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  "cells": [
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  {
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  "cell_type": "code",
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  "id": "e7e09770",
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  "metadata": {},
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  "outputs": [],
@@ -17,7 +17,7 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 3,
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  "id": "797deb25",
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  "metadata": {},
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  "outputs": [],
@@ -27,439 +27,23 @@
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  },
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  {
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  "cell_type": "code",
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- "execution_count": 4,
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  "id": "399483a9",
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  "metadata": {},
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  "outputs": [
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  {
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  "data": {
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- " cursor: pointer;\n",
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368
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375
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381
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386
- " background: var(--xr-background-color);\n",
387
- " padding-right: 10px;\n",
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397
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398
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403
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404
- " stroke: currentColor;\n",
405
- " fill: currentColor;\n",
406
- "}\n",
407
- "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 298MB\n",
408
- "Dimensions: (sample: 99766, x: 27, y: 27)\n",
409
- "Coordinates:\n",
410
- " * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
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- " * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
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- " * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
413
- "Data variables:\n",
414
- " images (sample, x, y) float32 291MB ...\n",
415
- " labels (sample) float64 798kB ...\n",
416
- " vx (sample) float64 798kB ...\n",
417
- " vy (sample) float64 798kB ...\n",
418
- " v (sample) float64 798kB ...\n",
419
- " smb (sample) float64 798kB ...\n",
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- " z (sample) float64 798kB ...\n",
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- " s (sample) float64 798kB ...\n",
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- " temp (sample) float64 798kB ...\n",
423
- "Attributes:\n",
424
- " description: CNN data with elevation images. Scalar features are everyth...</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-fab5bf5f-0a7d-4400-9852-9cd302ea8d15' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fab5bf5f-0a7d-4400-9852-9cd302ea8d15' 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>: 99766</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-32eaaf35-a186-4c6a-abb9-8a144e59074f' class='xr-section-summary-in' type='checkbox' checked><label for='section-32eaaf35-a186-4c6a-abb9-8a144e59074f' 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 ... 99762 99763 99764 99765</div><input id='attrs-a6eec0d7-e6f1-4f5c-8e5d-b836533cb8a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6eec0d7-e6f1-4f5c-8e5d-b836533cb8a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64e68426-0acf-4eac-9742-761d80fcc80f' class='xr-var-data-in' type='checkbox'><label for='data-64e68426-0acf-4eac-9742-761d80fcc80f' 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, ..., 99763, 99764, 99765], 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-a0867e38-ba6f-4e90-9e71-e7c7ccda4557' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0867e38-ba6f-4e90-9e71-e7c7ccda4557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dcb1984-8e80-49a2-a5e7-c33fd0eb7a2d' class='xr-var-data-in' type='checkbox'><label for='data-3dcb1984-8e80-49a2-a5e7-c33fd0eb7a2d' 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",
425
- " 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-cc877b04-3a3a-4b34-8a04-148b7d84b512' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cc877b04-3a3a-4b34-8a04-148b7d84b512' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d66f639-4bfb-4b40-92b5-64a39192a15e' class='xr-var-data-in' type='checkbox'><label for='data-4d66f639-4bfb-4b40-92b5-64a39192a15e' 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",
426
