diff --git a/POET_Training.ipynb b/POET_Training.ipynb index 99e33c2..5abcc83 100644 --- a/POET_Training.ipynb +++ b/POET_Training.ipynb @@ -27,11 +27,29 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-02-14 15:03:29.350232: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2025-02-14 15:03:29.368766: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running Keras in version 3.6.0\n" + ] + } + ], "source": [ "import keras\n", + "from keras.layers import Dense, Dropout, Input, BatchNormalization\n", "import tensorflow as tf\n", "import h5py\n", "import numpy as np\n", @@ -55,18 +73,9 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -81,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -118,17 +127,17 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
Model: \"sequential_7\"\n",
+       "
Model: \"sequential\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -140,11 +149,11 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_27 (Dense)                │ (None, 128)            │         1,280 │\n",
+       "│ dense (Dense)                   │ (None, 128)            │         1,280 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_28 (Dense)                │ (None, 128)            │        16,512 │\n",
+       "│ dense_1 (Dense)                 │ (None, 128)            │        16,512 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_29 (Dense)                │ (None, 9)              │         1,161 │\n",
+       "│ dense_2 (Dense)                 │ (None, 9)              │         1,161 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -152,11 +161,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_27 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,280\u001b[0m │\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,280\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_28 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m16,512\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_29 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │         \u001b[38;5;34m1,161\u001b[0m │\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │         \u001b[38;5;34m1,161\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -208,8 +217,9 @@
     "model_simple = keras.Sequential(\n",
     "    [\n",
     "        keras.Input(shape = (9,), dtype = \"float32\"),\n",
-    "        keras.layers.Dense(units = 128, activation = \"relu\", dtype = \"float32\"),\n",
-    "        keras.layers.Dense(units = 128, activation = \"relu\", dtype = \"float32\"),\n",
+    "        keras.layers.Dense(units = 128, activation = \"linear\", dtype = \"float32\"),\n",
+    "        # Dropout(0.2),\n",
+    "        keras.layers.Dense(units = 128, activation = \"elu\", dtype = \"float32\"),\n",
     "        keras.layers.Dense(units = 9, dtype = \"float32\")\n",
     "    ]\n",
     ")\n",
@@ -220,17 +230,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
Model: \"sequential_8\"\n",
+       "
Model: \"sequential_1\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_8\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -242,13 +252,13 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_30 (Dense)                │ (None, 512)            │         5,120 │\n",
+       "│ dense_3 (Dense)                 │ (None, 512)            │         5,120 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_31 (Dense)                │ (None, 1024)           │       525,312 │\n",
+       "│ dense_4 (Dense)                 │ (None, 1024)           │       525,312 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_32 (Dense)                │ (None, 512)            │       524,800 │\n",
+       "│ dense_5 (Dense)                 │ (None, 512)            │       524,800 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_33 (Dense)                │ (None, 9)              │         4,617 │\n",
+       "│ dense_6 (Dense)                 │ (None, 9)              │         4,617 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -256,13 +266,13 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_30 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m5,120\u001b[0m │\n",
+       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m5,120\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_31 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
+       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\u001b[0m)           │       \u001b[38;5;34m525,312\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_32 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
+       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m524,800\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_33 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │         \u001b[38;5;34m4,617\u001b[0m │\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │         \u001b[38;5;34m4,617\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -325,17 +335,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "
Model: \"sequential_5\"\n",
+       "
Model: \"sequential_2\"\n",
        "
\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -347,15 +357,15 @@
        "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_19 (Dense)                │ (None, 128)            │         1,664 │\n",
+       "│ dense_7 (Dense)                 │ (None, 128)            │         1,664 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_20 (Dense)                │ (None, 256)            │        33,024 │\n",
+       "│ dense_8 (Dense)                 │ (None, 256)            │        33,024 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_21 (Dense)                │ (None, 512)            │       131,584 │\n",
+       "│ dense_9 (Dense)                 │ (None, 512)            │       131,584 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_22 (Dense)                │ (None, 256)            │       131,328 │\n",
+       "│ dense_10 (Dense)                │ (None, 256)            │       131,328 │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_23 (Dense)                │ (None, 12)             │         3,084 │\n",
+       "│ dense_11 (Dense)                │ (None, 12)             │         3,084 │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "
\n"
       ],
@@ -363,15 +373,15 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_19 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,664\u001b[0m │\n",
+       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │         \u001b[38;5;34m1,664\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m33,024\u001b[0m │\n",
+       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │        \u001b[38;5;34m33,024\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m131,584\u001b[0m │\n",
+       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m131,584\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_22 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m131,328\u001b[0m │\n",
+       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m131,328\u001b[0m │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m3,084\u001b[0m │\n",
+       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m3,084\u001b[0m │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -443,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -467,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -513,12 +523,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "# os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n",
-    "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n",
+    "data_file = h5py.File(\"barite_50_4_corner.h5\")\n",
     "\n",
     "design = data_file[\"design\"]\n",
     "results = data_file[\"result\"]\n",
@@ -529,6 +539,84 @@
     "data_file.close()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "species_columns = ['H', 'O', 'Charge', 'Ba', 'Cl', 'S', 'Sr', 'Barite', 'Celestite']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/signer/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/base.py:1473: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
+      "  return fit_method(estimator, *args, **kwargs)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Amount class 0 before: 0.9521309523809524\n",
+      "Amount class 1 before: 0.04786904761904762\n",
+      "Using Oversampling\n",
+      "Amount class 0 after: 0.5\n",
+      "Amount class 1 after: 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "preprocess = preprocessing(func_dict_in=func_dict_in, func_dict_out=func_dict_out)\n",
+    "X, y = preprocess.cluster(df_design[species_columns], df_results[species_columns])\n",
+    "# X, y = preprocess.funcTranform(X, y)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = preprocess.split(X, y, ratio = 0.2)\n",
+    "X_train, y_train = preprocess.balancer(X_train, y_train, strategy = \"over\")\n",
+    "preprocess.scale_fit(X_train, y_train, scaling = \"individual\")\n",
+    "X_train, X_test, y_train, y_test = preprocess.scale_transform(X_train, X_test, y_train, y_test)\n",
+    "X_train, X_val, y_train, y_val = preprocess.split(X_train, y_train, ratio = 0.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       ""
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "timestep=250\n",
+    "plt.imshow(np.array(X[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')\n",
+    "plt.contour(np.array(X[\"Class\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower', colors='red')\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -547,16 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "species_columns = [\"H\", \"O\", \"Charge\", \"Ba\", \"Cl\", \"S_6_\", \"Sr\", \"Barite\", \"Celestite\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -568,11 +647,17 @@
