707 lines
18 KiB
Plaintext
707 lines
18 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# User Interface Configuration and Information\n",
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"We want to create a small user interface for our prototype in emoji prediction"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Needed\n",
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"We want to define needed components for this UI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import random\n",
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"import ipywidgets as widgets\n",
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"from IPython.display import display, clear_output\n",
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"import math\n",
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"import datetime"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Trigger refresh of prediction\n",
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"each action of typing and sending should yield a new updated prediction for best fitting emojis"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"Initial definition of emojis used later"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#locally defined based on the first analysis of parts of our twitter data: resulting in the 20 most used emojis\n",
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"#we used them for our first approaches of prediction\n",
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"\n",
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"top_emojis = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n",
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"#possible initial set of predictions, only used in naive test cases\n",
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"predictions = [\"🤐\",\"🤑\",\"🤒\",\"🤓\",\"🤔\",\"🤕\",\"🤗\",\"🤘\"]"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Advanced Approach\n",
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"define the classifier for advanced prediction, used for the sentiment prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using TensorFlow backend.\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package punkt to /Users/Carsten/nltk_data...\n",
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"[nltk_data] Package punkt is already up-to-date!\n",
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"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
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"[nltk_data] /Users/Carsten/nltk_data...\n",
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"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
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"[nltk_data] date!\n",
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"[nltk_data] Downloading package wordnet to /Users/Carsten/nltk_data...\n",
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"[nltk_data] Package wordnet is already up-to-date!\n"
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]
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}
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],
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"source": [
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"#navigation into right path and generating classifier\n",
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"import sys\n",
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"sys.path.append(\"..\")\n",
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"sys.path.append(\"../naive_approach\")\n",
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"\n",
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"import simple_approach.simple_twitter_learning as stl\n",
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"#clf_advanced = stl.pipeline_manager.load_pipeline_from_files( '\"/Users/Carsten/DataSets/NLP_LAB/tfidf_final/final_epoch01.pipeline', ['keras_model'], ['vectorizer', 'keras_model'])\n",
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"clf_advanced = stl.pipeline_manager.load_from_pipeline_file(\"/Users/Carsten/DataSets/NLP_LAB/tfidf_final/final_epoch01.pipeline\")\n",
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"import Tools.Emoji_Distance as ed"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Generate new Sample for online learning / reinforcement learning"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def generate_new_training_sample (msg, emoji):\n",
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" sentiment = ed.emoji_to_sentiment_vector(emoji)\n",
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" \n",
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" #TODO message msg could be filtred\n",
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" text = msg\n",
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" return text, sentiment"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Naive Approach\n",
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"for topic related emoji prediction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#sys.path.append(\"..\")\n",
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"#print(sys.path)\n",
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"\n",
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"import naive_approach as clf_naive"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"tmp_dict = clf_naive.prepareData()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Merge Predictions\n",
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"combine the predictions of both approaches"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def merged_prediction(msg , split = 0.5 , number = 8, target_emojis = top_emojis):\n",
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" \n",
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" #calc ratio of prediction splitted between advanced aprroach and naive approach\n",
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" number_advanced = round(split*number)\n",
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" number_naive = round((1-split)*number)\n",
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" \n",
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" #predict emojis with the naive approach\n",
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" prediction_naive , prediction_naive_values = clf_naive.predict(sentence = msg, lookup= tmp_dict, n = number_naive)\n",
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"\n",
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" #filter 0 values\n",
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" tmp1 = []\n",
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" tmp2 = []\n",
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" epsilon = 0.0001\n",
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"\n",
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" for i in range(len(prediction_naive)):\n",
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" if(abs(prediction_naive_values[i]) > epsilon):\n",