- " 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-3fec3564-48df-489e-a141-577c0013a24b' class='xr-section-summary-in' type='checkbox' checked><label for='section-3fec3564-48df-489e-a141-577c0013a24b' class='xr-section-summary' >Data variables: <span>(9)</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-e2df5768-3102-4b29-9bc2-be35d33a0e92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e2df5768-3102-4b29-9bc2-be35d33a0e92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a227dc8-87af-498f-b77f-bdb4aa284ac2' class='xr-var-data-in' type='checkbox'><label for='data-3a227dc8-87af-498f-b77f-bdb4aa284ac2' 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>[72729414 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-ca1f5cab-5812-4d9a-ad29-a9c07a924305' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca1f5cab-5812-4d9a-ad29-a9c07a924305' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b067e1b-1ecc-453e-88be-2aaf047146c2' class='xr-var-data-in' type='checkbox'><label for='data-9b067e1b-1ecc-453e-88be-2aaf047146c2' 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>[99766 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-722431de-0cd2-4da9-af31-f1b7119d1a7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-722431de-0cd2-4da9-af31-f1b7119d1a7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ade58dfa-9d6b-42d7-a700-9c68abaafaca' class='xr-var-data-in' type='checkbox'><label for='data-ade58dfa-9d6b-42d7-a700-9c68abaafaca' 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>[99766 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-debf125e-6404-474a-909c-b0b1a37e66d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-debf125e-6404-474a-909c-b0b1a37e66d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e73d079-8279-41ba-bb33-ae8a36fd4b08' class='xr-var-data-in' type='checkbox'><label for='data-2e73d079-8279-41ba-bb33-ae8a36fd4b08' 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>[99766 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-cfe7b680-4202-4450-aff7-6936810cea1e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfe7b680-4202-4450-aff7-6936810cea1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97bd7ed7-8130-4e75-bdcf-ee9fe9019e2f' class='xr-var-data-in' type='checkbox'><label for='data-97bd7ed7-8130-4e75-bdcf-ee9fe9019e2f' 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>[99766 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-74285667-e694-4834-803b-5100f1a32d9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74285667-e694-4834-803b-5100f1a32d9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60b09bc6-7c29-4b37-a062-2eccf148c095' class='xr-var-data-in' type='checkbox'><label for='data-60b09bc6-7c29-4b37-a062-2eccf148c095' 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>[99766 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-5d347239-c032-4499-91d0-1cf1aa488528' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d347239-c032-4499-91d0-1cf1aa488528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8966847d-0277-4575-ba90-d52c85bb412c' class='xr-var-data-in' type='checkbox'><label for='data-8966847d-0277-4575-ba90-d52c85bb412c' 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>[99766 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-392665da-bd3d-4155-961b-631d62d716c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-392665da-bd3d-4155-961b-631d62d716c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b606ad7f-0b0b-42a9-8304-c4150286b739' class='xr-var-data-in' type='checkbox'><label for='data-b606ad7f-0b0b-42a9-8304-c4150286b739' 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>[99766 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-6005ba2c-9dd4-4301-895d-c1aecbfc9ee7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6005ba2c-9dd4-4301-895d-c1aecbfc9ee7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ff47092-653a-4607-b7b1-38cbdf42298e' class='xr-var-data-in' type='checkbox'><label for='data-3ff47092-653a-4607-b7b1-38cbdf42298e' 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>[99766 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbae0e61-2071-4748-a7fa-d498d307aae7' class='xr-section-summary-in' type='checkbox' ><label for='section-fbae0e61-2071-4748-a7fa-d498d307aae7' 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-642735e3-9763-450c-9a72-a1029a9a77df' class='xr-index-data-in' type='checkbox'/><label for='index-642735e3-9763-450c-9a72-a1029a9a77df' 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,\n",
427
- " ...\n",
428
- " 99756, 99757, 99758, 99759, 99760, 99761, 99762, 99763, 99764, 99765],\n",
429
- " dtype=&#x27;int32&#x27;, name=&#x27;sample&#x27;, length=99766))</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-c585363f-c838-466b-b11d-352984aa5815' class='xr-index-data-in' type='checkbox'/><label for='index-c585363f-c838-466b-b11d-352984aa5815' 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",
430
- " 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
431
- " 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-23e0ae6e-a712-4383-8abb-b724f92a23e5' class='xr-index-data-in' type='checkbox'/><label for='index-23e0ae6e-a712-4383-8abb-b724f92a23e5' 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",
432
- " 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
433
- " dtype=&#x27;int32&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f85e146-1571-4421-9a55-fd6c39297966' class='xr-section-summary-in' type='checkbox' checked><label for='section-5f85e146-1571-4421-9a55-fd6c39297966' 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. Scalar features are everything from Niccolo 30M parquet. Images are 27x27 pixels.</dd></dl></div></li></ul></div></div>"
434
- ],
435
  "text/plain": [
436
- "<xarray.Dataset> Size: 298MB\n",
437
- "Dimensions: (sample: 99766, x: 27, y: 27)\n",
438
- "Coordinates:\n",
439
- " * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n",
440
- " * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
441
- " * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n",
442
- "Data variables:\n",
443
- " images (sample, x, y) float32 291MB ...\n",
444
- " labels (sample) float64 798kB ...\n",
445
- " vx (sample) float64 798kB ...\n",
446
- " vy (sample) float64 798kB ...\n",
447
- " v (sample) float64 798kB ...\n",
448
- " smb (sample) float64 798kB ...\n",
449
- " z (sample) float64 798kB ...\n",
450
- " s (sample) float64 798kB ...\n",
451
- " temp (sample) float64 798kB ...\n",
452
- "Attributes:\n",
453
- " description: CNN data with elevation images. Scalar features are everyth..."
