model-training/POET_Training.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is used to train a simple neural network model to predict the chemistry in the barite benchmark (50x50 grid). The training data is stored in the repository using **git large file storage** and can be downloaded after the installation of git lfs using the `git lfs pull` command.\n",
    "\n",
    "It is then recommended to create a Python environment using miniconda. The necessary dependencies are contained in `environment.yml` and can be installed using `conda env create -f environment.yml`.\n",
    "\n",
    "The data set is divided into a design and result part and consists of the iterations of a reference simulation. The design part of the data set contains the chemical concentrations at time $t$ and the result part at time $t+1$, which are to be learned by the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 3.6.0\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "import h5py\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import sklearn.model_selection as sk\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from collections import Counter\n",
    "import os\n",
    "from preprocessing import *\n",
    "from sklearn import set_config\n",
    "set_config(transform_output = \"pandas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = \"float32\"\n",
    "activation = \"relu\"\n",
    "\n",
    "lr = 0.001\n",
    "batch_size = 512\n",
    "epochs = 50 # default 400 epochs\n",
    "\n",
    "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate=lr,\n",
    "    decay_steps=2000,\n",
    "    decay_rate=0.9,\n",
    "    staircase=True\n",
    ")\n",
    "\n",
    "optimizer_simple = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
    "optimizer_large = keras.optimizers.Adam(learning_rate=lr_schedule)\n",
    "\n",
    "loss = keras.losses.MeanSquaredError()\n",
    "\n",
    "sample_fraction = 0.8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\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",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\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",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,724\u001b[0m (77.05 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# small model\n",
    "model_simple = keras.Sequential(\n",
    "    [\n",
    "        keras.Input(shape = (12,), 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",
    "    ]\n",
    ")\n",
    "\n",
    "model_simple.compile(optimizer=optimizer_simple, loss = loss)\n",
    "model_simple.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<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_3 (<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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_4 (<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_5 (<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_6 (<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",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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_3 (\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",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\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_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_6 (\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",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,062,924\u001b[0m (4.05 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# large model\n",
