MachineLearning_inMolecularBiology2022_GroupProject.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the first few assignments, we have learned how to infer part based components (known as mutational signatures) generated by particular mutational processes using Non-negative Matrix Factorization (NMF). By doing this, we are trying to reconstruct the mutation catalog in a given sample with mutational signatures and their contributions.\n",
    "\n",
    "In this group project, you will use similar mutational profiles and signature activities to predict cancer types but with much larger sample size. \n",
    "You should:\n",
    "* Separate the data into training and test groups within each cancer type.\n",
    "* Find out which features are informative for the prediction of the cancer type (label). You should combine the profiles and activities and use each data type independently.\n",
    "* Implement different models for classification of the samples given the input data and evaluate the model performance using test data to avoid overfitting. Explain briefly how does each model that you have used work.\n",
    "* Report model performance, using standard machine learning metrics such as confusion matrices etc. \n",
    "* Compare model performance across methods and across cancer types, are some types easier top predict than others.\n",
    "* Submit a single Jupyter notebook as the final report and present that during the last assignment session "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data include both mutational catalogs from multiple cancers and the predicted activities in the paper [\"Alexandrov LB, et al. (2020) The repertoire of mutational signatures in human cancer\"](https://www.nature.com/articles/s41586-020-1943-3). The data either are generated from whole human genome (WGS) or only exomes regions (WES). Since the exome region only constitutes about 1% of human genome, the total mutation numbers in these samples are, of course, much smaller. So if you plan to use WGS together with WES data, remember to normalize the profile for each sample to sum up to 1.\n",
    "\n",
    "Note that, the data is generated from different platforms by different research groups, some of them (e.g. labeled with PCAWG, TCGA) are processed with the same bioinformatics pipeline. Thus, these samples will have less variability related to data processing pipelines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cancer types might be labeled under the same tissue, e.g. 'Bone-Benign','Bone-Epith', which can also be combined together or take the one has more samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a link to background reading [\"Pan-Cancer Analysis of Whole Genomes\"](https://www.nature.com/collections/afdejfafdb). Have a look especially the paper [\"A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns\"](https://www.nature.com/articles/s41467-019-13825-8)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import scipy\n",
    "import numpy as np\n",
    "import sklearn\n",
    "from matplotlib import pyplot\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutational catalogs and activities - WGS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>Mutation type</th>\n",
       "      <th>Trinucleotide</th>\n",
       "      <th>Biliary-AdenoCA::SP117655</th>\n",
       "      <th>Biliary-AdenoCA::SP117556</th>\n",
       "      <th>Biliary-AdenoCA::SP117627</th>\n",
       "      <th>Biliary-AdenoCA::SP117775</th>\n",
       "      <th>Biliary-AdenoCA::SP117332</th>\n",
       "      <th>Biliary-AdenoCA::SP117712</th>\n",
       "      <th>Biliary-AdenoCA::SP117017</th>\n",
       "      <th>Biliary-AdenoCA::SP117031</th>\n",
       "      <th>...</th>\n",
       "      <th>Uterus-AdenoCA::SP94540</th>\n",
       "      <th>Uterus-AdenoCA::SP95222</th>\n",
       "      <th>Uterus-AdenoCA::SP89389</th>\n",
       "      <th>Uterus-AdenoCA::SP90503</th>\n",
       "      <th>Uterus-AdenoCA::SP92460</th>\n",
       "      <th>Uterus-AdenoCA::SP92931</th>\n",
       "      <th>Uterus-AdenoCA::SP91265</th>\n",
       "      <th>Uterus-AdenoCA::SP89909</th>\n",
       "      <th>Uterus-AdenoCA::SP90629</th>\n",
       "      <th>Uterus-AdenoCA::SP95550</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>269</td>\n",
       "      <td>114</td>\n",
       "      <td>105</td>\n",
       "      <td>217</td>\n",
       "      <td>52</td>\n",
       "      <td>192</td>\n",
       "      <td>54</td>\n",
       "      <td>196</td>\n",
       "      <td>...</td>\n",
       "      <td>117</td>\n",
       "      <td>233</td>\n",
       "      <td>94</td>\n",
       "      <td>114</td>\n",
       "      <td>257</td>\n",
       "      <td>139</td>\n",
       "      <td>404</td>\n",
       "      <td>97</td>\n",
       "      <td>250</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACC</td>\n",
       "      <td>148</td>\n",
       "      <td>56</td>\n",
       "      <td>71</td>\n",
       "      <td>123</td>\n",
       "      <td>36</td>\n",
       "      <td>139</td>\n",
       "      <td>54</td>\n",
       "      <td>102</td>\n",
       "      <td>...</td>\n",
       "      <td>90</td>\n",
       "      <td>167</td>\n",
       "      <td>59</td>\n",
       "      <td>64</td>\n",
       "      <td>268</td>\n",
       "      <td>75</td>\n",
       "      <td>255</td>\n",
       "      <td>78</td>\n",
       "      <td>188</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 2782 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mutation type Trinucleotide  Biliary-AdenoCA::SP117655  \\\n",
       "0           C>A           ACA                        269   \n",
       "1           C>A           ACC                        148   \n",
       "\n",
       "   Biliary-AdenoCA::SP117556  Biliary-AdenoCA::SP117627  \\\n",
       "0                        114                        105   \n",
       "1                         56                         71   \n",
       "\n",
       "   Biliary-AdenoCA::SP117775  Biliary-AdenoCA::SP117332  \\\n",
       "0                        217                         52   \n",
       "1                        123                         36   \n",
       "\n",
       "   Biliary-AdenoCA::SP117712  Biliary-AdenoCA::SP117017  \\\n",
       "0                        192                         54   \n",
       "1                        139                         54   \n",
       "\n",
       "   Biliary-AdenoCA::SP117031  ...  Uterus-AdenoCA::SP94540  \\\n",
       "0                        196  ...                      117   \n",
       "1                        102  ...                       90   \n",
       "\n",
       "   Uterus-AdenoCA::SP95222  Uterus-AdenoCA::SP89389  Uterus-AdenoCA::SP90503  \\\n",
       "0                      233                       94                      114   \n",
       "1                      167                       59                       64   \n",
       "\n",
       "   Uterus-AdenoCA::SP92460  Uterus-AdenoCA::SP92931  Uterus-AdenoCA::SP91265  \\\n",
       "0                      257                      139                      404   \n",
       "1                      268                       75                      255   \n",
       "\n",
       "   Uterus-AdenoCA::SP89909  Uterus-AdenoCA::SP90629  Uterus-AdenoCA::SP95550  \n",
       "0                       97                      250                      170  \n",
       "1                       78                      188                      137  \n",
       "\n",
       "[2 rows x 2782 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## PCAWG data is performed by the same pipeline\n",
    "PCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_PCAWG.96.csv\")\n",
    "PCAWG_wgs_mut.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is the cosine similarity of reconstruct catalog to the observed catalog "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>Cancer Types</th>\n",
       "      <th>Sample Names</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>SBS1</th>\n",
       "      <th>SBS2</th>\n",
       "      <th>SBS3</th>\n",
       "      <th>SBS4</th>\n",
       "      <th>SBS5</th>\n",
       "      <th>SBS6</th>\n",
       "      <th>SBS7a</th>\n",
       "      <th>...</th>\n",
       "      <th>SBS51</th>\n",
       "      <th>SBS52</th>\n",
       "      <th>SBS53</th>\n",
       "      <th>SBS54</th>\n",
       "      <th>SBS55</th>\n",
       "      <th>SBS56</th>\n",
       "      <th>SBS57</th>\n",
       "      <th>SBS58</th>\n",
       "      <th>SBS59</th>\n",
       "      <th>SBS60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117655</td>\n",
       "      <td>0.968</td>\n",
       "      <td>1496</td>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1825</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117556</td>\n",
       "      <td>0.963</td>\n",
       "      <td>985</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>922</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117627</td>\n",
       "      <td>0.973</td>\n",
       "      <td>1110</td>\n",
       "      <td>528</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1453</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117775</td>\n",
       "      <td>0.987</td>\n",
       "      <td>1803</td>\n",
       "      <td>1271</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2199</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117332</td>\n",
       "      <td>0.987</td>\n",
       "      <td>441</td>\n",
       "      <td>461</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117712</td>\n",
       "      <td>0.948</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6599</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117017</td>\n",
       "      <td>0.951</td>\n",
