.matrix, epochs=30, no_threads=4, verbose=True)\n",
+ "glove.add_dictionary(corpus.dictionary)\n",
+ "glove.save('glove.model')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting gensim\n",
+ " Downloading gensim-3.8.2-cp37-cp37m-manylinux1_x86_64.whl (24.2 MB)\n",
+ "\u001b[K |████████████████████████████████| 24.2 MB 4.2 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from gensim) (1.4.1)\n",
+ "Requirement already satisfied: six>=1.5.0 in /opt/conda/lib/python3.7/site-packages (from gensim) (1.14.0)\n",
+ "Collecting smart-open>=1.8.1\n",
+ " Downloading smart_open-1.11.1.tar.gz (105 kB)\n",
+ "\u001b[K |████████████████████████████████| 105 kB 90.1 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: numpy>=1.11.3 in /opt/conda/lib/python3.7/site-packages (from gensim) (1.18.2)\n",
+ "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from smart-open>=1.8.1->gensim) (2.23.0)\n",
+ "Requirement already satisfied: boto in /opt/conda/lib/python3.7/site-packages (from smart-open>=1.8.1->gensim) (2.49.0)\n",
+ "Collecting boto3\n",
+ " Downloading boto3-1.12.44-py2.py3-none-any.whl (128 kB)\n",
+ "\u001b[K |████████████████████████████████| 128 kB 75.9 MB/s eta 0:00:01\n",
+ "\u001b[?25hRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (1.25.7)\n",
+ "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (2019.11.28)\n",
+ "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (2.9)\n",
+ "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->smart-open>=1.8.1->gensim) (3.0.4)\n",
+ "Collecting s3transfer<0.4.0,>=0.3.0\n",
+ " Downloading s3transfer-0.3.3-py2.py3-none-any.whl (69 kB)\n",
+ "\u001b[K |████████████████████████████████| 69 kB 10.8 MB/s eta 0:00:01\n",
+ "\u001b[?25hCollecting botocore<1.16.0,>=1.15.44\n",
+ " Downloading botocore-1.15.44-py2.py3-none-any.whl (6.1 MB)\n",
+ "\u001b[K |████████████████████████████████| 6.1 MB 58.8 MB/s eta 0:00:01\n",
+ "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n",
+ " Downloading jmespath-0.9.5-py2.py3-none-any.whl (24 kB)\n",
+ "Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /opt/conda/lib/python3.7/site-packages (from botocore<1.16.0,>=1.15.44->boto3->smart-open>=1.8.1->gensim) (2.8.1)\n",
+ "Collecting docutils<0.16,>=0.10\n",
+ " Downloading docutils-0.15.2-py3-none-any.whl (547 kB)\n",
+ "\u001b[K |████████████████████████████████| 547 kB 66.1 MB/s eta 0:00:01\n",
+ "\u001b[?25hBuilding wheels for collected packages: smart-open\n",
+ " Building wheel for smart-open (setup.py) ... \u001b[?25ldone\n",
+ "\u001b[?25h Created wheel for smart-open: filename=smart_open-1.11.1-py3-none-any.whl size=95255 sha256=7d6b127140f07818d5fe03a40d4200ea5cb5d3f9d0c69634685434ec44988359\n",
+ " Stored in directory: /home/jupyter/.cache/pip/wheels/1a/8c/a2/7b24df77c58dab0aec275c1bd3d4392d54df941020199f6759\n",
+ "Successfully built smart-open\n",
+ "Installing collected packages: jmespath, docutils, botocore, s3transfer, boto3, smart-open, gensim\n",
+ " Attempting uninstall: docutils\n",
+ " Found existing installation: docutils 0.16\n",
+ " Uninstalling docutils-0.16:\n",
+ " Successfully uninstalled docutils-0.16\n",
+ "Successfully installed boto3-1.12.44 botocore-1.15.44 docutils-0.15.2 gensim-3.8.2 jmespath-0.9.5 s3transfer-0.3.3 smart-open-1.11.1\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install gensim "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from gensim.models import Word2Vec\n",
