minor fixes

Project/simple_approach/Continous_Learner.ipynb

@@ -11,9 +11,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "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",
@@ -37,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,9 +114,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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": "4fd5552e6a024dcaa0f35a594c77ae99",
+       "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",
@@ -174,6 +235,18 @@
     "                ]\n",
     "            ], \n",
     "            \"train\" )\n",
+    "create_area(\"playground 😎\",\n",
+    "           [\n",
+    "               [\n",
+    "                   (widgets.Text(),\"test_input\"),\n",
+    "                   (widgets.HTML(),\"prediction\")\n",
+    "               ],\n",
+    "               [\n",
+    "                   (widgets.Checkbox(),\"show_sorted_list\")\n",
+    "               ]\n",
+    "           ],\n",
+    "           \"playground\")\n",
+    "\n",
     "tab_manager"
    ]
   },
@@ -187,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -205,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -293,7 +366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -541,6 +614,46 @@
     "\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## testing area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_input(b):\n",
+    "    global sdm\n",
+    "    global pm\n",
+    "    global tr\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",
+    "        shown_widgets[\"prediction\"].value = \"<h1> \" + str(stl.sent2emoji(pred)[0]) + \"</h1>\"\n",
+    "        if shown_widgets[\"show_sorted_list\"].value:\n",
+    "            mp(\"## \" + \"\".join(stl.edist.sentiment_vector_to_emoji(pred, only_emoticons=True, n_results=100)))\n",
+    "\n",
+    "#link\n",
+    "shown_widgets[\"test_input\"].observe(test_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {