{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation\n", "We want to evaluate our approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Needed\n", "We want to define needed components for this UI" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "import ipywidgets as widgets\n", "from IPython.display import display, clear_output\n", "import math\n", "import datetime" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Trigger refresh of prediction\n", "each action of typing and sending should yield a new updated prediction for best fitting emojis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initial definition of emojis used later" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#locally defined based on the first analysis of parts of our twitter data: resulting in the 20 most used emojis\n", "#we used them for our first approaches of prediction\n", "top_emojis = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n", "#possible initial set of predictions, only used in naive test cases\n", "predictions = [\"🤐\",\"🤑\",\"🤒\",\"🤓\",\"🤔\",\"🤕\",\"🤗\",\"🤘\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Advanced Approach\n", "define the classifier for advanced prediction, used for the sentiment prediction" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /Users/Carsten/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "[nltk_data] Downloading package averaged_perceptron_tagger to\n", "[nltk_data] /Users/Carsten/nltk_data...\n", "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", "[nltk_data] date!\n", "[nltk_data] Downloading package wordnet to /Users/Carsten/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n" ] } ], "source": [ "#navigation into right path and generating classifier\n", "import sys\n", "sys.path.append(\"..\")\n", "sys.path.append(\"../naive_approach\")\n", "\n", "\n", "\n", "import simple_approach.simple_twitter_learning as stl\n", "clf_advanced = stl.pipeline_manager.load_from_pipeline_file(\"/Users/Carsten/DataSets/NLP_LAB/d2v_final/test_d2v_e2.pipeline\")\n", "\n", "import Tools.Emoji_Distance as ed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate new Sample for online learning / reinforcement learning" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generate_new_training_sample (msg, emoji):\n", " sentiment = ed.emoji_to_sentiment_vector(emoji)\n", " \n", " #TODO message msg could be filtred\n", " text = msg\n", " return text, sentiment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Naive Approach\n", "for topic related emoji prediction" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#sys.path.append(\"..\")\n", "#print(sys.path)\n", "\n", "import naive_approach as clf_naive" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tmp_dict = clf_naive.prepareData(stem=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Merge Predictions\n", "combine the predictions of both approaches" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def merged_prediction(msg , split = 0.5 , number = 8, target_emojis = top_emojis):\n", " \n", " #calc ratio of prediction splitted between advanced aprroach and naive approach\n", " number_advanced = round(split*number)\n", " number_naive = round((1-split)*number)\n", " \n", " #predict emojis with the naive approach\n", " prediction_naive , prediction_naive_values = clf_naive.predict(sentence = msg, lookup= tmp_dict, n = number_naive, embeddings = \"word2Vec\", stem = True)\n", "\n", " #filter 0 values\n", " tmp1 = []\n", " tmp2 = []\n", " epsilon = 0.0001\n", "\n", " for i in range(len(prediction_naive)):\n", " if(abs(prediction_naive_values[i]) > epsilon):\n", " tmp1.append(prediction_naive[i])\n", " tmp2.append(prediction_naive[i])\n", "\n", " prediction_naive = tmp1\n", " prediction_naive_values = tmp2\n", " \n", " if(len(prediction_naive) < number_naive):\n", " #print(\"only few matches\")\n", " number_advanced = number - len(prediction_naive)\n", " \n", " #print(number, number_advanced, number_naive)\n", " \n", " #predict the advanced approach\n", " sentiment = clf_advanced.predict([msg])\n", " prediction_advanced = ed.sentiment_vector_to_emoji(sentiment,n_results = number_advanced, custom_target_emojis=target_emojis)\n", " \n", " #concat both predictions\n", " prediction = list(prediction_advanced)+list(prediction_naive)\n", " \n", " return prediction[:number]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Actions triggered when something is changed" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def trigger_new_prediction(all_chat, current_message):\n", " global predictions\n", " \n", " #random prediction for initial test\n", " #random.shuffle(predictions)\n", " \n", " #first prediction only using advanced approach\n", " #sent = clf_advanced.predict([current_message])\n", " #p = ed.sentiment_vector_to_emoji(sent,n_results = 8, custom_target_emojis=top_emojis)\n", " \n", " #merged prediction\n", " if(current_message != \"\"):\n", " p = merged_prediction(msg = current_message, target_emojis=top_emojis)\n", "\n", " predictions = p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trigger Prediction for CSV Table" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sentencepredictionlabel
0I am so happyNaNp
1i love my lifeNaNp
2i really like this sunshineNaNp
3while doing sport i feel freeNaNp
4i is terrible to learn when the weather is thi...NaNn
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" ], "text/plain": [ " Sentence prediction label\n", "0 I am so happy NaN p\n", "1 i love my life NaN p\n", "2 i really like this sunshine NaN p\n", "3 while doing sport i feel free NaN p\n", "4 i is terrible to learn when the weather is thi... NaN n" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get table\n", "import pandas as pd\n", "df = pd.read_csv(\"Evaluation Sentences - Sentiment related sentences.csv\")#, sep=\"\\t\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "all_predictions = []\n", "\n", "for index, row in df.iterrows():\n", " sentence = row[\"Sentence\"]\n", " #print(sentence)\n", "\n", " trigger_new_prediction(all_chat=\"\", current_message = sentence)\n", " #print(predictions)\n", " \n", " #prediction to string\n", " tmp_prediction = \"\".join(predictions)\n", " \n", " #construct the preediction column\n", " all_predictions.append(tmp_prediction)\n", " " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Sentencepredictionlabel
0I am so happy😂😅😢😳😁😌😉😎p
1i love my life😅😂😢😳😁🏩💌🤟p
2i really like this sunshine😅😂😢😳😭😁😌😔p
3while doing sport i feel free😂😅😁😌😉😎😳😢p
4i is terrible to learn when the weather is thi...😂😅😁😉😌😎😳🙅n
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" ], "text/plain": [ " Sentence prediction label\n", "0 I am so happy 😂😅😢😳😁😌😉😎 p\n", "1 i love my life 😅😂😢😳😁🏩💌🤟 p\n", "2 i really like this sunshine 😅😂😢😳😭😁😌😔 p\n", "3 while doing sport i feel free 😂😅😁😌😉😎😳😢 p\n", "4 i is terrible to learn when the weather is thi... 😂😅😁😉😌😎😳🙅 n" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"prediction\"] = all_predictions\n", "\n", "df.head()\n", "\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df.to_csv(\"E_S - sentiment - d2v - w2v - no stemming.csv\", sep='\\t', encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }