diff --git a/Project/simple_approach/simple_twitter_learning.ipynb b/Project/simple_approach/simple_twitter_learning.ipynb
index 277e2b8..52f4f5b 100644
--- a/Project/simple_approach/simple_twitter_learning.ipynb
+++ b/Project/simple_approach/simple_twitter_learning.ipynb
@@ -2,43 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/jonas/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n",
-      "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"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "from IPython.display import clear_output, Markdown, Math\n",
@@ -59,6 +25,7 @@
     "import pickle\n",
     "import operator\n",
     "from sklearn.pipeline import Pipeline\n",
+    "import json\n",
     "nltk.download('punkt')\n",
     "nltk.download('averaged_perceptron_tagger')\n",
     "nltk.download('wordnet')"
@@ -66,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -129,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -194,13 +161,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "class sample_data_manager(object):\n",
     "    @staticmethod\n",
-    "    def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1):\n",
+    "    def generate_and_read(path:str, only_emoticons=True, apply_stemming=True, n_top_emojis=-1, file_range=None, n_kmeans_cluster=-1, progress_callback=None):\n",
     "        \"\"\"\n",
     "        generate, read and process train data in one step.\n",
     "        \n",
@@ -214,7 +181,7 @@
     "        @return: sample_data_manager object\n",
     "        \"\"\"\n",
     "        sdm = sample_data_manager(path)\n",
-    "        sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons)\n",
+    "        sdm.read_files(file_index_range=range(sdm.n_files) if file_range is None else file_range, only_emoticons=only_emoticons, progress_callback=progress_callback)\n",
     "        if apply_stemming:\n",
     "            sdm.apply_stemming_and_lemmatization()\n",
     "        \n",
@@ -254,7 +221,7 @@
     "        self.kmeans_cluster = None\n",
     "        self.label_binarizer = None\n",
     "    \n",
-    "    def read_files(self, file_index_range:list, only_emoticons=True):\n",
+    "    def read_files(self, file_index_range:list, only_emoticons=True, progress_callback=None):\n",
     "        \"\"\"\n",
     "        reading (multiple) files to one panda table.\n",
     "        \n",
@@ -268,7 +235,8 @@
     "                self.raw_data = pd.read_json(self.json_files[i], encoding=\"utf-8\")\n",
     "            else:\n",
     "                self.raw_data = self.raw_data.append(pd.read_json(self.json_files[i], encoding=\"utf-8\"))\n",
-    "        \n",
+    "            if progress_callback is not None:\n",
+    "                progress_callback()\n",
     "        self.emojis = self.raw_data['EMOJI']\n",
     "        self.plain_text = self.raw_data['text']\n",
     "        \n",
@@ -396,8 +364,7 @@
     "        \n",
     "        labels = self.binary_labels if self.use_binary_labels else self.labels\n",
     "        assert labels is not None\n",
-    "        self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state)\n",
-    "\n"
+    "        self.X, self.Xt, self.y, self.yt = train_test_split(self.plain_text, labels, test_size=split, random_state=random_state)\n"
    ]
   },
   {
@@ -409,12 +376,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "class pipeline_manager(object):\n",
     "    @staticmethod\n",
+    "    def load_from_pipeline_file(pipeline_file:str):\n",
+    "        \"\"\"\n",
+    "        loading a json configuration file and using it's paramters to call 'load_pipeline_from_files'\n",
+    "        \"\"\"\n",
+    "        with open(pipeline_file, 'r') as f:\n",
+    "            d = json.load(f)\n",
+    "        \n",
+    "        keras_models = d['keras_models']\n",
+    "        all_models = d['all_models']\n",
+    "        \n",
+    "        return pipeline_manager.load_pipeline_from_files(pipeline_file.rsplit('.',1)[0], keras_models, all_models)\n",
+    "\n",
+    "\n",
+    "    @staticmethod\n",
     "    def load_pipeline_from_files(file_prefix:str, keras_models = [], all_models = []):\n",
     "        \"\"\"\n",
     "        load a pipeline from files. A pipeline should be represented by multiple model files in the form '<file_prefix>.<model_name>'\n",
@@ -527,6 +508,7 @@
     "        @param prefix: file prefix for all models\n",
     "        \"\"\"\n",
     "        \n",
+    "\n",
     "        print(self.keras_models)\n",
     "        # doing this like explained here: https://stackoverflow.com/a/43415459\n",
     "        for step in self.pipeline.named_steps:\n",
@@ -538,6 +520,9 @@
     "        load_command = \"pipeline_manager.load_pipeline_from_files( '\"\n",
     "        load_command += prefix + \"', \" + str(self.keras_models) + \", \"\n",
     "        load_command += str(list(self.pipeline.named_steps.keys())) + \")\"\n",
+    "\n",
+    "        with open(prefix + '.pipeline', 'w') as outfile:\n",
+    "            json.dump({'keras_models': self.keras_models, 'all_models': [step for step in self.pipeline.named_steps]}, outfile)\n",
     "        \n",
     "        import __main__ as main\n",
     "        if not hasattr(main, '__file__'):\n",
@@ -584,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -594,7 +579,7 @@
     "        self.sdm = sdm\n",
     "        self.pm = pm\n",
     "    \n",
-    "    def fit(self, max_size=10000, disabled_fit_steps=['vectorizer']):\n",
+    "    def fit(self, max_size=10000, disabled_fit_steps=['vectorizer'], keras_batch_fitting_layer=['keras_model'], batch_size=None, n_epochs=1, progress_callback=None):\n",
     "        \"\"\"\n",
     "        fitting data in the pipeline. Because we don't want to refit the vectorizer, the pipeline models containing the vectorizer have to be named explicitly\n",
     "        \n",
@@ -607,22 +592,44 @@
     "        disabled_fits = {}\n",
     "        disabled_fit_transforms = {}\n",
     "        \n",
+    "        disabled_keras_fits = {}\n",
+    "        \n",
     "        named_steps = self.pm.pipeline.named_steps\n",
     "        \n",
     "        for s in disabled_fit_steps:\n",
-    "            # now it gets a little bit dirty:\n",
+    "            # now it gets really dirty:\n",
     "            # replace fit functions we don't want to call again (e.g. for vectorizers)\n",
     "            disabled_fits[s] = named_steps[s].fit\n",
     "            disabled_fit_transforms[s] = named_steps[s].fit_transform\n",
     "            named_steps[s].fit = lambda self, X, y=None: self\n",
     "            named_steps[s].fit_transform = named_steps[s].transform\n",
-    "            \n",
-    "        self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])\n",
+    "        \n",
+    "        for k in keras_batch_fitting_layer:\n",
+    "            # forcing batch fitting on keras\n",
+    "            disabled_keras_fits[k]=named_steps[k].fit\n",
+    "            named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(X.todense(), y) # ← why has keras no sparse support on batch progressing!?!?!\n",
+    "        \n",
+    "        if batch_size is None:\n",
+    "            self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])\n",
+    "        else:\n",
+    "            n = len(self.sdm.X) // batch_size\n",
+    "            for i in range(n_epochs):\n",
+    "                for j in range(n):\n",
+    "                    self.pm.fit(X = np.array(self.sdm.X[j*batch_size:(j+1)*batch_size]), y = np.array(self.sdm.y[j*batch_size:(j+1)*batch_size]))\n",
+    "                    if progress_callback is not None:\n",
+    "                        progress_callback()\n",
+    "                    pred, yt = self.test()\n",
+    "                    mean_squared_error = ((pred - yt)**2).mean(axis=0)\n",
+    "                    print(\"#\" + str(j) + \": loss: \", mean_squared_error)\n",
+    "\n",
     "        \n",
     "        # restore replaced fit functions:\n",
     "        for s in disabled_fit_steps:\n",
     "            named_steps[s].fit = disabled_fits[s]\n",
     "            named_steps[s].fit_transform = disabled_fit_transforms[s]\n",
+    "        \n",
+    "        for k in keras_batch_fitting_layer:\n",
+    "            named_steps[k].fit = disabled_keras_fits[k]\n",
     "    \n",
     "    def test(self):\n",
     "        '''\n",
@@ -630,9 +637,7 @@
     "        '''\n",
     "        if self.sdm.X is None:\n",
     "            self.sdm.create_train_test_split()\n",
-    "        return self.pm.predict(self.sdm.Xt), self.sdm.yt\n",
-    "\n",
-    "    "
+    "        return self.pm.predict(self.sdm.Xt), self.sdm.yt\n"
    ]
   },
   {
@@ -652,21 +657,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "reading file: ./data_en/2017-11-01.json...\n",
-      "imported 33368 samples\n",
-      "remaining samples after top emoji filtering:  26197\n",
-      "Epoch 1/1\n",
-      "100/100 [==============================] - 3s 28ms/step - loss: 0.1230\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import __main__ as main\n",
     "if not hasattr(main, '__file__'):\n",
@@ -698,40 +691,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['keras_model']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "'saved pipeline. It can be loaded the following way:'"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/markdown": [
-       "> ```\n",
-       "pipeline_manager.load_pipeline_from_files( 'custom_classifier', ['keras_model'], ['vectorizer', 'keras_model'])\n",
-       "```"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import __main__ as main\n",
     "if not hasattr(main, '__file__'):\n",
@@ -750,156 +712,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.16062996, 0.08324276, 0.09433182],\n",
-       "       [0.16413   , 0.09421383, 0.07578427],\n",
-       "       [0.11994962, 0.05705731, 0.06310127],\n",
-       "       ...,\n",
-       "       [0.13887292, 0.08502828, 0.08176519],\n",
-       "       [0.18185864, 0.09223703, 0.10704609],\n",
-       "       [0.17687687, 0.09147045, 0.10650696]], dtype=float32)"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "array([[0.46813021, 0.24716181, 0.28470797],\n",
-       "       [0.46813021, 0.24716181, 0.28470797],\n",
-       "       [0.70401758, 0.05932203, 0.23666039],\n",
-       "       ...,\n",
-       "       [0.46813021, 0.24716181, 0.28470797],\n",
-       "       [0.46813021, 0.24716181, 0.28470797],\n",
-       "       [0.46813021, 0.24716181, 0.28470797]])"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "prediction variance:  0.0005431187\n",
-      "teacher variance:  0.03341702104519965\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>predict</th>\n",
-       "      <th>predicted_sentiment</th>\n",
-       "      <th>teacher</th>\n",
-       "      <th>teacher_sentiment</th>\n",
-       "      <th>text</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>35671</th>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.16062995791435242, 0.0832427591085434, 0.09...</td>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
-       "      <td>i feel like i care so much more in everi situat</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25683</th>\n",
-       "      <td>😢</td>\n",
-       "      <td>[0.16413000226020813, 0.0942138284444809, 0.07...</td>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
-       "      <td>i did not meat to add that 2 there ... hav see...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8985</th>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.11994962394237518, 0.05705730617046356, 0.0...</td>\n",
-       "      <td>😊</td>\n",
-       "      <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
-       "      <td>never…</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5410</th>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.18114930391311646, 0.10199417173862457, 0.1...</td>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.46813021474490496, 0.24716181096977158, 0.2...</td>\n",
-       "      <td>lmao on me ! ! ! wtf wa he suppos to say</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62611</th>\n",
-       "      <td>😂</td>\n",
-       "      <td>[0.16997836530208588, 0.08633847534656525, 0.0...</td>\n",
-       "      <td>😊</td>\n",
-       "      <td>[0.7040175768989329, 0.059322033898305086, 0.2...</td>\n",
-       "      <td>this dude alway help me get through my school ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      predict                                predicted_sentiment teacher  \\\n",
-       "35671       😂  [0.16062995791435242, 0.0832427591085434, 0.09...       😂   \n",
-       "25683       😢  [0.16413000226020813, 0.0942138284444809, 0.07...       😂   \n",
-       "8985        😂  [0.11994962394237518, 0.05705730617046356, 0.0...       😊   \n",
-       "5410        😂  [0.18114930391311646, 0.10199417173862457, 0.1...       😂   \n",
-       "62611       😂  [0.16997836530208588, 0.08633847534656525, 0.0...       😊   \n",
-       "\n",
-       "                                       teacher_sentiment  \\\n",
-       "35671  [0.46813021474490496, 0.24716181096977158, 0.2...   \n",
-       "25683  [0.46813021474490496, 0.24716181096977158, 0.2...   \n",
-       "8985   [0.7040175768989329, 0.059322033898305086, 0.2...   \n",
-       "5410   [0.46813021474490496, 0.24716181096977158, 0.2...   \n",
-       "62611  [0.7040175768989329, 0.059322033898305086, 0.2...   \n",
-       "\n",
-       "                                                    text  \n",
-       "35671    i feel like i care so much more in everi situat  \n",
-       "25683  i did not meat to add that 2 there ... hav see...  \n",
-       "8985                                              never…  \n",
-       "5410            lmao on me ! ! ! wtf wa he suppos to say  \n",
-       "62611  this dude alway help me get through my school ...  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean Squared Error:  [0.13877691 0.04682433 0.02937794]\n",
-      "Variance teacher:  [0.02183094 0.02513847 0.00285735]\n",
-      "Variance prediction:  [0.00046378 0.00019441 0.00020516]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import __main__ as main\n",
     "if not hasattr(main, '__file__'):\n",
@@ -945,68 +760,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
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-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>Text</code>.</p>\n",
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-       "<p>\n",
-       "  If you're reading this message in another frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "Text(value='')"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4580af82b30545f197a41e4359010556",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
-      "text/plain": [
-       "VBox(children=(Button(description='get emoji', icon='check', style=ButtonStyle(), tooltip='Click me'), Output()))"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import __main__ as main\n",
     "if not hasattr(main, '__file__'):\n",