SVM&RF_CombinedModalities.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SVM&RF_CombinedModalities.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/notmanan/Depression-Detection-Through-Multi-Modal-Data/blob/master/SVM%26RF_CombinedModalities.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dho7dMmGuEI7",
        "colab_type": "code",
        "outputId": "63048705-2e3e-42b7-a488-bb41ac81d34d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "!pip install nltk\n",
        "import numpy as np\n",
        "import nltk\n",
        "import pandas as pd\n",
        "from gensim.models.keyedvectors import KeyedVectors\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from sklearn.metrics import classification_report\n",
        "import gc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNZhlsmvl8oi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(\"Text Results: \")\n",
        "\n",
        "dataset1 = np.array(pd.read_csv('/content/drive/My Drive/MCA Dataset/dev_split_Depression_AVEC2017.csv',delimiter=',',encoding='utf-8'))[:, 0:2]\n",
        "dataset2 = np.array(pd.read_csv('/content/drive/My Drive/MCA Dataset/full_test_split.csv',delimiter=',',encoding='utf-8'))[:, 0:2]\n",
        "dataset3 = np.array(pd.read_csv('/content/drive/My Drive/MCA Dataset/train_split_Depression_AVEC2017.csv',delimiter=',',encoding='utf-8'))[:, 0:2]\n",
        "\n",
        "\n",
        "countPos = 0\n",
        "dataset = np.concatenate((dataset1, np.concatenate((dataset2, dataset3))))\n",
        "def checkPosNeg(dataset, index):\n",
        "    for i in range(0, len(dataset)):\n",
        "        if(dataset[i][0] == index):\n",
        "            return dataset[i][1]\n",
        "    return 0\n",
        "\n",
        "Data = []\n",
        "Y = []\n",
        "\n",
        "Data_test = []\n",
        "Y_test = []\n",
        "index = -1\n",
        "for i in range(0, len(dataset3)):\n",
        "    val = checkPosNeg(dataset, dataset3[i][0])\n",
        "    if(val == 0 and countPos>38):\n",
        "        continue\n",
        "    Y.append(val)\n",
        "    index+=1\n",
        "    if(Y[index] == 0):\n",
        "        countPos+=1\n",
        "\n",
        "    \n",
        "    fileName = \"/content/drive/My Drive/MCA Dataset/train_data/\" + str(int(dataset3[i][0])) + \"_TRANSCRIPT.csv\"\n",
        "    Data.append(np.array(pd.read_csv(fileName,delimiter='\\t',encoding='utf-8'))[:, 2:4])\n",
        "\n",
        "\n",
        "for i in range(0, len(dataset1)):\n",
        "    val = checkPosNeg(dataset, dataset1[i][0])\n",
        "    if(val == 0):\n",
        "        continue\n",
        "    Y.append(val)\n",
        "    fileName = \"/content/drive/My Drive/MCA Dataset/dev_data/\" + str(int(dataset1[i][0])) + \"_TRANSCRIPT.csv\"\n",
        "    Data.append(np.array(pd.read_csv(fileName,delimiter='\\t',encoding='utf-8'))[:, 2:4])\n",
        "    \n",
        "for i in range(0, len(dataset2)):\n",
        "    Y_test.append(checkPosNeg(dataset, dataset2[i][0]))\n",
        "    fileName = \"/content/drive/My Drive/MCA Dataset/test_data/\" + str(int(dataset2[i][0])) + \"_TRANSCRIPT.csv\"\n",
        "    Data_test.append(np.array(pd.read_csv(fileName,delimiter='\\t',encoding='utf-8'))[:, 2:4])\n",
        "\n",
        "    \n",
        "Y = np.array(Y)\n",
        "Data2 = []\n",
        "\n",
        "Data2_test = []\n",
        "Y_test = np.array(Y_test)\n",
        "\n",
        "for i in range(0, len(Data)):\n",
        "    script = []\n",
        "    for k in range(1, len(Data[i])):\n",
        "        if(Data[i][k][0] == \"Participant\"):\n",
        "            script.append(Data[i][k][1])\n",
        "    Data2.append(script)\n",
        "    \n",
        "for i in range(0, len(Data_test)):\n",
        "    script = []\n",
        "    for k in range(1, len(Data_test[i])):\n",
        "        if(Data_test[i][k][0] == \"Participant\"):\n",
        "            script.append(Data_test[i][k][1])\n",
        "    Data2_test.append(script)\n",
        "        \n",
        "Data2 = np.array(Data2)\n",
