From 2714501ff6b41ce0435836f5522da1ae19308f5d Mon Sep 17 00:00:00 2001 From: Maren Date: Tue, 26 Jun 2018 15:12:07 +0200 Subject: [PATCH] naive approach test --- .../naive_approach/naiveApproachTest.ipynb | 130 ++++++++++++++++++ 1 file changed, 130 insertions(+) create mode 100644 Project/naive_approach/naiveApproachTest.ipynb diff --git a/Project/naive_approach/naiveApproachTest.ipynb b/Project/naive_approach/naiveApproachTest.ipynb new file mode 100644 index 0000000..104ad1f --- /dev/null +++ b/Project/naive_approach/naiveApproachTest.ipynb @@ -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\u001b[0m in \u001b[0;36m\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 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\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\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 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\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 +}