       "      <td>571</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3804</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117031</td>\n",
       "      <td>0.976</td>\n",
       "      <td>1926</td>\n",
       "      <td>547</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117759</td>\n",
       "      <td>0.982</td>\n",
       "      <td>2403</td>\n",
       "      <td>1191</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2684</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Biliary-AdenoCA</td>\n",
       "      <td>SP117621</td>\n",
       "      <td>0.989</td>\n",
       "      <td>2210</td>\n",
       "      <td>1605</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2631</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cancer Types Sample Names  Accuracy  SBS1  SBS2  SBS3  SBS4  SBS5  SBS6  \\\n",
       "0  Biliary-AdenoCA     SP117655     0.968  1496  1296     0     0  1825     0   \n",
       "1  Biliary-AdenoCA     SP117556     0.963   985     0     0     0   922     0   \n",
       "2  Biliary-AdenoCA     SP117627     0.973  1110   528     0     0  1453     0   \n",
       "3  Biliary-AdenoCA     SP117775     0.987  1803  1271     0     0  2199     0   \n",
       "4  Biliary-AdenoCA     SP117332     0.987   441   461     0     0   840     0   \n",
       "5  Biliary-AdenoCA     SP117712     0.948   122     0     0     0  6599     0   \n",
       "6  Biliary-AdenoCA     SP117017     0.951   571     0     0     0  3804     0   \n",
       "7  Biliary-AdenoCA     SP117031     0.976  1926   547     0     0   512     0   \n",
       "8  Biliary-AdenoCA     SP117759     0.982  2403  1191     0     0  2684     0   \n",
       "9  Biliary-AdenoCA     SP117621     0.989  2210  1605     0     0  2631     0   \n",
       "\n",
       "   SBS7a  ...  SBS51  SBS52  SBS53  SBS54  SBS55  SBS56  SBS57  SBS58  SBS59  \\\n",
       "0      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "1      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "2      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "3      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "4      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "5      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "6      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "7      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "8      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "9      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   SBS60  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "5      0  \n",
       "6      0  \n",
       "7      0  \n",
       "8      0  \n",
       "9      0  \n",
       "\n",
       "[10 rows x 68 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Activities:\n",
    "PCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_PCAWG.activities.csv\")\n",
    "PCAWG_wgs_act.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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>Mutation type</th>\n",
       "      <th>Trinucleotide</th>\n",
       "      <th>ALL::PD4020a</th>\n",
       "      <th>ALL::SJBALL011_D</th>\n",
       "      <th>ALL::SJBALL012_D</th>\n",
       "      <th>ALL::SJBALL020013_D1</th>\n",
       "      <th>ALL::SJBALL020422_D1</th>\n",
       "      <th>ALL::SJBALL020579_D1</th>\n",
       "      <th>ALL::SJBALL020589_D1</th>\n",
       "      <th>ALL::SJBALL020625_D1</th>\n",
       "      <th>...</th>\n",
       "      <th>Stomach-AdenoCa::pfg316T</th>\n",
       "      <th>Stomach-AdenoCa::pfg317T</th>\n",
       "      <th>Stomach-AdenoCa::pfg344T</th>\n",
       "      <th>Stomach-AdenoCa::pfg373T</th>\n",
       "      <th>Stomach-AdenoCa::pfg375T</th>\n",
       "      <th>Stomach-AdenoCa::pfg378T</th>\n",
       "      <th>Stomach-AdenoCa::pfg398T</th>\n",
       "      <th>Stomach-AdenoCa::pfg413T</th>\n",
       "      <th>Stomach-AdenoCa::pfg416T</th>\n",
       "      <th>Stomach-AdenoCa::pfg424T</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>35</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>133</td>\n",
       "      <td>185</td>\n",
       "      <td>202</td>\n",
       "      <td>185</td>\n",
       "      <td>96</td>\n",
       "      <td>134</td>\n",
       "      <td>12</td>\n",
       "      <td>279</td>\n",
       "      <td>75</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACC</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>70</td>\n",
       "      <td>126</td>\n",
       "      <td>88</td>\n",
       "      <td>35</td>\n",
       "      <td>54</td>\n",
       "      <td>16</td>\n",
       "      <td>112</td>\n",
       "      <td>31</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 1867 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mutation type Trinucleotide  ALL::PD4020a  ALL::SJBALL011_D  \\\n",
       "0           C>A           ACA            35                 9   \n",
       "1           C>A           ACC            16                 2   \n",
