1272 lines (1272 with data), 239.8 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": "3961eb2e-f99f-42b0-d94a-de78294c05c9"
      },
      "id": "dlV9LhAYX8NK",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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.model_selection import RandomizedSearchCV\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from collections import Counter\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from imblearn.pipeline import Pipeline as ImbPipeline\n",
        "from sklearn.metrics import classification_report, confusion_matrix\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": 3,
      "id": "63597d88-f48a-41c0-9afc-1d7199707141",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "63597d88-f48a-41c0-9afc-1d7199707141",
        "outputId": "9945a2a8-f527-4a32-ab93-914080b6985a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Name        FC     logFC    logCPM   P-Value       FDR  SCLC  NSCLC\n",
            "0  KRT16P5 -1.474275 -0.560006 -2.065784  0.423250  0.645529   0.0    0.0\n",
            "1  KRT16P3 -1.158475 -0.212227  0.698547  0.598622  0.779482   0.0    0.0\n",
            "2  KRT16P2  1.785481  0.836313  3.744968  0.060200  0.211667   0.0    0.0\n",
            "3  KRT16P6 -2.534136 -1.341494  0.404997  0.023716  0.123727   0.0    0.0\n",
            "4    CRHBP  1.441891  0.527962 -0.015277  0.034942  0.153404   0.0    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/MyDrive/ML_HW_4_5/labelled data.csv\",index_col=0).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": "92c04189-37b4-4afd-9eb9-d4a45c28291e"
      },
      "id": "mCs7A_6I6mOI",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(19778, 8)\n",
            "\n",
            "<bound method NDFrame.describe of                Name        FC     logFC    logCPM   P-Value       FDR  SCLC  \\\n",
            "0           KRT16P5 -1.474275 -0.560006 -2.065784  0.423250  0.645529   0.0   \n",
            "1           KRT16P3 -1.158475 -0.212227  0.698547  0.598622  0.779482   0.0   \n",
            "2           KRT16P2  1.785481  0.836313  3.744968  0.060200  0.211667   0.0   \n",
            "3           KRT16P6 -2.534136 -1.341494  0.404997  0.023716  0.123727   0.0   \n",
            "4             CRHBP  1.441891  0.527962 -0.015277  0.034942  0.153404   0.0   \n",
            "...             ...       ...       ...       ...       ...       ...   ...   \n",
            "19773  LOC105369958  2.196994  1.135531  1.382694  0.003199  0.040235   0.0   \n",
            "19774         ABCC2  3.461301  1.791314  5.336636  0.000671  0.015864   0.0   \n",
            "19775         TRAV6  3.849574  1.944699 -0.401490  0.000016  0.001372   0.0   \n",
            "19776  LOC105369904  2.226049  1.154485 -1.006838  0.002616  0.035713   0.0   \n",
            "19777  LOC101928636  2.279563  1.188757  0.151617  0.000219  0.007721   0.0   \n",
            "\n",
            "       NSCLC  \n",
            "0        0.0  \n",
            "1        0.0  \n",
            "2        0.0  \n",
            "3        0.0  \n",
            "4        0.0  \n",
            "...      ...  \n",
            "19773    0.0  \n",
            "19774    0.0  \n",
            "19775    0.0  \n",
            "19776    0.0  \n",
            "19777    0.0  \n",
            "\n",
            "[19778 rows x 8 columns]>\n",
            "\n",
            "<bound method DataFrame.info of                Name        FC     logFC    logCPM   P-Value       FDR  SCLC  \\\n",
            "0           KRT16P5 -1.474275 -0.560006 -2.065784  0.423250  0.645529   0.0   \n",
            "1           KRT16P3 -1.158475 -0.212227  0.698547  0.598622  0.779482   0.0   \n",
            "2           KRT16P2  1.785481  0.836313  3.744968  0.060200  0.211667   0.0   \n",
            "3           KRT16P6 -2.534136 -1.341494  0.404997  0.023716  0.123727   0.0   \n",
            "4             CRHBP  1.441891  0.527962 -0.015277  0.034942  0.153404   0.0   \n",
            "...             ...       ...       ...       ...       ...       ...   ...   \n",
            "19773  LOC105369958  2.196994  1.135531  1.382694  0.003199  0.040235   0.0   \n",
