ML_Project.ipynb

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      "source": [
        "<img src='http://www-scf.usc.edu/~ghasemig/images/sharif.png' alt=\"SUT logo\" width=200 height=200 align=left class=\"saturate\" >\n",
        "\n",
        "<br>\n",
        "<font face=\"Times New Roman\">\n",
        "<div dir=ltr align=center>\n",
        "<font color=0F5298 size=7>\n",
        "    Introduction to Machine Learning <br>\n",
        "<font color=2565AE size=5>\n",
        "    Computer Engineering Department <br>\n",
        "    Fall 2022<br>\n",
        "<font color=3C99D size=5>\n",
        "    Project <br>\n",
        "<font color=696880 size=4>\n",
        "    Project Team \n",
        "\n",
        "____\n"
      ]
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      "source": [
        "### Full Name : Mohammadreza Mofayezi\n",
        "### Student Number : 98106059\n",
        "___\n",
        "### Full Name : Ali Hatami Tajik\n",
        "### Student Number : 98101385\n",
        "___"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "40df5e74",
      "metadata": {
        "id": "40df5e74"
      },
      "source": [
        "# Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a627177",
      "metadata": {
        "id": "5a627177"
      },
      "source": [
        "In this project, we are going to have a brief and elementary hands-on real-world project, predicting breast cancer survival using machine learning models with clinical data and gene expression profiles."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b1e52cbc",
      "metadata": {
        "id": "b1e52cbc"
      },
      "source": [
        "# Data Documentation"
      ]
    },
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      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-01-29T19:31:29.604891Z",
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      "source": [
        "For this purpose, we will use \"Breast Cancer Gene Expression Profiles (METABRIC)\" data. \n",
        "The first 31 columns of data contain clinical information including death status.\n",
        "The next columns of the data contain gene's related information which includes both gene expressions and mutation information. (gene's mutation info columns have been marked with \"_mut\" at the end of the names of the columns) \n",
        "For more information please read the [data documentation](https://www.kaggle.com/datasets/raghadalharbi/breast-cancer-gene-expression-profiles-metabric)."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9cef1d4a",
      "metadata": {
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      },
      "source": [
        "# Data Preparation (15 Points)"
      ]
    },
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      "source": [
        "In this section you must first split data into three datasets:\n",
        "<br>\n",
        "1- clinical dataset\n",
        "<br>\n",
        "2- gene expressions dataset\n",
        "<br>\n",
        "3- gene mutation dataset. (We will not use this dataset in further steps of the project)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cc42f2b1",
      "metadata": {
        "id": "cc42f2b1"
      },
      "source": [
        "## Data Loading & Splitting"
      ]
    },
    {
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      "id": "BWn8yHHy--NA",
      "metadata": {
        "id": "BWn8yHHy--NA"
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      "source": [
        "from google.colab import files\n",
        "files.upload()\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d raghadalharbi/breast-cancer-gene-expression-profiles-metabric\n",
        "!unzip /content/breast-cancer-gene-expression-profiles-metabric.zip  -d /content"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Wboxank7062p",
      "metadata": {
        "id": "Wboxank7062p"
      },
      "outputs": [],
      "source": [
        "!pip install upsetplot\n",
        "!pip install umap-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "X4nUfY_lr_UH",
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      "source": [
        "# General\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import seaborn as sns\n",
        "from upsetplot import from_contents, UpSet\n",
        "\n",
        "# Stats\n",
        "from scipy import stats\n",
        "\n",
        "# Split and prepare data for training\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Evaluation\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import RocCurveDisplay\n",
        "from sklearn.metrics import f1_score, confusion_matrix\n",
        "\n",
        "# Classic Machine Learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from xgboost.sklearn import XGBClassifier\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# Deep Models\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "from torchvision import transforms, datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f9DvwHtqqKpE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "e3fa9a7e-2f36-4684-d5b6-35c01175a13a"
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            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (678,688,690,692) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ],
      "source": [
        "dataset = pd.read_csv(\"/content/METABRIC_RNA_Mutation.csv\")\n",
        "clinical = dataset.loc[:, \"patient_id\":\"death_from_cancer\"]\n",
        "gene_expr = dataset.loc[:, \"brca1\":\"ugt2b7\"]\n",
        "gene_mut = dataset.loc[:, \"pik3ca_mut\":]\n",
        "dataset = dataset.loc[:, :\"ugt2b7\"]"
      ]
    },
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              "      <th></th>\n",
              "      <th>patient_id</th>\n",
              "      <th>age_at_diagnosis</th>\n",
              "      <th>type_of_breast_surgery</th>\n",
              "      <th>cancer_type</th>\n",
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              "      <th>er_status_measured_by_ihc</th>\n",
              "      <th>...</th>\n",
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              "      <th>oncotree_code</th>\n",
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              "      <th>overall_survival</th>\n",
              "      <th>pr_status</th>\n",
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              "      <th>3-gene_classifier_subtype</th>\n",
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              "      <th>0</th>\n",
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              "      <td>75.65</td>\n",
              "      <td>MASTECTOMY</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>43.19</td>\n",
              "      <td>BREAST CONSERVING</td>\n",
              "      <td>Breast Cancer</td>\n",
              "      <td>Breast Invasive Ductal Carcinoma</td>\n",
              "      <td>High</td>\n",
              "      <td>0</td>\n",
              "      <td>LumA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Positve</td>\n",
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              "      <td>4.020</td>\n",
              "      <td>IDC</td>\n",
              "      <td>84.633333</td>\n",
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              "      <td>ER+/HER2- High Prolif</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>48.87</td>\n",
              "      <td>MASTECTOMY</td>\n",
              "      <td>Breast Cancer</td>\n",
              "      <td>Breast Invasive Ductal Carcinoma</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>LumB</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Positve</td>\n",
              "      <td>...</td>\n",
              "      <td>4.030</td>\n",
              "      <td>IDC</td>\n",
              "      <td>163.700000</td>\n",
              "      <td>0</td>\n",
              "      <td>Positive</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>47.68</td>\n",
              "      <td>MASTECTOMY</td>\n",
              "      <td>Breast Cancer</td>\n",
              "      <td>Breast Mixed Ductal and Lobular Carcinoma</td>\n",
              "      <td>Moderate</td>\n",
              "      <td>1</td>\n",
              "      <td>LumB</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Positve</td>\n",
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              "      <td>4.050</td>\n",
              "      <td>MDLC</td>\n",
              "      <td>164.933333</td>\n",
              "      <td>1</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>76.97</td>\n",
              "      <td>MASTECTOMY</td>\n",
              "      <td>Breast Cancer</td>\n",
              "      <td>Breast Mixed Ductal and Lobular Carcinoma</td>\n",
              "      <td>High</td>\n",
              "      <td>1</td>\n",
              "      <td>LumB</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>6.080</td>\n",
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            "text/plain": [
              "   patient_id  age_at_diagnosis type_of_breast_surgery    cancer_type  \\\n",
              "0           0             75.65             MASTECTOMY  Breast Cancer   \n",
              "1           2             43.19      BREAST CONSERVING  Breast Cancer   \n",
              "2           5             48.87             MASTECTOMY  Breast Cancer   \n",
              "3           6             47.68             MASTECTOMY  Breast Cancer   \n",
              "4           8             76.97             MASTECTOMY  Breast Cancer   \n",
              "\n",
              "                        cancer_type_detailed cellularity  chemotherapy  \\\n",
              "0           Breast Invasive Ductal Carcinoma         NaN             0   \n",
              "1           Breast Invasive Ductal Carcinoma        High             0   \n",
              "2           Breast Invasive Ductal Carcinoma        High             1   \n",
              "3  Breast Mixed Ductal and Lobular Carcinoma    Moderate             1   \n",
              "4  Breast Mixed Ductal and Lobular Carcinoma        High             1   \n",
              "\n",
              "  pam50_+_claudin-low_subtype  cohort er_status_measured_by_ihc  ...  \\\n",
              "0                 claudin-low     1.0                   Positve  ...   \n",
              "1                        LumA     1.0                   Positve  ...   \n",
              "2                        LumB     1.0                   Positve  ...   \n",
              "3                        LumB     1.0                   Positve  ...   \n",
              "4                        LumB     1.0                   Positve  ...   \n",
              "\n",
              "  nottingham_prognostic_index  oncotree_code overall_survival_months  \\\n",
              "0                       6.044            IDC              140.500000   \n",
              "1                       4.020            IDC               84.633333   \n",
              "2                       4.030            IDC              163.700000   \n",
              "3                       4.050           MDLC              164.933333   \n",
              "4                       6.080           MDLC               41.366667   \n",
              "\n",
              "  overall_survival pr_status  radio_therapy 3-gene_classifier_subtype  \\\n",
              "0                1  Negative              1                 ER-/HER2-   \n",
              "1                1  Positive              1     ER+/HER2- High Prolif   \n",
              "2                0  Positive              0                       NaN   \n",
              "3                1  Positive              1                       NaN   \n",
              "4                0  Positive              1     ER+/HER2- High Prolif   \n",
              "\n",
              "  tumor_size tumor_stage  death_from_cancer  \n",
              "0       22.0         2.0             Living  \n",
              "1       10.0         1.0             Living  \n",
              "2       15.0         2.0    Died of Disease  \n",
              "3       25.0         2.0             Living  \n",
              "4       40.0         2.0    Died of Disease  \n",
              "\n",
              "[5 rows x 31 columns]"
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>brca1</th>\n",
              "      <th>brca2</th>\n",
              "      <th>palb2</th>\n",
              "      <th>pten</th>\n",
              "      <th>tp53</th>\n",
              "      <th>atm</th>\n",
              "      <th>cdh1</th>\n",
              "      <th>chek2</th>\n",
              "      <th>nbn</th>\n",
              "      <th>nf1</th>\n",
              "      <th>...</th>\n",
              "      <th>srd5a1</th>\n",
              "      <th>srd5a2</th>\n",
              "      <th>srd5a3</th>\n",
              "      <th>st7</th>\n",
              "      <th>star</th>\n",
              "      <th>tnk2</th>\n",
              "      <th>tulp4</th>\n",
              "      <th>ugt2b15</th>\n",
              "      <th>ugt2b17</th>\n",
              "      <th>ugt2b7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.3990</td>\n",
              "      <td>-0.5738</td>\n",
              "      <td>-1.6217</td>\n",
              "      <td>1.4524</td>\n",
              "      <td>0.3504</td>\n",
              "      <td>1.1517</td>\n",
              "      <td>0.0348</td>\n",
              "      <td>0.1266</td>\n",
              "      <td>-0.8361</td>\n",
              "      <td>-0.8578</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.1877</td>\n",
              "      <td>-0.0194</td>\n",
              "      <td>-1.6345</td>\n",
              "      <td>-0.2142</td>\n",
              "      <td>-0.5698</td>\n",
              "      <td>-1.1741</td>\n",
              "      <td>-1.4779</td>\n",
              "      <td>-0.5954</td>\n",
              "      <td>-0.8847</td>\n",
              "      <td>-0.3354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.3800</td>\n",
              "      <td>0.2777</td>\n",
              "      <td>-1.2154</td>\n",
              "      <td>0.5296</td>\n",
              "      <td>-0.0136</td>\n",
              "      <td>-0.2659</td>\n",
              "      <td>1.3594</td>\n",
              "      <td>0.7961</td>\n",
              "      <td>0.5419</td>\n",
              "      <td>-2.6059</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.4412</td>\n",
              "      <td>0.4534</td>\n",
              "      <td>0.4068</td>\n",
              "      <td>0.7634</td>\n",
              "      <td>0.0231</td>\n",
              "      <td>0.9121</td>\n",
              "      <td>-0.9538</td>\n",
              "      <td>-0.2264</td>\n",
              "      <td>0.5398</td>\n",
              "      <td>-0.8920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0670</td>\n",
              "      <td>-0.8426</td>\n",
              "      <td>0.2114</td>\n",
              "      <td>-0.3326</td>\n",
              "      <td>0.5141</td>\n",
              "      <td>-0.0803</td>\n",
              "      <td>1.1398</td>\n",
              "      <td>0.4187</td>\n",
              "      <td>-0.4030</td>\n",
              "      <td>-1.1305</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5381</td>\n",
              "      <td>0.0668</td>\n",
              "      <td>0.8344</td>\n",
              "      <td>1.7227</td>\n",
              "      <td>0.4024</td>\n",
              "      <td>-3.7172</td>\n",
              "      <td>-1.5538</td>\n",
              "      <td>1.3701</td>\n",
              "      <td>-0.1078</td>\n",
              "      <td>0.3655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.6744</td>\n",
              "      <td>-0.5428</td>\n",
              "      <td>-1.6592</td>\n",
              "      <td>0.6369</td>\n",
              "      <td>1.6708</td>\n",
              "      <td>-0.8880</td>\n",
              "      <td>1.2491</td>\n",
              "      <td>-1.1889</td>\n",
              "      <td>-0.4174</td>\n",
              "      <td>-0.6165</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5630</td>\n",
              "      <td>-0.7078</td>\n",
              "      <td>0.8228</td>\n",
              "      <td>0.6819</td>\n",
              "      <td>-0.1948</td>\n",
              "      <td>-2.3286</td>\n",
              "      <td>-0.9924</td>\n",
              "      <td>-0.3154</td>\n",
              "      <td>0.2320</td>\n",
              "      <td>-0.4828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.2932</td>\n",
              "      <td>-0.9039</td>\n",
              "      <td>-0.7219</td>\n",
              "      <td>0.2168</td>\n",
              "      <td>0.3484</td>\n",
              "      <td>0.3897</td>\n",
              "      <td>0.9131</td>\n",
              "      <td>0.9356</td>\n",
              "      <td>0.7675</td>\n",
              "      <td>-0.2940</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.5845</td>\n",
              "      <td>-0.3544</td>\n",
              "      <td>-1.0150</td>\n",
              "      <td>2.2961</td>\n",
              "      <td>0.1817</td>\n",
              "      <td>-0.1572</td>\n",
              "      <td>0.0427</td>\n",
              "      <td>5.0048</td>\n",
              "      <td>3.8476</td>\n",
              "      <td>1.3223</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 489 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "    brca1   brca2   palb2    pten    tp53     atm    cdh1   chek2     nbn  \\\n",
              "0 -1.3990 -0.5738 -1.6217  1.4524  0.3504  1.1517  0.0348  0.1266 -0.8361   \n",
              "1 -1.3800  0.2777 -1.2154  0.5296 -0.0136 -0.2659  1.3594  0.7961  0.5419   \n",
              "2  0.0670 -0.8426  0.2114 -0.3326  0.5141 -0.0803  1.1398  0.4187 -0.4030   \n",
              "3  0.6744 -0.5428 -1.6592  0.6369  1.6708 -0.8880  1.2491 -1.1889 -0.4174   \n",
              "4  1.2932 -0.9039 -0.7219  0.2168  0.3484  0.3897  0.9131  0.9356  0.7675   \n",
              "\n",
              "      nf1  ...  srd5a1  srd5a2  srd5a3     st7    star    tnk2   tulp4  \\\n",
              "0 -0.8578  ... -1.1877 -0.0194 -1.6345 -0.2142 -0.5698 -1.1741 -1.4779   \n",
              "1 -2.6059  ... -0.4412  0.4534  0.4068  0.7634  0.0231  0.9121 -0.9538   \n",
              "2 -1.1305  ... -0.5381  0.0668  0.8344  1.7227  0.4024 -3.7172 -1.5538   \n",
              "3 -0.6165  ... -0.5630 -0.7078  0.8228  0.6819 -0.1948 -2.3286 -0.9924   \n",
              "4 -0.2940  ... -0.5845 -0.3544 -1.0150  2.2961  0.1817 -0.1572  0.0427   \n",
              "\n",
              "   ugt2b15  ugt2b17  ugt2b7  \n",
              "0  -0.5954  -0.8847 -0.3354  \n",
              "1  -0.2264   0.5398 -0.8920  \n",
              "2   1.3701  -0.1078  0.3655  \n",
              "3  -0.3154   0.2320 -0.4828  \n",
              "4   5.0048   3.8476  1.3223  \n",
              "\n",
              "[5 rows x 489 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gene_expr.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "j3sjccKpvBtu",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j3sjccKpvBtu",
        "outputId": "29799d0d-b6c7-4169-a4c3-883a3c1bf33b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1904 entries, 0 to 1903\n",
            "Data columns (total 31 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   patient_id                      1904 non-null   int64  \n",
            " 1   age_at_diagnosis                1904 non-null   float64\n",
            " 2   type_of_breast_surgery          1882 non-null   object \n",
            " 3   cancer_type                     1904 non-null   object \n",
            " 4   cancer_type_detailed            1889 non-null   object \n",
            " 5   cellularity                     1850 non-null   object \n",
            " 6   chemotherapy                    1904 non-null   int64  \n",
            " 7   pam50_+_claudin-low_subtype     1904 non-null   object \n",
            " 8   cohort                          1904 non-null   float64\n",
            " 9   er_status_measured_by_ihc       1874 non-null   object \n",
            " 10  er_status                       1904 non-null   object \n",
            " 11  neoplasm_histologic_grade       1832 non-null   float64\n",
            " 12  her2_status_measured_by_snp6    1904 non-null   object \n",
            " 13  her2_status                     1904 non-null   object \n",
            " 14  tumor_other_histologic_subtype  1889 non-null   object \n",
            " 15  hormone_therapy                 1904 non-null   int64  \n",
            " 16  inferred_menopausal_state       1904 non-null   object \n",
            " 17  integrative_cluster             1904 non-null   object \n",
            " 18  primary_tumor_laterality        1798 non-null   object \n",
            " 19  lymph_nodes_examined_positive   1904 non-null   float64\n",
            " 20  mutation_count                  1859 non-null   float64\n",
            " 21  nottingham_prognostic_index     1904 non-null   float64\n",
            " 22  oncotree_code                   1889 non-null   object \n",
            " 23  overall_survival_months         1904 non-null   float64\n",
            " 24  overall_survival                1904 non-null   int64  \n",
            " 25  pr_status                       1904 non-null   object \n",
            " 26  radio_therapy                   1904 non-null   int64  \n",
            " 27  3-gene_classifier_subtype       1700 non-null   object \n",
            " 28  tumor_size                      1884 non-null   float64\n",
            " 29  tumor_stage                     1403 non-null   float64\n",
            " 30  death_from_cancer               1903 non-null   object \n",
            "dtypes: float64(9), int64(5), object(17)\n",
            "memory usage: 461.2+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# change some lables\n",
        "clinical[\"death_from_cancer\"].replace({\"Died of Disease\":\"Disease\", \n",
        "                                       \"Died of Other Causes\": \"Other\"}, inplace=True)\n",
        "print(clinical.info())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "isQizSNX83BQ",
      "metadata": {
        "id": "isQizSNX83BQ"