       "\n",
       "   ALL::SJBALL012_D  ALL::SJBALL020013_D1  ALL::SJBALL020422_D1  \\\n",
       "0                 2                     7                     5   \n",
       "1                 4                    10                     5   \n",
       "\n",
       "   ALL::SJBALL020579_D1  ALL::SJBALL020589_D1  ALL::SJBALL020625_D1  ...  \\\n",
       "0                     7                     3                     5  ...   \n",
       "1                     9                     1                     2  ...   \n",
       "\n",
       "   Stomach-AdenoCa::pfg316T  Stomach-AdenoCa::pfg317T  \\\n",
       "0                       133                       185   \n",
       "1                        48                        70   \n",
       "\n",
       "   Stomach-AdenoCa::pfg344T  Stomach-AdenoCa::pfg373T  \\\n",
       "0                       202                       185   \n",
       "1                       126                        88   \n",
       "\n",
       "   Stomach-AdenoCa::pfg375T  Stomach-AdenoCa::pfg378T  \\\n",
       "0                        96                       134   \n",
       "1                        35                        54   \n",
       "\n",
       "   Stomach-AdenoCa::pfg398T  Stomach-AdenoCa::pfg413T  \\\n",
       "0                        12                       279   \n",
       "1                        16                       112   \n",
       "\n",
       "   Stomach-AdenoCa::pfg416T  Stomach-AdenoCa::pfg424T  \n",
       "0                        75                       135  \n",
       "1                        31                        91  \n",
       "\n",
       "[2 rows x 1867 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonPCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_Other.96.csv\")\n",
    "nonPCAWG_wgs_mut.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>Cancer Types</th>\n",
       "      <th>Sample Names</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>SBS1</th>\n",
       "      <th>SBS2</th>\n",
       "      <th>SBS3</th>\n",
       "      <th>SBS4</th>\n",
       "      <th>SBS5</th>\n",
       "      <th>SBS6</th>\n",
       "      <th>SBS7a</th>\n",
       "      <th>...</th>\n",
       "      <th>SBS51</th>\n",
       "      <th>SBS52</th>\n",
       "      <th>SBS53</th>\n",
       "      <th>SBS54</th>\n",
       "      <th>SBS55</th>\n",
       "      <th>SBS56</th>\n",
       "      <th>SBS57</th>\n",
       "      <th>SBS58</th>\n",
       "      <th>SBS59</th>\n",
       "      <th>SBS60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALL</td>\n",
       "      <td>PD4020a</td>\n",
       "      <td>0.995</td>\n",
       "      <td>208</td>\n",
       "      <td>3006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL011_D</td>\n",
       "      <td>0.905</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL012_D</td>\n",
       "      <td>0.921</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020013_D1</td>\n",
       "      <td>0.971</td>\n",
       "      <td>226</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020422_D1</td>\n",
       "      <td>0.937</td>\n",
       "      <td>104</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020579_D1</td>\n",
       "      <td>0.921</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020589_D1</td>\n",
       "      <td>0.904</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020625_D1</td>\n",
       "      <td>0.922</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>139</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020704_D1</td>\n",
       "      <td>0.934</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ALL</td>\n",
       "      <td>SJBALL020877_D1</td>\n",
       "      <td>0.941</td>\n",
       "      <td>98</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cancer Types     Sample Names  Accuracy  SBS1  SBS2  SBS3  SBS4  SBS5  SBS6  \\\n",
       "0          ALL          PD4020a     0.995   208  3006     0     0   365     0   \n",
       "1          ALL      SJBALL011_D     0.905    66     0     0     0   144     0   \n",
       "2          ALL      SJBALL012_D     0.921    68     0     0     0    72     0   \n",
       "3          ALL  SJBALL020013_D1     0.971   226     0     0     0    70     0   \n",
       "4          ALL  SJBALL020422_D1     0.937   104     0     0     0   247     0   \n",
       "5          ALL  SJBALL020579_D1     0.921    68     0     0     0   227     0   \n",
       "6          ALL  SJBALL020589_D1     0.904    59     0     0     0     0     0   \n",
       "7          ALL  SJBALL020625_D1     0.922    43     0     0     0   139     0   \n",
       "8          ALL  SJBALL020704_D1     0.934    65     0     0     0   169     0   \n",
       "9          ALL  SJBALL020877_D1     0.941    98     0     0     0   135     0   \n",
       "\n",
       "   SBS7a  ...  SBS51  SBS52  SBS53  SBS54  SBS55  SBS56  SBS57  SBS58  SBS59  \\\n",
       "0      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "1      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "2      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "3      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "4      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "5      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "6      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "7      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "8      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "9      0  ...      0      0      0      0      0      0      0      0      0   \n",
       "\n",
       "   SBS60  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  \n",
       "5      0  \n",
       "6      0  \n",
       "7      0  \n",
       "8      0  \n",
       "9      0  \n",
       "\n",
       "[10 rows x 68 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonPCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_Other.activities.csv\")\n",
    "nonPCAWG_wgs_act.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ALL' 'AML' 'Biliary-AdenoCa' 'Blood-CMDI' 'Bone-cancer' 'Breast-cancer'\n",
      " 'CNS-GBM' 'CNS-LGG' 'CNS-Medullo' 'CNS-glioma-NOS' 'ColoRect-AdenoCa'\n",
      " 'Eso-AdenoCa' 'Ewings' 'Eye-Melanoma' 'Kidney-RCC' 'Liver-HCC'\n",
      " 'Lung-AdenoCa' 'Lymph-BNHL' 'Lymph-CLL' 'Ovary-AdenoCa' 'Panc-AdenoCa'\n",
      " 'Panc-Endocrine' 'Prost-AdenoCa' 'Sarcoma-bone' 'Sarcoma' 'Skin-Melanoma'\n",
      " 'Stomach-AdenoCa']\n"
     ]
    }
   ],
   "source": [
    "print(nonPCAWG_wgs_act['Cancer Types'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mutational catalogs - WES data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "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>Mutation type</th>\n",
       "      <th>Trinucleotide</th>\n",
       "      <th>AML::TCGA-AB-2802-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2803-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2804-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2805-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2806-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2807-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2808-03B-01W-0728-08</th>\n",
       "      <th>AML::TCGA-AB-2809-03D-01W-0755-09</th>\n",
       "      <th>...</th>\n",
       "      <th>Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08</th>\n",
       "      <th>Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACC</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 9495 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mutation type Trinucleotide  AML::TCGA-AB-2802-03B-01W-0728-08  \\\n",
       "0           C>A           ACA                                  0   \n",
       "1           C>A           ACC                                  0   \n",
       "\n",
       "   AML::TCGA-AB-2803-03B-01W-0728-08  AML::TCGA-AB-2804-03B-01W-0728-08  \\\n",
       "0                                  0                                  0   \n",
       "1                                  2                                  0   \n",
       "\n",
       "   AML::TCGA-AB-2805-03B-01W-0728-08  AML::TCGA-AB-2806-03B-01W-0728-08  \\\n",
       "0                                  0                                  4   \n",
       "1                                  0                                  0   \n",
       "\n",
       "   AML::TCGA-AB-2807-03B-01W-0728-08  AML::TCGA-AB-2808-03B-01W-0728-08  \\\n",
       "0                                  0                                  2   \n",
       "1                                  1                                  3   \n",
       "\n",
       "   AML::TCGA-AB-2809-03D-01W-0755-09  ...  \\\n",
       "0                                  0  ...   \n",
       "1                                  0  ...   \n",
       "\n",
       "   Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08  \\\n",
       "0                                           1   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           1   \n",
       "\n",
       "   Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08  \\\n",
       "0                                           0   \n",
       "1                                           0   \n",
       "\n",
       "   Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08  \n",
       "0                                           0  \n",
       "1                                           0  \n",
       "\n",
       "[2 rows x 9495 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Performed by TCGA pipeline\n",
    "TCGA_wes_mut = pd.read_csv (\"./project_data/catalogs/WES/WES_TCGA.96.csv\")\n",
    "TCGA_wes_mut.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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>Cancer Types</th>\n",
       "      <th>Sample Names</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>SBS1</th>\n",