        "Data2_test = np.array(Data2_test)\n",
        "\n",
        "model = KeyedVectors.load_word2vec_format('/content/drive/My Drive/MCA Dataset/GoogleNews-vectors-negative300.bin', binary=True)\n",
        "\n",
        "stop_words = set(stopwords.words('english'))\n",
        "\n",
        "\n",
        "def remove_StopWOrds(sentence):\n",
        "    filtered_sentence = [] \n",
        "    for w in sentence: \n",
        "        if w not in stop_words: \n",
        "            filtered_sentence.append(w)\n",
        "    \n",
        "    return filtered_sentence\n",
        "\n",
        "def checkAcc(Y_pred, Y_test):\n",
        "    correct = 0\n",
        "    for i in range(len(Y_pred)):\n",
        "        if(Y_pred[i] == Y_test[i]):\n",
        "            correct+=1\n",
        "    \n",
        "    return float(correct)/len(Y_pred)\n",
        "\n",
        "\n",
        "max_num_words = 15\n",
        "max_num_sentence = 200\n",
        "\n",
        "#train_data\n",
        "finalMatrix = np.zeros((Data2.shape[0], max_num_sentence, max_num_words))\n",
        "print(finalMatrix.shape)\n",
        "for k in range(Data2.shape[0]):\n",
        "    for i in range(min(max_num_sentence, len(Data2[k]))):\n",
        "    \ttry:\n",
        "    \t  sentence = Data2[k][i].split(\" \")\n",
        "    \texcept:\n",
        "    \t  continue\n",
        "    \tsentence = remove_StopWOrds(sentence)\n",
        "    \tfor j in range(min(max_num_words, len(sentence))):\n",
        "    \t\ttry:\n",
        "    \t\t\tfinalMatrix[k][i][j] = np.average(np.array(model[sentence[j]]))\n",
        "    \t\texcept:\n",
        "    \t\t\tcontinue\n",
        "X = np.zeros((len(Data2), max_num_sentence*max_num_words))\n",
        "for i in range(len(finalMatrix)):\n",
        "    X[i] = finalMatrix[i].flatten().reshape(1, -1)\n",
        " \n",
        "#Test_data\n",
        "finalMatrix_test = np.zeros((Data2_test.shape[0], max_num_sentence, max_num_words))\n",
        "print(finalMatrix_test.shape)\n",
        "for k in range(Data2_test.shape[0]):\n",
        "    for i in range(min(max_num_sentence, len(Data2_test[k]))):\n",
        "    \ttry:\n",
        "    \t  sentence = Data2_test[k][i].split(\" \")\n",
        "    \texcept:\n",
        "    \t  continue\n",
        "    \tsentence = remove_StopWOrds(sentence)\n",
        "    \tfor j in range(min(max_num_words, len(sentence))):\n",
        "    \t\ttry:\n",
        "    \t\t\tfinalMatrix_test[k][i][j] = np.average(np.array(model[sentence[j]]))\n",
        "    \t\texcept:\n",
        "    \t\t\tcontinue\n",
        "X_test = np.zeros((len(Data2_test), max_num_sentence*max_num_words))\n",
        "for i in range(len(finalMatrix_test)):\n",
        "    X_test[i] = finalMatrix_test[i].flatten().reshape(1, -1)\n",
        "\n",
        "Data2 = []\n",
        "Data2_test = []\n",
        "gc.collect()\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf1 = RandomForestClassifier()\n",
        "clf1.fit(X,Y)\n",
        "Y_pred_rf_1 = clf1.predict(X_test)\n",
        "print(\"Random forests: \")\n",
        "print(classification_report(Y_test,Y_pred_rf_1))\n",
        "print(\"Train: \"+ str(clf1.score(X,Y)))\n",
        "print(\"Test: \" + str(clf1.score(X_test,Y_test)))\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X, Y)\n",
        "\n",
        "Y_pred_svm_1 = classifier.predict(X_test)\n",
        "print(\"SVM: \")\n",
        "print(classification_report(Y_test,Y_pred_svm_1))\n",
        "print(\"Train: \"+ str(classifier.score(X,Y)))\n",
        "print(\"Test: \" + str(classifier.score(X_test,Y_test)))\n",
        "\n",
        "y_pred_train_svm_1 = classifier.predict(X)\n",
        "y_pred_train_rf_1 = clf1.predict(X)\n",
        "\n",
        "\n",
        "#Audio Features \n",
        "print(\"Audio Results: \")\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "def makedata(X):\n",
        "    for i in range(X.shape[0]):\n",
        "        if(X[i,1] == 0):\n",
        "            X[i,0] = 0\n",
        "            for j in range(7):\n",
