CPTAC_trnsfrmr_v0.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e023e0d9-bb19-4d13-9689-5c21a8b4fc50",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dde382b-e6a4-41de-86c0-20d0528efaa1",
   "metadata": {},
   "source": [
    "Goal: predict cancer type based on integrated CPTAC omics data\n",
    "\n",
    "data url:\n",
    "https://figshare.com/s/7ffe48478ec907b36dfb\n",
    "\n",
    "CNV, proteomics, transcriptomics are X  \n",
    "cancer_type (10) is y, from sample file\n",
    "\n",
    "This notebook reads a latent feature file from the  \n",
    "VAE in CPTAC_VAE_v0.ipynb for each datatype  \n",
    "and the prediction targets from the sample file  \n",
    "then makes a categorical cancer type prediction  \n",
    "with a tabular transformer\n",
    "\n",
    "model template:\n",
    "https://keras.io/examples/structured_data/tabtransformer/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cdf473c-4ff3-48cc-ab58-022e184d136b",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "#### Schematic of experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "id": "0396b7c1-0cf1-48eb-9d9e-2b0656ae9345",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='CPTAC_schematic_v0.png',\n",
    "      # width=600,\n",
    "      # height=400\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777cef7e-3ea4-4e34-8f45-e0c322ea48f5",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7daedd28-1fea-40bc-ad55-05409e023564",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "01529c2a-a39f-46a8-960d-a7a9b77a6868",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Version</th>\n",
       "      <th>Build</th>\n",
       "      <th>Channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>absl-py</td>\n",
       "      <td>1.4.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aiofiles</td>\n",
       "      <td>22.1.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>aiosqlite</td>\n",
       "      <td>0.18.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>anyio</td>\n",
       "      <td>3.6.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>appnope</td>\n",
       "      <td>0.1.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>argon2-cffi</td>\n",
       "      <td>21.3.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>argon2-cffi-bindings</td>\n",
       "      <td>21.2.0</td>\n",
       "      <td>py39h02fc5c5_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>arrow</td>\n",
       "      <td>1.2.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>asttokens</td>\n",
       "      <td>2.2.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>astunparse</td>\n",
       "      <td>1.6.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>attrs</td>\n",
       "      <td>22.2.0</td>\n",
       "      <td>pyh71513ae_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>babel</td>\n",
       "      <td>2.11.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>backcall</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>pyh9f0ad1d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>backports</td>\n",
       "      <td>1.0</td>\n",
       "      <td>pyhd8ed1ab_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>backports.functools_lru_cache</td>\n",
       "      <td>1.6.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>beautifulsoup4</td>\n",
       "      <td>4.11.2</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bleach</td>\n",
       "      <td>6.0.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>boto3</td>\n",
       "      <td>1.26.165</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>botocore</td>\n",
       "      <td>1.29.165</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>brotli</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h1a8c8d9_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>brotli-bin</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h1a8c8d9_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>brotlipy</td>\n",
       "      <td>0.7.0</td>\n",
       "      <td>py39h02fc5c5_1005</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>bzip2</td>\n",
       "      <td>1.0.8</td>\n",
       "      <td>h3422bc3_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>c-ares</td>\n",
       "      <td>1.18.1</td>\n",
       "      <td>h3422bc3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ca-certificates</td>\n",
       "      <td>2023.5.7</td>\n",
       "      <td>hf0a4a13_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>cached-property</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>hd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>cached_property</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>pyha770c72_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>cachetools</td>\n",
       "      <td>5.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>certifi</td>\n",
       "      <td>2023.5.7</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>cffi</td>\n",
       "      <td>1.15.1</td>\n",
       "      <td>py39h7e6b969_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>charset-normalizer</td>\n",
       "      <td>2.1.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>comm</td>\n",
       "      <td>0.1.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>contourpy</td>\n",
       "      <td>1.0.7</td>\n",
       "      <td>py39haaf3ac1_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>cryptography</td>\n",
       "      <td>39.0.1</td>\n",
       "      <td>py39he2a39a8_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>cycler</td>\n",
       "      <td>0.11.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>debugpy</td>\n",
       "      <td>1.6.6</td>\n",
       "      <td>py39h23fbdae_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>decorator</td>\n",
       "      <td>5.1.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>defusedxml</td>\n",
       "      <td>0.7.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>deprecated</td>\n",
       "      <td>1.2.14</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>entrypoints</td>\n",
       "      <td>0.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>executing</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>filelock</td>\n",
       "      <td>3.12.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>flatbuffers</td>\n",
       "      <td>23.1.21</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>flit-core</td>\n",
       "      <td>3.8.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>fonttools</td>\n",
       "      <td>4.38.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>fqdn</td>\n",
       "      <td>1.5.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>freetype</td>\n",
       "      <td>2.12.1</td>\n",
       "      <td>hd633e50_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>gast</td>\n",
       "      <td>0.4.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>google-auth</td>\n",
       "      <td>2.16.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>google-auth-oauthlib</td>\n",
       "      <td>0.4.6</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>google-pasta</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>grpcio</td>\n",
       "      <td>1.46.3</td>\n",
       "      <td>py39h365d37b_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>h5py</td>\n",
       "      <td>3.8.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>hdf5</td>\n",
       "      <td>1.12.1</td>\n",
       "      <td>nompi_hd9dbc9e_104</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>idna</td>\n",
       "      <td>3.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>importlib-metadata</td>\n",
       "      <td>4.13.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>importlib_metadata</td>\n",
       "      <td>6.0.0</td>\n",
       "      <td>hd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>importlib_resources</td>\n",
       "      <td>5.10.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ipykernel</td>\n",
       "      <td>6.21.2</td>\n",
       "      <td>pyh736e0ef_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ipython</td>\n",
       "      <td>8.10.0</td>\n",
       "      <td>pyhd1c38e8_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>ipython_genutils</td>\n",
       "      <td>0.2.0</td>\n",
       "      <td>py_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ipywidgets</td>\n",
       "      <td>8.0.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>isoduration</td>\n",
       "      <td>20.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>jedi</td>\n",
       "      <td>0.18.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>jinja2</td>\n",
       "      <td>3.1.2</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>jmespath</td>\n",
       "      <td>1.0.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>joblib</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>json5</td>\n",
       "      <td>0.9.5</td>\n",
       "      <td>pyh9f0ad1d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>jsonpointer</td>\n",
       "      <td>2.4</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>jsonschema</td>\n",
       "      <td>4.17.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>jupyter</td>\n",
       "      <td>1.0.0</td>\n",
       "      <td>py39h2804cbe_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>jupyter-resource-usage</td>\n",
       "      <td>0.7.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>jupyter_client</td>\n",
       "      <td>8.0.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>jupyter_console</td>\n",
       "      <td>6.5.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>jupyter_core</td>\n",
       "      <td>5.2.0</td>\n",
       "      <td>py39h2804cbe_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>jupyter_events</td>\n",
       "      <td>0.5.0</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>jupyter_server</td>\n",
       "      <td>2.3.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>jupyter_server_fileid</td>\n",
       "      <td>0.6.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>jupyter_server_terminals</td>\n",
       "      <td>0.4.4</td>\n",
       "      <td>pyhd8ed1ab_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>jupyter_server_ydoc</td>\n",
       "      <td>0.6.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>jupyter_ydoc</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>jupyterlab</td>\n",
       "      <td>3.6.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>jupyterlab_pygments</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>jupyterlab_server</td>\n",
       "      <td>2.19.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>jupyterlab_widgets</td>\n",
       "      <td>3.0.5</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>keras</td>\n",
       "      <td>2.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>keras-preprocessing</td>\n",
       "      <td>1.1.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>keyring</td>\n",
       "      <td>23.4.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>kiwisolver</td>\n",
       "      <td>1.4.4</td>\n",
       "      <td>py39haaf3ac1_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>krb5</td>\n",
       "      <td>1.20.1</td>\n",
       "      <td>h69eda48_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>lcms2</td>\n",
       "      <td>2.15</td>\n",
       "      <td>hd835a16_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>lerc</td>\n",
       "      <td>4.0.0</td>\n",
       "      <td>h9a09cb3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>libblas</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>libbrotlicommon</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h1a8c8d9_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>libbrotlidec</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h1a8c8d9_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>libbrotlienc</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h1a8c8d9_8</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>libcblas</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>libclang</td>\n",
       "      <td>15.0.6.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>libcurl</td>\n",
       "      <td>7.88.0</td>\n",
       "      <td>h9049daf_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>libcxx</td>\n",
       "      <td>14.0.6</td>\n",
       "      <td>h2692d47_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>libdeflate</td>\n",
       "      <td>1.18</td>\n",
       "      <td>h1a8c8d9_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>libedit</td>\n",