    "model_large =   keras.Sequential(\n",
    "    [keras.layers.Input(shape=(12,), 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",
    "     ])\n",
    "\n",
    "model_large.compile(optimizer=optimizer_large, loss = loss)\n",
    "model_large.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model from paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define transformer functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Safelog(val):\n",
    "    # get range of vector\n",
    "    if val > 0:\n",
    "        return np.log10(val)\n",
    "    elif val < 0:\n",
    "        return -np.log10(-val)\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def Safeexp(val):\n",
    "    if val > 0:\n",
    "        return -10 ** -val\n",
    "    elif val < 0:\n",
    "        return 10 ** val\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ? Why does the charge is using another logarithm than the other species\n",
    "\n",
    "func_dict_in = {\n",
    "    \"H\" : np.log1p,\n",
    "    \"O\" : np.log1p,\n",
    "    \"Charge\" : Safelog,\n",
    "    \"H_0_\" : np.log1p,\n",
    "    \"O_0_\" : np.log1p,\n",
    "    \"Ba\" : np.log1p,\n",
    "    \"Cl\" : np.log1p,\n",
    "    \"S_2_\" : np.log1p,\n",
    "    \"S_6_\" : np.log1p,\n",
    "    \"Sr\" : np.log1p,\n",
    "    \"Barite\" : np.log1p,\n",
    "    \"Celestite\" : np.log1p,\n",
    "}\n",
    "\n",
    "func_dict_out = {\n",
    "    \"H\" : np.expm1,\n",
    "    \"O\" : np.expm1,\n",
    "    \"Charge\" : Safeexp,\n",
    "    \"H_0_\" : np.expm1,\n",
    "    \"O_0_\" : np.expm1,\n",
    "    \"Ba\" : np.expm1,\n",
    "    \"Cl\" : np.expm1,\n",
    "    \"S_2_\" : np.expm1,\n",
    "    \"S_6_\" : np.expm1,\n",
    "    \"Sr\" : np.expm1,\n",
    "    \"Barite\" : np.expm1,\n",
    "    \"Celestite\" : np.expm1,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data from `.h5` file and convert it to a `pandas.DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# os.chdir('/mnt/beegfs/home/signer/projects/model-training')\n",
    "data_file = h5py.File(\"Barite_50_Data_training.h5\")\n",
    "\n",
    "design = data_file[\"design\"]\n",
    "results = data_file[\"result\"]\n",
    "\n",
    "df_design = pd.DataFrame(np.array(design[\"data\"]).transpose(), columns = design[\"names\"].asstr())\n",
    "df_results = pd.DataFrame(np.array(results[\"data\"]).transpose(), columns = results[\"names\"].asstr())\n",
    "\n",
    "data_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess Data\n",
    "\n",
    "The data are preprocessed in the following way:\n",
    "\n",
    "1. Label data points in the `design`  dataset with `reactive` and `non-reactive` labels using kmeans clustering\n",
    "2. Transform `design` and `results` data set into log-scaled data.\n",
    "3. Split data into training and test sets.\n",
    "4. Learn scaler on training data for `design` and `results` together (option `global`) or individual (option `individual`).\n",
    "5. Transform training and test data.\n",
    "6. Split training data into training and validation dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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: 0.9879169719169719\n",
      "Amount class 1 before: 0.012083028083028084\n",
      "Using Oversampling\n",
      "Amount class 0 after: 0.5\n",