      },
      "source": [
        "We should change lable data to binary one so that they can be used as\n",
        "data to be trained. Also null numbers/values should be replaced."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aebfdb6b",
      "metadata": {
        "id": "aebfdb6b"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e87d59d7",
      "metadata": {
        "id": "e87d59d7"
      },
      "source": [
        "For each dataset, you must perform a sufficient EDA."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Tm_vaJ3WcbKC",
      "metadata": {
        "id": "Tm_vaJ3WcbKC"
      },
      "source": [
        "### Overall\n",
        "\n",
        "**Clinical:** In this section we will analyze the following effects on survival (Mostly Numerical Values):\n",
        "  * Age\n",
        "  * Tumor Stage\n",
        "  * Tumor Size\n",
        "  * Therapy Methods\n",
        "  * Survival Time\n",
        "  * Surgery Type\n",
        "\n",
        "Then we will visualize the correlation betwean the parameters to see if the\n",
        "dataset should be reduced or not.\n",
        "\n",
        "**Gene Expression**: For gene expression profile since we donot have that much\n",
        "of an expreience with genes, we leave the EDA part!"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UJRloLBJBEMH",
      "metadata": {
        "id": "UJRloLBJBEMH"
      },
      "source": [
        "### Nullity\n",
        "First we check how many null cells we have in the data (i.e. cells that has no\n",
        "data in them and are not usable in the dataset). In the results we can see that\n",
        "there is 25% missing data in the tumor stage which sounds like it should have a\n",
        "huge impact on the death form factor, which we are going to predict."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d3815455",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "d3815455",
        "outputId": "dfe3ba79-b2e4-4416-eb12-04347eea14c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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lCkSSJEkauknrXEuSJAGw/cGfXJKfc+E/P2JJfo7UpUnrXEuSJElaC5NrSZIkqSMm15IkSVJHpppcJ3l4kvOSnJ/k4HmO3z/JWUmuSbL3nGMHJPlu+3HANOOUJEmSujC15DrJhsDbaVZ23AXYL8kuc172Q+BpwIfmvPdWwCHAPYHdgUOS3HJasUqSJEldmGbL9e7A+VV1QVVdBRwNPGb8BVV1YVV9Hbh2znsfBpxYVb+oql8CJwIPn2KskiRJ0qJNM7neCvjR2PZF7b7O3pvkwCQrk6y89NJLFxyoJEmS1IVBT2isqsOrakVVrdhyyy37DkeSJEnruWkm1xcD24xtb93um/Z7JUmSpF5MM7k+A9gpyQ5Jbgw8CThuwveeADw0yS3biYwPbfdJkiRJy9bUkuuqugZ4IU1S/C3gmKo6N8mhSR4NkOQeSS4C9gHemeTc9r2/AF5Dk6CfARza7pMkSZKWrY2m+c2r6njg+Dn7XjX29Rk0Qz7me+97gPdMMz5JkiSpS4Oe0ChJkiQtJybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1xORakiRJ6ojJtSRJktQRk2tJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1xORakiRJ6ojJtSRJktQRk2tJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1xORakiRJ6ojJtSRJktQRk2tJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1xORakiRJ6ojJtSRJktQRk2tJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1ZKO+A5AkdWP7gz+5JD/nwn9+xJL8HEkaIluuJUmSpI5MNblO8vAk5yU5P8nB8xy/SZKPtMe/kmT7dv/2SX6X5Oz24x3TjFOSJEnqwtSGhSTZEHg7sCdwEXBGkuOq6ptjL3sm8Muqun2SJwFvAJ7YHvteVe06rfgkSZKkrk2z5Xp34PyquqCqrgKOBh4z5zWPAY5sv/4o8OAkmWJMkiRJ0tRMM7neCvjR2PZF7b55X1NV1wCXAbduj+2Q5KtJTk5yv/l+QJIDk6xMsvLSSy/tNnpJkiRpHS3XCY0/Abatqt2AlwIfSrLZ3BdV1eFVtaKqVmy55ZZLHqQkSZI0bprJ9cXANmPbW7f75n1Nko2AzYGfV9WVVfVzgKo6E/gesPMUY5UkSZIWbZrJ9RnATkl2SHJj4EnAcXNecxxwQPv13sDnq6qSbNlOiCTJ7YCdgAumGKskSZK0aFOrFlJV1yR5IXACsCHwnqo6N8mhwMqqOg44AjgqyfnAL2gScID7A4cmuRq4FnhuVf1iWrFKkiRJXZjqCo1VdTxw/Jx9rxr7+vfAPvO872PAx6YZmyRJktS15TqhUZIkSRock2tJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjoy1VJ8kmbP9gd/ckl+zoX//Igl+TmSJHXJlmtJkiSpIybXkiRJUkdMriVJkqSOmFxLkiRJHTG5liRJkjpici1JkiR1xORakiRJ6ojJtSRJktQRF5HRsuRCJZIkaYhsuZYkSZI6YnItSZIkdcTkWpIkSeqIybUkSZLUESc0SpKWJSc2SxoiW64lSZKkjphcS5IkSR0xuZYkSZI6YnItSZIkdcTkWpIkSeqIybUkSZLUEZNrSZIkqSPWuZaWgPV6JUlaP9hyLUmSJHXE5FqSJEnqiMm1JEmS1JH1esy142AlSZLUpfU6uZYkSes3G9rUNYeFSJIkSR0xuZYkSZI64rCQGWLXlrTuvG60FPw9k9YftlxLkiRJHTG5liRJkjpici1JkiR1ZKrJdZKHJzkvyflJDp7n+E2SfKQ9/pUk248de0W7/7wkD5tmnJIkSVIXppZcJ9kQeDuwF7ALsF+SXea87JnAL6vq9sC/Am9o37sL8CTgTsDDgX9vv58kSZK0bE2z5Xp34PyquqCqrgKOBh4z5zWPAY5sv/4o8OAkafcfXVVXVtX3gfPb7ydJkiQtW6mq6XzjZG/g4VX1rHb7qcA9q+qFY6/5Rvuai9rt7wH3BP4ROK2qPtDuPwL4VFV9dM7POBA4sN28A3DeVE5mVVsAP1uCn7NUZul8ZulcYLbOZ5bOBWbrfGbpXGC2zmeWzgVm63xm6Vxgts5nqc5lu6racr4Dg65zXVWHA4cv5c9MsrKqVizlz5ymWTqfWToXmK3zmaVzgdk6n1k6F5it85mlc4HZOp9ZOheYrfNZDucyzWEhFwPbjG1v3e6b9zVJNgI2B34+4XslSZKkZWWayfUZwE5JdkhyY5oJisfNec1xwAHt13sDn69mnMpxwJPaaiI7ADsBp08xVkmSJGnRpjYspKquSfJC4ARgQ+A9VXVukkOBlVV1HHAEcFSS84Ff0CTgtK87BvgmcA3wgqr6w7RiXUdLOgxlCczS+czSucBsnc8snQvM1vnM0rnAbJ3PLJ0LzNb5zNK5wGydT+/nMrUJjZIkSdL6xhUaJUmSpI6YXEuSJEkdMbmWJEmSOmJyPQGXXl++kty67xgk9SvJTSbZJ0lLweR6Mt9N8qYku/QdiG7gtCTHJvmLJOk7mMVKcrMkG4xtb5Dkpn3GtFhJNklyh77jWIwkOyf5XLuqLEnumuTv+45rMZI8avx3beC+POG+QUiyXZKHtF9vkuTmfce0ULN07ST54yRHJPlUu71Lkmf2HddCzMJ9eSTJhkk+2Hcc42blxjptdwO+A7w7yWlJDkyyWd9BraskH09y3Oo++o5vgXamKbvzVJqHoNcl2bnnmBbjc8B4Mn1T4LM9xbJoSR4FnA18ut3edaC/a+8CXgFcDVBVX6ctHTpgT6S5Zt6Y5I59B7MQSW6T5M+ATZLsluTu7ccDWfU6GowkzwY+Cryz3bU18N/9RbRos3TtvI+mvPBt2+3vAC/pLZoFmqH7MgBtqebt2jVVloVBL3++VKrqcpobxLuSPAD4EPCvST4KvKaqzu81wMn9S/v58cBtgA+02/sBP+0lokVqFx06ETgxyYNozun5Sb4GHFxVQ2u92riqfjPaqKrfDLzl+h+B3YGTAKrq7HZhqKG5aVWdPqdz5Jq+gulCVe3fNhLsB7wvSQHvBT7c3vOG4GHA02gS0LeM7b8c+Ls+AurAC2iuma8AVNV3k/xRvyEtyixdO1tU1TFJXgHXreexXNbgWBf/yGzcl8ddAJzaPiRcMdpZVW9Z/Vumx+R6Au2Y60cATwe2B94MfBC4H3A8TevpsldVJwMkeXNVrRg79PEkK3sKa1HaMdf707Rc/xR4Ec0Kn7sCxwJDu2FckeTuVXUWQNsq97ueY1qMq6vqsjl/WIdYXP9nSXakjT3J3sBP+g1p8arq120jwSY0LXCPA16e5G1VdVi/0a1dVR0JHJnkCVX1sb7j6ciVVXXV6JpJshHDvGZGZunauaL9mzM6l3sBl/Ub0oLMyn153Pfajw2A3odRmVxP5rvAF4A3VdWXxvZ/NMn9e4ppMW6W5HZVdQFA+8R6s55jWqgvA0cBj62qi8b2r0zyjp5iWoyXAMcm+TEQmh6GJ/Yb0qKcm+TJwIZJdgJeDHxpLe9Zjl5AM/zojkkuBr4PPKXfkBYnyWNoWn1vD7wf2L2qLml7Sr4JLPvkeswn2t+z7Rn7u1ZVh/YW0cKdnOTvaIa67Ak8H/h4zzEtxixdO39N03izY5JTgS2BffoNaUFm5b58nap6NUCSm1bVb/uOxxUa16JttX7lQG/S80rycJqb3QU0Cdx2wHOq6oReA1uAJPtW1TFz9u1TVcf2FdNiJbkRMJpocl5VXd1nPIvRJmqvBB5K87t2As1Qqt/3GtgCJbkZsMGAhk2sVpIjgSOq6ovzHHtwVX2uh7AWJMmnaVoQzwSu66avqjf3FtQCtZNMn8mq18y7a+B/rGfl2ml7Eu5A838zyPvzrN2XAZLsARwBbFpV2ya5G01e8/xe4hn49bokkpxeVbv3HUeX0pSpGk1i+nZVXdlnPAuV5Kyquvva9g1Jkntzwxa49/cWkEbDjw4B7kvTfXoKcGhV/bzXwBYhyRuq6m/Xtm8Iknyjqu7cdxy6oVm6dpJ8j6YH+x1j+z5RVY/sMSwBSb4C7A0cV1W7tft6uy84LGQypyb5N+AjrDpQ/qz+Qlq49qn1pcB2VfXsJDsluUNVfaLv2CaVZC/gL4Ctkrxt7NBmDHeyDEmOAnakmck9aoErmm77wUjycdYwhq+qHr2E4XThaOCLwBPa7afQ3A8e0ltEi7cnMDeR3muefUPwpSR3qapz+g5koZKcw5qvmbsuYThdmqVr52rgQUnuSdMqehWwVc8xrbPV3J8vA1YC7xxqC3ZV/WjOOPLeJpuaXE9m1/bz+NCQAv68h1i68F6a7tM92u2LaSb/DSa5Bn5McyN4NM25jFwO/FUvEXVjBbDL0LuAmb3KNH9SVa8Z235tkkGOhU/yPJpxvDsm+frYoZsDp/YT1aLdF3haku8DV9J0ddfAEtJR6+cL2s9HtZ/3Z9iTzWbm2gF+W1VPTPI3wP8m2Ydh/t9cQDNe/MPt9hNp/nbuTFMZ7ak9xbUYP2p7fasdWnkQ8K2+gnFYyHooycqqWpHkq2PdJ1+rqrv1Hdu6SrJRVQ22pXquJMcCL66qoc6mX8Xod21t+5a7JG8BTgdG4/v3ppkA+LL+olqYJJsDtwReDxw8dujyqvpFP1EtTpLt5ttfVT9Y6lgWa/y+PLZvsEPdZuzaGf+b+RDg34BbVdWgSiUmOaOq7jHfviTnVtWd+optoZJsAbyVpkckwGeAg/oafmTL9QSS/DHwOuC2VbVXmpUa96iqI3oObaGuSrIJ15cT2pGmtWcwkhxTVfsCX23r865iYC1W4910Nwe+meR0xv5PBjiMYmRWKtM8m6aSy6g1cUOaslzPoWkhHdKiUlVVFyZ5wdwDSW410AR7llqJkuQ+VXVqu3Fvhr3g2yxdO68afVFVn03yMOCAHuNZqE2TbFtVPwRIsi2waXvsqv7CWriq+hnLqAqNyfVk3kczlOKV7fZ3aMaMDTW5PoRmZaZt0iwZeh+aklxDclD7eVYmkvzL2l8ySH8FnJRklco0/Ya07qqq97qpHfoQzXVzJk1SOj5IsYDb9RHUIn2S689lY5r69ucBg2uBo6kU8p62hyHAL4Fn9BvSws3CtZPkjlX1beDiJHN7EIY0nHLkr4FT2gmaoblent9WdDmy18gWqK1+dFBV/ardviXw5qrq5dpxWMgExrpLxruEzq6qXdf23uWqncF9L5oL67T2qW9Q2jKJn62qB/Udi1ZvFirTJPkYzcP0p6vq2r7j0Zq1CdDzq+pZfceyUG1yTVUNcZGS68zCtZPk8Ko6MMkX5jlcVTW4+Vdz7svnDXUS48hqhlPdYN9SseV6MjOxKtM8T9yjcb3btl1Eg6p+UlV/SHJtks1n4A/QKVV13ySXs2oX92hi1pC6TufaiaYu7MbA3ZIMsbTgf9Cs0HpYOy7+vVV1Xs8xLcg894FVDO0+MJ+qOqut6DA4bVJ9CHD/dvtkmtJ1Q73HDf7aqaoD28+z1JAzC/flcRskuWVV/RKaIW70mOPacj2B9o/RYcCdgW/QzLLdu6q+vsY3LjOreeoeGerT9/8AuwEnsmqZxBf3FtQUjd88hiDJIcADgV2A42lKvZ1SVXv3GddCtYnPfjRDxH5EM7P+A0NaSGJG7wMvHdvcALg7cOuqelhPIS1Y29L7Da7vnn8qcLeqenx/US3ejFw7+9C0wF+e5O9pfs9eU1Vf7Tm0dTJr92WAJH8J/B1N5bPQTJz9p6o6ao1vnFY8JteTyQysyjSLksw7maSqBjlubG2GVjWgrd17N+CrVXW3dnLwB6pqz55DW2dt79X+NMnOj4EP0pSAu0tVPbDH0NZ7bbIwcg1wIfCxIXZ1zzfkcEaGIQ7+2kny9aq6a5L7Aq8F3gS8qqoG1UsyS/flcUnuBIx6Fz5fVd/sKxaHhUwgydwWg52TXAacU1WX9BHTQiT586r6/DznA0BV/edSx7RYs5pEr0HW/pJl5XdVdW2Sa4WrtE4AACAASURBVJJsBlwCbNN3UOsqyX/RPFwfBTxqrFTiR5Ks7C+yxUlyZ5rWq41H+4bYNVxVrwZIsmm7/Zt+I1qU3yW5b1WdApDkPsDveo5pwWbs2hktSvII4PCq+mSS1/YZ0ALNxH15Ht+mmQC8ETRVUEYVUZaayfVknkmz4MqoO/WBNDPtd0hyaF/dDgvwAODzwKPmOVbA4JLrJDvR1OudmyAMseLBJIbW1bQyyS1ouoDPBH4DfLnfkBbkXVV1/PiOJDepqiuHVrN7ZHVdwwxsNVC47iHhKOBW7fbPgAOq6hu9BrYwzwOOHE1opEkWntZfOIs2S9fOxUneSbO66RvaSYFDLJM4K/fl6yR5Ec1chZ/SPASF5u9lL2V5HRYygSQnAH9ZVT9tt/+Y5g/QfsAXq6e169VMBKS5oP6V5qHh6cAGVfWqNb5xoIY0LCRJgK2r6kft9vbAZkObqwDz/7sP6f9iPrPUNZzkS8Arq+oL7fYDgddV1b17DWwR2hZFqurXfceyGLN07SS5KfBwml7r7yb5E5qhLZ9pjw9qTgwM+748Lsn5wD2rp0Vj5rLlejLbjBLr1iXtvl8kGeTY6ySPoKkBO97ae+jq37FsbVJVn0uSalZj+8ckZzJW7H/GDGZYSFVVkuOBu7TbF/Yb0bpLchtgK2CTJLtx/b//ZsBNewusG7PUNXyzUWINUFUnpanZOzhJXge8cU693r+uqr/vN7J1M4vXTlX9lrEe3naIy/hqup+jmeS4rCX5XFU9GK6/L4/vG6gfsYyquJlcT+akJJ+gmYUK8IR2382AX/UX1sIkeQfNze1BwLtpZtWe3mtQC3dlkg2A7yZ5IXAx1680NThJ3gy8p6rOXc1LhnbzOyvJParqjL4DWaCH0XTJbw28ZWz/5TQz04dslrqGL0jyD1y/CuD+wAU9xrMYe1XVdb9bVfXLJH8BDCq5ZravndVZ1o0fSTam+du/RfvQNv7As1VvgXXjApq87JOsurrxW1b/lulxWMiEkjyBZnYzwKk0M9EH+Y83NuN59HlT4FNVdb++Y1tXSe4BfAu4BfAaYHOaVp/Teg1sgZI8i2Zoy0Y0q4J+eMD1bUnybeD2wA9oSiWO6nYPbXn6J1TVx/qOoyuzNGQHrmvdfTXNPbqA/wVePbQuemjuz8A9ql1sKckmwMqqGuJqkzN37azJch/ukuQgmqXob0vTEDVKrn9NMzb+3/qKbbHmVAy6zmiy81IzuV6LNKsAnltVd1zriwciyVeq6p5JTgMeD/yc5hxv33NoaiW5A02SvR/Nw9y7xru9hyLJdvPtb4fwDEZbSuwQrk/eTqFZ2GNZjO9biCTnVNVd+o5Dq0rytzTzR97b7no6cFxVvbG/qBZuFq+d1VnuyfVIkhdV1WF9xzHLHBayFtWsAnhenyVdpuATbXfwm4CzaG547+43pHWT5OOsoXJGVT16CcPpVPtAd8f242fA14CXJnlOVT2p1+DW3eUT7lvujga+SDMkDOApwEeAh/QW0eINfcjOdZKcCOwzZ5zy0TXARWSq6g1Jvsb1v1uvqaoT+oxpkWbx2lmdZT0sZMz/Jbl5rboYzmtrwKuzJtkS+BtuOJesl0WxbLmeQJIv0qwCeDqrrgI4yARuVAZp9DXNL+LvR/uGIMkD1nS8qk5eqli6lGRU9eRzwBFVdfrYsfOq6g69BbcASS6kmST3S5o/PLcA/o+mXNKzq+rM/qKbXJJvzK0KNPSW31kZsgOQ5KtVtdva9s2CJF+uqj36jmNSs3LtTNKLneRWVfWLJQxrQTIji+GMS/IZmoe2lwHPBQ4ALq2qv+0jHluuJ/MPfQfQsS/TzmhuE+ork5zFAGY5jww1eZ7A14G/r6or5jm2+1IH04ETgY+OWt6SPJSmBeu9wL8DQ7mZfybJk4Bj2u29gSG3JkIz4WxWXDveu9gOR5rVlqON1/6SZWUmrp1JerGHkFi3ZmUxnHG3rqojkhzU5gcnJ+mtV86W6/XIWGmkDwBPZtWZwu8Y4rjyJN9nnj+iNdBFZNqJZo9jbHxiVf1Xv1Et3HwtVGOtJst+Seckl9P8PwS4GXBte2gD4DdVtVlfsXWhbbnaqare23arblpV3+87rnWV5OHA4cDJNP9X9wMOHPhwinkNaFzvzF07s9KL3VY/u5hmMZy706wAenpV3a3XwBYhyWlVda8065K8DfgxTcPOjn3EY8v1BMZuEgA3Bm4EXDHAm8PqSiP9muGWRhpf4WtjYB/aVdoG6u00XfUfbrefk+QhVfWCHmNajJ+0E7SObrefCPy07WK9dvVvWx6q6uZ9xzAt7ez6FTRLU7+X5r72AeA+fca1EFX16SR3B+7V7npJVf1sdDzJndZQ3lJTMKPXzqz0Yu9LsxjOv1TVr9IshvPy0cEMcDEc4LVpVjX9a+AwmkbDv+orGFuu11HbsvgY4F5VdXDf8SzErJdGSnJmVf1Z33EsRDsO9k9HZR7bGt7nVtWf9hvZwiTZglUrBZwKHEpT7H/bqjq/x/AmluT+8+2vqi8udSxdSXI2TSvcWaOxyaNehX4j695QWnsnMbSx5LN27aRZyfQe7ebpVXVJn/FMwyxdL32x5XodtUnPf7etPoNMroFTkxwB3Laq9kqyC7BHVR3Rd2Drqm2tGtmApiVuyL/X5wPb0kwyg2Yy4CAS0Pm0rYcvWs3h85McVlWrO76cvHzs641pxr+fCfQyE70jV1VVJRk9yA1yRcMJDaWKA3DdmPGdquqzbZ3rjapqVGXnqT2GthAzc+0k2Zdm8t9JNL9ThyV5eVV9tNfAujeo6wUgyZHAQXMqBr25qp7RRzxDTkKWTJLHj22OErjf9xROF97bfryy3f4OzSzbwSXXwJvHvr4GuJCmy2tQxkoL3hz4VpLT2+17MtzVMycxiCEIVfWo8e0k2wD/r6dwunJMkncCt0jybOAZDKwk5zoYTBdt+39xIM3wth1phvG9g3Z11qr6Rn/RrbsZu3ZeSbPAzyVwXfm3zwKzllwP5noZc9dRYg3XrWzaWw+PyfVkxm8OowTuMf2E0oktquqYJK8AqKprkvxhbW9ajqrqQX3H0JF/6TsArZOLgEEO1Rmpqn9JsifNnIs70JTiOrHnsAQvoGnd/QpAVX03yR/1G1KnhnztbDBnGMjPaRrc1L8NxseKJ7kVPea4JtcTqKqn9x1Dx65oV80adQffi2YM7GAkeemajlfVW9Z0fLmZtLTg0Grczookh3F9a84GwK40CzANVpI3tDVgT5xn36y5qu8A1sGVVXVVM70HkmzEMFsSgZm7dj7dVqMYTTh/IvCpHuOZlsENC6Hpxf5ykmPb7X2Af+orGJPrCSR5I02h9d8BnwbuCvxVVX2g18AW7qXAccCOSU4FtqSpPTokszgTfRJDq3G7NkO5ia8c+/oa4MNVdWpfwXRkT2BuIr3XPPsGIclWwHaM/V0bTZqrqnut7n3L0MlJ/g7YpO1ZeD7w8Z5jWoyZuXaq6uVJnsD1w9kOH1qp1EkWw6EdgjQkVfX+JCu5fiz/46vqm6PjS10BxWohExjV403yOOCRNMnpFwdeE3Ijmq7gAOdV1dU9h6QJzNos7iRPq6r39R3HumgnymxTVV/vO5aFSPI8moTtdsD3xg7dHDi1qvbvJbBFSPIGmlbEb3L9Ahk1tPrDcF2FoGcCD6W5P58AvLtm4I/10K+dkSSbsepD3FAWjwEgyf8AL1rdYjizaKn/dtpyPZnRv9MjgGOr6rJRl92QzJmYOW7nJFTVfy5pQIuQ5G1rOl5VL16qWLR6SU4E9pkzg/voqnoYwFAS6yQnAY+muRecCVyS5EtV1Vsd1UX4EE1X9utZteLR5UNLEsY8FrhDu+LsoFXVtUk+QNOAc17f8SzWLF07SZ4DvJqmoMG1NA8/RfOgOiS3BM5tJ84PdjGcdbSkSZvJ9WQ+0dYf/h3wvHaG8BCrhYwmZv4RcG/g8+32g4AvAYNJrmlu0uujoT3VbTHPDO4hTs7avKp+neRZwPur6pAkg2x9q6rLaOZY7AfQ/n9sDGyaZNOBtmZdQLMIzuCT6ySPpin3dmNghyS7AocOOPGZmWsHeBlw5/EFigZqVhbDWRdL2vNjcj2Bqjq4HXd9WVX9IclvGasWkmTPIcyyH03MTPIZYJeq+km7/SfA+3oMbZ1V1ZHj20luWlW/7SueJTS0GrfXJtl2lLC19XuH2L29UXud7Mv1JSwHLcmjaFZqvS1wCc145W8Bd+ozrgX6LXB2ks8xlmAPtAfrEJpqIScBVNXZSXboNaLFmaVr53s0v2uDVlUnrw+L4fTJ5HpC492lVXUFY10pwBsYm3E/ANuMEuvWT2kWLhmcJHvQ1OfeFNg2yd2A51TV8/uNbN0kuZw1JJ1VtVn7eVA1bmn+mJ6S5GSaVvf70dTwHZpDaca+nlJVZyS5HfDdnmNarNfSLBf+2araLcmDgMGNt24d137MgqvnGXo4xAfSkVm6dl4BfCnJVxjwQ9x6tBjOuCXt9XVCYwcGuBztvwE7sWo5ofMHslLeKtqb3N7AcWNLOH+jqu7cb2QLk+Q1wE+Ao2huBk8B/qSqXtVrYIuQZgn0UbWG02agS/UGkryiql7fdxzrIsnKqlqR5GvAbu1Y368NdaJ2khsDO7ebg52k3a6e+zma8fBPAF4M3KiqnttrYFMypGunHaN8CnAOzZhr4IY9qctde83vOXcxnKFe+yOjCbOsOtn0rPbYrZZyTonJdQeGWMGhrXxy/3bzi0MrJzSS5CtVdc/xB5yBJwg3iH2I55PkjlX17ay6PP11Rje8WTHQe8BnaSYCvh7YgmZoyD2q6t69BrYASR4IHEmzwFdo/sAeMCrFNyRJbkrT4/PQdtcJwGuraojzfNZqSNfO0BrSVifJOVV1l7HtDYCvje8bmrZh6mk0Q3dGiW1V1Z+v9k1T5LCQ9VSbTM+bUA9soZIfJbk3UEluBBxEM250qK5I8hTgaJobxH6sOgRpKF5KM/zjzfMcK66vRTorhjbRFJp5I78H/oqmh2Rzmi78IXoz8NBRdY0kO9P0zP1Zr1Gto7YG8SerWXl26OOTJzWka+dTSQ6kqTs+PixkaFV2ZnExnH2BHatqWSwYZXI9gSQ3mVviac6+C5c+qqka0kIlzwXeCmwFXAx8hqaG71A9meZ83kqThJ7a7huUqjqw/Twry9OvzeC6ANu5IyOD6taex43Gy9ZV1Xfah+1BaSfMX5tk87aqy/pgSNfOfu3nV4ztG1wpvllYDGce3wBuQdMD1zuHhUxgvm6rIXVlrashnVuSI4GXjFZeasdcvbmqntFvZILV1la/DDhnlmanD6m7eA2TZ0PTjbrZEoe0aEneQzMGdrRq7lOADYd4H2gX+NiNZpL8eA3iQU2am9SQrp1ZM/TFcMYlWQH8D02SPd6r0EsJS1uu1yDJbWhaRDdJshvXd19tBty0t8A07q41tqRpW0d5sDfqtjv7P4A/rqo7J7kr8Oiqem3PoS3UM4E9gC+02w+kqVG+Q5JDq+qovgLr2LF9BzCpqrr5JK/LEi8XvEjPA15AM/kP4H+Bf+8vnEX5T2645sAgW8HaYS4vrqp/XcPLBnPtJPnL+fZX1fuXOpbFmKHFcMYdSVO5bZXJpn2x5XoNkhxAM0B+BXAG1yfXlwPvG9KKhutiSC0J7aznB461XN8KOHmoEzPaknUvB945I9VPTgD+sqp+2m7/MfB+mu7VLw7lvNoekoNq1ZUmZ7qHZEg9WLMkyUFV9da17RuKJKdX1e59x9GFJIeNbW4MPBg4q6r27imkBUnyXWCPWarclOSMqrrH2l+5NGy5XoO2vM6RSZ5QVR/rO54utYt57FRVn02yCbBRVV3eHh7SQiVvBr6cZNT6sQ/wTz3Gs1g3rarT59S4vaavYDqwzSixbl3S7vtFkiGVSrtr3XClyUE8gC7Csp9oluSYqto3yTnM07pbVXftIazFOoBmzsW4p82zbyhObcu/foRVh7kMrmLQ3HK1SW5BM/l8aGZiMZw5/jfJ62nq3Y8PC+nl98zkejJbt2OTLgfeBdwdOLiqPtNvWAuT5Nk0lRxuBewIbA28g+YpfFALlVTV+5Os5PrqE4+vqm/2GdMi/SzJjrSJQpK9aepeD9VJST7B9V2/e7f7bgb8avVvW3Y2GB8m0faQzPr9cwjdmge1nx/ZaxQdSLIfzeTlHZKML4izGTDYsbDAru3n8Uo0s1Ix6AqGOZRiJhbDmWPU2HGvsX29/Z7N+h+Hrjyjqt6a5GHArWlado+iqUwxRC+gWV73KwBV9d0kf9RvSAvXJtNDTqjHvQA4HLhjkouB79NMzhqqFwCPB+7bbh8JfKya8WhDqiQy3kMSmoeEIfeQzIS6fqXZ51fV344fS/IG4G9v+K5l60s0D9JbsGoJy8uBr/cSUQdmqWLQnIeeDYBdgGN6Cmcx3gl8nmUyPrkLy+33zOR6MqPu0b8A3l9V52ZOv/3AXFlVV41OIclGDKOVaqa1k3+eX1UPaVt2NxgbqjNIVVVJTgGuovkdO70GONFjBntIJjGke9ye3DCR3muefctWVf0A+AGwRzs3YTR+9FtVNdihYe25vA64bVXtlWQXmvG+R/Qc2kLchmZODDTD9X4IvLC/cBbsRlX10r6D6FKSzYFDuH5xvJOBQ/sqablBHz90gM5M8hma5PqEJDdn2E97Jyf5O5oqKHvSdNl/vOeY1ntV9QfaFt6qumLoiTVAkn2B02laevcFvtIOdRmEdjjYaBjI/wEfaj/+r903SEk2TPLttbzswUsSzCIkeV473voOSb4+9vF9Btram2QfmmtmHwZ4zczjfTSrTN623f4O8JLeolmcjarq5Pbj1Kr6Ec1D3NB8KsmBSf4kya1GH30HtUjvoenl2bf9+DXw3r6CsVrIBNqlQXcFLqiqXyW5NbBVVQ315r0BTYm0h9K0Tp0AvHuILYqzJsl/0JR/PJZVJ/8MsjJNW81lz1FN6yRbAp+tgSznnuQTVfXINlkbvz5GNaGHON4SuK6e8ouq6od9x7JQbWvVLWmWcD947NDlQ63ZO/RrZq5RFYfxKlRJzq6qXdf23uUiyfNoFie7Hc1kwJGbA6dW1f69BLZA7f1srqHfz27wO9Xn75nDQiZQVde2v4w7JxnS6oXzas/nSJox1wWcZ2K9bGwM/JxVJ2EUN6x7OxQbzFks5ucMqMesqh7Zft6h71im4JbAuUlOZ9UHuV4WXViItsv3MtqV89q5IxsDmybZdKAPDoO+ZuZxRdsgNZqkfS+a/7Mh+RDN8uAz8RA3o/ez3yW5b1WdApDkPsDv+grG5HoCSZ5FMyt9a+BsmtmoX2ags52TPIKmOsj3aFrgdkjynKr6VL+Rqaqe3ncMHft0W+v6w+32E4Hje4xnQZLMV+/5MuAHAx4P+w99B9CVJI8C3kIz9OASYDvgW8Cd+oxrgea7ZoZ8b34pTXm0HZOcCmxJM0xsMOY+xA3drCyGM8dzgfe3vVkAv6Qpa9kLh4VMoB3Tdw/gtKraNckdgddV1XxLOy977VjLR1bV+e32jsAnq+qO/UamJO9l/nq9g12sJMkTgPu0m/9bVf/VZzwLkeQ0mhKcX6d5IL0LzTK7mwPPG3BZzvGJc6fXQJekb4dS/DnN8IndkjwI2L+qntlzaAuSZLzCziCvmXHtpPk70Fw751XVkGrcz5xZWQxnpC0G8IaqetlonkxV/brPmGy5nszvq+r3SUhyk6r6dpI79B3UIlw+SqxbF9BMBFD/PjH29cbA44Af9xRLJ9oFmIa+CNOPgWdW1bkAbcWDQ4G/oRmyM7jkup1s+ibgJJqk57AkL6+qj/Ya2MJcXVU/T7JBkg2q6gtJ/l/fQS1Ekh2A40fzLJJskmT7qrqw38jWTZI/r6rPtw8K43ZOMth5JLNghhbDAZpiAElGxQB6TapHTK4nc1H7y/ffwIlJfklTMmmoViY5nqY+Z9HMSj9jdBP0ptefuSuBJvkwcEpP4SxYksuZv7zjaCLgZksc0mLtPEqsoamtnuSOVXXBgKtyvhK4x9yJc8AQk+tfJdkU+CLwwSSXMDaOfGCOBe49tv2Hdt+yWdp5QvenqaX8qHmODXkeySwa6mI4477a1iFfFsUATK4nUFWPa7/8xyRfoOkK/nSPIS3WxsBPgQe025cCm9DcBL3pLS87AYNb4Keqbt53DB07t63kMmrdeSLwzSQ3AYbaxT1LE+ceQzN56a9oFl3aHHh1rxEt3EZVddVoo12T4MZ9BrRAv2w/HzGaZKblYYYWwxm3rIoBmFxPqO1y2Kmq3tu28GxFs3reEL2sqn7edxC6oXlafP+PAS2EMZ8kdwPu125+caAlLJ9GU4prVJ/3VOBlNIn1sloZbB3M0sS5V7UrNF5LswroEFdoHLk0yaOr6jiAJI8BftZzTAvxdOCtwNto5ito+ZiVxXBI8ob22j++qo7tO54RJzROIMkhwArgDlW1c5LbAsdW1X3W8tZlKcl3aaqevBf4lGX4NC1JDgKezfWtB48DDq+qw1b/ruWpbT28A9eXrxxqi/V1ZmGyKUCSs6rq7nP2fb2q7tpXTAvVTjD/IE3lkwA/Av5yzjyZZa8d0raC5jzGa0OPhoYN7v9mVszY9XIOcFfgzLnn1CeT6wkkORvYjWY27agI/iB/EQHSDBJ9CPAMmnF8xwDvq6rv9BqYRrU5z66qK5LsT9Pi89ZqlkYenCRfp1nq+Ip2+2bAl4d27SR5IE2L6IU0ycE2wAFV9cUew+pEO7v+ul7MIdXtnbXFPca1Y8ipqt/0HctCJbkNzSJlN6idPtR72pDN4vWS5E00DTibAr8dP0SP83tMrieQ5PSq2n30tDfUBGE+bcmqDwA3A74GHFxVX+43qvVXm4zejeZJ/H3Au4F9q+oBa3rfcjUqY1lVv2+3NwbOqKq79BvZuklyJvDkqjqv3d4Z+HBV/Vm/kS1ckufQjEv+Pc1wisGtOjmjKzTeBHgCsD2rPvQc2ldMmg2zeL2MJPmfqnpM33GMOOZ6MsckeSdwiyTPpmnxfVfPMS1Yu1rW/sBTaSY2voimyP+uNDNtZ3H1pqG4pqqqHWf5b1V1RJJB1uptvRf4SpLRcIPHAu/pMZ6FutEosQaoqu8kuVGfAXXgZcCdq2qI43mB6xf3SDJ3bPWQV2j8H5oFS84Eruw5lgVLckxV7ds+YI+34jkspCezthjOuLUl1km+XFV7LFU8Jtdr0Q6h+AhwR+DXNGMuX1VVJ/Ya2OJ8GTgKeGxVXTS2f2WSd/QUkxqXJ3kFzcPP/ZNsAAw2iauqtyQ5iesXxHh6VX21x5AWamWSd9P08kBTkWJlj/F04Xus2o06ZJ+kSeBCUzVgB+A8hrlC49ZV9fC+g+jAQe3nR/YahdTYeCl/mMNCJpDknKF1Y69Jkn2r6pg5+/ZZTjNt11ftGMUn0wyd+N8k2wIPHOqytEmOqqqnrm3fctd21b+AsVXzgH+vqiG3LO5G27PAWAtpVb24t6A6kma5+udX1bP6jmVdJTkcOKyqzuk7li60wyh/V1XXtsOp7kgzkX7wE4I1HPNN4pzqzzO5XrskR9J00Z/RdyxdWM1M4SX9xdP6Ye7vVbtM7TlVtUuPYYlmLgnNAkXn0Iy5BqCqjuwtqA4NtVEkyTdp6ttfQPPQM+hhFO18hfvRjPU9FTgDuKqqntJrYFqvLHWO47CQydwT2D/JhTQr/wzyZpdkL+AvgK2SvG3s0GY0tS7VsyT3Ag4D/hS4MbAh8Juq2rzXwNZRO7Tl74BNkoyWow1wFXB4b4Gto3nGi65iaPeAOW5UVS/tO4guJBk/jw2AP6NZsn6I9qJJRK+rDQ/8qr9wFi1V9dt27si/V9Ub2wpc0lJa0qV0Ta4n87C+A+jIj2nGiT6aZrLMyOU0K5upf/8GPIlmYukK4C+BnXuNaAGq6vXA65O8vqpe0Xc8izDL40U/leRA4OOsOixkiFUDbs71D0HX0JzTx/oLZ1EeCzyLpjZ8aObHvIvmoXuIkmQPmnkKo8nZG/YYj9ZPSzoU0WEhE2rH8N2X5gZ+alWd1XNIC5bkRmsa75bkY1X1hKWMSY0kK6tqxXgd9SRfHdVXH5pZqtud5I9p6sIDnD5n6fDBSTLfCrODKsU3kuQeND0l23N9o9HgehdhdmrDjyR5APDXNH8335Dkdvz/9u48TNKyOv/49x5AlrArIP7c2EQHBUSQTQygJCKgqCAqbqgJSgKoP3EXkYhIwA0S44KCKKAgElFEQdZxIAyy71EWcQEJyBZ2hjt/PG9NV7e9TXf1PPXW3J/rqqur3nqr+/R0T9epp85zDrx/EGr7o39Iej1wGLA65UVp+lz3O0kHArszNGVuV8qExs/Wi2rmtDmZaztJF1AG/BxNGX1+O/BO2xtVDWyKBqVvt6Q3AocD51H+aG8DHGD7hzXjikLSjZTWgtcwvH68jS/iBqI3/Gia7kfL275/wpMjFoKk3wK72L6+diyQ5HpSmj/cG3X9sVuWshq3ft3IZkY2N9Yj6TmU3uNPoZTqrESpU2zV6OOOrsFLBwJ/bPp2t+73S9KVwA6d1WpJqwG/bOuLHgBJbx/teBs700j6le2XTXxm/2vqx98BdPeGP9b2l+tFNXWSTgDeC8ynbGZckfLu1eFVA4uBImmu7a1rx9GRmuvJ+ROlR+Ijze2lgT/WCycGle3fNS/e1rT9mdrx9MCg9O2eNaIM5G7Kxrk226zr+jLAK4DLgNYl18Cnmz7kZzO8fvxHYz+kPw1Qb/iO2bbvl7QncAZlMuCllHeCIqalKQeBMovgB8B/0gd/A5Jcj0PSUZQa6/uAayWd1dzeAZhXM7YZtkh31cYQSbsAR1BWrteStDFwsO3X1I1syvag9O1+t+07mr7dbXxS/bmkXwAnNrf3oCQKrWV73+7bklYGvl8pnOnai9I/eSmGykLMUClfbDJ8rAAAGi1JREFUqzR7elq7r2eEpZppprtSWto+LilvmUev7NJ1/SHg77puV/sbkLKQcUh6x3j3t7EfbNNn+LjxeoxK+jvbZy7CsKLR9ITdHjivU/fe1n69g6ZZIVkwRMb2qeOd3zZNAnRNG8vdJN3YxrgXB5L2Az4CXAnsBDwb+J7tbcZ9YMRCkLS17bkTHVtk8SS5nr62ddeQ9Ctge9uP1Y4lhpP0X7a36N5U2t05pC06NbCSHmB4n+iqO7inStJhtj8y0bE2kfQThn42s4DZlI3arfueJB0DHG77utqxxMQkLWk7sxWiZ/ptOF7KQnqjba2rbgbmSjqNMhQHKLV+9UKKxrWS3gIsIWk9YD/gwsoxLbTO5jLbK9SOpUd2oKy+ddtxlGNtckTX9SeA39n+Q61gpmkL4IqmvWDrpxoOGkk7ARtQavs7Dq4UTgyQpof6VsBqI4ZJrUjFfupJrnujbcv/NzWXWZThC9E/9gU+QUkQTgR+AfxL1YimqSlFWoOuvze2b6sX0eRJeh+wD7B201awYwXKKOc2e/UArca/qnYAMTpJXwOWA7ajtOLcjcHesxSL1lOA5SnPL935zP2U37UqUhbSA21sLQYgaTnbD9WOIwaXpH2BT1PaCy7YaNaWFUVJK1FGUR9K6XLQ8UBLJxkuMMbbqK0rQYr+1vmd6vq4PHBGaq6jlyQ9p5/62mflujda1V2jeRvlW5RXe8+WtBGwt+196kYWkjblryfN0eKEZ39gfdt31w5kKmzfB9wn6ZPAHbYflbQtsKGk42zfWzfChTfgq/HRfx5uPj4k6RmUNpZrVownBtNDkg5nRPmR7e1rBNP2Pq0zStLZzcfDJji1bW+jfhn4e8ofOWxfCby8akTRcTxlkuEbKC2GOpe2+j2llWXbnQLMl7Qu8A3gWcAJdUOashMov1OnMfx37CW239o5SdIqdcKLAfPTps3j4ZT2grcy1NIyoleOB24A1gI+Q/k9u6RWMCkLGYek64D3UFZ538KIFeqmF2nrSLrY9uYjOlJc2eZpc4NiUCbNdW0s2QBYHzid4Y39W7V5tmvS5IeBh20f1f3/ZxC1tdwt+pekpYFlmneEInpG0qW2X9Jd2ibpEtubTfTYmZCykPEdCHwKeCYwMhkwpR9xG/1e0laAm962+wPXV44pikGZNNfZWHJbc3lKc2mrxyW9GXg7Q+8ktHHS5MJoVblb9JeuyXmj3dfGv2nR3x5vPt7edKf5E7BqrWCycj0Jkj5lu9UdG7pJehrwFeCVlCfQM4H921oXO0gkfY8yae5ahm8AfFe9qGaOpKNGTgrsR5JmA+8FLrJ9oqS1gDfanqhkrLWych3T0fQeH8vA/k2LOiTtDMyhlOwdRWnF9xnbp1WJJ8n15Eh6DUN1yefZ/mnNeGIwLW6T5pLA9a/8bCIipiYbGidB0qGU0onrmsv+kj5XN6qpk/SdZoNJ5/Yqkr5dM6ZY4MJmlTT6gKSTmo9XS7pq5KV2fDMsZSExbZI+N8rzzWdrxhSDR9LzJJ0t6Zrm9oZNl6c68WTlemLNk+jGtp9sbi8BXN7W9mijbcQa9M1ZbSHpemAdYLGYNNfvq6OS1rR9u6TnjHZ/P/VVXViSvgB82/a1Y9y/att7eUd9Yzzf9PX/+2gfSecDBwBf72rUcI3tF9aIJxsaJ29loPNEs1LNQHpglqRVbN8D5UmU/C70i3EnzXX/3AZEX6+O2r69+djaJHoc1wPfkLQkcAxwYncXhyTW0SNLSFra9qMAkpYFlq4cUwye5WzPk4Y9pTxRK5gkVJNzKHC5pHMpycDLGT6trW2+AFwk6WTK97MbcEjdkAImlcSdDQzSis9XagcwGU3ng8OA1Sn/ZzrvKKxYNbBpsH00cLSk9YG9gKskzQW+afvcutHFADkeOLtrg+NewHcqxhOD6S5J61A6uSFpN+D2WsGkLGSSJK0JdPolzrN9R9d9G4z11mq/krQBsF1z8xzb19WMJyanbeU7zcTJTwDPobyYb2WZi6TfArvYHqiWlU2J286UhOdZwEnAy4AHbb+pZmwxOCS9itKdCuAs27+oGU8MHklrUwZ8bQXcQymt3LPWu45JrnugjfVjzZPqGgwfsX1bvYhiMtr2uybpRkod3NUMtRZsXZmFpLm2t64dRy9J+hIlsT4H+JbteV33LVZda6IeSRfZ3rJ2HNFOXQPLOpalNOt4EOoNLEtZSG/0dd3oSJL2BT4N/BmYT7OaCLRqNTFa4X9q9Rntha5BGL+W9APgP2n3cB8AVAoT/0LZqP3gKKe8dBGHFIuvZWoHEK3WGVi2PqW64MeUnOZtwLyxHjTTsnLdAy1cTfwtsHmGxrRPC8tCXgG8mZZOnBzkQRiSrrb9otpxxOKtbc+f0Z8kXQDsZPuB5vYKwOm2Xz7+I2dGVq4XT78H7pvwrFjkmg0Zf7D9qKRtKe8mHGf73uaUV1QLbmr2okycXIquiZNAK5Jr23tN5jxJH7N96EzH02OXSdrM9iW1A4mImKY1gMe6bj/WHKsiyXVvPDbxKX3lZuA8SaczfDWxSm1SDHMKsKmkdSmbM34MnAC8GlrZHm2zxaR2d3dKV6E22RzYU9LvKPWJrdxsGq3XqrLK6FvHAfMkndrc3hU4tlYwSa4noalP3BNY2/bBkp4NPL2zAcj2FlUDXHi3NZenNJfoH0/afkLS64CjbB8l6fLaQU3DhZJmLwbdaNqYIPx97QBi8dAMYVrP9i+bPtdLdt6+p9TGRkyL7UMknQFs0xzay3a1587UXE+CpP+gvKW9ve0XSFoFONP2ZhM8NGKhSLoY+DKlfd0utm+pOWVquhaXiZNtrhuVtDpdm8rSNSh6SdI/AP8IrGp7HUnrAV+z3bYSt4hJy8r15Gxue5POCqLteyS1dsVX0mrAh4ENGP6kun21oKJjL+C9wCFNYr0W8N3KMU3HuBMnB0jrVq4lvYYyUOoZwJ2UXuTXU/4uRPTKP1G6z1wMYPs3zQu6iIE1q3YALfF40xe6M/lnNbp69rbQ8cANwFrAZ4BbgWxq6gO2r7O9n+0Tm9u32D6sdlzTsCRwR9PXei3gtbRsM62kJSR9YILTTl4kwfTWvwBbAP9tey3KZtn/qhtSDKBHbS/YlyRpSZrn0ohBleR6co4ETgXWkHQI8Cvgc3VDmpan2v4W8Ljt85t2Ylm17gOSbpF088hL7bim4RRgftcGzWdRNmi2hu35lHaC453Txr8HjzftOGdJmtWMPN+0dlAxcM6X9HFgWUk7UF6I/qRyTBEzKmUhk2D7eEmXMtQGbdeWj0F+vPl4u6SdgD8Bq1aMJ4Z0JzfLULpQtPln09mg+XravUFzrqR/A35AM/kLwPZl9UKatnslLQ9cABwv6U66vreIHvko8G7KlNa9gZ8BR1eNKGKGZUPjJEnaBHgZ5e2suW1+UpW0MzCHsop4FLAicJDtrCb0IUmX2n5J7TimYlA2aEo6d5TDbvM+BUl/AzxCqRffE1gJOD7DpSIipicr15Mg6UDKCuIplCeiYySdbPuzdSObst2BX9m+BthO0qrAEeStuuqaF3Edsygr2W3+fzoQGzRtb1c7hl7rjD2XtCL5vx8zRNLWwEGUDbNLMtQxaO2acUXMpKxcT4KkG4GNbD/S3F4WuKKtwzFGG6HdtrHag2rECukTlBZ2X7B9Y6WQApC0BmWfxTNs7yhpNrBls3ehlSTtTdnQ/Ahlg3aSnug5STcAHwAuBeZ3jucdkhhkbV4RW5T+RKl/faS5vTTwx3rhTNssSavYvgegWbnO70J/eLftYRsYm9XeVmp62h4KzGZ428e2JXDHAsdQylsA/ptSf93a5Br4EPBC23fVDiQG2n22z6gdRMSilIRqcu4DrpV0FqXmegfKmM0jAWzvVzO4KfgCcJGkTvuw3YFDKsYTQ34IjBxG8kOglTXXlIT008CXgO0oZSJt7FL0NNsnSfoYQLNJc/5ED+pzNwEP1Q4iBt65kg4HfkQZJAW0fjNwxLiSXE/Oqc2l47xKcfSE7eMk/Zqh9nuvXwzGU/c1Sc+nDO9Yqems0bEiXSu+LbSs7bMlqel1fVDTeefA2oEtpAclPZWhXvdb0LJ+3aP4GGU8/cUMT3ratlgQ/W3z5mN3JyST9q8xwJJcT85fgNNtt3lwzDBNMp2Eun+sD+wMrAzs0nX8AeAfqkTUG49KmgX8RtI/U8qplq8c01R8EDgNWEfSXGA1YLe6IU3b14FzKC3SBuZvW/SXQdwMHDGRbGicBEnfA7akdAv5tu0bKocUA0rSlrYvqh1Hr0jajDJSe2XKRMCVgH+13bpJgM1kufUpG/9utP34BA/pa9nEHIvCIG4GjphIkutJatpVvZlSM2pKLemJth+oGlgMFEnLUAYubMDwDYDvqhZUACBpK+C5dL3jZ/u4agFNk6TPAbdS2vB1l4X8pVZMMXgknUGzGdj2Rs2L1Mttv6hyaBEzJsn1QmhqLt8GvJ+yGrcucKTto6oGFgOj2WR6A/AW4GDKcI/rbe9fNbCFJOknNPXJo7H9mkUYzrRJ+i6wDnAFQ+3E3Ob6ZEm3jHI4rfiipyRdYnuz7ndKJF1he+PasUXMlNRcT4Kk1wLvpCTTxwEvtX2npOUodctJrqNX1rW9u6TX2v6OpBMo0zTb5ojaAfTYpsBsD9BqhO3WtniMVhnEzcAR40pyPTlvAb5k+4LOAUmH2f6IpHdXjCsGT6eO915JLwTuAFavGM+U2D6/c13SU4DnU55cb7T9WLXApu4a4OnA7bUDmS5J29s+Z0RXmgVs/2hRxxQDbRA3A0eMK8n15KzXnVg3dgQ+YvvsGgHFwPqGpFWAT1KekJYHPlU3pKmTtBPwNUpPZQFrSdq7LUMluspbVgCukzSP4fXJrSpvafwtpUvILqPcZ0o/4oiesH2ZpL9lgDYDR0wkNdfjkPQ+YB9gbUpy0LECMNf2W6sEFgNL0lq2b5noWFs0o493tv3b5vY6lLaWz68b2eQ0ScGYulfo26Rpj7ib7ZNqxxKDbYx3SO4DrrZ956KOJ2JRSHI9DkkrAatQxjd/tOuuB7KjPmaCpMtsbzLi2KW2WzmhsbOZqeu2gHndx9qgUwY20bE2kfRr25tOfGbE1Ek6ndLK9tzm0LbApcBawMG2v1sptIgZk7KQcdi+j/IK+821Y4nBNsATGn8t6WfASZSSg92BSzrfY4vqe3cARibSO45yrE1+KelDwA+ABzsHs3AQPbYk8ALbf4YFfa+Po0xuvABIch0DJ8l1RH8Y1AmNywB/ptT5AvwPsCzle+z7+t7u0jBJV3XdtQIwt05UPbMH5Wewz4jjacUXvfSsTmLduLM59hdJqb2OgZSykIg+MmgTGttukEvDJC1LSaxfRkmy5wBfs/1w1cBioEj6KvBs4OTm0BuAPwAHAD/NePQYREmuI/qIpH8FPgs8DPwc2BD4gO3vVQ1siiQ9k9IHfuvm0Bxgf9t/qBfV1EjaCNimuTnH9pU145kuSScB9wPHN4feAqxk+431oopB0+yzeD3lRRyUd3xOGaSe8REjJbmO6COdyWWSXkcpE/kgcIHtjSqHNiWSzgJOYKiu8q3AnrZ3qBfVwpO0H/CPDJWxvA74Rpuns0q6zvbsiY5F9IqknW3/tHYcETNtVu0AImKYpZqPOwEnN5tq22w128fYfqK5HEsZItE27wE2t32g7QOBLWh3LTzAZc20PAAkbQ78umI8MfgOrh1AxKKQDY0R/eUnTW/oh4H3SVoNeKRyTNNxt6S3Aic2t98M3F0xnqkSML/r9vzmWJu9BLhQ0m3N7WcDN0q6GrDtDeuFFgOq7f9nIiYlZSERfUbSqsB9tudL+htgBdt3NPftYPusuhFOnqTnUGqut6RsmrsQ2M/2beM+sM9I+iDwDuDU5tCuwLG2v1wvqulpfjZjsv27RRVLDCZJL6W8ULtE0mxKqds1tn9WObSIGZXkOqJFRhsyE4uGpE0Y2pQ1x/blXfetYvueOpFF9B9Jn6b0gl8SOIvS1/pcSs/4X9g+pGJ4ETMqyXVEi0i63PaLa8cxWZKeB/wHsIbtF0raEHiN7c9WDq2n8qInYrimvGhjYGngDuCZtu9vWkBenLKjGGTZ0BjRLm17NfxN4GPA4wC2rwLeVDWimZFa0ojhnrA93/ZDwE227wdo+qg/WTe0iJmV5DoiZtJytueNOPZElUhmVtte9ETMtMckLddcf0nnYDOYKcl1DLQk1xF9QtIsSVtNcNqtiyKWHrpL0jo0yaek3YDb64YUEYvAy5tVa2x3J9NLUTYHRwys1FxH9JG21VRPRNLawDeArYB7gFsoQ2QGqhPFoP3cIiJi6pJcR/QRSUcAFwE/GoTxwJKWBnYDngusShm3bdutGSYhaQngWtvPH+ecVW3/ZRGGFRERfSrJdUQfkfQAsBxlSMkjlI1ytr1i1cCmSNLPgXuBy+gawmL7C9WCmgJJPwb2bVt/7oiIWPQyoTGiv6wE7AmsZftgSc8G1qwc03Q80/aragfRA6sA10qaBzzYOWj7NfVCioiIfpTkOqK//DtlJ/32wMHAA8ApwGY1g5qGCyW9yPbVtQOZpk/VDiAiItohZSERfaQzjKR7g5ykK21vVDu2qZB0HbAuZSPjowyVubRugISkNRh6kTPP9p0144mIiP6UleuI/vJ4s4Gu07puNdrdE3bH2gH0gqQ3AocD51FeIBwl6QDbP6waWERE9J2sXEf0EUl7AnsAmwDfoXTa+KTtk6sGtpiTdCWwQ2e1unnR88u2vqMQEREzJyvXEX3E9vGSLgVeQVkh3dX29ZXDCpg1ogzkbjKEKyIiRpHkOqLP2L4BuKF2HDHMzyX9Ajixub0HcEbFeCIiok+lLCQiYhIkvQHYurk5x/apNeOJiIj+lOQ6ImKSJK1I1zt+mcoYEREjpSwkImICkvYGPkOZmvkkTUtBYO2acUVERP/JynVExAQk/QbY0vZdtWOJiIj+lt3uERETuwl4qHYQERHR/7JyHRExAUkvBo4BLqZMmgTA9n7VgoqIiL6UmuuIiIl9HTgHuJp2T8yMiIgZlpXriIgJSLrc9otrxxEREf0vyXVExAQkfQ64FfgJw8tC0oovIiKGSXIdETEBSbeMcti204ovIiKGSXIdEREREdEj2dAYETEBSW8f7bjt4xZ1LBER0d+SXEdETGyzruvLAK8ALgOSXEdExDApC4mIWEiSVga+b/tVtWOJiIj+kgmNEREL70EgmxkjIuKvpCwkImICkk7rujkLmA2cVCmciIjoY0muIyIm9nTggOb6E8BtwD/XCyciIvpVaq4jIiYg6TLbm4w4dpXtDWvFFBER/Skr1xERY5D0PmAfYG1JV3XdtQIwt05UERHRz7JyHRExBkkrAasAhwIf7brrgYw+j4iI0SS5joiIiIjokbTii4iIiIjokSTXERERERE9kuQ6ImIMkuZLukLSNZJOlrRcH8S0raStKn3toyXNbq5/vOv4cyVdM4nHHyTpIUmrdx3730k87lhJuzXXz5O0aXP9Z5JWbi77TOV7iojotSTXERFje9j2xrZfCDwGvHcyD5I0k52YtgUWKrnuVTy232P7uubmx8c9eWx3Af+/R/G82va9wMqUri4REdUluY6ImJw5wLqSdpF0saTLJf1S0hqwYFX2u5LmAt9tVnPnSLqsuWzVnLetpPMl/VjSzZI+L2lPSfMkXS1pnea81SSdIumS5rK1pOdSEvwPNCvq24x23hjxbNB8jSskXSVpve5vTtLukr7YXN9f0s3N9bWbz7Fg1VjS54Flm891fPMplpD0TUnXSjpT0rJj/Dt+G9hD0qojvv6w1W9JH5J00Hg/EEm3Snoa8HlgnSaewyUdJ2nXrvOOl/Ta8T5XRESvJLmOiJhAs/K7I3A18CtgC9svBr4PfLjr1NnAK22/GbgT2KEZPrMHcGTXeRtRkuQXAG8Dnmf7pcDRwL7NOV8BvmR7M+ANwNG2bwW+1hzf2Pac0c4bI573Al+xvTGwKfCHEd/mHGCb5vo2wN2S/l9z/YLuE21/lKFV/T2bw+sB/257A+DeJpbR/C8lwd5/jPun4qPATU08BwDfAt4JC9opbgWc3sOvFxExpgyRiYgY27KSrmiuz6EkbesDP5C0JvAU4Jau80+z/XBzfSng3yRtDMwHntd13iW2bweQdBNwZnP8amC75vorgdmSOo9ZUdLyo8Q43nnd8VwEfELSM4Ef2f5N9yexfYek5SWtADwLOAF4OSW5/tHo/zzD3GK78291KfDccc49ErhC0hGT+LwLzfb5kr4qaTVKkn+K7Sdm4mtFRIyU5DoiYmwPNyu9C0g6Cvii7dMkbQsc1HX3g13XPwD8mbJKPQt4pOu+R7uuP9l1+0mG/i7PoqyQdz+OriSaSZy3IB7bJ0i6GNgJ+JmkvW2fM+JzXQjsBdxIeTHxLmBLJlcj3f09zQfGKgvB9r2STgD+qevwEwx/N3WZSXzN8RwHvBV4E+V7iohYJFIWEhGxcFYC/thcf8cE591u+0lK6ccSC/l1zmSoRIRmBRzgAcr49YnOG0bS2sDNto8EfgxsOMppc4APUcpALqesoj9q+75Rzn1c0lKT/m7+2heBvRl6MfFnYHVJT5W0NLDzQnyukf8mAMcC7wfo2oQZETHjklxHRCycg4CTJV1K6Xwxlq8C75B0JfB8hq9qT8Z+wKbN5sPrGOpU8hPgdZ0NjeOcN9IbgWuaMpcXUlZ2R5pDKQm5wPZ84PeUGvPRfAO4qmtD40KxfRdwKrB0c/tx4GBgHnAWcMNCfK67gbkqLRMPb479GbgeOGYq8UVETFXGn0dExMBR6Ul+NbDJGCvvEREzIivXERExUCS9krJqfVQS64hY1LJyHRERERHRI1m5joiIiIjokSTXERERERE9kuQ6IiIiIqJHklxHRERERPRIkuuIiIiIiB5Jch0RERER0SP/B65pIITDUbEVAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "nulls = clinical.isnull().mean()\n",
        "nulls = nulls[nulls != 0]\n",
        "nulls.plot.bar(figsize=(12,5))\n",
        "plt.ylabel(\"Percentage\")\n",
        "plt.xlabel(\"Parameters with Nullity\")\n",
        "plt.title(\"Nullity\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "r3wSjGRGCo7k",
      "metadata": {
        "id": "r3wSjGRGCo7k"
      },
      "source": [
        "### Survival\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bFeUmotWkbcp",
      "metadata": {
        "id": "bFeUmotWkbcp"
      },
      "source": [
        "#### Age\n",
        "From the charts below we can have the following interpretation that if the disease is diagnosed at early ages there is a better chance that the patient survive, but, we have to consider the fact that the death from the disease somehow overlaps with the living box and that chance isn't that much. At least we can say that diagnosis after age 85 is not effective."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "TTOmKwjeCifM",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "TTOmKwjeCifM",
        "outputId": "c33a1cc3-3325-461f-8413-f7c1a69b6433"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Survival vs Age\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n",
        "\n",
        "# Distibution of dead and alive according to age\n",
        "sns.histplot(clinical.loc[:,\"age_at_diagnosis\"][clinical[\"overall_survival\"]==1], \n",
        "            color='b', ax=ax[0], label='alive')\n",
        "sns.histplot(clinical.loc[:,\"age_at_diagnosis\"][clinical[\"overall_survival\"]==0], \n",
        "            color='r', ax=ax[0], label='dead')\n",
        "ax[0].set_xlabel(\"Age at Diagnosis\")\n",
        "ax[0].legend()\n",
        "\n",
        "# Box plot of the death vause vs age\n",
        "sns.boxplot(x='death_from_cancer', y='age_at_diagnosis', orient='v', \n",
        "            data=clinical, ax=ax[1])\n",
        "ax[1].set_ylabel(\"Age at Diagnosis\")\n",
        "ax[1].set_xlabel(\"Cause of Death\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "rcP9fDQ0pzCx",
      "metadata": {
        "id": "rcP9fDQ0pzCx"
      },
      "source": [
        "#### Tumor Stage\n",
        "From the chart below, we can tell that if the patient is in stage 1 the probability of surviving is much higher if she was in stage 2, 3 or 4. Although in stage 2 the chance of surviving is pretty high."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "GADsCgGamTC_",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "GADsCgGamTC_",
        "outputId": "07cfd0ca-ec7e-4c5c-f759-83d400b2ca9b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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nnXcC8Pbbb3PEEUcwf/58jjrqqGb/fjvD3ZuSJABOOeUUSktLKS0tpUePHqxcuZLp06czb948Bg8eDNTcz7NHjx5AzcjxjDPOaMmSm8zQ0zaKcQ/JeTd+qeB9Stqx/v37c++9927R9tZbb/Hqq6+y227b/vNfWlpaN11SUsLGjRtJKTF69Oi6G0jXV1ZW1iaO49Xn7k1JaqeOP/543nnnHSZPrvmP7KZNm7jiiis477zz2G+//Vi3bl2j+rj33nvrnrb++uuv88orrxS17mIy9CSpnYoIpk6dyj333EO/fv346Ec/SllZGT/4wQ/45Cc/ycKFC7c4kaUh/fv35/rrr+eEE05g0KBBfPrTn2bFihXN+C0Ky92bktQMGnOJQTEceOCBPPDAA9u0l5aW8vTTT293veeee65uetSoUXUnu9RX+4DZtqRoI72IODAiHomIhRHxfERcmrVfGxHLI6I6e51cb53vRMSSiHghIk4sVm2SpHwq5khvI3BFSumZiNgLmBcRf8zm3ZJS2uLS/4joD5wNDAD+GfhTRHw0pbSpiDVKknKkaCO9lNKKlNIz2fQ6YBFwwA5WGQnclVJ6L6X0V2AJcHix6pMk5U+znMgSEb2AQ4E5WdPFEfFsREyMiC5Z2wHAa/VWW8aOQ1KSmkVKqaVLUAN25s+l6KEXEZ2A+4BvpJTeAn4O9AUqgBXAzU3sb0xEzI2IuatXry54vZJUX1lZGWvWrDH4WpmUEmvWrKGsrKxJ6xX17M2I6EhN4N2ZUvotQEppZb35vwB+n71dDhxYb/WeWdsWUkoTgAkAVVVV/i2UVFQ9e/Zk2bJl+J/s1qesrIyePXs2aZ2ihV5EBHAHsCil9ON67funlGov8jgNqD0vdhrwPxHxY2pOZOkHPFWs+iSpMTp27Ejv3r1bugwVSDFHekOBc4EFEVGdtf078PmIqAASsBS4ACCl9HxE3A0spObMz6975qYkqZCKFnoppceBaGDWgw201a5zA3BDsWqSJOWbtyGTJOWGoSdJyg1DT5KUG4aeJCk3DD1JUm4YepKk3DD0JEm5YehJknLD0JMk5YahJ0nKDUNPkpQbhp4kKTcMPUlSbhh6kqTcMPQkSblh6EmScsPQkyTlhqEnScoNQ0+SlBuGniQpNww9SVJuGHqSpNww9CRJuWHoSZJyw9CTJOWGoSdJyg1DT5KUG4aeJCk3DD1JUm4YepKk3DD0JEm5YehJknKjaKEXEQdGxCMRsTAino+IS7P2fSPijxHxYvazS9YeETEuIpZExLMRcVixapMk5VMxR3obgStSSv2BIcDXI6I/cCUwPaXUD5ievQc4CeiXvcYAPy9ibZKkHCpa6KWUVqSUnsmm1wGLgAOAkcCkbLFJwKnZ9EhgcqrxJNA5IvYvVn2SpPxplmN6EdELOBSYA+yXUlqRzfobsF82fQDwWr3VlmVtkiQVRNFDLyI6AfcB30gpvVV/XkopAamJ/Y2JiLkRMXf16tUFrFSS1N4VNfQioiM1gXdnSum3WfPK2t2W2c9VWfty4MB6q/fM2raQUpqQUqpKKVV17969eMVLktqdYp69GcAdwKKU0o/rzZoGjM6mRwP312v/UnYW5xBgbb3doJIk7bLditj3UOBcYEFEVGdt/w78ELg7Ir4CvAKclc17EDgZWAK8A5xfxNqk3Bp629CC9znrklkF71MqhqKFXkrpcSC2M/v4BpZPwNeLVY8kSd6RRZKUG4aeJCk3DD1JUm4YepKk3DD0JEm5YehJknLD0JMk5YahJ0nKDUNPkpQbhp4kKTcaFXoRMb0xbZIktWY7vPdmRJQBewLdIqIL/7iX5t74gFdJUhvzYTecvgD4BvDPwDz+EXpvAT8tYl2SJBXcDkMvpfQT4CcRcUlK6bZmqkmSpKJo1KOFUkq3RcSRQK/666SUJhepLkmSCq5RoRcRvwL6AtXApqw5AYaeJKnNaOxDZKuA/tmDXiVJapMae53ec8A/FbMQSZKKrbEjvW7Awoh4CnivtjGlNKIoVUmSVASNDb1ri1mEJEnNobFnb/652IVIklRsjT17cx01Z2sC7A50BN5OKe1drMIkSSq0xo709qqdjogARgJDilWUJEnF0OSnLKQavwNOLEI9kiQVTWN3b55e720Haq7b21CUiiRJKpLGnr352XrTG4Gl1OzilCSpzWjsMb3zi12IJEnF1tiHyPaMiKkRsSp73RcRPYtdnCRJhdTYE1n+G5hGzXP1/hl4IGuTJKnNaGzodU8p/XdKaWP2+iXQvYh1SZJUcI0NvTUR8cWIKMleXwTWFLMwSZIKrbGh92XgLOBvwArgc8B5RapJkqSiaGzofQ8YnVLqnlLqQU0IXrejFSJiYnbSy3P12q6NiOURUZ29Tq437zsRsSQiXogIL3yXJBVcY0NvUErpjdo3KaXXgUM/ZJ1fAsMbaL8lpVSRvR4EiIj+wNnAgGyd/4qIkkbWJklSozQ29DpERJfaNxGxLx9yjV9K6VHg9Ub2PxK4K6X0Xkrpr8AS4PBGritJUqM0NvRuBp6IiO9HxPeB2cCPdvIzL46IZ7Pdn7VBegDwWr1llmVtkiQVTKNCL6U0GTgdWJm9Tk8p/WonPu/nQF+ggpoTYm5uagcRMSYi5kbE3NWrV+9ECZKkvGrsvTdJKS0EFu7Kh6WUVtZOR8QvgN9nb5cDB9ZbtGfW1lAfE4AJAFVVVamhZSRJakiTHy20KyJi/3pvTwNqz+ycBpwdEaUR0RvoBzzVnLVJktq/Ro/0mioifgMMA7pFxDLgu8CwiKig5insS4ELAFJKz0fE3dSMJDcCX08pbSpWbZKkfCpa6KWUPt9A8x07WP4G4IZi1SNJUrPu3pQkqSUZepKk3DD0JEm5YehJknLD0JMk5YahJ0nKDUNPkpQbhp4kKTcMPUlSbhh6kqTcMPQkSblh6EmScsPQkyTlhqEnScoNQ0+SlBuGniQpNww9SVJuGHqSpNww9CRJuWHoSZJyw9CTJOWGoSdJyg1DT5KUG4aeJCk3DD1JUm4YepKk3DD0JEm5YehJknLD0JMk5cZuLV2A8uHV7w0seJ8HXbOg4H1Kat8c6UmScsORntqsobcNLXifsy6ZVfA+JbUeRRvpRcTEiFgVEc/Va9s3Iv4YES9mP7tk7RER4yJiSUQ8GxGHFasuSVJ+FXP35i+B4Vu1XQlMTyn1A6Zn7wFOAvplrzHAz4tYlyQpp4oWeimlR4HXt2oeCUzKpicBp9Zrn5xqPAl0joj9i1WbJCmfmvtElv1SSiuy6b8B+2XTBwCv1VtuWda2jYgYExFzI2Lu6tWri1epJKndabGzN1NKCUg7sd6ElFJVSqmqe/fuRahMktReNXforazdbZn9XJW1LwcOrLdcz6xNkqSCae7QmwaMzqZHA/fXa/9SdhbnEGBtvd2gkiQVRNGu04uI3wDDgG4RsQz4LvBD4O6I+ArwCnBWtviDwMnAEuAd4Pxi1SVJyq+ihV5K6fPbmXV8A8sm4OvFqkWSJPA2ZJKkHDH0JEm5YehJknLD0JMk5YahJ0nKDUNPkpQbhp4kKTcMPUlSbhh6kqTcMPQkSblh6EmScsPQkyTlhqEnScoNQ0+SlBuGniQpNww9SVJuGHqSpNww9CRJuWHoSZJyw9CTJOWGoSdJyo3dWroASSq0V783sOB9HnTNgoL3qebnSE+SlBuGniQpNww9SVJuGHqSpNww9CRJuWHoSZJyw9CTJOWGoSdJyg1DT5KUGy1yR5aIWAqsAzYBG1NKVRGxLzAF6AUsBc5KKb3REvVJktqnlhzpfTKlVJFSqsreXwlMTyn1A6Zn7yVJKpjWtHtzJDApm54EnNqCtUiS2qGWCr0EPBwR8yJiTNa2X0ppRTb9N2C/lilNktRetdRTFo5KKS2PiB7AHyNicf2ZKaUUEamhFbOQHANw0EEHFb9SSVK70SIjvZTS8uznKmAqcDiwMiL2B8h+rtrOuhNSSlUpparu3bs3V8mSpHag2UMvIj4SEXvVTgMnAM8B04DR2WKjgfubuzZJUvvWErs39wOmRkTt5/9PSul/I+Jp4O6I+ArwCnBWC9QmSWrHmj30UkovA+UNtK8Bjm/ueiRJ+dGaLlmQJKmoDD1JUm4YepKk3DD0JEm5YehJknLD0JMk5YahJ0nKDUNPkpQbhp4kKTcMPUlSbhh6kqTcMPQkSbnRUg+RlSQAKsdOLnifU/cqeJdqJxzpSZJyw9CTJOWGoSdJyg1DT5KUG4aeJCk3DD1JUm4YepKk3PA6PUlqhKG3DS14n7MumVXwPrVjjvQkSblh6EmScsPQkyTlhqEnScoNQ0+SlBuGniQpNww9SVJuGHqSpNzw4nRJEgCvfm9gwfs86JoFBe9zVzjSkyTlhqEnScqNVhd6ETE8Il6IiCURcWVL1yNJaj9a1TG9iCgBfgZ8GlgGPB0R01JKC3emv8qxkwtZHgDzbvxSwfuUJDWP1jbSOxxYklJ6OaX0PnAXMLKFa5IktROtaqQHHAC8Vu/9MuCIFqql2fjIEklNVYw9WVP3KniXrU6klFq6hjoR8TlgeErpq9n7c4EjUkoX11tmDDAme3sw8EKzF9o43YC/t3QRbZDbbee57XaO223ntObt9i8ppe4NzWhtI73lwIH13vfM2uqklCYAE5qzqJ0REXNTSlUtXUdb43bbeW67neN22zltdbu1tmN6TwP9IqJ3ROwOnA1Ma+GaJEntRKsa6aWUNkbExcBDQAkwMaX0fAuXJUlqJ1pV6AGklB4EHmzpOgqg1e+CbaXcbjvPbbdz3G47p01ut1Z1IoskScXU2o7pSZJUNIbeLvqw26ZFRGlETMnmz4mIXs1fZesTERMjYlVEPLed+RER47Lt9mxEHNbcNbY2EXFgRDwSEQsj4vmIuLSBZdxuW4mIsoh4KiLmZ9vtugaW8fd0OyKiJCL+X0T8voF5bW67GXq7oN5t004C+gOfj4j+Wy32FeCNlNK/ArcA/9m8VbZavwSG72D+SUC/7DUG+Hkz1NTabQSuSCn1B4YAX2/g75vbbVvvAcellMqBCmB4RAzZahl/T7fvUmDRdua1ue1m6O2axtw2bSQwKZu+Fzg+IqIZa2yVUkqPAq/vYJGRwORU40mgc0Ts3zzVtU4ppRUppWey6XXU/EN0wFaLud22km2L9dnbjtlr65MZ/D1tQET0BE4Bbt/OIm1uuxl6u6ah26Zt/Y9Q3TIppY3AWqBrs1TXtjVm2+ZWthvpUGDOVrPcbg3IdtFVA6uAP6aUtrvd/D3dwq3At4DN25nf5raboSe1MRHRCbgP+EZK6a2WrqctSCltSilVUHOXp8Mj4uMtXVNrFxGfAVallOa1dC2FZOjtmg+9bVr9ZSJiN2AfYE2zVNe2NWbb5k5EdKQm8O5MKf22gUXcbjuQUnoTeIRtjyf7e7qtocCIiFhKzaGb4yLi11st0+a2m6G3axpz27RpwOhs+nPAjOTFkY0xDfhSdjbiEGBtSmlFSxfVkrJjJXcAi1JKP97OYm63rURE94jonE3vQc3zOhdvtZi/p1tJKX0npdQzpdSLmn/bZqSUvrjVYm1uu7W6O7K0Jdu7bVpEfA+Ym1KaRs0/Ur+KiCXUnLhxdstV3HpExG+AYUC3iFgGfJeaEwxIKY2n5q48JwNLgHeA81um0lZlKHAusCA7PgXw78BB4Hbbgf2BSdnZ1h2Au1NKv/f3dOe09e3mHVkkSbnh7k1JUm4YepKk3DD0JEm5YehJknLD0JMk5YaXLEgFEhFdgenZ238CNgGrs/eHZ/dnbY469gR+AQwCAniTmouxdwPOSSn9V3PUIbVGXrIgFUFEXAusTynd1AyftVt238Pa998BuqeULs/eHwwspeZ6td+nlLwFl3LL3ZtSEUXELyPic/Xer89+DouIP0fE/RHxckT8MCK+kD33bUFE9M2W6xURM7Jn402PiIPq9Ts+IuYAP9rqY/en3q3HUkovpJTeA34I9I2I6oi4MSI6ZX0+k31m3RNCIuL/RM1zIh+PiN9ExDez9r4R8b8RMS8iHouIjxVp00lF4e5NqeWUA4dQcyeLl4HbU0qHZw+HvQT4BnAbMCmlNCkivgyMA07N1u8JHJlS2rRVvxOBhzuAXnMAAAHuSURBVLOwnZ6t/yJwJfDx7MbLtfdKPC2l9FZEdAOejIhpQBVwRlZfR+AZoPamwxOAC1NKL0bEEcB/AccVdrNIxWPoSS3n6dr7YkbES8DDWfsC4JPZ9CeA07PpX7HlqO6eBgKPlFJ1RPQBTgA+BTwdEZ8A3t1q0QB+EBHHUPPomAOA/ai53dn9KaUNwIaIeCCrsRNwJHBPvUemle7MF5daiqEnFddGssMIEdEB2L3evPfqTW+u934zjfvdfHt7M7KHpv4W+G1EbKbmfpz3bbXYF4DuQGVK6YPsbvplO/i8DsCbtSNFqS3ymJ5UXEuBymx6BNlNtZtgNv+4ie8XgMc+bIWIGBoRXbLp3YH+wCvAOmCveovuQ83z0j6IiE8C/5K1zwI+GxFl2ejuMwDZs/v+GhFnZn1HRJQ38ftILcqRnlRcvwDuj4j5wP+yg9HZdlwC/HdEjKXm8ofGPDWhL/Dz7FFEHYD/C9yXUkoRMSsingP+APwn8EBELADmkj1uJ6X0dHZs71lgJTW7W9dmfX8h6/tqagL8LmB+E7+T1GK8ZEHSNiKiU0ppfXbN36PAmJTSMy1dl7SrHOlJasiEiOhPzTG+SQae2gtHepKk3PBEFklSbhh6kqTcMPQkSblh6EmScsPQkyTlhqEnScqN/w9iM0jGVYvGPwAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 1, figsize=(7,5))\n",
        "sns.countplot(x=\"tumor_stage\", hue=\"death_from_cancer\", ax=ax, data=clinical)\n",
        "ax.set_xlabel(\"Tumor Stage\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "JLKhnCfaqohi",
      "metadata": {
        "id": "JLKhnCfaqohi"
      },
      "source": [
        "#### Tumor Size\n",
        "From the charts below we can say that the every tumor sizes kills! But when it's bigger, it will kill more. Also, tumors with bigger sizes are probable to be in stages 3 that considering the results in the section above, they will be more dangerous. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UoOyp0XyrBod",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "UoOyp0XyrBod",
        "outputId": "4519f434-08e5-4e36-e9fd-b1e0693831a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Survival vs Size\n",
        "fig, ax = plt.subplots(1, 3, figsize=(15,5))\n",
        "\n",
        "# Distibution of dead and alive according to size\n",
        "sns.histplot(clinical.loc[:,\"tumor_size\"][clinical[\"overall_survival\"]==1], \n",
        "            color='b', ax=ax[0], label='alive')\n",
        "sns.histplot(clinical.loc[:,\"tumor_size\"][clinical[\"overall_survival\"]==0], \n",
        "            color='r', ax=ax[0], label='dead')\n",
        "ax[0].set_xlabel(\"Tumor Size\")\n",
        "ax[0].legend()\n",
        "\n",
        "# Box plot of the death vause vs size\n",
        "sns.boxplot(x='death_from_cancer', y='tumor_size', orient='v', \n",
        "            data=clinical, ax=ax[1])\n",
        "ax[1].set_ylabel(\"Tumor Size\")\n",
        "ax[1].set_xlabel(\"Cause of Death\")\n",
        "\n",
        "sns.boxplot(x='tumor_stage', y='tumor_size', orient='v', \n",
        "            data=clinical, ax=ax[2])\n",
        "ax[2].set_ylabel(\"Tumor Size\")\n",
        "ax[2].set_xlabel(\"Tumor Stage\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "EQao_jjouWc4",
      "metadata": {
        "id": "EQao_jjouWc4"
      },
      "source": [
        "#### Therapy Methods\n",
        "There are three therapy methods mentioned in the data set\n",
        "  1. `chemotherapy`                   \n",
        "  2. `radio_therapy`\n",
        "  3. `hormone_therapy`\n",
        "\n",
        "From the charts below we can find out:\n",
        "  * Every patient tried at least one type of therapy\n",
        "  * radio therapy and hormone therapies were more effective and \n",
        "      was used together more frequently."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "V8hkpU-Vu-NT",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "V8hkpU-Vu-NT",
        "outputId": "cc82b00b-a2dd-45bb-c013-f72940befcc2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(1, 3, figsize=(15,5), sharey=True)\n",
        "sns.countplot(x=\"chemotherapy\", hue=\"death_from_cancer\", ax=ax[0], data=clinical)\n",
        "ax[0].set_xlabel(\"Chemotherapy\")\n",
        "\n",
        "sns.countplot(x=\"radio_therapy\", hue=\"death_from_cancer\", ax=ax[1], data=clinical)\n",
        "ax[1].set_xlabel(\"Radio Therapy\")\n",
        "\n",
        "sns.countplot(x=\"hormone_therapy\", hue=\"death_from_cancer\", ax=ax[2], data=clinical)\n",
        "ax[2].set_xlabel(\"Hormone Therapy\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fGv60j964Nqz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "id": "fGv60j964Nqz",
        "outputId": "deac72a3-a02b-44ae-d48a-395dcdeffc8f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 384x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "C = clinical[\"chemotherapy\"].astype(bool)\n",
        "H = clinical[\"radio_therapy\"].astype(bool)\n",
        "R = clinical[\"hormone_therapy\"].astype(bool)\n",
        "\n",
        "C_i = C[C].index\n",
        "H_i = H[H].index\n",
        "R_i = R[R].index\n",
        "\n",
        "therapies = from_contents({'chem': C_i, 'Hor':H_i, 'Rad':R_i})\n",
        "UpSet(therapies, subset_size='count').plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "du2IKBXK7Vnt",
      "metadata": {
        "id": "du2IKBXK7Vnt"
      },
      "source": [
        "#### Survival Time\n",
        "From the charts below we can findout that as you survive the disease, it is more probable that you be cured in the future."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PUu-PdqW7U3p",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "PUu-PdqW7U3p",
        "outputId": "96ed9d1e-8526-4783-9935-625b4efe9c3b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Survival vs Time\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n",
        "\n",
        "# Distibution of dead and alive according to Time\n",
        "sns.histplot(clinical.loc[:,\"overall_survival_months\"][clinical[\"overall_survival\"]==1], \n",
        "            color='b', ax=ax[0], label='alive')\n",
        "sns.histplot(clinical.loc[:,\"overall_survival_months\"][clinical[\"overall_survival\"]==0], \n",
        "            color='r', ax=ax[0], label='dead')\n",
        "ax[0].set_xlabel(\"Overal Survival Month\")\n",
        "ax[0].legend()\n",
        "\n",
        "# Box plot of the death vause vs Time\n",
        "sns.boxplot(x='death_from_cancer', y='overall_survival_months', orient='v', \n",
        "            data=clinical, ax=ax[1])\n",
        "ax[1].set_ylabel(\"Overal Survival Month\")\n",
        "ax[1].set_xlabel(\"Cause of Death\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "-kQrSZNo8OSd",
      "metadata": {
        "id": "-kQrSZNo8OSd"
      },
      "source": [
        "#### Breast Surgery Type\n",
        "Although conserving breast surgeries are more probable to cure, they are not performed on big tumors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "jyJcUsCA8RxE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "jyJcUsCA8RxE",
        "outputId": "d98db1f9-3394-4c72-f0ae-2cd4ecbddfd8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(12,5))\n",
        "\n",
        "sns.countplot(x=\"type_of_breast_surgery\", hue=\"death_from_cancer\", ax=ax1, data=clinical)\n",
        "ax1.set_xlabel(\"Breast Surgery Type\")\n",
        "\n",
        "# Breast Surgery vs Tumor Size\n",
        "sns.boxplot(x='type_of_breast_surgery', y='tumor_size', data=clinical, \n",
        "            orient='v', ax=ax2)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Ox8OqL84_JxT",
      "metadata": {
        "id": "Ox8OqL84_JxT"
      },
      "source": [
        "### Correlations\n",
        "\n",
        "From the chart below we can see that the effect of multiple features on overal survival is almost nothing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cx5sBtKl_MZg",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 913
        },
        "id": "cx5sBtKl_MZg",
        "outputId": "49cf816e-2a79-4186-fe42-8900bb4c1f23"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 936x936 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "f, ax = plt.subplots(figsize=(13, 13))\n",
        "df = clinical.drop(['death_from_cancer', 'patient_id'], axis=1)\n",
        "\n",
        "object_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
        "for col in object_cols:\n",
