renamed a lot of stuff

Project/advanced_approach/Continous_Learner.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Continous Learner for Emoji classifier 🤓\n",
+    "# Learner for Emoji classifier 🤓\n",
     "**usage:**\n",
     "run all cells, then go to the [user interface](#User-Interface)"
    ]
@@ -46,7 +46,7 @@
     }
    ],
    "source": [
-    "import simple_twitter_learning as stl\n",
+    "import twitter_learning as twl\n",
     "import glob\n",
     "import sys\n",
     "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
@@ -158,7 +158,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9035abacb17b41e4ac3875663fb23014",
+       "model_id": "e94b33b8493a48798d3adda091986a78",
        "version_major": 2,
        "version_minor": 0
       },
@@ -493,7 +493,7 @@
     "        \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",
+    "        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",
@@ -546,7 +546,7 @@
     "        \n",
     "        p = progress_indicator()\n",
     "        \n",
-    "        tr = stl.trainer(sdm=sdm, pm=pm)\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",
@@ -613,7 +613,7 @@
     "            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",
+    "                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",
@@ -764,7 +764,7 @@
    "outputs": [],
    "source": [
     "top_20 = list(\"😳😋😀😌😏😔😒😎😢😅😁😉🙌🙏😘😊😩😍😭😂\")\n",
-    "top_20_sents = stl.emoji2sent(top_20)\n",
+    "top_20_sents = twl.emoji2sent(top_20)\n",
     "\n",
     "pred = None\n",
     "\n",
@@ -782,9 +782,9 @@
     "        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(stl.sent2emoji(pred,custom_target_emojis=target_list)[0]) + \"</h1>\"\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(stl.edist.sentiment_vector_to_emoji(pred, only_emoticons=True, n_results=100, custom_target_emojis=target_list)))\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",
@@ -810,7 +810,7 @@
     "            return\n",
     "        \n",
     "        if tr is None:\n",
-    "            tr = stl.trainer(sdm=sdm, pm=pm)\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",

Project/advanced_approach/README.md

@@ -0,0 +1,35 @@
+# Advanced Approach
+
+----
+
+## Folder Overview
+
+| Filename                                     | short_description                                            |
+| -------------------------------------------- | ------------------------------------------------------------ |
+| twitter_learning.py / twitter_learning.ipynb | module containing the main classes for the learning process  |
+| Learner.ipynb                                | notebook containing a user interface to control the learn process |
+| Evaluation_sentiment_dataset.ipynb           | notebook creating an evaluation on the sentiment dataset     |
+
+----
+
+## twitter_learning.py
+
+TODO
+
+----
+
+## Learner.ipynb
+
+This file provides all controls for the train process and feeds the classifier with data. To use it just run all cells and jump to the user interface Part. Some explanations for the options:
+
+### load datasets
+
+![1532531542185](README.assets/1532531542185.png)
+
+
+
+----
+
+## Evaluation_sentiment_dataset.ipynb  
+
+TODO

Project/simple_approach/README.md

@@ -1,6 +0,0 @@
-# Simple Approach
-
-----
-
-just a slightly better approach than the naive one 💁
-