      "Amount class 1 after: 0.5\n"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Loss function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess):\n",
    "    df_result = postprocess(df_result_log, func_dict_out, data_min_log, data_max_log)    \n",
    "    return keras.losses.Huber + np.sum(((df_result['H'] / df_result['O']) - 2)**2)\n",
    "\n",
    "def loss_wrapper(data_min_log, data_max_log, func_dict_out, postprocess):\n",
    "    def loss(df_design_log, df_result_log):\n",
    "        return custom_loss_H20(df_design_log, df_result_log, data_min_log, data_max_log, func_dict_out, postprocess)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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"
     ]
    }
   ],
   "source": [
    "# measure time\n",
    "start = time.time()\n",
    "callback = keras.callbacks.EarlyStopping(monitor='loss', patience=3)\n",
    "history = model_simple.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": 69,
   "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"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fa3db2b7010>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "species = \"O\"\n",
    "iterations = 1000\n",
    "cell_offset = 60\n",
    "y_design = []\n",
    "y_results = []\n",
    "for i in range(0,iterations):\n",
    "    idx = i*50*50 + cell_offset -1\n",
    "    y_design.append(df_design.iloc[idx, :])\n",
    "    y_results.append(df_results.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",
    "y = FuncTransform(func_dict_in, func_dict_out).fit_transform(y_design)\n",
    "y = scaler_X.transform(y)\n",
    "prediction = model_large.predict(y.iloc[:, y.columns != \"Class\"])\n",
    "prediction = pd.DataFrame(prediction, columns = y.columns[y.columns != \"Class\"])\n",
    "prediction_back = pd.DataFrame(scaler_X.inverse_transform(prediction), columns=prediction.columns)\n",
    "prediction_back = FuncTransform(func_dict_out, func_dict_in).inverse_transform(prediction_back.iloc[:, prediction_back.columns != \"Class\"])\n",
    "\n",
    "plt.plot(np.arange(0,iterations), prediction_back[species], label = \"Prediction\")\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel(species)\n",
    "plt.title(species+' Concentration over Iterations')\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')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aelUAALAKQSUIyUk2dXI23NldwoRaAAAsQ1AJEvNUAACwHkElSLksow8AgOUIKkFi0TcAAKxHUAmSd9G3UoIKAACWIagEKTutYY5KCfv9AABgGYJKkJijAgCA9QgqQcpJZxl9AACsRlAJEvv9AABgPYJKkFhHBQAA6xFUguS964c5KgAAWIegEqTDh37YmBAAAGsQVILkvT3ZY0oVNXURrg0AAPGJoBIkZ5L90MaETKgFAMASBJUOOLToG0EFAAArEFQ6gGX0AQCwFkGlA3wbE9KjAgCAJZKCedOXX36pt99+W19++aWqqqrUtWtXDRkyREVFRUpJSQl1HaNWbpp3LRWCCgAAVggoqCxcuFAPPfSQ1q1bp27duqlHjx5KTU1VSUmJtm/frpSUFE2ZMkW33XabevfubVWdo8ahZfRZ9A0AACv4HVROPPFE2Ww2XX311XrhhRfUq1evJq+7XC699957WrRokYYOHapHHnlEl156acgrHE1y2JgQAABL+R1U7rnnHp177rmtvu50OjVq1CiNGjVK9957r3bu3BmSCkYzb48Kc1QAALCG35NpvSGlrq5OCxYs0L59+1o9t0uXLjr55JM7Xrsol5vmveuHoR8AAKwQ8F0/SUlJuuGGG+RyuayoT0zJ8a6jwmRaAAAsEdTtycOGDdOmTZtCXJXYk8M6KgAAWCqo25N//vOfa8aMGdq9e7dOOukkpaenN3n9+OOPD0nlol3uYXf9eDymbDYjwjUCACC+BBVULr/8cknSzTff7DtmGIZM05RhGKqvrw9N7aKcdwn9eo+pipo6ZTU+BwAAoRFUUEmEO3r84UyyKz3Zrsraeh2oqiWoAAAQYkEFlURYzM1fOenJqqytVklVrY5SevtvAAAAfgt6r5//+7//04gRI1RQUKBdu3ZJkh588EH985//DFnlYgGLvgEAYJ2ggsrcuXM1Y8YMjR8/XqWlpb45KdnZ2XrwwQdDWb+oxzL6AABYJ6ig8te//lVPPPGE7rzzTtntdt/xoUOHavPmzSGrXCzwrqVCjwoAAKEXVFDZuXOnhgwZ0uy40+lUZWVlhysVS7xDPyz6BgBA6AUVVPr06dPigm+vvvqq+vfv39E6xZRcFn0DAMAyQd318+tf/1rTp09XTU2NTNPUunXr9Nxzz+m+++7T3//+91DXMar5ltFn6AcAgJALKqhcc801qqur029+8xtVVVVp8uTJ6tGjh/73f/9XkyZNCnUdoxqTaQEAsE5QQUWSrrvuOl133XUqLi6Wx+NRt27dQlmvmJHL7ckAAFgm6KAiSfv379fWrVtlGIYMw1DXrl1DVa+Yke0NKsxRAQAg5IKaTFteXq6pU6eqoKBAp59+uk477TQVFBToyiuvVFlZWajrGNUO35jQNM0I1wYAgPgSVFD5yU9+ovfff1/Lli1TaWmpysrK9O9//1sffPCBrrvuulDXMaodvjFheU1dhGsDAEB8CWroZ9myZXrttdc0cuRI37FzzjlHTzzxhMaOHRuyysWCFIddacl2VdXW60BlrbJS2ZgQAIBQCapHpXPnzsrKymp2PCsrSzk5OR2uVKzJYZ4KAACWCCqo3HXXXZoxY4b27t3rO7Zv3z79+te/1t133x2yysWKnPTGZfQJKgAAhJTfQz9DhgyRYRi+59u2bVPv3r3Vq1cvSdJXX30lp9Op7777Tj/72c9CX9Mo5ltGv5K1VAAACCW/g8rEiRMtrEZsYxl9AACs4XdQmTlzppX1iGmHelQIKgAAhFJQc1TQFJNpAQCwRlC3J9tstibzVY5UX18fdIViUa53Mi1zVAAACKmggsrixYubPHe73dq4caMWLFig2bNnh6RiscS7jH4JPSoAAIRUUEHlggsuaHbskksu0YABA/T888/r2muv7XDFYgmTaQEAsEZI56gMGzZMb7zxRiiLjAncngwAgDVCFlSqq6v117/+VT179gzq/ffdd58Mw9Att9wSqiqFjXfBt9KqWjYmBAAghIIa+snJyWkymdY0TVVUVCgtLU3PPPNMwOWtX79ejz/+uI4//vhgqhNx3h6VOo+pCledMlPY7wcAgFAIKqj85S9/aRJUbDabunbtqmHDhgW818/Bgwc1ZcoUPfHEE7r33nuDqU7EHbkxIUEFAIDQCCqoXH311SGrwPTp03Xuuefq7LPPjtmgIjX0qlTVVquksla9O6dHujoAAMSFgILKxx9/7Nd5/g7hLFq0SBs2bND69ev9Ot/lcsnlcvmel5eXS2q4PdrtDu1EVm95/pabnZakb0ql4opqud2dQloXqwXa1liWSG2VEqu9tDV+JVJ7E6WtgbTPMAOY/eld6M37Fu/wz+FFGIbh14Jvu3fv1tChQ/X6669r8ODBkqRRo0bphBNO0IMPPtjie2bNmtXiOi0LFy5UWlqav82wxCOf2bS1zKYpx9TrlK5MqAUAoDVVVVWaPHmyysrKlJmZ2ea5AQWVXbt2+b42TVMDBw7UK6+8ot69ezc578jnLVmyZIkuvPBC2e1237H6+noZhiGbzSaXy9XkNanlHpXCwkIVFxe329BAud1urVixQqNHj5bD0f6ck1++8LH+vXmf/mdsX/14xFEhrYvVAm1rLEuktkqJ1V7aGr8Sqb2J0tby8nJ16dLFr6AS0NDPkQHEMAz17NnTr2BypLPOOkubN29ucuyaa65Rv379dNtttzULKZLkdDrldDqbHXc4HJZdUH/L7pKRIkkqd9XH7A+Xld/HaJNIbZUSq720NX4lUnvjva2BtC2oybShkJGRoYEDBzY5lp6ers6dOzc7Hguy0xq+6Sz6BgBA6LB7coiwjD4AAKHX4R6VtnZRDtSqVatCVla4HVpGn6ACAECoBBRUhgwZ0iSYVFdXa8KECUpOTm5y3oYNG0JTuxjiDSoH6FEBACBkAgoqEydObPK8pV2UE5V3v58DVcxRAQAgVAIKKjNnzrSqHjHPO0flQGXDxoShHBIDACBRMZk2RI7cmBAAAHSc30Fl7NixWrNmTbvnVVRUaM6cOfrb3/7WoYrFmhSHXamOhrVfSrlFGQCAkPB76OfSSy/VZZddpoyMDJ1//vkaOnSoCgoKlJKSogMHDuizzz7TO++8o1deeUXnnXee/vSnP1lZ76iUk+ZQdVm9Sqpq1atzZJf0BwAgHvgdVK699lpNnTpV//jHP/T888/riSeeUGlpqaSGW5T79++vc845Rx9++KGOO+44q+ob1XLSk7WnrEYHuEUZAICQCGgybXJysiZPnqzJkydLksrKylRdXa3OnTvH9VK//vJNqOUWZQAAQqJDC75lZWUpKysrVHWJedks+gYAQEhx108I5aZ511IhqAAAEAoElRDK8Q39cNcPAAChQFAJId8y+gz9AAAQEgSVEMphMi0AACEVVFDZvXu3vv76a9/zdevW6ZZbbtHjjz8esorFolxfjwpDPwAAhEJQQWXy5MlauXKlJGnfvn0aPXq01q1bpzvuuEO/+93vQlrBWJLdOJm2hB4VAABCIqig8sknn+iUU06RJL3wwgsaOHCg1qxZo4ULF+qpp54KZf1iincdldKqho0JAQBAxwQVVNxut5xOpyTpjTfe0Pnnny9J6tevn/bu3Ru62sUY72Rad72pg2xMCABAhwUVVAYMGKBHH31Ub7/9tlasWKGxY8dKkvbs2aPOnTuHtIKxJDXZrhRHw7eUeSoAAHRcUEFlzpw5euyxxzRq1ChdccUVGjx4sCRp6dKlviGhROWbUMs8FQAAOiyoJfRHjRql4uJilZeXKycnx3f8pz/9qdLSEnvX4Oy0ho0JmVALAEDHBdWjUl1dLZfL5Qspu3bt0oMPPqitW7eqW7duIa1grPFtTMiibwAAdFhQQeWCCy7Q008/LUkqLS3VsGHD9Oc//1kTJ07U3LlzQ1rBWMMy+gAAhE5QQWXDhg069dRTJUn/+Mc/1L17d+3atUtPP/20HnrooZBWMNbkeDcmpEcFAIAOCyqoVFVVKSMjQ5L0+uuv66KLLpLNZtPw4cO1a9eukFYw1uQwmRYAgJAJKqgcc8wxWrJkiXbv3q3XXntNY8aMkSTt379fmZmZIa1grMllvx8AAEImqKDy29/+VrfeequOOuoonXLKKSoqKpLU0LsyZMiQkFYw1viW0WfoBwCADgvq9uRLLrlEI0eO1N69e31rqEjSWWedpQsvvDBklYtFh5bRZzItAAAdFVRQkaS8vDzl5eXp66+/lmEY6tGjR8Iv9iYdmqNCjwoAAB0X1NCPx+PR7373O2VlZal3797q1auXsrOzdc8998jj8YS6jjEl57A5KmxMCABAxwTVo3LnnXdq3rx5uv/++zVixAiZpql3331Xs2bNUk1NjX7/+9+Hup4xI/ewjQkra+vVyRl0pxUAAAkvqH9FFyxYoL///e++XZMlafDgwerRo4d+/vOfJ3RQSU22y5lkk6vOowOVtQQVAAA6IKihn5KSEvXr16/Z8X79+qmkpKTDlYp13gm1zFMBAKBjggoqgwcP1sMPP9zs+MMPP9zkLqBExaJvAACERlDjEn/84x917rnn6o033lBRUZEMw9CaNWu0e/duvfLKK6GuY8zJSW9cRp+gAgBAhwTVo3L