            "19774         ABCC2  3.461301  1.791314  5.336636  0.000671  0.015864   0.0   \n",
            "19775         TRAV6  3.849574  1.944699 -0.401490  0.000016  0.001372   0.0   \n",
            "19776  LOC105369904  2.226049  1.154485 -1.006838  0.002616  0.035713   0.0   \n",
            "19777  LOC101928636  2.279563  1.188757  0.151617  0.000219  0.007721   0.0   \n",
            "\n",
            "       NSCLC  \n",
            "0        0.0  \n",
            "1        0.0  \n",
            "2        0.0  \n",
            "3        0.0  \n",
            "4        0.0  \n",
            "...      ...  \n",
            "19773    0.0  \n",
            "19774    0.0  \n",
            "19775    0.0  \n",
            "19776    0.0  \n",
            "19777    0.0  \n",
            "\n",
            "[19778 rows x 8 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",
            "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": "26edde7a-6174-4c1f-ba99-f08379cd705a"
      },
      "id": "qfXHs_Hy6pP3",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index([], dtype='object')\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = data.corr()\n",
        "# Create a heatmap using Seaborn\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": "04377427-7af3-4624-ab1b-995a77a1ea8f"
      },
      "id": "f4DIAfhd6wiV",
      "execution_count": 6,
      "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": 7,
      "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": 8,
      "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": "a158631f-0e98-46f6-e717-ec8413b86b50"
      },
      "id": "Yr9LsDXb7kQk",
      "execution_count": 9,
      "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": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "8e9bd90d-605a-43f1-af50-cdd8a0b4359e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 963
        },
        "id": "8e9bd90d-605a-43f1-af50-cdd8a0b4359e",
        "outputId": "bb952016-a4e6-4bee-8d57-2c026b5e69f7"
      },
      "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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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#EDA\n",
        "#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": 12,
      "id": "1ae51af9-4174-4f78-9748-5ec9e61a06b1",
      "metadata": {
        "id": "1ae51af9-4174-4f78-9748-5ec9e61a06b1"
      },
      "outputs": [],
      "source": [
        "\n",
        "#data splitting\n",
        "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": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "da87f418-cda3-4f31-b596-54580eff3f40",
      "metadata": {
        "id": "da87f418-cda3-4f31-b596-54580eff3f40"
      },
      "outputs": [],
      "source": [
        "# Random forest pipeline\n",
        "pipeline = ImbPipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('rf', RandomForestClassifier(class_weight='balanced', n_jobs=-1))\n",
        "])\n",
        "# Parameter grid for random forest\n",
        "param_grid_rf = {\n",
        "    'rf__n_estimators': [int(x) for x in np.linspace(start=200, stop=2000, num=10)],\n",
        "    'rf__criterion': ['gini', 'entropy'],\n",
        "    'rf__max_depth': [int(x) for x in np.linspace(10, 110, num=11)],\n",
        "    'rf__min_samples_split': [2, 5, 10],\n",
        "    'rf__min_samples_leaf': [1, 2, 4],\n",
        "    'rf__bootstrap': [True, False],\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "aaadcd2a-5672-442a-924f-660e664fca2a",
      "metadata": {
        "id": "aaadcd2a-5672-442a-924f-660e664fca2a"
      },
      "outputs": [],
      "source": [
        "#RandomForest\n",
        "def evaluate_random_forest(x_train, y_train, x_test, y_test, param_grid):\n",
        "    grid_search = RandomizedSearchCV(estimator=pipeline, param_distributions=param_grid, n_iter=100, cv=3, verbose=1, n_jobs=-1)\n",
        "    grid_search.fit(x_train, y_train)\n",
        "    best_params = grid_search.best_params_\n",
        "    best_score = grid_search.best_score_\n",
        "    best_rf = pipeline.set_params(**best_params)\n",
        "    best_rf.fit(x_train, y_train)\n",
        "    y_test_pred = best_rf.predict(x_test)\n",
