373 lines
16 KiB
Plaintext
373 lines
16 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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"# Evaluation\n",
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"We want to evaluate our approach"
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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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"top_emojis = ['😂','😭','😍','😩','😊','😘','🙏','🙌','😉','😁','😅','😎','😢','😒','😏','😌','😔','😋','😀','😤']\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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"\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( '../simple_approach/custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
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"\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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"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]"
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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 = merged_prediction(msg = current_message, target_emojis=top_emojis)\n",
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"\n",
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" predictions = p"
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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 Prediction for CSV Table"
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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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"outputs": [
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{
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"ename": "ParserError",
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"evalue": "Error tokenizing data. C error: Expected 1 fields in line 27, saw 2\n",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mParserError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-9-7e24563a7fda>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# get table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Evaluation Sentences - Tabellenblatt1.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m 653\u001b[0m skip_blank_lines=skip_blank_lines)\n\u001b[1;32m 654\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 655\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 657\u001b[0m \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 412\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0mparser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1003\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'skipfooter not supported for iteration'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1004\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1005\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1006\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1007\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'as_recarray'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, nrows)\u001b[0m\n\u001b[1;32m 1746\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnrows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1747\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1748\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnrows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1749\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1750\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_first_chunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.read (pandas/_libs/parsers.c:10862)\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_low_memory (pandas/_libs/parsers.c:11138)\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._read_rows (pandas/_libs/parsers.c:11884)\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._tokenize_rows (pandas/_libs/parsers.c:11755)\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.raise_parser_error (pandas/_libs/parsers.c:28765)\u001b[0;34m()\u001b[0m\n",
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"\u001b[0;31mParserError\u001b[0m: Error tokenizing data. C error: Expected 1 fields in line 27, saw 2\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# get table\n",
|
|
"import pandas as pd\n",
|
|
"df = pd.read_csv(\"Evaluation Sentences - Tabellenblatt1.csv\")\n",
|
|
"df.head()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"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": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df[\"prediction\"] = all_predictions\n",
|
|
"\n",
|
|
"df.head()\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.to_csv(\"Evaluation Sentences - Tabellenblatt1.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": "3.6.3"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|