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" tmp1.append(prediction_naive[i])\n",
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" tmp2.append(prediction_naive[i])\n",
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"\n",
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" prediction_naive = tmp1\n",
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" prediction_naive_values = tmp2\n",
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" \n",
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" if(len(prediction_naive) < number_naive):\n",
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" #print(\"only few matches\")\n",
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" number_advanced = number - len(prediction_naive)\n",
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" \n",
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" #print(number, number_advanced, number_naive)\n",
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" \n",
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" #predict the advanced approach\n",
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" sentiment = clf_advanced.predict([msg])\n",
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" prediction_advanced = ed.sentiment_vector_to_emoji(sentiment,n_results = number_advanced, custom_target_emojis=target_emojis)\n",
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" \n",
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" #concat both predictions\n",
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" prediction = list(prediction_advanced)+list(prediction_naive)\n",
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" \n",
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" return prediction[:number], len(tmp1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Actions triggered when something is changed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def trigger_new_prediction(all_chat, current_message):\n",
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" global predictions\n",
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" \n",
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" #random prediction for initial test\n",
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" #random.shuffle(predictions)\n",
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" \n",
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" #first prediction only using advanced approach\n",
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" #sent = clf_advanced.predict([current_message])\n",
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" #p = ed.sentiment_vector_to_emoji(sent,n_results = 8, custom_target_emojis=top_emojis)\n",
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" \n",
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" #merged prediction\n",
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" if(current_message != \"\"):\n",
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" p,n = merged_prediction(msg = current_message, target_emojis=top_emojis)\n",
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"\n",
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" predictions = p\n",
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" update_descriptions()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### User Output\n",
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"the wiritten text as an overview or list of text"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"all_text = \"no text yet \\n\"\n",
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"\n",
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"out = widgets.Output(layout = widgets.Layout(max_height = \"500px\"))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### User Input\n",
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"the user has to interact with our UI so hee needs:"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Text Input field\n",
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"a simple line for text input on the bottom of UI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"text_input = widgets.Text()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def submit_new_message(p):\n",
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" global all_text\n",
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" bar = \"----------- \\n\"\n",
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" time = str(datetime.datetime.now())+\"\\n\"\n",
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" msg = text_input.value +\"\\n\"\n",
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" new_message = bar + time + msg\n",
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" all_text += new_message \n",
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" \n",
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" with out:\n",
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" clear_output()\n",
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" print(all_text)\n",
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" \n",
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" trigger_new_prediction(all_text, text_input.value)\n",
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" update_descriptions()\n",
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" text_input.value = \"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"text_input.on_submit(submit_new_message)\n",
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"\n",
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"#text_input.observe(lambda b: trigger_new_prediction(None, text_input.value))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### A sent button to enter yout typed in message\n",
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"alternatively it should be also possible to simply type enter"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"sent_button = widgets.Button(description = \"Sent\")\n",
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"\n",
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"sent_button.on_click(submit_new_message)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### A list of buttons for selecting predicted emojis\n",
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"a set of fixed size of buttons with a dynamic changeable labeling replaced by the unicode emoji"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p0_button = widgets.Button(description = \"p0\")\n",
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"\n",
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"def on_p0_button_click(p):\n",
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" update_descriptions() \n",
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" #with out:\n",
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" text_input.value += \" \"+predictions[0]\n",
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"\n",
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"p0_button.on_click(on_p0_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p1_button = widgets.Button(description = \"p1\")\n",