      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Amount class 0 before: 0.9879169719169719\n",
-      "Amount class 1 before: 0.012083028083028084\n"
+     "ename": "KeyError",
+     "evalue": "'S'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y \u001b[38;5;241m=\u001b[39m preprocessing_training(df_design[species_columns], df_results[species_columns], func_dict_in, func_dict_out, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moff\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mglobal\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m0.1\u001b[39m)\n",
+      "File \u001b[0;32m~/Documents/model-training/preprocessing.py:161\u001b[0m, in \u001b[0;36mpreprocessing_training\u001b[0;34m(df_design, df_targets, func_dict_in, func_dict_out, sampling, scaling, test_size)\u001b[0m\n\u001b[1;32m    158\u001b[0m df_design \u001b[38;5;241m=\u001b[39m clustering(df_design)\n\u001b[1;32m    159\u001b[0m df_targets \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mconcat([df_targets, df_design[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m'\u001b[39m]], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m--> 161\u001b[0m df_design_log \u001b[38;5;241m=\u001b[39m FuncTransform(func_dict_in, func_dict_out)\u001b[38;5;241m.\u001b[39mfit_transform(df_design)\n\u001b[1;32m    162\u001b[0m df_results_log \u001b[38;5;241m=\u001b[39m FuncTransform(func_dict_in, func_dict_out)\u001b[38;5;241m.\u001b[39mfit_transform(df_targets)\n\u001b[1;32m    164\u001b[0m X_train, X_test, y_train, y_test \u001b[38;5;241m=\u001b[39m sk\u001b[38;5;241m.\u001b[39mtrain_test_split(df_design_log, df_results_log, test_size \u001b[38;5;241m=\u001b[39m test_size, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n",
+      "File \u001b[0;32m~/Documents/model-training/preprocessing.py:63\u001b[0m, in \u001b[0;36mFuncTransform.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m---> 63\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(X, y)\n",
+      "File \u001b[0;32m~/Documents/model-training/preprocessing.py:58\u001b[0m, in \u001b[0;36mFuncTransform.transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m X\u001b[38;5;241m.\u001b[39mkeys():   \n\u001b[1;32m     57\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m key:\n\u001b[0;32m---> 58\u001b[0m         X[key] \u001b[38;5;241m=\u001b[39m X[key]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_transform[key])\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'S'"
      ]
     }
    ],
@@ -580,6 +665,50 @@
     "X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y = preprocessing_training(df_design[species_columns], df_results[species_columns], func_dict_in, func_dict_out, \"off\", 'global', 0.1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.88371754e-02, 2.38285692e-01, 1.25266821e-01, 4.02648011e-05,\n",
+       "       5.71730222e-02, 2.38302374e-01, 9.25432038e-02, 3.77910581e-07,\n",
+       "       9.99694424e-01])"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.iloc[12, :-1].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1.11012434e+02, 5.55068087e+01, 3.55966726e-08, 3.89751302e-06,\n",
+       "        1.12795836e-02, 1.47982437e-04, 5.78389634e-03, 9.99927111e-04,\n",
+       "        1.00047941e+00]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocess.scaler_X.inverse_transform(tf.keras.backend.constant(X_train.iloc[12, :-1].values.reshape(1, -1)))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -589,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -605,35 +734,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 133,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def custom_loss(scaler_X, scaler_y, FuncTransform, dict_in, dict_out, columns):\n",
-    "    def loss(results, predicted):\n",
-    "        \n",
-    "        predicted = pd.DataFrame(scaler_X.inverse_transform(predicted), columns = columns)\n",
-    "        results = pd.DataFrame(scaler_y.inverse_transform(results), columns = columns)\n",
-    "        \n",
-    "        predicted = FuncTransform(dict_in, dict_out).inverse_transform(predicted)\n",
-    "        results = FuncTransform(dict_in, dict_out).inverse_transform(results)\n",
-    "        \n",
-    "        dBa = tf.keras.backend.abs((predicted[\"Ba\"] + predicted[\"Barite\"]) - (results[\"Ba\"] + results[\"Barite\"]))\n",
-    "        dSr = tf.keras.backend.abs((predicted[\"Sr\"] + predicted[\"Celestite\"]) - (results[\"Sr\"] + results[\"Celestite\"]))\n",
-    "        total_loss = keras.loss.Huber(results, predicted) + 0.1 * dBa + 0.1 * dSr\n",
-    "        \n",
-    "        return total_loss\n",
-    "\n",
-    "    return loss"
+    "column_dict = {\"Ba\": X.columns.get_loc(\"Ba\"), \"Barite\":X.columns.get_loc(\"Barite\"), \"Sr\":X.columns.get_loc(\"Sr\"), \"Celestite\":X.columns.get_loc(\"Celestite\")}"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 160,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_simple.compile(optimizer=optimizer_simple, loss=custom_loss(scaler_X, scaler_y, FuncTransform, func_dict_in, func_dict_out, species_columns))"
+    "def custom_loss(preprocess, column_dict):\n",
+    "    def loss(results, predicted):\n",
+    "        \n",
+    "        # predicted = preprocess.funcInverse(predicted)\n",
+    "        # results = preprocess.funcInverse(results)\n",
+    "        # predicted = tf.keras.backend.constant(predicted)\n",
+    "        # results = tf.keras.backend.constant(results)\n",
+    "        \n",
+    "        # predicted = tf.keras.backend.constant(preprocess.scaler_X.inverse_transform(predicted))\n",
+    "        # results = tf.keras.backend.constant(preprocess.scaler_y.inverse_transform(results))\n",
+    "        \n",
+    "        # dBa = tf.keras.backend.abs((predicted[\"Ba\"] + predicted[\"Barite\"]) - (results[\"Ba\"] + results[\"Barite\"]))\n",
+    "        # dSr = tf.keras.backend.abs((predicted[\"Sr\"] + predicted[\"Celestite\"]) - (results[\"Sr\"] + results[\"Celestite\"]))\n",
+    "        # huber_loss = tf.keras.losses.Huber()(results, predicted)\n",
+    "        # total_loss = huber_loss + 0.1 * dBa + 0.1 * dSr\n",
+    "        \n",
+    "        predicted_inverse = predicted * tf.keras.backend.constant(preprocess.scaler_X.scale_) + tf.keras.backend.constant(preprocess.scaler_X.min_)\n",
+    "        results_inverse = results * tf.keras.backend.constant(preprocess.scaler_y.scale_) + tf.keras.backend.constant(preprocess.scaler_y.min_)\n",
+    "        \n",
+    "        dBa = tf.keras.backend.abs((predicted_inverse[:,column_dict[\"Ba\"]] + predicted_inverse[:, column_dict[\"Barite\"]]) - (results_inverse[:, column_dict[\"Ba\"]] + results_inverse[:, column_dict[\"Barite\"]]))\n",
+    "        dSa = tf.keras.backend.abs((predicted_inverse[:,column_dict[\"Sr\"]] + predicted_inverse[:, column_dict[\"Celestite\"]]) - (results_inverse[:, column_dict[\"Sr\"]] + results_inverse[:, column_dict[\"Celestite\"]]))\n",
+    "        \n",
+    "        huber_loss = tf.keras.losses.Huber()(results, predicted)\n",
+    "        total_loss = huber_loss # + 0.1 * dBa + 0.1 * dSa\n",
+    "        \n",
+    "        return total_loss\n",
+    "\n",
+    "    return loss\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_simple.compile(optimizer=optimizer_simple, loss=loss)#custom_loss(preprocess, column_dict))"
    ]
   },
   {
@@ -645,7 +796,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.15182858e+07, 4.02793041e+02, 1.88096044e+06, 1.03308956e+01,\n",
+       "       5.06871734e+00, 1.61113403e+03, 1.74937705e+01, 9.88625768e-01,\n",
+       "       9.99215371e-01])"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preprocess.scaler_X.scale_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -653,12 +826,12 @@
     "def model_training(model):\n",
     "    start = time.time()\n",
     "    callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
-    "    history = model.fit(X_train.iloc[:, X_train.columns != \"Class\"], \n",
-    "                y_train.iloc[:, y_train.columns != \"Class\"], \n",
-    "                batch_size = batch_size, \n",
-    "                epochs = 20, \n",
-    "                validation_data = (X_val.iloc[:, X_val.columns != \"Class\"], y_val.iloc[:, y_val.columns != \"Class\"]),\n",
-    "                callbacks = [callback])\n",
+    "    history = model.fit(X_train.loc[:, X_train.columns != \"Class\"], \n",
+    "                        y_train.loc[:, y_train.columns != \"Class\"], \n",
+    "                        batch_size=batch_size, \n",
+    "                        epochs=20, \n",
+    "                        validation_data=(X_val.loc[:, X_val.columns != \"Class\"], y_val.loc[:, y_val.columns != \"Class\"]),\n",
+    "                        callbacks=[callback])\n",
     "\n",
     "    end = time.time()\n",
     "\n",
@@ -667,588 +840,54 @@
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-   "metadata": {},
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HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestite
0111.01243455.506219-7.748371e-091.541375e-130.000000e+001.916106e-024.082138e-021.440295e-206.403132e-070.0012500.0022500.999244
1111.01243455.506220-4.672647e-090.000000e+006.197818e-149.637903e-032.189133e-020.000000e+007.449873e-070.0013090.0023080.999244
2111.01243455.508129-1.325997e-091.416456e-130.000000e+005.839037e-075.887491e-042.981540e-174.781732e-040.0007720.0012940.999847
3111.01243455.508676-1.223549e-094.449984e-140.000000e+004.536952e-072.183551e-053.803886e-196.148996e-040.0006250.0010100.999995
4111.01243455.508699-1.216518e-090.000000e+004.098861e-144.496372e-078.098352e-070.000000e+006.205560e-040.0006210.0010001.000000
.......................................
2502495111.01243455.5074883.573897e-090.000000e+001.374167e-109.953042e-072.266555e-030.000000e+003.178142e-040.0014500.0010001.000014
2502496111.01243455.5075013.494199e-090.000000e+001.378928e-109.814307e-072.217997e-030.000000e+003.210423e-040.0014290.0010001.000010
2502497111.01243455.5075123.429947e-090.000000e+001.376072e-109.704342e-072.179066e-030.000000e+003.236905e-040.0014120.0010001.000006
2502498111.01243455.5075203.381818e-090.000000e+001.368903e-109.632999e-072.149820e-030.000000e+003.257170e-040.0014000.0010001.000004
2502499111.01243455.5075253.349044e-090.000000e+001.378174e-109.563975e-072.129912e-030.000000e+003.271123e-040.0013910.0010001.000001
\n",
-       "
2502500 rows × 12 columns
\n",
-       "
"
-      ],
-      "text/plain": [
-       "                  H          O        Charge          H_0_          O_0_  \\\n",
-       "0        111.012434  55.506219 -7.748371e-09  1.541375e-13  0.000000e+00   \n",
-       "1        111.012434  55.506220 -4.672647e-09  0.000000e+00  6.197818e-14   \n",
-       "2        111.012434  55.508129 -1.325997e-09  1.416456e-13  0.000000e+00   \n",
-       "3        111.012434  55.508676 -1.223549e-09  4.449984e-14  0.000000e+00   \n",
-       "4        111.012434  55.508699 -1.216518e-09  0.000000e+00  4.098861e-14   \n",
-       "...             ...        ...           ...           ...           ...   \n",
-       "2502495  111.012434  55.507488  3.573897e-09  0.000000e+00  1.374167e-10   \n",
-       "2502496  111.012434  55.507501  3.494199e-09  0.000000e+00  1.378928e-10   \n",
-       "2502497  111.012434  55.507512  3.429947e-09  0.000000e+00  1.376072e-10   \n",
-       "2502498  111.012434  55.507520  3.381818e-09  0.000000e+00  1.368903e-10   \n",
-       "2502499  111.012434  55.507525  3.349044e-09  0.000000e+00  1.378174e-10   \n",
-       "\n",
-       "                   Ba            Cl          S_2_          S_6_        Sr  \\\n",
-       "0        1.916106e-02  4.082138e-02  1.440295e-20  6.403132e-07  0.001250   \n",
-       "1        9.637903e-03  2.189133e-02  0.000000e+00  7.449873e-07  0.001309   \n",
-       "2        5.839037e-07  5.887491e-04  2.981540e-17  4.781732e-04  0.000772   \n",
-       "3        4.536952e-07  2.183551e-05  3.803886e-19  6.148996e-04  0.000625   \n",
-       "4        4.496372e-07  8.098352e-07  0.000000e+00  6.205560e-04  0.000621   \n",
-       "...               ...           ...           ...           ...       ...   \n",
-       "2502495  9.953042e-07  2.266555e-03  0.000000e+00  3.178142e-04  0.001450   \n",
-       "2502496  9.814307e-07  2.217997e-03  0.000000e+00  3.210423e-04  0.001429   \n",
-       "2502497  9.704342e-07  2.179066e-03  0.000000e+00  3.236905e-04  0.001412   \n",
-       "2502498  9.632999e-07  2.149820e-03  0.000000e+00  3.257170e-04  0.001400   \n",
-       "2502499  9.563975e-07  2.129912e-03  0.000000e+00  3.271123e-04  0.001391   \n",
-       "\n",
-       "           Barite  Celestite  \n",
-       "0        0.002250   0.999244  \n",
-       "1        0.002308   0.999244  \n",
-       "2        0.001294   0.999847  \n",
-       "3        0.001010   0.999995  \n",
-       "4        0.001000   1.000000  \n",
-       "...           ...        ...  \n",
-       "2502495  0.001000   1.000014  \n",
-       "2502496  0.001000   1.000010  \n",
-       "2502497  0.001000   1.000006  \n",
-       "2502498  0.001000   1.000004  \n",
-       "2502499  0.001000   1.000001  \n",
-       "\n",
-       "[2502500 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
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-    "df_results"
-   ]
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-   "execution_count": 202,
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HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestite
0111.01243455.508192-7.779554e-092.697041e-262.210590e-152.041069e-024.082138e-020.000000e+000.0004940.0004940.0011.000000
1111.01243455.508427-4.736083e-091.446346e-262.473481e-151.094567e-022.189133e-020.000000e+000.0005530.0005530.0011.000000
2111.01243455.508691-1.311169e-093.889826e-282.769320e-152.943745e-045.887491e-040.000000e+000.0006190.0006190.0011.000000
3111.01243455.508698-1.220023e-091.442658e-292.777193e-151.091776e-052.183551e-050.000000e+000.0006200.0006200.0011.000000
4111.01243455.508699-1.216643e-095.350528e-312.777485e-154.049176e-078.098352e-070.000000e+000.0006200.0006200.0011.000000
.......................................
2502495111.01243455.5074883.573728e-095.424062e-1451.375204e-109.953520e-072.266555e-035.509534e-1490.0003180.0014500.0011.000014
2502496111.01243455.5075013.494007e-092.011675e-1461.377139e-109.817216e-072.217997e-032.043375e-1500.0003210.0014290.0011.000010
2502497111.01243455.5075123.429764e-097.460897e-1481.377819e-109.706451e-072.179066e-037.578467e-1520.0003240.0014120.0011.000006
2502498111.01243455.5075203.381745e-092.767237e-1491.371144e-109.621074e-072.149820e-032.810844e-1530.0003260.0014000.0011.000004
2502499111.01243455.5075253.348864e-095.321610e-1511.376026e-109.564401e-072.129912e-035.405468e-1550.0003270.0013910.0011.000001
\n",
-       "
2502500 rows × 12 columns
\n",
-       "
"
-      ],
-      "text/plain": [
-       "                  H          O        Charge           H_0_          O_0_  \\\n",
-       "0        111.012434  55.508192 -7.779554e-09   2.697041e-26  2.210590e-15   \n",
-       "1        111.012434  55.508427 -4.736083e-09   1.446346e-26  2.473481e-15   \n",
-       "2        111.012434  55.508691 -1.311169e-09   3.889826e-28  2.769320e-15   \n",
-       "3        111.012434  55.508698 -1.220023e-09   1.442658e-29  2.777193e-15   \n",
-       "4        111.012434  55.508699 -1.216643e-09   5.350528e-31  2.777485e-15   \n",
-       "...             ...        ...           ...            ...           ...   \n",
-       "2502495  111.012434  55.507488  3.573728e-09  5.424062e-145  1.375204e-10   \n",
-       "2502496  111.012434  55.507501  3.494007e-09  2.011675e-146  1.377139e-10   \n",
-       "2502497  111.012434  55.507512  3.429764e-09  7.460897e-148  1.377819e-10   \n",
-       "2502498  111.012434  55.507520  3.381745e-09  2.767237e-149  1.371144e-10   \n",
-       "2502499  111.012434  55.507525  3.348864e-09  5.321610e-151  1.376026e-10   \n",
-       "\n",
-       "                   Ba            Cl           S_2_      S_6_        Sr  \\\n",
-       "0        2.041069e-02  4.082138e-02   0.000000e+00  0.000494  0.000494   \n",
-       "1        1.094567e-02  2.189133e-02   0.000000e+00  0.000553  0.000553   \n",
-       "2        2.943745e-04  5.887491e-04   0.000000e+00  0.000619  0.000619   \n",
-       "3        1.091776e-05  2.183551e-05   0.000000e+00  0.000620  0.000620   \n",
-       "4        4.049176e-07  8.098352e-07   0.000000e+00  0.000620  0.000620   \n",
-       "...               ...           ...            ...       ...       ...   \n",
-       "2502495  9.953520e-07  2.266555e-03  5.509534e-149  0.000318  0.001450   \n",
-       "2502496  9.817216e-07  2.217997e-03  2.043375e-150  0.000321  0.001429   \n",
-       "2502497  9.706451e-07  2.179066e-03  7.578467e-152  0.000324  0.001412   \n",
-       "2502498  9.621074e-07  2.149820e-03  2.810844e-153  0.000326  0.001400   \n",
-       "2502499  9.564401e-07  2.129912e-03  5.405468e-155  0.000327  0.001391   \n",
-       "\n",
-       "         Barite  Celestite  \n",
-       "0         0.001   1.000000  \n",
-       "1         0.001   1.000000  \n",
-       "2         0.001   1.000000  \n",
-       "3         0.001   1.000000  \n",
-       "4         0.001   1.000000  \n",
-       "...         ...        ...  \n",
-       "2502495   0.001   1.000014  \n",
-       "2502496   0.001   1.000010  \n",
-       "2502497   0.001   1.000006  \n",
-       "2502498   0.001   1.000004  \n",
-       "2502499   0.001   1.000001  \n",
-       "\n",
-       "[2502500 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_design"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0          0.324673\n",
-       "1          0.307932\n",
-       "2         -2.590695\n",
-       "3         -1.376848\n",
-       "4          0.045476\n",
-       "             ...   \n",
-       "2502495   -0.000021\n",
-       "2502496   -0.000129\n",
-       "2502497   -0.000094\n",
-       "2502498    0.000537\n",
-       "2502499   -0.000019\n",
-       "Length: 2502500, dtype: float64"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(np.log10(df_results['Ba']) + np.log10(df_results['Barite'])) - (np.log10(df_design['Ba']) + np.log10(df_design['Barite']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 168,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/20\n"
-     ]
-    },
-    {
-     "ename": "NotImplementedError",
-     "evalue": "Cannot convert a symbolic tf.Tensor (sequential_7_1/dense_29_1/Add:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[201], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m model_training(model_simple)\n",
-      "Cell \u001b[0;32mIn[188], line 5\u001b[0m, in \u001b[0;36mmodel_training\u001b[0;34m(model)\u001b[0m\n\u001b[1;32m      3\u001b[0m start \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m      4\u001b[0m callback \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mEarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mloss\u001b[39m\u001b[38;5;124m'\u001b[39m, patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(X_train\u001b[38;5;241m.\u001b[39miloc[:, X_train\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      6\u001b[0m             y_train\u001b[38;5;241m.\u001b[39miloc[:, y_train\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], \n\u001b[1;32m      7\u001b[0m             batch_size \u001b[38;5;241m=\u001b[39m batch_size, \n\u001b[1;32m      8\u001b[0m             epochs \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m20\u001b[39m, \n\u001b[1;32m      9\u001b[0m             validation_data \u001b[38;5;241m=\u001b[39m (X_val\u001b[38;5;241m.\u001b[39miloc[:, X_val\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m], y_val\u001b[38;5;241m.\u001b[39miloc[:, y_val\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m]),\n\u001b[1;32m     10\u001b[0m             callbacks \u001b[38;5;241m=\u001b[39m [callback])\n\u001b[1;32m     12\u001b[0m end \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining took \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m seconds\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(end \u001b[38;5;241m-\u001b[39m start))\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:122\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    119\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m    120\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;66;03m# `keras.config.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    124\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
-      "Cell \u001b[0;32mIn[199], line 4\u001b[0m, in \u001b[0;36mcustom_loss..loss\u001b[0;34m(results, predicted)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mloss\u001b[39m(results, predicted):\n\u001b[0;32m----> 4\u001b[0m     predicted \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(scaler_X\u001b[38;5;241m.\u001b[39minverse_transform(predicted), columns \u001b[38;5;241m=\u001b[39m columns)\n\u001b[1;32m      5\u001b[0m     results \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(scaler_y\u001b[38;5;241m.\u001b[39minverse_transform(results), columns \u001b[38;5;241m=\u001b[39m columns)\n\u001b[1;32m      7\u001b[0m     predicted \u001b[38;5;241m=\u001b[39m FuncTransform(dict_in, dict_out)\u001b[38;5;241m.\u001b[39minverse_transform(predicted)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/preprocessing/_data.py:566\u001b[0m, in \u001b[0;36mMinMaxScaler.inverse_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    562\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m    564\u001b[0m xp, _ \u001b[38;5;241m=\u001b[39m get_namespace(X)\n\u001b[0;32m--> 566\u001b[0m X \u001b[38;5;241m=\u001b[39m check_array(\n\u001b[1;32m    567\u001b[0m     X,\n\u001b[1;32m    568\u001b[0m     copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcopy,\n\u001b[1;32m    569\u001b[0m     dtype\u001b[38;5;241m=\u001b[39m_array_api\u001b[38;5;241m.\u001b[39msupported_float_dtypes(xp),\n\u001b[1;32m    570\u001b[0m     force_writeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    571\u001b[0m     force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow-nan\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    572\u001b[0m )\n\u001b[1;32m    574\u001b[0m X \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmin_\n\u001b[1;32m    575\u001b[0m X \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale_\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/utils/validation.py:1012\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_writeable, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m   1010\u001b[0m         array \u001b[38;5;241m=\u001b[39m xp\u001b[38;5;241m.\u001b[39mastype(array, dtype, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1011\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1012\u001b[0m         array \u001b[38;5;241m=\u001b[39m _asarray_with_order(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype, xp\u001b[38;5;241m=\u001b[39mxp)\n\u001b[1;32m   1013\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexWarning \u001b[38;5;28;01mas\u001b[39;00m complex_warning:\n\u001b[1;32m   1014\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   1015\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mComplex data not supported\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m   1016\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcomplex_warning\u001b[39;00m\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/sklearn/utils/_array_api.py:745\u001b[0m, in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp, device)\u001b[0m\n\u001b[1;32m    743\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39marray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    744\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 745\u001b[0m     array \u001b[38;5;241m=\u001b[39m numpy\u001b[38;5;241m.\u001b[39masarray(array, order\u001b[38;5;241m=\u001b[39morder, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[1;32m    747\u001b[0m \u001b[38;5;66;03m# At this point array is a NumPy ndarray. We convert it to an array\u001b[39;00m\n\u001b[1;32m    748\u001b[0m \u001b[38;5;66;03m# container that is consistent with the input's namespace.\u001b[39;00m\n\u001b[1;32m    749\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m xp\u001b[38;5;241m.\u001b[39masarray(array)\n",
-      "\u001b[0;31mNotImplementedError\u001b[0m: Cannot convert a symbolic tf.Tensor (sequential_7_1/dense_29_1/Add:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported."
+      "Epoch 1/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.8797e-04 - val_loss: 2.8113e-04\n",
+      "Epoch 2/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.8380e-04 - val_loss: 2.7753e-04\n",
+      "Epoch 3/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.8036e-04 - val_loss: 2.7414e-04\n",
+      "Epoch 4/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.7782e-04 - val_loss: 2.7140e-04\n",
+      "Epoch 5/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.7578e-04 - val_loss: 2.6949e-04\n",
+      "Epoch 6/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.7396e-04 - val_loss: 2.6790e-04\n",
+      "Epoch 7/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.7167e-04 - val_loss: 2.6644e-04\n",
+      "Epoch 8/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.6910e-04 - val_loss: 2.6501e-04\n",
+      "Epoch 9/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.6823e-04 - val_loss: 2.6271e-04\n",
+      "Epoch 10/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.6424e-04 - val_loss: 2.5818e-04\n",
+      "Epoch 11/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.6078e-04 - val_loss: 2.5062e-04\n",
+      "Epoch 12/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.5335e-04 - val_loss: 2.4440e-04\n",
+      "Epoch 13/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.4686e-04 - val_loss: 2.4047e-04\n",
+      "Epoch 14/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.4252e-04 - val_loss: 2.3637e-04\n",
+      "Epoch 15/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 2.3795e-04 - val_loss: 2.3264e-04\n",
+      "Epoch 16/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.3383e-04 - val_loss: 2.2967e-04\n",
+      "Epoch 17/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.3237e-04 - val_loss: 2.2776e-04\n",
+      "Epoch 18/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.2965e-04 - val_loss: 2.2670e-04\n",
+      "Epoch 19/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.2929e-04 - val_loss: 2.2598e-04\n",
+      "Epoch 20/20\n",
+      "\u001b[1m1688/1688\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 2.2851e-04 - val_loss: 2.2543e-04\n",
+      "Training took 56.33732986450195 seconds\n"
      ]
     }
    ],
@@ -1265,22 +904,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 98,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def mass_balance(model, X, scaler_X, scaler_y, func_dict_in, func_dict_out):\n",
+    "def mass_balance(model, X, preprocess):\n",
     "    \n",
     "    # predict the chemistry\n",
     "    columns = X.iloc[:, X.columns != \"Class\"].columns\n",
     "    prediction = pd.DataFrame(model.predict(X[columns]), columns=columns)\n",
     "    # backtransform min/max\n",
-    "    X = pd.DataFrame(scaler_X.inverse_transform(X.iloc[:, X.columns != \"Class\"]), columns=columns)\n",
-    "    prediction = pd.DataFrame(scaler_y.inverse_transform(prediction), columns=columns)\n",
+    "    X = pd.DataFrame(preprocess.scaler_X.inverse_transform(X.iloc[:, X.columns != \"Class\"]), columns=columns)\n",
+    "    prediction = pd.DataFrame(preprocess.scaler_y.inverse_transform(prediction), columns=columns)\n",
     "    \n",
     "    # backtransform log\n",
-    "    X = FuncTransform(func_dict_in, func_dict_out).inverse_transform(X)\n",
-    "    prediction = FuncTransform(func_dict_in, func_dict_out).inverse_transform(prediction)\n",
+    "    if(preprocess.state['log'] == True):\n",
+    "        X, prediction = preprocess.funcInverse(X, prediction)\n",
     "        \n",
     "    # calculate mass balance\n",
     "    dBa = np.abs((prediction[\"Ba\"] + prediction[\"Barite\"]) - (X[\"Ba\"] + X[\"Barite\"]))\n",
@@ -1291,51 +930,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 99,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 328us/step\n"
+      "\u001b[1m26993/26993\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 358us/step\n"
      ]
     }
    ],
    "source": [
-    "columns = X_test.columns[X_test.columns != \"Class\"]\n",
-    "prediction = pd.DataFrame(model_simple.predict(X_test[columns]), columns = columns)"
+    "mass_balance_results = mass_balance(model_simple, X_train, preprocess)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 1ms/step\n"
-     ]
-    }
-   ],
-   "source": [
-    "mass_balance_results = mass_balance(model_large, X_test, scaler_X, scaler_y, func_dict_in, func_dict_out)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 100,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.022793206793206792"
+       "0.0"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1346,27 +967,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "4         0.000006\n",
-       "46        0.000010\n",
-       "157       0.000010\n",
-       "160       0.000003\n",
-       "189       0.000009\n",
-       "            ...   \n",
-       "250016    0.000007\n",
-       "250022    0.000007\n",
-       "250086    0.000007\n",
-       "250139    0.000003\n",
-       "250223    0.000006\n",
-       "Length: 5704, dtype: float64"
+       "Series([], dtype: float64)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1388,73 +998,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "argument of type 'int' is not iterable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[178], line 11\u001b[0m\n\u001b[1;32m      7\u001b[0m y_results \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m      8\u001b[0m y_differences \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m---> 11\u001b[0m df_design_transformed_scaled \u001b[38;5;241m=\u001b[39m scaler_X\u001b[38;5;241m.\u001b[39mtransform(FuncTransform(func_dict_in, func_dict_out)\u001b[38;5;241m.\u001b[39mfit_transform(df_design[species]))\n\u001b[1;32m     12\u001b[0m df_results_transformed_scaled \u001b[38;5;241m=\u001b[39m scaler_X\u001b[38;5;241m.\u001b[39mtransform(FuncTransform(func_dict_in, func_dict_out)\u001b[38;5;241m.\u001b[39mfit_transform(df_results[species]))\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m,iterations):\n",
-      "File \u001b[0;32m~/Documents/model-training/preprocessing.py:63\u001b[0m, in \u001b[0;36mFuncTransform.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     61\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     62\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m---> 63\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(X, y)\n",
-      "File \u001b[0;32m~/Documents/model-training/preprocessing.py:57\u001b[0m, in \u001b[0;36mFuncTransform.transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m     55\u001b[0m X \u001b[38;5;241m=\u001b[39m X\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m X\u001b[38;5;241m.\u001b[39mkeys():   \n\u001b[0;32m---> 57\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m key:\n\u001b[1;32m     58\u001b[0m         X[key] \u001b[38;5;241m=\u001b[39m X[key]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc_transform[key])\n\u001b[1;32m     59\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m X\n",
-      "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 712us/step\n"
      ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "species = \"Barite\"\n",
-    "iterations = 1000\n",
-    "cell_offset = 9\n",
+    "species = \"Ba\"\n",
+    "iterations = 250\n",
+    "cell_offset = 900\n",
     "y_design = []\n",
     "y_results = []\n",
     "y_differences = []\n",
     "\n",
+    "# if(preprocess.state['log'] == True):\n",
+    "#     df_design_transformed, df_results_transformed = preprocess.funcTranform(df_design[species_columns], df_results[species_columns])\n",
     "\n",
-    "df_design_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_design[species]))\n",
-    "df_results_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_results[species]))\n",
+    "df_design_transformed_scaled = preprocess.scaler_X.transform(df_design[species_columns])\n",
+    "df_results_transformed_scaled = preprocess.scaler_y.transform(df_results[species_columns])\n",
     "\n",
     "for i in range(0,iterations):\n",
-    "    idx = i*50*50 + cell_offset -1\n",
+    "    idx = i*50*50 + cell_offset-1\n",
     "    y_design.append(df_design_transformed_scaled.iloc[idx, :])\n",
     "    y_results.append(df_results_transformed_scaled.iloc[idx,:])\n",
     "    \n",
     "y_design = pd.DataFrame(y_design)\n",
     "y_results = pd.DataFrame(y_results)\n",
-    "# plt.plot(np.arange(0,iterations), y_design[species], label = \"Design\")\n",
-    "plt.plot(np.arange(0,iterations), y_results[species], label = \"Results\")\n",
     "\n",
     "prediction = model_simple.predict(y_design.iloc[:, y_design.columns != \"Class\"])\n",
     "prediction = pd.DataFrame(prediction, columns = y_results.columns)\n",
     "\n",
-    "y_results_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(y_results), columns=df_results.columns))\n",
-    "prediction_back = FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(prediction), columns=df_results.columns))\n",
+    "# y_results_back, prediction = preprocess.funcInverse(y_results, prediction)\n",
     "\n",
-    "plt.plot(np.arange(0,iterations), prediction[species], label = \"Prediction\")\n",
+    "y_results_back = pd.DataFrame(preprocess.scaler_y.inverse_transform(y_results), columns = species_columns)\n",
+    "prediction_back = pd.DataFrame(preprocess.scaler_X.inverse_transform(prediction), columns = species_columns)\n",
+    "\n",
+    "\n",
+    "plt.plot(np.arange(0,iterations), y_results_back[species], label = \"Results\")\n",
+    "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n",
+    "plt.legend()\n",
     "plt.xlabel('Iteration')\n",
     "plt.ylabel(species)\n",
-    "plt.title(species+' Concentration over Iterations')\n",
+    "plt.title(species+' Concentration over Iterations in cell ' + str(cell_offset))\n",
     "plt.legend()\n",
     "plt.show()\n",
     "\n",
-    "# plt.plot(np.arange(0,iterations), y_results_back[species], label = \"Results\")\n",
-    "# plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n",
-    "# plt.legend()\n",
     "\n",
-    "# plt.show()\n",
+    "mass_balance = np.abs((prediction_back[\"Ba\"] + prediction_back[\"Barite\"]) - (y_results_back[\"Ba\"] + y_results_back[\"Barite\"])) \\\n",
+    "    + np.abs((prediction_back[\"Sr\"] + prediction_back[\"Celestite\"]) - (y_results_back[\"Sr\"] + y_results_back[\"Celestite\"]))\n",
+    "plt.plot(np.arange(0,iterations), mass_balance, label = \"Results\")\n",
+    "plt.xlabel('Iteration')\n",
+    "plt.ylabel(species)\n",
+    "plt.title(species+' Absolute Differences between predictions and true values Iterations in cell ' + str(cell_offset))\n",
+    "plt.axhline(y=1e-5, color='r', linestyle='--', label='Threshold 1e-5')\n",
+    "plt.legend()\n",
     "\n",
-    "timestep = 1000\n",
-    "plt.imshow(np.array(df_results[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')"
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1481,11 +1114,8 @@
        "      H\n",
        "      O\n",
        "      Charge\n",
-       "      H_0_\n",
-       "      O_0_\n",
        "      Ba\n",
        "      Cl\n",
-       "      S_2_\n",
        "      S_6_\n",
        "      Sr\n",
        "      Barite\n",
@@ -1496,77 +1126,62 @@
        "    \n",
        "      0\n",
        "      111.012434\n",
-       "      55.508700\n",
-       "      -1.216415e-09\n",
-       "      0.0\n",
-       "      2.217711e-13\n",
-       "      4.495355e-07\n",
-       "      1.532249e-12\n",
-       "      0.0\n",
-       "      0.000621\n",
-       "      0.000620\n",
-       "      0.001000\n",
-       "      1.000000\n",
+       "      55.510420\n",
+       "      -5.285676e-07\n",
+       "      4.536952e-07\n",
+       "      0.000022\n",
+       "      1.050707e-03\n",
+       "      0.000625\n",
+       "      0.001010\n",
+       "      1.717461\n",
        "    \n",
        "    \n",
        "      1\n",
        "      111.012434\n",
-       "      55.508700\n",
-       "      -1.222504e-09\n",
-       "      0.0\n",
-       "      1.312902e-12\n",
-       "      4.500359e-07\n",
-       "      1.044603e-08\n",
-       "      0.0\n",
-       "      0.000621\n",
-       "      0.000620\n",
-       "      0.001000\n",
-       "      1.000000\n",
+       "      55.507697\n",
+       "      -5.292985e-07\n",
+       "      1.091671e-06\n",
+       "      0.002399\n",
+       "      3.700427e-04\n",
+       "      0.001488\n",
+       "      0.001738\n",
+       "      1.716139\n",
        "    \n",
        "    \n",
        "      2\n",
        "      111.012434\n",
-       "      55.508699\n",
-       "      -1.220407e-09\n",
-       "      0.0\n",
-       "      1.614098e-12\n",
-       "      4.500563e-07\n",
-       "      4.907802e-07\n",
-       "      0.0\n",
-       "      0.000621\n",
-       "      0.000620\n",
-       "      0.001000\n",
-       "      1.000000\n",
+       "      55.506335\n",
+       "      -5.311407e-07\n",
+       "      6.816584e-05\n",
+       "      0.008922\n",
+       "      2.946349e-05\n",
+       "      0.004445\n",
