{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa25df96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error as MAE\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow import keras\n",
    "import keras_tuner as kt\n",
    "r = np.random\n",
    "r.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fed09a5d",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 566MB\n",
       "Dimensions:     (sample: 189326, x: 27, y: 27)\n",
       "Coordinates:\n",
       "  * sample      (sample) int32 757kB 0 1 2 3 4 ... 189322 189323 189324 189325\n",
       "  * x           (x) int32 108B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "  * y           (y) int32 108B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    images      (sample, x, y) float32 552MB ...\n",
       "    labels      (sample) float64 2MB ...\n",
       "    vx          (sample) float64 2MB ...\n",
       "    vy          (sample) float64 2MB ...\n",
       "    v           (sample) float64 2MB ...\n",
       "    smb         (sample) float64 2MB ...\n",
       "    z           (sample) float64 2MB ...\n",
       "    s           (sample) float64 2MB ...\n",
       "    temp        (sample) float64 2MB ...\n",
       "    gridCellId  (sample) int32 757kB ...\n",
       "Attributes:\n",
       "    description:  CNN data with elevation images. N = 189326. Scalar features...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-64805055-d85b-45e1-9e39-d3af48324bec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-64805055-d85b-45e1-9e39-d3af48324bec' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sample</span>: 189326</li><li><span class='xr-has-index'>x</span>: 27</li><li><span class='xr-has-index'>y</span>: 27</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-df0121fe-a948-421c-bcad-a62456c07e63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df0121fe-a948-421c-bcad-a62456c07e63' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 189323 189324 189325</div><input id='attrs-683977ce-510b-4a3d-a334-7f35bbe24c88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-683977ce-510b-4a3d-a334-7f35bbe24c88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91c09dab-dcba-4a28-8c64-a3735212dfd6' class='xr-var-data-in' type='checkbox'><label for='data-91c09dab-dcba-4a28-8c64-a3735212dfd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([     0,      1,      2, ..., 189323, 189324, 189325], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-edb21f12-2ac8-4d57-92ba-463eb5d117ec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edb21f12-2ac8-4d57-92ba-463eb5d117ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4f7748a-fe69-4d4d-8460-b7e529645e9d' class='xr-var-data-in' type='checkbox'><label for='data-e4f7748a-fe69-4d4d-8460-b7e529645e9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 21 22 23 24 25 26</div><input id='attrs-e282c7d9-3059-4cb3-8204-b04253d13d55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e282c7d9-3059-4cb3-8204-b04253d13d55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84662f67-1d69-49a9-b7af-46f5a25ce309' class='xr-var-data-in' type='checkbox'><label for='data-84662f67-1d69-49a9-b7af-46f5a25ce309' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe3b8228-48e6-423c-a6e8-4c63015987b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe3b8228-48e6-423c-a6e8-4c63015987b4' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>images</span></div><div class='xr-var-dims'>(sample, x, y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d5cb0120-ce23-42d3-8c1a-c442a188124b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5cb0120-ce23-42d3-8c1a-c442a188124b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce07a535-a54f-4db6-a9e8-9cde99c4012c' class='xr-var-data-in' type='checkbox'><label for='data-ce07a535-a54f-4db6-a9e8-9cde99c4012c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[138018654 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>labels</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d038ac74-2d31-4a85-95ee-bf15870df0d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d038ac74-2d31-4a85-95ee-bf15870df0d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f521ee9-2ea1-4615-b8f6-d09b496910e5' class='xr-var-data-in' type='checkbox'><label for='data-3f521ee9-2ea1-4615-b8f6-d09b496910e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vx</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fd4d7e47-34ef-4418-8ed0-08cab7aa4d5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd4d7e47-34ef-4418-8ed0-08cab7aa4d5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-006afd86-82c7-4354-8b2b-d42e72c1c082' class='xr-var-data-in' type='checkbox'><label for='data-006afd86-82c7-4354-8b2b-d42e72c1c082' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vy</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-411a93ff-0abc-46ce-adfd-4548ad22e9b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-411a93ff-0abc-46ce-adfd-4548ad22e9b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a1ac552-f52b-4d3d-9b41-3d8d7ccd2858' class='xr-var-data-in' type='checkbox'><label for='data-8a1ac552-f52b-4d3d-9b41-3d8d7ccd2858' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fe1b9fab-27e6-4526-9431-83e0a714dc7e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fe1b9fab-27e6-4526-9431-83e0a714dc7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1cb671f2-d0a0-482f-bb5b-1841f870f8c1' class='xr-var-data-in' type='checkbox'><label for='data-1cb671f2-d0a0-482f-bb5b-1841f870f8c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smb</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-39274129-cd30-4d59-8775-7d18648d804b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39274129-cd30-4d59-8775-7d18648d804b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d94ac42-e12f-4db6-86b2-c1b1762bb146' class='xr-var-data-in' type='checkbox'><label for='data-2d94ac42-e12f-4db6-86b2-c1b1762bb146' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2961ab86-2583-4801-9a96-59dd6db22a00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2961ab86-2583-4801-9a96-59dd6db22a00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10512b75-0ec5-4ca1-928a-d167f6190cf5' class='xr-var-data-in' type='checkbox'><label for='data-10512b75-0ec5-4ca1-928a-d167f6190cf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-cacf6301-0af5-4751-b20d-85d93f63f812' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cacf6301-0af5-4751-b20d-85d93f63f812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1061d14-e006-49a3-99aa-0aae63afde0a' class='xr-var-data-in' type='checkbox'><label for='data-e1061d14-e006-49a3-99aa-0aae63afde0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ba30a2db-ae95-44db-929b-13074751d663' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba30a2db-ae95-44db-929b-13074751d663' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0b7f947-250b-489b-98aa-e42fc1673833' class='xr-var-data-in' type='checkbox'><label for='data-d0b7f947-250b-489b-98aa-e42fc1673833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gridCellId</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1ddf191-f8da-4460-b94d-7d7e8a8d2b9d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e1ddf191-f8da-4460-b94d-7d7e8a8d2b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb8c75c-a928-4c9b-9c88-e512ea1bb5e4' class='xr-var-data-in' type='checkbox'><label for='data-5cb8c75c-a928-4c9b-9c88-e512ea1bb5e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[189326 values with dtype=int32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c69fb7e5-f88b-4ec7-9dc8-5e4ed768f8e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-c69fb7e5-f88b-4ec7-9dc8-5e4ed768f8e0' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>sample</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fc0d5e1c-0972-4c24-aada-7fe18922d44b' class='xr-index-data-in' type='checkbox'/><label for='index-fc0d5e1c-0972-4c24-aada-7fe18922d44b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0,      1,      2,      3,      4,      5,      6,      7,      8,\n",
       "            9,\n",
       "       ...\n",
       "       189316, 189317, 189318, 189319, 189320, 189321, 189322, 189323, 189324,\n",
       "       189325],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;sample&#x27;, length=189326))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3a8b529a-2374-43f3-a3bc-828960e531db' class='xr-index-data-in' type='checkbox'/><label for='index-3a8b529a-2374-43f3-a3bc-828960e531db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-88143afc-5924-4acc-adf6-d3330dda4c45' class='xr-index-data-in' type='checkbox'/><label for='index-88143afc-5924-4acc-adf6-d3330dda4c45' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a6ef524-e397-482e-8110-be3f344f18ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a6ef524-e397-482e-8110-be3f344f18ce' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CNN data with elevation images. N = 189326. Scalar features are everything from Niccolo 30M parquet + gridCellId.Images are 27x27 pixels. Roughly uniform dist in THICK (50 bins)</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 566MB\n",
       "Dimensions:     (sample: 189326, x: 27, y: 27)\n",
       "Coordinates:\n",
       "  * sample      (sample) int32 757kB 0 1 2 3 4 ... 189322 189323 189324 189325\n",
       "  * x           (x) int32 108B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "  * y           (y) int32 108B 0 1 2 3 4 5 6 7 8 ... 18 19 20 21 22 23 24 25 26\n",
       "Data variables:\n",
       "    images      (sample, x, y) float32 552MB ...\n",
       "    labels      (sample) float64 2MB ...\n",
       "    vx          (sample) float64 2MB ...\n",
       "    vy          (sample) float64 2MB ...\n",
       "    v           (sample) float64 2MB ...\n",
       "    smb         (sample) float64 2MB ...\n",
       "    z           (sample) float64 2MB ...\n",
       "    s           (sample) float64 2MB ...\n",
       "    temp        (sample) float64 2MB ...\n",
       "    gridCellId  (sample) int32 757kB ...\n",
       "Attributes:\n",
       "    description:  CNN data with elevation images. N = 189326. Scalar features..."