       "      <th>SBS2</th>\n",
       "      <th>SBS3</th>\n",
       "      <th>SBS4</th>\n",
       "      <th>SBS5</th>\n",
       "      <th>SBS6</th>\n",
       "      <th>SBS7a</th>\n",
       "      <th>...</th>\n",
       "      <th>SBS51</th>\n",
       "      <th>SBS52</th>\n",
       "      <th>SBS53</th>\n",
       "      <th>SBS54</th>\n",
       "      <th>SBS55</th>\n",
       "      <th>SBS56</th>\n",
       "      <th>SBS57</th>\n",
       "      <th>SBS58</th>\n",
       "      <th>SBS59</th>\n",
       "      <th>SBS60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AML</td>\n",
       "      <td>TCGA-AB-2802-03B-01W-0728-08</td>\n",
       "      <td>0.811</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AML</td>\n",
       "      <td>TCGA-AB-2803-03B-01W-0728-08</td>\n",
       "      <td>0.608</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cancer Types                  Sample Names  Accuracy  SBS1  SBS2  SBS3  \\\n",
       "0          AML  TCGA-AB-2802-03B-01W-0728-08     0.811     3     0     0   \n",
       "1          AML  TCGA-AB-2803-03B-01W-0728-08     0.608     4     0     0   \n",
       "\n",
       "   SBS4  SBS5  SBS6  SBS7a  ...  SBS51  SBS52  SBS53  SBS54  SBS55  SBS56  \\\n",
       "0     0     0     0      0  ...      0      0      0      0      0      0   \n",
       "1     0     7     0      0  ...      0      0      0      0      0      0   \n",
       "\n",
       "   SBS57  SBS58  SBS59  SBS60  \n",
       "0      0      0      0      0  \n",
       "1      0      0      0      0  \n",
       "\n",
       "[2 rows x 68 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Activities\n",
    "TCGA_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_TCGA.activities.csv\")\n",
    "TCGA_wes_act.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "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>Mutation type</th>\n",
       "      <th>Trinucleotide</th>\n",
       "      <th>ALL::TARGET-10-PAIXPH-03A-01D</th>\n",
       "      <th>ALL::TARGET-10-PAKHZT-03A-01R</th>\n",
       "      <th>ALL::TARGET-10-PAKMVD-09A-01D</th>\n",
       "      <th>ALL::TARGET-10-PAKSWW-03A-01D</th>\n",
       "      <th>ALL::TARGET-10-PALETF-03A-01D</th>\n",
       "      <th>ALL::TARGET-10-PALLSD-09A-01D</th>\n",
       "      <th>ALL::TARGET-10-PAMDKS-03A-01D</th>\n",
       "      <th>ALL::TARGET-10-PAPJIB-04A-01D</th>\n",
       "      <th>...</th>\n",
       "      <th>Head-SCC::V-109</th>\n",
       "      <th>Head-SCC::V-112</th>\n",
       "      <th>Head-SCC::V-116</th>\n",
       "      <th>Head-SCC::V-119</th>\n",
       "      <th>Head-SCC::V-123</th>\n",
       "      <th>Head-SCC::V-124</th>\n",
       "      <th>Head-SCC::V-125</th>\n",
       "      <th>Head-SCC::V-14</th>\n",
       "      <th>Head-SCC::V-29</th>\n",
       "      <th>Head-SCC::V-98</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C&gt;A</td>\n",
       "      <td>ACC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 9693 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Mutation type Trinucleotide  ALL::TARGET-10-PAIXPH-03A-01D  \\\n",
       "0           C>A           ACA                              0   \n",
       "1           C>A           ACC                              0   \n",
       "\n",
       "   ALL::TARGET-10-PAKHZT-03A-01R  ALL::TARGET-10-PAKMVD-09A-01D  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "\n",
       "   ALL::TARGET-10-PAKSWW-03A-01D  ALL::TARGET-10-PALETF-03A-01D  \\\n",
       "0                              1                              0   \n",
       "1                              1                              0   \n",
       "\n",
       "   ALL::TARGET-10-PALLSD-09A-01D  ALL::TARGET-10-PAMDKS-03A-01D  \\\n",
       "0                              0                              0   \n",
       "1                              0                              0   \n",
       "\n",
       "   ALL::TARGET-10-PAPJIB-04A-01D  ...  Head-SCC::V-109  Head-SCC::V-112  \\\n",
       "0                              2  ...                0                0   \n",
       "1                              0  ...                1                0   \n",
       "\n",
       "   Head-SCC::V-116  Head-SCC::V-119  Head-SCC::V-123  Head-SCC::V-124  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "\n",
       "   Head-SCC::V-125  Head-SCC::V-14  Head-SCC::V-29  Head-SCC::V-98  \n",
       "0                0               0               0               1  \n",
       "1                0               1               0               0  \n",
       "\n",
       "[2 rows x 9693 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_wes_mut = pd.read_csv(\"./project_data/catalogs/WES/WES_Other.96.csv\")\n",
    "other_wes_mut.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "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>Cancer Types</th>\n",
