naive approach test
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							| @ -0,0 +1,130 @@ | |||||||
|  | { | ||||||
|  |  "cells": [ | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 1, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [], | ||||||
|  |    "source": [ | ||||||
|  |     "import naive_approach" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 2, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [], | ||||||
|  |    "source": [ | ||||||
|  |     "top_emojis = ['😂',\n", | ||||||
|  |     "         '😭',\n", | ||||||
|  |     "         '😍',\n", | ||||||
|  |     "         '😩',\n", | ||||||
|  |     "         '😊',\n", | ||||||
|  |     "         '😘',\n", | ||||||
|  |     "         '🙏',\n", | ||||||
|  |     "         '🙌',\n", | ||||||
|  |     "         '😉',\n", | ||||||
|  |     "         '😁',\n", | ||||||
|  |     "         '😅',\n", | ||||||
|  |     "         '😎',\n", | ||||||
|  |     "         '😢',\n", | ||||||
|  |     "         '😒',\n", | ||||||
|  |     "         '😏',\n", | ||||||
|  |     "         '😌',\n", | ||||||
|  |     "         '😔',\n", | ||||||
|  |     "         '😋',\n", | ||||||
|  |     "         '😀',\n", | ||||||
|  |     "         '😤']" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 3, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [], | ||||||
|  |    "source": [ | ||||||
|  |     "lookup = naive_approach.prepareData(emojis_to_consider=top_emojis)" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 4, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [], | ||||||
|  |    "source": [ | ||||||
|  |     "sentence=\"I am very happy today\"" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 7, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [ | ||||||
|  |     { | ||||||
|  |      "ename": "KeyError", | ||||||
|  |      "evalue": "357", | ||||||
|  |      "output_type": "error", | ||||||
|  |      "traceback": [ | ||||||
|  |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", | ||||||
|  |       "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)", | ||||||
|  |       "\u001b[1;32m<ipython-input-7-a7b8b0832a7d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnaive_approach\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msentence\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlookup\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0memojis_to_consider\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtop_emojis\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", | ||||||
|  |       "\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(sentence, lookup, emojis_to_consider, criteria, description_key, lang, n, t)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m     \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | ||||||
|  |       "\u001b[1;32m~\\Desktop\\NLP-LAB\\Project\\naive_approach\\naive_approach.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;31m# build a result table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 119\u001b[1;33m     \u001b[0mtable_array\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlookup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mindexes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdescription_key\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    121\u001b[0m     \u001b[0mtable_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtable_array\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcriteria\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", | ||||||
|  |       "\u001b[1;31mKeyError\u001b[0m: 357" | ||||||
|  |      ] | ||||||
|  |     } | ||||||
|  |    ], | ||||||
|  |    "source": [ | ||||||
|  |     "pred = naive_approach.predict(sentence, lookup, emojis_to_consider=top_emojis,  n=3)" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "cell_type": "code", | ||||||
|  |    "execution_count": 9, | ||||||
|  |    "metadata": {}, | ||||||
|  |    "outputs": [ | ||||||
|  |     { | ||||||
|  |      "data": { | ||||||
|  |       "text/plain": [ | ||||||
|  |        "['🎁', '🙋', '\\U0001f91f']" | ||||||
|  |       ] | ||||||
|  |      }, | ||||||
|  |      "execution_count": 9, | ||||||
|  |      "metadata": {}, | ||||||
|  |      "output_type": "execute_result" | ||||||
|  |     } | ||||||
|  |    ], | ||||||
|  |    "source": [ | ||||||
|  |     "pred" | ||||||
|  |    ] | ||||||
|  |   }, | ||||||
|  |   { | ||||||
|  |    "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": "3.6.4" | ||||||
|  |   } | ||||||
|  |  }, | ||||||
|  |  "nbformat": 4, | ||||||
|  |  "nbformat_minor": 2 | ||||||
|  | } | ||||||
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