66afr888/14UXXqjS0lKVlJTooosu0tatW317ACWyQ7cos5YKAAAdEfRMz4KCgmaTZnfv3q0f//jHevLJJztcsVh2aNE3elQAAOiIoHpUWlNSUqIFCxaEssiYlM2ibwAAhERIgwoa5Dbu98My+gAAdAxBxQI53J4MAEBIEFQswO3JAACERkCTaS+66KI2Xy8tLe1IXeJGbjpBBQCAUAgoqGRlZbX7+lVXXdWhCsUD38aElW6ZpinDMCJcIwAAYlNAQWX+/PlW1SOu5DROpq2t97AxIQAAHcAcFQukOho2JpSkA0yoBQAgaAQVCxiGwYRaAABCgKBiEW5RBgCg4wgqFslNZ9E3AAA6iqBiEZbRBwCg4wgqFslNY2NCAAA6iqBiEd8cFYIKAABBI6hYxLuWyoFK5qgAABAsgopFWEYfAICOI6hYJIfJtAAAdBhBxSIs+AYAQMcRVCyS07iOyoGqho0JAQBA4AgqFvH2qNTWeVRVWx/h2gAAEJsiGlTmzp2r448/XpmZmcrMzFRRUZFeffXVSFYpZNKS7Upu3JiQeSoAAAQnokGlZ8+euv/++/XBBx/ogw8+0JlnnqkLLrhAn376aSSrFRKGYRy26Bu3KAMAEIyIBpUJEyZo/Pjx6tu3r/r27avf//736tSpk9auXRvJaoVMduNaKiz6BgBAcJIiXQGv+vp6vfjii6qsrFRRUVGkqxMSvrVUGPoBACAoEQ8qmzdvVlFRkWpqatSpUyctXrxY/fv3b/Fcl8sll8vle15eXi5JcrvdcrtDO7ziLa8j5WanNnx7iyuqQ16/UApFW2NFIrVVSqz20tb4lUjtTZS2BtI+w4zwvbO1tbX66quvVFpaqpdeekl///vftXr16hbDyqxZszR79uxmxxcuXKi0tLRwVDcgL+yw6d1vbTqnh0fje3kiXR0AAKJCVVWVJk+erLKyMmVmZrZ5bsSDypHOPvtsHX300XrssceavdZSj0phYaGKi4vbbWig3G63VqxYodGjR8vhcARVxoP/+UJ/W7VDU04p1KwJPwxp/UIpFG2NFYnUVimx2ktb41citTdR2lpeXq4uXbr4FVQiPvRzJNM0m4SRwzmdTjmdzmbHHQ6HZRe0I2V3yUiRJJXW1MXED5yV38dok0htlRKrvbQ1fiVSe+O9rYG0LaJB5Y477tC4ceNUWFioiooKLVq0SKtWrdLy5csjWa2Q8S2jz2RaAACCEtGg8u2332rq1Knau3evsrKydPzxx2v58uUaPXp0JKsVMjm+HZTje1IUAABWiWhQmTdvXiQ/3nK59KgAANAh7PVjocMXfIuyOcsAAMQEgoqFvAu+1dZ5VO1mY0IAAAJFULFQWrJdyXY2JgQAIFgEFQsZhqGc9IbhnwOVTKgFACBQBBWL+W5RZmNCAAACRlCxGEEFAIDgEVQsxg7KAAAEj6BiMe8clRIWfQMAIGAEFYuxjD4AAMEjqFiMOSoAAASPoGIx3xwVggoAAAEjqFjMt4w+66gAABAwgorFvD0qpfSoAAAQMIKKxbxzVEoq2ZgQAIBAEVQsltPYo+JiY0IAAAJGULFY+mEbEx5gLRUAAAJCULGYYRi+CbWspQIAQGAIKmHgnVBbQlABACAgBJUwYNE3AACCQ1AJA+9+Pwz9AAAQGIJKGBzqUWEyLQAAgSCohAHL6AMAEByCShhkpzGZFgCAYBBUwiC3cY5KKUM/AAAEhKASBjn0qAAAEBSCShhwezIAAMEhqIQBk2kBAAgOQSUMvEvo17g9qq5lY0IAAPxFUAmDTs4kOeyGJKmEXhUAAPxGUAkDwzAOzVNhQi0AAH4jqIQJE2oBAAgcQSVMfPv9sJYKAAB+I6iEie/OH4Z+AADwG0ElTFhGHwCAwBFUwiS3MaiUMkcFAAC/EVTCJKdx6KeEOSoAAPiNoBImOY2LvjFHBQAA/xFUwiSHZfQBAAgYQSVMWPANAIDAEVTCxDuZliX0AQDwH0ElTLwLvrExIQAA/iOohEknZ5KSbA0bEzJPBQAA/xBUwsQwjEO3KDNPBQAAvxBUwujQom+spQIAgD8IKmGU3biWChNqAQDwD0EljLwbE7KMPgAA/iGohBFzVAAACAxBJYxYRh8AgMAQVMLItzotk2kBAPALQSWMctnvBwCAgBBUwsjbo8IcFQAA/ENQCaOcdNZRAQAgEASVMPJOpqVHBQAA/xBUwsjbo1LtrleNm40JAQBoD0EljDLYmBAAgIAQVMLIMAxlM6EWAAC/EVTCLDe9YZ4KE2oBAGhfRIPKfffdp5NPPlkZGRnq1q2bJk6cqK1bt0aySpbjFmUAAPwX0aCyevVqTZ8+XWvXrtWKFStUV1enMWPGqLKyMpLVstSh1WkJKgAAtCcpkh++fPnyJs/nz5+vbt266cMPP9Rpp50WoVpZy3vnz4FKhn4AAGhPRIPKkcrKyiRJubm5Lb7ucrnkcrl8z8vLyyVJbrdbbndo/+H3lhfqcrNT7JKk4oM1IS87WFa1NRolUlulxGovbY1fidTeRGlrIO0zTNM0LayL30zT1AUXXKADBw7o7bffbvGcWbNmafbs2c2OL1y4UGlpaVZXMSRW7jG0ZJddJ3b2aFpfT6SrAwBA2FVVVWny5MkqKytTZmZmm+dGTVCZPn26li1bpnfeeUc9e/Zs8ZyWelQKCwtVXFzcbkMD5Xa7tWLFCo0ePVoOhyNk5S7euEe/efkTjTyms+ZPOylk5XaEVW2NRonUVimx2ktb41citTdR2lpeXq4uXbr4FVSiYujnpptu0tKlS/XWW2+1GlIkyel0yul0NjvucDgsu6ChLrtLZookqbTaHXU/hFZ+H6NNIrVVSqz20tb4lUjtjfe2BtK2iAYV0zR10003afHixVq1apX69OkTyeqEhe+uHybTAgDQrogGlenTp2vhwoX65z//qYyMDO3bt0+SlJWVpdTU1EhWzTK56dyeDACAvyK6jsrcuXNVVlamUaNGKT8/3/d4/vnnI1ktS3mX0K+qZWNCAADaE/Ghn0STnmyXzZA8pvSfLd9q7MB82Rs3KgQAAE2x108YLf9kr07940p5GvPZ9IUbNXLOm1r+yd7IVgwAgChFUAmT5Z/s1Q3PbNDespomx/eV1eiGZzYQVgAAaAFBJQzqPaZm/+sztTTQ5T02+1+fqd6TeENhAAC0haASBut2ljTrSTmcKWlvWY3W7SwJX6UAAIgBBJUw2F/RekgJ5jwAABIFQSUMumWk+HXeis++1d6yaotrAwBA7CCohMEpfXKVn5Wi9m5C/vfHe3XqnJX61Qsfaeu+irDUDQCAaEZQCQO7zdDMCf0lqVlYMRof0884WsP65KrOY+qlDV/rnAff0jXz12ntju8Tcr0ZAACkKNmUMBGMHZivuVeeqNn/+qzJxNq8rBTNnNBfYwfmS5I27S7V429t16uf7NPKrd9p5dbvNLgwW9ef9gONGZDH4nAAgIRCUAmjsQPzNbp/ntbtLNH+ihp1y0jRKX1ym4SPEwqz9ciUk/RlcaX+/s4OvfjB1/pod6lueHaDjuqcpp+c+gNdclJPpTjsEWwJAADhQVAJM7vNUNHRnds976gu6bp34iDdcnZfPb3mSz29dpe+/L5Kdy35RH9Z8bmm/egoTR3eWzmNmxx61XvMNoMQAACxhKAS5bp0cmrGmON0/aij9cL63Xri7Z36prRaD6z4XHNXbdflJxfq2pF9VJibpuWf7G02tJR/xNASAACxhKASI9KSk3T1iD66cnhvLdu8V4+/tUOf7inXU2u+1P+t3aUhhdn6YNeBZu/zLtE/98oTCSsAgJjDXT8xJslu0wUn9NC/bxqpZ64dplOP7aJ6j9liSJFYoh8AENsIKjHKMAyNPLaL/u/aYfrDhQPbPJcl+gEAsYqhnziQ7vTvMt7+0scadVxXHd8zW4MLs/SDLp1kC3CiLZN1AQDhRFCJA/4u0b+rpEoL3tslaZckqZMzSQN7ZGpgQabqvjc06ECV+nTNlGG0HDyYrAsACDeCShzwLtG/r6xGLc1CMSR1yXDqjnH9tPmbcm3+plSffFOug646rd1RorU7SiTZ9dQD7yg3PVmDemRpcM8sHd8zW8cXZqlbRoqWf7JXNzyzoVn5TNYFAFiJoBIHvEv03/DMBhlSkzDh7Ru554IBGjswXxee2PC8rt6jL747qI93l2njVyV657Pd2ldjU0llrVZ//p1Wf/6dr4y8TKcOVLlbDEFm42fM/tdnGt2/4yvnMrQEADgcQSVO+LtEv1eS3aZ+eZnql5epC0/I0ytJX+qs0Wfri+9r9PHXpfpod5k+/rpUX3x3UPvKXW1+tney7uNvbdfo/nnqkZ2q1OTAV85laAkAcCSCShzxZ4n+tjgddp1QmK0TCrOlooZjla46PbZ6ux5684t23z9n+VbNWb5VktQ5PVk9c1LVIydVPbIbHj1z0hqe56QqM8XR5L3hGFqq95h6f2eJPiw21HlniYqO6UZvDQBEOYJKnPF3iX5/pTuTVHR0F7+CSmFOqg5UuXXQVafvK2v1fWWtPvq6rMVzM1OS1CMnTT2yU1WQnaLFG7+xdGipaW+NXU9v+4DeGgCIAQQVtMufybp5WSla9eszZDOk8uo67T5QpW9Kq/XNgWp9faBa35Q2PP/6QLVKq9wqr6lT+d5ybdlb3u7ne4eWfvXCJg3plaMunZzqmnHokZ5sb/VOJSk8vTUS82sAwAoEFbTLn8m6Myf09/2jnJXmUFZalgb2yGqxvEpX3WEhpkqrPv9O/9myv916LNm0R0s27Wl2PMVhawgtnZzNQkxuWrLuWvKJ5ROBwzG/hiAEIBERVOCXQCfrtiXdmaS+3TPUt3uGJOmYbhl+BZUx/bvLbjP0XYVL3x10qbjCpcraetW4PdpdUq3dJdUBt+vw3pr+BZnKTHEoK7Xhkdn4Z1aaQxnOpDbXl7G6x4YgBCBREVTgt45O1m2Nv0NLc688qdlnVbrqVHzQpeKDroYAU+HSdwdrfV9v+7ZCu0qq2q1Da701XjZDh4KLN8ikONQpJUn//mhPqz02kjRz6ac6s193JScFt2NFPAQhq0OQ1ROlraw/ARFoG0EFAQn1ZF1vmYEMLR0u3ZmkdGeSendOb7Hs97Z/ryueWNtuHc4Z0F1pyUkqq3Y3eZRXu+Wq88hjSqVVbpVWuQNu37flLvW961WlOuxKdyapk9OuTilJSk9OUidnUsPXzsavnUmHznE6lOqw6c7F1g5dWR2ErA5BVk+UtrL+8dBTFsshNB6+N7Fcvr8IKogKoRxaOpy/vTWPTGneW+NV465X+REBxvt4f2eJln+yz6+6VLvrVe2uV/HBoJrSIu/Q1cg5byo3PVmpDrtSfA+bnEmG9n9j08fLtyo9xfu6zXdest2mO9uZwzPrX5/prH7d5QiiRygcIShWy4+HnrJYDqHx9b2JvfIDYZim2dLfUTGhvLxcWVlZKisrU2ZmZkjLdrvdeuWVVzR+/Hg5HI723xDDoqmtViR47z8IUsu9NR35B8HfHpvHp56kH+Zn6qCrzveodNXpYI3363pV1tapoqbheKWrThWuOu05UK2vSwOfe2MFu81Qst2m5KTGh/3Qn46kw1+zK9luyGE3tHLrd6pxe1otMyMlSTeecYySk2xKshlKsttktzW8N8l26FjDn43H7IaSbIYMGfrJ0+tVfLC2xbINSd0znVox43Q5GstNshlt3iF2uHqPqZFz3mzyF/WR5edlpeid284M+Ge0o2X78zvbWhAKxc99rJcfzXVPhGsrBfbvNz0qiCpWDC1Z1Vsj+d9jc9YPuwcVuPwNQr8974f6QddOqnF7VOOuV01j701ljVubt2xVz959VFsv3/GGczz6prRaO4sr/apLvcdUtafh/aFSUVOn+179b8jKO5wpaV+5S4Nmvd7kuGFIdsOQ3XbE47BjNsNQncejb9tYldnbm3XeQ28rJz1ZNsOQzWbI1li+YTR+3Vje4a8VH3S1GlIOL/sXizaqR3aqryybYcgwJNPj0Re7DW1fuV0Ou923C7qt8TxTpv62cnubc6due2mzig/Wym4zZDR+XwzD+/XhxyRDRpPXTdPUb5d+2mb5//PyZtlkyG43fGU0XAD5PqPxaZPXG9pn6o42hjwl6c7Fnyg7LVlJtkPv833AYc+9h72f5/GYbQ6nStJdSz5RflaqkuyHfmcNHfk5R3zd+LrHNNu801CS7v7np+rTpVPD9/6Ivxbq6+q0v1raWVzZLKgYavg9vHtJ29/73/7zUx3XPbPF8tvjMU3d/c/Wyw/llin+okelFdHUy2C1RGlrvcfUe1/s1+tvv68xpw4L2Vi3lT023v95txeEgv2fdyA9Qsf3zFZtnUe19Z4mf7ob/3Q1HnM3/vnhlwf0jw1ft1v20N7ZystKVV29qTqPqTqPR/UeU+56758Nx7yve187WFOn0urA5wwB6Ljnrhveof9U0qMCtMBuMzSsT66+32JqWAgnhVnZY9ORicb+sLJH6KjO6X4FlV+N6RfUX3j+hqwF15ysE3vnyOOR6s2G0OPxyPdnvWmq3uNRvachGNZ7TNWbpj7afUAzl37Wbvk3n3WMjumWIY/HlMdseL9pNvzPtN405THV7LUdxQf13Lrd7ZY94fh85WWlNJbXUKbZWN+dX+5SYa9eMgybTNNsfK3hvK++r9T6XQfaLX9gj0zlZaZKOlRnU5JpqvHPhp+KhuemPJ6GP7+rcGn7d+33xPXKTVNOmsP3s+Utx/vf4yM/x3usrLq23T3GJPkWfJTU5DManptNnzf+WVVbpwN+TIrPTE1SStKhPcsO//1o+t/7pnV31dXroKv9Xse0ZLsc9qbzvrzfB7fbraQj/2PR+DHueo9q6lofTvVy2g3Z7S3PK2ute8KU6fvPQXv2V7TeIxhqBBUgBKy6ddtbdiwGIX9D0Cl9cgOveADljzy2a1D1H9QjS4+u3tFu+b84q29Qc1RWbf2u3bIfnDSkjZ6ynRo/vn+HesruHN/f0pA45+LjLS3/oUlDAi7f37Ifu3KopXWfN+3kFss/1At6Toeu7VM/HmZp/btlpARcdrCCW9gBQDPe+TUXnNBDRUd3Dun47diB+XrntjP13HXD9b+TTtBz1w3XO7edGZLZ994glJfV9C+ehrVrgh+28oYg6VDo8QpFb1Asl2913b0hrrV3G2q4g6OjITEWy4/lusdD+cEgqAAxIhaDkFUhKB7Kt7LsWA5xVpcfy3WPh/KDwdAPAEnW3HElWTssdnj5VkyUPrx8q4b1YnHIMNbLj+W6x0P5gSKoALCcVSHo8PKtmCh9ePlW1d/KssMVEmMxhMbL9yZWyw8EQQUA4lg4QmKshtB4+N7Ecvn+Yo4KAACIWgQVAAAQtQgqAAAgahFUAABA1CKoAACAqEVQAQAAUYugAgAAohZBBQAARC2CCgAAiFoxvTKtaTZskF5eXh7yst1ut6qqqlReXt7iVtvxhLbGr0RqL22NX4nU3kRpq/ffbe+/422J6aBSUVEhSSosLIxwTQAAQKAqKiqUlZXV5jmG6U+ciVIej0d79uxRRkaGDCO0GyWVl5ersLBQu3fvVmZmZkjLjja0NX4lUntpa/xKpPYmSltN01RFRYUKCgpks7U9CyWme1RsNpt69uxp6WdkZmbG9Q/L4Whr/Eqk9tLW+JVI7U2EtrbXk+LFZFoAABC1CCoAACBqEVRa4XQ6NXPmTDmdzkhXxXK0NX4lUntpa/xKpPYmUlv9FdOTaQEAQHyjRwUAAEQtggoAAIhaBBUAABC1EjqoPPLII+rTp49SUlJ00kkn6e23327z/NWrV+ukk05SSkqKfvCDH+jRRx8NU02Dd9999+nkk09WRkaGunXrpokTJ2rr1q1tvmfVqlUyDKPZ47///W+Yah2cWbNmNatzXl5em++JxWvqddRRR7V4naZPn97i+bF0Xd966y1NmDBBBQUFMgxDS5YsafK6aZqaNWuWCgoKlJqaqlGjRunTTz9tt9yXXnpJ/fv3l9PpVP/+/bV48WKLWuC/ttrqdrt12223adCgQUpPT1dBQYGuuuoq7dmzp80yn3rqqRavdU1NjcWtaV971/bqq69uVu/hw4e3W26sXVtJLV4jwzD0pz/9qdUyo/naWiVhg8rzzz+vW265RXfeeac2btyoU089VePGjdNXX33V4vk7d+7U+PHjdeqpp2rjxo264447dPPNN+ull14Kc80Ds3r1ak2fPl1r167VihUrVFdXpzFjxqiysrLd927dulV79+71PY499tgw1LhjBgwY0KTOmzdvbvXcWL2mXuvXr2/S1hUrVkiSLr300jbfFwvXtbKyUoMHD9bDDz/c4ut//OMf9cADD+jhhx/W+vXrlZeXp9GjR/u21WjJe++9p8svv1xTp07VRx99pKlTp+qyyy7T+++/b1Uz/NJWW6uqqrRhwwbdfffd2rBhg15++WV9/vnnOv/889stNzMzs8l13rt3r1JSUqxoQkDau7aSNHbs2Cb1fuWVV9osMxavraRm1+fJJ5+UYRi6+OKL2yw3Wq+tZcwEdcopp5jXX399k2P9+vUzb7/99hbP/81vfmP269evybGf/exn5vDhwy2roxX2799vSjJXr17d6jkrV640JZkHDhwIX8VCYObMmebgwYP9Pj9erqnXL37xC/Poo482PR5Pi6/H6nWVZC5evNj33OPxmHl5eeb999/vO1ZTU2NmZWWZjz76aKvlXHbZZebYsWObHDvnnHPMSZMmhbzOwTqyrS1Zt26dKcnctWtXq+fMnz/fzMrKCm3lLNBSe6dNm2ZecMEFAZUTL9f2ggsuMM8888w2z4mVaxtKCdmjUltbqw8//FBjxoxpcnzMmDFas2ZNi+957733mp1/zjnn6IMPPpDb7basrqFWVlYmScrNzW333CFDhig/P19nnXWWVq5caXXVQmLbtm0qKChQnz59NGnSJO3YsaPVc+PlmkoNP9PPPPOMfvzjH7e771UsXtfD7dy5U/v27Wty7ZxOp04//fRWf3+l1q93W++JRmVlZTIMQ9nZ2W2ed/DgQfXu3Vs9e/bUeeedp40bN4angiGwatUqdevWTX379tV1112n/fv3t3l+PFzbb7/9VsuWLdO1117b7rmxfG2DkZBBpbi4WPX19erevXuT4927d9e+fftafM++fftaPL+urk7FxcWW1TWUTNPUjBkzNHLkSA0cOLDV8/Lz8/X444/rpZde0ssvv6zjjjtOZ511lt56660w1jZww4YN09NPP63XXntNTzzxhPbt26cf/ehH+v7771s8Px6uqdeSJUtUWlqqq6++utVzYvW6Hsn7OxrI76/3fYG+J9rU1NTo9ttv1+TJk9vcB6Zfv3566qmntHTpUj333HNKSUnRiBEjtG3btjDWNjjjxo3Ts88+qzfffFN//vOftX79ep155plyuVytviceru2CBQuUkZGhiy66qM3zYvnaBiumNyXsqCP/52maZpv/G23p/JaOR6sbb7xRH3/8sd555502zzvuuON03HHH+Z4XFRVp9+7d+n//7//ptNNOs7qaQRs3bpzv60GDBqmoqEhHH320FixYoBkzZrT4nli/pl7z5s3TuHHjVFBQ0Oo5sXpdWxPo72+w74kWbrdbkyZNksfj0SOPPNLmucOHD28yAXXEiBE68cQT9de//lUPPfSQ1VXtkMsvv9z39cCBAzV06FD17t1by5Yta/Mf8Vi+tpL05JNPasqUKe3ONYnlaxushOxR6dKli+x2e7O0vX///map3CsvL6/F85OSktS5c2fL6hoqN910k5YuXaqVK1cGteP08OHDYy6xp6ena9CgQa3WO9avqdeuXbv0xhtv6Cc/+UnA743F6+q9kyuQ31/v+wJ9T7Rwu9267LLLtHPnTq1YsSLgXXVtNptOPvnkmLvWUkNPYO/evduseyxfW0l6++23tXXr1qB+h2P52vorIYNKcnKyTjrpJN9dEl4rVqzQj370oxbfU1RU1Oz8119/XUOHDpXD4bCsrh1lmqZuvPFGvfzyy3rzzTfVp0+foMrZuHGj8vPzQ1w7a7lcLm3ZsqXVesfqNT3S/Pnz1a1bN5177rkBvzcWr2ufPn2Ul5fX5NrV1tZq9erVrf7+Sq1f77beEw28IWXbtm164403ggrRpmlq06ZNMXetJen777/X7t2726x7rF5br3nz5umkk07S4MGDA35vLF9bv0VqFm+kLVq0yHQ4HOa8efPMzz77zLzlllvM9PR088svvzRN0zRvv/12c+rUqb7zd+zYYaalpZm//OUvzc8++8ycN2+e6XA4zH/84x+RaoJfbrjhBjMrK8tctWqVuXfvXt+jqqrKd86Rbf3LX/5iLl682Pz888/NTz75xLz99ttNSeZLL70UiSb47Ve