        "  df[col] = LabelEncoder().fit_transform(df[col].values)\n",
        "corr = df.corr()\n",
        "sns.heatmap(round(corr,2), annot=True, ax=ax, cmap=\"coolwarm\", fmt='.1f',\n",
        "                 linewidths=.05)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3mXzzARHHf2T",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mXzzARHHf2T",
        "outputId": "ce19a4c1-4aa6-4056-fe0e-7f9ee26c7a6a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Positive Correlations on Survival:\n",
            "overall_survival                  1.000000\n",
            "overall_survival_months           0.384467\n",
            "inferred_menopausal_state         0.170915\n",
            "radio_therapy                     0.112083\n",
            "pam50_+_claudin-low_subtype       0.063790\n",
            "chemotherapy                      0.045625\n",
            "tumor_other_histologic_subtype    0.033787\n",
            "cancer_type                       0.026900\n",
            "her2_status_measured_by_snp6      0.024611\n",
            "pr_status                         0.022425\n",
            "Name: overall_survival, dtype: float64\n",
            "---------------------------------------------------\n",
            "Negative Correlations on Survival:\n",
            "mutation_count                  -0.077741\n",
            "integrative_cluster             -0.082834\n",
            "neoplasm_histologic_grade       -0.086601\n",
            "nottingham_prognostic_index     -0.138000\n",
            "cohort                          -0.149645\n",
            "tumor_size                      -0.164361\n",
            "lymph_nodes_examined_positive   -0.164498\n",
            "type_of_breast_surgery          -0.182882\n",
            "tumor_stage                     -0.188790\n",
            "age_at_diagnosis                -0.303666\n",
            "Name: overall_survival, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "surv_corr_sorted = corr[\"overall_survival\"].sort_values(ascending = False)\n",
        "\n",
        "print(\"Positive Correlations on Survival:\")\n",
        "print(surv_corr_sorted.head(10))\n",
        "print(\"---------------------------------------------------\")\n",
        "print(\"Negative Correlations on Survival:\")\n",
        "print(surv_corr_sorted.tail(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "R3PFdbKMgzA4",
      "metadata": {
        "id": "R3PFdbKMgzA4"
      },
      "source": [
        "### Gene Expression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nmjMdcAJg1U1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "nmjMdcAJg1U1",
        "outputId": "f82103b0-9781-429a-93d1-18b24d32f442"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-112db74c-b47f-4809-8fa9-865e61159f14\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>brca1</th>\n",
              "      <th>brca2</th>\n",
              "      <th>palb2</th>\n",
              "      <th>pten</th>\n",
              "      <th>tp53</th>\n",
              "      <th>atm</th>\n",
              "      <th>cdh1</th>\n",
              "      <th>chek2</th>\n",
              "      <th>nbn</th>\n",
              "      <th>nf1</th>\n",
              "      <th>...</th>\n",
              "      <th>srd5a1</th>\n",
              "      <th>srd5a2</th>\n",
              "      <th>srd5a3</th>\n",
              "      <th>st7</th>\n",
              "      <th>star</th>\n",
              "      <th>tnk2</th>\n",
              "      <th>tulp4</th>\n",
              "      <th>ugt2b15</th>\n",
              "      <th>ugt2b17</th>\n",
              "      <th>ugt2b7</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1904.000000</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1904.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1904.000000</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1.904000e+03</td>\n",
              "      <td>1904.000000</td>\n",
              "      <td>1.904000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-6.302521e-07</td>\n",
              "      <td>-4.201681e-07</td>\n",
              "      <td>3.676471e-07</td>\n",
              "      <td>-8.928571e-07</td>\n",
              "      <td>6.302521e-07</td>\n",
              "      <td>-2.100840e-07</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>7.463684e-18</td>\n",
              "      <td>-3.676471e-07</td>\n",
              "      <td>-0.000001</td>\n",
              "      <td>...</td>\n",
              "      <td>4.726891e-07</td>\n",
              "      <td>-3.676471e-07</td>\n",
              "      <td>-9.453782e-07</td>\n",
              "      <td>-1.050420e-07</td>\n",
              "      <td>-0.000002</td>\n",
              "      <td>3.676471e-07</td>\n",
              "      <td>4.726891e-07</td>\n",
              "      <td>7.878151e-07</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.731842e-18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000263</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000262</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000262</td>\n",
              "      <td>1.000264e+00</td>\n",
              "      <td>1.000262e+00</td>\n",
              "      <td>1.000263e+00</td>\n",
              "      <td>1.000262</td>\n",
              "      <td>1.000262e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-2.444400e+00</td>\n",
              "      <td>-2.783500e+00</td>\n",
              "      <td>-4.384600e+00</td>\n",
              "      <td>-5.932800e+00</td>\n",
              "      <td>-2.481200e+00</td>\n",
              "      <td>-3.170500e+00</td>\n",
              "      <td>-3.323700</td>\n",
              "      <td>-2.688700e+00</td>\n",
              "      <td>-3.689800e+00</td>\n",
              "      <td>-2.968600</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.120800e+00</td>\n",
              "      <td>-3.364800e+00</td>\n",
              "      <td>-2.719400e+00</td>\n",
              "      <td>-4.982700e+00</td>\n",
              "      <td>-2.981700</td>\n",
              "      <td>-3.833300e+00</td>\n",
              "      <td>-3.609300e+00</td>\n",
              "      <td>-1.166900e+00</td>\n",
              "      <td>-2.112600</td>\n",
              "      <td>-1.051600e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-7.198500e-01</td>\n",
              "      <td>-6.994750e-01</td>\n",
              "      <td>-6.798250e-01</td>\n",
              "      <td>-5.586500e-01</td>\n",
              "      <td>-6.661500e-01</td>\n",
              "      <td>-6.602250e-01</td>\n",
              "      <td>-0.458600</td>\n",
              "      <td>-7.385500e-01</td>\n",
              "      <td>-6.667750e-01</td>\n",
              "      <td>-0.683925</td>\n",
              "      <td>...</td>\n",
              "      <td>-6.188500e-01</td>\n",
              "      <td>-6.104750e-01</td>\n",
              "      <td>-6.741750e-01</td>\n",
              "      <td>-6.136750e-01</td>\n",
              "      <td>-0.632900</td>\n",
              "      <td>-6.664750e-01</td>\n",
              "      <td>-7.102000e-01</td>\n",
              "      <td>-5.058250e-01</td>\n",
              "      <td>-0.476200</td>\n",
              "      <td>-7.260000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-1.244500e-01</td>\n",
              "      <td>-5.820000e-02</td>\n",
              "      <td>-2.210000e-02</td>\n",
              "      <td>1.098500e-01</td>\n",
              "      <td>-3.010000e-02</td>\n",
              "      <td>-4.200000e-03</td>\n",
              "      <td>0.115550</td>\n",
              "      <td>-1.311000e-01</td>\n",
              "      <td>-1.445000e-02</td>\n",
              "      <td>-0.054800</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.456500e-01</td>\n",
              "      <td>-4.690000e-02</td>\n",
              "      <td>-1.422500e-01</td>\n",
              "      <td>-5.175000e-02</td>\n",
              "      <td>-0.026650</td>\n",
              "      <td>7.000000e-04</td>\n",
              "      <td>-2.980000e-02</td>\n",
              "      <td>-2.885500e-01</td>\n",
              "      <td>-0.133400</td>\n",
              "      <td>-4.248000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.532250e-01</td>\n",
              "      <td>6.290000e-01</td>\n",
              "      <td>6.400000e-01</td>\n",
              "      <td>6.426250e-01</td>\n",
              "      <td>6.447500e-01</td>\n",
              "      <td>6.901500e-01</td>\n",
              "      <td>0.649800</td>\n",
              "      <td>5.431250e-01</td>\n",
              "      <td>6.387250e-01</td>\n",
              "      <td>0.668925</td>\n",
              "      <td>...</td>\n",
              "      <td>3.306000e-01</td>\n",
              "      <td>5.144500e-01</td>\n",
              "      <td>5.146000e-01</td>\n",
              "      <td>5.787750e-01</td>\n",
              "      <td>0.590350</td>\n",
              "      <td>6.429000e-01</td>\n",
              "      <td>5.957250e-01</td>\n",
              "      <td>6.022500e-02</td>\n",
              "      <td>0.270375</td>\n",
              "      <td>4.284000e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4.554200e+00</td>\n",
              "      <td>3.737900e+00</td>\n",
              "      <td>4.615000e+00</td>\n",
              "      <td>3.254100e+00</td>\n",
              "      <td>4.289800e+00</td>\n",
              "      <td>3.824200e+00</td>\n",
              "      <td>2.820900</td>\n",
              "      <td>4.538200e+00</td>\n",
              "      <td>5.371300e+00</td>\n",
              "      <td>6.178600</td>\n",
              "      <td>...</td>\n",
              "      <td>6.534900e+00</td>\n",
              "      <td>1.027030e+01</td>\n",
              "      <td>6.329000e+00</td>\n",
              "      <td>4.571300e+00</td>\n",
              "      <td>12.742300</td>\n",
              "      <td>3.938800e+00</td>\n",
              "      <td>3.833400e+00</td>\n",
              "      <td>1.088490e+01</td>\n",
              "      <td>12.643900</td>\n",
              "      <td>3.284400e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 489 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-112db74c-b47f-4809-8fa9-865e61159f14')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-112db74c-b47f-4809-8fa9-865e61159f14 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-112db74c-b47f-4809-8fa9-865e61159f14');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              brca1         brca2         palb2          pten          tp53  \\\n",
              "count  1.904000e+03  1.904000e+03  1.904000e+03  1.904000e+03  1.904000e+03   \n",
              "mean  -6.302521e-07 -4.201681e-07  3.676471e-07 -8.928571e-07  6.302521e-07   \n",
              "std    1.000262e+00  1.000262e+00  1.000263e+00  1.000263e+00  1.000263e+00   \n",
              "min   -2.444400e+00 -2.783500e+00 -4.384600e+00 -5.932800e+00 -2.481200e+00   \n",
              "25%   -7.198500e-01 -6.994750e-01 -6.798250e-01 -5.586500e-01 -6.661500e-01   \n",
              "50%   -1.244500e-01 -5.820000e-02 -2.210000e-02  1.098500e-01 -3.010000e-02   \n",
              "75%    5.532250e-01  6.290000e-01  6.400000e-01  6.426250e-01  6.447500e-01   \n",
              "max    4.554200e+00  3.737900e+00  4.615000e+00  3.254100e+00  4.289800e+00   \n",
              "\n",
              "                atm         cdh1         chek2           nbn          nf1  \\\n",
              "count  1.904000e+03  1904.000000  1.904000e+03  1.904000e+03  1904.000000   \n",
              "mean  -2.100840e-07     0.000002  7.463684e-18 -3.676471e-07    -0.000001   \n",
              "std    1.000262e+00     1.000263  1.000262e+00  1.000263e+00     1.000262   \n",
              "min   -3.170500e+00    -3.323700 -2.688700e+00 -3.689800e+00    -2.968600   \n",
              "25%   -6.602250e-01    -0.458600 -7.385500e-01 -6.667750e-01    -0.683925   \n",
              "50%   -4.200000e-03     0.115550 -1.311000e-01 -1.445000e-02    -0.054800   \n",
              "75%    6.901500e-01     0.649800  5.431250e-01  6.387250e-01     0.668925   \n",
              "max    3.824200e+00     2.820900  4.538200e+00  5.371300e+00     6.178600   \n",
              "\n",
              "       ...        srd5a1        srd5a2        srd5a3           st7  \\\n",
              "count  ...  1.904000e+03  1.904000e+03  1.904000e+03  1.904000e+03   \n",
              "mean   ...  4.726891e-07 -3.676471e-07 -9.453782e-07 -1.050420e-07   \n",
              "std    ...  1.000263e+00  1.000262e+00  1.000262e+00  1.000263e+00   \n",
              "min    ... -2.120800e+00 -3.364800e+00 -2.719400e+00 -4.982700e+00   \n",
              "25%    ... -6.188500e-01 -6.104750e-01 -6.741750e-01 -6.136750e-01   \n",
              "50%    ... -2.456500e-01 -4.690000e-02 -1.422500e-01 -5.175000e-02   \n",
              "75%    ...  3.306000e-01  5.144500e-01  5.146000e-01  5.787750e-01   \n",
              "max    ...  6.534900e+00  1.027030e+01  6.329000e+00  4.571300e+00   \n",
              "\n",
              "              star          tnk2         tulp4       ugt2b15      ugt2b17  \\\n",
              "count  1904.000000  1.904000e+03  1.904000e+03  1.904000e+03  1904.000000   \n",
              "mean     -0.000002  3.676471e-07  4.726891e-07  7.878151e-07     0.000000   \n",
              "std       1.000262  1.000264e+00  1.000262e+00  1.000263e+00     1.000262   \n",
              "min      -2.981700 -3.833300e+00 -3.609300e+00 -1.166900e+00    -2.112600   \n",
              "25%      -0.632900 -6.664750e-01 -7.102000e-01 -5.058250e-01    -0.476200   \n",
              "50%      -0.026650  7.000000e-04 -2.980000e-02 -2.885500e-01    -0.133400   \n",
              "75%       0.590350  6.429000e-01  5.957250e-01  6.022500e-02     0.270375   \n",
              "max      12.742300  3.938800e+00  3.833400e+00  1.088490e+01    12.643900   \n",
              "\n",
              "             ugt2b7  \n",
              "count  1.904000e+03  \n",
              "mean   3.731842e-18  \n",
              "std    1.000262e+00  \n",
              "min   -1.051600e+00  \n",
              "25%   -7.260000e-01  \n",
              "50%   -4.248000e-01  \n",
              "75%    4.284000e-01  \n",
              "max    3.284400e+00  \n",
              "\n",
              "[8 rows x 489 columns]"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gene_expr.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ks5TPPcNiR6u",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "id": "ks5TPPcNiR6u",
        "outputId": "3e60aae8-2bcc-4b06-a46b-203bbe340aaf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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dup5ON9P1dHoX8P8AtIkh3wh8vv39/sxkkZIkSUn+XZIbknw5yQeT7JXksCRXt1tqf6j1uibJnm19U9u/drTRS6vLUodRSBIAVfXinexaUE+nqnoP8J4eQ5MkSStAkoOB3wGeUFXfTfJh4CS6odrnVNVFSf4YOI3uttqnAXdX1WOTnAS8BXjRiMKXVh17NkiSJEmaFGuAvZOsAR4ObAOOprszHnS32n5+Wz6hrdP2H5MkQ4xVWtXs2SBJkiRp7FXV1iR/CHwd+C7wKeBa4J6qmrn/9OBtsx+4pXZV7UhyL3AAcOfgccftdtqLMbX35NxOuw+r7XbOk1pfkw2SJEmSxl6bQPoE4DDgHuDPgGOXetxxu532Ymxct4MTV9HtjVfb7Zwntb4Oo5AkSZI0CX4V+Ieq+mZV/QD4CPB0YL82rAJ+fDttGLjVdtu/L/Ct4YYsrV4mGyRJkiRNgq8DT03y8Db3wjHAjcCVwAtamdm32j6lLb8AuKJNUi1pCEw2SJIkSRp7VXU13USPXwCup/stcx7wOuA1STbRzclwfnvK+cABbftrgDOGHrS0ijlngyRJkqSJUFWvB14/a/PNwJPnKHs/8MJhxCXpJ9mzQZIkSZIk9cqeDZIkSZI04db2dGeLzWcd38txJHs2SJIkSZKkXplskCRJkiRJvTLZIEmSJEmSemWyQZIkSZIk9cpkgyRJkiRJ6pXJBkmSJEmS1CuTDZIkSZIkqVcmGyRJkiRJUq9MNkiSpLGQZI8kX0zy8bZ+WJKrk2xK8qEkD2vb92zrm9r+taOMW5Ik/SSTDZKWVZLHJblu4O8fk7w6yRuSbB3YftzAc85sPyK+muTXRhm/pKF6FXDTwPpbgHOq6rHA3cBpbftpwN1t+zmtnCRJGiNrRh2ApJWtqr4KHAndVUtgK/BR4GV0PyL+cLB8kicAJwE/DzwG+Ksk/6yqfjjUwCUNVZJDgOOBNwOvSRLgaOBftSIXAG8A3gmc0JYBLgbekSRVVcOMeTVbe8alvR1r81nH93YsSdL4MNkgaZiOAf6+qm7pfkfM6QTgoqr6HvAPSTYBTwY+O6QYJY3G24DXAo9s6wcA91TVjra+BTi4LR8M3ApQVTuS3NvK3zn7oElOB04HmJqaYnp6erni36nt27cv6XU3rtux+0I9mtp7uK+51H+Tpb6/wzZp8UrSYplsmADXb72XU3u4guCVA42Bk4APDqy/IslLgWuAjVV1N92PiKsGygz+wHjAKH9AzHUSvtiT80k54VwNJ8crvY7jXL8kzwXuqKprk2zo89hVdR5wHsD69etrw4ZeDz8v09PTLOV1+zgHWIiN63Zw9vXDO0XcfPKGJT1/qe/vsE1avJK0WCYbJA1Fm9jtecCZbdM7gTcC1R7PBn57vscb5Q+IuU78F3tyvtST7GFZDSfHK72OY16/pwPPa3O37AU8CjgX2C/Jmta74RC6YVi0x0OBLUnWAPsC3xp+2JIkaWecIFLSsDwH+EJV3Q5QVbdX1Q+r6kfAu+iGSsCPf0TMGPyBIWkFqqozq+qQqlpL1wPqiqo6GbgSeEErdgrwsbZ8SVun7b/C+Rqk1SHJfkkuTvKVJDcleVqS/ZNcluRr7fHRrWySvL1NOv2lJEeNOn5pNTHZIGlYXszAEIokBw3s+3Xgy235EuCkdmu7w4DDgc8NLUpJ4+R1dJNFbqKbk+H8tv184IC2/TXAGSOKT9LwnQt8sqoeDxxBdwebM4DLq+pw4HJ+3CY8h+484nC6oZfvHH640urlMApJyy7JPsCzgH8zsPm/JTmSbhjF5pl9VXVDkg8DNwI7gJd7Jwpp9aiqaWC6Ld/Mj3s9DZa5H3jhUAOTNHJJ9gWeCZwKUFXfB76f5ARgQyt2AV0b8jq6SacvbD2frmq9Ig6qqm1DDl1alZacbGi3srsG2FpVz21XIi+iuwJxLfCSqvp+kj2BC4En0Y2rfFFVbV7q60saf1X1Hbo2YXDbS3ZR/s10t7+TJEmacRjwTeC9SY6g+63xKmBqIIFwGzDVlh+4c00zM+m0yQZpCPro2fAquu5Lj2rrbwHOqaqLkvwxcBpdl6XTgLur6rFJTmrlXtTD60uSJEla+dYARwGvrKqrk5zLrGFUVVVJFjSHy7jd4Wox+rxl7bjeuWjQON9haTlMan2XlGxIcghwPN0VyNckCXA08K9akQuAN9AlG05oywAXA+9IEid0kiRJkjQPW4AtVXV1W7+YLtlw+8zwiDYn1B1t/7wmnR63O1wtRp+3rJ2EO2WN+R2Wejep9V3qJ/JtwGuBR7b1A4B72i2q4MddlWCgG1NV7Uhybyt/5+yD9p1dHEUmqK/MIvSXqRxVNmxSM3Ew2bFLkiStJFV1W5Jbkzyuqr4KHEM3x9ONdHeoOYufvHPNK5JcBDwFuNf5GqThWXSyIclzgTuq6tokG/oLqf/s4igyQX1lKaG/TOWospSTmomDyY5dkiRpBXol8P4kDwNuBl5Gd4e9Dyc5DbgFOLGV/QRwHLAJuK+VlTQkS/kF+3TgeUmOA/aim7PhXGC/JGta74bBrkoz3Zi2JFkD7Es3UaQkSZIk7VZVXQesn2PXMXOULeDlyx6UpDk9ZLFPrKozq+qQqloLnARcUVUnA1cCL2jFZndjOqUtv6CVd74GSZIkSZJWmEUnG3bhdXSTRW6im5Ph/Lb9fOCAtv01zJo5VpIkSZIkrQy9TFlaVdPAdFu+GXjyHGXuB17Yx+tJkiRJkqTx1c/9USRpzK3tcdJWSZIkSbu2HMMoJEmSJEnSKmbPhlm8+ilJkiRJ0tLYs0GSJEmSJPXKZIMkSZIkSeqVyQZJkiRJktQrkw2SJEmSJKlXJhskSZIkSVKvTDZIkiRJkqRemWyQJEmSJEm9Mtkgadkl2Zzk+iTXJbmmbds/yWVJvtYeH922J8nbk2xK8qUkR402ekmSJEkLZbJB0rD8SlUdWVXr2/oZwOVVdThweVsHeA5wePs7HXjn0COVJEmStCQmGySNygnABW35AuD5A9svrM5VwH5JDhpFgJIkSZIWZ82oA5C0KhTwqSQF/ElVnQdMVdW2tv82YKotHwzcOvDcLW3btoFtJDmdrucDU1NTTE9P7zKAjet2LLEKuza19+JeY3dxj4vt27dPTKyLtdLruNLrJ2n1SLIHcA2wtaqem+Qw4CLgAOBa4CVV9f0kewIXAk8CvgW8qKo2jyhsadUx2SBpGJ5RVVuT/DRwWZKvDO6sqmqJiHlrCYvzANavX18bNmzYZflTz7h0YREv0MZ1Ozj7+oU3qZtP3tB/MMtgenqa3b3Hk26l13Gl10/SqvIq4CbgUW39LcA5VXVRkj8GTqMbhnkacHdVPTbJSa3ci0YRsLQaOYxC0rKrqq3t8Q7go8CTgdtnhke0xzta8a3AoQNPP6RtkyRJq1ySQ4DjgXe39QBHAxe3IrOHZs4M2bwYOKaVlzQEJhskLask+yR55Mwy8Gzgy8AlwCmt2CnAx9ryJcBL210pngrcOzDcQpIkrW5vA14L/KitHwDcU1UzYxlnhl/CwNDMtv/eVl7SEDiMQtJymwI+2i4krAE+UFWfTPJ54MNJTgNuAU5s5T8BHAdsAu4DXjb8kCVJ0rhJ8lzgjqq6NsmGHo+7oHmg+tTXnFKLnTtqLpMwv89qm4doUutrskHSsqqqm4Ej5tj+LeCYObYX8PIhhCZJkibL04HnJTkO2ItuzoZz6e5ctab1XhgcfjkzNHNLkjXAvnQTRT7IQueB6lNfc0otdu6ouUzCfFKrbR6iSa2vwygkSZIkjb2qOrOqDqmqtcBJwBVVdTJwJfCCVmz20MyZIZsvaOUXNCG1pMUz2SBJkiRpkr0OeE2STXRzMpzftp8PHNC2vwY4Y0TxSauSwygkSZIkTZSqmgam2/LNdHe6ml3mfuCFQw1M0gPs2SBJkiRJknplskGSJI1UkkOTXJnkxiQ3JHlV275/ksuSfK09PrptT5K3J9mU5EtJjhptDSRJ0mwmGyRJ0qjtADZW1ROApwIvT/IEuvHVl1fV4cDl/Hi89XOAw9vf6cA7hx+yJEnaFZMNkiRppKpqW1V9oS1/G7gJOBg4AbigFbsAeH5bPgG4sDpX0d327qAhhy1JknbBCSIlSdLYSLIWeCJwNTBVVdvartuAqbZ8MHDrwNO2tG3bmCXJ6XS9H5iammJ6eno5wt6l7du3L+l1N67b0V8w8zC193Bfc6n/Jkt9f4dt0uKVpMVadLIhyaHAhXT/8RdwXlWdm2R/4EPAWmAzcGJV3Z0kwLnAccB9wKkzVzE0HGvPuLS3Y20+6/jejiVJEkCSRwB/Dry6qv6xO3XoVFUlqYUes6rOA84DWL9+fW3YsKGnaOdvenqapbzuqT3+/z0fG9ft4Ozrh3c9avPJG5b0/KW+v8M2afFK0mItZRiF4yslSVIvkjyULtHw/qr6SNt8+8zwiPZ4R9u+FTh04OmHtG2SJGlMLDrZ4PhKSZLUh9b78Xzgpqp668CuS4BT2vIpwMcGtr+03ZXiqcC9A8MtJEnSGOilj9y4j69cyNi4YY+LnI9hj52cj4X8m0zy2MRJjl2SJsjTgZcA1ye5rm37PeAs4MNJTgNuAU5s+z5BNyxzE93QzJcNN1xJkrQ7S042TML4yoWMjRv2uMj5GPbYyflYyPjKSR6bOMmxS9KkqKrPANnJ7mPmKF/Ay5c1KEmStCRLuvWl4yslSZIkSdJsi042OL5SkiRJkiTNZSl98x1fKUmSJEmSfsKikw2Or5QkSZIkSXMZr1kHJa04SQ4FLqS7M00B51XVuUneAPzfwDdb0d+rqk+055wJnAb8EPidqvrLoQcuSUuwdmDC6Y3rdozlBNSSJC0nkw2SltsOYGNVfSHJI4Frk1zW9p1TVX84WDjJE4CTgJ8HHgP8VZJ/VlU/HGrUkiRJkhZtSXejkKTdqaptVfWFtvxt4Cbg4F085QTgoqr6XlX9A908L09e/kglSdI4S3JokiuT3JjkhiSvatv3T3JZkq+1x0e37Uny9iSbknwpyVGjrYG0utizQdLQJFkLPBG4mm6S2VckeSlwDV3vh7vpEhFXDTxtC7tOTkiSJtjaJQ4xGRymsvms4/sISeNrZ70lTwUur6qzkpwBnAG8DngOcHj7ewrwzvYoaQhMNkgaiiSPAP4ceHVV/WOSdwJvpJvH4Y3A2cBvL+B4pwOnA0xNTTE9Pb3L8hvX7Vhc4PM0tffiXmN3cY+L7du3T0ysi7XS67jS6ydp5auqbcC2tvztJDO9JU8ANrRiFwDTdMmGE4AL20T1VyXZL8lB7TiSlpnJBknLLslD6RIN76+qjwBU1e0D+98FfLytbgUOHXj6IW3bg1TVecB5AOvXr68NGzbsMoblnpxt47odnH39wpvUzSdv6D+YZTA9Pc3u3uNJt9LruNLrJ2l1mdVbcmoggXAb3aTU0CUibh142kxvSZMN0hCYbJC0rJIEOB+4qareOrB98MrCrwNfbsuXAB9I8la6CSIPBz43xJAlSdIYm6O35AP7qqqS1AKPt6Dekn3qq+flYntYzmUSesGttt56k1pfkw2SltvTgZcA1ye5rm37PeDFSY6kG0axGfg3AFV1Q5IPAzfSjc18uXeikCRJMHdvSeD2mYsYSQ4C7mjbl6W3ZJ/66nm52B6Wc5mEXperrbfepNbXZIOkZVVVnwEyx65P7OI5bwbevGxBSZKkibOz3pJ0vSJPAc5qjx8b2P6KJBfRTQx5r/M1SMNjskGSJEnSJNhZb8mzgA8nOQ24BTix7fsEcBzdbbTvA1423HCl1c1kgyRJkqSxt4vekgDHzFG+gJcva1CSduohow5AkiRJkiStLCYbJEmSJElSr0w2SJIkSZKkXplskCRJkiRJvTLZIEmSJEmSeuXdKLQoa8+4dN5lN67bwam7KL/5rOP7CEmSJEmSNCbs2SBJkiRJknplskGSJEmSJPXKZIMkSZIkSeqVyQZJkiRJktQrkw2SJEmSJKlXK+JuFLu7M8Lu7oYgSaOykDu77Ip3dZEkSX3o69wEPD9Z7VZEskGTzR9bkiSpD55TaBj6/DEurWQOo5AkSZIkSb0y2SBJkiRJknplskGSJEmSJPVq6MmGJMcm+YWv7mUAACAASURBVGqSTUnOGPbrSxp/thOS5sO2QtLu2E5IozPUCSKT7AH8EfAsYAvw+SSXVNWNw4xDK5Mz564MthOS5mO52gonfpNWDs8ppNEa9t0ongxsqqqbAZJcBJwA+IWXNMN2YhFMtmkVsq3QsrFNXTFsJ6QRGnay4WDg1oH1LcBThhyDtFszJxkb1+3g1DG5yrWKTlZsJ0ZsrpPsUX8X+vz87+xHxKjr2BfbCmm8zG5zxqGtsZ3QsCxXb7Fx+B7BeH6XxilZOuxkw7wkOR04va1uT/LVpRzvd+BA4M4lBzYixj864xR73jLvoj+7jGGMjb7biaUap8/Kchh1/Rbw+V+0UdexL7t4rwbrtyraCRiPtmLSPlvGu7zGIV7PKR5sHNqJpRqHz9UwjUt9h3F+0oykvvOs307biWEnG7YChw6sH9K2PUhVnQec19eLJrmmqtb3dbxhM/7RmeTYJ9hI2omlWumflZVeP1j5dVyB9ZuYtmLS3nvjXV6TFu+Em5h2YqlW2+fK+k6GYd+N4vPA4UkOS/Iw4CTgkiHHIGm82U5Img/bCkm7YzshjdBQezZU1Y4krwD+EtgDeE9V3TDMGCSNN9sJSfNhWyFpd2wnpNEa+pwNVfUJ4BNDftmJ7haF8Y/SJMc+sUbUTizVSv+srPT6wcqv44qr3wS1FZP23hvv8pq0eCfaBLUTS7XaPlfWdwKkqkYdgyRJkiRJWkGGPWeDJEmSJEla4VZNsiHJHyT5SpIvJflokv1GHdPuJDk2yVeTbEpyxqjjWYgkhya5MsmNSW5I8qpRx7QYSfZI8sUkHx91LBpPk/w9na8km5Ncn+S6JNeMOp6lSvKeJHck+fLAtv2TXJbka+3x0aOMcal2Usc3JNna/h2vS3LcKGNcbSblPGSS2rRJPdfw3EJ9mqTvbF9W2nnJbCvpPGXVJBuAy4BfqKp/AfwdcOaI49mlJHsAfwQ8B3gC8OIkTxhtVAuyA9hYVU8Angq8fMLin/Eq4KZRB6HxtAK+pwvxK1V15CTedmkO7wOOnbXtDODyqjocuLytT7L38ZN1BDin/Tse2cYxa3jG/jxkAtu0ST3X8NxCvZjA72yfVtJ5yWzvY4Wcp6yaZENVfaqqdrTVq+juszvOngxsqqqbq+r7wEXACSOOad6qaltVfaEtf5vuP9WDRxvVwiQ5BDgeePeoY9HYmujv6WpVVZ8G7pq1+QTggrZ8AfD8oQbVs53UUSM0IechE9WmTeK5hucW6tlEfWc1PyvpPGXVJBtm+W3gf486iN04GLh1YH0LY/4f6M4kWQs8Ebh6tJEs2NuA1wI/GnUgGlsr5nu6GwV8Ksm1SU4fdTDLZKqqtrXl24CpUQazjF7RuvG/Z1K6YK5Q43oeMrFt2gSda3huoT5N7Hd2iVbDeclsE3mesqKSDUn+KsmX5/g7YaDMf6Drdvf+0UW6eiR5BPDnwKur6h9HHc98JXkucEdVXTvqWKQx8IyqOoqum+bLkzxz1AEtp+pu07QSb9X0TuDngCOBbcDZow1n5fE8ZDQm5VzDcwupN6vqvGS2STpPWTPqAPpUVb+6q/1JTgWeCxxT43/Pz63AoQPrh7RtEyPJQ+n+839/VX1k1PEs0NOB57UJ1PYCHpXkf1bVb404Lo2Xif+ezkdVbW2PdyT5KF23zU+PNqre3Z7koKraluQg4I5RB9S3qrp9ZjnJuwAnp+vZCjgPmbg2bcLONTy3UN8m7jvbh1VyXjLbRJ6nrKieDbuS5Fi6bmvPq6r7Rh3PPHweODzJYUkeBpwEXDLimOYtSYDzgZuq6q2jjmehqurMqjqkqtbSvfdXeDKgOUz093Q+kuyT5JEzy8CzgS/v+lkT6RLglLZ8CvCxEcayLNrJyYxfZ2X+O46tCTkPmag2bdLONTy30DKYqO9sH1bReclsE3mesqJ6NuzGO4A9gcu6/5u4qqr+7WhD2rmq2pHkFcBfAnsA76mqG0Yc1kI8HXgJcH2S69q233P2c60kK+B7Oh9TwEdbu7kG+EBVfXK0IS1Nkg8CG4ADk2wBXg+cBXw4yWnALcCJo4tw6XZSxw1JjqTrerkZ+DcjC3B1GvvzkAls0zzX0Ko2gd/ZPqy485LZVtJ5SsazF58kSZIkSZpUq2YYhSRJkiRJGg6TDZIkSZIkqVcmGyRJkiRJUq9MNkiSJEmSpF6ZbJAkSZIkSb0y2SBJkiRJknplskGSJEmSJPXKZIMkSZIkSeqVyYZVKMnjklyX5NtJ7kryplHHJGn8JNmc5FdHHYckgW2SJE0akw2r02uBK6vqkcAly/lCSX4myfZZf5Vk43K+rqTJk+TwJPcn+Z+jjkWSJE2OJK9Ick2S7yV53xz7j0nylST3Jbkyyc+OIMxVx2TD6vSzwA3DeKGq+npVPWLmD1gH/Aj482G8vqSJ8kfA50cdhCRJmjjfAN4EvGf2jiQHAh8B/hOwP3AN8KGhRrdKmWxYZZJcAfwK8I4k24GHAQcmuawNq/jrmUxfkv8ryZ1JDm3rRyS5O8njk7wsyV8MHPdrSf5sYP3WJEfOEcJLgU9X1eZlrKak/vxikhvbd/+9SfZKsiHJliSvTXJHkm1Jnp/kuCR/14Zn/d7MAZK8IcnFST7U2pkvJDli8EWSnATcA1w+7ApKWpo2vOHfJ/lSknvbd32vJKcm+cysspXksW35fUn+R5L/3Xo+/p8k/yTJ21qb85UkT5z1cj/RJg2topIWJMmhST6S5JtJvpXkHUl+LskVbf3OJO9Pst/Ac16XZGs7X/hqkmPa9l2eS1TVR6rqfwHfmiOU3wBuqKo/q6r7gTcARyR5/PK+AzLZsMpU1dHA3wCvaD0Nvg+cDLwROBC4Dnh/K/v/AX8CXJBkb+B/Av+pqr4C/DXwS0kekuQxdEmLpwEk+afAI4AvDb52ktAlGy5Y7npK6s3JwK8BPwf8M+A/tu3/BNgLOBj4z8C7gN8CngT8EvCfkhw2cJwTgD+ju6LwAeB/JXkoQJJHAb8PvGa5KyNp2ZwIHAscBvwL4NQFPO8/0p2DfA/4LPCFtn4x8NZZ5XfWJkkaI0n2AD4O3AKspTtfuAgI8P8CjwH+OXAo3Y9/kjwOeAXwi224968BmwcOu9Nzid34eeBvZ1aq6jvA37ftWkYmGwRwaVV9uqq+B/wH4GkzvRnovvz7Ap8DttJ1c6aqbga+DRwJPBP4S+AbLUP4y8DfVNWPZr3OM4ApupMHSZPhHVV1a1XdBbwZeHHb/gPgzVX1A7qThwOBc6vq21V1A3AjMNh74dqquriVfytdouKpbd8bgfOrassQ6iNpeby9qr7R2oq/oDs/mI+PVtW17WrjR4H7q+rCqvohXTfn2T0bdtYmSRovT6ZLKPxuVX2nqu6vqs9U1aaquqyqvldV36Q7J/jl9pwfAnsCT0jy0KraXFV/P3DMXZ1L7MojgHtnbbsXeOQS6qd5MNkggFtnFqpqO3AXXeNA+zK/D/gF4OyqqoHn/TWwgS7Z8NfANF1j8cttfbZTgD9vryFpMtw6sHwLrW0AvtV+DAB8tz3ePlD2u3T/uf/EcVoicgvwmDbc6leBc/oMWtLQ3TawfB8P/v7vyux2Y1ftCOy8TZI0Xg4FbqmqHYMbk0wluagNlfhHup7TBwJU1Sbg1XQXO+9o5Qa/43OeS8wjlu3Ao2ZtexTdhVMtI5MNgq4xACDJI+i6Jn2jrR8MvB54L3B2kj0HnjeTbPiltvzX7CTZ0IZhvBCHUEiT5tCB5Z+htQ1LOU6ShwCHtGNtoOte+fUktwH/HvjNJF9Y5OtIGh/fAR4+s5Lkn/RwzL7aJEnL61bgZ5KsmbX9vwIFrKuqR9ENwczMzqr6QFU9g25C+wLeMvDcnZ1L7M4NDPS2TLIP3VCsoUyYv5qZbBDAcUmekeRhdN2Zr6qqW9scC+8DzgdOA7a1/TP+mm6yyb1b9+e/oRuveQDwxVmv8evA3cCVy1kRSb17eZJDkuxPN8xqsbM3PynJb7STjlfTjc2+CjiP7j/8I9vfHwOX0o3TlDTZ/hb4+SRHtokc39DDMftqkyQtr8/R/XY4K8k+bdLYp9MNXdgO3Nsuav7uzBOSPC7J0e3i5v10vZsGh2Xv7FyCJGtaO7MHsEd7vZlEx0eBX0jym63Mfwa+1Oah0zIy2SDoJlh5Pd3wiSfRZRgBfgf4abpJIQt4GfCyJL8EUFV/R9dY/E1b/0fgZuD/DHSvnnEK8KezhmFIGn8fAD5F993+e7rbSi3Gx4AX0SUdXwL8RlX9oKruq6rbZv7o2pT72zhOSROsnSf8PvBXwNeAz+z6GfPSV5skaRm13wL/Engs8HW6IQ8vAv4LcBTdnAmX0t2ScsaewFnAnXRDs34aOHNg/5znEm3ff6RLTpxB91vmu20b7ZziN+nmebkbeApwUp/11dzibz9J0nJK8gbgsVX1W7srK0mSNJvnEpPJng2SJEmSJKlXJhskSZIkSVKvHEYhaSiS7AFcA2ytqucmOQy4iG5C0WuBl1TV99ukQBfSzR/yLeBFVbV5RGFLGpIk/w7413Szj19PN0/QQdhOSJI0kezZIGlYXgXcNLD+FuCcqnos3WQ9p7XtpwF3t+3n8OBbHklagdqM5L8DrK+qX6CbTfwkbCckSZpYJhskLbskhwDHA+9u6wGOBi5uRS4Ant+WT2jrtP3HtPKSVrY1wN7tVmUPp7tlmu2EJEkTas3ui4zWgQceWGvXrp1X2e985zvss88+yxvQiFi3yTSKul177bV3VtVPDfVFd+9twGvp7q0MXZfoe6pqR1vfAhzclg8GbgWoqh1J7m3l7xw8YJLTgdMB9t577ycdeuihuw3iRz/6EQ95yOrOsa7292C11x+692DTpk1j1U5U1dYkf0h3e7Tv0t3a8FqW2E7A4tqK5TDOn71xjg3GO75xjg2WHt/f/d3fjVVbMQwL+e2xHFbSefFKqctKqQcsT1129dtj7JMNa9eu5ZprrplX2enpaTZs2LC8AY2IdZtMo6hbkluG+oK7keS5wB1VdW2SDX0dt6rOA84DWL9+fc2nnVjJn7X5Wu3vwWqvP3Tvwa/8yq+MWzvxaLreCocB9wB/Bhzbx7EX01Ysh3H+7I1zbDDe8Y1zbLD0+MbtnGIYFvLbYzmM+2dqIVZKXVZKPWB56rKrdmJJqdgk+yW5OMlXktyU5GlJ9k9yWZKvtcdHt7JJ8vYkm5J8KclRS3ltSRPj6cDzkmymm+jtaOBcYL/WXRrgEGBrW94KHArQ9u9LNwGcpJXrV4F/qKpvVtUPgI/QtR22E5IkTail9vs6F/hkVT0eOIJu8rczgMur6nDg8rYO8Bzg8PZ3OvDOJb62pAlQVWdW1SFVtZZuwrcrqupk4ErgBa3YKcDH2vIlbZ22/4rytjnSSvd14KlJHt7mXjgGuBHbCUmSJtaikw1J9gWeCZwPUFXfr6p7ePCkTbMnc7qwOlfRXa04aNGRS5p0rwNek2QT3Vjr89v284ED2vbX8OOEpaQVqqquppvo8Qt0t718CN3QB9sJSZIm1FLmbDgM+Cbw3iRH0E3k9Cpgqqq2tTK3AVNt+YHJnJqZiZ62McvgZE5TU1NMT0/PK6Dt27fPu+yksW6TaSXXbTGqahqYbss3A0+eo8z9wAuHGpikkauq1wOvn7XZdkKSpAm1lGTDGuAo4JVVdXWSc5l1ZaGqKsmCuzXOnsxpvpNYjNPkHWvPuLS3Y20+6/ixqlvfrJtWs77ais1nHd/LcSRp2HbXDm5ct4NT59lW2haufG0OqG8DPwR2VNX6JPsDHwLWApuBE6vq7jYs61zgOOA+4NSq+sIo4p4kfZ2bbFy3gw29HEmTailzNmwBtrSuj9B1fzwKuH1meER7vKPtf2Ayp2ZwoidJkiRJmo9fqaojq2p9W3fOOGkMLTrZUFW3AbcmeVzbNDOZ0+CkTbMnc3ppuyvFU4F7B4ZbSJIkSdJiOGecNIaWMowC4JXA+5M8jG5c5cvoEhgfTnIacAtwYiv7CbouTJvoujG9bImvLUmSJGl1KeBTbaj2n7Th10uaM26x88Uth3GY72vjuh29HGdqb/jv7//Y7gvOw7qD9+3lOIsxDv8mfRl2XZaUbKiq64D1c+w6Zo6yBbx8Ka8nSZrbfMdXzmfss2OeJUlj7BlVtTXJTwOXJfnK4M7FzBm32PnilsM4zPc13zlSdmfjuh2cff1Sr213Np+8oZfjLMY4/Jv0Zdh1WcqcDZIkSZI0NFW1tT3eAXyU7o41zhknjSGTDZIkSZLGXpJ9kjxyZhl4NvBlnDNOGkv99GuRJEmSpOU1BXy0u6Mla4APVNUnk3we54yTxo7JBkmSJEljr6puBo6YY/u3cM44aew4jEKSJEmSJPXKZIMkSZIkSeqVyQZJkiRJktQrkw2SJEmSJKlXJhskSZIkSVKvTDZIkiRJkqRemWyQJEmSJEm9MtkgSZIkSZJ6ZbJBkiRJkiT1ymSDJEmSJEnqlckGSZIkSZLUqzWjDkCSNF7WnnFpb8fafNbxvR1LkiRJk8NkgyQtUJ8/xiVJkqSVyGEUkiRJkiSpVyYbJEmSJElSr0w2SFpWSfZK8rkkf5vkhiT/pW0/LMnVSTYl+VCSh7Xte7b1TW3/2lHGL2k4kuyX5OIkX0lyU5KnJdk/yWVJvtYeH93KJsnbWzvxpSRHjTp+SZL0YCYbJC237wFHV9URwJHAsUmeCrwFOKeqHgvcDZzWyp8G3N22n9PKSVr5zgU+WVWPB44AbgLOAC6vqsOBy9s6wHOAw9vf6cA7hx+upFFJskeSLyb5eFv3AoY0hkw2SFpW1dneVh/a/go4Gri4bb8AeH5bPqGt0/YfkyRDClfSCCTZF3gmcD5AVX2/qu7hwe3B7Hbiwta+XAXsl+SgIYctaXReRZeQnOEFDGkMeTcKScsuyR7AtcBjgT8C/h64p6p2tCJbgIPb8sHArQBVtSPJvcABwJ2zjnk63RVNpqammJ6e3m0c27dvn1e53dm4bsfuC42pqb2HG38f73ef+voMTLLt27fvvtDwHQZ8E3hvkiPo2otXAVNVta2VuQ2YassPtBPNTBuyjVkW01Ysh3H+7I06tt21SQtpt4Zdj1G/d7sz7vEtRpJDgOOBNwOvaRckjgb+VStyAfAGuh5PJ7Rl6C5gvCNJqqqGGbO0WplskLTsquqHwJFJ9gM+Cjy+h2OeB5wHsH79+tqwYcNunzM9Pc18yu3OqRN868uN63Zw9vXDa/o3n7xhaK81H319BibZmP7wWAMcBbyyqq5Oci4/HjIBdL2kkiz4B8Ji2orlMM6fvVHHtrs2dSHt1rDbnFG/d7sz7vEt0tuA1wKPbOsHMIILGMtlHBJEfV2U6PMCx2r/N+nLsOtiskHS0FTVPUmuBJ5G1+15TTs5OATY2optBQ4FtiRZA+wLfGskAUsali3Alqq6uq1fTJdsuD3JQVW1rQ2TuKPtn2knZgy2IZJWqCTPBe6oqmuTbOjruOOSlITxSBD1dVGlzwsco7x4MQ7/Jn0Zdl2cs0HSskryU61HA0n2Bp5FN87ySuAFrdgpwMfa8iVtnbb/Crs7SitbVd0G3JrkcW3TMcCNPLg9mN1OvLTdleKpwL0Dwy0krVxPB56XZDNwEd3wiXNpFzBambkuYOAFDGn47NkgabkdBFzQ5m14CPDhqvp4khuBi5K8CfgibWK49vinSTYBdwEnjSJoSUP3SuD9bRb5m4GX0dqMJKcBtwAntrKfAI4DNgH3tbKSVriqOhM4E6D1bPj3VXVykj+ju0BxEXNfwPgsXsCQhs5kg6RlVVVfAp44x/abgSfPsf1+4IVDCE3SGKmq64D1c+w6Zo6yBbx82YOSNClehxcwpLFjskGSJEnSRKmqaWC6LXsBQxpDJhskSZIkacKtneC7ZWllcoJISZIkSZLUK5MNkiRJkiSpVyYbJEmSJElSr5acbEiyR5IvJvl4Wz8sydVJNiX5ULuFFUn2bOub2v61S31tSZIkSZI0fvqYIPJVwE3Ao9r6W4BzquqiJH8MnAa8sz3eXVWPTXJSK/eiHl5/xVt7xqVsXLeDU5c46cvms47vKSJJkiRJknZuST0bkhwCHA+8u60HOBq4uBW5AHh+Wz6hrdP2H9PKS5IkSZKkFWSpPRveBrwWeGRbPwC4p6p2tPUtwMFt+WDgVoCq2pHk3lb+ztkHTXI6cDrA1NQU09PT8wpm+/bt8y673Dau27H7QgswtffSjzku781s4/Tv1reVXDdJkiRJ2plFJxuSPBe4o6quTbKhv5Cgqs4DzgNYv359bdgwv8NPT08z37LLbalDHmbbuG4HZ1+/tNzQ5pM39BNMz8bp361vK7lukiRJkrQzS/n1+nTgeUmOA/aim7PhXGC/JGta74ZDgK2t/FbgUGBLkjXAvsC3lvD6kiRJkiRpDC16zoaqOrOqDqmqtcBJwBVVdTJwJfCCVuwU4GNt+ZK2Ttt/RVXVYl9fkiRJkiSNpz7uRjHb64CLkrwJ+CJwftt+PvCnSTYBd9ElKCRJK9janoaUeTcdSZKkydJLsqGqpoHptnwz8OQ5ytwPvLCP15MkSZIkSeNrSbe+lCRJkiRJmm05hlFMtL66/EqSJEmStFrZs0GSJEnS2EuyV5LPJfnbJDck+S9t+2FJrk6yKcmHkjysbd+zrW9q+9eOMn5ptTHZIEmSJGkSfA84uqqOAI4Ejk3yVOAtwDlV9VjgbuC0Vv404O62/ZxWTtKQmGyQJEmSNPaqs72tPrT9FXA0cHHbfgHw/LZ8Qlun7T8mSYYUrrTqmWz4/9m7/zjJ6vrO96/3AgqLKCJJB2cmjhuJLjezRjMX9EFu0pGY5deKu6uoIciw5M7dDRpcJ+qoycbs3SR4s6h49WF2ItEhkiBBvXCV/GCRXtd9BKIgwgIaR+4QZjIIKCCjMdr6uX/Uaa0Zenr6x6mqU9Wv5+PRj67zPadOfU5XnU+f+p7vD0mSJEljIckhSW4DHgCuB74MPFJVs80mu4A1zeM1wH0AzfpHgacNN2Jp9XKASEmSJEljoaq+C/xkkqOBjwHPWek+k2wGNgNMTU0xMzOz0l0u2969e5f9+ls2zB58oyGaOqK9mMb1PemaYR+LlQ2SJEmaCG3NKrbz4jNa2Y8Gp6oeSXIj8ELg6CSHNq0X1gK7m812A+uAXUkOBZ4CfHWefW0DtgFs3Lixpqenh3AE85uZmWG5r7+pY7PqbdkwyyV3tPN1c+c5063sZzlW8p50zbCPxW4UkiRJkjovyQ81LRpIcgTwYuBu4EbgZc1m5wHXNI+vbZZp1n+yqmp4EUurmy0bJEmStCRttSCQlug4YHuSQ+jdNL2qqj6e5C7gyiT/CfgccFmz/WXAHyXZAXwNeOUogpZWKysbJA1UknXA5cAUvRGjt1XVpUmOAT4MrAd2AmdX1cPNKNGXAqcD3wQ2VdWto4hd0nA1XyA+C+yuqjOTPBO4kt6AbrcA51bVt5M8kV5e+Sl6TaJfUVU7RxS2pCGpqtuB581Tfg9w4jzl3wJePoTQJM3DbhSSBm0W2FJVJwAvAC5McgKwFbihqo4HbmiWAU4Djm9+NgPvG37IkkbkInpNoue8HXhnVT0LeBi4oCm/AHi4KX9ns50kSeoQKxskDVRV7ZlrmVBVj9H7IrGGfee+3n9O7MububRvojfo03FDDlvSkCVZC5wBvL9ZDvAi4Opmk/3zxFz+uBo4pdlekiR1hN0oJA1NkvX0mj/eDExV1Z5m1f30ullA35zYjbn5svf0lS1rmqq2pvvp2tRSS9HmNFTD1NY0TZM0fdVy7d27d9QhHMi7gDcCRzXLTwMeaUaXhx/kAujLE1U1m+TRZvuHhheuJGlY2hwnxtlmhsfKBklDkeRJwEeA11XV1/tvQlZVJVnS6NDLmaaqrel+uja11FK0OQ3VMLU15dUkTV+1XF2sbElyJvBAVd2SZLrlfS+5YnIQulzRtZzYhllpOYpK0sX+Pbr8vkL345M02cbvilPS2ElyGL2Khiuq6qNN8VeSHFdVe5puEg805XNzYs/pny9b0mQ6GXhJktOBw4En0xso9ugkhzatG/pzwVye2JXkUOAp9AaKfJzlVEwOQpcrupYT2zArXUdRSbrYCs4uv6/Q/fgkTTbHbJA0UE0/6suAu6vqHX2r+ue+3n9O7Fen5wXAo33dLSRNoKp6c1Wtrar19Kam+2RVnQPcCLys2Wz/PDGXP17WbL+k1lGSJGmwbNkgadBOBs4F7khyW1P2FuBi4KokFwD3Amc3666jN+3lDnpTX54/3HAldcibgCuT/Cfgc/QqLml+/1GSHcDX6FVQSJKkDrGyQdJAVdWngQONEn/KPNsXcOFAg5LUWVU1A8w0j+8BTpxnm28BLx9qYJIkaUnsRiFJkiRJklplZYMkSZIkSWqVlQ2SJEmSJKlVVjZIkiRJkqRWWdkgSZIkSZJaZWWDJEmSJElqlZUNkiRJkiSpVYeOOgANz/qtn2htXzsvPqO1fUmSJEmSJostGyRJkiR1XpJ1SW5McleSO5Nc1JQfk+T6JF9qfj+1KU+SdyfZkeT2JM8f7RFIq4uVDZIkSZLGwSywpapOAF4AXJjkBGArcENVHQ/c0CwDnAYc3/xsBt43/JCl1cvKBkmSJEmdV1V7qurW5vFjwN3AGuAsYHuz2Xbgpc3js4DLq+cm4Ogkxw05bGnVcswGSZIkSWMlyXrgecDNwFRV7WlW3Q9MNY/XAPf1PW1XU7anr4wkm+m1fGBqaoqZmZlBhX1Qe/fuXfbrb9kw224wKzR1RPdiApb8913Je9I1wz4WKxskSZIkjY0kTwI+Aryuqr6e5PvrqqqS1FL2V1XbgG0AGzdurOnp6RajXZqZmRmW+/qbWhwMvg1bNsxyyR3d+7q585zpJW2/kveka4Z9LHajkCRJkjQWkhxGr6Lhiqr6aFP8lbnurAtFkgAAIABJREFUEc3vB5ry3cC6vqevbcokDYGVDZIkSZI6L70mDJcBd1fVO/pWXQuc1zw+D7imr/zVzawULwAe7etuIWnAll3Z4NQzkiRJkoboZOBc4EVJbmt+TgcuBl6c5EvAzzfLANcB9wA7gD8AfmUEMUur1ko60cxNPXNrkqOAW5JcD2yiN/XMxUm20pt65k3sO/XMSfSmnjlpJcFLkiRJWh2q6tNADrD6lHm2L+DCgQYl6YCW3bLBqWckSZIkSdJ8WhketM2pZ5r9LWv6mTam8uji9CzQvalj2pwyZZKmk9nfJB+bJEmSJB3Iiisb2p56pnnesqafaWMqj65NGTOna1PHLHXKmIVM0nQy+5vkY5MkSZKkA1nRbBROPSNJkiRJkva3ktkonHpGkiRJkiQ9zkra5c9NPXNHktuasrfQm2rmqiQXAPcCZzfrrgNOpzf1zDeB81fw2pIkSZIkqaOWXdng1DOSJEmSJGk+KxqzQZIOJskfJnkgyf/sKzsmyfVJvtT8fmpTniTvTrIjye1Jnj+6yCUNS5J1SW5McleSO5Nc1JSbKyRJGlPdmd5A0qT6IPAe4PK+sq3ADVV1cZKtzfKbgNOA45ufk4D3Nb9XbP3WT7Blw2xnZ5zRwta39L5t2TDLdCt7UstmgS1VdWuSo4BbklwPbGLIuUKSJLXDlg2SBqqqPgV8bb/is4DtzePtwEv7yi+vnpuAo+dmt5E0uapqT1Xd2jx+DLgbWIO5QpKksWXLBkmjMNU3G839wFTzeA1wX992u5qyx81ck2QzsBlgamqKmZmZBV9wy4ZZpo7o/V7NVvvfYOoIDvpZmXR79+4ddQgLSrIeeB5wMy3kCkmSNBpWNkgaqaqqJLWM520DtgFs3LixpqenF9x+U9ON4pI7VnfaW+1/gy0bZjn7IJ+VSdflypYkTwI+Aryuqr7em2W7Z7m5YqkVk4Oyd+/ezv7tlxPbMCstR1FJuti/R5ffV+h+fJIm2+q94pQ0Sl9JclxV7WmaPj/QlO8G1vVtt7YpkzThkhxGr6Lhiqr6aFO84lyx1IrJQZmZmWFUr30wy4ltmOPfjKKSdOc504varsvvK3Q/PkmTzTEbJI3CtcB5zePzgGv6yl/djDT/AuDRvibUkiZUek0YLgPurqp39K0yV0iSNKZs2SBpoJL8CTANHJtkF/CbwMXAVUkuAO4Fzm42vw44HdgBfBM4f+gBSxqFk4FzgTuS3NaUvQVzhSRJY8vKBkkDVVWvOsCqU+bZtoALBxuRpK6pqk8DOcBqc4UkSWPIbhSSJEmSOi/JHyZ5IMn/7Cs7Jsn1Sb7U/H5qU54k706yI8ntSZ4/usil1cnKBkmSJEnj4IPAqfuVbQVuqKrjgRuaZYDTgOObn83A+4YUo6SG3SgkSZIkdV5VfSrJ+v2Kz6I3NhTAdmAGeFNTfnnT7eqmJEfPzW4znGgXZ/1+M7ts2TA71NleVqP9/+YHs9B7svPiM9oIaWJZ2SBJkrRKzHeR7ZcbjbmpvgqE+4Gp5vEa4L6+7XY1ZY+rbEiymV7rB6amppiZmRlYsPvbsmF2n+WpIx5fNq4m5VgWOo5hflbasHfv3qHGbGWDJEmSpLFXVZWklvG8bcA2gI0bN9b09HTboR3Q/hV9WzbMcskdk/EVbVKOZaHj2HnO9HCDWaGZmRmG+fl2zAZJkiRJ4+orSY4DaH4/0JTvBtb1bbe2KZM0JONf1aSRWGpfp4V88NQjW9uXJEmSVpVrgfOAi5vf1/SVvybJlcBJwKNdG69BmnRWNkiSJEnqvCR/Qm8wyGOT7AJ+k14lw1VJLgDuBc5uNr8OOB3YAXwTOH/oAUur3ERUNszdZXeAI0mSJGkyVdWrDrDqlHm2LeDCwUYkaSGO2SBJkiRJklplZYMkSZIkSWqVlQ2SJEmSJKlVVjZIkiRJkqRWWdkgSZIkSZJaZWWDJEmSJElq1URMfSlJ0mKtb2mK5J0Xn9HKfiR1z2LzxGKmXTdXSFqtbNkgSZIkSZJaZcsGjdwdux896F2BxfDOgSRJkiR1gy0bJEmSJElSq6xskCRJkiRJrbKyQZIkSZIktcrKBkmSJEmS1CorGyRJkiRJUqusbJAkSZIkSa0a+tSXSU4FLgUOAd5fVRcPOwZNpvUtTJ85x2k0R8s8IWkxzBWSDmYQeaLNa06Nt7Y+C5P63WOolQ1JDgHeC7wY2AV8Jsm1VXXXMOOQ1F3mCY0LKzhHazXlCr/YjDdzxeispjyh8TapeWLYLRtOBHZU1T0ASa4EzgI84dUpbZ3wWzbMMt3KnlYV84RWnUm9yBgwc4WkgzFPSCM07MqGNcB9fcu7gJOGHIM0VDavWjLzhKTFGEiuGFQrgi0bZtlkCwWt0FI/nwf63HlNIU2uhfLEUv8XrTRXDH3MhsVIshnY3CzuTfLFxTzvV+FY4KGBBTZCHtt4avPY8vZFb/qMNl6v65aTJyb5s7ZYq/1vsBqOfxG54lhWSZ6A5V9TtK3Ln70uxwbdjq/LscGB4/OaYl9dyRPQ/c/UUkzKsUzKccDSj2WRueKAeWLYlQ27gXV9y2ubsn1U1TZg21J3nuSzVbVx+eF1l8c2nib52AZoYHnC98O/wWo/fvj+32D9qONowUCvKdrW5c9el2ODbsfX5dig+/ENwVjlCZis92xSjmVSjgOGfyzDnvryM8DxSZ6Z5AnAK4FrhxyDpG4zT0haDHOFpIMxT0gjNNSWDVU1m+Q1wF/Qm37mD6vqzmHGIKnbzBOSFsNcIelgzBPSaA19zIaqug64bkC770TzpwHx2MbTJB/bwAwwT/h++DdY7ccPE/Q3GPA1Rdu6/HfvcmzQ7fi6HBt0P76BG7M8AZP1nk3KsUzKccCQjyVVNczXkyRJkiRJE27YYzZIkiRJkqQJN3GVDUl+L8kXktye5GNJjh51TCuR5NQkX0yyI8nWUcfTliTrktyY5K4kdya5aNQxtS3JIUk+l+Tjo45FPzBpOWKxJjWXLNZqyDmLYV7qhiRbklSSY0cdy5wu5sYu561xyCme7+Ori+fjUnT53F2KcTjPl2IUOWHiKhuA64GfqKp/BvwN8OYRx7NsSQ4B3gucBpwAvCrJCaONqjWzwJaqOgF4AXDhBB3bnIuAu0cdhB5nYnLEYk14Llms1ZBzFsO8NGJJ1gG/APztqGPZT6dy4xjkrXHIKZ7v46tT5+NSjMG5uxTjcJ4vxdBzwsRVNlTVX1bVbLN4E735dMfVicCOqrqnqr4NXAmcNeKYWlFVe6rq1ubxY/Q++GtGG1V7kqwFzgDeP+pYtK8JyxGLNbG5ZLEmPecshnmpM94JvBHo1KBZHcyNnc5bXc8pnu/jrYPn41J0+txdiq6f50sxqpwwcZUN+/k3wJ+NOogVWAPc17e8izH9gC8kyXrgecDNo42kVe+idzH5vVEHogWNe45YrFWRSxZrQnPOYpiXRizJWcDuqvr8qGM5iC7kxrHJWx3NKZ7vk6ML5+NSjM25uxQdPc+XYiQ5YehTX7YhyX8FfmSeVW+tqmuabd5Kr+nLFcOMTUuT5EnAR4DXVdXXRx1PG5KcCTxQVbckmR51PKuROUIHMok5ZzHMS8OzUP4B3kKvC8VImBvb18Wc4vk+Hjwfx0cXz/OlGGVOGMvKhqr6+YXWJ9kEnAmcUuM9t+duYF3f8tqmbCIkOYzeiXtFVX101PG06GTgJUlOBw4HnpzkQ1X1SyOOa9VYRTlisSY6lyzWBOecxTAvDcmB8k+SDcAzgc8ngd55eGuSE6vq/lHGNqdjubHzeavDOcXzfQyM2fm4FJ0/d5eiw+f5UowsJ2S8PrsHl+RU4B3Az1bVg6OOZyWSHEpvUJhT6J2knwF+saruHGlgLUjvSms78LWqet2o4xmUpvbw16rqzFHHop5JyhGLNcm5ZLFWS85ZDPNSNyTZCWysqodGHQt0Lzd2PW+NS07xfB9PXTsfl6Lr5+5SjMt5vhTDzgmTOGbDe4CjgOuT3Jbk90cd0HI1A8O8BvgLegOSXDWOJ+oBnAycC7yoeZ9ua2rbpEGbmByxWBOeSxbLnCMtrFO5cQzyljlFg9Sp83EpxuDcXQrP8xWauJYNkiRJkiRptCaxZYMkSZIkSRohKxskSZIkSVKrrGyQJEmSJEmtsrJBkiRJkiS1ysoGSZIkSZLUKisbJEmSJElSq6xskCRJkiRJrbKyQZIkSZIktcrKBi0oyaYkn15g/VSSTyV5LMklSd6W5EPDjFHS5EhSSZ416jgkLU+S9c15fGhL+5tJ8stt7EvS6LSZG5LsTPLzbcSlwbKyQSu1GXgIeHJVbRl1MJIGZ6mViUmmk+zar2xTku8m2dv3M916sJKGxgt/SfMZRm5I8vv7XVP8Q5LHBvmaWrxWap21qj0DuKuqatSBSBobf1VVPz3qICRJ0nirqn8L/Nu55SQfBL43soC0D1s26PuSrEvy0SQPJvlqkvf8YFXek+TRJF9IckpT+EHgPOCNTU3iXM3l4Uk+3HStuDXJc0dwOJJWIMmbkuxuzuMvJjkDeAvwiuZ8/3yz3flJ7m62uyfJ/9GUHwn8GfD0vrsNT1/ky5/e7OuhJL+XxP9VUkcl+SPgR4H/N8le4Oxm1TlJ/rY5j9/at/2JSf4qySNJ9jTXF0/oW//i5lrj0eY6JH3rfizJJ5trlIeSXJHk6CEdqqQlWGZu+GySryf5SpJ39K07N8m9zbn/Vg6gufb418D2vrKtSb7cXKfcleRftn2sOjAv4ARAkkOAjwP3AuuBNcCVzeqTgC8DxwK/CXw0yTFVtQm4Avi/qupJVfVfm+3PAv4UOAb4Y+D/SXLYkA5F0goleTbwGuB/raqjgH8OfAH4HeDDzfk+V4n4AHAm8GTgfOCdSZ5fVd8ATgP+rtn+SVX1d81zntdcZPxNkt+Yp//mvwQ2As+nl0/+zQAPV9IKVNW5wN8C/6KqngRc1az6aeDZwCnAf0jyT5vy7wL/nt41xQub9b8CkORY4KPArzfrvwyc3PdyAX4XeDrwT4F1wNsGdGiSVmAZueFS4NKqejLwY3PbJzkBeB9wLr1z/2nA2gO87L8GHgQ+1Vf2ZeB/A54C/BbwoSTHtXGMOjgrGzTnRHon8Buq6htV9a2qmhsY8gHgXVX1nar6MPBF4IwF9nVLVV1dVd8B3gEcDrxgkMFLatV3gScCJyQ5rKp2VtWX59uwqj5RVV+unv8G/CW9f+oH8ingJ4AfpndR8CrgDftt8/aq+lpV/S3wrmYbSePlt6rq76vq88DngecCVNUtVXVTVc1W1U7gvwA/2zzndODOvmuIdwH3z+2wqnZU1fVV9Q9V9SC9a4yfRdI4mTc3AN8BnpXk2KraW1U3NeUvAz5eVZ+qqn8AfoMDd5M4D7i8v3t3Vf1pVf1dVX2v+R7zJXrfezQEVjZozjrg3qqanWfd7v3GZLiXXsXEgdw396CqvgfsOsj2kjqkqnYAr6N3x/CBJFceqAtEktOS3JTka0keofdl4dgF9n1PVf1/zT/9O4D/SO9Cot99fY8Plm8kddP9fY+/CTwJIMmPJ/l4kvuTfJ1ei6m5nPF09r2GqP7l9GbAurLp4vV14EMskG8kddK8uQG4APhx4AtJPpPkzKZ8/7zwDeCr++80yY8C08Dl+5W/OsltTdetR+jd8DBvDImVDZpzH/Cj8zRnBliTJH3LPwr83TzbzVk396Dpa732INtL6piq+uNmEMdnAAW8vfn9fUmeCHwE+M/AVFUdDVzHD/pYL2bg2Orbfs66vscHyzeSRm8pg0S/j163rOOb5tJv4Qc5YA/7XkOEffPB7zSvtaF57i/x+PwhqTsWnRuq6ktV9Sp6LR/fDlzdjMGwf174x/S6UuzvXOB/VNU9fds+A/gDel1Dn9Zcp/xPzBtDY2WD5vw1vZP54iRHJjk8yVw/yR8GfjXJYUleTq+f5HUL7OunkvyrpuLidcA/ADctsL2kDkny7CQvaioTvgX8Pb0mi18B1vcN2PgEet0tHgRmk5wG/ELfrr4CPC3JU/r2fVqSqebxc+g1h7xmvxDekOSpSdYBFwEfbv0gJbXpK8A/WeS2RwFfB/Y2OeDf9a37BPC/9F1D/CrwI/s9dy/waJI1PL4LlqRuWXRuSPJLSX6oaRX9SFP8PeBq4MwkP90MJvsfmf877KuBD+5XdiS9Co8Hm9c4n17LBg2JlQ0CoKq+C/wL4Fn0BnPZBbyiWX0zcDzwEPDbwMuq6nHNl/pc0zz3YXq1jP+q6XspaTw8EbiY3jl/P70KxzfTG/gV4KtJbq2qx+h9GbiK3vn+i8C1czupqi8AfwLc0zRffDq9AaFuT/INepWWH6V3t7LfNcAtwG30vnxcNoiDlNSa3wV+vWmivH+3qP39Gr1c8Ri9O47fr0ysqoeAl9PLP1+ld+3xP/qe+1v0Bo59lF5u+GhL8UsajKXkhlOBO5uZKy4FXtmM7XAncCG9Qef30Lve2NX/xCQvpNeS+k/7y6vqLuAS4K/oVXxsYN+cogHLvl3xJUmSJEmSVsaWDZIkSZIkqVVWNkiSJEmSpFZZ2SBJkiRJklplZYMkSZIkSWqVlQ2SJEmSJKlVh446gIM59thja/369SN57W984xsceeSRI3nt5RrHmGE84+5qzLfccstDVfVDo45jmEaZJ6C7n4Xl8ni6rY3jWY15AgaXK8bpMzYusY5LnDA+sS4nztWYK7ymWJjxrVzXY1xqfAvlic5XNqxfv57PfvazI3ntmZkZpqenR/LayzWOMcN4xt3VmJPcO+oYhm2UeQK6+1lYLo+n29o4ntWYJ2BwuWKcPmPjEuu4xAnjE+ty4lyNucJrioUZ38p1PcalxrdQnrAbhSRJkiRJatWKKhuSHJ3k6iRfSHJ3khcmOSbJ9Um+1Px+arNtkrw7yY4ktyd5fjuHIEmSJEmSumSlLRsuBf68qp4DPBe4G9gK3FBVxwM3NMsApwHHNz+bgfet8LUlSZIkSVIHLbuyIclTgJ8BLgOoqm9X1SPAWcD2ZrPtwEubx2cBl1fPTcDRSY5bduSSJEmSJKmTVjJA5DOBB4EPJHkucAtwETBVVXuabe4HpprHa4D7+p6/qynbg8bO+q2faG1fOy8+o7V9qZuS7AQeA74LzFbVxiTHAB8G1gM7gbOr6uEkoddq6nTgm8Cmqrp1FHGPkzbPyQ+e2t0RkiVNnvny15YNs2xaRl7zmkJauYWuKZZ6bnpOrm4rqWw4FHg+8NqqujnJpfygywQAVVVJaqk7TrKZXlcLpqammJmZWUGYy7d3796RvfZyDSvmLRtmW9vX/33FNUwd0fu9EhvWPKWliBZnHD8fI/ZzVfVQ3/Jcl6uLk2xtlt/Evl2uTqLX5eqkYQcrSZIkaflWUtmwC9hVVTc3y1fT+7LwlSTHVdWeppvEA8363cC6vuevbcoep6q2AdsANm7cWKOaGqTr05LMZ1gxL+duw0K2bJjlkjtWNhPrznOm2wlmkcbx89ExZwHTzePtwAy9yobvd7kCbmoGoj2ur8WUJEmSpI5b9pgNVXU/cF+SZzdFpwB3AdcC5zVl5wFzt6uvBV7dzErxAuBRvzxIq0YBf5nklqblEiy9y5UkSZKkMbGyW8nwWuCKJE8A7gHOp1eBcVWSC4B7gbObba+j1wd7B71+2Oev8LUljY+frqrdSX4YuD7JF/pXLqfLVVe6W0E3utS02bWpC8fTJo9HkiRp+FZU2VBVtwEb51l1yjzbFnDhSl5P0niqqt3N7weSfAw4kRV2uepKdyvoRpeaNrs2ffDUI0d+PG3qwvvTpkk7HkmSNJlW2rJBkhaU5EjgH1XVY83jXwD+Iz/ocnUxj+9y9ZokV9IbGNIuV0N2x+5HW6u8cBRqSZKk1cnKBkmDNgV8rDejJYcCf1xVf57kM9jlSpIkSZpIVjZIGqiqugd47jzlX8UuV5IkSdJEWvZsFJIkSZIkSfOxskGSJI1ckqOTXJ3kC0nuTvLCJMckuT7Jl5rfT222TZJ3J9mR5PYkzx91/JIkaV9WNkiSpC64FPjzqnoOva5XdwNbgRuq6njghmYZ4DTg+OZnM/C+4YcrSZIWYmWDJEkaqSRPAX4GuAygqr5dVY8AZwHbm822Ay9tHp8FXF49NwFHN1PoSpKkjnCASEmSNGrPBB4EPpDkucAtwEXAVN/Ut/fTm90GYA1wX9/zdzVlj5smN8lmeq0fmJqaYmZmpvXg9+7dO5D9DkIXY92yYfZxZVNHzF9+MKM4ti7+TeczLnFKmhxWNkiSpFE7FHg+8NqqujnJpfygywTQm6kmSS11x1W1DdgGsHHjxpqenm4h3H3NzMwwiP0OQhdj3bT1E48r27JhlkvuWPpl6s5zpluIaGm6+Dedz7jEKWly2I1CkiSN2i5gV1Xd3CxfTa/y4Stz3SOa3w8063cD6/qev7YpkyRJHWFlgyRJGqmquh+4L8mzm6JTgLuAa4HzmrLzgGuax9cCr25mpXgB8GhfdwtJktQBdqOQJEld8FrgiiRPAO4Bzqd3U+SqJBcA9wJnN9teB5wO7AC+2WwrSZI6xMoGSZI0clV1G7BxnlWnzLNtARcOPChJkrRsdqOQJEmSJEmtsrJBkiRJkiS1ysoGSZIkSZLUKisbJEmSJElSq6xskCRJkjQWkhyd5OokX0hyd5IXJjkmyfVJvtT8fmqzbZK8O8mOJLcnef6o45dWEysbJEmSJI2LS4E/r6rnAM8F7ga2AjdU1fHADc0ywGnA8c3PZuB9ww9XWr2c+lLSUCQ5BPgssLuqzkzyTOBK4GnALcC5VfXtJE8ELgd+Cvgq8Iqq2jmisCVJ81i/9ROjDkGrUJKnAD8DbAKoqm8D305yFjDdbLYdmAHeBJwFXN5Ml3tT0yriuKraM+TQpVXJlg2ShuUiencf5rwdeGdVPQt4GLigKb8AeLgpf2eznSRJ0jOBB4EPJPlckvcnORKY6qtAuB+Yah6vAe7re/6upkzSENiyQdLAJVkLnAH8NvD6JAFeBPxis8l24G30mjee1TwGuBp4T5I0dyUkSdLqdSjwfOC1VXVzkkv5QZcJAKqqkizpmiHJZnrdLJiammJmZqalcJdu7969I319gC0bZg+4buqIhdfvb9jH0oW/38F0PcY247OyQdIwvAt4I3BUs/w04JGqmvtv1X+n4ft3IapqNsmjzfYP9e/QC4N9LeUf/8Es9UJiIaP+u0A33p82TdrxSNIS7AJ2VdXNzfLV9CobvjLXPSLJccADzfrdwLq+569tyvZRVduAbQAbN26s6enpAYV/cDMzM4zy9QE2LdBNasuGWS65Y/FfIXeeM91CRIvXhb/fwXQ9xjbjs7JB0kAlORN4oKpuSTLd1n69MNjXQhcGS7XUC4mFDPsiYz5deH/aNGnHI0mLVVX3J7kvybOr6ovAKcBdzc95wMXN72uap1wLvCbJlcBJwKOO1yANj5UNkgbtZOAlSU4HDgeeTG8k6aOTHNq0bui/0zB3F2JXkkOBp9AbKFKSJOm1wBVJngDcA5xPbxy6q5JcANwLnN1sex1wOrAD+GazraQhsbJB0kBV1ZuBNwM0LRt+rarOSfKnwMvozUix/12I84C/atZ/0vEaJEkSQFXdBmycZ9Up82xbwIUDD0rSvJyNQtKovIneYJE76I3JcFlTfhnwtKb89ew38JMkSZKk7rNlg6ShqaoZenNfU1X3ACfOs823gJcPNTBJkiRJrbJlgyRJkiRJapUtGyRJkiRpzK1vcWYqqQ22bJAkSZIkSa2yskGSJEmSJLXKygZJkiRJktSqFVc2JDkkyeeSfLxZfmaSm5PsSPLhJE9oyp/YLO9o1q9f6WtLkiRJkqTuaaNlw0XA3X3LbwfeWVXPAh4GLmjKLwAebsrf2WwnSZIkSZImzIoqG5KsBc4A3t8sB3gRcHWzyXbgpc3js5plmvWnNNtLkiRJkqQJstKWDe8C3gh8r1l+GvBIVc02y7uANc3jNcB9AM36R5vtJUmSJEnSBDl0uU9McibwQFXdkmS6vZAgyWZgM8DU1BQzMzNt7n7R9u7dO7LXXq5hxbxlw+zBN1qCqSNWvs9hv1fj+PmQJEmSpGFYdmUDcDLwkiSnA4cDTwYuBY5OcmjTemEtsLvZfjewDtiV5FDgKcBX59txVW0DtgFs3LixpqenVxDm8s3MzDCq116uYcW8aesnWt3flg2zXHLHSj6OsPOc6XaCWaRx/HxIkiRJ0jAsuxtFVb25qtZW1XrglcAnq+oc4EbgZc1m5wHXNI+vbZZp1n+yqmq5ry9JkiRJkrqpjdko9vcm4PVJdtAbk+Gypvwy4GlN+euBrQN4bUmSJEmSNGIra7feqKoZYKZ5fA9w4jzbfAt4eRuvJ0mSJEmSuquVygZJ0vKsb3n8E2mcJTkE+Cywu6rOTPJM4Ep6LSVvAc6tqm8neSJwOfBT9MZ/ekVV7RxR2JIkaR6D6EYhSZK0HBcBd/ctvx14Z1U9C3gYuKApvwB4uCl/Z7OdJEnqECsbJA1UksOT/HWSzye5M8lvNeXPTHJzkh1JPpzkCU35E5vlHc369aOMX9JwJFkLnAG8v1kO8CLg6maT7cBLm8dnNcs0609ptpckSR1hNwpJg/YPwIuqam+Sw4BPJ/kzegPFvrOqrkzy+/TuVL6PvjuWSV5J747lK0YVvFamrW4iOy8+o5X9qNPeBbwROKpZfhrwSDOVNsAuYE3zeA1wH0BVzSZ5tNn+of13mmQzsBlgamqKmZmZ1gPfu3fvQPY7CG3FumXD7ME3WoGpI5b3GqN4H8bl/R+XOCVNDisbJA1UM8Xt3mbxsOan6N2x/MWmfDvwNnqVDWc1j6F3x/I9SeJUudLkSnIm8EBV3ZJkus19V9U2YBvAxo0ba3q61d0DvS+4g9jvILQV66YBjzezZcMsl9yx9MvUnedMtx/MQYzL+z8ucUqaHFY2SBr5O3qVAAAgAElEQVS4ZtC3W4BnAe8FvswK71gO427lYq3kbtGg7w4ux3LvKA7SSt7fSbubN2nH0zgZeEmS04HDgScDlwJHJzm0yRVrgd3N9ruBdcCuJIcCT6E3UKQkSeoIKxskDVxVfRf4ySRHAx8DntPCPgd+t3KxVnK3aNB3B5djuXcUB2kldysn7W7epB0PQFW9GXgzQNOy4deq6pwkfwq8jN6MFOcB1zRPubZZ/qtm/Sdt/SRJUrc4QKSkoamqR4AbgRfS3LFsVs13xxLvWEqr3puA1yfZQa+F02VN+WXA05ry1wNbRxSfJEk6gG7dupI0cZL8EPCdqnokyRHAi+kN+ngj3rGUtJ+qmgFmmsf3ACfOs823gJcPNTBJkrQktmyQNGjHATcmuR34DHB9VX0c71hKkqRlSHJIks8l+Xiz7HTaUgfZskHSQFXV7cDz5in3jqUkSVqOi4C76Q0mC70Wk06nLXWMLRskSZIkjYUka4EzgPc3y6E3nfbVzSbbgZc2j89qlmnWn9JsL2kIbNkgSZIkaVy8C3gjcFSz/DScThsYzrTVS50ee9h/y3GYHrrrMbYZn5UNkiRJmgjrW5pOeOfFZ7SyH7UryZnAA1V1SzNNbiucTnvxljo99kqmrl6OcZgeuusxthmflQ2SJEmSxsHJwEuSnA4cTm/MhktpptNuWjfMN532LqfTlobPMRskSZIkdV5Vvbmq1lbVeuCV9KbHPocfTKcN80+nDU6nLQ2dlQ2SJEmSxpnTaUsdZDcKSZIkSWOlqmaAmeax02lLHWTLBkmSJEmS1CorGyRJkiRJUqvsRqGJ0dZ0V+CUV5IkSZK0ErZskCRJkiRJrbKyQZIkSZIktcrKBkmSJEmS1CorGyRJkiRJUqusbJAkSZIkSa1yNgpJA5VkHXA5MAUUsK2qLk1yDPBhYD2wEzi7qh5OEuBS4HTgm8Cmqrp1FLFLkiRp+dqaLc6Z4saTlQ2rSJtTQ0pLMAtsqapbkxwF3JLkemATcENVXZxkK7AVeBNwGnB883MS8L7mtyRJkqQxYTcKSQNVVXvmWiZU1WPA3cAa4Cxge7PZduClzeOzgMur5ybg6CTHDTlsSZIkSStgywZJQ5NkPfA84GZgqqr2NKvup9fNAnoVEff1PW1XU7anr4wkm4HNAFNTU8zMzAwq7IPau3fvsl9/y4bZdoNpwdQR3YtrJe/vSt6fLpq045EkSZPJygZJQ5HkScBHgNdV1dd7QzP0VFUlqaXsr6q2AdsANm7cWNPT0y1GuzQzMzMs9/U3dbB705YNs1xyR7f+Pew8Z3rZz13J+9NFk3Y8kiRpMtmNQtLAJTmMXkXDFVX10ab4K3PdI5rfDzTlu4F1fU9f25RJkiRJGhNWNkgaqGZ2icuAu6vqHX2rrgXOax6fB1zTV/7q9LwAeLSvu4UkSZKkMbDsyoYk65LcmOSuJHcmuagpPybJ9Um+1Px+alOeJO9OsiPJ7Ume39ZBSOq0k4FzgRclua35OR24GHhxki8BP98sA1wH3APsAP4A+JURxCxJkiRpBVbSKdfp7CQdVFV9GsgBVp8yz/YFXDjQoCRJkiQN1LJbNjidnSRJkiRJmk8rYzascDo7SZIkSZI0QVY8t1nb09k1+9wMbAaYmpoa2Xzi4ziX+UIxb9kwO9xglmDqiG7Ft5j3fRw/H5IkSZI0DCuqbFhoOruq2rPc6eyqahuwDWDjxo01qvnEx3Eu84Vi3rT1E8MNZgm2bJjlkjtWXPfVmp3nTB90m3H8fEhSFyVZB1xOrzVkAduq6tIkxwAfBtYDO4Gzq+rhZpabS4HTgW8Cm+a6dkqSpG5YyWwUTmcnSZLaMDfo9AnAC4ALk5xAb5DpG6rqeOCGZhn2HXR6M71BpyVJUoes5Fby3HR2dyS5rSl7C73p665KcgFwL3B2s+46encgdtC7C3H+Cl5bkiRNiObmw57m8WNJ+gednm422w7M0Jvh6vuDTgM3JTl6rlXlsGOXJEnzW3Zlg9PZSZKktq1w0OnHVTYMYxyocRrDp61YBz3O0qjHclrK32hc3v9xiVPS5OhOJ3lJkrSqDWLQ6WGMAzVOY/i0Feugx