+       "      0.004898\n",
+       "      1.708478\n",
        "    \n",
        "    \n",
        "      3\n",
        "      111.012434\n",
-       "      55.508695\n",
-       "      -1.216831e-09\n",
-       "      0.0\n",
-       "      2.293739e-12\n",
-       "      4.504482e-07\n",
-       "      4.772370e-06\n",
-       "      0.0\n",
-       "      0.000620\n",
-       "      0.000622\n",
-       "      0.001000\n",
-       "      1.000000\n",
+       "      55.506229\n",
+       "      -5.326179e-07\n",
+       "      1.435037e-03\n",
+       "      0.017414\n",
+       "      3.035681e-06\n",
+       "      0.007281\n",
+       "      0.008778\n",
+       "      1.698481\n",
        "    \n",
        "    \n",
        "      4\n",
        "      111.012434\n",
-       "      55.508679\n",
-       "      -1.216842e-09\n",
-       "      0.0\n",
-       "      2.641545e-12\n",
-       "      4.534070e-07\n",
-       "      2.200220e-05\n",
-       "      0.0\n",
-       "      0.000616\n",
-       "      0.000626\n",
-       "      0.001000\n",
-       "      1.000000\n",
+       "      55.506224\n",
+       "      -5.354202e-07\n",
+       "      3.264876e-03\n",
+       "      0.026235\n",
+       "      1.872898e-06\n",
+       "      0.009764\n",
+       "      0.012641\n",
+       "      1.688408\n",
        "    \n",
        "    \n",
        "      ...\n",
@@ -1579,377 +1194,109 @@
        "      ...\n",
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        "      995\n",
        "      111.012434\n",
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-       "      1.550089e-04\n",
-       "      9.784038e-02\n",
-       "      0.0\n",
-       "      0.000048\n",
-       "      0.048814\n",
-       "      0.149476\n",
-       "      0.846327\n",
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+       "      -5.369526e-07\n",
+       "      6.381593e-02\n",
+       "      0.223770\n",
+       "      1.220403e-07\n",
+       "      0.032096\n",
+       "      1.714723\n",
+       "      0.000000\n",
        "    \n",
        "    \n",
        "      996\n",
        "      111.012434\n",
-       "      55.506410\n",
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-       "      0.0\n",
-       "      4.808612e-11\n",
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-       "      0.000048\n",
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+       "      1.714723\n",
+       "      0.000000\n",
        "    \n",
        "    \n",
        "      997\n",
        "      111.012434\n",
-       "      55.506409\n",
-       "      2.162167e-08\n",
-       "      0.0\n",
-       "      4.811205e-11\n",
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-       "      0.000048\n",
-       "      0.048829\n",
-       "      0.153945\n",
-       "      0.841856\n",
+       "      55.506217\n",
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+       "      6.391481e-02\n",
+       "      0.223807\n",
+       "      1.219644e-07\n",
+       "      0.032017\n",
+       "      1.714723\n",
+       "      0.000000\n",
        "    \n",
        "    \n",
        "      998\n",
        "      111.012434\n",
-       "      55.506409\n",
-       "      2.157995e-08\n",
-       "      0.0\n",
-       "      4.815004e-11\n",
-       "      1.574812e-04\n",
-       "      9.789167e-02\n",
-       "      0.0\n",
-       "      0.000048\n",
-       "      0.048836\n",
-       "      0.156185\n",
-       "      0.839614\n",
+       "      55.506217\n",
+       "      -5.372196e-07\n",
+       "      6.395922e-02\n",
+       "      0.223826\n",
+       "      1.219672e-07\n",
+       "      0.031982\n",
+       "      1.714723\n",
+       "      0.000000\n",
        "    \n",
        "    \n",
        "      999\n",
        "      111.012434\n",
-       "      55.506409\n",
-       "      2.153938e-08\n",
-       "      0.0\n",
-       "      4.815067e-11\n",
-       "      1.581835e-04\n",
-       "      9.790872e-02\n",
-       "      0.0\n",
-       "      0.000048\n",
-       "      0.048844\n",
-       "      0.158428\n",
-       "      0.837370\n",
+       "      55.506217\n",
+       "      -5.372770e-07\n",
+       "      6.400057e-02\n",
+       "      0.223844\n",
+       "      1.220142e-07\n",
+       "      0.031950\n",
+       "      1.714723\n",
+       "      0.000000\n",
        "    \n",
        "  \n",
        "\n",
-       "
1000 rows × 12 columns
\n",
+       "
1000 rows × 9 columns
\n",
        ""
       ],
       "text/plain": [
-       "              H          O        Charge  H_0_          O_0_            Ba  \\\n",
-       "0    111.012434  55.508700 -1.216415e-09   0.0  2.217711e-13  4.495355e-07   \n",
-       "1    111.012434  55.508700 -1.222504e-09   0.0  1.312902e-12  4.500359e-07   \n",
-       "2    111.012434  55.508699 -1.220407e-09   0.0  1.614098e-12  4.500563e-07   \n",
-       "3    111.012434  55.508695 -1.216831e-09   0.0  2.293739e-12  4.504482e-07   \n",
-       "4    111.012434  55.508679 -1.216842e-09   0.0  2.641545e-12  4.534070e-07   \n",
-       "..          ...        ...           ...   ...           ...           ...   \n",
-       "995  111.012434  55.506410  2.170844e-08   0.0  4.777415e-11  1.550089e-04   \n",
-       "996  111.012434  55.506410  2.166504e-08   0.0  4.808612e-11  1.559012e-04   \n",
-       "997  111.012434  55.506409  2.162167e-08   0.0  4.811205e-11  1.567226e-04   \n",
-       "998  111.012434  55.506409  2.157995e-08   0.0  4.815004e-11  1.574812e-04   \n",
-       "999  111.012434  55.506409  2.153938e-08   0.0  4.815067e-11  1.581835e-04   \n",
+       "              H          O        Charge            Ba        Cl  \\\n",
+       "0    111.012434  55.510420 -5.285676e-07  4.536952e-07  0.000022   \n",
+       "1    111.012434  55.507697 -5.292985e-07  1.091671e-06  0.002399   \n",
+       "2    111.012434  55.506335 -5.311407e-07  6.816584e-05  0.008922   \n",
+       "3    111.012434  55.506229 -5.326179e-07  1.435037e-03  0.017414   \n",
+       "4    111.012434  55.506224 -5.354202e-07  3.264876e-03  0.026235   \n",
+       "..          ...        ...           ...           ...       ...   \n",
+       "995  111.012434  55.506217 -5.369526e-07  6.381593e-02  0.223770   \n",
+       "996  111.012434  55.506217 -5.370535e-07  6.386712e-02  0.223789   \n",
+       "997  111.012434  55.506217 -5.371457e-07  6.391481e-02  0.223807   \n",
+       "998  111.012434  55.506217 -5.372196e-07  6.395922e-02  0.223826   \n",
+       "999  111.012434  55.506217 -5.372770e-07  6.400057e-02  0.223844   \n",
        "\n",
-       "               Cl  S_2_      S_6_        Sr    Barite  Celestite  \n",
-       "0    1.532249e-12   0.0  0.000621  0.000620  0.001000   1.000000  \n",
-       "1    1.044603e-08   0.0  0.000621  0.000620  0.001000   1.000000  \n",
-       "2    4.907802e-07   0.0  0.000621  0.000620  0.001000   1.000000  \n",
-       "3    4.772370e-06   0.0  0.000620  0.000622  0.001000   1.000000  \n",
-       "4    2.200220e-05   0.0  0.000616  0.000626  0.001000   1.000000  \n",
-       "..            ...   ...       ...       ...       ...        ...  \n",
-       "995  9.784038e-02   0.0  0.000048  0.048814  0.149476   0.846327  \n",
-       "996  9.785750e-02   0.0  0.000048  0.048821  0.151708   0.844093  \n",
-       "997  9.787459e-02   0.0  0.000048  0.048829  0.153945   0.841856  \n",
-       "998  9.789167e-02   0.0  0.000048  0.048836  0.156185   0.839614  \n",
-       "999  9.790872e-02   0.0  0.000048  0.048844  0.158428   0.837370  \n",
+       "             S_6_        Sr    Barite  Celestite  \n",
+       "0    1.050707e-03  0.000625  0.001010   1.717461  \n",
+       "1    3.700427e-04  0.001488  0.001738   1.716139  \n",
+       "2    2.946349e-05  0.004445  0.004898   1.708478  \n",
+       "3    3.035681e-06  0.007281  0.008778   1.698481  \n",
+       "4    1.872898e-06  0.009764  0.012641   1.688408  \n",
+       "..            ...       ...       ...        ...  \n",
+       "995  1.220403e-07  0.032096  1.714723   0.000000  \n",
+       "996  1.220029e-07  0.032055  1.714723   0.000000  \n",
+       "997  1.219644e-07  0.032017  1.714723   0.000000  \n",
+       "998  1.219672e-07  0.031982  1.714723   0.000000  \n",
+       "999  1.220142e-07  0.031950  1.714723   0.000000  \n",
        "\n",
-       "[1000 rows x 12 columns]"
+       "[1000 rows x 9 columns]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(y_results), columns=df_results.columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
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-       "
HOChargeH_0_O_0_BaClS_2_S_6_SrBariteCelestite
0111.01242855.508682-1.193141e-093.397941e-152.128961e-13-0.0000120.000021-1.191799e-170.0006200.0006300.0009851.000231
1111.01242855.508682-1.202185e-092.918474e-151.019832e-12-0.0000160.000008-7.920033e-180.0006200.0006300.0009461.000121
2111.01242855.508682-1.203471e-091.785468e-152.398433e-12-0.000015-0.000013-9.158671e-180.0006200.0006270.0009130.999977
3111.01242855.508682-1.199235e-091.746077e-152.357316e-12-0.000016-0.000011-9.246642e-180.0006190.0006290.0009160.999936
4111.01242855.508682-1.197043e-091.533956e-152.670053e-12-0.0000190.000003-9.144569e-180.0006150.0006350.0009430.999769
.......................................
