Fucntional_Connectivity_Feature_Selection_Feature_Shuffling.ipynb

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        "<a href=\"https://colab.research.google.com/github/ayushi15092002/mental-health-workload/blob/main/Fucntional_Connectivity_Feature_Selection_Feature_Shuffling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the Drive helper and mount\n",
        "from google.colab import drive\n",
        "\n",
        "# This will prompt for authorization.\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0_ghmkonvbU-",
        "outputId": "522db3b2-8c15-4d8f-c308-bbf98b3cae92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "OcFsY0n4u44O"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "# import seaborn as sns\n",
        "# import matplotlib.pyplot as plt\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_selection import SelectFromModel\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# plt.style.use('seaborn-colorblind')\n",
        "# %matplotlib inline\n",
        "from sklearn.metrics import roc_auc_score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_QVcZXLu44S"
      },
      "source": [
        "## Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "3msZVBBEu44U"
      },
      "outputs": [],
      "source": [
        "data = pd.read_excel(r'/content/drive/My Drive/drdo/features/min_max_fun_conn.xlsx')"
      ]
    },
    {
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              "   Coherence  Imaginary Coherence  Phase Locking Value  \\\n",
              "0        0.0             0.000000                  0.0   \n",
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              "\n",
              "   corrected imaginary PLV  Pairwise Phase Consistency  Phase Lag Index (PLI)  \\\n",
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              "   Directed Phase Lag Index (DPLI)  Weighted Phase Lag Index (WPLI)  \\\n",
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              "   Debiased estimator of squared WPLI    y  \n",
              "0                            0.000000  1.0  \n",
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              "2                            0.069174  1.0  \n",
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        "data.head(5)"
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iR5Vop-Yu44W",
        "outputId": "4f6b27d4-1be5-4537-f3a4-0376195ad4ce",
        "colab": {
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1880, 9), (471, 9))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data.drop(labels=['y'], axis=1), \n",
        "                                                    data.y, test_size=0.2,\n",
        "                                                    random_state=0)\n",
        "X_train.shape, X_test.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
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        "colab": {
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        "id": "hwMHoaw-zAb0",
        "outputId": "612e7c97-364b-4486-97fb-a6904c5d5fa1"
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              "2108    3.0\n",
              "436     1.0\n",
              "1538    2.0\n",
              "342     1.0\n",
              "713     1.0\n",
              "       ... \n",
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              "835     2.0\n",
              "1653    3.0\n",
              "Name: y, Length: 1881, dtype: float64"
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          "execution_count": 6
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    {
      "cell_type": "markdown",
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        "id": "LBzSlYqMu44X"
      },
      "source": [
        "##  Feature Shuffling\n",
        "permute the values of each feature, one at the time, and measure how much the permutation decreases the accuracy, or the roc_auc, or the mse of the machine learning model.\n",
        "If the variables are important, this is, highly predictive, a random permutation of their values will decrease dramatically any of these metrics."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rOarLVr5u44X",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "fe3cb460-50ee-41ad-9f43-df9f8c7e31ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.08       0.34117574 0.34117574 ... 0.18       0.34117574 0.1       ]\n",
            "0.8270189952517566\n",
            "0.8376249537015961\n",
            "0.844167088212791\n",
            "0.8300759715249278\n",
            "0.8324521564703139\n",
            "0.8384596585710463\n",
            "0.8263559243028987\n",
            "0.8339155372341067\n",
            "0.8407233809513391\n"
          ]
        }
      ],
      "source": [
        "auc_drop, selected_features = feature_shuffle_rf(X_train=X_train,y_train=y_train,random_state=0)"
      ]
    },
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              "                              feature  auc_drop\n",
              "6     Directed Phase Lag Index (DPLI)  0.040014\n",
              "0                           Coherence  0.039351\n",
              "3             corrected imaginary PLV  0.036294\n",
              "4          Pairwise Phase Consistency  0.033917\n",
              "7     Weighted Phase Lag Index (WPLI)  0.032454\n",
              "1                 Imaginary Coherence  0.028745\n",
              "5               Phase Lag Index (PLI)  0.027910\n",
              "8  Debiased estimator of squared WPLI  0.025646\n",
              "2                 Phase Locking Value  0.022202"
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              "      <th>6</th>\n",
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              "      <th>4</th>\n",
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              "    [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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-24e455df-308a-49e5-8188-5ed27670709a 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-24e455df-308a-49e5-8188-5ed27670709a');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#  we select features that have auc_drop > 0\n",
        "auc_drop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "59dAUqtxu44Z",
        "outputId": "64313f03-2c68-40b0-a59a-25035bfc9470",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6       Directed Phase Lag Index (DPLI)\n",
              "0                             Coherence\n",
              "3               corrected imaginary PLV\n",
              "4            Pairwise Phase Consistency\n",
              "7       Weighted Phase Lag Index (WPLI)\n",
              "1                   Imaginary Coherence\n",
              "5                 Phase Lag Index (PLI)\n",
              "8    Debiased estimator of squared WPLI\n",
              "2                   Phase Locking Value\n",
              "Name: feature, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "selected_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "kr7WcuHqu44a"
      },
      "outputs": [],
      "source": [
        "def feature_shuffle_rf(X_train,y_train,max_depth=None,class_weight=None,top_n=15,n_estimators=50,random_state=0):\n",
        "    \n",
        "    model = RandomForestClassifier(n_estimators=n_estimators,max_depth=max_depth,\n",
        "                                    random_state=random_state,class_weight=class_weight,\n",
        "                                    n_jobs=-1)\n",
        "    model.fit(X_train, y_train)\n",
        "    print(model.predict_proba(X_train)[:, 1])\n",
        "    train_auc = roc_auc_score(y_train, (model.predict_proba(X_train)),multi_class = 'ovr', average = 'weighted')\n",
        "    feature_dict = {}\n",
        "\n",
        "    # selection  logic\n",
        "    for feature in X_train.columns:\n",
        "        X_train_c = X_train.copy().reset_index(drop=True)\n",
        "        y_train_c = y_train.copy().reset_index(drop=True)\n",
        "        \n",
        "        # shuffle individual feature\n",
        "        X_train_c[feature] = X_train_c[feature].sample(frac=1,random_state=random_state).reset_index(\n",
        "            drop=True)\n",
        "        #print(X_train_c.isnull().sum())\n",
        "        # make prediction with shuffled feature and calculate roc-auc\n",
        "        shuff_auc = roc_auc_score(y_train_c,\n",
        "                                  (model.predict_proba(X_train_c)),multi_class = 'ovr', average = 'weighted')\n",
        "        print(shuff_auc)\n",
        "        # save the drop in roc-auc\n",
        "        feature_dict[feature] = (train_auc - shuff_auc)\n",
        "        #print(feature_dict)\n",
        "    \n",
        "    auc_drop = pd.Series(feature_dict).reset_index()\n",
        "    auc_drop.columns = ['feature', 'auc_drop']\n",
        "    auc_drop.sort_values(by=['auc_drop'], ascending=False, inplace=True)\n",
        "    selected_features = auc_drop[auc_drop.auc_drop>0]['feature']\n",
        "\n",
        "    return auc_drop, selected_features"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0tIIZ1-7zxun"
      },
      "execution_count": null,
      "outputs": []
    }
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