4Ea9VhOixm7ac64vP/jEudCHN9FGi9WNkiSpJEb1KDT2tcdux/t9IDR0kHMje9ya5KjgFuSXA9soje+y8VJttIb3+VN7Du+y0n0xnc5aSSRS6vQsgeIlCRJaoODTktajKraM9cyoaoeA/rHd9nebLYdeGnz+Pvju1TVTcDRTcWlpCGwZYMkSRo1B52WtCRtju8yjLFdFmslY2sMY5yTUY2nsti/yTiMTdL1GNuMz8oGSZI0Ug46LWkp2h7fZRhjuyzWSsbWGEYXqVGNp7LYcVTGYWySrsfYZnx2o5AkSZI0FhYa36VZ7/guUkdY2SBJkiSp8xzfRRovdqOQJEmSNA4c30UaI1Y2SJIkSeo8x3eRxouVDZIGKskfAmcCD1TVTzRlxwAfBtYDO4Gzq+rhpnnkpfTuQnwT2DQ3xVWXrN9vAKYtG2adt16SJEnq45gNkgbtg8Cp+5VtBW6oquOBG5plgNOA45ufzcD7hhSjJEmSpBZZ2SBpoKrqU8DX9is+C9jePN4OvLSv/PLquQk4em50aUmSJEnjw24UkkZhqm806PuBqebxGuC+vu12NWWPGzk6yWZ6rR+YmppiZmZmYMHub8uG2X2Wp454fNk46+LxrOT93bt371A/H4M2accjSZImk5UNkkaqqipJLeN524BtABs3bqzp6em2Qzug/cdn2LJhlkvumJx02snjueMby37qlg3f5ZJP/+D5Oy8+o42IRmZmZoZhft4lSZKWo2NXk5JWia8kOa6q9jTdJB5oyncD6/q2W9uUSZIkTRwHndYkc8wGSaNwLXBe8/g84Jq+8len5wXAo33dLSRJkiSNCVs2SBqoJH8CTAPHJtkF/CZwMXBVkguAe4Gzm82vozft5Q56U1+eP/SAJUmSJK2YlQ2SBqqqXnWAVafMs20BFw42IkmSJEmDZmWDNI/9+8/NZ7F96sZ9MDpJkiRJWirHbJAkSZIkSa2yZYMkSZLUZzEtHOccrKWjLRwlrVZWNkiSJEmSOmuxFYCL6eZsBeDw2I1CkiRJkiS1ysoGSZIkSZLUKrtRjIE2+w1KkiRJkjRotmyQJEmSJEmtsrJBkiRJkiS1ysoGSZIkSZLUKisbJEmSJElSq6xskCRJkiRJrXI2CkmrwlJmddFka+uzsPPiM1rZj3QwbeavLRta25UkSQuyZYMkSZIkSWqVlQ2SJEmSJKlVQ+9GkeRU4FLgEOD9VXXxsGOQ1G3mCUmLYa7QOGizG4zdt5bOPKH9eU4Oz1ArG5IcArwXeDGwC/hMkmur6q5hxjEM9g/XHPuHL81qyhOSls9cIelgzBPSaA27ZcOJwI6qugcgyZXAWYAnvKQ55gmNBe+MjJy5QqvOSvLOlg2zbGqev4pyzkDyhDcVpcUZdmXDGuC+vuVdwEkr3emgTvj+pCxpaAaSJ6QuW8r/sYP9b1pFXyI6nSv8MiJ1QqfzhMbfcnJ9179jtlkxmapqI6bFvVjyMuDUqvrlZvlc4KSqes1+220GNjeLzwa+OLQg93Us8NCIXnu5xjFmGM+4uxrzM6rqh0YdxHKNYZ6A7n4Wlsvj6bY2jmes8wR0LleM02dsXGIdlzhhfGJdTpxjnSs6licWq+ufJ+Nbua7HuNT4Dpgnht2yYTewrm95bVO2j6raBmwbVlAHkuSzVbVx1HEsxTjGDOMZ9zjGPCbGKk/A5H0WPJ5um7TjWYHO5Ipxek/GJdZxiRPGJ9ZxibNlnckTi9X198n4Vq7rMbYZ37CnvvwMcHySZyZ5AvBK4NohxyCp28wTkhbDXCHpYMwT0ggNtWVDVc0meQ3wF/Smn/nDqrpzmDFI6jbzhKTFMFdIOhjzhDRaw+5GQVVdB1w37Nddpk40p1qicYwZxjPucYx5LIxZnoDJ+yx4PN02acezbB3KFeP0noxLrOMSJ4xPrOMSZ6s6lCcWq+vvk/GtXNdjbC2+oQ4QKUmSJEmSJt+wx2yQJEmSJEkTzsqGRUqyJUklOXbUsRxMkt9L8oUktyf5WJKjRx3TgSQ5NckXk+xIsnXU8RxMknVJbkxyV5I7k1w06pg0euN0zi1k3M7HhUzquZrkkCSfS/LxUcei+Y3D9ULXc9Y45KJxyzHmjvHS1XO0y+fmuJyTXT4Xkxyd5Orms3d3kheudJ9WNixCknXALwB/O+pYFul64Ceq6p8BfwO8ecTxzCvJIcB7gdOAE4BXJTlhtFEd1CywpapOAF4AXDgGMWvwxuKcW8iYno8LmdRz9SLg7lEHofmN0fVCZ3PWGOWiccsx5o7x0rlzdAzOzXE5J7t8Ll4K/HlVPQd4Li3EaWXD4rwTeCMwFgNcVNVfVtVss3gTvTmFu+hEYEdV3VNV3wauBM4acUwLqqo9VXVr8/gxeifhmtFGpVEbo3NuIWN3Pi5kEs/VJGuBM4D3jzoWHdBYXC90PGeNRS4apxxj7hg/HT1HO31ujsM52eVzMclTgJ8BLgOoqm9X1SMr3a+VDQeR5Cxgd1V9ftSxLNO/Af5s1EEcwBrgvr7lXXQsKSwkyXrgecDNo41EHdPlc24hY30+LmSCztV30fsi+71RB6LHG+Prha7lrLHLRWOQY8wd460r5+jYnJsdPie7fC4+E3gQ+EDTzeP9SY5c6U6HPvVlFyX5r8CPzLPqrcBb6DWJ7JSFYq6qa5pt3kqvSdEVw4xtNUjyJOAjwOuq6uujjkeD5zk3niblXE1yJvBAVd2SZHrU8axW43S9YM4ajq7nGHNHd3mODkZXz8kxOBcPBZ4PvLaqbk5yKbAV+I2V7nTVq6qfn688yQZ6tTyfTwK9Jky3Jjmxqu4fYoiPc6CY5yTZBJwJnFLdnd90N7Cub3ltU9ZpSQ6jl8SuqKqPjjoeDceEnHMLGcvzcSETdq6eDLwkyenA4cCTk3yoqn5pxHGtKuN0vTDGOWtsctGY5BhzR0eN4Tna+XOz4+dk18/FXcCuqpprDXI1vcqGFUk3PrvjIclOYGNVPTTqWBaS5FTgHcDPVtWDo47nQJIcSm/Qm1PoJavPAL9YVXeONLAFpHcVuR34WlW9btTxqBvG5ZxbyDiejwuZ5HO1uSPya1V15qhj0fy6fr3Q5Zw1LrloHHOMuWN8dPEc7fq5OU7nZFfPxST/HfjlqvpikrcBR1bVG1ayT8dsmEzvAY4Crk9yW5LfH3VA82kGvnkN8Bf0BnG5qisJawEnA+cCL2r+trc1NZRa3cbinFvImJ6PC/FclQ6sszlrjHKROUaD1LlzdAzOTc/JlXstcEWS24GfBH5npTu0ZYMkSZIkSWqVLRskSZIkSVKrrGyQJEmSJEmtsrJBkiRJkiS1ysoGSZIkSZLUKisbJEmSJElSq6xskCRJkiRJrbKyQZIkSZIktcrKBkmSJEmS1CorGwRAkmcnuS3JY0l+dYHtNiX59DBjk9RNi80bklafJOuTVJJDm+WZJL886rgkdV+S6SS7Rh2HVs7KBs15I3BjVR1VVe9eyY6S3JjkwSRfT/L5JGe1FKOkbll03kjyfya5I8lskrcNJzxJkyDJtiRfTPK9JJtGHY+k7kryc813kUeT7Bx1PKudlQ2a8wzgzpXsYO7uBXARcFxVPRnYDHwoyXErjE9S9ywlb+ygVznxicGFI2lCfR74FeDWUQciabD6vk8s1zeAPwTe0EI4WiErG0SSTwI/B7wnyd4kz01yedM64d4kv55k3s9K00TywiRfAr4EUFW3V9Vss0kBhwHrhnEskoZjnrzxx0nem+QTTbeKm5P82Nz2VbW9qv4MeGxkQUtqRZJ1ST7aXCd8Ncl7khyS5D8neSjJPcAZCzz/uCS3J3lDszzTtH76H03++Mskx85tX1XvraobgG/Ns68Tk/xVkkeS7GliecIgjlvS8iXZmeTNSe5K8nCSDyQ5fK7LRJI3Jbkf+MA8z/3V5nlrm+U3Nuf73yX55eb7yLMAquqvq+qPgHuGe4Saj5UNoqpeBPx34DVV9SRgC/AU4J8APwu8Gjh/gV28FDgJOGGuIMnHk3wLuBmYAT47kOAljcQ8eePbwCuB3wKeSq8lw2+PLkJJg5DkEODjwL3AemANcCXwvwNnAs8DNgIvO8Dznwn8N+A9VfV7fat+kd61xg8DTwB+bZEhfRf498CxwAuBU+i1gpDUPecA/xz4MeDHgV9vyn8EOIZei8nN/U9I8h+ATcDPVtWuJKcCrwd+HngWMD2MwLU8VjZoH81FxCuBN1fVY1W1E7gEOHeBp/1uVX2tqv5+rqCqzgSOAk4H/rKqvjfAsCV1w8eaOwqzwBXAT446IEmtOxF4OvCGqvpGVX2rqj4NnA28q6ruq6qvAb87z3NPAG4EfrOqtu237gNV9TfNtcRVLDJ/VNUtVXVTVc021yz/hd6NEknd856+HPHbwKua8u/Rywv/0Pd9IkneAfwC8HNV9WBTfja9fHFnVX0TeNsQ49cSWdmg/R1Lr9vDvX1l99K7c3Eg981XWFXfaZpN/0KSl7QXoqSOur/v8TeBJ40qEEkDsw64t6+75Jyns+/1wL083jnAbuDqedYtK38k+fGmNeX9Sb4O/A69axlJ3bN/jnh68/jBqtq/m9TR9Fo5/G5VPdpXvn+umfd7iLrBygbt7yHgO/SaMc35UXoXBwdSB9nnofSaS0mSpPF2H/Cj8wzitod9x2f60Xme+zZ61xl/3LSkbMP7gC8AxzcDU78FSEv7ltSu/XPE3zWP5/su8TC9rlkfSHJyX/keYO0B9qmOsbJB+6iq79JrvvjbSY5K8gx6/aI+tJjnJ3lOktOSHJHksCS/BPwMvf6ZklapJh8cTu//zqHNoFBtfdmQNDx/Te9i/+IkRzbn8sn0rh1+NcnaJE8Fts7z3O8ALweOBC4/0ODT+0vyhCZ/BDisec255x4FfB3Ym+Q5wL9b0dFJGqQLmxxxDPBW4MMLbVxVM/RaRH00yYlN8VXA+Un+aZJ/DPxG/3OS/KMmXxzWW8zhDho7OlY2aD6vpTdtzD3Ap4E/pjeFzGKE3p2LB4AH6U2D+YqqcroqaXX7A+Dv6fXPfGvz+P9v7/6DJK/rO48/XyWREKKCIZkiLHfDVYg54kYlW2DOHDcJF4PAueTKEDxOWSTZpA49rWxKF3N1pqJWkUtQ8ZIitRESyKHIoRwbISqic1aqApFfEQSNK1nCbi1iAqKridya9/3R39FmmZ3pmflOf78983xUTU1/P/3pb7+7e/o93e/v5/v5LDQXjKQeag5K/AcGE7P9HbAH+CUG7/GPMVim8m7gw4e4/VPAfwSmgKtGLDh8nEHO+DfAjubyac11v8FgcsmvNzEs+OVFUqfez+D9/BDwJeAdi92gqm4FXgf8WZKTm1O038tg/pddwO1N1281v09jkCNuYTB64h+b+1QHUrXYCHhJkiRJkpYnyW7gl6vqEy3v918D9wOHzzOXjDrmyAZJkiRJ0kRI8gtJDm9O2fod4M8sNPSTxQZJkiRJ0qT4VQanbH8J+DbO1dJbnkYhSZIkSZJa5cgGSZIkSZLUKosNkiRJkiSpVYd1HcBijjnmmJqenu46jGf4xje+wZFHHtl1GJ1Z748f+vsc3HXXXX9fVT/YdRzjNEqe6OvrNYpJjh0mO/5Jjh0OHf96zBPQzWeKPv4NGdNojGl95opR80Qf/z4O1vcY+x4f9D/GPsS3UJ7ofbFhenqaO++8s+swnmF2dpaZmZmuw+jMen/80N/nIMnDXccwbqPkib6+XqOY5NhhsuOf5Njh0PGvxzwB3Xym6OPfkDGNxpjWZ64YNU/08e/jYH2Pse/xQf9j7EN8C+UJT6OQJEmSJEmtstggSZIkSZJatWixIclVSR5Lcv9Q2+8m+XySzya5MclRTft0kn9Mcm/z84dDt/nJJPcl2ZXkvUmyOg9JkiRJkiR1aZSRDX8CnHFQ263AC6vqJ4C/AS4Zuu5LVfXi5ufXhtqvAH4FOLH5OXifkiRJkiRpDVh0gsiq+nSS6YPaPj60eTvwqoX2keRY4LlVdXuzfQ1wDvDnS4x31U1vv3mkfts2HmDLAn13X3pWWyFJWsNGyTmL5Rsw50gazfT2m0fKKYsx50gaxUKfc5aSi8w5k6mNORtex9OLBickuSfJ/03yb5u244A9Q332NG2SJEmSJGmNWdHSl0l+EzgAXNs07QP+RVX9Q5KfBP5Pkh9fxn63AlsBpqammJ2dXUmYS7Jt44GR+k0dsXDfccbchf3796/5x7gYn4PRNfO6vA94IVAMipRfAD4ITAO7gXOr6olmPpfLgTOBbwJbquruDsKWJEmStEzLLjYk2QKcDZxeVQVQVd8CvtVcvivJl4AfBfYCG4ZuvqFpm1dV7QB2AGzatKnGuXboqEN5tm08wGX3Hfrp233+TEsR9VMf1nTtms/BklwOfLSqXpXk2cD3AW8FbquqS5NsB7YDbwFewXfndjmVwXwvp3YT9vxGPd1KkiRJWq+WVWxIcgbwZuDfVdU3h9p/EHi8qr6d5F8x+LLwUFU9nuRrSV4K3AG8FvifKw9fUt8leR5wGrAFoKqeAp5KshmYabpdDcwyKDZsBq5pipi3JzkqybFVtW/MoUtSL1jglCRNokWLDUk+wOALwTFJ9gBvY7D6xOHArc0Klrc3K0+cBvx2kv8H/DPwa1X1eLOr/8JgZYsjGMzx0LvJISWtihOArwB/nORFwF3AG4GpoQLCo8BUc/k44JGh28/N8fK0YsNST7dq87SXUU+3astip21Bv0/dmuRTjiY5dpj8+CVJ0uQaZTWKV8/TfOUh+n4I+NAhrruTwfnaktaXw4CTgTdU1R1JLmdwysR3VFUlqaXsdKmnW7V52stKZ3FfqsVO24J+n7o1yaccTXLsMPnxS5KkydXGahSStJA9wJ6quqPZvoFB8eHLzbK4c8vjPtZcvxc4fuj2C87xIkmSJKl/LDZIWlVV9SjwSJIXNE2nAw8AO4ELmrYLgJuayzuB12bgpcCTztcgSZIkTZYVLX0pSSN6A3BtsxLFQ8CFDIqd1ye5CHgYOLfpewuDZS93MVj68sLxhytJkiRpJSw2SFp1VXUvsGmeq06fp28BF696UJIkSZJWjadRSJIkSZKkVllskCRJkiRJrbLYIEmSJGkiJDkqyQ1JPp/kwSQ/leT5SW5N8sXm99FN3yR5b5JdST6b5OSu45fWE4sNkiRJkibF5cBHq+rHgBcBDwLbgduq6kTgtmYb4BXAic3PVuCK8YcrrV8WGyRJUuc8WilpMUmeB5wGXAlQVU9V1VeBzcDVTbergXOay5uBa2rgduCoJMeOOWxp3XI1CkmS1AdzRytf1SyT+33AWxkcrbw0yXYGRyvfwtOPVp7K4Gjlqd2ELWmMTgC+AvxxkhcBdwFvBKaqal/T51Fgqrl8HPDI0O33NG37htpIspXByAempqaYnZ1dNJD9+/eP1K9LfYhx28YDh7xu6oiFrx/W1ePow3O4kL7HZ7FBkiR1auho5RYYHK0EnkqyGZhpul0NzDIoNnznaCVwezMq4tihLxuS1qbDgJOBN1TVHUku57unTACDJbST1FJ2WlU7gB0AmzZtqpmZmUVvMzs7yyj9utSHGLdsv/mQ123beIDL7hvt6+ju82daimhp+vAcLqTv8XkahSRJ6trw0cp7krwvyZEs/WilpLVtD7Cnqu5otm9gUHz48tzpEc3vx5rr9wLHD91+Q9MmaQwc2SBJkrq2KkcrYXnDo9vUxhDXUYcZj2opQ5cPpe3nsY9DgY2pf6rq0SSPJHlBVX0BOB14oPm5ALi0+X1Tc5OdwOuTXMfgVKsnHQEljY/FBkmS1LX5jlZupzlaWVX7lnu0cjnDo9vUxhDXhYYhL8dShi4fSttDmvs4FNiYeusNwLXN3C4PARcyGK19fZKLgIeBc5u+twBnAruAbzZ9JY2JxQZJktQpj1ZKGlVV3Qtsmueq0+fpW8DFqx6UpHlZbJAkSX3g0UpJ6oHplkdTaf0aqdiQ5CrgbOCxqnph0/Z84IPANLAbOLeqnkgSBstXncngA8CWqrq7uc0FwH9rdvuOqroaSZK07nm0UpKktWXU1Sj+BDjjoLbtDNa+PhG4je9O5DS89vVWBmtfzxUn3sZguOMpwNuSHL2S4CVJkiRJUv+MVGyoqk8Djx/UvJnBmtc0v88Zar+mBm4Hjmomdfp54NaqeryqngBu5ZkFDEmSJEmSNOFGHdkwn6Wufe2a2JIkSZIkrQOtTBC53LWvD6XLNbFHXXd6sTWq1/oayOt9nWfwOZAkSZKkQ1lJsWGpa1/vBWYOap+db8ddrok96lrWi61R3fb6033jOs8+B5IkSZJ0KCs5jWIngzWv4ZlrX782Ay/lu2tffwx4eZKjm4khX960SZIkSZKkNWTUpS8/wGBUwjFJ9jBYVeJSlrD2dVU9nuTtwGeafr9dVQdPOilJkiRJkibcSMWGqnr1Ia5a0trXVXUVcNXI0UmSJEmSpImzktMoJEmSJEmSnsFigyRJkiRJapXFBkmSJEmS1CqLDZIkSZIkqVUWGyRJkiRJUqssNkiSJEmSpFZZbJAkSZIkSa2y2CBJkiRJklplsUGSJEmSJLXKYoMkSZIkSWqVxQZJY5HkWUnuSfKRZvuEJHck2ZXkg0me3bQf3mzvaq6f7jJuSZIkSUtnsUHSuLwReHBo+3eAd1fVjwBPABc17RcBTzTt7276SZIkSZogFhskrbokG4CzgPc12wF+Frih6XI1cE5zeXOzTXP96U1/SZIkSRPCYoOkcXgP8Gbgn5vtHwC+WlUHmu09wHHN5eOARwCa659s+kuSJEmaEId1HYCktS3J2cBjVXVXkpkW97sV2AowNTXF7Ozsgv3379+/aJ9Rbdt4YPFOLeed7HMAABOHSURBVJo6YvH7bOuxrYY2n/txm+TYYfLjlyRJk8tig6TV9jLglUnOBL4XeC5wOXBUksOa0QsbgL1N/73A8cCeJIcBzwP+4eCdVtUOYAfApk2bamZmZsEgZmdnWazPqLZsv7mV/Yxq28YDXHbfwul69/kz4wlmGdp87sdtkmOHyY9fkiRNrmWfRpHkBUnuHfr5WpI3JfmtJHuH2s8cus0lzQzzX0jy8+08BEl9VlWXVNWGqpoGzgM+WVXnA58CXtV0uwC4qbm8s9mmuf6TVVVjDFmSJEnSCi17ZENVfQF4MQyWtGNwNPJG4EIGM8z/3nD/JCcx+KLx48APA59I8qNV9e3lxiBpor0FuC7JO4B7gCub9iuBP02yC3icQd6QJEmSNEHaOo3idOBLVfXwApPGbwauq6pvAX/bfJE4BfjLlmKQ1HNVNQvMNpcfYpADDu7zT8AvjjUwSZI0MZoDnXcCe6vq7CQnANcxmFD6LuA1VfVUksOBa4CfZHBK5i9V1e6OwpbWnbZWozgP+MDQ9uuTfDbJVUmObtq+M8N8Y3j2eUmStM4leVaSe5J8pNk+IckdzSmYH0zy7Kb98GZ7V3P9dJdxSxq7NwIPDm3/DoOR1T8CPAFc1LRfBDzRtL+76SdpTFY8sqH5x/9K4JKm6Qrg7UA1vy8DXrfEfS5plvk2jTrL/GKzw6/12b+d4dznQJJWwdwXiOc223NfIK5L8ocMvjhcwdAXiCTnNf1+qYuAJY1Xkg3AWcA7gV/PYFj1zwL/qelyNfBbDHLF5uYywA3A7yeJc0FJ49HGaRSvAO6uqi8DzP0GSPJHwEeazbkZ5ucMzz7/NEudZb5No84yv9js8H2eGb4NznDucyBJbfILhKQRvQd4M/CcZvsHgK82q1vB00dPf2dkdVUdSPJk0//vh3e4nAOdk3DQabkxjmuJ71GW9p7T1XPd99e57/G1UWx4NUOnUCQ5tqr2NZu/ANzfXN4JvD/JuxhMEHki8Fct3L8kSZp8rX+BkLS2JDkbeKyq7koy09Z+l3OgcxIOOi03xnEt8T3K0t5zujqQ2/fXue/xrajYkORI4OeAXx1q/h9JXszgNIrdc9dV1eeSXA88ABwALnYlCkmStFpfIJp9d3ZqJrRz1Knto4xLOZp4KG0/j308OmdMvfQy4JVJzgS+l8EpV5cDRyU5rClODo+enhtZvSfJYcDzGEwUKWkMVlRsqKpvMDiSMNz2mgX6v5PB8EhJkqQ5q/YFostTM6Gdo05tH2VcytHEQ2n7KGMfj84ZU/9U1SU088Q1hcnfqKrzk/xv4FUMVqS4ALipucnOZvsvm+s/6elW0vi0tRqFJEnSslTVJVW1oaqmGaxw9cmqOh/4FIMvCDD/FwjwC4QkeAuDuV52MTgQemXTfiXwA037rwPbO4pPWpfamLNBkiRpNbwFuC7JO4B7ePoXiD9tvkA8zqBAIWkdqapZYLa5/BBwyjx9/gn4xbEGplUx3eIIr92XntXavrQwiw2SJKk3/AIhSdLa4GkUkiRJkiSpVRYbJEmSJElSqyw2SJIkSZKkVllskCRJkiRJrbLYIEmSJEmSWmWxQZIkSZIktcqlLyVpDXD9aUmSJPWJIxskSZIkSVKrLDZIkiRJkqRWWWyQJEmSJEmtstggSZIkSZJaZbFBkiRJkiS1ymKDJEmSJElq1YqLDUl2J7kvyb1J7mzanp/k1iRfbH4f3bQnyXuT7Ery2SQnr/T+JUmSJElSv7Q1suFnqurFVbWp2d4O3FZVJwK3NdsArwBObH62Ale0dP+SJEmSJKknVus0is3A1c3lq4FzhtqvqYHbgaOSHLtKMUiSJEmSpA60UWwo4ONJ7kqytWmbqqp9zeVHganm8nHAI0O33dO0SZIkSZKkNeKwFvbx01W1N8kPAbcm+fzwlVVVSWopO2yKFlsBpqammJ2dbSHM0WzbeGCkflNHLNx3nDF3Yf/+/Wv+MS7G50CSJEmS5rfiYkNV7W1+P5bkRuAU4MtJjq2qfc1pEo813fcCxw/dfEPTdvA+dwA7ADZt2lQzMzMrDXNkW7bfPFK/bRsPcNl9h376dp8/01JE/TQ7O8s4X5c+8jmQJEmSpPmt6DSKJEcmec7cZeDlwP3ATuCCptsFwE3N5Z3Aa5tVKV4KPDl0uoUkSZIkSVoDVjqyYQq4Mcncvt5fVR9N8hng+iQXAQ8D5zb9bwHOBHYB3wQuXOH9S5IkSZKknllRsaGqHgJeNE/7PwCnz9NewMUruU9JkiRJktRvq7X0pSRJkiRJWqcsNkhaVUmOT/KpJA8k+VySNzbtz09ya5IvNr+PbtqT5L1JdiX5bJKTu30EkiRJkpbKYoOk1XYA2FZVJwEvBS5OchKwHbitqk4Ebmu2AV4BnNj8bAWuGH/IkiRJklbCYoOkVVVV+6rq7uby14EHgeOAzcDVTbergXOay5uBa2rgduCoZgldSZIkSRNipatRSNLIkkwDLwHuAKaGlr59lMHqNjAoRDwydLM9TdvTlslNspXByAempqaYnZ1d8L7379+/aJ9Rbdt4oJX9jGrqiPHeZ1vP05w2n/txm+TYYfLjlyRJk8tig6SxSPL9wIeAN1XV15olc4HBSjVJain7q6odwA6ATZs21czMzIL9Z2dnWazPqLZsv7mV/Yxq28YDXHbf+NL17vNnWt1fm8/9uE1y7DA58Sc5HriGQdGxgB1VdXmS5wMfBKaB3cC5VfVEBgnkcgbLaX8T2DI3gqovpps8sW3jgbHnDGmtWou5QlrLLDZIWnVJvodBoeHaqvpw0/zlJMdW1b7mNInHmva9wPFDN9/QtElau+bmdrk7yXOAu5LcCmxhMLfLpUm2M5jb5S08fW6XUxnM7XJqJ5GvU9MtFlB2X3pWa/vSmmeukCaIczZIWlXNUYUrgQer6l1DV+0ELmguXwDcNNT+2mZVipcCTw6dbiFpDXJuF0mjMFdIk8WRDZJW28uA1wD3Jbm3aXsrcClwfZKLgIeBc5vrbmEw3HEXgyGPF443XEldanNuF0lrV5fzQMFkzImz3BjHNU/UuOekmrOU56Tvr3Pf47PYIGlVVdVfADnE1afP07+Ai1c1KEm91PbcLs0+l/wlog1zH6C7+jC9kL7FNDs728sPzMbUX13PAwWTMSfOcmMc1zwz456Tas5S5qbq++vc9/gsNkiSpM6t1twuy/kS0YYtQxNEdvFheiF9i2n3+TO9/MBsTP3kPFDS5HDOBkmS1CnndpE0CnOFNFn6U9aWJEnrlXO7SBqFuUKaIBYbJElSp5zbRdIozBXSZFkTxYY213qWJEmSJEkrsyaKDZKk9rRZwN196Vmt7UuSJEmTY9nFhiTHA9cwWMe2gB1VdXmS3wJ+BfhK0/WtVXVLc5tLgIuAbwP/tao+toLYe80P65IkSZKk9WolIxsOANuq6u4kzwHuSnJrc927q+r3hjsnOQk4D/hx4IeBTyT50ar69gpikKSR3Lf3ybGtGy1JkiStd8te+rKq9lXV3c3lrwMPAsctcJPNwHVV9a2q+lsGs8Kestz7lyRJkiRJ/dTKnA1JpoGXAHcwWJLm9UleC9zJYPTDEwwKEbcP3WwPCxcnJEmSJEkjOPg07m0bDziqU51acbEhyfcDHwLeVFVfS3IF8HYG8zi8HbgMeN0S97kV2AowNTXF7Ozsgv23bTyw9MBXaOqI8d3vYo+/C/v37+9lXOPkcyBJkiRJ81tRsSHJ9zAoNFxbVR8GqKovD13/R8BHms29wPFDN9/QtD1DVe0AdgBs2rSpZmZmFoyji4rdto0HuOy+8Szmsfv8mbHcz1LMzs6y2Ouy1vkcSJIkSdL8lj1nQ5IAVwIPVtW7htqPHer2C8D9zeWdwHlJDk9yAnAi8FfLvX9JkiRJktRPKzk0/zLgNcB9Se5t2t4KvDrJixmcRrEb+FWAqvpckuuBBxisZHGxK1FIkiRJkrT2LLvYUFV/AWSeq25Z4DbvBN653PuUJEmSJEn9t+zTKCRJkiRJkuZjsUGSJEmSJLXKYoMkSZIkSWqVxQZJkiRJktSqlaxGIUmSJEnSxJjefvPIfbdtPMCWBfrvvvSsNkJasxzZIEmSJEmSWmWxQZIkSZIktcpigyRJkiRJapXFBkmSJEmS1ConiJQkrZrp7TcvOrnSKJyASZIkabI4skGSJEmSJLXKkQ2SJEnqTFsjoMBRUJLUJ45skCRJkiRJrbLYIEmSJEmSWuVpFJIkSZIkLdF0C6d/wdo9BcxigySp99r6Zw5r9x+6JElSn3gahSRJkiRJatXYRzYkOQO4HHgW8L6qunTcMUjqN/OEpFGsRq5ocxSNpO75mULqzliLDUmeBfwB8HPAHuAzSXZW1QPjjGPSeC6Q1hPzhFbbUnLqQsvxmVO7Za7QfPzMpGGTkCcscGotG/fIhlOAXVX1EECS64DNQG/e8GtZm8msrfWwwX/oegbzhCaC80h0zlyhVTO9/ebWPuv4/u6UeUITYbmfKdr8TjafleavVFVLoYxwZ8mrgDOq6peb7dcAp1bV6w/qtxXY2my+APjC2IIc3THA33cdRIfW++OH/j4H/7KqfrDrIJZrFfNEX1+vUUxy7DDZ8U9y7HDo+Cc6T8BEfabo49+QMY3GmCY8V6xynujj38fB+h5j3+OD/sfYh/gOmSd6uRpFVe0AdnQdx0KS3FlVm7qOoyvr/fGDz0HXlponJvn1muTYYbLjn+TYYfLjb0PXnyn6+BoY02iMaf1YTp6YhNei7zH2PT7of4x9j2/cq1HsBY4f2t7QtEnSHPOEpFGYKyQtxjwhdWjcxYbPACcmOSHJs4HzgJ1jjkFSv5knJI3CXCFpMeYJqUNjPY2iqg4keT3wMQbLz1xVVZ8bZwwt6vVpHmOw3h8/+BysilXME5P8ek1y7DDZ8U9y7DD58R/SBH2m6ONrYEyjMaYJt8p5YhJei77H2Pf4oP8x9jq+sU4QKUmSJEmS1r5xn0YhSZIkSZLWOIsNkiRJkiSpVRYblijJGUm+kGRXku1dx9OFJLuT3Jfk3iR3dh3POCS5KsljSe4fant+kluTfLH5fXSXMWpxSX43yeeTfDbJjUmO6jqmxUxqzklyfJJPJXkgyeeSvLHrmJYqybOS3JPkI13HslRJjkpyQ/P3/mCSn+o6pvWsL7mnb/mkz3mib+9/39P9lmRbkkpyTNexDOtL7plP3/LRsD7npmF9y1PzsdiwBEmeBfwB8ArgJODVSU7qNqrO/ExVvbjP67q27E+AMw5q2w7cVlUnArc12+q3W4EXVtVPAH8DXNJxPAua8JxzANhWVScBLwUunqDY57wReLDrIJbpcuCjVfVjwIuY3MexVnSee3qaT/qcJ/r2/vc93VNJjgdeDvxd17HMo/PcM5+e5qNhfc5Nw/qWp57BYsPSnALsqqqHquop4Dpgc8cxaQyq6tPA4wc1bwaubi5fDZwz1qC0ZFX18ao60GzezmC97T6b2JxTVfuq6u7m8tcZ/DM8rtuoRpdkA3AW8L6uY1mqJM8DTgOuBKiqp6rqq91Gtb71JPf0Lp/0NU/07f3ve7r33g28GejdrPs9yT3z6V0+GtbX3DSsb3nqUCw2LM1xwCND23vo2R/emBTw8SR3JdnadTAdmqqqfc3lR4GpLoPRkr0O+POug1jEmsg5SaaBlwB3dBvJkryHwYfHf+46kGU4AfgK8MfN8Mr3JTmy66D0HV3lnl7nk57lib69/31P91SSzcDeqvrrrmMZQZ8+9/Q6Hw3rWW4a1rc8NS+LDVqOn66qkxkMfbo4yWldB9S1Gqwh27uK9nqU5BNJ7p/nZ/NQn99kMETu2u4iXR+SfD/wIeBNVfW1ruMZRZKzgceq6q6uY1mmw4CTgSuq6iXAN/A0r1Vn7lm+PuWJnr7/fU93aJH39luB/97j+Ob6mHuWoU+5aVhP89S8Dus6gAmzFzh+aHtD07auVNXe5vdjSW5kMBTq091G1YkvJzm2qvYlORZ4rOuABFX17xe6PskW4Gzg9KZI1GcTnXOSfA+Df9LXVtWHu45nCV4GvDLJmcD3As9N8r+q6j93HNeo9gB7qmruKMwN+MVk1U1A7ullPulhnujj+9/3dIcO9d5OspHBqJO/TgKD99TdSU6pqke7jm9OD3LPfHqZj4b1MDcN62OempcjG5bmM8CJSU5I8mzgPGBnxzGNVZIjkzxn7jKDCXHuX/hWa9ZO4ILm8gXATR3GohEkOYPBkLNXVtU3u45nBBObczL45HUl8GBVvavreJaiqi6pqg1VNc3gOf9kH/+BH0rzIfeRJC9omk4HHugwpHWvJ7mnd/mkj3mij+9/39P9VFX3VdUPVdV08/eyBzh5nIWGxfQk98ynd/loWB9z07A+5qlDcWTDElTVgSSvBz4GPAu4qqo+13FY4zYF3NhUcA8D3l9VH+02pNWX5APADHBMkj3A24BLgeuTXAQ8DJzbXYQa0e8DhwO3Nn/Dt1fVr3Ub0qFNeM55GfAa4L4k9zZtb62qWzqMaT15A3Bt8yHuIeDCjuNZ7zrPPT3NJ+aJ0fme1nJ0nnvm09N8NMzc1JL0ZzSNJEmSJElaCzyNQpIkSZIktcpigyRJkiRJapXFBkmSJEmS1CqLDZIkSZIkqVUWGyRJkiRJUqssNkiSJEmSpFZZbJAkSZIkSa36/5su18Pd5p5YAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1296x864 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "gene_expr.sample(n=16,axis='columns').hist(figsize=(18,12))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "wO3UQ3sujCB-",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "wO3UQ3sujCB-",
        "outputId": "618c6117-5528-4381-ad81-63b9ecd08812"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#how varied are genes and how well do they correlate with eventdeath?\n",
        "fig, ax = plt.subplots(figsize=(10,4))\n",
        "\n",
        "#plot histogram of variation using standard deviation as a measure\n",
        "corrs=[]\n",
        "for col in gene_expr.columns:\n",
        "    corr = gene_expr[col].corr(clinical['overall_survival'])\n",
        "    corrs.append(corr)\n",
        "\n",
        "ax.hist(corrs, bins=30)\n",
        "ax.set_xlabel(\"Correlation\")\n",
        "ax.set_ylabel(\"#Genes\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "OVaYF388Xmh6",
      "metadata": {
        "id": "OVaYF388Xmh6"
      },
      "source": [
        "# Train Test Split\n",
        "\n",
        "In the following cells, we split the data into train, validation and test sets. `death_from_cancer` is removed from clinical dataset because it will determine output exactly and there will nothing remain to learn! object data (Which is string data) is encoded using scikitlearn `LabelEncoder`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UuLfwOW3NJTi",
      "metadata": {
        "id": "UuLfwOW3NJTi"
      },
      "outputs": [],
      "source": [
        "X = dataset.dropna()\n",
        "y = X[['overall_survival']].to_numpy()\n",
        "y = y.reshape(y.shape[0])\n",
        "X = X.drop(['death_from_cancer', 'overall_survival', 'patient_id'], axis=1)\n",
        "\n",
        "object_cols = X.select_dtypes(include=['object']).columns.tolist()\n",
        "for col in object_cols:\n",
        "  X[col] = LabelEncoder().fit_transform(X[col].values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Uam70JHDNZ--",
      "metadata": {
        "id": "Uam70JHDNZ--"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d1WLbr3eN3yP",
      "metadata": {
        "id": "d1WLbr3eN3yP"
      },
      "outputs": [],
      "source": [
        "X_clinical_train = X_train.loc[:, :'tumor_stage'].to_numpy()\n",
        "X_gene_train = X_train.loc[:, \"brca1\":].to_numpy()\n",
        "X_clinical_val = X_val.loc[:, :'tumor_stage'].to_numpy()\n",
        "X_gene_val = X_val.loc[:, \"brca1\":].to_numpy()\n",
        "X_clinical_test = X_test.loc[:, :'tumor_stage'].to_numpy()\n",
        "X_gene_test = X_test.loc[:, \"brca1\":].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "IcvdY6MUgCm5",
      "metadata": {
        "id": "IcvdY6MUgCm5"
      },
      "outputs": [],
      "source": [
        "# Normalize Data of clinical data\n",
        "clinical_scaler = StandardScaler()\n",
        "clinical_scaler.fit(X_clinical_train)\n",
        "X_clinical_train_norm = clinical_scaler.transform(X_clinical_train)\n",
        "X_clinical_val_norm = clinical_scaler.transform(X_clinical_val)\n",
        "X_clinical_test_norm = clinical_scaler.transform(X_clinical_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e2c3821f",
      "metadata": {
        "id": "e2c3821f"
      },
      "source": [
        "# Dimension Reduction (20 + Up to 10 Points Optional)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b925e2cf",
      "metadata": {
        "id": "b925e2cf"
      },
      "source": [
        "For each dataset, investigate whether it is needed to use a dimensionality reduction approach or not. If yes, please reduce the dataset's dimension. You can use UMAP for this purpose but any other approach is acceptable. Finding the most important features contains extra points."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f1TAEJ6Xg_N",
      "metadata": {
        "id": "1f1TAEJ6Xg_N"
      },
      "source": [
        "**NOTE:** We used only the training data to reduce the dimention of the test and validation sets so that the results matches realworld practice and to be unbiased.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5j9jigsMetbe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "5j9jigsMetbe",
        "outputId": "221f346d-eff5-4aae-f150-0d5617403346"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(200).fit(X_gene_train)\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.xlabel('Number of Principal Components')\n",
        "plt.ylabel('Eigenvalues')\n",
        "plt.title('PCA Eigenvalues')\n",
        "plt.style.context('seaborn-whitegrid')\n",
        "plt.plot(pca.explained_variance_)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "uuOCsY-me8TG",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "uuOCsY-me8TG",
        "outputId": "77d957a7-c0e6-4507-c04b-89d03bac95c4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "variance = np.cumsum(pca.explained_variance_ratio_)\n",
        "\n",
        "fig = plt.figure()\n",
        "plt.xlabel('Number of Principal Components')\n",
        "plt.ylabel('Proportion of the Variance Explained')\n",
        "plt.title('PCA Variance Explained')\n",
        "plt.style.context('seaborn-whitegrid')\n",
        "plt.axhline(y=0.9, color='r', linestyle='--')\n",
        "plt.plot(variance)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "259a3804",
      "metadata": {
        "id": "259a3804"
      },
      "outputs": [],
      "source": [
        "from umap import UMAP\n",
        "\n",
        "umap_enc = UMAP(n_components=10, random_state=42)\n",
        "umap_enc.fit(X_gene_train)\n",
        "X_gene_reduced_train = umap_enc.transform(X_gene_train)\n",
        "X_gene_reduced_val = umap_enc.transform(X_gene_val)\n",
        "X_gene_reduced_test = umap_enc.transform(X_gene_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bf0ea4bb",
      "metadata": {
        "id": "bf0ea4bb"
      },
      "source": [
        "# Classic Model (25 Points)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "389c5d75",
      "metadata": {
        "id": "389c5d75"
      },
      "source": [
        "In this section, you must implement a classic classification model for clinical, gene expressions, and reduced gene expressions datasets. Using Random Forest is suggested. (minimum acceptable accuracy = 60%)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56xr0OhFVo4U",
      "metadata": {
        "id": "56xr0OhFVo4U"
      },
      "source": [
        "## Foreword\n",
        "\n",
        "As it was mentioned in the Quera that we can use any module and library that we want, we will use scikit learn library to build and train our model. We didn't stopped at Random Forest model and examined different methods for the datasets and compared them together. This work is done for each dataset (i.e. clinical, gene expression and reduced gene expression). The models that are used are as follow:\n",