       "      <td>3.1.20191231</td>\n",
       "      <td>hc8eb9b7_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>libev</td>\n",
       "      <td>4.33</td>\n",
       "      <td>h642e427_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>libffi</td>\n",
       "      <td>3.4.2</td>\n",
       "      <td>h3422bc3_5</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>libgfortran</td>\n",
       "      <td>5.0.0</td>\n",
       "      <td>11_3_0_hd922786_27</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>libgfortran5</td>\n",
       "      <td>11.3.0</td>\n",
       "      <td>hdaf2cc0_27</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>libjpeg-turbo</td>\n",
       "      <td>2.1.5.1</td>\n",
       "      <td>h1a8c8d9_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>liblapack</td>\n",
       "      <td>3.9.0</td>\n",
       "      <td>16_osxarm64_openblas</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>libnghttp2</td>\n",
       "      <td>1.51.0</td>\n",
       "      <td>hae82a92_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>libopenblas</td>\n",
       "      <td>0.3.21</td>\n",
       "      <td>openmp_hc731615_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>libpng</td>\n",
       "      <td>1.6.39</td>\n",
       "      <td>h76d750c_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>libprotobuf</td>\n",
       "      <td>3.19.4</td>\n",
       "      <td>hccf11d3_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>libsodium</td>\n",
       "      <td>1.0.18</td>\n",
       "      <td>h27ca646_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>libsqlite</td>\n",
       "      <td>3.40.0</td>\n",
       "      <td>h76d750c_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>libssh2</td>\n",
       "      <td>1.10.0</td>\n",
       "      <td>h7a5bd25_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>libtiff</td>\n",
       "      <td>4.5.0</td>\n",
       "      <td>h4f7d55c_6</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>libwebp-base</td>\n",
       "      <td>1.3.0</td>\n",
       "      <td>h1a8c8d9_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>libxcb</td>\n",
       "      <td>1.13</td>\n",
       "      <td>h9b22ae9_1004</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>libzlib</td>\n",
       "      <td>1.2.13</td>\n",
       "      <td>h03a7124_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>llvm-openmp</td>\n",
       "      <td>15.0.7</td>\n",
       "      <td>h7cfbb63_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>markdown</td>\n",
       "      <td>3.4.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>markupsafe</td>\n",
       "      <td>2.1.2</td>\n",
       "      <td>py39h02fc5c5_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>matplotlib</td>\n",
       "      <td>3.7.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>matplotlib-base</td>\n",
       "      <td>3.6.3</td>\n",
       "      <td>py39h35e9e80_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>matplotlib-inline</td>\n",
       "      <td>0.1.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>matplotlib-venn</td>\n",
       "      <td>0.11.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>mistune</td>\n",
       "      <td>2.0.5</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>mpmath</td>\n",
       "      <td>1.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>munkres</td>\n",
       "      <td>1.1.4</td>\n",
       "      <td>pyh9f0ad1d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>nbclassic</td>\n",
       "      <td>0.5.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>nbclient</td>\n",
       "      <td>0.7.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>nbconvert</td>\n",
       "      <td>7.2.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>nbconvert-core</td>\n",
       "      <td>7.2.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>nbconvert-pandoc</td>\n",
       "      <td>7.2.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>nbformat</td>\n",
       "      <td>5.7.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>ncurses</td>\n",
       "      <td>6.3</td>\n",
       "      <td>h07bb92c_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>nest-asyncio</td>\n",
       "      <td>1.5.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>networkx</td>\n",
       "      <td>3.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>notebook</td>\n",
       "      <td>6.5.2</td>\n",
       "      <td>pyha770c72_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>notebook-shim</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>numpy</td>\n",
       "      <td>1.24.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>oauthlib</td>\n",
       "      <td>3.2.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>openjpeg</td>\n",
       "      <td>2.5.0</td>\n",
       "      <td>hbc2ba62_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>openssl</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>h53f4e23_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>opt-einsum</td>\n",
       "      <td>3.3.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>packaging</td>\n",
       "      <td>23.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>pandas</td>\n",
       "      <td>1.5.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>pandoc</td>\n",
       "      <td>2.19.2</td>\n",
       "      <td>hce30654_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>pandocfilters</td>\n",
       "      <td>1.5.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>parso</td>\n",
       "      <td>0.8.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>patsy</td>\n",
       "      <td>0.5.3</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>pexpect</td>\n",
       "      <td>4.8.0</td>\n",
       "      <td>pyh1a96a4e_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>pickleshare</td>\n",
       "      <td>0.7.5</td>\n",
       "      <td>py_1003</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>pillow</td>\n",
       "      <td>9.4.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>pip</td>\n",
       "      <td>23.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>pkgutil-resolve-name</td>\n",
       "      <td>1.3.10</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>platformdirs</td>\n",
       "      <td>3.0.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>pooch</td>\n",
       "      <td>1.7.0</td>\n",
       "      <td>pyha770c72_3</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>prometheus_client</td>\n",
       "      <td>0.16.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>prompt-toolkit</td>\n",
       "      <td>3.0.36</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>prompt_toolkit</td>\n",
       "      <td>3.0.36</td>\n",
       "      <td>hd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>protobuf</td>\n",
       "      <td>3.19.6</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>psutil</td>\n",
       "      <td>5.9.4</td>\n",
       "      <td>py39h02fc5c5_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>pthread-stubs</td>\n",
       "      <td>0.4</td>\n",
       "      <td>h27ca646_1001</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>ptyprocess</td>\n",
       "      <td>0.7.0</td>\n",
       "      <td>pyhd3deb0d_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>pure_eval</td>\n",
       "      <td>0.2.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>pyasn1</td>\n",
       "      <td>0.4.8</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>pyasn1-modules</td>\n",
       "      <td>0.2.8</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>pycparser</td>\n",
       "      <td>2.21</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>pydot</td>\n",
       "      <td>1.4.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>pygments</td>\n",
       "      <td>2.14.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>pyopenssl</td>\n",
       "      <td>23.0.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>pyparsing</td>\n",
       "      <td>3.0.9</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>pyrsistent</td>\n",
       "      <td>0.19.3</td>\n",
       "      <td>py39h02fc5c5_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>pysocks</td>\n",
       "      <td>1.7.1</td>\n",
       "      <td>pyha2e5f31_6</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>python</td>\n",
       "      <td>3.9.16</td>\n",
       "      <td>hea58f1e_0_cpython</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>python-dateutil</td>\n",
       "      <td>2.8.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>python-fastjsonschema</td>\n",
       "      <td>2.16.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>python-json-logger</td>\n",
       "      <td>2.0.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>python_abi</td>\n",
       "      <td>3.9</td>\n",
       "      <td>3_cp39</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>pytz</td>\n",
       "      <td>2022.7.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>pyyaml</td>\n",
       "      <td>6.0</td>\n",
       "      <td>py39h02fc5c5_5</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>pyzmq</td>\n",
       "      <td>25.0.0</td>\n",
       "      <td>py39h0553236_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>qtconsole</td>\n",
       "      <td>5.4.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>qtpy</td>\n",
       "      <td>2.3.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>readline</td>\n",
       "      <td>8.1.2</td>\n",
       "      <td>h46ed386_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>requests</td>\n",
       "      <td>2.28.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>requests-oauthlib</td>\n",
       "      <td>1.3.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>rfc3339-validator</td>\n",
       "      <td>0.1.4</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>rfc3986-validator</td>\n",
       "      <td>0.1.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>rsa</td>\n",
       "      <td>4.9</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>s3transfer</td>\n",
       "      <td>0.6.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>scikit-learn</td>\n",
       "      <td>1.2.2</td>\n",
       "      <td>py39h6180588_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>scipy</td>\n",
       "      <td>1.10.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>seaborn</td>\n",
       "      <td>0.12.2</td>\n",
       "      <td>hd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>seaborn-base</td>\n",
       "      <td>0.12.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>send2trash</td>\n",
       "      <td>1.8.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>setuptools</td>\n",
       "      <td>68.0.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>six</td>\n",
       "      <td>1.16.0</td>\n",
       "      <td>pyh6c4a22f_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>sklearn</td>\n",
       "      <td>0.0.post4</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>sniffio</td>\n",
       "      <td>1.3.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>soupsieve</td>\n",
       "      <td>2.3.2.post1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>stack_data</td>\n",
       "      <td>0.6.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>statsmodels</td>\n",
       "      <td>0.14.0</td>\n",
       "      <td>py39h8a366b7_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>sympy</td>\n",
       "      <td>1.11.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>synapseclient</td>\n",
       "      <td>2.7.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>tensorboard</td>\n",
       "      <td>2.11.2</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>tensorboard-data-server</td>\n",
       "      <td>0.6.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>tensorboard-plugin-wit</td>\n",
       "      <td>1.8.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>tensorflow-addons</td>\n",
       "      <td>0.20.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>tensorflow-deps</td>\n",
       "      <td>2.10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>tensorflow-estimator</td>\n",
       "      <td>2.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>tensorflow-macos</td>\n",
       "      <td>2.11.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>tensorflow-metal</td>\n",
       "      <td>0.7.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>termcolor</td>\n",
