diff --git a/POET_Training.ipynb b/POET_Training.ipynb
index 22262c4..99e33c2 100644
--- a/POET_Training.ipynb
+++ b/POET_Training.ipynb
@@ -27,28 +27,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 197,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "2025-01-23 14:37:53.766781: 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",
-      "2025-01-23 14:37:53.786741: 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 3.8.0\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import keras\n",
+    "import tensorflow as tf\n",
     "import h5py\n",
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -71,9 +55,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 136,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -88,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +104,7 @@
     "optimizer_paper = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
     "\n",
     "\n",
-    "loss = keras.losses.MeanSquaredError()\n",
+    "loss = keras.losses.Huber()\n",
     "\n",
     "sample_fraction = 0.8"
    ]
@@ -125,17 +118,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 150,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_7\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_7\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -147,11 +140,11 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> │\n",
+       "│ dense_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,280</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
+       "│ dense_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,548</span> │\n",
+       "│ dense_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,161</span> │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "</pre>\n"
       ],
@@ -159,11 +152,11 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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_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",
+       "│ 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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_8 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m1,548\u001b[0m │\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",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -173,11 +166,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,724</span> (77.05 KB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,953</span> (74.04 KB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,953\u001b[0m (74.04 KB)\n"
       ]
      },
      "metadata": {},
@@ -186,11 +179,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,724</span> (77.05 KB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">18,953</span> (74.04 KB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,953\u001b[0m (74.04 KB)\n"
       ]
      },
      "metadata": {},
@@ -214,10 +207,10 @@
     "# small model\n",
     "model_simple = keras.Sequential(\n",
     "    [\n",
-    "        keras.Input(shape = (12,), dtype = \"float32\"),\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 = 12, dtype = \"float32\")\n",
+    "        keras.layers.Dense(units = 9, dtype = \"float32\")\n",
     "    ]\n",
     ")\n",
     "\n",
@@ -227,17 +220,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 161,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_8\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_8\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -249,13 +242,13 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,656</span> │\n",
+       "│ dense_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">5,120</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">525,312</span> │\n",
+       "│ dense_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1024</span>)           │       <span style=\"color: #00af00; text-decoration-color: #00af00\">525,312</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</span> │\n",
+       "│ dense_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,800</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">6,156</span> │\n",
+       "│ dense_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)              │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,617</span> │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "</pre>\n"
       ],
@@ -263,13 +256,13 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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_20 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m6,656\u001b[0m │\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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_21 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_22 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_23 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m)             │         \u001b[38;5;34m6,156\u001b[0m │\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",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -279,11 +272,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,062,924</span> (4.05 MB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,059,849</span> (4.04 MB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n"
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,059,849\u001b[0m (4.04 MB)\n"
       ]
      },
      "metadata": {},
@@ -292,11 +285,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,062,924</span> (4.05 MB)\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,059,849</span> (4.04 MB)\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n"
