From 771b710007120cf0e0cf14fe8292e125ff3beba3 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Fri, 27 Jul 2018 15:23:49 +0200 Subject: [PATCH] added missing notebook --- Project/advanced_approach/Learner.ipynb | 840 ++++++++++++++++++++++++ 1 file changed, 840 insertions(+) create mode 100644 Project/advanced_approach/Learner.ipynb diff --git a/Project/advanced_approach/Learner.ipynb b/Project/advanced_approach/Learner.ipynb new file mode 100644 index 0000000..0e8aa1f --- /dev/null +++ b/Project/advanced_approach/Learner.ipynb @@ -0,0 +1,840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Learner for Emoji classifier πŸ€“\n", + "**usage:**\n", + "run all cells, then go to the [user interface](#User-Interface)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n", + "[nltk_data] Downloading package averaged_perceptron_tagger to\n", + "[nltk_data] /home/jonas/nltk_data...\n", + "[nltk_data] Package averaged_perceptron_tagger is already up-to-\n", + "[nltk_data] date!\n", + "[nltk_data] Downloading package wordnet to /home/jonas/nltk_data...\n", + "[nltk_data] Package wordnet is already up-to-date!\n" + ] + } + ], + "source": [ + "import twitter_learning as twl\n", + "import glob\n", + "import sys\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n", + "import pickle\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## user interface area:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* UI helper functions and global states" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import clear_output, Markdown, Math\n", + "import ipywidgets as widgets\n", + "\n", + "out_areas = {}\n", + "shown_widgets = {}\n", + "tab_manager = widgets.Tab()\n", + "\n", + "def mp(obj):\n", + " display(Markdown(obj))\n", + "\n", + "def set_widget_visibility(widget_names, visible=True):\n", + " for w in widget_names:\n", + " shown_widgets[w].disabled = not visible\n", + "\n", + "def create_area(area_name:str, list_widgets:list, out_name:str, tab=tab_manager):\n", + " \"\"\"\n", + " creates a table of widgets with corresponding output area below\n", + " \n", + " @param area_name: title of the area\n", + " @param list_widgets: list of tuples: (widget, name:str)\n", + " @param out_name: name for the output area\n", + " \"\"\"\n", + " if out_name is not None:\n", + " out = widgets.Output()\n", + " out_areas[out_name] = out\n", + " h_box_widgets = []\n", + " v_box_widgets = []\n", + " for v in list_widgets:\n", + " for h in v:\n", + " if 'description' in h[0].__dir__() and h[1] is not None:\n", + " h[0].description = h[1]\n", + " if h[1] is not None:\n", + " shown_widgets[h[1]] = h[0]\n", + " h_box_widgets.append(h[0])\n", + " v_box_widgets.append(widgets.HBox(h_box_widgets))\n", + " h_box_widgets = []\n", + " \n", + " if out_name is not None:\n", + " v_box_widgets += [out]\n", + " tab.children = list(tab.children) + [widgets.VBox(v_box_widgets)]\n", + " tab.set_title(len(tab.children) - 1, area_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "* build UI" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "----" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "## User Interface" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "faee52cdf0cf4e9da752a7d818d0d011", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Tab(children=(VBox(children=(HBox(children=(HTML(value=' Data Root Folder:
setup the folder conta…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mp(\"----\")\n", + "mp(\"## User Interface\")\n", + "# create widgets\n", + "create_area(\"load dataset πŸ’Ύ\",\n", + " [\n", + " [\n", + " (widgets.HTML(\" Data Root Folder:
setup the folder containing *.json train data \"), None)\n", + " ],\n", + " [\n", + " (widgets.Text(value=\"./data_en/\"), \"root_path\"),\n", + " (widgets.Button(), \"set_path\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Loading and preprocessing options:
setup the range of files to load. Only_emoticons will filter out 'non-smiley' emojis, min_words is the minimum amount of words for one document. Also you can setup top-emoji filtering or only load samples containing a custom emoji set\"), None)\n", + " ],\n", + " [\n", + " (widgets.IntRangeSlider(disabled=True, min=0, max=0), \"file_range\"),\n", + " (widgets.Checkbox(value=True,disabled=True), \"only_emoticons\"),\n", + " (widgets.Checkbox(value=False,disabled=True), \"apply_lemmatization_and_stemming\"),\n", + " (widgets.BoundedIntText(value=5,min=0, max=10), \"min_words\")\n", + " ],\n", + " [\n", + " #(widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\")\n", + " (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\"),\n", + " (widgets.Dropdown(options=[\"latest\", \"mean\"], value=\"latest\"), \"label_criteria\"),\n", + " (widgets.Text(value=\"\"), \"custom_emojis\")\n", + " ],\n", + " [\n", + " (widgets.Button(disabled=True),\"load_data\")\n", + " ]\n", + " ],\n", + " \"load\")\n", + "\n", + "classifier_tab = widgets.Tab()\n", + "\n", + "create_area(\"keras\",\n", + " [\n", + " [\n", + " (widgets.IntSlider(min=0, max=10), \"n_keras_layer\")\n", + " ],\n", + " [\n", + " (widgets.HBox([]), \"n_keras_neurons\")\n", + " ],\n", + " [\n", + " (widgets.HBox([]), \"keras_funcs\")\n", + " ]\n", + " ],\n", + " None,\n", + " classifier_tab)\n", + "\n", + "create_area(\"create/save/load classifier\",\n", + " [\n", + " [\n", + " (classifier_tab, \"classifier_tab\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Create new Classifier:
create a new keras classifier with layer options from above. Also a vectorizer will be trained on loaded sample data. If doc2vec is disabled, TFIDF is used\"), None)\n", + " ],\n", + " [\n", + " (widgets.Checkbox(value=True),\"use_doc2vec\"),\n", + " (widgets.Checkbox(value=True),\"d2v_use_pretrained\"),\n", + " (widgets.IntText(value=100),\"d2v_size\"),\n", + " (widgets.IntText(value=8), \"d2v_window\"),\n", + " (widgets.IntSlider(value=5, min=0, max=32), \"d2v_min_count\")\n", + " ],\n", + " [\n", + " (widgets.Button(), \"create_classifier\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Save Classifier: \"), None)\n", + " ],\n", + " [\n", + " (widgets.Text(), \"classifier_name\"),\n", + " (widgets.Button(), \"save_classifier\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Load Classifier: \"), None)\n", + " ],\n", + " [\n", + " (widgets.Select(options=sorted(glob.glob(\"./*.pipeline\"))), \"clf_file_selector\"),\n", + " (widgets.Text(), \"clf_file\"),\n", + " (widgets.Button(), \"load_classifier\")\n", + " ]\n", + " ],\n", + " \"create\")\n", + "\n", + "create_area(\"train classifier πŸŽ“\", \n", + " [\n", + " [\n", + " (widgets.HTML(\" Custom Batch Settings:
(Ignored if batch_size is 0)\"), None)\n", + " ],\n", + " [\n", + " (widgets.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n", + " (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Train: \"), None)\n", + " ],\n", + " [\n", + " (widgets.IntText(value=1), \"n_epochs\"),\n", + " (widgets.Button(),\"train\")\n", + " ]\n", + " ], \n", + " \"train\" )\n", + "create_area(\"playground 😎\",\n", + " [\n", + " [\n", + " (widgets.HTML(\" predict single sentence
(uses min distance to given emojis in prediction_ground_set)\"), None)\n", + " ],\n", + " [\n", + " (widgets.Text(),\"test_input\"),\n", + " (widgets.Text(value=\"πŸ˜³πŸ˜‹πŸ˜€πŸ˜ŒπŸ˜πŸ˜”πŸ˜’πŸ˜ŽπŸ˜’πŸ˜…πŸ˜πŸ˜‰πŸ™ŒπŸ™πŸ˜˜πŸ˜ŠπŸ˜©πŸ˜πŸ˜­πŸ˜‚\"),\"prediction_ground_set\"),\n", + " (widgets.HTML(\"

βˆ…

\"),\"prediction\"),\n", + " ],\n", + " [\n", + " (widgets.Checkbox(),\"show_sorted_list\"),\n", + " (widgets.Button(),\"show_plot\")\n", + " ],\n", + " [\n", + " (widgets.HTML(\" Test on loaded validation set:
