diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb
index 01533e4..30d0557 100644
--- a/Project/simple_approach/Continous_Learner.ipynb
+++ b/Project/simple_approach/Continous_Learner.ipynb
@@ -105,9 +105,10 @@
" v_box_widgets = []\n",
" for v in list_widgets:\n",
" for h in v:\n",
- " if 'description' in h[0].__dir__():\n",
+ " if 'description' in h[0].__dir__() and h[1] is not None:\n",
" h[0].description = h[1]\n",
- " shown_widgets[h[1]] = h[0]\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",
@@ -157,12 +158,12 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e508c9a1f8634b7ba790cf2f291e215a",
+ "model_id": "9035abacb17b41e4ac3875663fb23014",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
- "Tab(children=(VBox(children=(HBox(children=(Text(value='./data_en/', description='root_path'), Button(descriptβ¦"
+ "Tab(children=(VBox(children=(HBox(children=(HTML(value=' Data Root Folder:
setup the folder contaβ¦"
]
},
"metadata": {},
@@ -176,17 +177,23 @@
"create_area(\"load dataset πΎ\",\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=-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",
@@ -220,6 +227,9 @@
" (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",
@@ -230,14 +240,14 @@
" (widgets.Button(), \"create_classifier\")\n",
" ],\n",
" [\n",
- " (widgets.Label(\"save_area:\"), \"save_area:\")\n",
+ " (widgets.HTML(\" Save Classifier: \"), None)\n",
" ],\n",
" [\n",
" (widgets.Text(), \"classifier_name\"),\n",
" (widgets.Button(), \"save_classifier\")\n",
" ],\n",
" [\n",
- " (widgets.Label(\"load_area:\"), \"load_area:\")\n",
+ " (widgets.HTML(\" Load Classifier: \"), None)\n",
" ],\n",
" [\n",
" (widgets.Select(options=sorted(glob.glob(\"./*.pipeline\"))), \"clf_file_selector\"),\n",
@@ -250,11 +260,17 @@
"create_area(\"train classifier π\", \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",
+ " (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",
@@ -262,16 +278,22 @@
"create_area(\"playground π\",\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.HTML(),\"prediction\"),\n",
- " (widgets.Text(value=\"π³ππππππππ’π
πππππππ©πππ\"),\"prediction_ground_set\")\n",
+ " (widgets.Text(value=\"π³ππππππππ’π
πππππππ©πππ\"),\"prediction_ground_set\"),\n",
+ " (widgets.HTML(\"