       "      <td>2.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>terminado</td>\n",
       "      <td>0.17.1</td>\n",
       "      <td>pyhd1c38e8_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>threadpoolctl</td>\n",
       "      <td>3.1.0</td>\n",
       "      <td>pyh8a188c0_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>tinycss2</td>\n",
       "      <td>1.2.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>tk</td>\n",
       "      <td>8.6.12</td>\n",
       "      <td>he1e0b03_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>tomli</td>\n",
       "      <td>2.0.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>torch</td>\n",
       "      <td>2.0.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>torchvision</td>\n",
       "      <td>0.15.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>tornado</td>\n",
       "      <td>6.2</td>\n",
       "      <td>py39h02fc5c5_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>traitlets</td>\n",
       "      <td>5.9.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>typeguard</td>\n",
       "      <td>2.13.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>typing-extensions</td>\n",
       "      <td>4.4.0</td>\n",
       "      <td>hd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>typing_extensions</td>\n",
       "      <td>4.4.0</td>\n",
       "      <td>pyha770c72_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>tzdata</td>\n",
       "      <td>2022g</td>\n",
       "      <td>h191b570_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>unicodedata2</td>\n",
       "      <td>15.0.0</td>\n",
       "      <td>py39h02fc5c5_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>upsetplot</td>\n",
       "      <td>0.8.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>uri-template</td>\n",
       "      <td>1.2.0</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>urllib3</td>\n",
       "      <td>1.26.14</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>wcwidth</td>\n",
       "      <td>0.2.6</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>webcolors</td>\n",
       "      <td>1.13</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>webencodings</td>\n",
       "      <td>0.5.1</td>\n",
       "      <td>py_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>websocket-client</td>\n",
       "      <td>1.5.1</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>werkzeug</td>\n",
       "      <td>2.2.3</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>wheel</td>\n",
       "      <td>0.38.4</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>widgetsnbextension</td>\n",
       "      <td>4.0.5</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>wrapt</td>\n",
       "      <td>1.14.1</td>\n",
       "      <td>pypi_0</td>\n",
       "      <td>pypi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>xorg-libxau</td>\n",
       "      <td>1.0.9</td>\n",
       "      <td>h27ca646_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>xorg-libxdmcp</td>\n",
       "      <td>1.1.3</td>\n",
       "      <td>h27ca646_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>xz</td>\n",
       "      <td>5.2.6</td>\n",
       "      <td>h57fd34a_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>y-py</td>\n",
       "      <td>0.5.5</td>\n",
       "      <td>py39hb39fadb_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>yaml</td>\n",
       "      <td>0.2.5</td>\n",
       "      <td>h3422bc3_2</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>ypy-websocket</td>\n",
       "      <td>0.8.2</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>zeromq</td>\n",
       "      <td>4.3.4</td>\n",
       "      <td>hbdafb3b_1</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>zipp</td>\n",
       "      <td>3.13.0</td>\n",
       "      <td>pyhd8ed1ab_0</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>zlib</td>\n",
       "      <td>1.2.13</td>\n",
       "      <td>h03a7124_4</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>zstd</td>\n",
       "      <td>1.5.2</td>\n",
       "      <td>hf913c23_6</td>\n",
       "      <td>conda-forge</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name       Version                 Build  \\\n",
       "0                          absl-py         1.4.0                pypi_0   \n",
       "1                         aiofiles        22.1.0          pyhd8ed1ab_0   \n",
       "2                        aiosqlite        0.18.0          pyhd8ed1ab_0   \n",
       "3                            anyio         3.6.2          pyhd8ed1ab_0   \n",
       "4                          appnope         0.1.3          pyhd8ed1ab_0   \n",
       "5                      argon2-cffi        21.3.0          pyhd8ed1ab_0   \n",
       "6             argon2-cffi-bindings        21.2.0        py39h02fc5c5_3   \n",
       "7                            arrow         1.2.3                pypi_0   \n",
       "8                        asttokens         2.2.1          pyhd8ed1ab_0   \n",
       "9                       astunparse         1.6.3                pypi_0   \n",
       "10                           attrs        22.2.0          pyh71513ae_0   \n",
       "11                           babel        2.11.0          pyhd8ed1ab_0   \n",
       "12                        backcall         0.2.0          pyh9f0ad1d_0   \n",
       "13                       backports           1.0          pyhd8ed1ab_3   \n",
       "14   backports.functools_lru_cache         1.6.4          pyhd8ed1ab_0   \n",
       "15                  beautifulsoup4        4.11.2          pyha770c72_0   \n",
       "16                          bleach         6.0.0          pyhd8ed1ab_0   \n",
       "17                           boto3      1.26.165                pypi_0   \n",
       "18                        botocore      1.29.165                pypi_0   \n",
       "19                          brotli         1.0.9            h1a8c8d9_8   \n",
       "20                      brotli-bin         1.0.9            h1a8c8d9_8   \n",
       "21                        brotlipy         0.7.0     py39h02fc5c5_1005   \n",
       "22                           bzip2         1.0.8            h3422bc3_4   \n",
       "23                          c-ares        1.18.1            h3422bc3_0   \n",
       "24                 ca-certificates      2023.5.7            hf0a4a13_0   \n",
       "25                 cached-property         1.5.2            hd8ed1ab_1   \n",
       "26                 cached_property         1.5.2          pyha770c72_1   \n",
       "27                      cachetools         5.3.0                pypi_0   \n",
       "28                         certifi      2023.5.7          pyhd8ed1ab_0   \n",
       "29                            cffi        1.15.1        py39h7e6b969_3   \n",
       "30              charset-normalizer         2.1.1          pyhd8ed1ab_0   \n",
       "31                            comm         0.1.2          pyhd8ed1ab_0   \n",
       "32                       contourpy         1.0.7        py39haaf3ac1_0   \n",
       "33                    cryptography        39.0.1        py39he2a39a8_0   \n",
       "34                          cycler        0.11.0          pyhd8ed1ab_0   \n",
       "35                         debugpy         1.6.6        py39h23fbdae_0   \n",
       "36                       decorator         5.1.1          pyhd8ed1ab_0   \n",
       "37                      defusedxml         0.7.1          pyhd8ed1ab_0   \n",
       "38                      deprecated        1.2.14                pypi_0   \n",
       "39                     entrypoints           0.4          pyhd8ed1ab_0   \n",
       "40                       executing         1.2.0          pyhd8ed1ab_0   \n",
       "41                        filelock        3.12.0                pypi_0   \n",
       "42                     flatbuffers       23.1.21                pypi_0   \n",
       "43                       flit-core         3.8.0          pyhd8ed1ab_0   \n",
       "44                       fonttools        4.38.0                pypi_0   \n",
       "45                            fqdn         1.5.1                pypi_0   \n",
       "46                        freetype        2.12.1            hd633e50_1   \n",
       "47                            gast         0.4.0                pypi_0   \n",
       "48                     google-auth        2.16.0                pypi_0   \n",
       "49            google-auth-oauthlib         0.4.6                pypi_0   \n",
       "50                    google-pasta         0.2.0                pypi_0   \n",
       "51                          grpcio        1.46.3        py39h365d37b_0   \n",
       "52                            h5py         3.8.0                pypi_0   \n",
       "53                            hdf5        1.12.1    nompi_hd9dbc9e_104   \n",
       "54                            idna           3.4          pyhd8ed1ab_0   \n",
       "55              importlib-metadata        4.13.0                pypi_0   \n",
       "56              importlib_metadata         6.0.0            hd8ed1ab_0   \n",
       "57             importlib_resources        5.10.2          pyhd8ed1ab_0   \n",
       "58                       ipykernel        6.21.2          pyh736e0ef_0   \n",
       "59                         ipython        8.10.0          pyhd1c38e8_0   \n",
       "60                ipython_genutils         0.2.0                  py_1   \n",
       "61                      ipywidgets         8.0.4          pyhd8ed1ab_0   \n",
       "62                     isoduration       20.11.0                pypi_0   \n",
       "63                            jedi        0.18.2          pyhd8ed1ab_0   \n",
       "64                          jinja2         3.1.2          pyhd8ed1ab_1   \n",
       "65                        jmespath         1.0.1                pypi_0   \n",
       "66                          joblib         1.2.0          pyhd8ed1ab_0   \n",
       "67                           json5         0.9.5          pyh9f0ad1d_0   \n",
       "68                     jsonpointer           2.4                pypi_0   \n",
       "69                      jsonschema        4.17.3          pyhd8ed1ab_0   \n",
       "70                         jupyter         1.0.0        py39h2804cbe_8   \n",
       "71          jupyter-resource-usage         0.7.2                pypi_0   \n",
       "72                  jupyter_client         8.0.3          pyhd8ed1ab_0   \n",
       "73                 jupyter_console         6.5.1          pyhd8ed1ab_0   \n",
       "74                    jupyter_core         5.2.0        py39h2804cbe_0   \n",
       "75                  jupyter_events         0.5.0          pyhd8ed1ab_1   \n",
       "76                  jupyter_server         2.3.0          pyhd8ed1ab_0   \n",
       "77           jupyter_server_fileid         0.6.0          pyhd8ed1ab_0   \n",
       "78        jupyter_server_terminals         0.4.4          pyhd8ed1ab_1   \n",
       "79             jupyter_server_ydoc         0.6.1          pyhd8ed1ab_0   \n",
       "80                    jupyter_ydoc         0.2.2          pyhd8ed1ab_0   \n",
       "81                      jupyterlab         3.6.1          pyhd8ed1ab_0   \n",
       "82             jupyterlab_pygments         0.2.2          pyhd8ed1ab_0   \n",
       "83               jupyterlab_server        2.19.0          pyhd8ed1ab_0   \n",
       "84              jupyterlab_widgets         3.0.5          pyhd8ed1ab_0   \n",
       "85                           keras        2.11.0                pypi_0   \n",
       "86             keras-preprocessing         1.1.2                pypi_0   \n",
       "87                         keyring        23.4.1                pypi_0   \n",
       "88                      kiwisolver         1.4.4        py39haaf3ac1_1   \n",
       "89                            krb5        1.20.1            h69eda48_0   \n",
       "90                           lcms2          2.15            hd835a16_1   \n",
       "91                            lerc         4.0.0            h9a09cb3_0   \n",
       "92                         libblas         3.9.0  16_osxarm64_openblas   \n",
       "93                 libbrotlicommon         1.0.9            h1a8c8d9_8   \n",
       "94                    libbrotlidec         1.0.9            h1a8c8d9_8   \n",
       "95                    libbrotlienc         1.0.9            h1a8c8d9_8   \n",
       "96                        libcblas         3.9.0  16_osxarm64_openblas   \n",
       "97                        libclang      15.0.6.1                pypi_0   \n",
       "98                         libcurl        7.88.0            h9049daf_0   \n",