(performs prediction plot on all validation samples that are labeled with given emojis)\"), None)\n", + " ],\n", + " [\n", + " (widgets.Text(value=\"πŸ˜³πŸ˜‹πŸ˜€πŸ˜ŒπŸ˜πŸ˜”πŸ˜’πŸ˜ŽπŸ˜’πŸ˜…πŸ˜πŸ˜‰πŸ™ŒπŸ™πŸ˜˜πŸ˜ŠπŸ˜©πŸ˜πŸ˜­πŸ˜‚\"), \"validation_emojis\"),\n", + " (widgets.Button(),\"show_validation_plot\")\n", + " ]\n", + " ],\n", + " \"playground\")\n", + "\n", + "tab_manager" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## global variables:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sdm = None\n", + "pm = None\n", + "tr = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## pretty jupyter print" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import collections\n", + "import traceback\n", + "from pprint import pprint as pp\n", + "\n", + "def jupyter_print(obj, cell_w = 10, headers=None, p_type=True, ret_mdown=False, index_offset=0, list_horizontal=False):\n", + " \"\"\"\n", + " pretty hacky function to convert arrays, lists and matrices into\n", + " nice readable markdown code and render that in jupyter. if that is not possible\n", + " it will use pretty print instead\n", + " \"\"\"\n", + " try:\n", + " ts = \"**Type:** \" + str(type(obj)).strip(\"<>\") + \"\\n\\n\"\n", + " if type(obj) == str:\n", + " display(Markdown(obj))\n", + " elif isinstance(obj, collections.Iterable):\n", + " if isinstance(obj[0], collections.Iterable) and type(obj[0]) is not str:\n", + " # we have a table\n", + " \n", + " if headers is None:\n", + " headers = [str(i) for i in range(len(obj[0]))]\n", + " \n", + " if len(headers) < len(obj[0]):\n", + " headers += [\" \" for i in range(len(obj[0]) - len(headers))]\n", + " \n", + " s = \"|\" + \" \" * cell_w + \"|\"\n", + " \n", + " for h in headers:\n", + " s += str(h) + \" \" * (cell_w - len(h)) + \"|\"\n", + " s += \"\\n|\" + \"-\" * (len(headers) + (len(headers) + 1) * cell_w) + \"|\\n\"\n", + " \n", + " #s = (\"|\" + (\" \" * (cell_w))) * len(obj[0]) + \"|\\n\" + \"|\" + (\"-\" * (cell_w + 1)) * len(obj[0])\n", + " #s += '|\\n'\n", + " \n", + " row = index_offset\n", + " \n", + " for o in obj:\n", + " s += \"|**\" + str(row) + \"**\" + \" \" * (cell_w - (len(str(row))+4))\n", + " row += 1\n", + " for i in o:\n", + " s += \"|\" + str(i) + \" \" * (cell_w - len(str(i)))\n", + " s+=\"|\" + '\\n'\n", + " s += ts\n", + " display(Markdown(s))\n", + " return s if ret_mdown else None\n", + " else:\n", + " # we have a list\n", + " \n", + " \n", + " if headers is None:\n", + " headers = [\"index\",\"value\"]\n", + " \n", + " index_title = headers[0]\n", + " value_title = headers[1]\n", + " \n", + " s = \"|\" + index_title + \" \" * (cell_w - len(value_title)) + \"|\" + value_title + \" \" * (cell_w - len(value_title)) + \"|\" + '\\n'\n", + " s += \"|\" + \"-\" * (1 + 2 * cell_w) + '|\\n'\n", + " i = index_offset\n", + " for o in obj:\n", + " s_i = str(i)\n", + " s_o = str(o)\n", + " s += \"|\" + s_i + \" \" * (cell_w - len(s_i)) + \"|\" + s_o + \" \" * (cell_w - len(s_o)) + \"|\" + '\\n'\n", + " i+=1\n", + " s += ts\n", + " #print(s)\n", + " display(Markdown(s))\n", + " return s if ret_mdown else None\n", + " else:\n", + " jupyter_print([obj])\n", + " except Exception as e:\n", + " print(ts)\n", + " pp(obj) \n", + "\n", + "jp = jupyter_print" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## output progress printing:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class progress_indicator(object):\n", + " \n", + " def __init__(self, description=\"progress\"):\n", + " self.w = widgets.FloatProgress(value=0, min=0,max=1, description = description)\n", + " display(self.w)\n", + " def update(self, val):\n", + " self.w.value = val\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "----\n", + "## load datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def