galaxias.ipynb

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  "metadata": {
    "colab": {
      "name": "galaxias.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/davidrmh/CIC-A18/blob/master/galaxias.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "xZ5HgW6CBSXz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TDwBTmuOBUyP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Preprocesamiento\n",
        "\n",
        "+ Imágenes de tamaño (120, 120)\n",
        "\n",
        "+ Imágenes en blanco y negro (las preguntas del cuestionario no involucran los colores)\n",
        "\n",
        "+ Para data augmentation:\n",
        "\n",
        "    + Rotación de la imagen hasta 180 grados.\n",
        "    \n",
        "    + Se giran las imágenes de forma vertical u horizontal.\n",
        "    \n",
        "    + Zoom entre 1X - 3X.\n",
        "\n",
        "## Arquitectura\n",
        "\n",
        "+ Similar a la arquitectura del tercer lugar aunque se omitieron ciertas capas que consideramos **redundantes**, además se utilizó lo siguiente:\n",
        "\n",
        "    + Leaky ReLu en lugar de ReLu como función de activación (para evitar gradientes iguales a cero).\n",
        "    \n",
        "    + Batch normalization (para evitar gradientes saturados o iguales a cero)\n",
        "\n",
        "## Datos utilizados\n",
        "\n",
        "+ Por la falta de  GPU's  :-(, no fue posible utilizar en su totalidad el conjunto de imágenes de entrenamiento. En cambio se utilizaron **15,000** imágenes de este conjunto de las cuales el **90%** fue utilizado como conjunto de entrenamiento y el **10%** como conjunto de validación."
      ]
    },
    {
      "metadata": {
        "id": "SWfZrCWXdT7q",
        "colab_type": "code",
        "outputId": "201d30dc-ef3a-413a-d3a5-5cb502e9eb3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "cell_type": "code",
      "source": [
        "!apt-get install -y -qq software-properties-common python-software-properties module-init-tools\n",
        "!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null\n",
        "!apt-get update -qq 2>&1 > /dev/null\n",
        "!apt-get -y install -qq google-drive-ocamlfuse fuse\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "creds = GoogleCredentials.get_application_default()\n",
        "import getpass\n",
        "!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL\n",
        "vcode = getpass.getpass()\n",
        "!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "E: Package 'python-software-properties' has no installation candidate\n",
            "Selecting previously unselected package libfuse2:amd64.\n",
            "(Reading database ... 22280 files and directories currently installed.)\n",
            "Preparing to unpack .../libfuse2_2.9.7-1ubuntu1_amd64.deb ...\n",
            "Unpacking libfuse2:amd64 (2.9.7-1ubuntu1) ...\n",
            "Selecting previously unselected package fuse.\n",
            "Preparing to unpack .../fuse_2.9.7-1ubuntu1_amd64.deb ...\n",
            "Unpacking fuse (2.9.7-1ubuntu1) ...\n",
            "Selecting previously unselected package google-drive-ocamlfuse.\n",
            "Preparing to unpack .../google-drive-ocamlfuse_0.7.0-0ubuntu1~ubuntu18.04.1_amd64.deb ...\n",
            "Unpacking google-drive-ocamlfuse (0.7.0-0ubuntu1~ubuntu18.04.1) ...\n",
            "Setting up libfuse2:amd64 (2.9.7-1ubuntu1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1) ...\n",
            "Setting up fuse (2.9.7-1ubuntu1) ...\n",
            "Setting up google-drive-ocamlfuse (0.7.0-0ubuntu1~ubuntu18.04.1) ...\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "··········\n",
            "Please, open the following URL in a web browser: https://accounts.google.com/o/oauth2/auth?client_id=32555940559.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive&response_type=code&access_type=offline&approval_prompt=force\n",
            "Please enter the verification code: Access token retrieved correctly.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "fl8RHo6Hd2Ts",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir -p drive\n",
        "!google-drive-ocamlfuse drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OQoINZYoKwF8",
        "colab_type": "code",