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,059,849\u001b[0m (4.04 MB)\n"
       ]
      },
      "metadata": {},
@@ -319,11 +312,11 @@
    "source": [
     "# large model\n",
     "model_large =   keras.Sequential(\n",
-    "    [keras.layers.Input(shape=(12,), dtype=dtype),\n",
+    "    [keras.layers.Input(shape=(9,), dtype=dtype),\n",
     "     keras.layers.Dense(512, activation='relu', dtype=dtype),\n",
     "     keras.layers.Dense(1024, activation='relu', dtype=dtype),\n",
     "     keras.layers.Dense(512, activation='relu', dtype=dtype),\n",
-    "     keras.layers.Dense(12, dtype=dtype)\n",
+    "     keras.layers.Dense(9, dtype=dtype)\n",
     "     ])\n",
     "\n",
     "model_large.compile(optimizer=optimizer_large, loss = loss)\n",
@@ -332,17 +325,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 140,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
+       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
       ]
      },
      "metadata": {},
@@ -354,15 +347,15 @@
        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
        "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
-       "│ dense_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> │\n",
+       "│ dense_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
+       "│ dense_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">33,024</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,584</span> │\n",
+       "│ dense_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,584</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │\n",
+       "│ dense_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │\n",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,084</span> │\n",
+       "│ dense_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">3,084</span> │\n",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
        "</pre>\n"
       ],
@@ -370,15 +363,15 @@
        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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_24 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_25 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_26 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_27 (\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_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",
        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
-       "│ dense_28 (\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_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",
        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
       ]
      },
@@ -450,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 141,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -474,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 142,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 143,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,14 +547,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "species_columns = [\"H\", \"O\", \"Charge\", \"Ba\", \"Cl\", \"S_6_\", \"Sr\", \"Barite\", \"Celestite\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/hannessigner/miniforge3/envs/ai/lib/python3.12/site-packages/sklearn/base.py:1474: ConvergenceWarning: Number of distinct clusters (1) found smaller than n_clusters (2). Possibly due to duplicate points in X.\n",
+      "/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"
      ]
     },
@@ -575,7 +577,7 @@
     }
    ],
    "source": [
-    "X_train, X_val, X_test, y_train, y_val, y_test, scaler_X, scaler_y = preprocessing_training(df_design, df_results, func_dict_in, func_dict_out, \"over\", 'individual', 0.1)"
+    "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)"
    ]
   },
   {
@@ -587,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 146,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -601,6 +603,39 @@
     "    return loss"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "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))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -610,54 +645,610 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 188,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# measure time\n",
+    "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",
+    "\n",
+    "    end = time.time()\n",
+    "\n",
+    "    print(\"Training took {} seconds\".format(end - start))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>H</th>\n",
+       "      <th>O</th>\n",
+       "      <th>Charge</th>\n",
+       "      <th>H_0_</th>\n",
+       "      <th>O_0_</th>\n",
+       "      <th>Ba</th>\n",
+       "      <th>Cl</th>\n",
+       "      <th>S_2_</th>\n",
+       "      <th>S_6_</th>\n",
+       "      <th>Sr</th>\n",
+       "      <th>Barite</th>\n",
+       "      <th>Celestite</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.506219</td>\n",
+       "      <td>-7.748371e-09</td>\n",
+       "      <td>1.541375e-13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.916106e-02</td>\n",
+       "      <td>4.082138e-02</td>\n",
+       "      <td>1.440295e-20</td>\n",
+       "      <td>6.403132e-07</td>\n",
+       "      <td>0.001250</td>\n",
+       "      <td>0.002250</td>\n",
+       "      <td>0.999244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.506220</td>\n",
+       "      <td>-4.672647e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>6.197818e-14</td>\n",