set_path(b):\n", + " with out_areas[\"load\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " files = sorted(glob.glob(shown_widgets[\"root_path\"].value + \"/*.json\"))\n", + " \n", + " if len(files) == 0:\n", + " sys.stderr.write(\"ERROR: no json files available in \" + shown_widgets[\"root_path\"].value + \"\\n\")\n", + " set_widget_visibility([\"file_range\",\n", + " \"only_emoticons\",\n", + " \"n_top_emojis\",\n", + " \"apply_lemmatization_and_stemming\",\n", + " \"load_data\"], False)\n", + " return\n", + " \n", + " mp(\"**available files:**\")\n", + " jp(files, headers=[\"fileindex\",\"filepath\"])\n", + " set_widget_visibility([\"file_range\",\n", + " \"only_emoticons\",\n", + " \"n_top_emojis\",\n", + " \"apply_lemmatization_and_stemming\",\n", + " \"load_data\"], True)\n", + " shown_widgets[\"file_range\"].min=0\n", + " shown_widgets[\"file_range\"].max=len(files) -1\n", + "\n", + "def load_data(b):\n", + " global sdm\n", + " with out_areas[\"load\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " \n", + " r = shown_widgets[\"file_range\"].value\n", + " r = (r[0], r[1] + 1) # range has to be exclusive according to the last element!\n", + " \n", + " p_r = progress_indicator(\"reading progress\")\n", + " \n", + " lemm_and_stemm = shown_widgets[\"apply_lemmatization_and_stemming\"].value\n", + " \n", + " if lemm_and_stemm:\n", + " p_s = progress_indicator(\"stemming progress\")\n", + " \n", + " emoji_mean = shown_widgets[\"label_criteria\"].value == \"mean\"\n", + " \n", + " custom_emojis = list(shown_widgets[\"custom_emojis\"].value)\n", + " \n", + " min_words = shown_widgets[\"min_words\"].value\n", + " \n", + " sdm = twl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n", + " n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n", + " file_range=range(r[0], r[1]),\n", + " n_kmeans_cluster=-1,\n", + " read_progress_callback=p_r.update,\n", + " stem_progress_callback=p_s.update if lemm_and_stemm else None,\n", + " apply_stemming = lemm_and_stemm,\n", + " emoji_mean=emoji_mean,\n", + " custom_target_emojis=custom_emojis if len(custom_emojis) > 0 else None,\n", + " min_words=min_words)\n", + " shown_widgets[\"batch_size\"].max = len(sdm.labels)\n", + " \n", + " \n", + "# linking functions with buttons:\n", + "shown_widgets[\"set_path\"].on_click(set_path)\n", + "shown_widgets[\"load_data\"].on_click(load_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## train" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def train(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " with out_areas[\"train\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " if sdm is None or pm is None:\n", + " sys.stderr.write(\"ERROR: sample data and/or classifier missing!\\n\")\n", + " return\n", + " \n", + " batch_size = shown_widgets[\"batch_size\"].value\n", + " val_split = shown_widgets[\"val_split\"].value\n", + " n_epochs = shown_widgets[\"n_epochs\"].value\n", + " \n", + " print(\"update train test split:\")\n", + " sdm.create_train_test_split(split=val_split)\n", + " \n", + " print(\"fit\")\n", + " \n", + " p = progress_indicator()\n", + " \n", + " tr = twl.trainer(sdm=sdm, pm=pm)\n", + " tr.fit(progress_callback=p.update, batch_size=batch_size if batch_size > 0 else None, n_epochs=n_epochs)\n", + " \n", + "\n", + "# linking:\n", + "shown_widgets[\"train\"].on_click(train)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## create classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "keras_acivations = [\n", + " \"softmax\",\n", + " \"elu\",\n", + " \"selu\",\n", + " \"softplus\",\n", + " \"softsign\",\n", + " \"relu\",\n", + " \"tanh\",\n", + " \"sigmoid\",\n", + " \"hard_sigmoid\",\n", + " \"linear\",\n", + " \"None\"\n", + "]\n", + "\n", + "def populate_keras_options(b):\n", + " n_layers = shown_widgets[\"n_keras_layer\"].value\n", + " hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n", + " hbox_funcs = shown_widgets[\"keras_funcs\"]\n", + " \n", + " hbox_neurons.children = [widgets.IntText(description = str(i)) for i in range(n_layers)]\n", + " hbox_funcs.children = [widgets.Dropdown(options=keras_acivations,description = str(i)) for i in range(n_layers)]\n", + " \n", + " #hbox_neurons.children[-1].disabled = True\n", + "\n", + "def create_classifier(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " with out_areas[\"create\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " if sdm is None:\n", + " sys.stderr.write(\"load a dataset first!\\n\")\n", + " return\n", + " \n", + " chosen_classifier = classifier_tab.get_title(classifier_tab.selected_index)\n", + " \n", + " mp(\"**chosen classifier**: `\" + chosen_classifier + \"`\")\n", + " \n", + " # creating the vectorizer\n", + " vectorizer = None\n", + " if shown_widgets[\"use_doc2vec\"].value:\n", + " if shown_widgets[\"d2v_use_pretrained\"].value:\n", + " vectorizer = pickle.load( open( \"doc2VecModel.p\", \"rb\" ) )\n", + " else:\n", + " vectorizer = twl.skd2v.Doc2VecTransformer(size=shown_widgets[\"d2v_size\"].value,\n", + " window=shown_widgets[\"d2v_window\"].value,\n", + " min_count=shown_widgets[\"d2v_min_count\"].value)\n", + " else:\n", + " vectorizer=TfidfVectorizer(stop_words='english')\n", + " \n", + " # TODO: add more classifier options here:\n", + " if chosen_classifier is 'keras':\n", + " sdm.create_train_test_split()\n", + " \n", + " n_layers = shown_widgets[\"n_keras_layer\"].value\n", + " hbox_neurons = shown_widgets[\"n_keras_neurons\"]\n", + " hbox_funcs = shown_widgets[\"keras_funcs\"]\n", + "\n", + " layers = []\n", + " for i in range(n_layers):\n", + " func = hbox_funcs.children[i].value\n", + " if func == 'None':\n", + " func = None\n", + " layers.append((hbox_neurons.children[i].value, func))\n", + " \n", + " # modify last layer:\n", + " layers[-1] = (sdm.y.shape[1], layers[-1][1])\n", + " \n", + " mp(\"**layers:** \")\n", + " jp(layers, headers=['#neurons', 'activation_func'])\n", + "\n", + " pm = stl.pipeline_manager.create_keras_pipeline_with_vectorizer(vectorizer, layers=layers, sdm=sdm, fit_vectorizer=not shown_widgets[\"d2v_use_pretrained\"].value)\n", + "\n", + "def save_classifier(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " with out_areas[\"create\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " if pm is None:\n", + " sys.stderr.write(\"ERROR: create classifier first\")\n", + " return\n", + " \n", + " pm.save(shown_widgets[\"classifier_name\"].value)\n", + "\n", + "\n", + "def update_file_selector(b):\n", + " shown_widgets[\"clf_file_selector\"].options = sorted(glob.glob(\"./*.pipeline\"))\n", + "\n", + "def clf_file_selector(b):\n", + " shown_widgets[\"clf_file\"].value = shown_widgets[\"clf_file_selector\"].value\n", + " update_file_selector(b)\n", + "\n", + "def load_classifier(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " with out_areas[\"create\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " clf_file = shown_widgets[\"clf_file\"].value\n", + " pm = twl.pipeline_manager.load_from_pipeline_file(clf_file)\n", + " \n", + "\n", + "# link\n", + "shown_widgets[\"n_keras_layer\"].observe(populate_keras_options)\n", + "shown_widgets[\"create_classifier\"].on_click(create_classifier)\n", + "shown_widgets[\"save_classifier\"].on_click(save_classifier)\n", + "shown_widgets[\"load_classifier\"].on_click(load_classifier)\n", + "shown_widgets[\"clf_file_selector\"].observe(clf_file_selector)\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## plotting stuff for testing area" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def sentiment_score(s):\n", + " #(pos, neg, neu)^T\n", + " return