454
  ]
455
  },
456
- "execution_count": 4,
457
  "metadata": {},
458
  "output_type": "execute_result"
459
  }
460
  ],
461
  "source": [
462
- "data"
463
  ]
464
  },
465
  {
@@ -907,35 +491,7 @@
907
  },
908
  {
909
  "cell_type": "code",
910
- "execution_count": 22,
911
- "id": "702c1265",
912
- "metadata": {},
913
- "outputs": [
914
- {
915
- "ename": "TypeError",
916
- "evalue": "Input should have at least 1 dimension i.e. satisfy `len(x.shape) > 0`, got scalar `array(ValuesView(<xarray.Dataset> Size: 298MB\nDimensions: (sample: 99766, x: 27, y: 27)\nCoordinates:\n * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\nData variables:\n images (sample, x, y) float32 291MB ...\n labels (sample) float64 798kB 1.064e+03 1.194e+03 ... 2.453e+03 1.68e+03\n vx (sample) float64 798kB -2.619 31.06 -2.924 ... -167.4 -43.76 -4.671\n vy (sample) float64 798kB 3.084 -41.06 8.954 ... -27.1 47.03 -1.08\n v (sample) float64 798kB 4.046 51.48 9.419 ... 169.6 64.24 4.794\n smb (sample) float64 798kB 62.64 451.3 490.5 ... 1.294e+03 738.1 31.25\n z (sample) float64 798kB 1.162e+03 1.204e+03 ... 1.22e+03 2.154e+03\n s (sample) float64 798kB 0.007977 0.01595 ... 0.00455 0.008152\n temp (sample) float64 798kB 244.0 252.7 246.3 ... 258.2 249.8 234.5\nAttributes:\n description: CNN data with elevation images. Scalar features are everyth...),\n dtype=object)` instead.",
917
- "output_type": "error",
918
- "traceback": [
919
- "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
920
- "\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
921
- "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m train_data, test_data, ydatatrain, ydatatest = \u001b[43mtrain_test_split\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m.\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtest_size\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m0.2\u001b[39;49m\u001b[43m)\u001b[49m\n",
922
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\utils\\_param_validation.py:216\u001b[39m, in \u001b[36mvalidate_params.<locals>.decorator.<locals>.wrapper\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m 210\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 211\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[32m 212\u001b[39m skip_parameter_validation=(\n\u001b[32m 213\u001b[39m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[32m 214\u001b[39m )\n\u001b[32m 215\u001b[39m ):\n\u001b[32m--> \u001b[39m\u001b[32m216\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\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 217\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m InvalidParameterError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 218\u001b[39m \u001b[38;5;66;03m# When the function is just a wrapper around an estimator, we allow\u001b[39;00m\n\u001b[32m 219\u001b[39m \u001b[38;5;66;03m# the function to delegate validation to the estimator, but we replace\u001b[39;00m\n\u001b[32m 220\u001b[39m \u001b[38;5;66;03m# the name of the estimator by the name of the function in the error\u001b[39;00m\n\u001b[32m 221\u001b[39m \u001b[38;5;66;03m# message to avoid confusion.\u001b[39;00m\n\u001b[32m 222\u001b[39m msg = re.sub(\n\u001b[32m 223\u001b[39m \u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[33m\\\u001b[39m\u001b[33mw+ must be\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 224\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc.\u001b[34m__qualname__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m must be\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 225\u001b[39m \u001b[38;5;28mstr\u001b[39m(e),\n\u001b[32m 226\u001b[39m )\n",
923
