optimierungen für observe und vorbereitung für parallele ansätze der
prediction
This commit is contained in:
Carsten
parent
43e9ace028
commit
d6e24c2065
@ -26,7 +26,9 @@
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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"
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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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@ -37,9 +39,38 @@
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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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"top_emojis = ['😂','😭','😍','😩','😊','😘','🙏','🙌','😉','😁','😅','😎','😢','😒','😏','😌','😔','😋','😀','😤']\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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@ -69,53 +100,114 @@
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"sys.path.append(\"..\")\n",
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"\n",
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"import simple_approach.simple_twitter_learning as stl\n",
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"clf = stl.pipeline_manager.load_pipeline_from_files( '../simple_approach/custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\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": null,
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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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"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": 3,
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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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"#TODO"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"top_emojis = ['😂',\n",
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" '😭',\n",
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" '😍',\n",
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" '😩',\n",
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" '😊',\n",
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" '😘',\n",
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" '🙏',\n",
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" '🙌',\n",
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" '😉',\n",
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" '😁',\n",
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" '😅',\n",
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" '😎',\n",
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" '😢',\n",
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" '😒',\n",
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" '😏',\n",
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" '😌',\n",
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" '😔',\n",
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" '😋',\n",
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" '😀',\n",
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" '😤']\n",
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"predictions = [\"🤐\",\"🤑\",\"🤒\",\"🤓\",\"🤔\",\"🤕\",\"🤗\",\"🤘\"]\n",
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"\n",
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"def merged_prediction(msg , split = 1 , 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 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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" #predict emojis with the naive approach\n",
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" #prediction_naive = clf_naive.predict(msg, target_emojis)\n",
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" \n",
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" #concat both predictions\n",
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" prediction = prediction_advanced#.append(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": 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 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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" sent = clf.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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" #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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" p = 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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@ -130,7 +222,7 @@
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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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"execution_count": 8,
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"metadata": {
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"collapsed": true
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},
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@ -159,18 +251,27 @@
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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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"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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"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": 23,
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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()\n",
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"\n",
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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 = \"12:00 \\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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@ -181,12 +282,19 @@
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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.description = \"\"\n",
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" \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": 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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"text_input.on_submit(submit_new_message)\n",
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"\n",
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"\n",
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"#TODO\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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@ -200,7 +308,7 @@
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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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"execution_count": 12,
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"metadata": {
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"collapsed": true
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},
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@ -221,7 +329,7 @@
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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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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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@ -239,7 +347,7 @@
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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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"execution_count": 14,
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"metadata": {
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"collapsed": true
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},
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@ -257,7 +365,7 @@
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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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"execution_count": 15,
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"metadata": {
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"collapsed": true
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},
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@ -275,7 +383,7 @@
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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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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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@ -293,7 +401,7 @@
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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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"execution_count": 17,
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"metadata": {
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"collapsed": true
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},
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@ -311,7 +419,7 @@
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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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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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@ -329,7 +437,7 @@
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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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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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@ -347,7 +455,7 @@
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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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"execution_count": 20,
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"metadata": {
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"collapsed": true
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},
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@ -365,7 +473,7 @@
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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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"execution_count": 21,
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"metadata": {
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"collapsed": true
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},
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@ -403,13 +511,13 @@
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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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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "29e1cbab23c841a8b201b95c3d75da01",
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"model_id": "dc2c580ed3224ae7b2582c7c039813dd",
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"version_major": 2,
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"version_minor": 0
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},
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