+       "      <td>9.637903e-03</td>\n",
+       "      <td>2.189133e-02</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>7.449873e-07</td>\n",
+       "      <td>0.001309</td>\n",
+       "      <td>0.002308</td>\n",
+       "      <td>0.999244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508129</td>\n",
+       "      <td>-1.325997e-09</td>\n",
+       "      <td>1.416456e-13</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>5.839037e-07</td>\n",
+       "      <td>5.887491e-04</td>\n",
+       "      <td>2.981540e-17</td>\n",
+       "      <td>4.781732e-04</td>\n",
+       "      <td>0.000772</td>\n",
+       "      <td>0.001294</td>\n",
+       "      <td>0.999847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508676</td>\n",
+       "      <td>-1.223549e-09</td>\n",
+       "      <td>4.449984e-14</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>4.536952e-07</td>\n",
+       "      <td>2.183551e-05</td>\n",
+       "      <td>3.803886e-19</td>\n",
+       "      <td>6.148996e-04</td>\n",
+       "      <td>0.000625</td>\n",
+       "      <td>0.001010</td>\n",
+       "      <td>0.999995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508699</td>\n",
+       "      <td>-1.216518e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>4.098861e-14</td>\n",
+       "      <td>4.496372e-07</td>\n",
+       "      <td>8.098352e-07</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>6.205560e-04</td>\n",
+       "      <td>0.000621</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502495</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507488</td>\n",
+       "      <td>3.573897e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.374167e-10</td>\n",
+       "      <td>9.953042e-07</td>\n",
+       "      <td>2.266555e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>3.178142e-04</td>\n",
+       "      <td>0.001450</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502496</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507501</td>\n",
+       "      <td>3.494199e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.378928e-10</td>\n",
+       "      <td>9.814307e-07</td>\n",
+       "      <td>2.217997e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>3.210423e-04</td>\n",
+       "      <td>0.001429</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502497</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507512</td>\n",
+       "      <td>3.429947e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.376072e-10</td>\n",
+       "      <td>9.704342e-07</td>\n",
+       "      <td>2.179066e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>3.236905e-04</td>\n",
+       "      <td>0.001412</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502498</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507520</td>\n",
+       "      <td>3.381818e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.368903e-10</td>\n",
+       "      <td>9.632999e-07</td>\n",
+       "      <td>2.149820e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>3.257170e-04</td>\n",
+       "      <td>0.001400</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502499</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507525</td>\n",
+       "      <td>3.349044e-09</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>1.378174e-10</td>\n",
+       "      <td>9.563975e-07</td>\n",
+       "      <td>2.129912e-03</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>3.271123e-04</td>\n",
+       "      <td>0.001391</td>\n",
+       "      <td>0.001000</td>\n",
+       "      <td>1.000001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2502500 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "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"
+    }
+   ],
+   "source": [
+    "df_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>H</th>\n",
+       "      <th>O</th>\n",
+       "      <th>Charge</th>\n",
+       "      <th>H_0_</th>\n",
+       "      <th>O_0_</th>\n",
+       "      <th>Ba</th>\n",
+       "      <th>Cl</th>\n",
+       "      <th>S_2_</th>\n",
+       "      <th>S_6_</th>\n",
+       "      <th>Sr</th>\n",
+       "      <th>Barite</th>\n",
+       "      <th>Celestite</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508192</td>\n",
+       "      <td>-7.779554e-09</td>\n",
+       "      <td>2.697041e-26</td>\n",
+       "      <td>2.210590e-15</td>\n",
+       "      <td>2.041069e-02</td>\n",
+       "      <td>4.082138e-02</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000494</td>\n",
+       "      <td>0.000494</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508427</td>\n",
+       "      <td>-4.736083e-09</td>\n",
+       "      <td>1.446346e-26</td>\n",
+       "      <td>2.473481e-15</td>\n",
+       "      <td>1.094567e-02</td>\n",
+       "      <td>2.189133e-02</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000553</td>\n",
+       "      <td>0.000553</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508691</td>\n",
+       "      <td>-1.311169e-09</td>\n",
+       "      <td>3.889826e-28</td>\n",
+       "      <td>2.769320e-15</td>\n",
+       "      <td>2.943745e-04</td>\n",
+       "      <td>5.887491e-04</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000619</td>\n",
+       "      <td>0.000619</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508698</td>\n",