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = xr.open_dataset('Gridded_CNN_train_uniform.nc')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "909cbf50",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = xr.open_dataset('D:/DOCS/AntarticaBedmap/Gridded_CNN_train_uniform_VelSurface.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0f7348dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1892, 27, 27)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = data.z_images.values[::100]\n",
    "VX = data.vx_images.values[::100]\n",
    "VY = data.vy_images.values[::100]\n",
    "V = VX+VY\n",
    "labels = data.labels.values[::100]\n",
    "V.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cb8f54e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 758.71625,  747.8932 ,  738.0409 ,  739.1947 ,  751.47314,\n",
       "         759.60144,  757.624  ,  754.82355,  752.2944 ,  746.8666 ,\n",
       "         741.5578 ,  739.4795 ,  737.69904,  734.3263 ,  725.3909 ,\n",
       "         719.0792 ,  717.751  ,  715.6693 ,  706.9947 ,  691.98285,\n",
       "         671.292  ,  648.18   ,  623.81177,  598.2093 ,  566.3176 ,\n",
       "         546.02673,  538.538  ],\n",
       "       [ 770.47107,  759.8682 ,  750.09674,  750.1514 ,  757.9271 ,\n",
       "         765.0736 ,  766.16455,  764.747  ,  762.61786,  757.2117 ,\n",
       "         755.9828 ,  757.45013,  753.0962 ,  744.70215,  742.4739 ,\n",
       "         738.70306,  734.77515,  731.87823,  722.5432 ,  707.0293 ,\n",
       "         688.7833 ,  664.0321 ,  638.65735,  615.30597,  583.8299 ,\n",
       "         558.4388 ,  550.8086 ],\n",
       "       [ 780.8736 ,  771.5699 ,  766.12274,  767.2503 ,  769.3253 ,\n",
       "         770.27814,  771.6918 ,  771.61316,  769.7759 ,  769.973  ,\n",
       "         770.2005 ,  767.3624 ,  763.5513 ,  762.19635,  761.91595,\n",
       "         759.16876,  751.696  ,  743.63855,  733.94586,  719.81116,\n",
       "         703.264  ,  682.588  ,  657.96173,  633.3504 ,  598.25037,\n",
       "         569.9233 ,  557.1749 ],\n",
       "       [ 791.57294,  785.0429 ,  783.4848 ,  784.19977,  784.2918 ,\n",
       "         783.29517,  782.1616 ,  781.33575,  780.06415,  781.1813 ,\n",
       "         777.0083 ,  773.6228 ,  776.6602 ,  778.10596,  777.3625 ,\n",
       "         775.22437,  768.10156,  756.8365 ,  742.8248 ,  728.1376 ,\n",
       "         709.8567 ,  691.7619 ,  668.1707 ,  641.88635,  607.9533 ,\n",
       "         575.4935 ,  562.66547],\n",
       "       [ 800.3752 ,  798.3191 ,  798.4311 ,  796.57477,  795.6261 ,\n",
       "         794.62354,  792.5196 ,  789.13727,  787.2162 ,  788.0271 ,\n",
       "         789.87946,  791.7681 ,  794.0805 ,  793.995  ,  792.3673 ,\n",
       "         787.2922 ,  779.0692 ,  766.17944,  751.8914 ,  735.79144,\n",
       "         716.7624 ,  697.88   ,  677.3073 ,  647.75073,  614.6159 ,\n",
       "         583.1597 ,  569.0245 ],\n",
       "       [ 810.7943 ,  811.19116,  811.6967 ,  807.85614,  804.7235 ,\n",
       "         799.9889 ,  791.6099 ,  787.1421 ,  792.857  ,  801.3254 ,\n",
       "         807.36005,  810.68005,  810.3839 ,  810.51666,  807.46954,\n",
       "         796.9021 ,  783.60455,  772.02563,  758.37427,  745.7291 ,\n",
       "         728.9701 ,  708.47705,  687.5087 ,  656.64386,  626.8473 ,\n",
       "         592.19763,  576.7242 ],\n",
       "       [ 826.54553,  823.6415 ,  820.9048 ,  816.25494,  806.9499 ,\n",
       "         798.473  ,  794.3941 ,  799.307  ,  810.4939 ,  816.3273 ,\n",
       "         817.5107 ,  819.33453,  820.55676,  817.4888 ,  814.69904,\n",
       "         811.77856,  801.7271 ,  790.79156,  778.1195 ,  766.47015,\n",
       "         746.7677 ,  724.9917 ,  692.99603,  672.55597,  647.93854,\n",
       "         616.0635 ,  595.76465],\n",
       "       [ 837.17847,  831.3097 ,  824.3972 ,  816.41144,  807.6082 ,\n",
       "         803.0547 ,  811.22656,  820.1113 ,  825.0324 ,  827.7279 ,\n",
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       "         884.17413,  862.88025,  851.20044,  839.401  ,  818.305  ,\n",
       "         803.7593 ,  785.19403,  769.0501 ,  750.6378 ,  740.3166 ,\n",
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       "         892.4579 ,  873.308  ,  866.61163,  857.91425,  840.6952 ,\n",
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       "         942.4506 ,  939.01715,  932.723  ,  924.25366,  918.30884,\n",
       "         906.57947,  890.0969 ,  876.43085,  869.84375,  857.62164,\n",
       "         852.21295,  840.5641 ,  812.293  ,  785.413  ,  770.88824,\n",
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       "         955.83105,  956.81433,  954.7377 ,  953.2581 ,  951.4033 ,\n",
       "         948.93854,  948.2558 ,  943.51135,  934.3973 ,  924.5845 ,\n",
       "         922.54175,  912.0469 ,  894.9299 ,  878.04913,  860.8506 ,\n",
       "         850.1877 ,  839.316  ,  819.4294 ,  793.96246,  771.69617,\n",
       "         769.07196,  770.727  ],\n",
       "       [ 945.19574,  947.9343 ,  951.98834,  956.6935 ,  961.21216,\n",
       "         966.3961 ,  970.1522 ,  969.98914,  966.9092 ,  962.45544,\n",
       "         959.11755,  953.8786 ,  949.6395 ,  945.4058 ,  936.6641 ,\n",
       "         926.76904,  917.0323 ,  902.4238 ,  891.11005,  876.294  ,\n",
       "         859.24243,  841.24475,  822.9987 ,  805.6927 ,  784.43286,\n",
       "         773.19446,  792.2541 ],\n",
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       "         973.82526,  977.30334,  980.3193 ,  980.6614 ,  977.7013 ,\n",
       "         971.02734,  964.7686 ,  957.54626,  953.70795,  950.1616 ,\n",
       "         941.72485,  932.0618 ,  913.36584,  894.74023,  886.6212 ,\n",
       "         872.9527 ,  855.8629 ,  841.5984 ,  835.44135,  828.7736 ,\n",
       "         816.0017 ,  820.9006 ],\n",
       "       [ 963.8245 ,  961.9323 ,  965.16614,  971.90936,  977.8903 ,\n",
       "         982.0299 ,  983.0382 ,  985.33295,  988.1451 ,  987.9563 ,\n",
       "         983.36914,  974.7366 ,  965.9115 ,  960.195  ,  955.1482 ,\n",
       "         949.7486 ,  939.6857 ,  928.9512 ,  911.0213 ,  899.17676,\n",
       "         885.6603 ,  872.1875 ,  865.74506,  874.5106 ,  880.18304,\n",
       "         870.77936,  860.97784],\n",
       "       [ 978.4339 ,  978.8483 ,  979.67224,  981.07623,  986.07275,\n",
       "         991.7258 ,  994.8542 ,  996.8698 ,  998.3976 ,  997.2902 ,\n",
       "         993.57526,  988.93665,  979.367  ,  973.9712 ,  970.80054,\n",
       "         963.29016,  950.7066 ,  937.3827 ,  924.3625 ,  915.07654,\n",
       "         906.5525 ,  897.3263 ,  890.5516 ,  889.0563 ,  898.81116,\n",