        "    report = classification_report(y_test, y_test_pred)\n",
        "    return best_params, best_score, report, y_test_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "20f5780a-2552-4225-8877-f5e5b2a679fd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20f5780a-2552-4225-8877-f5e5b2a679fd",
        "outputId": "1e8080f7-cbb0-4292-ea0d-39b41497fee3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
            "Best Parameters for NSCLC: {'rf__n_estimators': 1000, 'rf__min_samples_split': 2, 'rf__min_samples_leaf': 2, 'rf__max_depth': 50, 'rf__criterion': 'gini', 'rf__bootstrap': True}\n",
            "Best F1 Score for NSCLC: 0.8398310160637839\n",
            "Classification Report for NSCLC (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.79      0.90      0.84       142\n",
            "         1.0       0.88      0.74      0.80       135\n",
            "\n",
            "    accuracy                           0.82       277\n",
            "   macro avg       0.83      0.82      0.82       277\n",
            "weighted avg       0.83      0.82      0.82       277\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Evaluate for NSCLC\n",
        "best_params_nsclc, best_score_nsclc, report_nsclc, y_test_pred_nsclc = evaluate_random_forest(xtrain_nsclc, ytrain_nsclc, xtest_nsclc, ytest_nsclc, param_grid_rf)\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)\n",
        "cm = confusion_matrix(ytest_nsclc, y_test_pred_nsclc)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a confusion matrix visualization\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm, annot=True, fmt='g', cmap='Blues')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix for NSCLC Model')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "mfmbAhhdHNGI",
        "outputId": "44d7a045-9c2e-4692-fe8b-6eee6989305e"
      },
      "id": "mfmbAhhdHNGI",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "1ca5b4d6-6d66-48a2-bb0c-ad322034dc73",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ca5b4d6-6d66-48a2-bb0c-ad322034dc73",
        "outputId": "0a7dc11d-42f5-4191-ffca-ea87bb6b5f1e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n",
            "Best Parameters for SCLC: {'rf__n_estimators': 200, 'rf__min_samples_split': 10, 'rf__min_samples_leaf': 4, 'rf__max_depth': 70, 'rf__criterion': 'entropy', 'rf__bootstrap': True}\n",
            "Best F1 Score for SCLC: 0.8187372708757638\n",
            "Classification Report for SCLC (Test Data):\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.82      0.88      0.85       188\n",
            "         1.0       0.86      0.80      0.83       181\n",
            "\n",
            "    accuracy                           0.84       369\n",
            "   macro avg       0.84      0.84      0.84       369\n",
            "weighted avg       0.84      0.84      0.84       369\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#Evaluate for SCLC\n",
        "best_params_sclc, best_score_sclc, report_sclc, y_test_pred_sclc = evaluate_random_forest(xtrain_sclc, ytrain_sclc, xtest_sclc, ytest_sclc, param_grid_rf)\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)\n",
        "cm_sclc = confusion_matrix(ytest_sclc, y_test_pred_sclc)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a confusion matrix visualization\n",
        "plt.figure(figsize=(10, 7))\n",
        "sns.heatmap(cm_sclc, annot=True, fmt='g', cmap='Blues')\n",
        "\n",
        "# Adding labels and title\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix for SCLC Model')\n",
        "\n",
        "# Adjust layout\n",
        "plt.tight_layout()\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "r8pSPsQwN1Kn",
        "outputId": "64cba4f1-4bdc-4e69-863c-43986107cbc2"
      },
      "id": "r8pSPsQwN1Kn",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_input = data.iloc[12238:12257,:]\n",
        "test_input"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "_dO-kmhKjkHT",
        "outputId": "fb726802-57e6-4209-d163-e39d8c8b574d"
      },
      "id": "_dO-kmhKjkHT",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Name        FC     logFC    logCPM   P-Value       FDR  SCLC  \\\n",
              "12238          FAR2 -1.036364 -0.051530  6.047611  0.768052  0.889167   0.0   \n",
              "12239          ATF6 -1.074289 -0.103382  8.154670  0.260387  0.490329   1.0   \n",
              "12240          ATF3 -1.197653 -0.260209  6.107613  0.301848  0.533602   1.0   \n",
              "12241     LOC441087  1.365118  0.449026 -1.056675  0.084938  0.258926   0.0   \n",
              "12242         GATA6 -1.275932 -0.351551  4.885739  0.114056  0.305799   0.0   \n",
              "12243        TPRG1L  1.004909  0.007065  4.665519  0.976440  0.994635   0.0   \n",
              "12244         PDGFA -1.243270 -0.314139  4.715532  0.088348  0.265474   1.0   \n",
              "12245         GATA2  1.032892  0.046689  2.803374  0.842767  0.931684   0.0   \n",
              "12246         CIAO3 -1.148757 -0.200073  3.933105  0.075367  0.242021   0.0   \n",
              "12247         CIAO1  1.123722  0.168285  5.935793  0.131505  0.332089   0.0   \n",
              "12248       EPB41L5 -1.504651 -0.589428  7.674006  0.000487  0.012852   0.0   \n",
              "12249         PDGFD -1.260623 -0.334137  5.855667  0.125071  0.322722   1.0   \n",
              "12250       EPB41L2  1.179566  0.238256  7.043599  0.144521  0.350371   0.0   \n",
              "12251         PDGFC -1.506741 -0.591432  7.891498  0.000298  0.009267   1.0   \n",
              "12252  LOC100507053 -1.215763 -0.281862  5.163591  0.148860  0.356350   0.0   \n",
              "12253       EPB41L3  1.221163  0.288255  6.511856  0.165384  0.378512   0.0   \n",
              "12254       PIP5K1A  1.011516  0.016518  7.225447  0.857265  0.939069   0.0   \n",
              "12255       PIP5K1B -1.036260 -0.051386  6.607497  0.810784  0.913194   1.0   \n",
              "12256       PIP5K1C  1.009869  0.014168  5.455605  0.907544  0.961805   1.0   \n",
              "\n",
              "       NSCLC  \n",
              "12238    0.0  \n",
              "12239    0.0  \n",
              "12240    0.0  \n",
              "12241    0.0  \n",
              "12242    0.0  \n",
              "12243    0.0  \n",
              "12244    1.0  \n",
              "12245    0.0  \n",
              "12246    0.0  \n",
              "12247    0.0  \n",
              "12248    0.0  \n",
              "12249    1.0  \n",
              "12250    0.0  \n",
              "12251    1.0  \n",
              "12252    0.0  \n",
              "12253    0.0  \n",
              "12254    0.0  \n",
              "12255    0.0  \n",
              "12256    0.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-511ec19f-1d08-4daa-b7d3-742e9642471e\" 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>Name</th>\n",
              "      <th>FC</th>\n",
              "      <th>logFC</th>\n",
              "      <th>logCPM</th>\n",
              "      <th>P-Value</th>\n",
              "      <th>FDR</th>\n",
              "      <th>SCLC</th>\n",
              "      <th>NSCLC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12238</th>\n",
              "      <td>FAR2</td>\n",
              "      <td>-1.036364</td>\n",
              "      <td>-0.051530</td>\n",
              "      <td>6.047611</td>\n",
              "      <td>0.768052</td>\n",
              "      <td>0.889167</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12239</th>\n",
              "      <td>ATF6</td>\n",
              "      <td>-1.074289</td>\n",
              "      <td>-0.103382</td>\n",
              "      <td>8.154670</td>\n",
              "      <td>0.260387</td>\n",
              "      <td>0.490329</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12240</th>\n",
              "      <td>ATF3</td>\n",
              "      <td>-1.197653</td>\n",
              "      <td>-0.260209</td>\n",
              "      <td>6.107613</td>\n",
              "      <td>0.301848</td>\n",
              "      <td>0.533602</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12241</th>\n",
              "      <td>LOC441087</td>\n",
              "      <td>1.365118</td>\n",
              "      <td>0.449026</td>\n",
              "      <td>-1.056675</td>\n",
              "      <td>0.084938</td>\n",