       "99                          libcxx        14.0.6            h2692d47_0   \n",
       "100                     libdeflate          1.18            h1a8c8d9_0   \n",
       "101                        libedit  3.1.20191231            hc8eb9b7_2   \n",
       "102                          libev          4.33            h642e427_1   \n",
       "103                         libffi         3.4.2            h3422bc3_5   \n",
       "104                    libgfortran         5.0.0    11_3_0_hd922786_27   \n",
       "105                   libgfortran5        11.3.0           hdaf2cc0_27   \n",
       "106                  libjpeg-turbo       2.1.5.1            h1a8c8d9_0   \n",
       "107                      liblapack         3.9.0  16_osxarm64_openblas   \n",
       "108                     libnghttp2        1.51.0            hae82a92_0   \n",
       "109                    libopenblas        0.3.21     openmp_hc731615_3   \n",
       "110                         libpng        1.6.39            h76d750c_0   \n",
       "111                    libprotobuf        3.19.4            hccf11d3_0   \n",
       "112                      libsodium        1.0.18            h27ca646_1   \n",
       "113                      libsqlite        3.40.0            h76d750c_0   \n",
       "114                        libssh2        1.10.0            h7a5bd25_3   \n",
       "115                        libtiff         4.5.0            h4f7d55c_6   \n",
       "116                   libwebp-base         1.3.0            h1a8c8d9_0   \n",
       "117                         libxcb          1.13         h9b22ae9_1004   \n",
       "118                        libzlib        1.2.13            h03a7124_4   \n",
       "119                    llvm-openmp        15.0.7            h7cfbb63_0   \n",
       "120                       markdown         3.4.1                pypi_0   \n",
       "121                     markupsafe         2.1.2        py39h02fc5c5_0   \n",
       "122                     matplotlib         3.7.0                pypi_0   \n",
       "123                matplotlib-base         3.6.3        py39h35e9e80_0   \n",
       "124              matplotlib-inline         0.1.6          pyhd8ed1ab_0   \n",
       "125                matplotlib-venn        0.11.9          pyhd8ed1ab_0   \n",
       "126                        mistune         2.0.5          pyhd8ed1ab_0   \n",
       "127                         mpmath         1.3.0                pypi_0   \n",
       "128                        munkres         1.1.4          pyh9f0ad1d_0   \n",
       "129                      nbclassic         0.5.1          pyhd8ed1ab_0   \n",
       "130                       nbclient         0.7.2          pyhd8ed1ab_0   \n",
       "131                      nbconvert         7.2.9          pyhd8ed1ab_0   \n",
       "132                 nbconvert-core         7.2.9          pyhd8ed1ab_0   \n",
       "133               nbconvert-pandoc         7.2.9          pyhd8ed1ab_0   \n",
       "134                       nbformat         5.7.3          pyhd8ed1ab_0   \n",
       "135                        ncurses           6.3            h07bb92c_1   \n",
       "136                   nest-asyncio         1.5.6          pyhd8ed1ab_0   \n",
       "137                       networkx           3.1                pypi_0   \n",
       "138                       notebook         6.5.2          pyha770c72_1   \n",
       "139                  notebook-shim         0.2.2          pyhd8ed1ab_0   \n",
       "140                          numpy        1.24.2                pypi_0   \n",
       "141                       oauthlib         3.2.2                pypi_0   \n",
       "142                       openjpeg         2.5.0            hbc2ba62_2   \n",
       "143                        openssl         3.1.0            h53f4e23_3   \n",
       "144                     opt-einsum         3.3.0                pypi_0   \n",
       "145                      packaging          23.0          pyhd8ed1ab_0   \n",
       "146                         pandas         1.5.3                pypi_0   \n",
       "147                         pandoc        2.19.2            hce30654_1   \n",
       "148                  pandocfilters         1.5.0          pyhd8ed1ab_0   \n",
       "149                          parso         0.8.3          pyhd8ed1ab_0   \n",
       "150                          patsy         0.5.3          pyhd8ed1ab_0   \n",
       "151                        pexpect         4.8.0          pyh1a96a4e_2   \n",
       "152                    pickleshare         0.7.5               py_1003   \n",
       "153                         pillow         9.4.0                pypi_0   \n",
       "154                            pip          23.0          pyhd8ed1ab_0   \n",
       "155           pkgutil-resolve-name        1.3.10          pyhd8ed1ab_0   \n",
       "156                   platformdirs         3.0.0          pyhd8ed1ab_0   \n",
       "157                          pooch         1.7.0          pyha770c72_3   \n",
       "158              prometheus_client        0.16.0          pyhd8ed1ab_0   \n",
       "159                 prompt-toolkit        3.0.36          pyha770c72_0   \n",
       "160                 prompt_toolkit        3.0.36            hd8ed1ab_0   \n",
       "161                       protobuf        3.19.6                pypi_0   \n",
       "162                         psutil         5.9.4        py39h02fc5c5_0   \n",
       "163                  pthread-stubs           0.4         h27ca646_1001   \n",
       "164                     ptyprocess         0.7.0          pyhd3deb0d_0   \n",
       "165                      pure_eval         0.2.2          pyhd8ed1ab_0   \n",
       "166                         pyasn1         0.4.8                pypi_0   \n",
       "167                 pyasn1-modules         0.2.8                pypi_0   \n",
       "168                      pycparser          2.21          pyhd8ed1ab_0   \n",
       "169                          pydot         1.4.2                pypi_0   \n",
       "170                       pygments        2.14.0          pyhd8ed1ab_0   \n",
       "171                      pyopenssl        23.0.0          pyhd8ed1ab_0   \n",
       "172                      pyparsing         3.0.9          pyhd8ed1ab_0   \n",
       "173                     pyrsistent        0.19.3        py39h02fc5c5_0   \n",
       "174                        pysocks         1.7.1          pyha2e5f31_6   \n",
       "175                         python        3.9.16    hea58f1e_0_cpython   \n",
       "176                python-dateutil         2.8.2          pyhd8ed1ab_0   \n",
       "177          python-fastjsonschema        2.16.2          pyhd8ed1ab_0   \n",
       "178             python-json-logger         2.0.6          pyhd8ed1ab_0   \n",
       "179                     python_abi           3.9                3_cp39   \n",
       "180                           pytz      2022.7.1          pyhd8ed1ab_0   \n",
       "181                         pyyaml           6.0        py39h02fc5c5_5   \n",
       "182                          pyzmq        25.0.0        py39h0553236_0   \n",
       "183                      qtconsole         5.4.3                pypi_0   \n",
       "184                           qtpy         2.3.1                pypi_0   \n",
       "185                       readline         8.1.2            h46ed386_0   \n",
       "186                       requests        2.28.2          pyhd8ed1ab_0   \n",
       "187              requests-oauthlib         1.3.1                pypi_0   \n",
       "188              rfc3339-validator         0.1.4                pypi_0   \n",
       "189              rfc3986-validator         0.1.1                pypi_0   \n",
       "190                            rsa           4.9                pypi_0   \n",
       "191                     s3transfer         0.6.1                pypi_0   \n",
       "192                   scikit-learn         1.2.2        py39h6180588_1   \n",
       "193                          scipy        1.10.1                pypi_0   \n",
       "194                        seaborn        0.12.2            hd8ed1ab_0   \n",
       "195                   seaborn-base        0.12.2          pyhd8ed1ab_0   \n",
       "196                     send2trash         1.8.0          pyhd8ed1ab_0   \n",
       "197                     setuptools        68.0.0                pypi_0   \n",
       "198                            six        1.16.0          pyh6c4a22f_0   \n",
       "199                        sklearn     0.0.post4                pypi_0   \n",
       "200                        sniffio         1.3.0          pyhd8ed1ab_0   \n",
       "201                      soupsieve   2.3.2.post1          pyhd8ed1ab_0   \n",
       "202                     stack_data         0.6.2          pyhd8ed1ab_0   \n",
       "203                    statsmodels        0.14.0        py39h8a366b7_1   \n",
       "204                          sympy        1.11.1                pypi_0   \n",
       "205                  synapseclient         2.7.2                pypi_0   \n",
       "206                    tensorboard        2.11.2                pypi_0   \n",
       "207        tensorboard-data-server         0.6.1                pypi_0   \n",
       "208         tensorboard-plugin-wit         1.8.1                pypi_0   \n",
       "209              tensorflow-addons        0.20.0                pypi_0   \n",
       "210                tensorflow-deps        2.10.0                     0   \n",
       "211           tensorflow-estimator        2.11.0                pypi_0   \n",
       "212               tensorflow-macos        2.11.0                pypi_0   \n",
       "213               tensorflow-metal         0.7.1                pypi_0   \n",
       "214                      termcolor         2.2.0                pypi_0   \n",
       "215                      terminado        0.17.1          pyhd1c38e8_0   \n",
       "216                  threadpoolctl         3.1.0          pyh8a188c0_0   \n",
       "217                       tinycss2         1.2.1          pyhd8ed1ab_0   \n",
       "218                             tk        8.6.12            he1e0b03_0   \n",
       "219                          tomli         2.0.1          pyhd8ed1ab_0   \n",
       "220                          torch         2.0.0                pypi_0   \n",
       "221                    torchvision        0.15.1                pypi_0   \n",
       "222                        tornado           6.2        py39h02fc5c5_1   \n",
       "223                      traitlets         5.9.0          pyhd8ed1ab_0   \n",
       "224                      typeguard        2.13.3                pypi_0   \n",
       "225              typing-extensions         4.4.0            hd8ed1ab_0   \n",
       "226              typing_extensions         4.4.0          pyha770c72_0   \n",
       "227                         tzdata         2022g            h191b570_0   \n",
       "228                   unicodedata2        15.0.0        py39h02fc5c5_0   \n",
       "229                      upsetplot         0.8.0                pypi_0   \n",
       "230                   uri-template         1.2.0                pypi_0   \n",
       "231                        urllib3       1.26.14          pyhd8ed1ab_0   \n",
       "232                        wcwidth         0.2.6          pyhd8ed1ab_0   \n",
       "233                      webcolors          1.13                pypi_0   \n",
       "234                   webencodings         0.5.1                  py_1   \n",
       "235               websocket-client         1.5.1          pyhd8ed1ab_0   \n",
       "236                       werkzeug         2.2.3                pypi_0   \n",
       "237                          wheel        0.38.4          pyhd8ed1ab_0   \n",
       "238             widgetsnbextension         4.0.5          pyhd8ed1ab_0   \n",
       "239                          wrapt        1.14.1                pypi_0   \n",
       "240                    xorg-libxau         1.0.9            h27ca646_0   \n",
       "241                  xorg-libxdmcp         1.1.3            h27ca646_0   \n",
       "242                             xz         5.2.6            h57fd34a_0   \n",
       "243                           y-py         0.5.5        py39hb39fadb_2   \n",
       "244                           yaml         0.2.5            h3422bc3_2   \n",
       "245                  ypy-websocket         0.8.2          pyhd8ed1ab_0   \n",
       "246                         zeromq         4.3.4            hbdafb3b_1   \n",
       "247                           zipp        3.13.0          pyhd8ed1ab_0   \n",
       "248                           zlib        1.2.13            h03a7124_4   \n",
       "249                           zstd         1.5.2            hf913c23_6   \n",
       "\n",
       "         Channel  \n",
       "0           pypi  \n",
       "1    conda-forge  \n",
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       "17          pypi  \n",
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       "27          pypi  \n",
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       "37   conda-forge  \n",
       "38          pypi  \n",
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       "40   conda-forge  \n",
       "41          pypi  \n",
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       "43   conda-forge  \n",
       "44          pypi  \n",
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       "46   conda-forge  \n",
       "47          pypi  \n",
       "48          pypi  \n",
       "49          pypi  \n",
       "50          pypi  \n",
       "51   conda-forge  \n",
       "52          pypi  \n",
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       "55          pypi  \n",
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       "60   conda-forge  \n",
       "61   conda-forge  \n",
       "62          pypi  \n",
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       "64   conda-forge  \n",
       "65          pypi  \n",
       "66   conda-forge  \n",
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       "68          pypi  \n",
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       "71          pypi  \n",
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       "84   conda-forge  \n",
       "85          pypi  \n",
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       "95   conda-forge  \n",
       "96   conda-forge  \n",
       "97          pypi  \n",
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       "100  conda-forge  \n",
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      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conda envrionment, Apple M1\n",
    "trsfrmr_conda_env = pd.read_csv('CPTAC_trsfrmr_conda_packages.tsv', sep = '\\t',\n",
    "           header = None)\n",
    "headers = ['Name', 'Version', 'Build', 'Channel']\n",
    "trsfrmr_conda_env.columns = headers\n",
    "trsfrmr_conda_env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2ddb6e15-9c59-43fd-985b-6153ec33768a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(trsfrmr_conda_env)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1836021f-54d5-47a5-8c58-481697fc79db",
   "metadata": {},
   "source": [
    "#### Model template\n",
    "https://keras.io/examples/structured_data/tabtransformer/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3a9a65-3271-477b-8e47-c32b1c26c447",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06bdd744-2e86-4450-b84f-13df0905b887",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/trnsfrmr/lib/python3.9/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
      "\n",
      "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
      "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
      "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
      "\n",
      "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
      "\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import tensorflow_addons as tfa\n",
    "import matplotlib.pyplot as plt\n",
    "import collections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6456b36c-8411-44da-939f-169749b7f4f5",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Data prep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7bff227-540d-4e9e-91fe-538926533fcd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "samples = pd.read_csv('data/samples.csv')\n",
    "samples.index = samples.improve_sample_id\n",
    "y = pd.DataFrame(samples['cancer_type'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99eb04cf-ca6c-44a9-8358-4651519a1c4e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cnv = pd.read_csv('latent_features/cnv_latent.tsv', sep = '\\t',\n",
    "                   index_col = 0)\n",
    "prt = pd.read_csv('latent_features/proteomics_latent.tsv', sep = '\\t',\n",
    "                   index_col = 0)\n",
    "trs = pd.read_csv('latent_features/transcriptomics_latent.tsv', sep = '\\t',\n",
    "                   index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6091c872-e28a-4559-9075-cfe165a774ad",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original sample count 1386\n",
      "Validation sum check  1386\n"
     ]
    }
   ],
   "source": [
    "# Combined numeric feature and y target (cancer_type) object\n",
    "y_X = pd.concat([y, cnv, prt, trs], axis=1, join='inner')\n",
    "print('Original sample count', y_X.shape[0])\n",
    "\n",
    "y_X['plchldr_cat'] = '1' # Placeholder column for categorical features\n",
    "y_X['fnlwgt'] = 1. # optional wieght variable - increase for rare classes\n",
    "\n",
    "# Take out 100 random samples for validation\n",
    "n_vld = 100\n",
    "smpl_srs = y_X.index.to_series().sample(n_vld)\n",
    "validation_set = y_X.loc[smpl_srs, :]\n",
    "\n",
    "train_test_set = y_X.loc[~y_X.index.isin(validation_set.index)]\n",
    "num_classes = len(train_test_set.cancer_type.unique())\n",
    "print('Validation sum check ', len(train_test_set)+len(validation_set))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65eeb4d7-3062-459d-9442-e7fda106b523",
   "metadata": {},
   "source": [
    "### Transformer setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0a33fd24-e3bc-4b2c-9cb9-41daa4cd4cae",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cross validation insertion point\n",
    "train_data = train_test_set.sample(int(len(train_test_set)*.75))\n",
    "test_data = train_test_set.loc[~train_test_set.index.isin(train_data.index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "e5dead36-4710-4b0b-a26b-e85e3c8ddb63",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data_file = \"train_data.csv\"\n",
    "test_data_file = \"test_data.csv\"\n",
    "\n",
    "train_data.to_csv(train_data_file, index=False, header=False)\n",
    "test_data.to_csv(test_data_file, index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a5f72048-d8d3-4521-9aca-d7f951e06f9a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CSV_HEADER = y_X.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b5483668-5dc2-4d60-9fe3-9adfaac32545",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "NUMERIC_FEATURE_NAMES = list(y_X.columns[1:-2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3641b380-0fb3-464b-93dd-9e106267e037",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "750"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(NUMERIC_FEATURE_NAMES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ae8f1b34-c4bd-434a-9168-01094fe43a3f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CATEGORICAL_FEATURES_WITH_VOCABULARY = {\n",
    "    \"plchldr_cat\": sorted(list(train_data[\"plchldr_cat\"].unique())),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8ddd3d47-6518-427f-a53f-050a2177e7df",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(CATEGORICAL_FEATURES_WITH_VOCABULARY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cc5b5c34-9332-4429-a6a9-1badaaf0a2ee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "WEIGHT_COLUMN_NAME = \"fnlwgt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "2c0f5237-70dc-470f-80e7-30a175e2b698",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TARGET_FEATURE_NAME = 'cancer_type'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0f20f3dd-ca3f-448b-b946-b9b09e2d2532",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "COLUMN_DEFAULTS = [\n",
    "    [0.0] if feature_name in NUMERIC_FEATURE_NAMES + [WEIGHT_COLUMN_NAME] else [\"NA\"]\n",
    "    for feature_name in CSV_HEADER\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "142f9619-96ee-43a1-bfb6-e05932f1d886",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "TARGET_LABELS = y_X.cancer_type.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "41743a3d-cc0d-4fd3-9caa-097f7fe3daf3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.001\n",
    "WEIGHT_DECAY = 0.0001\n",
    "DROPOUT_RATE = 0.2\n",
    "BATCH_SIZE = 265\n",
    "NUM_EPOCHS = 15\n",
    "\n",
    "NUM_TRANSFORMER_BLOCKS = 3  # Number of transformer blocks.\n",
    "NUM_HEADS = 4  # Number of attention heads.\n",
    "EMBEDDING_DIMS = 16  # Embedding dimensions of the categorical features.\n",
    "MLP_HIDDEN_UNITS_FACTORS = [\n",
    "    2,\n",
    "    1,\n",
    "]  # MLP hidden layer units, as factors of the number of inputs.\n",
    "NUM_MLP_BLOCKS = 2  # Number of MLP blocks in the baseline model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4471131d-7df1-4b2e-8baf-46186b9bd668",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/trnsfrmr/lib/python3.9/site-packages/numpy/core/numeric.py:2463: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    }
   ],
   "source": [
    "target_label_lookup = layers.StringLookup(\n",
    "    vocabulary=TARGET_LABELS, mask_token=None, num_oov_indices=0\n",
    ")\n",
    "\n",
    "def prepare_example(features, target):\n",
    "    target_index = target_label_lookup(target)\n",
    "    weights = features.pop(WEIGHT_COLUMN_NAME)\n",
    "    return features, target_index, weights\n",
    "\n",
    "\n",
    "def get_dataset_from_csv(csv_file_path, batch_size=128, shuffle=False):\n",
    "    dataset = tf.data.experimental.make_csv_dataset(\n",
    "        csv_file_path,\n",
    "        batch_size=batch_size,\n",
    "        column_names=CSV_HEADER,\n",
    "        column_defaults=COLUMN_DEFAULTS,\n",
    "        label_name=TARGET_FEATURE_NAME,\n",
    "        num_epochs=1,\n",
    "        header=False,\n",
    "        na_value=\"?\",\n",
    "        shuffle=shuffle,\n",
    "    ).map(prepare_example, num_parallel_calls=tf.data.AUTOTUNE, deterministic=False)\n",
    "    return dataset.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ed686b00-f071-48ff-9062-3c3a3e56c45d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def run_experiment(\n",
    "    model,            # <--- toggle point for baseline MLP or tabular transformer\n",
    "    train_data_file,\n",
    "    test_data_file,\n",
    "    num_epochs,\n",
    "    learning_rate,\n",
    "    weight_decay,\n",
    "    batch_size,\n",
    "):\n",
    "\n",
    "    optimizer = tfa.optimizers.AdamW(\n",
    "        learning_rate=learning_rate, weight_decay=weight_decay\n",
    "    )\n",
    "\n",
    "    # ~~~~  Multi-class categorical prediction  ~~~~\n",
    "    model.compile(\n",
    "    optimizer=optimizer,\n",
    "    loss=keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
    "    metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"accuracy\")],\n",
    "    )\n",
    "\n",
    "    train_dataset = get_dataset_from_csv(train_data_file, batch_size, shuffle=True)\n",
    "    validation_dataset = get_dataset_from_csv(test_data_file, batch_size)\n",
    "\n",
    "    print(\"Start training the model...\")\n",
    "    history = model.fit(\n",
    "        train_dataset, epochs=num_epochs, validation_data=validation_dataset\n",
    "    )\n",
    "    print(\"Model training finished\")\n",
    "\n",
    "    _, accuracy = model.evaluate(validation_dataset, verbose=0)\n",
    "\n",
    "    print(f\"Validation accuracy: {round(accuracy * 100, 2)}%\")\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "73976f80-1bbe-41b9-b991-50bf9533d055",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_model_inputs():\n",
    "    inputs = {}\n",
    "    for feature_name in FEATURE_NAMES:\n",
    "        if feature_name in NUMERIC_FEATURE_NAMES:\n",
    "            inputs[feature_name] = layers.Input(\n",
    "                name=feature_name, shape=(), dtype=tf.float32\n",
    "            )\n",
    "        else:\n",
    "            inputs[feature_name] = layers.Input(\n",
    "                name=feature_name, shape=(), dtype=tf.string\n",
    "            )\n",
    "    # print()\n",
    "    return inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "211047e7-0990-4fcd-b166-10464d98302e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def encode_inputs(inputs, embedding_dims):\n",
    "\n",
    "    encoded_categorical_feature_list = []\n",
    "    numerical_feature_list = []\n",
    "\n",
    "    for feature_name in inputs:\n",
    "        if feature_name in CATEGORICAL_FEATURE_NAMES:\n",
    "\n",
    "            # Get the vocabulary of the categorical feature.\n",
    "            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]\n",
    "\n",
    "            # Create a lookup to convert string values to an integer indices.\n",
    "            # Since we are not using a mask token nor expecting any out of vocabulary\n",
    "            # (oov) token, we set mask_token to None and  num_oov_indices to 0.\n",
    "            lookup = layers.StringLookup(\n",
    "                vocabulary=vocabulary,\n",
    "                mask_token=None,\n",
    "                num_oov_indices=0,\n",
    "                output_mode=\"int\",\n",
    "            )\n",
    "\n",
    "            # Convert the string input values into integer indices.\n",
    "            encoded_feature = lookup(inputs[feature_name])\n",
    "\n",
    "            # Create an embedding layer with the specified dimensions.\n",
    "            embedding = layers.Embedding(\n",
    "                input_dim=len(vocabulary), output_dim=embedding_dims\n",
    "            )\n",
    "\n",
    "            # Convert the index values to embedding representations.\n",
    "            encoded_categorical_feature = embedding(encoded_feature)\n",
    "            encoded_categorical_feature_list.append(encoded_categorical_feature)\n",
    "\n",
    "        else:\n",
    "\n",
    "            # Use the numerical features as-is.\n",
    "            numerical_feature = tf.expand_dims(inputs[feature_name], -1)\n",
    "            numerical_feature_list.append(numerical_feature)\n",
    "\n",
    "    return encoded_categorical_feature_list, numerical_feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6b783ff1-ef74-49e8-a80c-a1c755aedbec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_mlp(hidden_units, dropout_rate, activation, normalization_layer, name=None):\n",
    "\n",
    "    mlp_layers = []\n",
    "    for units in hidden_units:\n",
    "        mlp_layers.append(normalization_layer),\n",
    "        mlp_layers.append(layers.Dense(units, activation=activation))\n",
    "        mlp_layers.append(layers.Dropout(dropout_rate))\n",
    "\n",
    "    return keras.Sequential(mlp_layers, name=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a2557b5d-a614-4aca-8964-50e65b3c6c57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f3d087dd-ab16-4c67-9e7e-c437f07ac605",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "65d3397d-3d0d-4712-b19a-e3e28bf989fa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total model weights: 3535879\n"
     ]
    }
   ],
   "source": [
    "# MLP\n",
    "def create_baseline_model(\n",
    "    embedding_dims, num_mlp_blocks, mlp_hidden_units_factors, dropout_rate\n",
    "):\n",
    "\n",
    "    # Create model inputs.\n",
    "    inputs = create_model_inputs()\n",
    "    # encode features.\n",
    "    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(\n",
    "        inputs, embedding_dims\n",
    "    )\n",
    "    # Concatenate all features.\n",
    "    features = layers.concatenate(\n",
    "        encoded_categorical_feature_list + numerical_feature_list\n",
    "    )\n",
    "    # Compute Feedforward layer units.\n",
    "    feedforward_units = [features.shape[-1]]\n",
    "\n",
    "    # Create several feedforwad layers with skip connections.\n",
    "    for layer_idx in range(num_mlp_blocks):\n",
    "        features = create_mlp(\n",
    "            hidden_units=feedforward_units,\n",
    "            dropout_rate=dropout_rate,\n",
    "            activation=keras.activations.gelu,\n",
    "            normalization_layer=layers.LayerNormalization(epsilon=1e-6),\n",
    "            name=f\"feedforward_{layer_idx}\",\n",
    "        )(features)\n",
    "\n",
    "    # Compute MLP hidden_units.\n",
    "    mlp_hidden_units = [\n",
    "        factor * features.shape[-1] for factor in mlp_hidden_units_factors\n",
    "    ]\n",
    "    # Create final MLP.\n",
    "    features = create_mlp(\n",
    "        hidden_units=mlp_hidden_units,\n",
    "        dropout_rate=dropout_rate,\n",
    "        activation=keras.activations.selu,\n",
    "        normalization_layer=layers.BatchNormalization(),\n",
    "        name=\"MLP\",\n",
    "    )(features)\n",
    "\n",
    "    # ~~~~  Add a sigmoid as a binary classifer ~~~~\n",
    "    # outputs = layers.Dense(units=1, activation=\"sigmoid\", name=\"sigmoid\")(features)\n",
    "    \n",
    "    # ~~~~  Softmax for multi-class classifer ~~~~\n",
    "    outputs = layers.Dense(units=num_classes, activation=\"softmax\", name=\"softmax\")(features)\n",
    "    \n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "baseline_model = create_baseline_model(\n",
    "    embedding_dims=EMBEDDING_DIMS,\n",
    "    num_mlp_blocks=NUM_MLP_BLOCKS,\n",
    "    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,\n",
    "    dropout_rate=DROPOUT_RATE,\n",
    ")\n",
    "\n",
    "print(\"Total model weights:\", baseline_model.count_params())\n",
    "# keras.utils.plot_model(baseline_model, show_shapes=True, rankdir=\"LR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e56ee444-b875-4e3f-8cf8-a896e45df3a1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training the model...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-17 16:02:35.979643: W tensorflow/tsl/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/trnsfrmr/lib/python3.9/site-packages/keras/backend.py:5585: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
      "  output, from_logits = _get_logits(\n",
      "2023-07-17 16:02:37.777139: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n",
      "2023-07-17 16:02:49.371930: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:392] Filling up shuffle buffer (this may take a while): 901 of 10000\n",
      "2023-07-17 16:02:59.640022: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:392] Filling up shuffle buffer (this may take a while): 954 of 10000\n",
      "2023-07-17 16:03:02.009744: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:417] Shuffle buffer filled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      4/Unknown - 27s 281ms/step - loss: 1.8169 - accuracy: 0.6006WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-17 16:03:05.096663: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 29s 1s/step - loss: 1.8169 - accuracy: 0.6006 - val_loss: 1.4600 - val_accuracy: 0.7640\n",
      "Epoch 2/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 1.0884 - accuracy: 0.8745WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 155ms/step - loss: 1.0884 - accuracy: 0.8745 - val_loss: 1.1696 - val_accuracy: 0.8634\n",
      "Epoch 3/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.5535 - accuracy: 0.9263WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 154ms/step - loss: 0.5535 - accuracy: 0.9263 - val_loss: 0.3864 - val_accuracy: 0.9224\n",
      "Epoch 4/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1674 - accuracy: 0.9678WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 153ms/step - loss: 0.1674 - accuracy: 0.9678 - val_loss: 0.8664 - val_accuracy: 0.8571\n",
      "Epoch 5/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1480 - accuracy: 0.9564WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.1480 - accuracy: 0.9564 - val_loss: 0.2408 - val_accuracy: 0.8727\n",
      "Epoch 6/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0981 - accuracy: 0.9751WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 163ms/step - loss: 0.0981 - accuracy: 0.9751 - val_loss: 0.1514 - val_accuracy: 0.9503\n",
      "Epoch 7/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0272 - accuracy: 0.9896WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 0s 130ms/step - loss: 0.0272 - accuracy: 0.9896 - val_loss: 0.7647 - val_accuracy: 0.7981\n",
      "Epoch 8/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0356 - accuracy: 0.9876WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 0s 133ms/step - loss: 0.0356 - accuracy: 0.9876 - val_loss: 0.2921 - val_accuracy: 0.8478\n",
      "Epoch 9/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0268 - accuracy: 0.9938WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 149ms/step - loss: 0.0268 - accuracy: 0.9938 - val_loss: 0.1718 - val_accuracy: 0.9627\n",
      "Epoch 10/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0134 - accuracy: 0.9948WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 0s 129ms/step - loss: 0.0134 - accuracy: 0.9948 - val_loss: 0.3958 - val_accuracy: 0.8789\n",
      "Epoch 11/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0243 - accuracy: 0.9938WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 0s 129ms/step - loss: 0.0243 - accuracy: 0.9938 - val_loss: 0.3617 - val_accuracy: 0.8944\n",
      "Epoch 12/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0046 - accuracy: 0.9979WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 0s 133ms/step - loss: 0.0046 - accuracy: 0.9979 - val_loss: 0.2300 - val_accuracy: 0.9627\n",
      "Epoch 13/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0059 - accuracy: 0.9959WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 162ms/step - loss: 0.0059 - accuracy: 0.9959 - val_loss: 0.2236 - val_accuracy: 0.9596\n",
      "Epoch 14/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0036 - accuracy: 0.9979WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 153ms/step - loss: 0.0036 - accuracy: 0.9979 - val_loss: 0.2552 - val_accuracy: 0.9410\n",
      "Epoch 15/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0037 - accuracy: 0.9979WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 156ms/step - loss: 0.0037 - accuracy: 0.9979 - val_loss: 0.2364 - val_accuracy: 0.9503\n",
      "Model training finished\n",
      "WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "Validation accuracy: 95.03%\n"
     ]
    }
   ],
   "source": [
    "history = run_experiment(\n",
    "    model=baseline_model,\n",
    "    train_data_file=train_data_file,\n",
    "    test_data_file=test_data_file,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    weight_decay=WEIGHT_DECAY,\n",
    "    batch_size=BATCH_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8fa191-1270-4e3f-bbbf-99f32dee6d8f",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MLP predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "01a6ec3a-a922-4516-ba28-949ede4c3eb3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cancer_type</th>\n",
       "      <th>ltnt_cnv_0</th>\n",
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       "      <th>ltnt_trs_249</th>\n",
       "      <th>plchldr_cat</th>\n",
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       "      <th>improve_sample_id</th>\n",
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       "      <th>109820.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.433715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.907877</td>\n",
       "      <td>4.122471</td>\n",
       "      <td>...</td>\n",
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       "      <td>Breast carcinoma</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.398782</td>\n",
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       "      <td>1.031150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.901609</td>\n",
       "      <td>4.713804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.437247</td>\n",
       "      <td>0.506141</td>\n",
       "      <td>0.481366</td>\n",
       "      <td>0.811322</td>\n",
       "      <td>0.425224</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>109964.0</th>\n",
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       "      <td>0.115932</td>\n",
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       "      <td>1.078823</td>\n",
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       "      <td>Breast carcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.130291</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.733532</td>\n",
       "      <td>0.864370</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110145.0</th>\n",
       "      <td>Colon adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.576671</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.173397</td>\n",
       "      <td>4.261923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.613996</td>\n",
       "      <td>0.577007</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109376.0</th>\n",
       "      <td>Lung adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.960527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.416421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.516705</td>\n",