       "         905.55316,  891.7181 ],\n",
       "       [ 987.4494 ,  988.03   ,  987.7845 ,  988.4791 ,  993.1834 ,\n",
       "         998.51575, 1002.9361 , 1005.3111 , 1006.36304, 1006.079  ,\n",
       "        1003.27924,  999.69775,  995.13837,  985.8965 ,  979.92694,\n",
       "         973.3434 ,  960.8178 ,  947.55493,  936.8066 ,  925.81903,\n",
       "         922.22797,  918.60284,  914.3651 ,  912.86194,  908.35333,\n",
       "         910.49365,  908.7345 ],\n",
       "       [ 996.8387 ,  996.2997 ,  995.0454 ,  994.35126,  997.34985,\n",
       "        1002.9524 , 1007.7489 , 1011.0999 , 1012.50714, 1011.9428 ,\n",
       "        1010.2651 , 1008.5286 , 1005.57776,  996.86633,  984.1445 ,\n",
       "         971.0647 ,  961.5647 ,  954.77277,  951.01465,  940.4914 ,\n",
       "         929.7272 ,  929.1489 ,  930.19794,  938.13245,  943.9457 ,\n",
       "         936.6311 ,  930.4469 ],\n",
       "       [1009.50366, 1009.22815, 1006.4334 , 1004.08923, 1003.9203 ,\n",
       "        1007.31226, 1013.04926, 1017.59607, 1020.0408 , 1020.2671 ,\n",
       "        1017.4877 , 1012.6673 , 1005.11945,  996.67596,  989.1752 ,\n",
       "         976.95746,  964.444  ,  962.6547 ,  964.7617 ,  958.9493 ,\n",
       "         947.6009 ,  943.1143 ,  944.104  ,  952.6283 ,  959.26465,\n",
       "         957.6259 ,  954.4685 ],\n",
       "       [1016.28064, 1018.542  , 1019.4889 , 1018.0632 , 1016.73346,\n",
       "        1017.039  , 1020.797  , 1024.7007 , 1025.9926 , 1024.7734 ,\n",
       "        1022.11456, 1016.2408 , 1009.8903 , 1001.32104,  992.27045,\n",
       "         988.3388 ,  979.1502 ,  973.17334,  975.29535,  974.8518 ,\n",
       "         967.454  ,  958.2576 ,  954.3507 ,  957.6213 ,  969.5873 ,\n",
       "         981.197  ,  986.73065]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[200]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "117884cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=5, figsize=(10,6))\n",
    "\n",
    "offset = 110\n",
    "save_label = None\n",
    "for i in range(5):\n",
    "    # train\n",
    "    if i == 0: save_label = labels[i+offset]\n",
    "    ax[0,i].imshow(Z[i+offset], cmap=plt.cm.Blues)\n",
    "    ax[0,i].set_xlabel(f'{labels[i+offset]:.1f} [m]', fontsize=12, labelpad = 20)\n",
    "    ax[0,i].set_xticks([]); ax[0,i].set_yticks([]); ax[0,i].grid(False)\n",
    "    # val\n",
    "    ax[1,i].imshow(V[i+offset], cmap=plt.cm.Oranges)\n",
    "    # ax[1,i].set_xlabel(labels[i+offset], fontsize=10)\n",
    "    ax[1,i].set_xticks([]); ax[1,i].set_yticks([]); ax[1,i].grid(False)\n",
    "    # test\n",
    "    # ax[2,i].imshow(VY[i+offset], cmap=plt.cm.Blues)\n",
    "    # ax[2,i].set_xlabel(labels[i+offset], fontsize=10)\n",
    "    # ax[2,i].set_xticks([]); ax[2,i].set_yticks([]); ax[2,i].grid(False)\n",
    "\n",
    "# Add row labels\n",
    "ax[0,0].set_ylabel(\"Surface elevation\", fontsize=12, rotation=90, labelpad=20)\n",
    "ax[1,0].set_ylabel(\"Velocity\", fontsize=12, rotation=90, labelpad=20)\n",
    "ax[0,0].set_xlabel(f'DEPTH:    {save_label} [m]', fontsize=12, labelpad=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37ae90fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples: (189326, 27, 27)\n",
      "Data.images shape: (189326, 27, 27)\n",
      "# Training samples: 146820\n",
      "# Testing samples: 42506\n",
      "Total samples: 189326\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = xr.open_dataset('Gridded_CNN_train_uniform.nc')\n",
    "\n",
    "Nsamples = data.images.values.shape\n",
    "\n",
    "print('Number of samples:', Nsamples)\n",
    "print('Data.images shape:', data.images.values.shape)\n",
    "\n",
    "cellIds = data.gridCellId.values\n",
    "\n",
    "mask = cellIds % 4 == 0#cellIds % 2 == cellIds // 15 % 2\n",
    "\n",
    "even_count = np.sum(mask)\n",
    "\n",
    "testingIds = np.argwhere(mask).flatten()\n",
    "trainingIds = np.argwhere(~mask).flatten()\n",
    "\n",
    "print('# Training samples:', len(trainingIds))\n",
    "print('# Testing samples:', len(testingIds))\n",
    "print('Total samples:', len(trainingIds) + len(testingIds))\n",
    "\n",
    "plt.hist(cellIds[trainingIds])\n",
    "plt.hist(cellIds[testingIds])\n",
    "plt.show()\n",
    "\n",
    "scalar_feats = ['vx', 'vy', 'v', 'smb', 'z', 's', 'temp']\n",
    "\n",
    "feats = np.array([data[feat].values for feat in scalar_feats]).T #(200_000, 7)\n",
    "\n",
    "images = data.images.values#np.stack((data.vx_images.values, data.vy_images.values, data.z_images.values), axis=-1)\n",
    "#(190_000, 27, 27, 3)\n",
    "\n",
    "train_images = images[trainingIds]\n",
    "ytrain = data.labels.values[trainingIds]\n",
    "train_feats = feats[trainingIds]\n",
    "\n",
    "test_images = images[testingIds]\n",
    "ytest = data.labels.values[testingIds]\n",
    "test_feats = feats[testingIds]\n",
    "\n",
    "val_images, test_images, val_feats, test_feats, yval, ytest =\\\n",
    "      train_test_split(test_images, test_feats, ytest, test_size=0.5, random_state = 42)\n",
    "\n",
    "#---------standardize labels-------------------\n",
    "# Compute mean and std from training labels\n",
    "y_mean = ytrain.mean()\n",
    "y_std = ytrain.std()\n",
    "\n",
    "# Standardize ytrain and ytest using training statistics\n",
    "ytrain_std = (ytrain - y_mean) / y_std\n",
    "ytest_std = (ytest - y_mean) / y_std\n",
    "yval_std = (yval - y_mean) / y_std\n",
    "#to convert back to original scale, use:\n",
    "# ypreds_orig = ypreds * y_std + y_mean\n",
    "#----------------------------------------------\n",
    "\n",
    "# ytrain = ytrain.values\n",
    "# ytest = ytest.values\n",
    "\n",
    "# explicitly illustrating standardization\n",
    "def standardizeimg(img, mu, sigma):\n",
    "    return (img - mu) / sigma\n",
    "\n",
    "# save for scaling test dataA\n",
    "mu_train = np.mean(train_images, axis = (0,1,2))\n",
    "sigma_train = np.std(train_images, axis = (0,1,2))\n",
    "\n",
    "# Standardize pixel distribution to have zero mean and unit variance\n",
    "train_images = standardizeimg(img=train_images, mu=mu_train, sigma=sigma_train)\n",
    "test_images = standardizeimg(img=test_images, mu=mu_train, sigma=sigma_train)\n",
    "val_images = standardizeimg(img=val_images, mu=mu_train, sigma=sigma_train)\n",
    "\n",
    "#---------standardize scalar features-------------------\n",
    "# Compute mean and std from training features\n",
    "feats_mean = train_feats.mean(axis=0)\n",
    "feats_std = train_feats.std(axis=0)\n",
    "\n",
    "# Standardize train, val, and test features using training statistics\n",
    "train_feats = (train_feats - feats_mean) / feats_std\n",