995111.01242855.5064162.189995e-08-3.792428e-154.785985e-110.0002790.0976421.182626e-160.0000510.0487380.1490160.844585
996111.01242855.5064162.188781e-08-3.910681e-154.822730e-110.0002790.0976651.169111e-160.0000510.0487510.1512410.842272
997111.01242855.5064162.184875e-08-3.749360e-154.824932e-110.0002790.0976871.146696e-160.0000510.0487610.1534770.840015
998111.01242855.5064162.180415e-08-3.500642e-154.816323e-110.0002790.0977101.119656e-160.0000510.0487700.1557200.837773
999111.01242855.5064162.177183e-08-3.375572e-154.822685e-110.0002790.0977321.095921e-160.0000500.0487800.1579620.835504
\n",
-       "
1000 rows × 12 columns
\n",
-       "
"
-      ],
-      "text/plain": [
-       "              H          O        Charge          H_0_          O_0_  \\\n",
-       "0    111.012428  55.508682 -1.193141e-09  3.397941e-15  2.128961e-13   \n",
-       "1    111.012428  55.508682 -1.202185e-09  2.918474e-15  1.019832e-12   \n",
-       "2    111.012428  55.508682 -1.203471e-09  1.785468e-15  2.398433e-12   \n",
-       "3    111.012428  55.508682 -1.199235e-09  1.746077e-15  2.357316e-12   \n",
-       "4    111.012428  55.508682 -1.197043e-09  1.533956e-15  2.670053e-12   \n",
-       "..          ...        ...           ...           ...           ...   \n",
-       "995  111.012428  55.506416  2.189995e-08 -3.792428e-15  4.785985e-11   \n",
-       "996  111.012428  55.506416  2.188781e-08 -3.910681e-15  4.822730e-11   \n",
-       "997  111.012428  55.506416  2.184875e-08 -3.749360e-15  4.824932e-11   \n",
-       "998  111.012428  55.506416  2.180415e-08 -3.500642e-15  4.816323e-11   \n",
-       "999  111.012428  55.506416  2.177183e-08 -3.375572e-15  4.822685e-11   \n",
-       "\n",
-       "           Ba        Cl          S_2_      S_6_        Sr    Barite  Celestite  \n",
-       "0   -0.000012  0.000021 -1.191799e-17  0.000620  0.000630  0.000985   1.000231  \n",
-       "1   -0.000016  0.000008 -7.920033e-18  0.000620  0.000630  0.000946   1.000121  \n",
-       "2   -0.000015 -0.000013 -9.158671e-18  0.000620  0.000627  0.000913   0.999977  \n",
-       "3   -0.000016 -0.000011 -9.246642e-18  0.000619  0.000629  0.000916   0.999936  \n",
-       "4   -0.000019  0.000003 -9.144569e-18  0.000615  0.000635  0.000943   0.999769  \n",
-       "..        ...       ...           ...       ...       ...       ...        ...  \n",
-       "995  0.000279  0.097642  1.182626e-16  0.000051  0.048738  0.149016   0.844585  \n",
-       "996  0.000279  0.097665  1.169111e-16  0.000051  0.048751  0.151241   0.842272  \n",
-       "997  0.000279  0.097687  1.146696e-16  0.000051  0.048761  0.153477   0.840015  \n",
-       "998  0.000279  0.097710  1.119656e-16  0.000051  0.048770  0.155720   0.837773  \n",
-       "999  0.000279  0.097732  1.095921e-16  0.000050  0.048780  0.157962   0.835504  \n",
-       "\n",
-       "[1000 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "FuncTransform(func_dict_in, func_dict_out).inverse_transform(pd.DataFrame(scaler_X.inverse_transform(prediction), columns=prediction.columns))"
+    "y"
    ]
   },
   {
@@ -2012,56 +1359,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 1ms/step - loss: 7.4076e-07\n"
+      "\u001b[1m15641/15641\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 351us/step - loss: 5.1847e-07\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "1.0149801710213069e-06"
+       "3.571243496480747e-07"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on all test data\n",
-    "model_large.evaluate(X_test[species_columns], y_test[species_columns])"
+    "model_simple.evaluate(X_test.loc[:, X_test.columns != \"Class\"], y_test.loc[:, y_test.columns != \"Class\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
-     "ename": "IndexError",
-     "evalue": ".iloc requires numeric indexers, got ['H' 'O' 'Charge' 'Ba' 'Cl' 'S_6_' 'Sr' 'Barite' 'Celestite']",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[185], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# test on non-reactive data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m model_large\u001b[38;5;241m.\u001b[39mevaluate(X_test[X_test[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[:,species_columns], y_test[X_test[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mClass\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39miloc[:,species_columns])\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/pandas/core/indexing.py:1184\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1182\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[0;32m-> 1184\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[1;32m   1185\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1186\u001b[0m     \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[1;32m   1187\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/pandas/core/indexing.py:1690\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m   1689\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m-> 1690\u001b[0m     tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n\u001b[1;32m   1691\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[1;32m   1692\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/pandas/core/indexing.py:966\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    964\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(key):\n\u001b[1;32m    965\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 966\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(k, i)\n\u001b[1;32m    967\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[1;32m    968\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    969\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    970\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    971\u001b[0m         ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n",
-      "File \u001b[0;32m~/bin/miniconda3/envs/training/lib/python3.11/site-packages/pandas/core/indexing.py:1608\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1606\u001b[0m \u001b[38;5;66;03m# check that the key has a numeric dtype\u001b[39;00m\n\u001b[1;32m   1607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_numeric_dtype(arr\u001b[38;5;241m.\u001b[39mdtype):\n\u001b[0;32m-> 1608\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.iloc requires numeric indexers, got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marr\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1610\u001b[0m \u001b[38;5;66;03m# check that the key does not exceed the maximum size of the index\u001b[39;00m\n\u001b[1;32m   1611\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(arr) \u001b[38;5;129;01mand\u001b[39;00m (arr\u001b[38;5;241m.\u001b[39mmax() \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmin() \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis):\n",
-      "\u001b[0;31mIndexError\u001b[0m: .iloc requires numeric indexers, got ['H' 'O' 'Charge' 'Ba' 'Cl' 'S_6_' 'Sr' 'Barite' 'Celestite']"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m15452/15452\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 385us/step - loss: 5.2313e-07\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.601293485644419e-07"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on non-reactive data\n",
-    "model_large.evaluate(X_test[X_test['Class'] == 0].iloc[:,species_columns], y_test[X_test['Class'] == 0].iloc[:,species_columns])"
+    "model_simple.evaluate(X_test[X_test['Class'] == 0].iloc[:,X_test.columns != \"Class\"], y_test[X_test['Class'] == 0].iloc[:, y_test.columns != \"Class\"])"
    ]
   },
   {
@@ -2083,23 +1432,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1m189/189\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 393us/step - loss: 1.2226e-07\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "4.047931361128576e-05"
+       "1.1114495634956256e-07"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on reactive data\n",
-    "model_large.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1].iloc[:, :-1])"
+    "model_simple.evaluate(X_test[X_test['Class'] == 1].iloc[:,:-1], y_test[X_test['Class'] == 1].iloc[:, :-1])"
    ]
   },
   {
@@ -2118,82 +1474,6 @@
     "# Save the model\n",
     "model.save(\"Barite_50_Model_additional_species.keras\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Legacy Code"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "def log_scale(df_design, df_result, func_dict):\n",
-    "    \n",
-    "    df_design = df_design.copy()\n",
-    "    df_result = df_result.copy()\n",
-    "    \n",
-    "    for key in df_design.keys():\n",
-    "        if key != \"Class\":\n",
-    "            df_design[key] = np.vectorize(func_dict[key])(df_design[key])\n",
-    "            df_result[key] = np.vectorize(func_dict[key])(df_result[key])\n",
-    "        \n",
-    "    return df_design, df_result\n",
-    "\n",
-    "# Get minimum and maximum values for each column\n",
-    "def get_min_max(df_design, df_result):\n",
-    "    \n",
-    "    min_vals_des = df_design.min()\n",
-    "    max_vals_des = df_design.max()\n",
-    "    \n",
-    "    min_vals_res = df_result.min()\n",
-    "    max_vals_res = df_result.max()\n",
-    "\n",
-    "    # minimum of input and output data to get global minimum/maximum\n",
-    "    data_min = np.minimum(min_vals_des, min_vals_res).to_dict()\n",
-    "    data_max = np.maximum(max_vals_des, max_vals_res).to_dict()\n",
-    "\n",
-    "    return data_min, data_max\n",
-    "\n",
-    "df_design_log, df_results_log = log_scale(df_design, df_results, func_dict_in)\n",
-    "data_min_log, data_max_log = get_min_max(df_design_log, df_design_log)\n",
-    "\n",
-    "train_min_log, train_max_log = get_min_max(X_train_log, y_train_log)\n",
-    "test_min_log, test_max_log = get_min_max(X_test_log, y_test_log)\n",