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\model_selection\\_split.py:2848\u001b[39m, in \u001b[36mtrain_test_split\u001b[39m\u001b[34m(test_size, train_size, random_state, shuffle, stratify, *arrays)\u001b[39m\n\u001b[32m 2845\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m n_arrays == \u001b[32m0\u001b[39m:\n\u001b[32m 2846\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mAt least one array required as input\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m-> \u001b[39m\u001b[32m2848\u001b[39m arrays = \u001b[43mindexable\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43marrays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 2850\u001b[39m n_samples = _num_samples(arrays[\u001b[32m0\u001b[39m])\n\u001b[32m 2851\u001b[39m n_train, n_test = _validate_shuffle_split(\n\u001b[32m 2852\u001b[39m n_samples, test_size, train_size, default_test_size=\u001b[32m0.25\u001b[39m\n\u001b[32m 2853\u001b[39m )\n",
924
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:532\u001b[39m, in \u001b[36mindexable\u001b[39m\u001b[34m(*iterables)\u001b[39m\n\u001b[32m 502\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Make arrays indexable for cross-validation.\u001b[39;00m\n\u001b[32m 503\u001b[39m \n\u001b[32m 504\u001b[39m \u001b[33;03mChecks consistent length, passes through None, and ensures that everything\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 528\u001b[39m \u001b[33;03m[[1, 2, 3], array([2, 3, 4]), None, <...Sparse...dtype 'int64'...shape (3, 1)>]\u001b[39;00m\n\u001b[32m 529\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 531\u001b[39m result = [_make_indexable(X) \u001b[38;5;28;01mfor\u001b[39;00m X \u001b[38;5;129;01min\u001b[39;00m iterables]\n\u001b[32m--> \u001b[39m\u001b[32m532\u001b[39m \u001b[43mcheck_consistent_length\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 533\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
925
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:472\u001b[39m, in \u001b[36mcheck_consistent_length\u001b[39m\u001b[34m(*arrays)\u001b[39m\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcheck_consistent_length\u001b[39m(*arrays):\n\u001b[32m 455\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Check that all arrays have consistent first dimensions.\u001b[39;00m\n\u001b[32m 456\u001b[39m \n\u001b[32m 457\u001b[39m \u001b[33;03m Checks whether all objects in arrays have the same shape or length.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 469\u001b[39m \u001b[33;03m >>> check_consistent_length(a, b)\u001b[39;00m\n\u001b[32m 470\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m lengths = \u001b[43m[\u001b[49m\u001b[43m_num_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43marrays\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m]\u001b[49m\n\u001b[32m 473\u001b[39m uniques = np.unique(lengths)\n\u001b[32m 474\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) > \u001b[32m1\u001b[39m:\n",
926
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:472\u001b[39m, in \u001b[36m<listcomp>\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m 454\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcheck_consistent_length\u001b[39m(*arrays):\n\u001b[32m 455\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Check that all arrays have consistent first dimensions.\u001b[39;00m\n\u001b[32m 456\u001b[39m \n\u001b[32m 457\u001b[39m \u001b[33;03m Checks whether all objects in arrays have the same shape or length.\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 469\u001b[39m \u001b[33;03m >>> check_consistent_length(a, b)\u001b[39;00m\n\u001b[32m 470\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m lengths = [\u001b[43m_num_samples\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m X \u001b[38;5;129;01min\u001b[39;00m arrays \u001b[38;5;28;01mif\u001b[39;00m X \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[32m 473\u001b[39m uniques = np.unique(lengths)\n\u001b[32m 474\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(uniques) > \u001b[32m1\u001b[39m:\n",
927
