added README.md for advanced approach
This commit is contained in:
		| @ -1,847 +0,0 @@ | ||||
| { | ||||
|  "cells": [ | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "# Learner for Emoji classifier 🤓\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": [ | ||||
|        "<IPython.core.display.Markdown object>" | ||||
|       ] | ||||
|      }, | ||||
|      "metadata": {}, | ||||
|      "output_type": "display_data" | ||||
|     }, | ||||
|     { | ||||
|      "data": { | ||||
|       "text/markdown": [ | ||||
|        "## User Interface" | ||||
|       ], | ||||
|       "text/plain": [ | ||||
|        "<IPython.core.display.Markdown object>" | ||||
|       ] | ||||
|      }, | ||||
|      "metadata": {}, | ||||
|      "output_type": "display_data" | ||||
|     }, | ||||
|     { | ||||
|      "data": { | ||||
|       "application/vnd.jupyter.widget-view+json": { | ||||
|        "model_id": "e94b33b8493a48798d3adda091986a78", | ||||
|        "version_major": 2, | ||||
|        "version_minor": 0 | ||||
|       }, | ||||
|       "text/plain": [ | ||||
|        "Tab(children=(VBox(children=(HBox(children=(HTML(value='<b> Data Root Folder: </b> <br> setup the folder conta…" | ||||
|       ] | ||||
|      }, | ||||
|      "metadata": {}, | ||||
|      "output_type": "display_data" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "mp(\"----\")\n", | ||||
|     "mp(\"## User Interface\")\n", | ||||
|     "# create widgets\n", | ||||
|     "create_area(\"load dataset 💾\",\n", | ||||
|     "           [\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.HTML(\"<b> Data Root Folder: </b> <br> 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(\"<b> Loading and preprocessing options: </b> <br> 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(\"<b> Create new Classifier: </b> <br> 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(\"<b> Save Classifier: </b>\"), None)\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.Text(), \"classifier_name\"),\n", | ||||
|     "                   (widgets.Button(), \"save_classifier\")\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.HTML(\"<b> Load Classifier: </b>\"), 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 🎓\", \n", | ||||
|     "            [\n", | ||||
|     "                [\n", | ||||
|     "                    (widgets.HTML(\"<b> Custom Batch Settings: </b> <br> (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(\"<b> Train: </b>\"), 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(\"<b> predict single sentence </b> <br> (uses min distance to given emojis in prediction_ground_set)\"), None)\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.Text(),\"test_input\"),\n", | ||||
|     "                   (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"),\"prediction_ground_set\"),\n", | ||||
|     "                   (widgets.HTML(\"<h1>∅</h1>\"),\"prediction\"),\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.Checkbox(),\"show_sorted_list\"),\n", | ||||
|     "                   (widgets.Button(),\"show_plot\")\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.HTML(\"<b> Test on loaded validation set: </b> <br> (performs prediction plot on all validation samples that are labeled with given emojis)\"), None)\n", | ||||
|     "               ],\n", | ||||
|     "               [\n", | ||||
|     "                   (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"), \"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", | ||||
|     "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(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\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 = \"<h1> \" + str(twl.sent2emoji(pred,custom_target_emojis=target_list)[0]) + \"</h1>\"\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 | ||||
| } | ||||
										
											
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		Reference in New Issue
	
	Block a user