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"\n",
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"def on_p1_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[1]\n",
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"\n",
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"p1_button.on_click(on_p1_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p2_button = widgets.Button(description = \"p2\")\n",
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"\n",
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"def on_p2_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[2]\n",
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"\n",
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"p2_button.on_click(on_p2_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p3_button = widgets.Button(description = \"p3\")\n",
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"\n",
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"def on_p3_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[3]\n",
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"\n",
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"p3_button.on_click(on_p3_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p4_button = widgets.Button(description = \"p4\")\n",
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"\n",
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"def on_p4_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[4]\n",
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"\n",
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"p4_button.on_click(on_p4_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p5_button = widgets.Button(description = \"p5\")\n",
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"\n",
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"def on_p5_button_click(p):\n",
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" update_descriptions()\n",
|
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" with out:\n",
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" text_input.value += \" \"+predictions[5]\n",
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"\n",
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"p5_button.on_click(on_p5_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p6_button = widgets.Button(description = \"p6\")\n",
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"\n",
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"def on_p6_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[6]\n",
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"\n",
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"p6_button.on_click(on_p6_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"p7_button = widgets.Button(description = \"p7\")\n",
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"\n",
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"def on_p7_button_click(p):\n",
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" update_descriptions()\n",
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" with out:\n",
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" text_input.value += \" \"+predictions[7]\n",
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"\n",
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"p7_button.on_click(on_p7_button_click)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def update_descriptions():\n",
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" global predictions\n",
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" p0_button.description = predictions[0]\n",
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" p1_button.description = predictions[1]\n",
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" p2_button.description = predictions[2]\n",
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" p3_button.description = predictions[3]\n",
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" p4_button.description = predictions[4]\n",
|
|
" p5_button.description = predictions[5]\n",
|
|
" p6_button.description = predictions[6]\n",
|
|
" p7_button.description = predictions[7]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Additional\n",
|
|
"Developer Information\n",
|
|
"#### Output of in and out commands\n",
|
|
"#### Prob distribution or whole list of sorted emojis\n",
|
|
"#### configuration information"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Prototype UI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 23,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "6c16f36840984c1e9749b70846926f66",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"A Jupyter Widget"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"text_output = widgets.VBox([out])\n",
|
|
"all_prediction_buttons = widgets.HBox([p0_button,p1_button,p2_button,p3_button,p4_button,p5_button,p6_button,p7_button])\n",
|
|
"user_input = widgets.HBox([text_input,sent_button])\n",
|
|
"total_layout = widgets.VBox([text_output,all_prediction_buttons,user_input],layout = widgets.Layout(max_width = \"450px\"))\n",
|
|
"display(total_layout)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"improve naive approach\n",
|
|
"word2vec\n",
|
|
"\n",
|
|
"longer sentences\n",
|
|
"own training set"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"source": [
|
|
"## I Am Groot"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"text_input_groot = widgets.Text()\n",
|
|
"out_groot = widgets.Output(layout = widgets.Layout(max_height = \"500px\"))\n",
|
|
"all_text_groot = \"no text yet \\n\"\n",
|
|
"text_output_groot = widgets.VBox([out_groot])\n",
|
|
"user_input_groot = widgets.HBox([text_input_groot])\n",
|
|
"total_layout_groot = widgets.VBox([text_output_groot,user_input_groot],layout = widgets.Layout(max_width = \"450px\"))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def submit_new_message_groot(p):\n",
|
|
" global all_text_groot\n",
|
|
" bar = \"----------- \\n\"\n",
|
|
" time = str(datetime.datetime.now())+\"\\n\"\n",
|
|
" msg = \"User: \"+text_input_groot.value +\"\\n\"\n",
|
|
" new_message = bar + time + msg\n",
|
|
" all_text_groot += new_message \n",
|
|
" \n",
|
|
" mp, n_topic = merged_prediction(msg)\n",
|
|
" \n",
|
|
" answer = \"ChatGROOT: I AM GROOT\" + mp[0]\n",
|
|
" \n",
|
|
" if n_topic > 0:\n",
|
|
" answer += mp[8 - n_topic]\n",
|
|
" \n",
|
|
" all_text_groot += answer + \"\\n\"\n",
|
|
" \n",
|
|
" with out_groot:\n",
|
|
" clear_output()\n",
|
|
" print(all_text_groot)\n",
|
|
" \n",
|
|
" text_input_groot.value = \"\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"application/vnd.jupyter.widget-view+json": {
|
|
"model_id": "503a0e49088a4374972a5f16abfa7e79",
|
|
"version_major": 2,
|
|
"version_minor": 0
|
|
},
|
|
"text/plain": [
|
|
"A Jupyter Widget"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"text_input_groot.on_submit(submit_new_message_groot)\n",
|
|
"display(total_layout_groot)"
|
|
]
|
|
},
|
|
{
|
|
"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": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|