/+pW5atUqc8eOHebatWvN8847z8zIyIi7a3q4+vp6s1evXuZtt93W7LVYvq4VFRXmxo0bzY0bN5qSzAceeMDcuHGj706X+++/38zKyjJffvllc/PmzeYVV1xh5ufnm+Xl5b4ypk6d2uQuvnfffde02+3m/fffb27ZssW8//77zaSkJHPt2rVhb9/h2mqr2+02zz//fLNnz57mpk2bmvwOu1wuXxlHtnXWrFnm8uXLze3bt5sbN240r7nmGjMpKcl8//33I9HEJtpqb0VFhfmrX/3KXLNmjblz505z5cqVZlFRkdmjR4+4u7ZeZWVlZlpamjl37twWy4ila2uVhA0qpmmaf/vb38zevXubycnJ5oknntjklt1p06aZp59+epPzV61aZQ4ZMsRMTk42jzrqqFZ/sKKJpBYf8+fP951zZFvnzJljHn300WZKSoqZk5Njjhw50ly2bFn4Kx+gyy+/3MzPzzcdDodZUFBgXnTRReann37qez1erunhXnvtNVOSuXXr1mavxfJ19d5KfeRj2rRppmk23KI8c+ZMMy8vz3Q6neZpp51mbt68uUkZp59+uu98rxdffNE87rjjTIfDYfbr1y8qQlpbbd25c2erv8MrV670lXFkW2+55RazV69eZnJystm1a1dzzJgx5po1a8LfuBa01d6qqipzzJgxZteuXU2Hw2H26tXLnDZtmvnVV181KSMerq3XY489ZqamppqlpaUtlhFL19Yq7J4MAACiVkLOUQEAALGBoAIAAKIWQQUAAEQtggoAAIhaBBUAABC1CCoAACBqEVQAAEDUIqgAAICoRVABEFcMw9CSJUsiXQ0AIUJQARAyV199tQzDaPYYO3ZspKsGIEYlRboCAOLL2LFjNX/+/CbHnE5nhGoDINbRowIgpJxOp/Ly8po8cnJyJDUMy8ydO1fjxo1Tamqq+vTpoxdffLHJ+zdv3qwzzzxTqamp6ty5s37605/q4MGDTc558sknNWDAADmdTuXn5+vGG29s8npxcbEuvPBCpaWl6dhjj9XSpUutbTQAyxBUAITV3XffrYsvvlgfffSRrrzySl1xxRXasmWLJKmqqkpjx45VTk6O1q9frxdffFFvvPFGkyAyd+5cTZ8+XT/96U+1efNmLV26VMccc0yTz5g9e7Yuu+wyffzxxxo/frymTJmikpKSsLYTQIhEevtmAPFj2rRppt1uN9PT05s8fve735mmaZqSzOuvv77Je4YNG2becMMNpmma5uOPP27m5OSYBw8e9L2+bNky02azmfv27TNN0zQLCgrMO++8s9U6SDLvuusu3/ODBw+ahmGYr776asjaCSB8mKMCIKTOOOMMzZ07t8mx3Nxc39dFRUVNXisqKtKmTZskSVu2bNHgwYOVnp7ue33EiBHyeDzaunWrDMPQnj17dNZZZ7VZh+OPP973dXp6ujIyMrR///5gmwQggggqAEIqPT292VBMewzDkCSZpun7uqVzUlNT/SrP4XA0e6/H4wmoTgCiA3NUAITV2rVrmz3v16+fJKl///7atGmTKisrfa+/++67stls6tu3rzIyMnTUUUfpP//5T1jrDCBy6FEBEFIul0v79u1rciwpKUldunSRJL344osaOnSoRo4cqWeffVbr1q3TvHnzJElTpkzRzJkzNW3aNM2aNUvfffedbrrpJk2dOlXdu3eXJM2aNUvXX3+9unXrpnHjxqmiokLvvvuubrrppvA2FEBYEFQAhNTy5cuVn5/f5Nhxxx2n//73v5Ia7shZtGiRfv7znysvL0/PPvus+vfvL0lKS0vTa6+9pl/84hc6+eSTlZaWposvvlgPPPCAr6xp06appqZGf/nLX3TrrbeqS5cuuuSSS8LXQABhZZimaUa6EgASg2EYWrx4sSZOnBjpqgCIEcxRAQAAUYugAgAAohZzVACEDSPNAAJFjwoAAIhaBBUAABC1CCoAACBqEVQAAEDUIqgAAICoRVABAABRi6ACAACiFkEFAABELYIKAACIWv8ffjVm25L6Qy0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"], \"o-\", label = \"Training Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "# plt.yscale('log')\n",
    "plt.ylabel(\"Loss (Huber)\")\n",
    "plt.grid('on')\n",
    "\n",
    "plt.savefig(\"loss_all.png\", dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history[\"loss\"][1:], \"o-\", label = \"Training Loss\")\n",
    "plt.xlabel(\"Epoch\")\n",
    "# plt.yscale('log')\n",
    "plt.ylabel(\"Loss (Huber)\")\n",
    "plt.grid('on')\n",
    "plt.savefig(\"loss_1_to_end.png\", dpi=300)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9.875676596493577e-07"
      ]
     },
     "execution_count": 15,
     "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\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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"
     ]
    },
    {
     "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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m94/94\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4.0710e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4.047931361128576e-05"
      ]
     },
     "execution_count": 17,
     "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])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": {
  "kernelspec": {
   "display_name": "training",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
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