        "* Random Forest\n",
        "* Logistic Regression\n",
        "* AdaBoost\n",
        "* XGBoost\n",
        "* Support Vector Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Ks3mWUy9Y2z0",
      "metadata": {
        "id": "Ks3mWUy9Y2z0"
      },
      "source": [
        "## Clinical\n",
        "\n",
        "In this section we use clinical data to classify the `overall_survival` feature."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "34ec1dd7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34ec1dd7",
        "outputId": "0a4b0891-2a9c-4188-93e0-5dfbb916284f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(gamma='auto')"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Random Forest\n",
        "rf_clf = RandomForestClassifier(random_state=42, max_depth=50)\n",
        "rf_clf.fit(X_clinical_train, y_train)\n",
        "\n",
        "# Logestic Regression\n",
        "lr_clf = LogisticRegression(random_state=42, penalty='l2')\n",
        "lr_clf.fit(X_clinical_train_norm, y_train)\n",
        "\n",
        "# AdaBoost\n",
        "ada_clf = AdaBoostClassifier(n_estimators=100)\n",
        "ada_clf.fit(X_clinical_train, y_train)\n",
        "\n",
        "# XGBoost\n",
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(X_clinical_train, y_train)\n",
        "\n",
        "# SVC\n",
        "svc_clf = SVC(gamma='auto')\n",
        "svc_clf.fit(X_clinical_train_norm, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "nst3q40zjU5b",
      "metadata": {
        "id": "nst3q40zjU5b"
      },
      "outputs": [],
      "source": [
        "# Generate Predictions\n",
        "predictions = {\n",
        "  \"Train\": {\n",
        "    \"Random Forest\": rf_clf.predict(X_clinical_train),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_clinical_train_norm),\n",
        "    \"Ada Boost\": ada_clf.predict(X_clinical_train),\n",
        "    \"XGBoost\": xgb_clf.predict(X_clinical_train),\n",
        "    \"SVC\": svc_clf.predict(X_clinical_train_norm),\n",
        "  },\n",
        "  \"Test\":{\n",
        "    \"Random Forest\": rf_clf.predict(X_clinical_test),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_clinical_test_norm),\n",
        "    \"Ada Boost\": ada_clf.predict(X_clinical_test),\n",
        "    \"XGBoost\": xgb_clf.predict(X_clinical_test),\n",
        "    \"SVC\": svc_clf.predict(X_clinical_test_norm)\n",
        "  }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ijgZsVG_ne51",
      "metadata": {
        "id": "ijgZsVG_ne51"
      },
      "outputs": [],
      "source": [
        "# Calculate Accuracy Scores\n",
        "accuracy = {}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  accuracy[stage] = {}\n",
        "  for model, y_pred in models.items():\n",
        "    accuracy[stage][model] = accuracy_score(y_true[stage], y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4ttL24mLsfLz",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ttL24mLsfLz",
        "outputId": "d613e0aa-ceee-42ee-d8db-51e565bc666c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "========== Train =========================\n",
            "      Random Forest :  1.00\n",
            "      Logistic Regression :  0.79\n",
            "      Ada Boost :  0.87\n",
            "      XGBoost :  0.90\n",
            "      SVC :  0.87\n",
            "========== Test ==========================\n",
            "      Random Forest :  0.76\n",
            "      Logistic Regression :  0.71\n",
            "      Ada Boost :  0.75\n",
            "      XGBoost :  0.75\n",
            "      SVC :  0.76\n"
          ]
        }
      ],
      "source": [
        "# Print and Visualize Accuracies\n",
        "for stage, models in accuracy.items():\n",
        "  print(\"=\"*10, stage, \"=\"*(30 - len(stage)))\n",
        "  for model, acc in models.items():\n",
        "    print(\" \" * 5, model, \": \", \"%.2f\"%(acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dNTri-o6qpiR",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "dNTri-o6qpiR",
        "outputId": "590c2de9-3193-46db-9de9-afb8bfd600d1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize RoC Curves\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "ax = {\"Train\": ax1, \"Test\": ax2}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  ax[stage].set_title(stage + \" RoC\")\n",
        "  for model, y_pred in models.items():\n",
        "    RocCurveDisplay.from_predictions(y_true[stage], y_pred, name=model, \n",
        "                                       ax=ax[stage])\n",
        "    \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "WEAxBgQxySd-",
      "metadata": {
        "id": "WEAxBgQxySd-"
      },
      "source": [
        "## Gene Expression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "SAMQWTKvySeD",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SAMQWTKvySeD",
        "outputId": "0a3b18e4-5027-4155-c797-37a0d448f792"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "SVC(gamma='auto')"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Random Forest\n",
        "rf_clf = RandomForestClassifier(random_state=42, max_depth=5)\n",
        "rf_clf.fit(X_gene_train, y_train)\n",
        "\n",
        "# Logestic Regression\n",
        "lr_clf = LogisticRegression(random_state=42, penalty='l2')\n",
        "lr_clf.fit(X_gene_train, y_train)\n",
        "\n",
        "# AdaBoost\n",
        "ada_clf = AdaBoostClassifier(n_estimators=100)\n",
        "ada_clf.fit(X_gene_train, y_train)\n",
        "\n",
        "# XGBoost\n",
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(X_gene_train, y_train)\n",
        "\n",
        "# SVC\n",
        "svc_clf = SVC(gamma='auto')\n",
        "svc_clf.fit(X_gene_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PunkO8n4ySeD",
      "metadata": {
        "id": "PunkO8n4ySeD"
      },
      "outputs": [],
      "source": [
        "# Generate Predictions\n",
        "predictions = {\n",
        "  \"Train\": {\n",
        "    \"Random Forest\": rf_clf.predict(X_gene_train),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_gene_train),\n",
        "    \"Ada Boost\": ada_clf.predict(X_gene_train),\n",
        "    \"XGBoost\": xgb_clf.predict(X_gene_train),\n",
        "    \"SVC\": svc_clf.predict(X_gene_train),\n",
        "  },\n",
        "  \"Test\":{\n",
        "    \"Random Forest\": rf_clf.predict(X_gene_test),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_gene_test),\n",
        "    \"Ada Boost\": ada_clf.predict(X_gene_test),\n",
        "    \"XGBoost\": xgb_clf.predict(X_gene_test),\n",
        "    \"SVC\": svc_clf.predict(X_gene_test)\n",
        "  }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "m1QgkZmHySeD",
      "metadata": {
        "id": "m1QgkZmHySeD"
      },
      "outputs": [],
      "source": [
        "# Calculate Accuracy Scores\n",
        "accuracy = {}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  accuracy[stage] = {}\n",
        "  for model, y_pred in models.items():\n",
        "    accuracy[stage][model] = accuracy_score(y_true[stage], y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PA01imHLySeE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PA01imHLySeE",
        "outputId": "75cbbda0-10bd-43a9-ab48-e4abd50eaafc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "========== Train =========================\n",
            "      Random Forest :  0.95\n",
            "      Logistic Regression :  1.00\n",
            "      Ada Boost :  0.98\n",
            "      XGBoost :  1.00\n",
            "      SVC :  0.93\n",
            "========== Test ==========================\n",
            "      Random Forest :  0.64\n",
            "      Logistic Regression :  0.63\n",
            "      Ada Boost :  0.58\n",
            "      XGBoost :  0.66\n",
            "      SVC :  0.70\n"
          ]
        }
      ],
      "source": [
        "# Print and Visualize Accuracies\n",
        "for stage, models in accuracy.items():\n",
        "  print(\"=\"*10, stage, \"=\"*(30 - len(stage)))\n",
        "  for model, acc in models.items():\n",
        "    print(\" \" * 5, model, \": \", \"%.2f\"%(acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rYF95Z3EySeE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "rYF95Z3EySeE",
        "outputId": "9d903494-92c8-4134-b5e4-b5783d153ea8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize RoC Curves\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "ax = {\"Train\": ax1, \"Test\": ax2}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  ax[stage].set_title(stage + \" RoC\")\n",
        "  for model, y_pred in models.items():\n",
        "    RocCurveDisplay.from_predictions(y_true[stage], y_pred, name=model, \n",
        "                                       ax=ax[stage])\n",
        "    \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0PEVvsHfzUhw",
      "metadata": {
        "id": "0PEVvsHfzUhw"
      },
      "source": [
        "## Reduced Gene Expression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "z1_NDcr8zUh1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z1_NDcr8zUh1",
        "outputId": "2b2c0e94-c8ce-4164-a74f-17dd962c498f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(gamma='auto')"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Random Forest\n",
        "rf_clf = RandomForestClassifier(random_state=42, max_depth=5)\n",
        "rf_clf.fit(X_gene_reduced_train, y_train)\n",
        "\n",
        "# Logestic Regression\n",
        "lr_clf = LogisticRegression(random_state=42, penalty='l2')\n",
        "lr_clf.fit(X_gene_reduced_train, y_train)\n",
        "\n",
        "# AdaBoost\n",
        "ada_clf = AdaBoostClassifier(n_estimators=100)\n",
        "ada_clf.fit(X_gene_reduced_train, y_train)\n",
        "\n",
        "# XGBoost\n",
        "xgb_clf = XGBClassifier()\n",
        "xgb_clf.fit(X_gene_reduced_train, y_train)\n",
        "\n",
        "# SVC\n",
        "svc_clf = SVC(gamma='auto')\n",
        "svc_clf.fit(X_gene_reduced_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "GihilXJkzUh1",
      "metadata": {
        "id": "GihilXJkzUh1"
      },
      "outputs": [],
      "source": [
        "# Generate Predictions\n",
        "predictions = {\n",
        "  \"Train\": {\n",
        "    \"Random Forest\": rf_clf.predict(X_gene_reduced_train),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_gene_reduced_train),\n",
        "    \"Ada Boost\": ada_clf.predict(X_gene_reduced_train),\n",
        "    \"XGBoost\": xgb_clf.predict(X_gene_reduced_train),\n",
        "    \"SVC\": svc_clf.predict(X_gene_reduced_train),\n",
        "  },\n",
        "  \"Test\":{\n",
        "    \"Random Forest\": rf_clf.predict(X_gene_reduced_test),\n",
        "    \"Logistic Regression\": lr_clf.predict(X_gene_reduced_test),\n",
        "    \"Ada Boost\": ada_clf.predict(X_gene_reduced_test),\n",
        "    \"XGBoost\": xgb_clf.predict(X_gene_reduced_test),\n",
        "    \"SVC\": svc_clf.predict(X_gene_reduced_test)\n",
        "  }\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "CSAKoLtZzUh1",
      "metadata": {
        "id": "CSAKoLtZzUh1"
      },
      "outputs": [],
      "source": [
        "# Calculate Accuracy Scores\n",
        "accuracy = {}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  accuracy[stage] = {}\n",
        "  for model, y_pred in models.items():\n",
        "    accuracy[stage][model] = accuracy_score(y_true[stage], y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Onpro9dEzUh2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Onpro9dEzUh2",
        "outputId": "5d7cab0c-0582-41bb-ffe9-80a2b11250c9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "========== Train =========================\n",
            "      Random Forest :  0.80\n",
            "      Logistic Regression :  0.60\n",
            "      Ada Boost :  0.80\n",
            "      XGBoost :  0.85\n",
            "      SVC :  0.60\n",
            "========== Test ==========================\n",
            "      Random Forest :  0.65\n",
            "      Logistic Regression :  0.64\n",
            "      Ada Boost :  0.60\n",
            "      XGBoost :  0.62\n",
            "      SVC :  0.66\n"
          ]
        }
      ],
      "source": [
        "# Print and Visualize Accuracies\n",
        "for stage, models in accuracy.items():\n",
        "  print(\"=\"*10, stage, \"=\"*(30 - len(stage)))\n",
        "  for model, acc in models.items():\n",
        "    print(\" \" * 5, model, \": \", \"%.2f\"%(acc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "iEE7Ggz0zUh2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "iEE7Ggz0zUh2",
        "outputId": "8b72e216-f66c-4368-b966-cc8b1b6190bf"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Visualize RoC Curves\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n",
        "ax = {\"Train\": ax1, \"Test\": ax2}\n",
        "y_true = {\"Train\": y_train, \"Test\": y_test}\n",
        "\n",
        "for stage, models in predictions.items():\n",
        "  ax[stage].set_title(stage + \" RoC\")\n",
        "  for model, y_pred in models.items():\n",
        "    RocCurveDisplay.from_predictions(y_true[stage], y_pred, name=model, \n",
        "                                       ax=ax[stage])\n",
        "    \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "50146d67",
      "metadata": {
        "id": "50146d67"
      },
      "source": [
        "# Neural Network (30 Points)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "485c4047",
      "metadata": {
        "id": "485c4047"
      },
      "source": [
        "In this section, you must implement a neural network model for clinical, gene expressions and reduced gene expressions datasets. Using the MPL models is suggested. (minimum acceptable accuracy = 60%)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "z6MAFUNNvb-R",
      "metadata": {
        "id": "z6MAFUNNvb-R"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "xun0vdbOxzy1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xun0vdbOxzy1",
        "outputId": "0948a00b-5217-45c5-c96d-dc2b39774590"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cpu')"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "SX4MKj0xaoZi",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SX4MKj0xaoZi",
        "outputId": "6fdf9748-2aa0-452e-e916-46c604e7411b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f39e59941f0>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "torch.manual_seed(42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "Do8IctJlzcEO",
      "metadata": {
        "id": "Do8IctJlzcEO"
      },
      "outputs": [],
      "source": [
        "class TabularDataset(Dataset):\n",
        "  def __init__(self, X, y, ytype=torch.long):\n",
        "    self.X = torch.tensor(X, dtype=torch.float32)\n",
        "    self.y = torch.tensor(y, dtype=ytype)\n",
        " \n",
        "  def __len__(self):\n",
        "    return len(self.y)\n",
        "   \n",
        "  def __getitem__(self,idx):\n",
        "    return self.X[idx], self.y[idx]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "L7MEiO0RrtMQ",
      "metadata": {
        "id": "L7MEiO0RrtMQ"
      },
      "outputs": [],
      "source": [
        "clinical_features = X_clinical_train.shape[1]\n",
        "gene_exp_features = X_gene_train.shape[1]\n",
        "gene_exp_reduced_features = X_gene_reduced_train.shape[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "omI2qPP6vgEJ",
      "metadata": {
        "id": "omI2qPP6vgEJ"
      },
      "source": [
        "## Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2992ff5a",
      "metadata": {
        "id": "2992ff5a"
      },
      "outputs": [],
      "source": [
        "class ClinicalClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ClinicalClassifier, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(clinical_features, 16),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(16, 8),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(8, 4),\n",
        "            nn.ReLU(),\n",
        "            nn.Linear(4, 2),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "class GeneExpressionClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(GeneExpressionClassifier, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(gene_exp_features, 16),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.5),\n",
        "            nn.Linear(16, 2)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)\n",
        "\n",
        "class ReducedGeneExpressionClassifier(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(ReducedGeneExpressionClassifier, self).__init__()\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(gene_exp_reduced_features, 7),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(0.4),\n",
        "            nn.Linear(7, 2)\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bLnk0uDhH8Jg",
      "metadata": {
        "id": "bLnk0uDhH8Jg"
      },
      "source": [
        "## Clinical Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "np_CdqoJxrzA",
      "metadata": {
        "id": "np_CdqoJxrzA"
      },
      "outputs": [],
      "source": [
        "clinical_model = ClinicalClassifier().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(clinical_model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "MnkqeGt5JPQ2",
      "metadata": {
        "id": "MnkqeGt5JPQ2"
      },
      "source": [
        "### Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "-y_GqV871jxi",
      "metadata": {
        "id": "-y_GqV871jxi"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "\n",
        "train_dataset = TabularDataset(X_clinical_train, y_train)\n",
        "val_dataset = TabularDataset(X_clinical_val, y_val)\n",
        "test_dataset = TabularDataset(X_clinical_test, y_test)\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "xc4Q8Ut9JXxF",
      "metadata": {
        "id": "xc4Q8Ut9JXxF"
      },
      "source": [
        "### Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "MQ5kpH1BHmkk",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MQ5kpH1BHmkk",
        "outputId": "98e37a91-cb2e-4f1a-d48b-2a79c499d393"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train 0 | Loss:0.6769: 100%|██████████| 11/11 [00:00<00:00, 135.10it/s]\n",
            "Val 0 | Loss:0.6600: 100%|██████████| 3/3 [00:00<00:00, 277.58it/s]\n",
            "Train 1 | Loss:0.6365: 100%|██████████| 11/11 [00:00<00:00, 148.18it/s]\n",
            "Val 1 | Loss:0.6336: 100%|██████████| 3/3 [00:00<00:00, 254.86it/s]\n",
            "Train 2 | Loss:0.6055: 100%|██████████| 11/11 [00:00<00:00, 102.07it/s]\n",
            "Val 2 | Loss:0.6159: 100%|██████████| 3/3 [00:00<00:00, 234.76it/s]\n",
            "Train 3 | Loss:0.5856: 100%|██████████| 11/11 [00:00<00:00, 113.43it/s]\n",
            "Val 3 | Loss:0.6100: 100%|██████████| 3/3 [00:00<00:00, 232.35it/s]\n",
            "Train 4 | Loss:0.5736: 100%|██████████| 11/11 [00:00<00:00, 93.94it/s]\n",
            "Val 4 | Loss:0.6095: 100%|██████████| 3/3 [00:00<00:00, 168.69it/s]\n",
            "Train 5 | Loss:0.5630: 100%|██████████| 11/11 [00:00<00:00, 116.46it/s]\n",
            "Val 5 | Loss:0.5863: 100%|██████████| 3/3 [00:00<00:00, 193.27it/s]\n",
            "Train 6 | Loss:0.5475: 100%|██████████| 11/11 [00:00<00:00, 127.59it/s]\n",
            "Val 6 | Loss:0.5853: 100%|██████████| 3/3 [00:00<00:00, 228.15it/s]\n",
            "Train 7 | Loss:0.5397: 100%|██████████| 11/11 [00:00<00:00, 141.97it/s]\n",
            "Val 7 | Loss:0.5779: 100%|██████████| 3/3 [00:00<00:00, 176.67it/s]\n",
            "Train 8 | Loss:0.5348: 100%|██████████| 11/11 [00:00<00:00, 115.85it/s]\n",
            "Val 8 | Loss:0.5775: 100%|██████████| 3/3 [00:00<00:00, 213.65it/s]\n",
            "Train 9 | Loss:0.5336: 100%|██████████| 11/11 [00:00<00:00, 93.76it/s]\n",
            "Val 9 | Loss:0.5753: 100%|██████████| 3/3 [00:00<00:00, 191.84it/s]\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 10\n",
        "\n",
        "train_losses, val_losses = [], []\n",
        "best_loss = np.inf\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    train_loss, val_loss = 0, 0\n",
        "    clinical_model.train()\n",
        "    with tqdm(enumerate(train_loader), total=len(train_loader)) as pbar:\n",
        "        for i, (X, y) in pbar:  \n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            optimizer.zero_grad()\n",
        "            scores = clinical_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            train_loss += batch_loss * batch_size\n",
        "            batch_loss.backward()\n",
        "            optimizer.step()\n",
        "            pbar.set_description(f'Train {epoch} | Loss:{train_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    train_loss /= len(train_loader.dataset)\n",
        "    train_losses.append(train_loss.detach().cpu().numpy())\n",
        "\n",
        "    with torch.no_grad():\n",
        "        with tqdm(enumerate(val_loader), total=len(val_loader)) as pbar:\n",
        "            for i, (X, y) in pbar:\n",
        "                X = X.to(device=device)\n",
        "                y = y.to(device=device)\n",
        "                scores = clinical_model(X)\n",
        "                batch_loss = criterion(scores, y)\n",
        "                val_loss += batch_loss * batch_size\n",
        "                pbar.set_description(f'Val {epoch} | Loss:{val_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    val_losses.append(val_loss.detach().cpu().numpy())\n",
        "\n",
        "    if val_loss < best_loss:\n",
        "        torch.save(clinical_model.state_dict(), 'best_clinical.pth')\n",
        "        best_loss = val_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "QclqQgeSJnQ0",
      "metadata": {
        "id": "QclqQgeSJnQ0"
      },
      "source": [
        "### Plot Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "_LfYnJ81Jh8X",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "_LfYnJ81Jh8X",
        "outputId": "af4d707b-407a-4158-e3ff-ab23adeee5e7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "epochs = [str(i + 1) for i in range(num_epochs)]\n",
        "ax.plot(epochs, train_losses, label='Train')\n",
        "ax.plot(epochs, val_losses, label='Validation')\n",