              "      <td>0.258926</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12242</th>\n",
              "      <td>GATA6</td>\n",
              "      <td>-1.275932</td>\n",
              "      <td>-0.351551</td>\n",
              "      <td>4.885739</td>\n",
              "      <td>0.114056</td>\n",
              "      <td>0.305799</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12243</th>\n",
              "      <td>TPRG1L</td>\n",
              "      <td>1.004909</td>\n",
              "      <td>0.007065</td>\n",
              "      <td>4.665519</td>\n",
              "      <td>0.976440</td>\n",
              "      <td>0.994635</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12244</th>\n",
              "      <td>PDGFA</td>\n",
              "      <td>-1.243270</td>\n",
              "      <td>-0.314139</td>\n",
              "      <td>4.715532</td>\n",
              "      <td>0.088348</td>\n",
              "      <td>0.265474</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12245</th>\n",
              "      <td>GATA2</td>\n",
              "      <td>1.032892</td>\n",
              "      <td>0.046689</td>\n",
              "      <td>2.803374</td>\n",
              "      <td>0.842767</td>\n",
              "      <td>0.931684</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12246</th>\n",
              "      <td>CIAO3</td>\n",
              "      <td>-1.148757</td>\n",
              "      <td>-0.200073</td>\n",
              "      <td>3.933105</td>\n",
              "      <td>0.075367</td>\n",
              "      <td>0.242021</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12247</th>\n",
              "      <td>CIAO1</td>\n",
              "      <td>1.123722</td>\n",
              "      <td>0.168285</td>\n",
              "      <td>5.935793</td>\n",
              "      <td>0.131505</td>\n",
              "      <td>0.332089</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12248</th>\n",
              "      <td>EPB41L5</td>\n",
              "      <td>-1.504651</td>\n",
              "      <td>-0.589428</td>\n",
              "      <td>7.674006</td>\n",
              "      <td>0.000487</td>\n",
              "      <td>0.012852</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12249</th>\n",
              "      <td>PDGFD</td>\n",
              "      <td>-1.260623</td>\n",
              "      <td>-0.334137</td>\n",
              "      <td>5.855667</td>\n",
              "      <td>0.125071</td>\n",
              "      <td>0.322722</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12250</th>\n",
              "      <td>EPB41L2</td>\n",
              "      <td>1.179566</td>\n",
              "      <td>0.238256</td>\n",
              "      <td>7.043599</td>\n",
              "      <td>0.144521</td>\n",
              "      <td>0.350371</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12251</th>\n",
              "      <td>PDGFC</td>\n",
              "      <td>-1.506741</td>\n",
              "      <td>-0.591432</td>\n",
              "      <td>7.891498</td>\n",
              "      <td>0.000298</td>\n",
              "      <td>0.009267</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12252</th>\n",
              "      <td>LOC100507053</td>\n",
              "      <td>-1.215763</td>\n",
              "      <td>-0.281862</td>\n",
              "      <td>5.163591</td>\n",
              "      <td>0.148860</td>\n",
              "      <td>0.356350</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12253</th>\n",
              "      <td>EPB41L3</td>\n",
              "      <td>1.221163</td>\n",
              "      <td>0.288255</td>\n",
              "      <td>6.511856</td>\n",
              "      <td>0.165384</td>\n",
              "      <td>0.378512</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12254</th>\n",
              "      <td>PIP5K1A</td>\n",
              "      <td>1.011516</td>\n",
              "      <td>0.016518</td>\n",
              "      <td>7.225447</td>\n",
              "      <td>0.857265</td>\n",
              "      <td>0.939069</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12255</th>\n",
              "      <td>PIP5K1B</td>\n",
