{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Continous 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 simple_twitter_learning as stl\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__():\n", " h[0].description = h[1]\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": "e508c9a1f8634b7ba790cf2f291e215a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Tab(children=(VBox(children=(HBox(children=(Text(value='./data_en/', description='root_path'), Button(descript…" ] }, "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.Text(value=\"./data_en/\"), \"root_path\"),\n", " (widgets.Button(), \"set_path\")\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.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.Label(\"save_area:\"), \"save_area:\")\n", " ],\n", " [\n", " (widgets.Text(), \"classifier_name\"),\n", " (widgets.Button(), \"save_classifier\")\n", " ],\n", " [\n", " (widgets.Label(\"load_area:\"), \"load_area:\")\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.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n", " (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\"),\n", " (widgets.IntText(value=1), \"n_epochs\")\n", " ],\n", " [\n", " (widgets.Button(),\"train\")\n", " ]\n", " ], \n", " \"train\" )\n", "create_area(\"playground 😎\",\n", " [\n", " [\n", " (widgets.Text(),\"test_input\"),\n", " (widgets.HTML(),\"prediction\"),\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", " ],\n", " [\n", " (widgets.Checkbox(),\"show_sorted_list\"),\n", " (widgets.Button(),\"show_plot\")\n", " ],\n", " [\n", " (widgets.Text(), \"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", " \"k_means_cluster\",\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", " \"k_means_cluster\",\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 = stl.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=shown_widgets[\"k_means_cluster\"].value,\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 = stl.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 = stl.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", "def load_classifier(b):\n", " global sdm\n", " global pm\n", " global tr\n", " with out_areas[\"create\"]:\n", " clear_output()\n", " mp(\"----\")\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 = stl.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 = stl.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(stl.sent2emoji(pred,custom_target_emojis=target_list)[0]) + \"

\"\n", " if shown_widgets[\"show_sorted_list\"].value:\n", " mp(\"## \" + \"\".join(stl.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 = stl.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)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }