588 lines (588 with data), 168.3 kB

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dlV9LhAYX8NK",
        "outputId": "f7fc136f-fe10-4796-c589-86eedf0e1b41"
      },
      "id": "dlV9LhAYX8NK",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "d2b1d10d-868f-4b29-b48a-8482c2e887b9",
      "metadata": {
        "id": "d2b1d10d-868f-4b29-b48a-8482c2e887b9"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from collections import Counter\n",
        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
        "from sklearn.metrics import classification_report\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"joblib\")\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"sklearn\")\n",
        "warnings.filterwarnings('ignore', category=UserWarning, message=\"Line Search failed\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "63597d88-f48a-41c0-9afc-1d7199707141",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63597d88-f48a-41c0-9afc-1d7199707141",
        "outputId": "8250d37a-48b8-4a66-d494-26fe7ab8be96"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   Unnamed: 0     Name        FC     logFC    logCPM   P-Value       FDR  \\\n",
            "0           0  KRT16P5 -1.474275 -0.560006 -2.065784  0.423250  0.645529   \n",
            "1           1  KRT16P3 -1.158475 -0.212227  0.698547  0.598622  0.779482   \n",
            "2           2  KRT16P2  1.785481  0.836313  3.744968  0.060200  0.211667   \n",
            "3           3  KRT16P6 -2.534136 -1.341494  0.404997  0.023716  0.123727   \n",
            "4           4    CRHBP  1.441891  0.527962 -0.015277  0.034942  0.153404   \n",
            "\n",
            "   SCLC  NSCLC  \n",
            "0   0.0    0.0  \n",
            "1   0.0    0.0  \n",
            "2   0.0    0.0  \n",
            "3   0.0    0.0  \n",
            "4   0.0    0.0  \n",
            "Unnamed: 0    0\n",
            "Name          0\n",
            "FC            0\n",
            "logFC         0\n",
            "logCPM        0\n",
            "P-Value       0\n",
            "FDR           0\n",
            "SCLC          0\n",
            "NSCLC         0\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "#load data and preprocess\n",
        "data = pd.read_csv('/content/drive/My Drive/ML_project/labelled_data.csv').fillna(0)\n",
        "print(data.head())\n",
        "print(data.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.shape)\n",
        "print()\n",
        "print(data.describe)\n",
        "print()\n",
        "print(data.info)\n",
        "print()\n",
        "print(data.duplicated())\n",
        "print()\n",
        "print(data.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCs7A_6I6mOI",
        "outputId": "4f8d10b8-689e-43af-8d13-d1c3cdcdcb89"
      },
      "id": "mCs7A_6I6mOI",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19778, 9)\n",
            "\n",
            "<bound method NDFrame.describe of        Unnamed: 0          Name        FC     logFC    logCPM   P-Value  \\\n",
            "0               0       KRT16P5 -1.474275 -0.560006 -2.065784  0.423250   \n",
            "1               1       KRT16P3 -1.158475 -0.212227  0.698547  0.598622   \n",
            "2               2       KRT16P2  1.785481  0.836313  3.744968  0.060200   \n",
            "3               3       KRT16P6 -2.534136 -1.341494  0.404997  0.023716   \n",
            "4               4         CRHBP  1.441891  0.527962 -0.015277  0.034942   \n",
            "...           ...           ...       ...       ...       ...       ...   \n",
            "19773       19773  LOC105369958  2.196994  1.135531  1.382694  0.003199   \n",
            "19774       19774         ABCC2  3.461301  1.791314  5.336636  0.000671   \n",
            "19775       19775         TRAV6  3.849574  1.944699 -0.401490  0.000016   \n",
            "19776       19776  LOC105369904  2.226049  1.154485 -1.006838  0.002616   \n",
            "19777       19777  LOC101928636  2.279563  1.188757  0.151617  0.000219   \n",
            "\n",
            "            FDR  SCLC  NSCLC  \n",
            "0      0.645529   0.0    0.0  \n",
            "1      0.779482   0.0    0.0  \n",
            "2      0.211667   0.0    0.0  \n",
            "3      0.123727   0.0    0.0  \n",
            "4      0.153404   0.0    0.0  \n",
            "...         ...   ...    ...  \n",
            "19773  0.040235   0.0    0.0  \n",
            "19774  0.015864   0.0    0.0  \n",
            "19775  0.001372   0.0    0.0  \n",
            "19776  0.035713   0.0    0.0  \n",
            "19777  0.007721   0.0    0.0  \n",
            "\n",
            "[19778 rows x 9 columns]>\n",
            "\n",
            "<bound method DataFrame.info of        Unnamed: 0          Name        FC     logFC    logCPM   P-Value  \\\n",
            "0               0       KRT16P5 -1.474275 -0.560006 -2.065784  0.423250   \n",
            "1               1       KRT16P3 -1.158475 -0.212227  0.698547  0.598622   \n",
            "2               2       KRT16P2  1.785481  0.836313  3.744968  0.060200   \n",
            "3               3       KRT16P6 -2.534136 -1.341494  0.404997  0.023716   \n",
            "4               4         CRHBP  1.441891  0.527962 -0.015277  0.034942   \n",
            "...           ...           ...       ...       ...       ...       ...   \n",
            "19773       19773  LOC105369958  2.196994  1.135531  1.382694  0.003199   \n",
            "19774       19774         ABCC2  3.461301  1.791314  5.336636  0.000671   \n",
            "19775       19775         TRAV6  3.849574  1.944699 -0.401490  0.000016   \n",
            "19776       19776  LOC105369904  2.226049  1.154485 -1.006838  0.002616   \n",
            "19777       19777  LOC101928636  2.279563  1.188757  0.151617  0.000219   \n",
            "\n",
            "            FDR  SCLC  NSCLC  \n",
            "0      0.645529   0.0    0.0  \n",
            "1      0.779482   0.0    0.0  \n",
            "2      0.211667   0.0    0.0  \n",
            "3      0.123727   0.0    0.0  \n",
            "4      0.153404   0.0    0.0  \n",
            "...         ...   ...    ...  \n",
            "19773  0.040235   0.0    0.0  \n",
            "19774  0.015864   0.0    0.0  \n",
            "19775  0.001372   0.0    0.0  \n",
            "19776  0.035713   0.0    0.0  \n",
            "19777  0.007721   0.0    0.0  \n",
            "\n",
            "[19778 rows x 9 columns]>\n",
            "\n",
            "0        False\n",
            "1        False\n",
            "2        False\n",
            "3        False\n",
            "4        False\n",
            "         ...  \n",
            "19773    False\n",
            "19774    False\n",
            "19775    False\n",
            "19776    False\n",
            "19777    False\n",
            "Length: 19778, dtype: bool\n",
            "\n",
            "Unnamed: 0      int64\n",
            "Name           object\n",
            "FC            float64\n",
            "logFC         float64\n",
            "logCPM        float64\n",
            "P-Value       float64\n",
            "FDR           float64\n",
            "SCLC          float64\n",
            "NSCLC         float64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.columns[data.isna().any()])\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qfXHs_Hy6pP3",
        "outputId": "40376d0d-52d7-48d5-fe27-e1bf43b06118"
      },
      "id": "qfXHs_Hy6pP3",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([], dtype='object')\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = data.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap=\"coolwarm\", fmt=\".2f\", linewidths=.5)\n",
        "plt.title(\"Correlation Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "f4DIAfhd6wiV",
        "outputId": "d5b3da5f-2d1c-430b-91e3-6bedd6b1e801"
      },
      "id": "f4DIAfhd6wiV",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "e2d61a8f-9b18-4b6e-b776-c4d0d9e6b891",
      "metadata": {
        "id": "e2d61a8f-9b18-4b6e-b776-c4d0d9e6b891"
      },
      "outputs": [],
      "source": [
        "#feature selection\n",
        "features = data[['FC', 'logFC', 'P-Value']]\n",
        "targets = {'NSCLC': data['NSCLC'], 'SCLC': data['SCLC']}"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nsclc = data['NSCLC']\n",
        "sclc = data['SCLC']"
      ],
      "metadata": {
        "id": "OoVONtzu7NTD"
      },