        "ax.grid()\n",
        "ax.set_xlabel('Epoch')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.set_title('Train and validation loss')\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "UP_yTS1QJ84O",
      "metadata": {
        "id": "UP_yTS1QJ84O"
      },
      "source": [
        "### Test the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "QE6R0Ek5J-5i",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "QE6R0Ek5J-5i",
        "outputId": "14927e49-6829-4ebe-bad8-226b00cd2874"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Test Loss:0.5270: 100%|██████████| 4/4 [00:00<00:00, 98.60it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "158 / 219 correct (Acc.: 72.1461 and loss: 0.6161)\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 144x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "confusion = np.zeros(shape=(2, 2))\n",
        "num_samples = 0\n",
        "num_correct = 0\n",
        "loss = 0\n",
        "\n",
        "clinical_model.eval()\n",
        "with torch.no_grad():\n",
        "     with tqdm(enumerate(test_loader), total=len(test_loader)) as pbar:\n",
        "       for i, (X, y) in pbar:\n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            scores = clinical_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            loss += batch_loss * batch_size\n",
        "            _, preds = scores.max(1)\n",
        "            confusion += confusion_matrix(y.detach().cpu().numpy(), preds.detach().cpu().numpy(), labels=np.arange(2))\n",
        "            num_correct += (preds == y).sum()\n",
        "            num_samples += preds.size(0)\n",
        "            pbar.set_description(f'Test Loss:{loss/((i + 1) * batch_size):.4f}')\n",
        "loss /= len(test_loader.dataset)\n",
        "\n",
        "acc = float(num_correct) / num_samples\n",
        "print(f'\\n{num_correct} / {num_samples} correct (Acc.: {100 * acc:.4f} and loss: {loss:.4f})')\n",
        "\n",
        "plt.figure(figsize = (2,2))\n",
        "sns.heatmap(confusion, annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "pj2GXMmTIpfi",
      "metadata": {
        "id": "pj2GXMmTIpfi"
      },
      "source": [
        "## Gene Expression Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PL5x_80FIpfq",
      "metadata": {
        "id": "PL5x_80FIpfq"
      },
      "outputs": [],
      "source": [
        "torch.manual_seed(5)\n",
        "\n",
        "gene_exp_model = GeneExpressionClassifier().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(gene_exp_model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GDW7NFxvJch0",
      "metadata": {
        "id": "GDW7NFxvJch0"
      },
      "source": [
        "### Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "IaiuVYE0Ipfq",
      "metadata": {
        "id": "IaiuVYE0Ipfq"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "\n",
        "train_dataset = TabularDataset(X_gene_train, y_train)\n",
        "val_dataset = TabularDataset(X_gene_val, y_val)\n",
        "test_dataset = TabularDataset(X_gene_test, y_test)\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "_k0WnOfHJeZT",
      "metadata": {
        "id": "_k0WnOfHJeZT"
      },
      "source": [
        "### Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "WAfoyAu9Ipfq",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WAfoyAu9Ipfq",
        "outputId": "11e29c3e-4044-4664-d2f5-ca681b22502b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train 0 | Loss:0.7030: 100%|██████████| 11/11 [00:00<00:00, 136.53it/s]\n",
            "Val 0 | Loss:0.6384: 100%|██████████| 3/3 [00:00<00:00, 194.14it/s]\n",
            "Train 1 | Loss:0.6459: 100%|██████████| 11/11 [00:00<00:00, 97.71it/s] \n",
            "Val 1 | Loss:0.6398: 100%|██████████| 3/3 [00:00<00:00, 160.11it/s]\n",
            "Train 2 | Loss:0.6268: 100%|██████████| 11/11 [00:00<00:00, 97.68it/s]\n",
            "Val 2 | Loss:0.6443: 100%|██████████| 3/3 [00:00<00:00, 221.42it/s]\n",
            "Train 3 | Loss:0.6021: 100%|██████████| 11/11 [00:00<00:00, 122.00it/s]\n",
            "Val 3 | Loss:0.6369: 100%|██████████| 3/3 [00:00<00:00, 106.86it/s]\n",
            "Train 4 | Loss:0.5834: 100%|██████████| 11/11 [00:00<00:00, 109.49it/s]\n",
            "Val 4 | Loss:0.6120: 100%|██████████| 3/3 [00:00<00:00, 204.04it/s]\n",
            "Train 5 | Loss:0.5554: 100%|██████████| 11/11 [00:00<00:00, 97.66it/s] \n",
            "Val 5 | Loss:0.6250: 100%|██████████| 3/3 [00:00<00:00, 197.93it/s]\n",
            "Train 6 | Loss:0.5427: 100%|██████████| 11/11 [00:00<00:00, 124.08it/s]\n",
            "Val 6 | Loss:0.6512: 100%|██████████| 3/3 [00:00<00:00, 250.78it/s]\n",
            "Train 7 | Loss:0.5277: 100%|██████████| 11/11 [00:00<00:00, 91.02it/s]\n",
            "Val 7 | Loss:0.6433: 100%|██████████| 3/3 [00:00<00:00, 167.37it/s]\n",
            "Train 8 | Loss:0.4821: 100%|██████████| 11/11 [00:00<00:00, 86.83it/s]\n",
            "Val 8 | Loss:0.6553: 100%|██████████| 3/3 [00:00<00:00, 179.28it/s]\n",
            "Train 9 | Loss:0.4582: 100%|██████████| 11/11 [00:00<00:00, 74.89it/s]\n",
            "Val 9 | Loss:0.6344: 100%|██████████| 3/3 [00:00<00:00, 142.68it/s]\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 10\n",
        "\n",
        "train_losses, val_losses = [], []\n",
        "best_loss = np.inf\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    train_loss, val_loss = 0, 0\n",
        "    gene_exp_model.train()\n",
        "    with tqdm(enumerate(train_loader), total=len(train_loader)) as pbar:\n",
        "        for i, (X, y) in pbar:  \n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            optimizer.zero_grad()\n",
        "            scores = gene_exp_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            train_loss += batch_loss * batch_size\n",
        "            batch_loss.backward()\n",
        "            optimizer.step()\n",
        "            pbar.set_description(f'Train {epoch} | Loss:{train_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    train_loss /= len(train_loader.dataset)\n",
        "    train_losses.append(train_loss.detach().cpu().numpy())\n",
        "\n",
        "    with torch.no_grad():\n",
        "        with tqdm(enumerate(val_loader), total=len(val_loader)) as pbar:\n",
        "            for i, (X, y) in pbar:\n",
        "                X = X.to(device=device)\n",
        "                y = y.to(device=device)\n",
        "                scores = gene_exp_model(X)\n",
        "                batch_loss = criterion(scores, y)\n",
        "                val_loss += batch_loss * batch_size\n",
        "                pbar.set_description(f'Val {epoch} | Loss:{val_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    val_losses.append(val_loss.detach().cpu().numpy())\n",
        "\n",
        "    if val_loss < best_loss:\n",
        "        torch.save(gene_exp_model.state_dict(), 'best_gene.pth')\n",
        "        best_loss = val_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "CP49rPiPJsCq",
      "metadata": {
        "id": "CP49rPiPJsCq"
      },
      "source": [
        "### Plot Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "PGDSrn51JpnE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "PGDSrn51JpnE",
        "outputId": "ab25b8c4-c6c1-42c4-98dd-29c0cac6fc87"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "epochs = [str(i + 1) for i in range(num_epochs)]\n",
        "ax.plot(epochs, train_losses, label='Train')\n",
        "ax.plot(epochs, val_losses, label='Validation')\n",
        "ax.grid()\n",
        "ax.set_xlabel('Epoch')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.set_title('Train and validation loss')\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "VFd3PZGfKLZY",
      "metadata": {
        "id": "VFd3PZGfKLZY"
      },
      "source": [
        "### Test the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "K4IafdFdKNCA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "K4IafdFdKNCA",
        "outputId": "96114612-3726-4841-d838-ffafaa659c12"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Test Loss:0.6108: 100%|██████████| 4/4 [00:00<00:00, 132.25it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "152 / 219 correct (Acc.: 69.40639269406392 and loss: 0.7140)\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 144x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "confusion = np.zeros(shape=(2, 2))\n",
        "num_samples = 0\n",
        "num_correct = 0\n",
        "loss = 0\n",
        "\n",
        "gene_exp_model.eval()\n",
        "with torch.no_grad():\n",
        "     with tqdm(enumerate(test_loader), total=len(test_loader)) as pbar:\n",
        "       for i, (X, y) in pbar:\n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            scores = gene_exp_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            loss += batch_loss * batch_size\n",
        "            _, preds = scores.max(1)\n",
        "            confusion += confusion_matrix(y.detach().cpu().numpy(), preds.detach().cpu().numpy(), labels=np.arange(2))\n",
        "            num_correct += (preds == y).sum()\n",
        "            num_samples += preds.size(0)\n",
        "            pbar.set_description(f'Test Loss:{loss/((i + 1) * batch_size):.4f}')\n",
        "loss /= len(test_loader.dataset)\n",
        "\n",
        "acc = float(num_correct) / num_samples\n",
        "print(f'\\n{num_correct} / {num_samples} correct (Acc.: {100 * acc} and loss: {loss:.4f})')\n",
        "\n",
        "plt.figure(figsize = (2,2))\n",
        "sns.heatmap(confusion, annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "I4ARPXnfFO_J",
      "metadata": {
        "id": "I4ARPXnfFO_J"
      },
      "source": [
        "## Reduced Gene Expression Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ixc30ccaFO_W",
      "metadata": {
        "id": "ixc30ccaFO_W"
      },
      "outputs": [],
      "source": [
        "reduced_gene_exp_model = ReducedGeneExpressionClassifier().to(device)\n",
        "criterion = nn.CrossEntropyLoss()\n",
        "learning_rate = 1e-3\n",
        "optimizer = torch.optim.Adam(reduced_gene_exp_model.parameters(), lr=learning_rate)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "tM_css4QFO_W",
      "metadata": {
        "id": "tM_css4QFO_W"
      },
      "source": [
        "### Dataloaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "uVWQePYKFO_X",
      "metadata": {
        "id": "uVWQePYKFO_X"
      },
      "outputs": [],
      "source": [
        "batch_size = 64\n",
        "\n",
        "train_dataset = TabularDataset(X_gene_reduced_train, y_train)\n",
        "val_dataset = TabularDataset(X_gene_reduced_val, y_val)\n",
        "test_dataset = TabularDataset(X_gene_reduced_test, y_test)\n",
        "\n",
        "train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "GVbB9cYMFO_X",
      "metadata": {
        "id": "GVbB9cYMFO_X"
      },
      "source": [
        "### Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "UxyoCLQEFO_X",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxyoCLQEFO_X",
        "outputId": "35a7aa4d-ac5d-4af7-9136-2cc934d94db9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Train 0 | Loss:2.2341: 100%|██████████| 11/11 [00:00<00:00, 115.37it/s]\n",
            "Val 0 | Loss:1.9769: 100%|██████████| 3/3 [00:00<00:00, 126.87it/s]\n",
            "Train 1 | Loss:1.9040: 100%|██████████| 11/11 [00:00<00:00, 90.12it/s]\n",
            "Val 1 | Loss:1.6057: 100%|██████████| 3/3 [00:00<00:00, 141.47it/s]\n",
            "Train 2 | Loss:1.7708: 100%|██████████| 11/11 [00:00<00:00, 103.49it/s]\n",
            "Val 2 | Loss:1.6497: 100%|██████████| 3/3 [00:00<00:00, 258.63it/s]\n",
            "Train 3 | Loss:1.8149: 100%|██████████| 11/11 [00:00<00:00, 111.16it/s]\n",
            "Val 3 | Loss:1.6439: 100%|██████████| 3/3 [00:00<00:00, 235.17it/s]\n",
            "Train 4 | Loss:1.5963: 100%|██████████| 11/11 [00:00<00:00, 105.09it/s]\n",
            "Val 4 | Loss:1.4240: 100%|██████████| 3/3 [00:00<00:00, 252.79it/s]\n",
            "Train 5 | Loss:1.4490: 100%|██████████| 11/11 [00:00<00:00, 80.78it/s]\n",
            "Val 5 | Loss:1.1790: 100%|██████████| 3/3 [00:00<00:00, 106.79it/s]\n",
            "Train 6 | Loss:1.2494: 100%|██████████| 11/11 [00:00<00:00, 75.62it/s]\n",
            "Val 6 | Loss:1.3737: 100%|██████████| 3/3 [00:00<00:00, 187.25it/s]\n",
            "Train 7 | Loss:1.2902: 100%|██████████| 11/11 [00:00<00:00, 82.20it/s]\n",
            "Val 7 | Loss:1.0810: 100%|██████████| 3/3 [00:00<00:00, 175.22it/s]\n",
            "Train 8 | Loss:1.2357: 100%|██████████| 11/11 [00:00<00:00, 65.86it/s]\n",
            "Val 8 | Loss:1.3024: 100%|██████████| 3/3 [00:00<00:00, 169.31it/s]\n",
            "Train 9 | Loss:1.1136: 100%|██████████| 11/11 [00:00<00:00, 80.98it/s]\n",
            "Val 9 | Loss:0.9361: 100%|██████████| 3/3 [00:00<00:00, 187.01it/s]\n",
            "Train 10 | Loss:1.0559: 100%|██████████| 11/11 [00:00<00:00, 91.22it/s]\n",
            "Val 10 | Loss:1.0338: 100%|██████████| 3/3 [00:00<00:00, 125.77it/s]\n",
            "Train 11 | Loss:1.0314: 100%|██████████| 11/11 [00:00<00:00, 115.31it/s]\n",
            "Val 11 | Loss:0.9031: 100%|██████████| 3/3 [00:00<00:00, 133.36it/s]\n",
            "Train 12 | Loss:0.9890: 100%|██████████| 11/11 [00:00<00:00, 108.10it/s]\n",
            "Val 12 | Loss:0.9728: 100%|██████████| 3/3 [00:00<00:00, 149.10it/s]\n",
            "Train 13 | Loss:0.8781: 100%|██████████| 11/11 [00:00<00:00, 81.35it/s]\n",
            "Val 13 | Loss:0.9024: 100%|██████████| 3/3 [00:00<00:00, 119.49it/s]\n",
            "Train 14 | Loss:0.8980: 100%|██████████| 11/11 [00:00<00:00, 94.26it/s]\n",
            "Val 14 | Loss:0.8641: 100%|██████████| 3/3 [00:00<00:00, 205.89it/s]\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 15\n",
        "train_losses, val_losses = [], []\n",
        "best_loss = np.inf\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    train_loss, val_loss = 0, 0\n",
        "    reduced_gene_exp_model.train()\n",
        "    with tqdm(enumerate(train_loader), total=len(train_loader)) as pbar:\n",
        "        for i, (X, y) in pbar:  \n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            optimizer.zero_grad()\n",
        "            scores = reduced_gene_exp_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            train_loss += batch_loss * batch_size\n",
        "            batch_loss.backward()\n",
        "            optimizer.step()\n",
        "            pbar.set_description(f'Train {epoch} | Loss:{train_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    train_loss /= len(train_loader.dataset)\n",
        "    train_losses.append(train_loss.detach().cpu().numpy())\n",
        "\n",
        "    with torch.no_grad():\n",
        "        with tqdm(enumerate(val_loader), total=len(val_loader)) as pbar:\n",
        "            for i, (X, y) in pbar:\n",
        "                X = X.to(device=device)\n",
        "                y = y.to(device=device)\n",
        "                scores = reduced_gene_exp_model(X)\n",
        "                batch_loss = criterion(scores, y)\n",
        "                val_loss += batch_loss * batch_size\n",
        "                pbar.set_description(f'Val {epoch} | Loss:{val_loss/((i + 1) * batch_size):.4f}')\n",
        "\n",
        "    val_loss /= len(val_loader.dataset)\n",
        "    val_losses.append(val_loss.detach().cpu().numpy())\n",
        "\n",
        "    if val_loss < best_loss:\n",
        "        torch.save(reduced_gene_exp_model.state_dict(), 'best_gene_reduced.pth')\n",
        "        best_loss = val_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "tHJPC3ncFO_Y",
      "metadata": {
        "id": "tHJPC3ncFO_Y"
      },
      "source": [
        "### Plot Losses"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "rKiJZCMAFO_Y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "rKiJZCMAFO_Y",
        "outputId": "942a7c3e-68e4-4f6e-ac85-34bccd1eac1f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "epochs = [str(i + 1) for i in range(num_epochs)]\n",
        "ax.plot(epochs, train_losses, label='Train')\n",
        "ax.plot(epochs, val_losses, label='Validation')\n",
        "ax.grid()\n",
        "ax.set_xlabel('Epoch')\n",
        "ax.set_ylabel('Loss')\n",
        "ax.set_title('Train and validation loss')\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "KpVSqKDmFO_Y",
      "metadata": {
        "id": "KpVSqKDmFO_Y"
      },
      "source": [
        "### Test the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "WVcgqQ9UFO_Y",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "WVcgqQ9UFO_Y",
        "outputId": "19641188-c689-4077-9fb8-615992cad203"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Test Loss:0.6870: 100%|██████████| 4/4 [00:00<00:00, 127.75it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "142 / 219 correct (Acc.: 64.84018264840182 and loss: 0.8030)\n"
          ]
        },
        {
          "data": {
            "image/png": 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s6IzrLOiM6yzojOss6IzrLOiM6yzojOv+H2252truN6ewAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 144x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "confusion = np.zeros(shape=(2, 2))\n",
        "num_samples = 0\n",
        "num_correct = 0\n",
        "loss = 0\n",
        "\n",
        "reduced_gene_exp_model.eval()\n",
        "with torch.no_grad():\n",
        "     with tqdm(enumerate(test_loader), total=len(test_loader)) as pbar:\n",
        "       for i, (X, y) in pbar:\n",
        "            X = X.to(device=device)\n",
        "            y = y.to(device=device)\n",
        "            scores = reduced_gene_exp_model(X)\n",
        "            batch_loss = criterion(scores, y)\n",
        "            loss += batch_loss * batch_size\n",
        "            _, preds = scores.max(1)\n",
        "            confusion += confusion_matrix(\n",
        "                y.detach().cpu().numpy(), \n",
        "                preds.detach().cpu().numpy(), \n",
        "                labels=np.arange(2))\n",
        "            num_correct += (preds == y).sum()\n",
        "            num_samples += preds.size(0)\n",
        "            pbar.set_description(f'Test Loss:{loss/((i + 1) * batch_size):.4f}')\n",
        "loss /= len(test_loader.dataset)\n",
        "\n",
        "acc = float(num_correct) / num_samples\n",
        "print(f'\\n{num_correct} / {num_samples} correct (Acc.: {100 * acc} and loss: {loss:.4f})')\n",
        "\n",
        "plt.figure(figsize = (2,2))\n",
        "sns.heatmap(confusion, annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56877dc4",
      "metadata": {
        "id": "56877dc4"
      },
      "source": [
        "# Model Comparison (10 Points)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "509fe56a",
      "metadata": {
        "id": "509fe56a"
      },
      "source": [
        "Compare different models and different datasets (clinical, gene expressions, and gene reduced expressions) and try to explain their differences."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "w2xEojoX1Z5O",
      "metadata": {
        "id": "w2xEojoX1Z5O"
      },
      "source": [
        "## Classical vs Neural Network Model"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c2f05a73",
      "metadata": {
        "id": "c2f05a73"
      },
      "source": [
        "According to the results, classical models work better on the given datasets.\n",
        "Because the data is sparse and building a complex model over it results in overfitting.\n",
        "So, simpler models such as Random Forrest and XGBoost reach better results on Clinical and Reduced Gene Expression datasets and on par on Gene Expression dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "9tj6YuZx0sgA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9tj6YuZx0sgA",
        "outputId": "c930de84-8f69-40e2-d56d-1b0f26903285"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "datasets = ['Clinical', 'Gene Expression', 'Reduced Gene Expression']\n",
        "classical_accuracies = [0.75, 0.67, 0.65]\n",
        "nn_accuracies = [0.72, 0.69, 0.64]\n",
        "ax.plot(classical_accuracies, label='Classical')\n",
        "ax.plot(nn_accuracies, label='Neural Network')\n",
        "ax.grid()\n",
        "ax.set_xlabel('Dataset')\n",
        "ax.set_xticks(np.arange(3))\n",
        "ax.set_xticklabels(datasets)\n",
        "ax.set_ylabel('Accuracy')\n",
        "ax.set_title('Comparison of Classical and Neural Network models on different datasets')\n",
        "ax.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wqqTt3g34Yk-",
      "metadata": {
        "id": "wqqTt3g34Yk-"
      },
      "source": [
        "## Different Datasets"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aX7fVJvF4ave",
      "metadata": {
        "id": "aX7fVJvF4ave"
      },
      "source": [
        "And comparing different datasets we get the best accuracy on the Clinical dataset which shows that it gives us better information for predicting cancer survival.\n",
        "The Gene Expression dataset features have low correlation and though it is difficult to infer cancer survival from this data.\n",
        "Detailed exploration has been done in the EDA section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "LyfZRr7p6DQH",
      "metadata": {
        "id": "LyfZRr7p6DQH"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "UJRloLBJBEMH",
        "r3wSjGRGCo7k",
        "Ox8OqL84_JxT"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.7.9"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
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      "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
    },
    "varInspector": {
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "delete_cmd_postfix": "",
          "delete_cmd_prefix": "del ",
          "library": "var_list.py",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "delete_cmd_postfix": ") ",
          "delete_cmd_prefix": "rm(",
          "library": "var_list.r",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
        "module",
        "function",
        "builtin_function_or_method",
        "instance",
        "_Feature"
      ],
      "window_display": false
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}