-    "\n",
-    "X_train_preprocess = preprocess(X_train_log, func_dict_in, train_min_log, train_max_log)\n",
-    "y_train_preprocess = preprocess(y_train_log, func_dict_in, train_min_log, train_max_log)\n",
-    "\n",
-    "X_test_preprocess = preprocess(X_test_log, func_dict_in, test_min_log, test_max_log)\n",
-    "y_test_preprocess = preprocess(y_test_log, func_dict_in, test_min_log, test_max_log)\n",
-    "\n",
-    "X_train_log, y_train_log = log_scale(X_train, y_train, func_dict_in)\n",
-    "X_test_log, y_test_log = log_scale(X_test, y_test, func_dict_in)\n",
-    "\n",
-    "\n",
-    "def preprocess(data, func_dict, data_min, data_max):\n",
-    "    data = data.copy()\n",
-    "    for key in data.keys():\n",
-    "        if key != \"Class\":\n",
-    "            data[key] = (data[key] - data_min[key]) / (data_max[key] - data_min[key])\n",
-    "\n",
-    "    return data\n",
-    "\n",
-    "def postprocess(data, func_dict, data_min, data_max):\n",
-    "    data = data.copy()\n",
-    "    for key in data.keys():\n",
-    "        if key != \"Class\":\n",
-    "            data[key] = data[key] * (data_max[key] - data_min[key]) + data_min[key]\n",
-    "            data[key] = np.vectorize(func_dict[key])(data[key])\n",
-    "    return data\n",
-    "\n",
-    "X_train, X_val, y_train, y_val = sk.train_test_split(X_train_preprocess, y_train_preprocess, test_size = 0.1)\n",
-    "\n",
-    "pp_design = preprocess(df_design_log, func_dict_in, data_min_log, data_max_log)\n",
-    "pp_results = preprocess(df_results_log, func_dict_in, data_min_log, data_max_log)"
-   ]
   }
  ],
  "metadata": {
diff --git a/__pycache__/preprocessing.cpython-311.pyc b/__pycache__/preprocessing.cpython-311.pyc
new file mode 100644
index 0000000..ebe217b
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diff --git a/loss_1_to_end.png b/loss_1_to_end.png
new file mode 100644
index 0000000..fe5b726
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diff --git a/loss_all.png b/loss_all.png
new file mode 100644
index 0000000..9b45cb2
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diff --git a/model_large.keras b/model_large.keras
new file mode 100644
index 0000000..8bfca12
Binary files /dev/null and b/model_large.keras differ
diff --git a/preprocessing.py b/preprocessing.py
index 813f619..4597334 100644
--- a/preprocessing.py
+++ b/preprocessing.py
@@ -190,50 +190,143 @@ def preprocessing_training(df_design, df_targets, func_dict_in, func_dict_out, s
     
 class preprocessing:
 
-    def __init__(self, df_design, df_targets, random_state=42):
-        self.X = df_design
-        self.y = df_targets
+    def __init__(self, func_dict_in, func_dict_out, random_state=42):
         self.random_state = random_state
+        self.scaler_X = None
+        self.scaler_y = None
+        self.func_dict_in = func_dict_in
+        self.func_dict_out = func_dict_out
         self.state = {"cluster": False, "log": False, "balance": False, "scale": False}
         
-    def funcTranform(self, func_dict_in):
-        for key in self.X.keys():   
+    def funcTranform(self, X, y):
+        for key in X.keys():   
             if "Class" not in key:
-                self.X[key] = self.X[key].apply(func_dict_in[key])
-                self.y[key] = self.y[key].apply(func_dict_in[key])
+                X[key] = X[key].apply(self.func_dict_in[key])
+                y[key] = y[key].apply(self.func_dict_in[key])
         self.state["log"] = True
         
-    def funcInverse(self, func_dict_out):
-    
-        if(self.state["log"] == False):
-            raise Exception("Data has to be transformed first")
-        for key in self.X.keys():
-            if "Class" not in key:
-                self.X[key] = self.X[key].apply(func_dict_out[key])
-                self.y[key] = self.y[key].apply(func_dict_out[key])
-                
-    def cluster(self, species='Barite', n_clusters=2, x_length=50, y_length=50):
+        return X, y
+        
+    def funcInverse(self, X, y):
+    
+        for key in X.keys():
+            if "Class" not in key:
+                X[key] = X[key].apply(self.func_dict_out[key])
+                y[key] = y[key].apply(self.func_dict_out[key])
+        self.state["log"] = False
+        return X, y
+        
+    def cluster(self, X, y, species='Barite', n_clusters=2, x_length=50, y_length=50):
         
-        if(self.state["log"] == False):
-            raise Exception("Data has to be transformed first")
         class_labels = np.array([])
         grid_length = x_length * y_length
-        iterations = int(len(self.X) / grid_length)
+        iterations = int(len(X) / grid_length)
 
         for i in range(0, iterations):
-            field = np.array(self.X['Barite'][(i*grid_length):(i*grid_length+grid_length)]
+            field = np.array(X[species][(i*grid_length):(i*grid_length+grid_length)]
                          ).reshape(x_length, y_length)
             kmeans = KMeans(n_clusters=n_clusters, random_state=self.random_state).fit(field.reshape(-1, 1))
+            class_labels = np.append(class_labels.astype(int), kmeans.labels_)
 
-        class_labels = np.append(class_labels.astype(int), kmeans.labels_)
-
-        if ("Class" in self.X.columns and "Class" in self.y.columns):
+        if ("Class" in X.columns and "Class" in y.columns):
             print("Class column already exists")
         else:
             class_labels_df = pd.DataFrame(class_labels, columns=['Class'])
-            self.X = pd.concat([self.X, class_labels_df], axis=1)
-            self.y = pd.concat([self.y, class_labels_df], axis=1)
+            X = pd.concat([X, class_labels_df], axis=1)
+            y = pd.concat([y, class_labels_df], axis=1)
             self.state["cluster"] = True
             
+        return X, y
+            
+            
+    def balancer(self, X, y, strategy, sample_fraction=0.5):
+        
+        number_features = (X.columns != "Class").sum()
+        if("Class" not in X.columns):
+            if("Class" in y.columns):
+                classes = y['Class']
+            else:
+                raise Exception("No class column found")
+        else:
+            classes = X['Class']
+            counter = classes.value_counts()
+            print("Amount class 0 before:", counter[0] / (counter[0] + counter[1]) )
+            print("Amount class 1 before:", counter[1] / (counter[0] + counter[1]) )
+            df = pd.concat([X.loc[:,X.columns != "Class"], y.loc[:, y.columns != "Class"],  classes], axis=1)
+
+        if strategy == 'smote':
+            print("Using SMOTE strategy")
+            smote = SMOTE(sampling_strategy=sample_fraction)
+            df_resampled, classes_resampled = smote.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df.    columns == "Class"])
+
+        elif strategy == 'over':
+            print("Using Oversampling")
+            over = RandomOverSampler()
+            df_resampled, classes_resampled = over.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df. columns == "Class"])
+
+        elif strategy == 'under':
+            print("Using Undersampling")
+            under = RandomUnderSampler()
+            df_resampled, classes_resampled = under.fit_resample(df.loc[:, df.columns != "Class"], df.loc[:, df.    columns == "Class"])
+
+        else:
+            return X, y
+
+        counter = classes_resampled["Class"].value_counts()
+        print("Amount class 0 after:", counter[0] / (counter[0] + counter[1]) )
+        print("Amount class 1 after:", counter[1] / (counter[0] + counter[1]) )
+
+        design_resampled = pd.concat([df_resampled.iloc[:,0:number_features], classes_resampled], axis=1)
+        target_resampled = pd.concat([df_resampled.iloc[:,number_features:], classes_resampled], axis=1)
+
+        self.state['balance'] = True
+        return design_resampled, target_resampled
+    
+    
+    def scale_fit(self, X, y, scaling):
+        
+        if scaling == 'individual':
+            self.scaler_X = MinMaxScaler()
+            self.scaler_y = MinMaxScaler()
+            self.scaler_X.fit(X.iloc[:, X.columns != "Class"])
+            self.scaler_y.fit(y.iloc[:, y.columns != "Class"])
+            
+        elif scaling == 'global':
+            self.scaler_X = MinMaxScaler()
+            self.scaler_X.fit(pd.concat([X.iloc[:, X.columns != "Class"], y.iloc[:, y.columns != "Class"]], axis=0))
+            self.scaler_y = self.scaler_X
+            
+        self.state['scale'] = True
+        
+    def scale_transform(self, X_train, X_test, y_train, y_test):
+        X_train = pd.concat([self.scaler_X.transform(X_train.loc[:, X_train.columns != "Class"]), X_train.loc[:, "Class"]], axis=1)
+        
+        X_test = pd.concat([self.scaler_X.transform(X_test.loc[:, X_test.columns != "Class"]), X_test.loc[:, "Class"]], axis=1)
+
+        y_train = pd.concat([self.scaler_y.transform(y_train.loc[:, y_train.columns != "Class"]), y_train.loc[:, "Class"]], axis=1)
+        
+        y_test = pd.concat([self.scaler_y.transform(y_test.loc[:, y_test.columns != "Class"]), y_test.loc[:, "Class"]], axis=1)
+        
+        return X_train, X_test, y_train, y_test
+    
+    def scale_inverse(self, X):
+        
+        if("Class" in X.columns):
+            X = pd.concat([self.scaler_X.inverse_transform(X.loc[:, X.columns != "Class"]), X.loc[:, "Class"]], axis=1)
+        else:
+            X = self.scaler_X.inverse_transform(X)  
+        
+        return X
+          
+    def split(self, X, y, ratio=0.8):
+        X_train, y_train, X_test, y_test = sk.train_test_split(X, y, test_size = ratio, random_state=self.random_state)
+        
+        return X_train, y_train, X_test, y_test
+    
+    
+
+
+
+
 
    
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