       "      <td>4.300665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.530324</td>\n",
       "      <td>0.497336</td>\n",
       "      <td>0.450473</td>\n",
       "      <td>0.647615</td>\n",
       "      <td>0.492134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110385.0</th>\n",
       "      <td>Pancreatic ductal adenocarcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.336474</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.101984</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.383413</td>\n",
       "      <td>5.456665</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.753509</td>\n",
       "      <td>0.857809</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108274.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.998241</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.642601</td>\n",
       "      <td>3.125448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.499556</td>\n",
       "      <td>0.528515</td>\n",
       "      <td>0.507634</td>\n",
       "      <td>0.867145</td>\n",
       "      <td>0.498092</td>\n",
       "      <td>0.005660</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109704.0</th>\n",
       "      <td>Breast carcinoma</td>\n",
       "      <td>0.335723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.926236</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.808175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.705284</td>\n",
       "      <td>3.615990</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.472129</td>\n",
       "      <td>0.508779</td>\n",
       "      <td>0.507059</td>\n",
       "      <td>0.867221</td>\n",
       "      <td>0.519571</td>\n",
       "      <td>0.101845</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 753 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        cancer_type  ltnt_cnv_0  ltnt_cnv_1  \\\n",
       "improve_sample_id                                                             \n",
       "109820.0                           Breast carcinoma    0.000000         0.0   \n",
       "108270.0                           Breast carcinoma    0.000000         0.0   \n",
       "109964.0                           Breast carcinoma    0.000000         0.0   \n",
       "108165.0                           Breast carcinoma    0.000000         0.0   \n",
       "110419.0           Pancreatic ductal adenocarcinoma    0.026880         0.0   \n",
       "...                                             ...         ...         ...   \n",
       "110145.0                       Colon adenocarcinoma    0.000000         0.0   \n",
       "109376.0                        Lung adenocarcinoma    0.000000         0.0   \n",
       "110385.0           Pancreatic ductal adenocarcinoma    0.000000         0.0   \n",
       "108274.0                           Breast carcinoma    0.000000         0.0   \n",
       "109704.0                           Breast carcinoma    0.335723         0.0   \n",
       "\n",
       "                   ltnt_cnv_2  ltnt_cnv_3  ltnt_cnv_4  ltnt_cnv_5  ltnt_cnv_6  \\\n",
       "improve_sample_id                                                               \n",
       "109820.0             0.000000    1.433715    0.000000    0.000000         0.0   \n",
       "108270.0             0.000000    1.398782    0.000000    1.031150         0.0   \n",
       "109964.0             0.115932    1.860882    0.000000    1.078823         0.0   \n",
       "108165.0             0.000000    1.130291    0.000000    1.189532         0.0   \n",
       "110419.0             0.000000    1.405521    0.000000    1.022672         0.0   \n",
       "...                       ...         ...         ...         ...         ...   \n",
       "110145.0             0.000000    2.576671    0.000000    0.000000         0.0   \n",
       "109376.0             0.000000    0.960527    0.000000    1.416421         0.0   \n",
       "110385.0             0.000000    1.336474    0.000000    1.101984         0.0   \n",
       "108274.0             0.000000    0.000000    0.998241    0.966667         0.0   \n",
       "109704.0             0.000000    1.926236    0.000000    0.808175         0.0   \n",
       "\n",
       "                   ltnt_cnv_7  ltnt_cnv_8  ...  ltnt_trs_242  ltnt_trs_243  \\\n",
       "improve_sample_id                          ...                               \n",
       "109820.0             6.907877    4.122471  ...           0.0      0.448051   \n",
       "108270.0             6.901609    4.713804  ...           0.0      0.437247   \n",
       "109964.0             5.853739    4.319950  ...           0.0      0.466553   \n",
       "108165.0             7.821920    5.560207  ...           0.0      0.511565   \n",
       "110419.0             7.507570    5.328773  ...           0.0      0.000000   \n",
       "...                       ...         ...  ...           ...           ...   \n",
       "110145.0             5.173397    4.261923  ...           0.0      0.000000   \n",
       "109376.0             7.516705    4.300665  ...           0.0      0.530324   \n",
       "110385.0             7.383413    5.456665  ...           0.0      0.000000   \n",
       "108274.0             6.642601    3.125448  ...           0.0      0.499556   \n",
       "109704.0             6.705284    3.615990  ...           0.0      0.472129   \n",
       "\n",
       "                   ltnt_trs_244  ltnt_trs_245  ltnt_trs_246  ltnt_trs_247  \\\n",
       "improve_sample_id                                                           \n",
       "109820.0               0.518651      0.515649      0.876546      0.513258   \n",
       "108270.0               0.506141      0.481366      0.811322      0.425224   \n",
       "109964.0               0.507196      0.497130      0.870872      0.554220   \n",
       "108165.0               0.564085      0.635782      1.039949      0.658557   \n",
       "110419.0               0.000000      0.000000      0.000000      0.000000   \n",
       "...                         ...           ...           ...           ...   \n",
       "110145.0               0.000000      0.000000      0.000000      0.000000   \n",
       "109376.0               0.497336      0.450473      0.647615      0.492134   \n",
       "110385.0               0.000000      0.000000      0.000000      0.000000   \n",
       "108274.0               0.528515      0.507634      0.867145      0.498092   \n",
       "109704.0               0.508779      0.507059      0.867221      0.519571   \n",
       "\n",
       "                   ltnt_trs_248  ltnt_trs_249  plchldr_cat  fnlwgt  \n",
       "improve_sample_id                                                   \n",
       "109820.0               0.083375      0.000000            1     1.0  \n",
       "108270.0               0.000000      0.000000            1     1.0  \n",
       "109964.0               0.097657      0.000000            1     1.0  \n",
       "108165.0               0.152952      0.000000            1     1.0  \n",
       "110419.0               1.733532      0.864370            1     1.0  \n",
       "...                         ...           ...          ...     ...  \n",
       "110145.0               1.613996      0.577007            1     1.0  \n",
       "109376.0               0.000000      0.000000            1     1.0  \n",
       "110385.0               1.753509      0.857809            1     1.0  \n",
       "108274.0               0.005660      0.000000            1     1.0  \n",
       "109704.0               0.101845      0.000000            1     1.0  \n",
       "\n",
       "[100 rows x 753 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d4f13f4b-4465-46e6-87f7-a98d9ce1a6cf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# toggle number of predictions\n",
    "n_vld_smpls_to_check = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "db5d724a-ea61-478f-8452-bc8a2d9c2f48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "smpl_srs = validation_set.index.to_series().sample(n_vld_smpls_to_check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "488a9a1c-a113-4af4-885d-33bcc3c5ef16",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "improve_sample_id\n",
       "108270.0    108270.0\n",
       "110381.0    110381.0\n",
       "110338.0    110338.0\n",
       "109153.0    109153.0\n",
       "108258.0    108258.0\n",
       "108953.0    108953.0\n",
       "109877.0    109877.0\n",
       "109926.0    109926.0\n",
       "110409.0    110409.0\n",
       "109937.0    109937.0\n",
       "Name: improve_sample_id, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smpl_srs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "b35f29e1-33b5-4f83-a31b-2d493f94edd2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 346ms/step\n",
      "109820.0  predicted\n",
      "1/1 [==============================] - 0s 330ms/step\n",
      "108270.0  predicted\n",
      "1/1 [==============================] - 0s 353ms/step\n",
      "109964.0  predicted\n",
      "1/1 [==============================] - 0s 335ms/step\n",
      "108165.0  predicted\n",
      "1/1 [==============================] - 0s 330ms/step\n",
      "110419.0  predicted\n",
      "1/1 [==============================] - 0s 330ms/step\n",
      "110381.0  predicted\n",
      "1/1 [==============================] - 0s 329ms/step\n",
      "108458.0  predicted\n",
      "1/1 [==============================] - 0s 323ms/step\n",
      "108252.0  predicted\n",
      "1/1 [==============================] - 0s 321ms/step\n",
      "109714.0  predicted\n",
      "1/1 [==============================] - 0s 332ms/step\n",
      "109137.0  predicted\n"
     ]
    }
   ],
   "source": [
    "truth_list = []\n",
    "prediction_list = []\n",
    "# for isi in smpl_srs: # isi is improve sample id, for random sample validation set\n",
    "for i in list(range(10)):\n",
    "# for i in list(range(len(validation_set))):\n",
    "\n",
    "    hold_out = pd.DataFrame(validation_set.iloc[i]).T\n",
    "    improve_samble_id = hold_out.index[0]\n",
    "    truth_val = hold_out.loc[improve_samble_id, 'cancer_type']\n",
    "    truth_list.append(truth_val)\n",
    "    hold_out = hold_out.drop(columns=hold_out.columns[0]) # Label is always pos 0 here\n",
    "    # to prediction formatting function\n",
    "    ch_lst = []\n",
    "    val_lst = []\n",
    "    for clmn_hdr in hold_out.columns:\n",
    "        ch_lst.append(clmn_hdr)\n",
    "        val_lst.append(hold_out[clmn_hdr][improve_samble_id])\n",
    "        result_dict = dict(zip(ch_lst, val_lst)) \n",
    "        \n",
    "        # break\n",
    "    result_dict.pop('fnlwgt')\n",
    "    input_dict = {name: tf.convert_to_tensor([value]) for name,value in result_dict.items()}\n",
    "    prediction_list.append(baseline_model.predict(input_dict))\n",
    "    print(improve_samble_id, ' predicted')\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "75d25969-d587-4935-9da4-1e7fe3676270",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "human_readable_predictions = []\n",
    "for p in prediction_list:\n",
    "    human_readable_predictions.append(TARGET_LABELS[np.argmax(p)])\n",
    "    # print(p)\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "2fcdb948-efd8-4f30-84de-8583b76be676",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breast carcinoma Breast carcinoma\n",
      "Breast carcinoma Breast carcinoma\n",
      "Breast carcinoma Breast carcinoma\n",
      "Breast carcinoma Breast carcinoma\n",
      "Pancreatic ductal adenocarcinoma Head and neck squamous cell carcinoma\n",
      "Pancreatic ductal adenocarcinoma Pancreatic ductal adenocarcinoma\n",
      "Endometrial carcinoma Endometrial carcinoma\n",
      "Breast carcinoma Breast carcinoma\n",
      "Breast carcinoma Breast carcinoma\n",
      "Lung squamous cell carcinoma Lung squamous cell carcinoma\n"
     ]
    }
   ],
   "source": [
    "for i, truth_val in enumerate(truth_list):\n",
    "    print(truth_val, human_readable_predictions[i])\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "d98f34c6-840d-476b-8751-4fc4a06281d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sklearn.metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4ef25cae-9c75-4570-850c-14770228d8d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(truth_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "452beb61-2d59-4491-8d1b-c21e3ac43d33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(prediction_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "d9a3d2e3-56d8-4f62-a017-8e62af7461f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(human_readable_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "2328e52a-f907-4fc6-afce-075b43470c20",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sklearn.metrics.accuracy_score(truth_list, human_readable_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e62880d6-d8fb-4f1d-94c8-bc5e3f90e2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# End MLP version zero, ~90% accurate at predicting \n",