    "val_feats = (val_feats - feats_mean) / feats_std\n",
    "test_feats = (test_feats - feats_mean) / feats_std\n",
    "#------------------------------------------------------\n",
    "\n",
    "# adapt to format required by tensorflow; Using channels_last --> (n_samples, img_rows, img_cols, n_channels)\n",
    "img_rows, img_cols = 27, 27 # input image dimensions\n",
    "train_images = train_images.reshape(train_images.shape[0], img_rows, img_cols, 1)\n",
    "test_images = test_images.reshape(test_images.shape[0], img_rows, img_cols, 1)\n",
    "val_images = val_images.reshape(val_images.shape[0], img_rows, img_cols, 1)\n",
    "\n",
    "# Data augmentation for high target values\n",
    "def augment_images(images, feats, targets, threshold=4257, n_aug=3):\n",
    "    augmented_images = []\n",
    "    augmented_feats = []\n",
    "    augmented_targets = []\n",
    "    for img, feat, target in zip(images, feats, targets):\n",
    "        if target > threshold:\n",
    "            for _ in range(n_aug):\n",
    "                # Randomly rotate 0, 90, 180, or 270 degrees\n",
    "                k = np.random.choice([1, 2, 3])\n",
    "                aug_img = np.rot90(img.squeeze(), k)\n",
    "                aug_img = np.expand_dims(aug_img, axis=-1)\n",
    "                # Optionally, add horizontal flip\n",
    "                if np.random.rand() > 0.5:\n",
    "                    aug_img = np.fliplr(aug_img)\n",
    "                augmented_images.append(aug_img)\n",
    "                augmented_feats.append(feat)\n",
    "                augmented_targets.append(target)\n",
    "    if augmented_images:\n",
    "        images = np.concatenate([images, np.array(augmented_images)], axis=0)\n",
    "        feats = np.concatenate([feats, np.array(augmented_feats)], axis=0)\n",
    "        targets = np.concatenate([targets, np.array(augmented_targets)], axis=0)\n",
    "    return images, feats, targets\n",
    "\n",
    "thrsh = (4400 - y_mean) / y_std\n",
    "# Apply augmentation to high-target samples only\n",
    "train_images, train_feats, ytrain_std = augment_images(train_images, train_feats, ytrain_std, threshold=thrsh, n_aug=3)\n",
    "\n",
    "plt.hist(ytrain_std, bins = 50)\n",
    "# plt.vlines(x=thrsh, ymin=0, ymax=3000, color='red')\n",
    "plt.show()\n",
    "\n",
    "fig, ax = plt.subplots(nrows=3, ncols=5, figsize=(10,6))\n",
    "\n",
    "offset = 20\n",
    "for i in range(5):\n",
    "    # train\n",
    "    im = train_images[i+offset]\n",
    "    ax[0,i].imshow(im[:,:,0], cmap=plt.cm.Blues)\n",
    "    ax[0,i].set_xlabel(ytrain[i], fontsize=10)\n",
    "    ax[0,i].set_xticks([]); ax[0,i].set_yticks([]); ax[0,i].grid(False)\n",
    "    # val\n",
    "    ax[1,i].imshow(im[:,:,0], cmap=plt.cm.Blues)\n",
    "    ax[1,i].set_xlabel(ytrain[i], fontsize=10)\n",
    "    ax[1,i].set_xticks([]); ax[1,i].set_yticks([]); ax[1,i].grid(False)\n",
    "    # test\n",
    "    ax[2,i].imshow(im[:,:,0], cmap=plt.cm.Blues)\n",
    "    ax[2,i].set_xlabel(ytrain[i], fontsize=10)\n",
    "    ax[2,i].set_xticks([]); ax[2,i].set_yticks([]); ax[2,i].grid(False)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "44b788ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d30c4a6610>]"
      ]
     },
     "execution_count": 32,
     "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": [
    "plt.imshow(train_images[0][:,:,0], cmap = plt.cm.Blues)\n",
    "plt.plot(13,13, 'X', color='r', markersize=15, label = 'Center Pixel')\n",
    "# plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8f92cbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras_tuner as kt\n",
    "from tensorflow.keras import Input, Model\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Concatenate, Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "def build_model_2(hp):\n",
    "    img_input = Input(shape=(img_rows, img_cols, 3), name='img_input')\n",
    "    x = img_input\n",
    "\n",
    "    # 2-3 Conv blocks, doubling filters each time\n",
    "    n_blocks = hp.Int('n_blocks', 2, 3)\n",
    "    filters = 32\n",
    "    for block in range(n_blocks):\n",
    "        x = Conv2D(filters, kernel_size=hp.Int(f'kSize_{block}_1', 3,7), activation='relu', padding='same')(x)\n",
    "        if hp.Boolean(f'batchnorm_{block}'):\n",
    "            x = layers.BatchNormalization()(x)\n",
    "        x = Conv2D(filters, kernel_size=hp.Int(f'kSize_{block}_2', 3, 7), activation='relu', padding='same')(x)\n",
    "        x = MaxPooling2D(pool_size=2)(x)\n",
    "        filters *= 2\n",
    "\n",
    "    x = Flatten()(x)\n",
    "    feats_input = Input(shape=(train_feats.shape[1],), name='feats_input')\n",
    "    concat = Concatenate()([x, feats_input])\n",
    "\n",
    "    # 1-2 dense layers\n",
    "    n_dense = hp.Int('n_dense', 1, 2)\n",
    "    for i in range(n_dense):\n",
    "        concat = Dense(hp.Int(f'dense_units_{i}', 64, 256, step=64), activation='relu')(concat)\n",
    "        if hp.Boolean(f'dense_dropout_{i}'):\n",
    "            concat = Dropout(rate=hp.Float(f'dense_dropout_rate_{i}', 0.1, 0.5, step=0.1))(concat)\n",
    "\n",
    "    output = Dense(1, activation='linear')(concat)\n",
    "    model = Model(inputs=[img_input, feats_input], outputs=output)\n",
    "    model.compile(\n",
    "        optimizer=Adam(hp.Float('lr', 1e-4, 1e-2, sampling='log')),\n",
    "        loss='mse',\n",
    "        metrics=['mae']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e1ca8134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading Tuner from C:/Users/Cap/Documents/Python_Scripts/AppML/FinalProject/CNN_tuner_output\\cnn_bayes_bayesopt_surfVel\\tuner0.json\n"
     ]
    }
   ],
   "source": [
    "# Model-building function for Keras Tuner\n",
    "def build_model(hp):\n",
    "    # Image input\n",
    "    img_input = Input(shape=(img_rows, img_cols, 1), name='img_input')\n",
    "    x = img_input\n",
    "    # Convolutional layers\n",
    "    for i in range(hp.Int('conv_layers', 1, 3)):\n",
    "        x = Conv2D(\n",
    "            filters=hp.Int(f'filters_{i}', 16, 64, step=16),\n",
    "            kernel_size=hp.Choice(f'kernel_size_{i}', [3, 5]),\n",
    "            activation='relu',\n",
    "            padding='same')(x)\n",
    "        x = MaxPooling2D(pool_size=2)(x)\n",
    "    x = Flatten()(x)\n",
    "    # Scalar features input\n",
    "    feats_input = Input(shape=(train_feats.shape[1],), name='feats_input')\n",
    "    # Concatenate\n",
    "    concat = Concatenate()([x, feats_input])\n",
    "    # Dense layers\n",
    "    for j in range(hp.Int('dense_layers', 1, 3)):\n",
    "        concat = Dense(hp.Int(f'dense_units_{j}', 32, 128, step=32), activation='relu')(concat)\n",
    "        if hp.Boolean(f'dropout_{j}'):\n",
    "            concat = Dropout(rate=hp.Float(f'dropout_rate_{j}', 0.1, 0.5, step=0.1))(concat)\n",
    "    output = Dense(1, activation='linear')(concat)\n",
    "    model = Model(inputs=[img_input, feats_input], outputs=output)\n",
    "    model.compile(\n",
    "        optimizer=Adam(hp.Float('lr', 1e-4, 1e-2, sampling='log')),\n",
    "        loss='mse',\n",
    "        metrics=['mae']\n",
    "    )\n",
    "\n",
    "    print(hp.values)\n",
    "    return model\n",
    "\n",
    "directory = 'C:/Users/Cap/Documents/Python_Scripts/AppML/FinalProject/CNN_tuner_output'\n",
    "# Set up the BayesianOptimization tuner\n",
    "bayes_tuner = kt.BayesianOptimization(\n",
    "    build_model_2,\n",
    "    objective='val_mae',\n",
    "    max_trials=15,\n",
    "    directory=directory,\n",
    "    project_name='cnn_bayes_bayesopt_surfVel'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b4b52e1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 11 Complete [00h 33m 24s]\n",
      "val_mae: 0.23299406468868256\n",
      "\n",
      "Best val_mae So Far: 0.23299406468868256\n",
      "Total elapsed time: 07h 06m 30s\n",
      "\n",
      "Search: Running Trial #12\n",
      "\n",
      "Value             |Best Value So Far |Hyperparameter\n",
      "3                 |2                 |n_blocks\n",
      "False             |False             |batchnorm_0\n",
      "False             |True              |batchnorm_1\n",
      "2                 |2                 |n_dense\n",
      "256               |64                |dense_units_0\n",
      "True              |False             |dense_dropout_0\n",
      "0.00016356        |0.00056609        |lr\n",
      "0.1               |0.3               |dense_dropout_rate_0\n",
      "4                 |3                 |kSize_0_1\n",
      "3                 |4                 |kSize_0_2\n",
      "7                 |3                 |kSize_1_1\n",
      "7                 |4                 |kSize_1_2\n",
      "128               |128               |dense_units_1\n",
      "False             |False             |dense_dropout_1\n",
      "6                 |4                 |kSize_2_1\n",
      "True              |False             |batchnorm_2\n",
      "6                 |3                 |kSize_2_2\n",
      "0.1               |0.1               |dense_dropout_rate_1\n",
      "\n",
      "Epoch 1/20\n",
      "\u001b[1m2290/2290\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m353s\u001b[0m 153ms/step - loss: 0.2001 - mae: 0.3297 - val_loss: 0.1565 - val_mae: 0.2938\n",
      "Epoch 2/20\n",
      "\u001b[1m2290/2290\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m347s\u001b[0m 152ms/step - loss: 0.1014 - mae: 0.2269 - val_loss: 0.1269 - val_mae: 0.2672\n",
      "Epoch 3/20\n",
      "\u001b[1m2290/2290\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m350s\u001b[0m 153ms/step - loss: 0.0865 - mae: 0.2049 - val_loss: 0.1250 - val_mae: 0.2617\n",
      "Epoch 4/20\n",
      "\u001b[1m1173/2290\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m2:45\u001b[0m 148ms/step - loss: 0.0775 - mae: 0.1916"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Run the search with a single progress bar per epoch\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43mbayes_tuner\u001b[49m\u001b[43m.\u001b[49m\u001b[43msearch\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[43m    \u001b[49m\u001b[43m[\u001b[49m\u001b[43mtrain_images\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_feats\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mytrain_std\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      4\u001b[39m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[43m=\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mval_images\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_feats\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43myval_std\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      5\u001b[39m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m20\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      6\u001b[39m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m64\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m      7\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkeras\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[43m.\u001b[49m\u001b[43mEarlyStopping\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpatience\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrestore_best_weights\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m      8\u001b[39m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Ensures only one progress bar per epoch\u001b[39;49;00m\n\u001b[32m      9\u001b[39m \u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\base_tuner.py:234\u001b[39m, in \u001b[36mBaseTuner.search\u001b[39m\u001b[34m(self, *fit_args, **fit_kwargs)\u001b[39m\n\u001b[32m    231\u001b[39m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m    233\u001b[39m     \u001b[38;5;28mself\u001b[39m.on_trial_begin(trial)\n\u001b[32m--> \u001b[39m\u001b[32m234\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_try_run_and_update_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    235\u001b[39m     \u001b[38;5;28mself\u001b[39m.on_trial_end(trial)\n\u001b[32m    236\u001b[39m \u001b[38;5;28mself\u001b[39m.on_search_end()\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\base_tuner.py:274\u001b[39m, in \u001b[36mBaseTuner._try_run_and_update_trial\u001b[39m\u001b[34m(self, trial, *fit_args, **fit_kwargs)\u001b[39m\n\u001b[32m    272\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_try_run_and_update_trial\u001b[39m(\u001b[38;5;28mself\u001b[39m, trial, *fit_args, **fit_kwargs):\n\u001b[32m    273\u001b[39m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m274\u001b[39m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run_and_update_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    275\u001b[39m         trial.status = trial_module.TrialStatus.COMPLETED\n\u001b[32m    276\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\base_tuner.py:239\u001b[39m, in \u001b[36mBaseTuner._run_and_update_trial\u001b[39m\u001b[34m(self, trial, *fit_args, **fit_kwargs)\u001b[39m\n\u001b[32m    238\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_run_and_update_trial\u001b[39m(\u001b[38;5;28mself\u001b[39m, trial, *fit_args, **fit_kwargs):\n\u001b[32m--> \u001b[39m\u001b[32m239\u001b[39m     results = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrun_trial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mfit_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    240\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.oracle.get_trial(trial.trial_id).metrics.exists(\n\u001b[32m    241\u001b[39m         \u001b[38;5;28mself\u001b[39m.oracle.objective.name\n\u001b[32m    242\u001b[39m     ):\n\u001b[32m    243\u001b[39m         \u001b[38;5;66;03m# The oracle is updated by calling `self.oracle.update_trial()` in\u001b[39;00m\n\u001b[32m    244\u001b[39m         \u001b[38;5;66;03m# `Tuner.run_trial()`. For backward compatibility, we support this\u001b[39;00m\n\u001b[32m    245\u001b[39m         \u001b[38;5;66;03m# use case. No further action needed in this case.