       "      <th>Sample Names</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>SBS1</th>\n",
       "      <th>SBS2</th>\n",
       "      <th>SBS3</th>\n",
       "      <th>SBS4</th>\n",
       "      <th>SBS5</th>\n",
       "      <th>SBS6</th>\n",
       "      <th>SBS7a</th>\n",
       "      <th>...</th>\n",
       "      <th>SBS51</th>\n",
       "      <th>SBS52</th>\n",
       "      <th>SBS53</th>\n",
       "      <th>SBS54</th>\n",
       "      <th>SBS55</th>\n",
       "      <th>SBS56</th>\n",
       "      <th>SBS57</th>\n",
       "      <th>SBS58</th>\n",
       "      <th>SBS59</th>\n",
       "      <th>SBS60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALL</td>\n",
       "      <td>TARGET-10-PAIXPH-03A-01D</td>\n",
       "      <td>0.529</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALL</td>\n",
       "      <td>TARGET-10-PAKHZT-03A-01R</td>\n",
       "      <td>0.696</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Cancer Types              Sample Names  Accuracy  SBS1  SBS2  SBS3  SBS4  \\\n",
       "0          ALL  TARGET-10-PAIXPH-03A-01D     0.529     0     0     0     0   \n",
       "1          ALL  TARGET-10-PAKHZT-03A-01R     0.696     0     0     0     0   \n",
       "\n",
       "   SBS5  SBS6  SBS7a  ...  SBS51  SBS52  SBS53  SBS54  SBS55  SBS56  SBS57  \\\n",
       "0     0     0      0  ...      0      0      0      1      0      0      0   \n",
       "1     0     0      0  ...      0      0      0      1      0      0      0   \n",
       "\n",
       "   SBS58  SBS59  SBS60  \n",
       "0      0      0      0  \n",
       "1      0      0      0  \n",
       "\n",
       "[2 rows x 68 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_Other.activities.csv\")\n",
    "other_wes_act.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38\n"
     ]
    }
   ],
   "source": [
    "### Aggregate mutation type and trinucleotides to one column\n",
    "PCAWG_wgs_mut['tri_mut'] = PCAWG_wgs_mut[['Mutation type', 'Trinucleotide']].agg('_'.join, axis=1)\n",
    "# \n",
    "nonPCAWG_wgs_mut['tri_mut'] = nonPCAWG_wgs_mut[['Mutation type', 'Trinucleotide']].agg('_'.join, axis=1)\n",
    "# Concatenate both mutation count data sets\n",
    "#total_wgs_mut = pd.concat([PCAWG_wgs_mut, nonPCAWG_wgs_mut], axis=1)\n",
    "#total_wgs_mut.shape\n",
    "#(96, 4651)\n",
    "### Get cancer types\n",
    "new_list = [re.sub(r'\\:.*', '', i) for i in PCAWG_wgs_mut.columns]\n",
    "# Transpose\n",
    "PCAWG_wgs_mut_T = PCAWG_wgs_mut.T\n",
    "# Rename columns\n",
    "PCAWG_wgs_mut_T.columns = PCAWG_wgs_mut[\"tri_mut\"]\n",
    "PCAWG_wgs_mut_T[\"label\"] = new_list[:]\n",
    "PCAWG_wgs_mut_T = PCAWG_wgs_mut_T.iloc[2:,:] # we don't need to first 2 rows\n",
    "print(len(PCAWG_wgs_mut_T['label'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36]),\n",
       " [Text(0, 0, 'Biliary-AdenoCA'),\n",
       "  Text(1, 0, 'Bladder-TCC'),\n",
       "  Text(2, 0, 'Bone-Benign'),\n",
       "  Text(3, 0, 'Bone-Epith'),\n",
       "  Text(4, 0, 'Bone-Osteosarc'),\n",
       "  Text(5, 0, 'Breast-AdenoCA'),\n",
       "  Text(6, 0, 'Breast-DCIS'),\n",
       "  Text(7, 0, 'Breast-LobularCA'),\n",
       "  Text(8, 0, 'CNS-GBM'),\n",
       "  Text(9, 0, 'CNS-Medullo'),\n",
       "  Text(10, 0, 'CNS-Oligo'),\n",
       "  Text(11, 0, 'CNS-PiloAstro'),\n",
       "  Text(12, 0, 'Cervix-AdenoCA'),\n",
       "  Text(13, 0, 'Cervix-SCC'),\n",
       "  Text(14, 0, 'ColoRect-AdenoCA'),\n",
       "  Text(15, 0, 'Eso-AdenoCA'),\n",
       "  Text(16, 0, 'Head-SCC'),\n",
       "  Text(17, 0, 'Kidney-ChRCC'),\n",
       "  Text(18, 0, 'Kidney-RCC'),\n",
       "  Text(19, 0, 'Liver-HCC'),\n",
       "  Text(20, 0, 'Lung-AdenoCA'),\n",
       "  Text(21, 0, 'Lung-SCC'),\n",
       "  Text(22, 0, 'Lymph-BNHL'),\n",
       "  Text(23, 0, 'Lymph-CLL'),\n",
       "  Text(24, 0, 'Myeloid-AML'),\n",
       "  Text(25, 0, 'Myeloid-MDS'),\n",
       "  Text(26, 0, 'Myeloid-MPN'),\n",
       "  Text(27, 0, 'Ovary-AdenoCA'),\n",
       "  Text(28, 0, 'Panc-AdenoCA'),\n",
       "  Text(29, 0, 'Panc-Endocrine'),\n",
       "  Text(30, 0, 'Prost-AdenoCA'),\n",
       "  Text(31, 0, 'Skin-Melanoma'),\n",
       "  Text(32, 0, 'SoftTissue-Leiomyo'),\n",
       "  Text(33, 0, 'SoftTissue-Liposarc'),\n",
       "  Text(34, 0, 'Stomach-AdenoCA'),\n",
       "  Text(35, 0, 'Thy-AdenoCA'),\n",
       "  Text(36, 0, 'Uterus-AdenoCA')])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot the amount of sampels in each cancer type.\n",
    "plt.figure(figsize=(10, 2))\n",
    "sns.countplot(x=new_list[2:-1], palette=sns.hls_palette(2))\n",
    "plt.xticks(rotation=90)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder \n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[14 18 35 ... 21 26 30]\n",
      "['ColoRect-AdenoCA' 'Kidney-RCC' 'Thy-AdenoCA' ... 'Lung-SCC'\n",
      " 'Myeloid-MPN' 'Prost-AdenoCA']\n"
     ]
    }
   ],
