ml_randomness_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9beaf855",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00923e98",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 92ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n"
     ]
    }
   ],
   "source": [
    "linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\")\n",
    "\n",
    "da = df_all_chan.to_numpy()\n",
    "\n",
    "window_input_width = 200\n",
    "\n",
    "multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "    input_width=window_input_width, label_width=1, shift=0,\n",
    "    label_columns=['Discharge'],\n",
    "    input_columns=input_columns,\n",
    "    shuffle=True)\n",
    "\n",
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}\n",
    "\n",
    "history['lstm_'+str(window_input_width)] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "\n",
    "val_performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a32b6769",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2592x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting one graph\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, ax0 = plt.subplots(nrows=1, \n",
    "                         ncols=1, \n",
    "                         sharey=False, \n",
    "                         sharex=False,\n",
    "                         figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "\n",
    "ax0.plot(history['lstm_200'].history['loss'], label='Train Loss',linewidth=3)\n",
    "ax0.plot(history['lstm_200'].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "ax0.grid(True)\n",
    "ax0.legend()\n",
    "ax0.set_title('lstm_200',size=50)\n",
    "ax0.set_ylabel('Loss', size=30)\n",
    "ax0.set_xlabel('Epoch', size=30)\n",
    "#ax0.set_ylim([0,1])\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aad95fcb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ede7d213",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}