s[0] - s[1]\n", + "\n", + "def plot_sentiment_space(predicted_sentiment_vectors, top_sentiments, top_emojis):\n", + " # sentiment score axis\n", + " top_X = np.array([sentiment_score(x) for x in top_sentiments])\n", + " pred_X = np.array([sentiment_score(x) for x in predicted_sentiment_vectors])\n", + " \n", + " # neutral axis:\n", + " top_Y = np.array([x[2] for x in top_sentiments])\n", + " pred_Y = np.array([x[2] for x in predicted_sentiment_vectors])\n", + " \n", + " fig_1, ax_1 = plt.subplots()#figsize=(15,10))\n", + " plt.title(\"sentiment-score-plot\")\n", + " plt.xlabel(\"sentiment score\")\n", + " plt.ylabel(\"neutrality\")\n", + " plt.xlim([-1,1])\n", + " plt.ylim([0,1])\n", + " for i in range(len(top_X)):\n", + " plt.text(top_X[i], top_Y[i], top_emojis[i])\n", + " plt.plot(pred_X, pred_Y, 'bo')\n", + " #plt.savefig(title + \" -- sentiment-plot.png\", bbox_inches='tight')\n", + " \n", + " # sentiment score axis\n", + " top_X = np.array([x[0] for x in top_sentiments])\n", + " pred_X = np.array([x[0] for x in predicted_sentiment_vectors])\n", + " \n", + " # neutral axis:\n", + " top_Y = np.array([x[1] for x in top_sentiments])\n", + " pred_Y = np.array([x[1] for x in predicted_sentiment_vectors])\n", + " \n", + " fig_2, ax_2 = plt.subplots()#figsize=(15,10))\n", + " plt.title(\"positive-negative-plot\")\n", + " plt.xlabel(\"positive\")\n", + " plt.ylabel(\"negative\")\n", + " plt.xlim([0,1])\n", + " plt.ylim([0,1])\n", + " for i in range(len(top_X)):\n", + " plt.text(top_X[i], top_Y[i], top_emojis[i])\n", + " plt.plot(pred_X, pred_Y, 'bo')\n", + " #plt.savefig(title + \" -- positive-negative-plot.png\", bbox_inches='tight')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## testing area" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "top_20 = list(\"πŸ˜³πŸ˜‹πŸ˜€πŸ˜ŒπŸ˜πŸ˜”πŸ˜’πŸ˜ŽπŸ˜’πŸ˜…πŸ˜πŸ˜‰πŸ™ŒπŸ™πŸ˜˜πŸ˜ŠπŸ˜©πŸ˜πŸ˜­πŸ˜‚\")\n", + "top_20_sents = twl.emoji2sent(top_20)\n", + "\n", + "pred = None\n", + "\n", + "def test_input(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " global pred\n", + " with out_areas[\"playground\"]:\n", + " clear_output()\n", + " mp(\"----\")\n", + " if pm is None:\n", + " sys.stderr.write(\"ERROR: load or create classifier first\")\n", + " return\n", + " X = shown_widgets[\"test_input\"].value\n", + " pred = pm.predict([X])\n", + " target_list=list(shown_widgets[\"prediction_ground_set\"].value)\n", + " shown_widgets[\"prediction\"].value = \"

\" + str(twl.sent2emoji(pred,custom_target_emojis=target_list)[0]) + \"

\"\n", + " if shown_widgets[\"show_sorted_list\"].value:\n", + " mp(\"## \" + \"\".join(twl.edist.sentiment_vector_to_emoji(pred, only_emoticons=True, n_results=100, custom_target_emojis=target_list)))\n", + " \n", + "\n", + "\n", + "def plot_pred(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " global pred\n", + " with out_areas[\"playground\"]:\n", + " plot_sentiment_space(pred, top_20_sents, top_20)\n", + " \n", + " \n", + "def plot_subset_pred(b):\n", + " global sdm\n", + " global pm\n", + " global tr\n", + " global pred\n", + " with out_areas[\"playground\"]:\n", + " clear_output()\n", + " \n", + " if sdm is None or pm is None:\n", + " sys.stderr.write(\"ERROR: sample data and/or classifier missing!\\n\")\n", + " return\n", + " \n", + " if tr is None:\n", + " tr = twl.trainer(sdm=sdm, pm=pm)\n", + " \n", + " pred, y = tr.test(emoji_subset=list(shown_widgets[\"validation_emojis\"].value))\n", + " print(len(pred))\n", + " plot_sentiment_space(pred, top_20_sents, top_20)\n", + "\n", + "#link\n", + "shown_widgets[\"test_input\"].observe(test_input)\n", + "shown_widgets[\"show_plot\"].on_click(plot_pred)\n", + "shown_widgets[\"show_validation_plot\"].on_click(plot_subset_pred)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}