    "# tissue-of-origin based on latent features of\n",
    "# CNV, proteomics, and gene expression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0500c604-0ca4-49aa-bd2e-5717e26a97ef",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "dbb7f0b9-c692-4314-8ecf-0ea460396acc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/trnsfrmr/lib/python3.9/site-packages/numpy/core/numeric.py:2463: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  return bool(asarray(a1 == a2).all())\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total model weights: 2373287\n"
     ]
    }
   ],
   "source": [
    "# Multi-categorical\n",
    "\n",
    "def create_tabtransformer_classifier(\n",
    "    num_transformer_blocks,\n",
    "    num_heads,\n",
    "    embedding_dims,\n",
    "    mlp_hidden_units_factors,\n",
    "    dropout_rate,\n",
    "    use_column_embedding=False,\n",
    "):\n",
    "\n",
    "    # Create model inputs.\n",
    "    inputs = create_model_inputs()\n",
    "    # encode features.\n",
    "    encoded_categorical_feature_list, numerical_feature_list = encode_inputs(\n",
    "        inputs, embedding_dims\n",
    "    )\n",
    "    # Stack categorical feature embeddings for the Tansformer.\n",
    "    encoded_categorical_features = tf.stack(encoded_categorical_feature_list, axis=1)\n",
    "    # Concatenate numerical features.\n",
    "    numerical_features = layers.concatenate(numerical_feature_list)\n",
    "\n",
    "    # Add column embedding to categorical feature embeddings.\n",
    "    if use_column_embedding:\n",
    "        num_columns = encoded_categorical_features.shape[1]\n",
    "        column_embedding = layers.Embedding(\n",
    "            input_dim=num_columns, output_dim=embedding_dims\n",
    "        )\n",
    "        column_indices = tf.range(start=0, limit=num_columns, delta=1)\n",
    "        encoded_categorical_features = encoded_categorical_features + column_embedding(\n",
    "            column_indices\n",
    "        )\n",
    "\n",
    "    # Create multiple layers of the Transformer block.\n",
    "    for block_idx in range(num_transformer_blocks):\n",
    "        # Create a multi-head attention layer.\n",
    "        attention_output = layers.MultiHeadAttention(\n",
    "            num_heads=num_heads,\n",
    "            key_dim=embedding_dims,\n",
    "            dropout=dropout_rate,\n",
    "            name=f\"multihead_attention_{block_idx}\",\n",
    "        )(encoded_categorical_features, encoded_categorical_features)\n",
    "        # Skip connection 1.\n",
    "        x = layers.Add(name=f\"skip_connection1_{block_idx}\")(\n",
    "            [attention_output, encoded_categorical_features]\n",
    "        )\n",
    "        # Layer normalization 1.\n",
    "        x = layers.LayerNormalization(name=f\"layer_norm1_{block_idx}\", epsilon=1e-6)(x)\n",
    "        # Feedforward.\n",
    "        feedforward_output = create_mlp(\n",
    "            hidden_units=[embedding_dims],\n",
    "            dropout_rate=dropout_rate,\n",
    "            activation=keras.activations.gelu,\n",
    "            normalization_layer=layers.LayerNormalization(epsilon=1e-6),\n",
    "            name=f\"feedforward_{block_idx}\",\n",
    "        )(x)\n",
    "        # Skip connection 2.\n",
    "        x = layers.Add(name=f\"skip_connection2_{block_idx}\")([feedforward_output, x])\n",
    "        # Layer normalization 2.\n",
    "        encoded_categorical_features = layers.LayerNormalization(\n",
    "            name=f\"layer_norm2_{block_idx}\", epsilon=1e-6\n",
    "        )(x)\n",
    "\n",
    "    # Flatten the \"contextualized\" embeddings of the categorical features.\n",
    "    categorical_features = layers.Flatten()(encoded_categorical_features)\n",
    "    # Apply layer normalization to the numerical features.\n",
    "    numerical_features = layers.LayerNormalization(epsilon=1e-6)(numerical_features)\n",
    "    # Prepare the input for the final MLP block.\n",
    "    features = layers.concatenate([categorical_features, numerical_features])\n",
    "\n",
    "    # Compute MLP hidden_units.\n",
    "    mlp_hidden_units = [\n",
    "        factor * features.shape[-1] for factor in mlp_hidden_units_factors\n",
    "    ]\n",
    "    # Create final MLP.\n",
    "    features = create_mlp(\n",
    "        hidden_units=mlp_hidden_units,\n",
    "        dropout_rate=dropout_rate,\n",
    "        activation=keras.activations.selu,\n",
    "        normalization_layer=layers.BatchNormalization(),\n",
    "        name=\"MLP\",\n",
    "    )(features)\n",
    "\n",
    "    # Add a sigmoid as a binary classifer.\n",
    "    # outputs = layers.Dense(units=1, activation=\"sigmoid\", name=\"sigmoid\")(features)\n",
    "    \n",
    "    # ~~~~  Softmax for multi-class classifer ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <---\n",
    "    outputs = layers.Dense(units=num_classes, activation=\"softmax\", name=\"softmax\")(features)\n",
    "    model = keras.Model(inputs=inputs, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "\n",
    "tabtransformer_model = create_tabtransformer_classifier(\n",
    "    num_transformer_blocks=NUM_TRANSFORMER_BLOCKS,\n",
    "    num_heads=NUM_HEADS,\n",
    "    embedding_dims=EMBEDDING_DIMS,\n",
    "    mlp_hidden_units_factors=MLP_HIDDEN_UNITS_FACTORS,\n",
    "    dropout_rate=DROPOUT_RATE,\n",
    ")\n",
    "\n",
    "print(\"Total model weights:\", tabtransformer_model.count_params())\n",
    "# keras.utils.plot_model(tabtransformer_model, show_shapes=True, rankdir=\"LR\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "2d1a18ac-83f5-487c-bf17-ce187f53d7bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start training the model...\n",
      "Epoch 1/15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/karlberb/miniforge3/envs/trnsfrmr/lib/python3.9/site-packages/keras/backend.py:5585: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a Softmax activation and thus does not represent logits. Was this intended?\n",
      "  output, from_logits = _get_logits(\n",
      "2023-07-17 20:03:23.946297: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      4/Unknown - 6s 542ms/step - loss: 2.9394 - accuracy: 0.5985WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-17 20:03:29.808536: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 9s 2s/step - loss: 2.9394 - accuracy: 0.5985 - val_loss: 5.7565 - val_accuracy: 0.4658\n",
      "Epoch 2/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.7891 - accuracy: 0.8579WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 263ms/step - loss: 0.7891 - accuracy: 0.8579 - val_loss: 0.9627 - val_accuracy: 0.7453\n",
      "Epoch 3/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.5865 - accuracy: 0.8963WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 263ms/step - loss: 0.5865 - accuracy: 0.8963 - val_loss: 4.6863 - val_accuracy: 0.4379\n",
      "Epoch 4/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.8120 - accuracy: 0.9025WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 274ms/step - loss: 0.8120 - accuracy: 0.9025 - val_loss: 2.3591 - val_accuracy: 0.6335\n",
      "Epoch 5/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.2277 - accuracy: 0.9627WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 253ms/step - loss: 0.2277 - accuracy: 0.9627 - val_loss: 1.2014 - val_accuracy: 0.7205\n",
      "Epoch 6/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1697 - accuracy: 0.9678WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 245ms/step - loss: 0.1697 - accuracy: 0.9678 - val_loss: 0.9089 - val_accuracy: 0.8416\n",
      "Epoch 7/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0598 - accuracy: 0.9834WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 203ms/step - loss: 0.0598 - accuracy: 0.9834 - val_loss: 1.6917 - val_accuracy: 0.7112\n",
      "Epoch 8/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1089 - accuracy: 0.9741WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 214ms/step - loss: 0.1089 - accuracy: 0.9741 - val_loss: 1.9393 - val_accuracy: 0.7609\n",
      "Epoch 9/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0564 - accuracy: 0.9865WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 225ms/step - loss: 0.0564 - accuracy: 0.9865 - val_loss: 2.5794 - val_accuracy: 0.6708\n",
      "Epoch 10/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0975 - accuracy: 0.9782WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 204ms/step - loss: 0.0975 - accuracy: 0.9782 - val_loss: 2.5377 - val_accuracy: 0.7267\n",
      "Epoch 11/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.1000 - accuracy: 0.9803WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 218ms/step - loss: 0.1000 - accuracy: 0.9803 - val_loss: 2.7682 - val_accuracy: 0.7236\n",
      "Epoch 12/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0538 - accuracy: 0.9865WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 194ms/step - loss: 0.0538 - accuracy: 0.9865 - val_loss: 2.5430 - val_accuracy: 0.7609\n",
      "Epoch 13/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0171 - accuracy: 0.9948WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 210ms/step - loss: 0.0171 - accuracy: 0.9948 - val_loss: 2.1576 - val_accuracy: 0.7764\n",
      "Epoch 14/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9969WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 204ms/step - loss: 0.0033 - accuracy: 0.9969 - val_loss: 1.9580 - val_accuracy: 0.7764\n",
      "Epoch 15/15\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.0057 - accuracy: 0.9969WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "4/4 [==============================] - 1s 228ms/step - loss: 0.0057 - accuracy: 0.9969 - val_loss: 1.7528 - val_accuracy: 0.7857\n",
      "Model training finished\n",
      "WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "Validation accuracy: 78.57%\n"
     ]
    }
   ],
   "source": [
    "history = run_experiment(\n",
    "    model=tabtransformer_model,\n",
    "    train_data_file=train_data_file,\n",
    "    test_data_file=test_data_file,\n",
    "    num_epochs=NUM_EPOCHS,\n",
    "    learning_rate=LEARNING_RATE,\n",
    "    weight_decay=WEIGHT_DECAY,\n",
    "    batch_size=BATCH_SIZE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "4bd6a9f4-ec3a-44a9-9244-4e1e582b293b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-17 20:10:46.964207: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 2s 2s/step\n",
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      "1/1 [==============================] - 0s 338ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make 100 predictions on validation set,\n",
    "# Multi-categorical modified Tabular Transformer\n",
    "truth_list = []\n",
    "prediction_list = []\n",
    "for i in list(range(len(validation_set))):\n",
    "\n",
    "    hold_out = pd.DataFrame(validation_set.iloc[i]).T\n",
    "    improve_samble_id = hold_out.index[0]\n",
    "    truth_val = hold_out.loc[improve_samble_id, 'cancer_type']\n",
    "    truth_list.append(truth_val)\n",
    "    hold_out = hold_out.drop(columns=hold_out.columns[0])\n",
    "\n",
    "    ch_lst = []\n",
    "    val_lst = []\n",
    "    for clmn_hdr in hold_out.columns:\n",
    "        ch_lst.append(clmn_hdr)\n",
    "        val_lst.append(hold_out[clmn_hdr][improve_samble_id])\n",
    "        result_dict = dict(zip(ch_lst, val_lst)) \n",
    "        \n",
    "        # break\n",
    "    result_dict.pop('fnlwgt')\n",
    "    input_dict = {\n",
    "        name: tf.convert_to_tensor(\n",
    "            [value]) for name,value in result_dict.items()}\n",
    "    prediction_list.append(tabtransformer_model.predict(input_dict)) # <--- replaced MLP\n",
    "    # print(improve_samble_id, ' predicted')\n",
    "human_readable_predictions = []\n",
    "for p in prediction_list:\n",
    "    human_readable_predictions.append(TARGET_LABELS[np.argmax(p)])\n",
    "sklearn.metrics.accuracy_score(truth_list, human_readable_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6d980d-623f-4a34-a6c8-52abeae38435",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 80%\n",
    "# MLP scores ~10% higher"
   ]
  }
 ],
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