      "id": "OoVONtzu7NTD",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nm = NearMiss()\n",
        "print('SCLC Original Shape:', Counter(sclc))\n",
        "features_nm_sclc, nm_sclc = nm.fit_resample(features, sclc)\n",
        "print('SCLC Resample Shape:', Counter(nm_sclc))\n",
        "print('NSCLC Original Shape:', Counter(nsclc))\n",
        "features_nm_nsclc, nm_nsclc = nm.fit_resample(features, nsclc)\n",
        "print('NSCLC Resample Shape:', Counter(nm_nsclc))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yr9LsDXb7kQk",
        "outputId": "22988d4f-b71e-4d3b-ef81-52f199b5a6db"
      },
      "id": "Yr9LsDXb7kQk",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SCLC Original Shape: Counter({0.0: 18857, 1.0: 921})\n",
            "SCLC Resample Shape: Counter({0.0: 921, 1.0: 921})\n",
            "NSCLC Original Shape: Counter({0.0: 19087, 1.0: 691})\n",
            "NSCLC Resample Shape: Counter({0.0: 691, 1.0: 691})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_sclc = features_nm_sclc\n",
        "features_nsclc = features_nm_nsclc\n",
        "sclc = nm_sclc\n",
        "nsclc = nm_nsclc"
      ],
      "metadata": {
        "id": "BVeBjCmw9AKc"
      },
      "id": "BVeBjCmw9AKc",
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "8e9bd90d-605a-43f1-af50-cdd8a0b4359e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "8e9bd90d-605a-43f1-af50-cdd8a0b4359e",
        "outputId": "f235971b-4fe3-44f3-f3eb-f9dddb3e9087"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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7KykpyaY9KSlJ/v7+OZ4zcuRIde3aVS+88IIkqU6dOkpJSVGfPn302muvye0GdSPX4t0EAADYc8GmQ56enmrYsKFiYmKsbVlZWYqJibHJFFzr4sWL2X7h/7OZlAOJf8cyAyaTSSa76mb7zwAAFHguerQwLCxM3bp10/3336/AwECNGzdOKSkp6tGjhyQpNDRUAQEB1qx827ZtNWbMGDVo0MC6TDBy5Ei1bdvWoR0mHV4m6N69uzXdcfnyZfXt21fFitlWLc+bN8+RYQEAyF8cfCTQWTp27KgTJ05o1KhRSkxMVP369bVkyRJrbV5CQoJNJiAiIkImk0kRERE6cuSIypYtq7Zt2+rtt9926LoOFRD+E5n8m2nTpjk0CYkCQtiigBDXooAQ9vK8gPCr4U4bq0i3d502Vl5xKDOQm1/yAAAUOAZ7N4FDwQAAAIZgsGCApwkAADA4MgMAANhz4JHAOwHBAAAAdixZxnoZGMsEAAAYHJkBAADsGayAkGAAAAB7BqsZYJkAAACDIzMAAIA9gxUQEgwAAGCPmgEAAAzOYMEANQMAABgcmQEAAOy56BXGrkIwAACAPZYJAACAkZAZAADAHo8WAgBgcOxACAAAjITMAAAA9lgmAADA2Cw8TQAAAIyEzAAAAPZYJgAAwOAM9jQBwQAAAPYMlhmgZgAAAIMjMwAAgD2DPU1AMAAAgD2WCQAAgJGQGQAAwB5PEwAAYHAsEwAAACMhMwAAgB2jvZuAYAAAAHssEwAAACMhMwAAgD2DZQYIBgAAsMejhQAAGJzBMgPUDAAAYHBkBgAAsGMxWGaAYAAAAHsGCwZYJgAAwODIDAAAYI8dCAEAMDiWCQAAgJGQGQAAwJ7BMgMEAwAA2LFYjBUMsEwAAIDBkRkAAMAeywQAABgcwQAAAMbGdsQukrFylqungHyk0COdXT0F5CMX0j519RSAO1q+CQYAAMg3yAwAAGBwxtqNmEcLAQAwOoIBAADsWLIsTjscNXHiRFWqVEmFCxdWUFCQYmNjb9j/7NmzGjBggMqVKyez2awaNWooOjraoWuyTAAAgD0X1QzMmTNHYWFhmjx5soKCgjRu3DiFhIRo165d8vX1zdY/LS1NLVu2lK+vr+bOnauAgAAdOnRIJUuWdOi6BAMAAOSh1NRUpaam2rSZzWaZzeZsfceMGaPevXurR48ekqTJkydr0aJFmjp1qoYPH56t/9SpU3X69GmtWbNGHh4ekqRKlSo5PEeWCQAAsJflvCMqKkre3t42R1RUVLZLpqWlaePGjQoODra2ubm5KTg4WGvXrs1xmgsWLFDjxo01YMAA+fn5qXbt2nrnnXeUmZnp0O2SGQAAwI4zNx0KDw9XWFiYTVtOWYGTJ08qMzNTfn5+Nu1+fn6Kj4/Pcez9+/dr+fLlev755xUdHa29e/eqf//+Sk9PV2Rk5E3PkWAAAIA8dL0lAWfIysqSr6+vPv/8c7m7u6thw4Y6cuSIPvjgA4IBAABuiQv2GfDx8ZG7u7uSkpJs2pOSkuTv75/jOeXKlZOHh4fc3d2tbffcc48SExOVlpYmT0/Pm7o2NQMAANhxxaOFnp6eatiwoWJiYqxtWVlZiomJUePGjXM8p2nTptq7d6+ysq5GL7t371a5cuVuOhCQCAYAAMjOiQWEjggLC9OUKVP01VdfaefOnerXr59SUlKsTxeEhoYqPDzc2r9fv346ffq0Bg8erN27d2vRokV65513NGDAAIeuyzIBAAD5RMeOHXXixAmNGjVKiYmJql+/vpYsWWItKkxISJCb29W/4ytUqKClS5dqyJAhqlu3rgICAjR48GC9+uqrDl3XZLFY8sXbGC7NfcvVU0A+wlsLca0i5Zu5egrIZzLSjuTp+KfaPuy0scos/M1pY+UVMgMAANjjRUUAAMBIyAwAAGDHYrDMAMEAAAD2DBYMsEwAAIDBkRkAAMAOywQAABgcwQAAAAZntGCAmgEAAAyOzAAAAPYsJlfP4LYiGAAAwA7LBAAAwFDIDAAAYMeSxTIBAACGxjIBAAAwFDIDAADYsfA0AQAAxsYyAQAAMBQyAwAA2OFpAgAADM5icfUMbi+CAQAA7BgtM0DNAAAABkdmAAAAO0bLDBAMAABgx2g1AywTAABgcGQGAACwwzIBAAAGZ7TtiFkmAADA4MgMAABgx2jvJiAYAADAThbLBAAAwEgcCgYyMzO1detWXbp0KdvXLl68qK1btyory2C5FQDAHcdiMTntKAgcCga+/vpr9ezZU56entm+5unpqZ49e2rWrFlOmxwAAK5gyTI57SgIHAoGvvzySw0dOlTu7u7ZvlaoUCENGzZMn3/+udMmBwCAK1gszjsKAoeCgV27dumBBx647tcbNWqknTt33vKkAADA7ePQ0wQpKSk6f/78db9+4cIFXbx48ZYnBQCAKxWU9L6zOJQZqF69utasWXPdr69atUrVq1e/5UkBAOBKWRaT046CwKFgoHPnzoqIiNDWrVuzfS0uLk6jRo1S586dnTY5AACQ9xxaJhgyZIgWL16shg0bKjg4WLVq1ZIkxcfH69dff1XTpk01ZMiQPJkoAAC3S0F5JNBZHAoGPDw89Msvv2js2LGaNWuWfv/9d1ksFtWoUUNvv/22Xn75ZXl4eOTVXAEAuC0KylMAzuLwdsQeHh4aNmyYhg0blhfzAQAAt5lDNQNTp05VampqXs3ljjN73S61+mCeAiNnqsun0dp2+OQN+3+zeqfajf1JQZGzFPL+D/pg0Z9KTc+0fn3jgSQNmrFcLd+dq/qvfa3lfyXk9S3ABTZs2aYBwyLV/MnnVbtpK8X8fv2iXRR8oyOH6vChTbpwbq+WLp6tatUq37B/sweDNP/H6Uo4uFEZaUf05JMh2fqMGhmm7dt+07kze3QiaYeWLp6twEYN8uoW7kgUEN5A7969de7cOevn8uXL6+DBg86e0x1h6daD+ih6g15sUVffDnhCNfxLqf/0GJ1Ozr6VsyRFxx3Q+F826cUWdTXv5ScV2aGxftl2SJ8s22ztcyktQzXKlVJ428DbdRtwgUuXLqtmtSp67ZX+rp4K8tj/hvbXSwN6qv9Lw9XkwbZKuXhR0T/PlNlsvu45xYoV1datf2ng4Neu22f3nv0aPDhC9f/zqB5u3kEHDx3W4uhZ8vEpnRe3cUcy2nbEDi0TWOwWUS5cuMC7CK7j69V/6an7q6t9w2qSpIh2D+iPXUc0f+M+9Xy4drb+cYdOqH5FX7Wud+WvgoBSxfV43Ura9vfVbMKDNQP0YM2A23MDcJlmjRupWeNGrp4GboNBA1/QO1Efa+HCXyRJ3XsM1tG/t6hduxB9992CHM9ZsnSFlixdccNxZ8+eb/N56P9eV6+enVW3zr1avmKVU+aOOwtvLcwD6RmZ2nn0tIKq+Vvb3NxMCqpWTlsTTuR4Tr27y+qvo6esSwl/n76gVbuP6MEa/PIH7kSVK1dUuXJ+ill+9Zfz+fMXFBu7WQ8ENXTadTw8PNT7hed19uw5xW3d4bRx73RG247YocyAyWSSyWS67ueblZqamq32ICs9Q2YPh+sZ86UzF1OVmWVRmeJFbNrLFC+sgyfO5XhO63qVdTblsnpMWSpZLMrIsujZwBp64ZE6t2PKAG4zfz9fSVJSku0fCEnHT8rf3/eWx3+idbBmfjNJRYsW0bFjSXq8VSedOnXmlsc1ioKy1u8sDmUG/nmMsHTp0ipdurSSk5PVoEED6+d/jn8TFRUlb29vm+ODH3/P9U3cCf7cn6gvf9uuEW0D9e2AJzSm88P6Y9ff+nx59g2eABQ8nTp10NnTu62HRx7/8bNi5Wo1bPSYmj3UTkt/WalvZ01W2bJl8vSadxJqBm5g2rRpTrloeHi4wsLCbNqyFo1xytj5QamiZrm7mXTKrljwVPJl+dhlC/4x6dc4PVG/ip5qdGU75+r+pXQpPUNvzl+nFx6pIze3gvENBSBnCxf+otjYqwXBZvOVV8H7+ZVVYuJxa7ufr4+2xN16Ov/ixUvat++g9u07qPWxm7Rzxyr17NFJ770/4ZbHxp3HoWCgW7duTrmo2WzOVi176Q5ZIpAkj0Luuqd8acXuS1SLeytKkrKyLIrdl6jnHqiZ4zmX0zPkZrfk8s9niyySCAaAgiw5OUXJySk2bceOJalF8wcV9/+//EuUKK7AwAaa/PkMp1/fzc1kDUDw74y2THDn/AbOZ7o2vVcjf1itewPKqPZdPpq5ZqcupWWoXcOqkqSI71fL16uIBoX8R5L0UK279M3qnapVvpTq3OWjhNMXNOnXOD1U6y65u11ZzbmYmq6EUxes1zhyJlnxR0/Lu6hZ5UoWu/03iTxx8eIlJfx91Pr5yNEkxe/eJ2+vEirnhLVk5B/jP/lCI8IHac/e/Tp48LBeH/0/HT2apJ9+Wmrt88uSOZr/02JN+nS6pCuPFl67F0HlShVVr959On36jA4fPqqiRYtoRPhgLVz4i44lJsmnTGn169ddAQH+mvvDz7f7FgusAlL35zS5CgZKlSqVY+GgyWRS4cKFVa1aNXXv3l09evS45QkWVCF1K+lMymV9GhOnkxcuqWa5UprUvYW1qPDYuRRd+4+w9yN1ZJI0cVmcjp+/qFLFzHqo1l16qeXVjUJ2HDml3l8us37+KHqjJKltgyp685mmt+W+kPe2x+9Rz4GvWj+//8nnkqR2rYL1dsQrrpoW8sAHH05SsWJFNXnS+ypZ0kurV/+pJ9p2sSmwrlLlbpv9Ae5vWE8xv861fv7ow9GSpK9mfKdeLwxRZmaWatasqq5dPpePT2mdOnVGGzbG6ZHmT+mvv3bftntDwWKy2G8ecBPGjh2rt99+W61atVJg4JUNcGJjY7VkyRINGTJEBw4c0Ndff61PPvlEvXv3vqkxL819y9Fp4A5W6BHefomripRv5uopIJ/JSDuSp+OvKfe008ZqcuwHp42VV3KVGVi1apXeeust9e3b16b9s88+0y+//KIffvhBdevW1fjx4286GAAAIL8oKE8BOEuuNh1aunSpgoODs7U/+uijWrr0ylpX69attX///lubHQAAyHO5CgZKly6thQsXZmtfuHChdZ+BlJQUlShR4tZmBwCAC2Q58XDUxIkTValSJRUuXFhBQUGKjY29qfNmz54tk8mk9u3bO3zNXC0TjBw5Uv369dOKFSusNQN//vmnoqOjNXnyZEnSsmXL9PDDD+dmeAAAXMriose558yZo7CwME2ePFlBQUEaN26cQkJCtGvXLvn6Xv9pooMHD2ro0KFq1ix39TW5KiCUpNWrV2vChAnatWuXJKlmzZoaOHCgmjRpkquJUECIa1FAiGtRQAh7eV1A+Lv/s04b66HE72+6b1BQkBo1aqQJE65sDpWVlaUKFSpo4MCBGj58eI7nZGZm6qGHHlLPnj31xx9/6OzZs5o/f75Dc8z1PgNNmzZV06Y8zgYAuPNkOXGjgZzex5PT5ntpaWnauHGjwsPDrW1ubm4KDg7W2rVrrzv+G2+8IV9fX/Xq1Ut//PFHruaY62AgMzNT8+fP186dOyVJ9913n5588km5u7vndkgAAPKFLCcuE0RFRen111+3aYuMjNTo0aNt2k6ePKnMzEz5+fnZtPv5+Sk+Pj7HsVetWqUvv/xSW7ZsuaU55ioY2Lt3r1q3bq0jR46oZs0r2+tGRUWpQoUKWrRokapWrXpLkwIAwJWcWTOQ0/t47LMCuXHhwgV17dpVU6ZMkY+Pzy2NlatgYNCgQapatarWrVtnfXrg1KlT6tKliwYNGqRFixbd0qQAALhT5LQkkBMfHx+5u7srKSnJpj0pKUn+/v7Z+u/bt08HDx5U27ZtrW1ZWVeeXyhUqJB27dp103+c5yoY+O2332wCAUkqU6aM3n33XeoIAAAFXm4eCbxVnp6eatiwoWJiYqyPB2ZlZSkmJkYvvfRStv61atXStm3bbNoiIiJ04cIFffzxx6pQocJNXztXwYDZbNaFCxeytScnJ8vTk7diAQAKNlc9WhgWFqZu3brp/vvvV2BgoMaNG6eUlBTru35CQ0MVEBCgqKgoFS5cWLVr17Y5v2TJkpKUrf3f5CoYaNOmjfr06aMvv/zSus/A+vXr1bdvXz355JO5GRIAAMPr2LGjTpw4oVGjRikxMVH169fXkiVLrEWFCQkJcnPL1X6BN5SrfQbOnj2rbt26aeHChfLw8JAkpaenq127dpo2bZo1MnEE+wzgWuwzgGuxzwDs5fU+A0v8nnPaWI8nzXbaWHklV5mBkiVL6qefftLevXutjxbec889qlatmlMnBwCAK7iiZsCVbjoYsH8swt6KFSus/3/MmDG5nxEAALitbjoY2Lx58031M5mM9dpHAMCdx1UFhK5y08HAtX/5AwBwJ8syViyQu1cYAwCAO0eu300AAMCdypnvJigICAYAALDjxJcWFggEAwAA2DHao4XUDAAAYHBkBgAAsJNlsMfkCQYAALBjtJoBlgkAADA4MgMAANgxWgEhwQAAAHbYgRAAABgKmQEAAOywAyEAAAbH0wQAAMBQyAwAAGDHaAWEBAMAANjh0UIAAAyOmgEAAGAoZAYAALBDzQAAAAZntJoBlgkAADA4MgMAANgxWmaAYAAAADsWg9UMsEwAAIDBkRkAAMAOywQAABic0YIBlgkAADA4MgMAANgx2nbEBAMAANhhB0IAAAyOmgEAAGAoZAYAALBjtMwAwQAAAHaMVkDIMgEAAAZHZgAAADs8TQAAgMEZrWaAZQIAAAyOzAAAAHaMVkBIMAAAgJ0sg4UD+SYYKN99uqungHzkQtqnrp4C8pFLR/9w9RSAO1q+CQYAAMgvjFZASDAAAIAdYy0SEAwAAJCN0TIDPFoIAIDBkRkAAMAOOxACAGBwRnu0kGUCAAAMjswAAAB2jJUXIBgAACAbniYAAACGQjAAAICdLFmcdjhq4sSJqlSpkgoXLqygoCDFxsZet++UKVPUrFkzlSpVSqVKlVJwcPAN+18PwQAAAHYsTjwcMWfOHIWFhSkyMlKbNm1SvXr1FBISouPHj+fYf+XKlerUqZNWrFihtWvXqkKFCnrsscd05MgRh65rslgs+aJOolTxaq6eAvKRC2mXXD0F5CO8qAj2PHyq5On4wyp1ctpY7x/89qb7BgUFqVGjRpowYYIkKSsrSxUqVNDAgQM1fPjwfz0/MzNTpUqV0oQJExQaGnrT16WAEAAAO84sIExNTVVqaqpNm9lsltlstmlLS0vTxo0bFR4ebm1zc3NTcHCw1q5de1PXunjxotLT01W6dGmH5sgyAQAAdpxZMxAVFSVvb2+bIyoqKts1T548qczMTPn5+dm0+/n5KTEx8abm/eqrr6p8+fIKDg526H7JDAAAYMeZ6+fh4eEKCwuzabPPCjjDu+++q9mzZ2vlypUqXLiwQ+cSDAAAkIdyWhLIiY+Pj9zd3ZWUlGTTnpSUJH9//xue++GHH+rdd9/Vr7/+qrp16zo8R5YJAACwk+XE42Z5enqqYcOGiomJuTqPrCzFxMSocePG1z3v/fff15tvvqklS5bo/vvvd+CKV5EZAADAjsVFGxKHhYWpW7duuv/++xUYGKhx48YpJSVFPXr0kCSFhoYqICDAWnPw3nvvadSoUZo1a5YqVapkrS0oXry4ihcvftPXJRgAACCf6Nixo06cOKFRo0YpMTFR9evX15IlS6xFhQkJCXJzu5rU//TTT5WWlqZnnnnGZpzIyEiNHj36pq/LPgPIl9hnANdinwHYy+t9Bl6q1NFpY004OMdpY+UVMgMAANjJzTbCBRkFhAAAGByZAQAA7BgrL0AwAABANiwTAAAAQyEzAACAHWe+qKggIBgAAMCOqzYdchWCAQAA7BgtM0DNAAAABkdmAAAAOywTAABgcCwTAAAAQyEzAACAnaz88Q6/24ZgAAAAO8YKBVgmAADA8MgMAABgx2jvJiAYAADAjtEeLWSZAAAAgyMzAACAHfYZuEn79u1TRESEOnXqpOPHj0uSFi9erB07djhtcgAAuEKWLE47CoJcBQO//fab6tSpo/Xr12vevHlKTk6WJMXFxSkyMtKpEwQA4HazOPF/BUGugoHhw4frrbfe0rJly+Tp6Wltb9GihdatW+e0yQEAgLyXq5qBbdu2adasWdnafX19dfLkyVueFAAArkTNwE0oWbKkjh07lq198+bNCggIuOVJAQDgShaLxWlHQZCrYOC5557Tq6++qsTERJlMJmVlZWn16tUaOnSoQkNDnT1HAACQh3IVDLzzzjuqVauWKlSooOTkZN1777166KGH1KRJE0VERDh7jgAA3FZGe5ogVzUDnp6emjJlikaOHKnt27crOTlZDRo0UPXq1Z09PwAAbjuj1Qzc0qZDFStWVMWKFZ01FwAA4AK5CgZ69ux5w69PnTo1V5MBACA/KCj7AzhLroKBM2fO2HxOT0/X9u3bdfbsWbVo0cIpEwMAwFUKylq/s+QqGPjxxx+ztWVlZalfv36qWrXqLU8KAADcPk57a6Gbm5vCwsI0duxYZw0JAIBLGG2fAae+tXDfvn3KyMhw5pAAANx2PE1wE8LCwmw+WywWHTt2TIsWLVK3bt2cMjEAAFyFAsKbsHnzZpvPbm5uKlu2rD766KN/fdIAAADkL7mqGVixYoXNERMTo9mzZ6tPnz4qVMipKw8FTnjEYO3cu0ZHT2zXjwu/UpWqd//rOS/06aK4HSt17OQOLVsxV/9pWNfm676+Ppo85UPF71urv5O2auWqn9S2XYhNn7gdK3Umea/N8XLYi069NzjH6MihOnxoky6c26uli2erWrXKN+zf7MEgzf9xuhIOblRG2hE9+WRItj6jRoZp+7bfdO7MHp1I2qGli2crsFGDvLoF3GYbtmzTgGGRav7k86rdtJVifl/j6ind8Yy2A6HTCgghDR7SRy/27aawwaPU8pGndTHlkn6YP01ms+d1z+nwdGu9FTVC70V9okcebKft2+P1w/xp8ilb2trn0ykfqlr1yur83xfVNOgJLVywVNNmjFeduvfajPX2m2NVs8oD1uPzyTPy7F6RO/8b2l8vDeip/i8NV5MH2yrl4kVF/zxTZrP5uucUK1ZUW7f+pYGDX7tun9179mvw4AjV/8+jerh5Bx08dFiLo2fJx6f0dc9BwXHp0mXVrFZFr73S39VTMQyjFRCaLDc50wYNGshkMt3UoJs2bXJ4IqWKV3P4nPxm5941mjj+S00Y/6UkycuruHbtX68BfYdp3txFOZ6zbMVcbd60TcNeeV2SZDKZtH3XH5oy+WuNG/OZJOlwYpyGvhypObPnW8/bd+hPjR71gb7+6jtJVzIDn06crsmTpufdDd5GF9IuuXoKeeLwoU0aO+4zjRl75d+tl1cJHf17i3q+METffbfgX8/PSDuip57pqQULlt6wX4kSxXXm1C49FtJRy1escsrcXenS0T9cPYV8o3bTVvo4aqQefaiJq6fiUh4+VfJ0/EfvesxpY8X8/YvTxsorN53Tb9++fR5Oo+C7u1IF+fv7auWKq+m78+eTtXFDnBoFNsgxGPDw8FD9BrU19qPJ1jaLxaLfVqxRo8CrKd7Y9ZvV4enWWrp0hc6dPa8OT7eWubBZq/5YbzPey6+8qP+9OkB//31Mc79boEkTpikzMzMP7ha5UblyRZUr56eY5Vd/OZ8/f0GxsZv1QFDDmwoGboaHh4d6v/C8zp49p7itO5wyJmA0BSW97yw3HQxERkY67aKpqalKTU21abNYLDedeciP/Px8JEknjp+0aT9+/KR8/crmeE6ZMqVUqFAhnTh+yqb9xPGTql7jatTbI3Sgpn41XgcOb1R6erouXbysrp3668D+Q9Y+n306Q3FxO3T29FkFPvAfjRo9VH7+vooIf8dZt4hb5O/nK0lKSjph0550/KT8/X1vefwnWgdr5jeTVLRoER07lqTHW3XSqVNn/v1EANkY7WkCl9QMREVFydvb2+a4nF6wfmg9+98ndTgxznoU8vDIs2u9NnKIvL1LqF2brmrRrIMmTpiqaTPG6977alj7TJowVav/WK8dO3Zp2pffKmJElPr07SpPz+vXKyBvderUQWdP77YeHh55W1y7YuVqNWz0mJo91E5Lf1mpb2dNVtmyZfL0mgDuDLn66ZSZmamxY8fqu+++U0JCgtLS0my+fvr06RueHx4enm2vgorlClbl8+LoGG3YEGf9/E+RYFlfH5u//Hx9fbRt6185jnHq1BllZGSorK/tD+yyvj46nnQlw1CpckX16Ruqxo1aKX7nHknS9u3xatzkfr3Qp4vCBo/KceyNG+Lk4eGhincHaO+eA7m/UeTawoW/KDb26mO4/3yP+PmVVWLicWu7n6+PtsTdejr/4sVL2rfvoPbtO6j1sZu0c8cq9ezRSe+9P+GWxwaMJquAFP45S64yA6+//rrGjBmjjh076ty5cwoLC9NTTz0lNzc3jR49+l/PN5vN8vLysjkK2hJBcnKKDuw/ZD3id+5RYuJxPfzI1aKeEiWKq+H99fRn7OYcx0hPT9eWzdttzjGZTHrokSbWc4oWLSzpyrsfrpWZmSWT2/X/9dWpc48yMzN14sSp6/ZB3kpOTrH+ct6376D++mu3jh1LUovmD1r7lChRXIGBDbRu/UanX9/NzXTDJ1kAXJ/FiUdBkKvMwMyZMzVlyhQ98cQTGj16tDp16qSqVauqbt26WrdunQYNGuTseRYIkydO19Bh/bV/30EdOnRYIyKGKPFYkhYtXGbtM//nGVq0cJmmfPa1pCvp/UmffaDNm7Zp08at6jegu4oVLaKZ38yVJO3etV/79h7U2PFvauSId3X69Fk90aalmrdoquee6S1JahTYQA3vr6dVv6/TheQUBQY20NvvvabvZv+kc2fP3/5/ELiu8Z98oRHhg7Rn734dPHhYr4/+n44eTdJPP119OuCXJXM0/6fFmvTpdElXHi28di+CypUqql69+3T69BkdPnxURYsW0YjwwVq48BcdS0yST5nS6tevuwIC/DX3h59v9y0iD1y8eEkJfx+1fj5yNEnxu/fJ26uEyjmh3gTIVTCQmJioOnXqSJKKFy+uc+fOSZLatGmjkSNHOm92BczHYz9X0WJFNPaTt+Tt7aV1azfomQ49lZp6dRmlcuWKKl2mlPXzjz9Ey8enjEZEvCxfv7LatvUvPdOhp7WoMCMjQ/99upci3/ifvv3+cxUrVlQH9h9S/z7DtOyX3yRJqalpeuqZNho+YpA8zZ46dOhvfTphmiZ+MvX2/gPAv/rgw0kqVqyoJk96XyVLemn16j/1RNsuNgW1VarcbbM/wP0N6ynm17nWzx99OFqS9NWM79TrhSHKzMxSzZpV1bXL5/LxKa1Tp85ow8Y4PdL8Kf311+7bdm/IO9vj96jnwFetn9//5HNJUrtWwXo74hVXTeuOZrSnCW56n4Fr1axZUzNmzFBQUJAefPBBtWnTRsOHD9ecOXM0cOBAHT9+/N8HsXMn7DMA57lT9xlA7rDPAOzl9T4DjQOaO22stUdWOG2svJKrmoEOHTooJiZGkjRw4ECNHDlS1atXV2hoKO8mAAAUeEbbgdChZYIJEyaoS5cuevfdd61tHTt2VMWKFbV27VpVr15dbdu2dfokAQBA3nFomcDb21vp6enq0KGDevXqpRYtWjhtIiwT4FosE+BaLBPAXl4vEwSWf9hpY8Ue/c1pY+UVh5YJEhMTNXnyZB09elQtW7ZU5cqV9eabb+rw4cN5NT8AAG47ixP/VxA4FAwUKVJEoaGhWrFihfbs2aOuXbvqyy+/VOXKlfX444/r+++/V3p6el7NFQAA5IFcb0dcpUoVvfHGGzpw4IAWL16sMmXKqHv37goICHDm/AAAuO0oIHSQyWRSoUKFZDKZZLFYyAwAAAo8o+0zkOvMwOHDh/XGG2+oSpUqatmypY4ePaopU6bo2LFjzpwfAADIYw5lBtLS0jRv3jxNnTpVy5cvV7ly5dStWzf17NlTVarkbWUnAAC3S0FJ7zuLQ5kBf39/de/eXV5eXlq4cKEOHTqkt956i0AAAHBHyZLFaYejJk6cqEqVKqlw4cIKCgpSbGzsDft///33qlWrlgoXLqw6deooOjra4Ws6FAxERETo8OHDmjt3rlq1aiU3Nzd9++23SklJcfjCAADA1pw5cxQWFqbIyEht2rRJ9erVU0hIyHW3+V+zZo06deqkXr16afPmzWrfvr3at2+v7du3O3TdXL2b4FpeXl7asmXLLWcH2HQI12LTIVyLTYdgL683Harr39hpY/15aKXNy8gkyWw2y2w2Z+sbFBSkRo0aacKECZKuvL6+QoUKGjhwoIYPH56tf8eOHZWSkqKff776htIHHnhA9evX1+TJk296jrkuIPyH0dZVAAB3viyLxWlHVFSUvL29bY6oqKhs10xLS9PGjRsVHBxsbXNzc1NwcLDWrl2b4zzXrl1r01+SQkJCrtv/em750UIAAO40ztw5MDw8XGFhYTZtOWUFTp48qczMTPn5+dm0+/n5KT4+PsexExMTc+yfmJjo0BwdCgaysrL0wQcfaMGCBUpLS9Ojjz6q6OhoNhoCAOA6rrckkJ84FAy8/fbbGj16tIKDg1WkSBF9/PHHOn78uJo1a5ZX8wMA4LbLcsESuI+Pj9zd3ZWUlGTTnpSUJH9//xzP8ff3d6j/9ThUMzBjxgxNmjRJS5cu1fz587Vw4ULNnDlTWVlZDl0UAID8zBUvKvL09FTDhg0VExNjbcvKylJMTIwaN865oLFx48Y2/SVp2bJl1+1/PQ4FAwkJCWrdurX1c3BwsEwmk44ePerQRQEAQHZhYWGaMmWKvvrqK+3cuVP9+vVTSkqKevToIUkKDQ1VeHi4tf/gwYO1ZMkSffTRR4qPj9fo0aO1YcMGvfTSSw5d16FlgoyMDBUuXNimzcPDg/cRAADuKK5YJpCuPCp44sQJjRo1SomJiapfv76WLFliLRJMSEiQm9vVv+ObNGmiWbNmKSIiQiNGjFD16tU1f/581a5d26HrOrTPgJubm1q1amVTCLFw4UK1aNFCxYoVs7bNmzfPoUlI7DMAW+wzgGuxzwDs5fU+A9XLNnTaWHtObHTaWHnFocxAt27dsrV16dLFaZMBAAC3n0PBwLRp0/JqHgAA5BuuWiZwFTYdAgDAjjM3HSoIbnk7YgAAULCRGQAAwI7FYqz9cwgGAACwk2WwZQKCAQAA7BjtjbzUDAAAYHBkBgAAsMMyAQAABscyAQAAMBQyAwAA2GEHQgAADI4dCAEAgKGQGQAAwI7RCggJBgAAsGO0RwtZJgAAwODIDAAAYIdlAgAADI5HCwEAMDijZQaoGQAAwODIDAAAYMdoTxMQDAAAYIdlAgAAYChkBgAAsMPTBAAAGBwvKgIAAIZCZgAAADssEwAAYHA8TQAAAAyFzAAAAHaMVkBIMAAAgB2jLRMQDAAAYMdowQA1AwAAGByZAQAA7BgrLyCZLEbLheRjqampioqKUnh4uMxms6unAxfj+wHX4vsBeYlgIB85f/68vL29de7cOXl5ebl6OnAxvh9wLb4fkJeoGQAAwOAIBgAAMDiCAQAADI5gIB8xm82KjIykOAiS+H6ALb4fkJcoIAQAwODIDAAAYHAEAwAAGBzBAAAABkcwAACAwREMAHngkUce0csvv+zqacAARo8erfr167t6GijgCAZus+7du8tkMmU79u7dK0lKTEzUwIEDVaVKFZnNZlWoUEFt27ZVTEyMi2cOV5o+fXqO3zdffPGFtU9aWpref/991atXT0WLFpWPj4+aNm2qadOmKT093YWzN5Zr/xv39PRUtWrV9MYbbygjI8Om30cffaRSpUrp8uXL2ca4ePGivLy8NH78+Ns1bRgcby10gccff1zTpk2zaStbtqwOHjyopk2bqmTJkvrggw9Up04dpaena+nSpRowYIDi4+NdNGPkB15eXtq1a5dNm7e3t6QrgUBISIji4uL05ptvqmnTpvLy8tK6dev04YcfqkGDBvz1eBv98994amqqoqOjNWDAAHl4eCg8PNzap2vXrgoPD9e8efPUuXNnm/Pnzp2rtLQ0denS5XZPHQZFZsAFzGaz/P39bQ53d3f1799fJpNJsbGxevrpp1WjRg3dd999CgsL07p161w9beTSmTNnFBoaqlKlSqlo0aJq1aqV9uzZY9NnypQpqlChgooWLaoOHTpozJgxKlmypE0fk8mU7fumSJEikqRx48bp999/V0xMjAYMGKD69eurSpUq6ty5s9avX6/q1avfrtuFrv43fvfdd6tfv34KDg7WggULbPr4+vqqbdu2mjp1arbzp06dqvbt26t06dJ69dVXVaNGDRUtWlRVqlTRyJEjb5jpyWmJqn379urevbv1c2pqqoYOHaqAgAAVK1ZMQUFBWrly5a3cMgo4goF84vTp01qyZIkGDBigYsWKZfu6/S8GFBzdu3fXhg0btGDBAq1du1YWi0WtW7e2/kBfvXq1+vbtq8GDB2vLli1q2bKl3n77bYeuMXPmTAUHB6tBgwbZvubh4ZHj9xRunyJFiigtLS1be69evbR8+XIdOnTI2rZ//379/vvv6tWrlySpRIkSmj59uv766y99/PHHmjJlisaOHXtL83nppZe0du1azZ49W1u3btWzzz6rxx9/PFuQCuMgGHCBn3/+WcWLF7cezz77rPbu3SuLxaJatWq5enpwoj179mjBggX64osv1KxZM9WrV08zZ87UkSNHNH/+fEnSJ598olatWmno0KGqUaOG+vfvr1atWmUb69y5czbfN/7+/jbX4Xsn/7FYLPr111+1dOlStWjRItvXQ0JCVL58eZtlw+nTp6tChQp69NFHJUkRERFq0qSJKlWqpLZt22ro0KH67rvvcj2nhIQETZs2Td9//72aNWumqlWraujQoXrwwQezLV/COKgZcIHmzZvr008/tX4uVqyYEhISXDgj5JWdO3eqUKFCCgoKsraVKVNGNWvW1M6dOyVJu3btUocOHWzOCwwM1M8//2zTVqJECW3atMn62c3taizPruL5yz8Bf3p6urKystS5c2e1adNGxYsXt/b57LPP9Pzzz6tbt26aPn26IiMjZbFY9NVXX6lHjx7Wf79z5szR+PHjtW/fPiUnJysjI0NeXl65ntu2bduUmZmpGjVq2LSnpqaqTJkyuR4XBRvBgAsUK1ZM1apVs2kzm80ymUwUCeK63Nzcsn3f/KNGjRp87+Qj/wT8np6eKl++vAoVKqRLly5py5Yt1j5+fn6SpJ49eyoqKkrLly9XVlaWDh8+rB49ekiS1q5dq+eff16vv/66QkJC5O3trdmzZ+ujjz667rXd3NyyBYfX1hgkJyfL3d1dGzdulLu7u02/a4MVGAvBQD5RunRphYSEaOLEiRo0aFC2Nd6zZ89SN1AA3XPPPcrIyND69evVpEkTSdKpU6e0a9cu3XvvvZKkmjVr6s8//7Q5z/7zv+ncubNGjBihzZs3Z6sbSE9PV1paGnUDt1FOAX+RIkVyDOaqVq2qhx9+WFOnTpXFYlFwcLDuvvtuSdKaNWt0991367XXXrP2v7a+ICdly5bVsWPHrJ8zMzO1fft2NW/eXJLUoEEDZWZm6vjx42rWrFmu7xF3FmoG8pGJEycqMzNTgYGB+uGHH7Rnzx7t3LlT48ePV+PGjV09PeRC9erV1a5dO/Xu3VurVq1SXFycunTpooCAALVr106SNHDgQEVHR2vMmDHas2ePPvvsMy1evFgmk+mmr/Pyyy+radOmevTRRzVx4kTFxcVp//79+u677/TAAw9QGJbP9erVS/PmzdOPP/5oLRyUrnz/JCQkaPbs2dq3b5/Gjx+vH3/88YZjtWjRQosWLdKiRYsUHx+vfv366ezZs9av16hRQ88//7xCQ0M1b948HThwQLGxsYqKitKiRYvy6haRzxEM5CNVqlTRpk2b1Lx5c73yyiuqXbu2WrZsqZiYGJsaAxQs06ZNU8OGDdWmTRs1btxYFotF0dHR8vDwkCQ1bdpUkydP1pgxY1SvXj0tWbJEQ4YMUeHChW/6GmazWcuWLdOwYcP02Wef6YEHHlCjRo00fvx4DRo0SLVr186r24MTPP300zKbzSpatKjat29vbX/yySc1ZMgQvfTSS6pfv77WrFmjkSNH3nCsnj17qlu3bgoNDdXDDz+sKlWqWLMC/5g2bZpCQ0P1yiuvqGbNmmrfvr3+/PNPVaxYMS9uDwWAyULlEZDv9O7dW/Hx8frjjz9cPRUABkDNAJAPfPjhh2rZsqWKFSumxYsX66uvvtKkSZNcPS0ABkFmAMgH/vvf/2rlypW6cOGCqlSpooEDB6pv376unhYAgyAYAADA4CggBADA4AgGAAAwOIIBAAAMjmAAAACDIxgAAMDgCAYAADA4ggEAAAyOYAAAAIP7P14DaoYqTG6LAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#histograms and correlation matrix\n",
        "features.hist(bins=15, figsize=(15, 6), layout=(2, 2))\n",
        "plt.show()\n",
        "sns.heatmap(features.corr(), annot=True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "1ae51af9-4174-4f78-9748-5ec9e61a06b1",
      "metadata": {
        "id": "1ae51af9-4174-4f78-9748-5ec9e61a06b1"
      },
      "outputs": [],
      "source": [
        "def train_test_and_standardize(features, target, test_size=0.2, random_state=42):\n",
        "    # Split the data into train and test sets\n",
        "    x_train, x_test, y_train, y_test = train_test_split(features, target, test_size=test_size, random_state=random_state)\n",
        "\n",
        "    # Standardize the features using StandardScaler\n",
        "    scaler = StandardScaler()\n",
        "    x_train = scaler.fit_transform(x_train)\n",
        "    x_test = scaler.transform(x_test)\n",
        "\n",
        "    return x_train, x_test, y_train, y_test"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xtrain_sclc, xtest_sclc, ytrain_sclc, ytest_sclc = train_test_and_standardize(features_sclc, sclc)\n",
        "xtrain_nsclc, xtest_nsclc, ytrain_nsclc, ytest_nsclc = train_test_and_standardize(features_nsclc, nsclc)"
      ],
      "metadata": {
        "id": "SkKbZBLJ-HRB"
      },
      "id": "SkKbZBLJ-HRB",
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "da87f418-cda3-4f31-b596-54580eff3f40",
      "metadata": {
        "id": "da87f418-cda3-4f31-b596-54580eff3f40"
      },
      "outputs": [],
      "source": [
        "#logistic regression pipeline\n",
        "pipeline = ImbPipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('lr', LogisticRegression(class_weight='balanced', max_iter=10000))\n",
        "])\n",
        "\n",
        "#parameter grid for Logistic Regression\n",
        "param_grid_lr = [\n",
        "    {'lr__penalty': ['l1', 'l2'], 'lr__C': np.logspace(-4, 4, 10), 'lr__solver': ['liblinear'], 'lr__tol': [1e-3],'lr__fit_intercept': [True, False],\n",
        "     'lr__class_weight': [None, 'balanced'], 'lr__random_state': [None], 'lr__max_iter': [5000, 10000, 15000],\n",
        "      'lr__multi_class': ['auto', 'ovr', 'multinomial'], 'lr__verbose': [0], 'lr__warm_start': [True, False],'lr__n_jobs': [None, -1]},\n",
        "    {'lr__penalty': ['l2'], 'lr__C': np.logspace(-4, 4, 10), 'lr__solver': ['lbfgs', 'newton-cg', 'sag'], 'lr__tol': [1e-3],'lr__fit_intercept': [True, False],\n",
        "      'lr__class_weight': [None, 'balanced'], 'lr__random_state': [None], 'lr__max_iter': [5000, 10000, 15000],\n",
        "      'lr__multi_class': ['auto', 'ovr', 'multinomial'], 'lr__verbose': [0], 'lr__warm_start': [True, False],'lr__n_jobs': [None, -1]},\n",
        "    {'lr__penalty': ['elasticnet'], 'lr__C': np.logspace(-4, 4, 10), 'lr__solver': ['saga'], 'lr__l1_ratio': np.linspace(0, 1, 10), 'lr__tol': [1e-3],'lr__fit_intercept': [True, False],\n",
        "      'lr__class_weight': [None, 'balanced'], 'lr__random_state': [None], 'lr__max_iter': [5000, 10000, 15000],\n",
        "      'lr__multi_class': ['auto', 'ovr', 'multinomial'], 'lr__verbose': [0], 'lr__warm_start': [True, False],'lr__n_jobs': [None, -1]}\n",
        "]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "aaadcd2a-5672-442a-924f-660e664fca2a",
      "metadata": {
        "id": "aaadcd2a-5672-442a-924f-660e664fca2a"
      },
      "outputs": [],
      "source": [
        "def evaluate_lr(x_train, y_train, x_test, y_test, param_grid):\n",
        "    grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid_lr, scoring='f1', cv=5, verbose=1, n_jobs=-1)\n",
        "    grid_search.fit(x_train, y_train)  #fit on training data\n",
        "    best_params = grid_search.best_params_\n",
        "    best_score = grid_search.best_score_\n",
        "    best_lr = pipeline.set_params(**best_params)\n",
        "    best_lr.fit(x_train, y_train)  #refit on training data\n",
        "    y_test_pred = best_lr.predict(x_test)\n",
        "    report = classification_report(y_test, y_test_pred)\n",
        "    return best_params, best_score, report\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "20f5780a-2552-4225-8877-f5e5b2a679fd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20f5780a-2552-4225-8877-f5e5b2a679fd",
        "outputId": "d0d8958b-3842-43b4-d170-8fe25193ff73"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 21600 candidates, totalling 108000 fits\n",
            "Best Parameters for NSCLC: {'lr__C': 0.0001, 'lr__class_weight': None, 'lr__fit_intercept': True, 'lr__max_iter': 10000, 'lr__multi_class': 'auto', 'lr__n_jobs': -1, 'lr__penalty': 'l2', 'lr__random_state': None, 'lr__solver': 'sag', 'lr__tol': 0.001, 'lr__verbose': 0, 'lr__warm_start': False}\n",
            "Best F1 Score for NSCLC: 0.8068794072621254\n",
            "Classification Report for NSCLC (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.82      0.73      0.77       142\n",
            "         1.0       0.74      0.84      0.79       135\n",
            "\n",
            "    accuracy                           0.78       277\n",
            "   macro avg       0.78      0.78      0.78       277\n",
            "weighted avg       0.78      0.78      0.78       277\n",
            "\n"
          ]
        }
      ],
      "source": [
        "best_params_nsclc, best_score_nsclc, report_nsclc = evaluate_lr(xtrain_nsclc, ytrain_nsclc, xtest_nsclc, ytest_nsclc, param_grid_lr)\n",
        "print(\"Best Parameters for NSCLC:\", best_params_nsclc)\n",
        "print(\"Best F1 Score for NSCLC:\", best_score_nsclc)\n",
        "print(\"Classification Report for NSCLC (Test Data):\\n\", report_nsclc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "1ca5b4d6-6d66-48a2-bb0c-ad322034dc73",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ca5b4d6-6d66-48a2-bb0c-ad322034dc73",
        "outputId": "4015a2e5-dd68-430e-9aad-d78aca1aff31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 21600 candidates, totalling 108000 fits\n",
            "Best Parameters for SCLC: {'lr__C': 0.0001, 'lr__class_weight': None, 'lr__fit_intercept': True, 'lr__max_iter': 5000, 'lr__multi_class': 'ovr', 'lr__n_jobs': -1, 'lr__penalty': 'l2', 'lr__random_state': None, 'lr__solver': 'sag', 'lr__tol': 0.001, 'lr__verbose': 0, 'lr__warm_start': False}\n",
            "Best F1 Score for SCLC: 0.7635247723067767\n",
            "Classification Report for SCLC (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.74      0.68      0.71       188\n",
            "         1.0       0.69      0.75      0.72       181\n",
            "\n",
            "    accuracy                           0.71       369\n",
            "   macro avg       0.71      0.71      0.71       369\n",
            "weighted avg       0.71      0.71      0.71       369\n",
            "\n"
          ]
        }
      ],
      "source": [
        "best_params_sclc, best_score_sclc, report_sclc = evaluate_lr(xtrain_sclc, ytrain_sclc, xtest_sclc, ytest_sclc, param_grid_lr)\n",
        "print(\"Best Parameters for SCLC:\", best_params_sclc)\n",
        "print(\"Best F1 Score for SCLC:\", best_score_sclc)\n",
        "print(\"Classification Report for SCLC (Test Data):\\n\", report_sclc)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FjtZWq8jNaLg"
      },
      "id": "FjtZWq8jNaLg",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.18"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}