        "                X[i,j+1] = 0\n",
        "    X = np.array(X)\n",
        "    X = np.average(X, axis = 0)\n",
        "    X = np.array(X.reshape(1, -1))\n",
        "    return X\n",
        " \n",
        "X_train = np.zeros((1,74))\n",
        "Y_train = []\n",
        "\n",
        "X_test = np.zeros((1,74))\n",
        "#Y_test = []\n",
        "\n",
        "for i in range(len(dataset2)):\n",
        "    data = pd.read_csv('/content/drive/My Drive/MCA Dataset/test_data/'+str(int(dataset2[i][0]))+\"_COVAREP.csv\",header = None)\n",
        "    X_temp = data.iloc[:,:].values\n",
        "    X_temp = makedata(X_temp)\n",
        "    X_test = np.concatenate((X_test,X_temp),0)\n",
        "    #Y_test.append(dataset2[i][1])\n",
        "    \n",
        "\n",
        "X_test = np.delete(X_test,0,0)\n",
        "#Y_test = np.array(Y_test)\n",
        "count_0 = 0\n",
        "count_1 = 0\n",
        "\n",
        "for i in range(len(dataset3)):\n",
        "    data = pd.read_csv('/content/drive/My Drive/MCA Dataset/train_data/'+str(int(dataset3[i][0]))+\"_COVAREP.csv\",header = None)\n",
        "    if(dataset3[i][1] == 0):\n",
        "      count_0 +=1\n",
        "      if(count_0<=39):\n",
        "        X_temp = data.iloc[:,:].values\n",
        "        X_temp = makedata(X_temp)\n",
        "        X_train = np.concatenate((X_train,X_temp),0)\n",
        "        Y_train.append(dataset3[i][1])\n",
        "    else:\n",
        "      count_1 +=1\n",
        "      if(count_1<=28):\n",
        "        X_temp = data.iloc[:,:].values\n",
        "        X_temp = makedata(X_temp)\n",
        "        X_train = np.concatenate((X_train,X_temp),0)\n",
        "        Y_train.append(dataset3[i][1])\n",
        "\n",
        "count_0 = 0\n",
        "count_1 = 0    \n",
        "\n",
        "for i in range(len(dataset1)):\n",
        "    data = pd.read_csv('/content/drive/My Drive/MCA Dataset/dev_data/'+str(int(dataset1[i][0]))+\"_COVAREP.csv\",header = None)\n",
        "    if(dataset1[i][1] == 0):\n",
        "      continue\n",
        "    else:\n",
        "      count_1+= 1\n",
        "      if(count_1<=11):\n",
        "        X_temp = data.iloc[:,:].values\n",
        "        X_temp = makedata(X_temp)\n",
        "        X_train = np.concatenate((X_train,X_temp),0)\n",
        "        Y_train.append(dataset1[i][1])\n",
        "    \n",
        "\n",
        "X_train = np.delete(X_train,0,0)\n",
        "Y_train = np.array(Y_train)\n",
        "    \n",
        "clf1 = RandomForestClassifier()\n",
        "clf1.fit(X_train,Y_train)\n",
        "Y_pred_rf_2 = clf1.predict(X_test)\n",
        "print(\"Random forests: \")\n",
        "print(classification_report(Y_test,Y_pred_rf_2))\n",
        "print(\"Train: \"+ str(clf1.score(X_train,Y_train)))\n",
        "print(\"Test: \" + str(clf1.score(X_test,Y_test)))\n",
        "\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_pred_svm_2 = classifier.predict(X_test)\n",
        "print(\"SVM: \")\n",
        "print(classification_report(Y_test,Y_pred_svm_2))\n",
        "print(\"Train: \"+ str(classifier.score(X_train,Y_train)))\n",
        "print(\"Test: \" + str(classifier.score(X_test,Y_test)))\n",
        "\n",
        "y_pred_train_svm_2 = classifier.predict(X_train)\n",
        "y_pred_train_rf_2 = clf1.predict(X_train)\n",
        "\n",
        "\n",
        "#Video\n",
        "\n",
        "print(\"Video Results: \")\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "def processData(data):\n",
        "    # print(data)\n",
        "    X = data.iloc[:,:].values\n",
        "    X = np.delete(X, 0, 1)\n",
        "    X = np.delete(X, 1, 1)\n",
        "    for i in range(len(X)):\n",
        "        if(isinstance(X[i][5],str) or isinstance(X[i][7],str)):\n",
        "            X[i] = np.zeros((1, X.shape[1]))\n",
        "\n",
        "    X = np.mean(X, 0)\n",
        "    X = np.array(X.reshape(1, -1))\n",
        "    # print(X)\n",
        "    # print(X.shape)\n",
        "    return X\n",
        "\n",