+ "model = Word2Vec(sentences=X3, size=110, window=5, min_count=101, workers=4, sg=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('▁lady', 0.5997246503829956), ('▁guy', 0.591339647769928), ('▁woman', 0.5895590782165527), ('▁gentleman', 0.5367828607559204), ('▁girl', 0.5270550847053528), ('▁dude', 0.4889143705368042), ('▁jo', 0.48389774560928345), ('▁boss', 0.4744197726249695), ('▁owner', 0.4523840844631195), ('▁ben', 0.45194077491760254)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_result = model.wv.most_similar(\"▁man\")\n",
+ "print(model_result)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
+ " This is separate from the ipykernel package so we can avoid doing imports until\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "X_em=model[model.wv.vocab]\n",
+ "pca = PCA(n_components=2)\n",
+ "result = pca.fit_transform(X_em)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 112,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.collections.PathCollection at 0x7f96d41fb710>"
+ ]
+ },
+ "execution_count": 112,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "plt.scatter(result[:, 0], result[:, 1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "words = list(model.wv.vocab)\n",
+ "for i, word in enumerate(words):\n",
+ "\tplt.annotate(word, xy=(result[i, 0], result[i, 1]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.savefig('wvplot.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# 1D_CNN"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from __future__ import print_function\n",
+ "\n",
+ "from keras.preprocessing import sequence\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense, Dropout, Activation\n",
+ "from keras.layers import Embedding\n",
+ "from keras.layers import Conv1D, GlobalMaxPooling1D\n",
+ "from keras.datasets import imdb\n",
+ "\n",
+ "# set parameters:\n",
+ "max_features = 5000\n",
+ "maxlen = 400\n",
+ "batch_size = 32\n",
+ "embedding_dims = 50\n",
+ "filters = 250\n",
+ "kernel_size = 3\n",
+ "hidden_dims = 250\n",
+ "epochs = 2\n",
+ "\n",
+ "print('Loading data...')\n",
+ "(x_train, y_train), (x_test, y_test) = \\\n",
+ "print(len(x_train), 'train sequences')\n",
+ "print(len(x_test), 'test sequences')\n",
+ "\n",
+ "print('Pad sequences (samples x time)')\n",
+ "x_train = sequence.pad_sequences(x_train, maxlen=maxlen)\n",
+ "x_test = sequence.pad_sequences(x_test, maxlen=maxlen)\n",
+ "print('x_train shape:', x_train.shape)\n",
+ "print('x_test shape:', x_test.shape)\n",
+ "\n",
+ "print('Build model...')\n",
+ "model = Sequential()\n",
+ "\n",
+ "# we start off with an efficient embedding layer which maps\n",
+ "# our vocab indices into embedding_dims dimensions\n",
+ "model.add(Embedding(max_features,\n",
+ " embedding_dims,\n",
+ " input_length=maxlen))\n",
+ "model.add(Dropout(0.2))\n",
+ "\n",
+ "# we add a Convolution1D, which will learn filters\n",
+ "# word group filters of size filter_length:\n",
+ "model.add(Conv1D(filters,\n",
+ " kernel_size,\n",
+ " padding='valid',\n",
+ " activation='relu',\n",
+ " strides=1))\n",
+ "# we use max pooling:\n",
+ "model.add(GlobalMaxPooling1D())\n",
+ "\n",
+ "# We add a vanilla hidden layer:\n",
+ "model.add(Dense(hidden_dims))\n",
+ "model.add(Dropout(0.2))\n",
+ "model.add(Activation('relu'))\n",
+ "\n",
+ "# We project onto a single unit output layer, and squash it with a sigmoid:\n",
+ "model.add(Dense(1))\n",
+ "model.add(Activation('sigmoid'))\n",
+ "\n",
+ "model.compile(loss='binary_crossentropy',\n",
+ " optimizer='adam',\n",
+ " metrics=['accuracy'])\n",
+ "model.fit(x_train, y_train,\n",
+ " batch_size=batch_size,\n",
+ " epochs=epochs,\n",
+ " validation_data=(x_test, y_test))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/week5/note5.pdf b/week5/note5.pdf
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--
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