   "source": [
    "X = PCAWG_wgs_mut_T.values[:-1,:-1]\n",
    "y = PCAWG_wgs_mut_T['label'].values[:-1]\n",
    "features = list(PCAWG_wgs_mut_T.columns)[:-1]\n",
    "\n",
    "\n",
    "lc = LabelEncoder() \n",
    "lc = lc.fit(y) \n",
    "lc_y = lc.transform(y)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, lc_y, test_size=0.2, random_state=5)\n",
    "\n",
    "# print(X_train.shape)\n",
    "# print(X_test.shape)\n",
    "# print(y_train.shape)\n",
    "# print(y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "model = XGBClassifier(verbosity = 0, use_label_encoder=False, objective = 'multi:softmax', num_class = 37, n_estimators = 500)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = [round(pred) for pred in y_pred] \n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(accuracy) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "learning_rate = [0.1,0.3,0.5,0.8] #default = 0.3\n",
    "gamma = [0,0.5,1,5] #default = 0\n",
    "max_depth = [4,6,10,20] #default = 6\n",
    "max_delta_step = [0,2,4,6] #default = 0\n",
    "\n",
    "column_list = ['learning_rate','gamma','max_depth','max_delta_step','accuracy']\n",
    "results = pd.DataFrame(columns = column_list)\n",
    "i = 1\n",
    "for a in learning_rate:\n",
    "    for b in gamma:\n",
    "        for c in max_depth:\n",
    "            for d in max_delta_step:\n",
    "                print(f\"training model {i}\\n\")\n",
    "                i += 1\n",
    "                model = XGBClassifier(verbosity = 0,learning_rate=a, gamma=b, max_depth=c, max_delta_step=d, use_label_encoder=False)\n",
    "                model.fit(X_train,y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                predictions = [round(pred) for pred in y_pred] \n",
    "                accuracy = accuracy_score(y_test, predictions) \n",
    "                results = results.append({'learning_rate':a,'gamma':b,'max_depth':c,'max_delta_step':d,'accuracy':accuracy},ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "results.to_csv(\"results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = [0.01,0.1,0.3] #default = 0.3\n",
    "gamma = [0,1,5] #default = 0\n",
    "max_depth = [3,6,10] #default = 6\n",
    "colsample_bytree = [0.3,0.55,0.8]\n",
    "\n",
    "\n",
    "column_list = ['learning_rate','gamma','max_depth','colsample_bytree','accuracy']\n",
    "results2 = pd.DataFrame(columns = column_list)\n",
    "i = 1\n",
    "for a in learning_rate:\n",
    "    for b in gamma:\n",
    "        for c in max_depth:\n",
    "            for d in colsample_bytree:\n",
    "                print(f\"training model {i}\")\n",
    "                i += 1\n",
    "                model = XGBClassifier(objective = 'multi:softmax', num_class = 37,n_estimators = 1000, verbosity = 0,learning_rate=a, gamma=b, max_depth=c, colsample_bytree=d, use_label_encoder=False)\n",
    "                model.fit(X_train,y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                accuracy = accuracy_score(y_test, y_pred) \n",
    "                print(accuracy,\"\\n\")\n",
    "                results2 = results2.append({'learning_rate':a,'gamma':b,'max_depth':c,'colsample_bytree':d,'accuracy':accuracy},ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "results2.to_csv(\"results2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier(objective = 'multi:softmax', num_class = 37,n_estimators = 1000, verbosity = 0,learning_rate=0.1, gamma=0, max_depth=6, colsample_bytree=0.55, use_label_encoder=False)\n",
    "model.fit(X_train,y_train)\n",
    "y_pred = model.predict(X_test)\n",
    "predictions = lc.inverse_transform(y_pred)\n",
    "actuals = lc.inverse_transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cf_matrix = confusion_matrix(actuals, predictions)\n",
    "tp_and_fn = cf_matrix.sum(1)\n",
    "\n",
    "plt.figure(figsize=(25, 25)) # Choose figure size\n",
    "sns.set(font_scale = 1) # set font size\n",
    "\n",
    "#ax = sns.heatmap(cf_matrix, annot=True, cmap='Blues') # do heat map, cmap = color scheme\n",
    "ax = sns.heatmap(100*(cf_matrix/tp_and_fn), annot=True, cmap='Blues') # plot as precentages\n",
    "\n",
    "ax.set_title('Confusion matrix of Extreme Gradient Boosting Classifier \\n\\n') # set plot title\n",
    "ax.set_xlabel('\\nPredicted Cancer types') # set x label\n",
    "ax.set_ylabel('Actual Cancer types ') # set y label\n",
    "\n",
    "## Ticket labels - List must be in alphabetical order\n",
    "ax.xaxis.set_ticklabels(set(list(actuals))) # set x ticks as cancer types\n",
    "ax.yaxis.set_ticklabels(set(list(actuals))) # set y ticks as cancer types\n",
    "plt.xticks(rotation=90) # rotate ticks\n",
    "plt.yticks(rotation=0)   # rotate ticsk\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all the functions\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC # support vector machine\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}