        "def getData(filename):\n",
        "    # print(filename)\n",
        "    data = pd.read_csv(filename,delimiter=',')\n",
        "    X = processData(data)\n",
        "    return X\n",
        "\n",
        "def makeDataPoint(patientNo):\n",
        "    file1 = (patientNo)+\"_CLNF_AUs.txt\"\n",
        "    file2 = (patientNo)+\"_CLNF_features.txt\"\n",
        "    file3 = (patientNo)+\"_CLNF_features3D.txt\"\n",
        "    file4 = (patientNo)+\"_CLNF_gaze.txt\"\n",
        "    file5 = (patientNo)+\"_CLNF_hog.txt\"\n",
        "    file6 = (patientNo)+\"_CLNF_pose.txt\"\n",
        "\n",
        "    X1 = getData(file1)\n",
        "    X2 = getData(file2)\n",
        "    X3 = getData(file3)\n",
        "    X4 = getData(file4)\n",
        "    # X5 = getData(file5)\n",
        "    X6 = getData(file6)\n",
        "\n",
        "    X = np.concatenate((X1, X2, X3, X4, X6), 1)\n",
        "    # print(X)\n",
        "    # print(X.shape)\n",
        "    return X\n",
        "\n",
        "count0 = 0\n",
        "count1 = 0\n",
        "\n",
        "def makeDataset(location, folder):\n",
        "    global count0\n",
        "    global count1\n",
        "    file  = np.array(pd.read_csv(location,delimiter=',',encoding='utf-8'))[:, 0:2]\n",
        "    X_temp = np.zeros((1,388))\n",
        "    Y_temp = []\n",
        "    for i in range(len(file)):\n",
        "      if(int(file[i][1]) == 0):\n",
        "        count0 += 1\n",
        "        if(count0 >39):\n",
        "          continue\n",
        "      patientID = (str(int(file[i][0])))\n",
        "      #print(patientID)\n",
        "      string = '/content/drive/My Drive/MCA Dataset/' + folder +\"/\"+ patientID\n",
        "      X_temp = np.concatenate((X_temp, makeDataPoint(string)), 0)\n",
        "      Y_temp.append(int(file[i][1]))\n",
        "    X_temp = np.delete(X_temp,0,0)\n",
        "    Y_temp = np.asarray(Y_temp)\n",
        "    return X_temp, Y_temp\n",
        "\n",
        "print(\"train\")\n",
        "X_train, Y_train = makeDataset('/content/drive/My Drive/MCA Dataset/train_split_Depression_AVEC2017.csv', 'train_data')\n",
        "print(\"dev\")\n",
        "X_dev, Y_dev = makeDataset('/content/drive/My Drive/MCA Dataset/dev_split_Depression_AVEC2017.csv', 'dev_data')\n",
        "count0 = 0\n",
        "X_train = np.concatenate((X_train, X_dev), 0  )\n",
        "Y_train = np.concatenate((Y_train, Y_dev), 0  )\n",
        "\n",
        "print(\"test\")\n",
        "X_test, Y_test = makeDataset('/content/drive/My Drive/MCA Dataset/full_test_split.csv', 'test_data')\n",
        "\n",
        "print(Y_train)\n",
        "\n",
        "clf1 = RandomForestClassifier()\n",
        "clf1.fit(X_train,Y_train)\n",
        "Y_pred_rf_3 = clf1.predict(X_test)\n",
        "print(\"Random forests: \")\n",
        "print(classification_report(Y_test,Y_pred_rf_3))\n",
        "print(\"Train: \"+ str(clf1.score(X_train,Y_train)))\n",
        "print(\"Test: \" + str(clf1.score(X_test,Y_test)))\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "classifier.fit(X_train, Y_train)\n",
        "Y_pred_svm_3 = classifier.predict(X_test)\n",
        "print(\"SVM: \")\n",
        "print(classification_report(Y_test,Y_pred_svm_3))\n",
        "print(\"Train: \"+ str(classifier.score(X_train,Y_train)))\n",
        "print(\"Test: \" + str(classifier.score(X_test,Y_test)))\n",
        "\n",
        "y_pred_train_svm_3 = classifier.predict(X_train)\n",
        "y_pred_train_rf_3 = clf1.predict(X_train)\n",
        "\n",
        "\n",
        "# Combine\n",
        "print(\"Combined Results: \") \n",
        "X_2 = np.column_stack((y_pred_train1,y_pred_train2,y_pred_train3))\n",
        "from sklearn.svm import SVC\n",
        "classifier2 = SVC(kernel = 'rbf', random_state = 0, gamma = 'auto')\n",
        "classifier2.fit(X_2, Y_train)\n",
        "y_pred_combine = classifier.predict(np.column_stack((y_pred1, y_pred2,y_pred3)))\n",
        "print(classification_report(Y_test, y_pred_combine))\n"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}