- "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Cap\\Documents\\Python_Scripts\\AppML\\.appMLvenv\\Lib\\site-packages\\sklearn\\utils\\validation.py:399\u001b[39m, in \u001b[36m_num_samples\u001b[39m\u001b[34m(x)\u001b[39m\n\u001b[32m 397\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[33m\"\u001b[39m\u001b[33mshape\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m x.shape \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[32m 398\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(x.shape) == \u001b[32m0\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m399\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m 400\u001b[39m \u001b[33m\"\u001b[39m\u001b[33mInput should have at least 1 dimension i.e. satisfy \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 401\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m`len(x.shape) > 0`, got scalar `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[33m` instead.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 402\u001b[39m )\n\u001b[32m 403\u001b[39m \u001b[38;5;66;03m# Check that shape is returning an integer or default to len\u001b[39;00m\n\u001b[32m 404\u001b[39m \u001b[38;5;66;03m# Dask dataframes may not return numeric shape[0] value\u001b[39;00m\n\u001b[32m 405\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x.shape[\u001b[32m0\u001b[39m], numbers.Integral):\n",
928
- "\u001b[31mTypeError\u001b[39m: Input should have at least 1 dimension i.e. satisfy `len(x.shape) > 0`, got scalar `array(ValuesView(<xarray.Dataset> Size: 298MB\nDimensions: (sample: 99766, x: 27, y: 27)\nCoordinates:\n * sample (sample) int32 399kB 0 1 2 3 4 5 ... 99761 99762 99763 99764 99765\n * x (x) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\n * y (y) int32 108B 0 1 2 3 4 5 6 7 8 9 ... 18 19 20 21 22 23 24 25 26\nData variables:\n images (sample, x, y) float32 291MB ...\n labels (sample) float64 798kB 1.064e+03 1.194e+03 ... 2.453e+03 1.68e+03\n vx (sample) float64 798kB -2.619 31.06 -2.924 ... -167.4 -43.76 -4.671\n vy (sample) float64 798kB 3.084 -41.06 8.954 ... -27.1 47.03 -1.08\n v (sample) float64 798kB 4.046 51.48 9.419 ... 169.6 64.24 4.794\n smb (sample) float64 798kB 62.64 451.3 490.5 ... 1.294e+03 738.1 31.25\n z (sample) float64 798kB 1.162e+03 1.204e+03 ... 1.22e+03 2.154e+03\n s (sample) float64 798kB 0.007977 0.01595 ... 0.00455 0.008152\n temp (sample) float64 798kB 244.0 252.7 246.3 ... 258.2 249.8 234.5\nAttributes:\n description: CNN data with elevation images. Scalar features are everyth...),\n dtype=object)` instead."
929
- ]
930
- }
931
- ],
932
- "source": [
933
- "train_data, test_data, ydatatrain, ydatatest = train_test_split(data.values(), data.labels.values, test_size=0.2)"
934
- ]
935
- },
936
- {
937
- "cell_type": "code",
938
- "execution_count": 36,
939
  "id": "f57481cb",
940
  "metadata": {},
941
  "outputs": [],
@@ -970,7 +526,7 @@
970
  },
971
  {
972
  "cell_type": "code",
973
- "execution_count": 41,
974
  "id": "73c5f289",
975
  "metadata": {},
976
  "outputs": [
 
2
  "cells": [
3
  {
4
  "cell_type": "code",
5
+ "execution_count": 1,
6
  "id": "e7e09770",
7
  "metadata": {},
8
  "outputs": [],
 
17
  },
18
  {
19
  "cell_type": "code",
20
+ "execution_count": 2,
21
  "id": "797deb25",
22
  "metadata": {},
23
  "outputs": [],
 
27
  },
28
  {
29
  "cell_type": "code",
30
+ "execution_count": 14,
31
  "id": "399483a9",
32
  "metadata": {},
33
  "outputs": [
34
  {
35
  "data": {
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
36
  "text/plain": [
37
+ "(99766, 27, 27)"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
38
  ]
39
  },
40
+ "execution_count": 14,
41
  "metadata": {},
42
  "output_type": "execute_result"
43
  }
44
  ],
45
  "source": [
46
+ "data.images.values.shape"
47
  ]
48
  },
49
  {
 
491
  },
492
  {
493
  "cell_type": "code",
494
+ "execution_count": 10,
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
495
  "id": "f57481cb",
496
  "metadata": {},
497
  "outputs": [],
 
526
  },
527
  {
528
  "cell_type": "code",
529
+ "execution_count": 11,
530
  "id": "73c5f289",
531
  "metadata": {},
532
  "outputs": [