\u001b[39;00m\n\u001b[32m    246\u001b[39m         warnings.warn(\n\u001b[32m    247\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33mThe use case of calling \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    248\u001b[39m             \u001b[33m\"\u001b[39m\u001b[33m`self.oracle.update_trial(trial_id, metrics)` \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   (...)\u001b[39m\u001b[32m    254\u001b[39m             stacklevel=\u001b[32m2\u001b[39m,\n\u001b[32m    255\u001b[39m         )\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\tuner.py:314\u001b[39m, in \u001b[36mTuner.run_trial\u001b[39m\u001b[34m(self, trial, *args, **kwargs)\u001b[39m\n\u001b[32m    312\u001b[39m     callbacks.append(model_checkpoint)\n\u001b[32m    313\u001b[39m     copied_kwargs[\u001b[33m\"\u001b[39m\u001b[33mcallbacks\u001b[39m\u001b[33m\"\u001b[39m] = callbacks\n\u001b[32m--> \u001b[39m\u001b[32m314\u001b[39m     obj_value = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_build_and_fit_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mcopied_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    316\u001b[39m     histories.append(obj_value)\n\u001b[32m    317\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m histories\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\tuner.py:233\u001b[39m, in \u001b[36mTuner._build_and_fit_model\u001b[39m\u001b[34m(self, trial, *args, **kwargs)\u001b[39m\n\u001b[32m    231\u001b[39m hp = trial.hyperparameters\n\u001b[32m    232\u001b[39m model = \u001b[38;5;28mself\u001b[39m._try_build(hp)\n\u001b[32m--> \u001b[39m\u001b[32m233\u001b[39m results = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mhypermodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    235\u001b[39m \u001b[38;5;66;03m# Save the build config for model loading later.\u001b[39;00m\n\u001b[32m    236\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m backend.config.multi_backend():\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras_tuner\\src\\engine\\hypermodel.py:149\u001b[39m, in \u001b[36mHyperModel.fit\u001b[39m\u001b[34m(self, hp, model, *args, **kwargs)\u001b[39m\n\u001b[32m    125\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfit\u001b[39m(\u001b[38;5;28mself\u001b[39m, hp, model, *args, **kwargs):\n\u001b[32m    126\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Train the model.\u001b[39;00m\n\u001b[32m    127\u001b[39m \n\u001b[32m    128\u001b[39m \u001b[33;03m    Args:\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    147\u001b[39m \u001b[33;03m        If return a float, it should be the `objective` value.\u001b[39;00m\n\u001b[32m    148\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m149\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:117\u001b[39m, in \u001b[36mfilter_traceback.<locals>.error_handler\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    115\u001b[39m filtered_tb = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    116\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m117\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    118\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m    119\u001b[39m     filtered_tb = _process_traceback_frames(e.__traceback__)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:371\u001b[39m, in \u001b[36mTensorFlowTrainer.fit\u001b[39m\u001b[34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[39m\n\u001b[32m    369\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator:\n\u001b[32m    370\u001b[39m     callbacks.on_train_batch_begin(step)\n\u001b[32m--> \u001b[39m\u001b[32m371\u001b[39m     logs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    372\u001b[39m     callbacks.on_train_batch_end(step, logs)\n\u001b[32m    373\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.stop_training:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras\\src\\backend\\tensorflow\\trainer.py:219\u001b[39m, in \u001b[36mTensorFlowTrainer._make_function.<locals>.function\u001b[39m\u001b[34m(iterator)\u001b[39m\n\u001b[32m    215\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mfunction\u001b[39m(iterator):\n\u001b[32m    216\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\n\u001b[32m    217\u001b[39m         iterator, (tf.data.Iterator, tf.distribute.DistributedIterator)\n\u001b[32m    218\u001b[39m     ):\n\u001b[32m--> \u001b[39m\u001b[32m219\u001b[39m         opt_outputs = \u001b[43mmulti_step_on_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    220\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m opt_outputs.has_value():\n\u001b[32m    221\u001b[39m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[39m, in \u001b[36mfilter_traceback.<locals>.error_handler\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    148\u001b[39m filtered_tb = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    149\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m150\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    151\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m    152\u001b[39m   filtered_tb = _process_traceback_frames(e.__traceback__)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:833\u001b[39m, in \u001b[36mFunction.__call__\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m    830\u001b[39m compiler = \u001b[33m\"\u001b[39m\u001b[33mxla\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mnonXla\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    832\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m._jit_compile):\n\u001b[32m--> \u001b[39m\u001b[32m833\u001b[39m   result = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    835\u001b[39m new_tracing_count = \u001b[38;5;28mself\u001b[39m.experimental_get_tracing_count()\n\u001b[32m    836\u001b[39m without_tracing = (tracing_count == new_tracing_count)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:878\u001b[39m, in \u001b[36mFunction._call\u001b[39m\u001b[34m(self, *args, **kwds)\u001b[39m\n\u001b[32m    875\u001b[39m \u001b[38;5;28mself\u001b[39m._lock.release()\n\u001b[32m    876\u001b[39m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[32m    877\u001b[39m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m878\u001b[39m results = \u001b[43mtracing_compilation\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    879\u001b[39m \u001b[43m    \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[32m    880\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    881\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._created_variables:\n\u001b[32m    882\u001b[39m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mCreating variables on a non-first call to a function\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    883\u001b[39m                    \u001b[33m\"\u001b[39m\u001b[33m decorated with tf.function.