+       "      <td>-1.220023e-09</td>\n",
+       "      <td>1.442658e-29</td>\n",
+       "      <td>2.777193e-15</td>\n",
+       "      <td>1.091776e-05</td>\n",
+       "      <td>2.183551e-05</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.508699</td>\n",
+       "      <td>-1.216643e-09</td>\n",
+       "      <td>5.350528e-31</td>\n",
+       "      <td>2.777485e-15</td>\n",
+       "      <td>4.049176e-07</td>\n",
+       "      <td>8.098352e-07</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.000620</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502495</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507488</td>\n",
+       "      <td>3.573728e-09</td>\n",
+       "      <td>5.424062e-145</td>\n",
+       "      <td>1.375204e-10</td>\n",
+       "      <td>9.953520e-07</td>\n",
+       "      <td>2.266555e-03</td>\n",
+       "      <td>5.509534e-149</td>\n",
+       "      <td>0.000318</td>\n",
+       "      <td>0.001450</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502496</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507501</td>\n",
+       "      <td>3.494007e-09</td>\n",
+       "      <td>2.011675e-146</td>\n",
+       "      <td>1.377139e-10</td>\n",
+       "      <td>9.817216e-07</td>\n",
+       "      <td>2.217997e-03</td>\n",
+       "      <td>2.043375e-150</td>\n",
+       "      <td>0.000321</td>\n",
+       "      <td>0.001429</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502497</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507512</td>\n",
+       "      <td>3.429764e-09</td>\n",
+       "      <td>7.460897e-148</td>\n",
+       "      <td>1.377819e-10</td>\n",
+       "      <td>9.706451e-07</td>\n",
+       "      <td>2.179066e-03</td>\n",
+       "      <td>7.578467e-152</td>\n",
+       "      <td>0.000324</td>\n",
+       "      <td>0.001412</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502498</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507520</td>\n",
+       "      <td>3.381745e-09</td>\n",
+       "      <td>2.767237e-149</td>\n",
+       "      <td>1.371144e-10</td>\n",
+       "      <td>9.621074e-07</td>\n",
+       "      <td>2.149820e-03</td>\n",
+       "      <td>2.810844e-153</td>\n",
+       "      <td>0.000326</td>\n",
+       "      <td>0.001400</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2502499</th>\n",
+       "      <td>111.012434</td>\n",
+       "      <td>55.507525</td>\n",
+       "      <td>3.348864e-09</td>\n",
+       "      <td>5.321610e-151</td>\n",
+       "      <td>1.376026e-10</td>\n",
+       "      <td>9.564401e-07</td>\n",
+       "      <td>2.129912e-03</td>\n",
+       "      <td>5.405468e-155</td>\n",
+       "      <td>0.000327</td>\n",
+       "      <td>0.001391</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>1.000001</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2502500 rows × 12 columns</p>\n",
+       "</div>"
+      ],
+      "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,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 0.0018 - val_loss: 3.6601e-05\n",
-      "Epoch 2/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.6899e-05 - val_loss: 3.6822e-05\n",
-      "Epoch 3/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.5005e-05 - val_loss: 3.5655e-05\n",
-      "Epoch 4/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.4032e-05 - val_loss: 3.3455e-05\n",
-      "Epoch 5/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.3279e-05 - val_loss: 3.3064e-05\n",
-      "Epoch 6/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.3023e-05 - val_loss: 3.3338e-05\n",
-      "Epoch 7/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2532e-05 - val_loss: 3.2765e-05\n",
-      "Epoch 8/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2749e-05 - val_loss: 3.2730e-05\n",
-      "Epoch 9/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2961e-05 - val_loss: 3.2593e-05\n",
-      "Epoch 10/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2573e-05 - val_loss: 3.2576e-05\n",
-      "Epoch 11/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 2ms/step - loss: 3.2442e-05 - val_loss: 3.2507e-05\n",
-      "Epoch 12/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2135e-05 - val_loss: 3.2548e-05\n",
-      "Epoch 13/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2451e-05 - val_loss: 3.2482e-05\n",
-      "Epoch 14/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2296e-05 - val_loss: 3.2475e-05\n",
-      "Epoch 15/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2081e-05 - val_loss: 3.2470e-05\n",
-      "Epoch 16/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2440e-05 - val_loss: 3.2471e-05\n",
-      "Epoch 17/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2050e-05 - val_loss: 3.2460e-05\n",
-      "Epoch 18/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 2ms/step - loss: 3.2444e-05 - val_loss: 3.2452e-05\n",
-      "Epoch 19/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2259e-05 - val_loss: 3.2452e-05\n",
-      "Epoch 20/20\n",
-      "\u001b[1m7823/7823\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 2ms/step - loss: 3.2442e-05 - val_loss: 3.2448e-05\n",
-      "Training took 276.5459449291229 seconds\n"
+      "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.<locals>.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.<locals>.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."
