annotated widgets in user interface
This commit is contained in:
Jonas Weinz
parent
051c34ad5d
commit
89e4122187
@ -105,8 +105,9 @@
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" v_box_widgets = []\n",
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" for v in list_widgets:\n",
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" for h in v:\n",
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" if 'description' in h[0].__dir__():\n",
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" if 'description' in h[0].__dir__() and h[1] is not None:\n",
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" h[0].description = h[1]\n",
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" if h[1] is not None:\n",
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" shown_widgets[h[1]] = h[0]\n",
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" h_box_widgets.append(h[0])\n",
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" v_box_widgets.append(widgets.HBox(h_box_widgets))\n",
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@ -157,12 +158,12 @@
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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": "e508c9a1f8634b7ba790cf2f291e215a",
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"model_id": "9035abacb17b41e4ac3875663fb23014",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Tab(children=(VBox(children=(HBox(children=(Text(value='./data_en/', description='root_path'), Button(descript…"
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"Tab(children=(VBox(children=(HBox(children=(HTML(value='<b> Data Root Folder: </b> <br> setup the folder conta…"
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]
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},
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"metadata": {},
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@ -176,17 +177,23 @@
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"create_area(\"load dataset 💾\",\n",
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" [\n",
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" [\n",
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" (widgets.HTML(\"<b> Data Root Folder: </b> <br> setup the folder containing *.json train data \"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.Text(value=\"./data_en/\"), \"root_path\"),\n",
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" (widgets.Button(), \"set_path\")\n",
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" ],\n",
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" [\n",
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" (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",
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" ],\n",
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" [\n",
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" (widgets.IntRangeSlider(disabled=True, min=0, max=0), \"file_range\"),\n",
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" (widgets.Checkbox(value=True,disabled=True), \"only_emoticons\"),\n",
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" (widgets.Checkbox(value=False,disabled=True), \"apply_lemmatization_and_stemming\"),\n",
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" (widgets.BoundedIntText(value=5,min=0, max=10), \"min_words\")\n",
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" ],\n",
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" [\n",
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" (widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\"),\n",
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" #(widgets.BoundedIntText(value=-1,disabled=True,min=-1, max=10), \"k_means_cluster\")\n",
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" (widgets.BoundedIntText(value=20,disabled=True,min=-1, max=100), \"n_top_emojis\"),\n",
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" (widgets.Dropdown(options=[\"latest\", \"mean\"], value=\"latest\"), \"label_criteria\"),\n",
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" (widgets.Text(value=\"\"), \"custom_emojis\")\n",
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@ -220,6 +227,9 @@
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" (classifier_tab, \"classifier_tab\")\n",
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" ],\n",
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" [\n",
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" (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",
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" ],\n",
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" [\n",
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" (widgets.Checkbox(value=True),\"use_doc2vec\"),\n",
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" (widgets.Checkbox(value=True),\"d2v_use_pretrained\"),\n",
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" (widgets.IntText(value=100),\"d2v_size\"),\n",
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@ -230,14 +240,14 @@
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" (widgets.Button(), \"create_classifier\")\n",
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" ],\n",
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" [\n",
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" (widgets.Label(\"save_area:\"), \"save_area:\")\n",
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" (widgets.HTML(\"<b> Save Classifier: </b>\"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.Text(), \"classifier_name\"),\n",
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" (widgets.Button(), \"save_classifier\")\n",
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" ],\n",
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" [\n",
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" (widgets.Label(\"load_area:\"), \"load_area:\")\n",
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" (widgets.HTML(\"<b> Load Classifier: </b>\"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.Select(options=sorted(glob.glob(\"./*.pipeline\"))), \"clf_file_selector\"),\n",
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@ -250,11 +260,17 @@
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"create_area(\"train classifier 🎓\", \n",
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" [\n",
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" [\n",
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" (widgets.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n",
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" (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\"),\n",
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" (widgets.IntText(value=1), \"n_epochs\")\n",
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" (widgets.HTML(\"<b> Custom Batch Settings: </b> <br> (Ignored if batch_size is 0)\"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.IntSlider(value=0,min=0,max=0), \"batch_size\"),\n",
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" (widgets.FloatSlider(value=0.15, min=0, max=1), \"val_split\")\n",
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" ],\n",
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" [\n",
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" (widgets.HTML(\"<b> Train: </b>\"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.IntText(value=1), \"n_epochs\"),\n",
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" (widgets.Button(),\"train\")\n",
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" ]\n",
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" ], \n",
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@ -262,16 +278,22 @@
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"create_area(\"playground 😎\",\n",
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" [\n",
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" [\n",
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" (widgets.HTML(\"<b> predict single sentence </b> <br> (uses min distance to given emojis in prediction_ground_set)\"), None)\n",
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" ],\n",
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" [\n",
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" (widgets.Text(),\"test_input\"),\n",
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" (widgets.HTML(),\"prediction\"),\n",
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" (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"),\"prediction_ground_set\")\n",
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" (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"),\"prediction_ground_set\"),\n",
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" (widgets.HTML(\"<h1>∅</h1>\"),\"prediction\"),\n",
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" ],\n",
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" [\n",
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" (widgets.Checkbox(),\"show_sorted_list\"),\n",
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" (widgets.Button(),\"show_plot\")\n",
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" ],\n",
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" [\n",
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" (widgets.Text(), \"validation_emojis\"),\n",
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" (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",
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" ],\n",
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" [\n",
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" (widgets.Text(value=\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\"), \"validation_emojis\"),\n",
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" (widgets.Button(),\"show_validation_plot\")\n",
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" ]\n",
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" ],\n",
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@ -434,7 +456,6 @@
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" sys.stderr.write(\"ERROR: no json files available in \" + shown_widgets[\"root_path\"].value + \"\\n\")\n",
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" set_widget_visibility([\"file_range\",\n",
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" \"only_emoticons\",\n",
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" \"k_means_cluster\",\n",
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" \"n_top_emojis\",\n",
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" \"apply_lemmatization_and_stemming\",\n",
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" \"load_data\"], False)\n",
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@ -444,7 +465,6 @@
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" jp(files, headers=[\"fileindex\",\"filepath\"])\n",
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" set_widget_visibility([\"file_range\",\n",
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" \"only_emoticons\",\n",
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" \"k_means_cluster\",\n",
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" \"n_top_emojis\",\n",
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" \"apply_lemmatization_and_stemming\",\n",
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" \"load_data\"], True)\n",
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@ -476,7 +496,7 @@
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" sdm = stl.sample_data_manager.generate_and_read(path=shown_widgets[\"root_path\"].value,\n",
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" n_top_emojis=shown_widgets[\"n_top_emojis\"].value,\n",
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" file_range=range(r[0], r[1]),\n",
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" n_kmeans_cluster=shown_widgets[\"k_means_cluster\"].value,\n",
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" n_kmeans_cluster=-1,\n",
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" read_progress_callback=p_r.update,\n",
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" stem_progress_callback=p_s.update if lemm_and_stemm else None,\n",
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" apply_stemming = lemm_and_stemm,\n",
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@ -801,13 +821,6 @@
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"shown_widgets[\"show_plot\"].on_click(plot_pred)\n",
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"shown_widgets[\"show_validation_plot\"].on_click(plot_subset_pred)"
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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