\u001b[39m\u001b[33m\"\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[39m, in \u001b[36mcall_function\u001b[39m\u001b[34m(args, kwargs, tracing_options)\u001b[39m\n\u001b[32m    137\u001b[39m bound_args = function.function_type.bind(*args, **kwargs)\n\u001b[32m    138\u001b[39m flat_inputs = function.function_type.unpack_inputs(bound_args)\n\u001b[32m--> \u001b[39m\u001b[32m139\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[32m    140\u001b[39m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mfunction\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[32m    141\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1322\u001b[39m, in \u001b[36mConcreteFunction._call_flat\u001b[39m\u001b[34m(self, tensor_inputs, captured_inputs)\u001b[39m\n\u001b[32m   1318\u001b[39m possible_gradient_type = gradients_util.PossibleTapeGradientTypes(args)\n\u001b[32m   1319\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type == gradients_util.POSSIBLE_GRADIENT_TYPES_NONE\n\u001b[32m   1320\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[32m   1321\u001b[39m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1322\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_inference_function\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1323\u001b[39m forward_backward = \u001b[38;5;28mself\u001b[39m._select_forward_and_backward_functions(\n\u001b[32m   1324\u001b[39m     args,\n\u001b[32m   1325\u001b[39m     possible_gradient_type,\n\u001b[32m   1326\u001b[39m     executing_eagerly)\n\u001b[32m   1327\u001b[39m forward_function, args_with_tangents = forward_backward.forward()\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[39m, in \u001b[36mAtomicFunction.call_preflattened\u001b[39m\u001b[34m(self, args)\u001b[39m\n\u001b[32m    214\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core.Tensor]) -> Any:\n\u001b[32m    215\u001b[39m \u001b[38;5;250m  \u001b[39m\u001b[33;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m216\u001b[39m   flat_outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    217\u001b[39m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.function_type.pack_output(flat_outputs)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[39m, in \u001b[36mAtomicFunction.call_flat\u001b[39m\u001b[34m(self, *args)\u001b[39m\n\u001b[32m    249\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m record.stop_recording():\n\u001b[32m    250\u001b[39m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._bound_context.executing_eagerly():\n\u001b[32m--> \u001b[39m\u001b[32m251\u001b[39m     outputs = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_bound_context\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m    252\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    253\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    254\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfunction_type\u001b[49m\u001b[43m.\u001b[49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    255\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    256\u001b[39m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    257\u001b[39m     outputs = make_call_op_in_graph(\n\u001b[32m    258\u001b[39m         \u001b[38;5;28mself\u001b[39m,\n\u001b[32m    259\u001b[39m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[32m    260\u001b[39m         \u001b[38;5;28mself\u001b[39m._bound_context.function_call_options.as_attrs(),\n\u001b[32m    261\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1688\u001b[39m, in \u001b[36mContext.call_function\u001b[39m\u001b[34m(self, name, tensor_inputs, num_outputs)\u001b[39m\n\u001b[32m   1686\u001b[39m cancellation_context = cancellation.context()\n\u001b[32m   1687\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1688\u001b[39m   outputs = \u001b[43mexecute\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1689\u001b[39m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mutf-8\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1690\u001b[39m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1691\u001b[39m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1692\u001b[39m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m=\u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1693\u001b[39m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m   1694\u001b[39m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1695\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1696\u001b[39m   outputs = execute.execute_with_cancellation(\n\u001b[32m   1697\u001b[39m       name.decode(\u001b[33m\"\u001b[39m\u001b[33mutf-8\u001b[39m\u001b[33m\"\u001b[39m),\n\u001b[32m   1698\u001b[39m       num_outputs=num_outputs,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1702\u001b[39m       cancellation_manager=cancellation_context,\n\u001b[32m   1703\u001b[39m   )\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[39m, in \u001b[36mquick_execute\u001b[39m\u001b[34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[39m\n\u001b[32m     51\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m     52\u001b[39m   ctx.ensure_initialized()\n\u001b[32m---> \u001b[39m\u001b[32m53\u001b[39m   tensors = \u001b[43mpywrap_tfe\u001b[49m\u001b[43m.\u001b[49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m     54\u001b[39m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     55\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m core._NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m     56\u001b[39m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    }
   ],
   "source": [
    "# Run the search with a single progress bar per epoch\n",
    "bayes_tuner.search(\n",
    "    [train_images, train_feats], ytrain_std,\n",
    "    validation_data=([val_images, val_feats], yval_std),\n",
    "    epochs=20,\n",
    "    batch_size=64,\n",
    "    callbacks=[keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True)],\n",