      ]
     }
    ],
@@ -665,63 +1256,168 @@
     "model_training(model_simple)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Test Mass Balance"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mass_balance(model, X, scaler_X, scaler_y, func_dict_in, func_dict_out):\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",
+    "    \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",
+    "        \n",
+    "    # calculate mass balance\n",
+    "    dBa = np.abs((prediction[\"Ba\"] + prediction[\"Barite\"]) - (X[\"Ba\"] + X[\"Barite\"]))\n",
+    "    dSr = np.abs((prediction[\"Sr\"] + prediction[\"Celestite\"]) - (X[\"Sr\"] + X[\"Celestite\"]))\n",
+    "    \n",
+    "    return dBa + dSr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step \n"
+      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 328us/step\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "columns = X_test.columns[X_test.columns != \"Class\"]\n",
+    "prediction = pd.DataFrame(model_simple.predict(X_test[columns]), columns = columns)"
+   ]
+  },
+  {
+   "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,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "0.022793206793206792"
       ]
      },
+     "execution_count": 176,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mass_balance_results[mass_balance_results < 1e-5]) / len(mass_balance_results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fa3db2b7010>"
+       "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"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 169,
      "metadata": {},
-     "output_type": "display_data"
-    },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mass_balance_results[mass_balance_results < 1e-5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Percentage of cells that pass the mass balance condition\n",
+    "\n",
+    "small_modell_20_epochs = 0.0031088911088911087\n",
+    "\n",
+    "large_modell_20_epochs = 0.022793206793206792"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {},
+   "outputs": [
     {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "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"
+     ]
     }
    ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "species = \"O\"\n",
+    "species = \"Barite\"\n",
     "iterations = 1000\n",
-    "cell_offset = 60\n",
+    "cell_offset = 9\n",
     "y_design = []\n",
     "y_results = []\n",
     "y_differences = []\n",
     "\n",
     "\n",
-    "df_design_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_design))\n",
-    "df_results_transformed_scaled = scaler_X.transform(FuncTransform(func_dict_in, func_dict_out).fit_transform(df_results))\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",
     "\n",
     "for i in range(0,iterations):\n",
     "    idx = i*50*50 + cell_offset -1\n",
@@ -743,12 +1439,14 @@
     "plt.xlabel('Iteration')\n",
     "plt.ylabel(species)\n",
     "plt.title(species+' Concentration over Iterations')\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.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",
+    "# plt.show()\n",
     "\n",
     "timestep = 1000\n",
     "plt.imshow(np.array(df_results[\"Barite\"][(timestep*2500):(timestep*2500+2500)]).reshape(50,50), origin='lower')"
@@ -1314,65 +2012,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 184,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[1m  20/7821\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m44s\u001b[0m 6ms/step - loss: 5.1914e-06"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 4ms/step - loss: 1.0395e-06\n"
+      "\u001b[1m7821/7821\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 1ms/step - loss: 7.4076e-07\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "9.875676596493577e-07"
+       "1.0149801710213069e-06"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on all test data\n",
-    "model_large.evaluate(X_test.iloc[:,X_test.columns != \"Class\"], y_test.iloc[:, y_test.columns != \"Class\"])"
+    "model_large.evaluate(X_test[species_columns], y_test[species_columns])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 185,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[1m7727/7727\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 4ms/step - loss: 5.4493e-07\n"
+     "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']"
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "5.075861508885282e-07"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
     "# test on non-reactive data\n",
-    "model_large.evaluate(X_test[X_test['Class'] == 0].iloc[:,:-1], y_test[X_test['Class'] == 0].iloc[:,:-1])"
+    "model_large.evaluate(X_test[X_test['Class'] == 0].iloc[:,species_columns], y_test[X_test['Class'] == 0].iloc[:,species_columns])"
    ]
   },
   {
@@ -1509,7 +2198,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ai",
+   "display_name": "training",
    "language": "python",
    "name": "python3"
   },
@@ -1523,7 +2212,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.8"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,