              "      <td>-1.036260</td>\n",
              "      <td>-0.051386</td>\n",
              "      <td>6.607497</td>\n",
              "      <td>0.810784</td>\n",
              "      <td>0.913194</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12256</th>\n",
              "      <td>PIP5K1C</td>\n",
              "      <td>1.009869</td>\n",
              "      <td>0.014168</td>\n",
              "      <td>5.455605</td>\n",
              "      <td>0.907544</td>\n",
              "      <td>0.961805</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-511ec19f-1d08-4daa-b7d3-742e9642471e 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-511ec19f-1d08-4daa-b7d3-742e9642471e');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0880d51e-1090-4dd1-b94d-51746b4da238')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0880d51e-1090-4dd1-b94d-51746b4da238 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setting up and training the final models\n",
        "rfc_sclc = pipeline.set_params(**best_params_sclc)\n",
        "rfc_sclc.fit(xtrain_sclc, ytrain_sclc)\n",
        "\n",
        "rfc_nsclc = pipeline.set_params(**best_params_nsclc)\n",
        "rfc_nsclc.fit(xtrain_nsclc, ytrain_nsclc)\n",
        "\n",
        "\n",
        "def predict_biomarkers(test_input):\n",
        "    # Assuming 'Name', 'FC', 'P-Value', 'SCLC', and 'NSCLC' are present in the DataFrame\n",
        "\n",
        "    test_input['NSCLC'] = test_input['NSCLC'].fillna(0)\n",
        "    test_input['SCLC'] = test_input['SCLC'].fillna(0)\n",
        "\n",
        "    # Selecting the desired columns for test_x\n",
        "    text_x = test_input[['FC', 'logFC', 'P-Value']]\n",
        "\n",
        "    # Selecting the desired column for test_y_sclc\n",
        "    test_y_sclc = test_input['SCLC']\n",
        "\n",
        "    # Selecting the desired column for test_y_nsclc\n",
        "    test_y_nsclc = test_input['NSCLC']\n",
        "\n",
        "    sc_x = StandardScaler()\n",
        "    test_x = sc_x.fit_transform(text_x)\n",
        "\n",
        "    rfc_predict_sclc = np.array(rfc_sclc.predict(test_x))\n",
        "    rfc_predict_nsclc = np.array(rfc_nsclc.predict(test_x))\n",
        "    common_biomarkers = (rfc_predict_sclc == 1) & (rfc_predict_nsclc == 1)\n",
        "    common_biomarkers = common_biomarkers.astype(int)\n",
        "\n",
        "    return rfc_predict_sclc, rfc_predict_nsclc, common_biomarkers\n",
        "\n",
        "rfc_predict_sclc, rfc_predict_nsclc, common_biomarkers = predict_biomarkers(test_input)\n",
        "\n",
        "# Print the predicted biomarkers\n",
        "print(\"SCLC Biomarkers\")\n",
        "for (i, val) in enumerate(rfc_predict_sclc):\n",
        "    if val == 1:\n",
        "        if test_input.iloc[i, :]['SCLC'] == 1.0:\n",
        "            print(test_input.iloc[i, :]['Name'])\n",
        "\n",
        "print(\"\\nNSCLC Biomarkers\")\n",
        "for (i, val) in enumerate(rfc_predict_nsclc):\n",
        "    if val == 1:\n",
        "        if test_input.iloc[i, :]['NSCLC'] == 1.0:\n",
        "            print(test_input.iloc[i, :]['Name'])\n",
        "\n",
        "print(\"\\nCommon Biomarkers\")\n",
        "for (i, val) in enumerate(common_biomarkers):\n",
        "    if val == 1:\n",
        "        if test_input.iloc[i, :]['SCLC'] == 1.0 and test_input.iloc[i, :]['NSCLC'] == 1.0:\n",
        "            print(test_input.iloc[i, :]['Name'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEyx8aR9joSK",
        "outputId": "0adfcb55-7223-45d0-9524-2948d9454347"
      },
      "id": "PEyx8aR9joSK",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SCLC Biomarkers\n",
            "ATF6\n",
            "ATF3\n",
            "PDGFA\n",
            "PDGFD\n",
            "PDGFC\n",
            "PIP5K1C\n",
            "\n",
            "NSCLC Biomarkers\n",
            "PDGFA\n",
            "PDGFD\n",
            "PDGFC\n",
            "\n",
            "Common Biomarkers\n",
            "PDGFA\n",
            "PDGFD\n",
            "PDGFC\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
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