    "    verbose=1  # Ensures only one progress bar per epoch\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b873689",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'conv_layers': 1, 'filters_0': 48, 'kernel_size_0': 5, 'dense_layers': 2, 'dense_units_0': 32, 'dropout_0': False, 'lr': 0.002841818806123182, 'filters_1': 16, 'kernel_size_1': 3, 'dropout_rate_0': 0.1, 'dense_units_1': 128, 'dropout_1': False, 'filters_2': 48, 'kernel_size_2': 3}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 18 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.0965 - mae: 0.2220\n",
      "Test MAE: 0.2238\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "291.7641483525876"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the best model\n",
    "best_model = bayes_tuner.get_best_models(1)[0]\n",
    "\n",
    "# Evaluate on test set\n",
    "test_loss, test_mae = best_model.evaluate([test_images, test_feats], ytest_std)\n",
    "print(f'Test MAE: {test_mae:.4f}')\n",
    "\n",
    "# Make predictions\n",
    "preds = best_model.predict([test_images, test_feats]) * y_std + y_mean\n",
    "preds = np.clip(preds, 0, None)  # Ensure predictions are non-negative\n",
    "# Calculate MAE on original scale\n",
    "mae = MAE(ytest, preds)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "96e71df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "ith = pd.read_parquet('CNN_uniform_medGrid.parquet', columns = ['ith_bm', 'gridCellId'])\n",
    "ith = ith.ith_bm[ith.gridCellId % 4 == 0].to_numpy().flatten()\n",
    "ith = ith[:len(ytest)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "149f7ed4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step\n"
     ]
    }
   ],
   "source": [
    "pred_meters_y = best_model.predict([test_images, test_feats]) * y_std + y_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "595155ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_meters_y = pred_meters_y.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "fb4daba1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21253\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error (MAE) NN: 292.4415\n",
      "Mean Absolute Error (MAE) BedMachine: 1489.5653\n"
     ]
    }
   ],
   "source": [
    "#Testing on Uncorrelated Data:\n",
    "#Test size:\n",
    "print(len(ith))\n",
    "\n",
    "# benchmark_data  = test_data['ith_bm'][:N]\n",
    "# test_input_data = test_data[input_variables][:N]\n",
    "# test_truth_data = test_data['THICK'][:N]\n",
    "\n",
    "# input_test = scaler.transform(test_input_data)\n",
    "# truth_test = test_truth_data.values.reshape(-1, 1)\n",
    "# benchmark_test = benchmark_data.values.reshape(-1, 1)\n",
    "\n",
    "# Scatter plot: predicted vs true\n",
    "# truth_score = model.predict(np.array(input_test))\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(ytest, pred_meters_y, alpha=0.2, label='NN')\n",
    "# plt.scatter(ytest, ith, alpha=0.2, label='BedMachine')\n",
    "plt.plot([ytest.min(), ytest.max()], [ytest.min(), ytest.max()], 'r--')  # Ideal line\n",
    "plt.xlabel('True values')\n",
    "plt.ylabel('Predicted values')\n",
    "plt.title('Predicted vs. True')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# Residual plot: true - predicted\n",
    "residuals = ytest - pred_meters_y\n",
    "# residualsbenchmark = ytest - ith\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.hist(residuals, bins=50, alpha=0.5, label='NN')\n",
    "# plt.hist(residualsbenchmark, bins=50, alpha=0.5, label='BedMachine')\n",
    "plt.xlabel(\"Residual (True - Predicted)\")\n",
    "plt.title(\"Residual Distribution\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mae = mean_absolute_error(ytest, pred_meters_y)\n",
    "maebenchmark = mean_absolute_error(ytest, ith)\n",
    "print(f\"Mean Absolute Error (MAE) NN: {mae:.4f}\")\n",
    "print(f\"Mean Absolute Error (MAE) BedMachine: {maebenchmark:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8f4b581d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1019.23464578],\n",
       "       [ 668.83271954],\n",
       "       [2891.67993721],\n",
       "       ...,\n",
       "       [1040.93782386],\n",
       "       [3285.05157819],\n",
       "       [1129.04554624]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# residuals = ytest - pred_meters_y\n",
    "\n",
    "pred_meters_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "fa3b3ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 465.73584036,  191.02320265,    3.24253853, ..., 2401.25065562,\n",
       "       2180.60587413, 2184.89760737])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ith"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "09aa533e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:757: UserWarning: Skipping variable loading for optimizer 'adam', because it has 18 variables whereas the saved optimizer has 2 variables. \n",
      "  saveable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    }
   ],
   "source": [
    "best_model = keras.models.load_model('bestSurfaceCNN.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cebdadb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 6ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 5ms/step\n",
      "\u001b[1m665/665\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step\n",
      "Baseline MAE: 291.7641\n",
      "MAE after permuting images: 540.5966\n",
      "Image importance (MAE increase): 248.8325\n"
     ]
    }
   ],
   "source": [
    "# Baseline MAE\n",
    "baseline_preds = best_model.predict([test_images, test_feats]) * y_std + y_mean\n",
    "baseline_preds = np.clip(baseline_preds, 0, None)\n",
    "baseline_mae = MAE(ytest, baseline_preds)\n",
    "\n",
    "permMAES = []\n",
    "for i in range(10):\n",
    "    # Permute images\n",
    "    shuffled_images = np.random.permutation(test_images)\n",
    "    permuted_preds = best_model.predict([shuffled_images, test_feats]) * y_std + y_mean\n",
    "    permuted_preds = np.clip(permuted_preds, 0, None)\n",
    "    permuted_mae = MAE(ytest, permuted_preds)\n",
    "    permMAES.append(permuted_mae)\n",
    "permuted_mae = np.mean(permMAES)\n",
    "\n",
    "#Permute scalars:\n",
    "# shuffled_scalars = np.random.permutation(test_feats)\n",
    "# permuted_preds_feats = best_model.predict([test_images, shuffled_scalars]) * y_std + y_mean\n",
    "# permuted_preds_feats = np.clip(permuted_preds_feats, 0, None)\n",
    "# permuted_mae_feats = MAE(ytest, permuted_preds_feats)\n",
    "\n",
    "print(f\"Baseline MAE: {baseline_mae:.4f}\")\n",
    "print(f\"MAE after permuting images: {permuted_mae:.4f}\")\n",
    "# print(f\"MAE after permuting scalar features: {permuted_mae_feats:.4f}\")\n",
    "print(f\"Image importance (MAE increase): {permuted_mae - baseline_mae:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7942032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21253, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_feats.shape"
   ]
  }
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