        "outputId": "3df8bc90-19c6-4442-d7fb-f77f14da5b27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "cd 'drive/Colab Notebooks/kaggle-galaxias/codigos'\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/Colab Notebooks/kaggle-galaxias/codigos\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gpTz0i5rK1KL",
        "colab_type": "code",
        "outputId": "8ca2c34f-8e31-4bf0-fc67-cc68da4da5d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# coding: utf-8\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers.convolutional import MaxPooling2D\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras.layers.normalization import BatchNormalization\n",
        "from keras.layers.core import Activation\n",
        "from keras.layers.core import Flatten\n",
        "from keras.layers.core import Dense\n",
        "from keras.layers import LeakyReLU\n",
        "from keras.optimizers import Nadam\n",
        "\n",
        "import time\n",
        "import preprocesamiento as pre\n",
        "import pandas as pd\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "r6EqMC-SPL35",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "##==============================================================================\n",
        "## Variables globales\n",
        "##==============================================================================\n",
        "\n",
        "#Optimizador (Nesterov + Adam)\n",
        "learning_rate = 0.002\n",
        "opt = Nadam(learning_rate)\n",
        "\n",
        "##==============================================================================\n",
        "## Función para crear el modelo\n",
        "##==============================================================================\n",
        "def crea_modelo(inputShape = (1, 120, 120)):\n",
        "    '''\n",
        "    ENTRADA\n",
        "    inputShape: Tupla. Dimensiones los arreglos de entrada. Por default se\n",
        "    utiliza la forma channels_first\n",
        "    '''\n",
        "    model = Sequential()\n",
        "    #Convolucional con 48 filtros de 5x5 cada uno\n",
        "    model.add(Conv2D(filters = 48, kernel_size = (5,5), padding = 'same', input_shape = inputShape, data_format = 'channels_first'))\n",
        "    model.add(BatchNormalization())\n",
        "    #Función de activación ReLu\n",
        "    model.add((LeakyReLU(alpha=0.3)))\n",
        "    #Max pooling\n",
        "    model.add(MaxPooling2D(pool_size=(3,3), strides=(3, 3), padding = 'same', data_format = 'channels_first'))\n",
        "    model.add(Conv2D(filters = 96, kernel_size = (5,5), padding = 'same'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add((LeakyReLU(alpha=0.3)))\n",
        "    model.add(MaxPooling2D(pool_size=(2,2), strides=(2, 2), padding = 'same', data_format = 'channels_first'))\n",
        "\n",
        "    model.add(Conv2D(filters = 192, kernel_size = (3,3), padding = 'same', data_format = 'channels_first'))\n",
        "    model.add((LeakyReLU(alpha=0.3)))\n",
        "\n",
        "    #model.add(Conv2D(filters = 192, kernel_size = (3,3), padding = 'same', data_format = 'channels_first'))\n",
        "    #model.add(BatchNormalization())\n",
        "    #model.add((LeakyReLU(alpha=0.3)))\n",
        "\n",
        "    #model.add(Conv2D(filters = 384, kernel_size = (3,3), padding = 'same', data_format = 'channels_first'))\n",
        "    #model.add((LeakyReLU(alpha=0.3)))\n",
        "\n",
        "    model.add(Conv2D(filters = 384, kernel_size = (3,3), padding = 'same', data_format = 'channels_first'))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add((LeakyReLU(alpha=0.3)))\n",
        "    model.add(MaxPooling2D(pool_size=(3,3), strides=(3, 3), padding = 'same', data_format = 'channels_first'))\n",
        "\n",
        "    #Fully connected\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(2048))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add((LeakyReLU(alpha=0.3)))\n",
        "\n",
        "    #model.add(Dense(2048))\n",
        "    #model.add(BatchNormalization())\n",
        "    #model.add((LeakyReLU(alpha=0.3)))\n",
        "\n",
        "    #Última capa para predecir las probabilidades\n",
        "    model.add(Dense(37))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Activation(\"relu\"))\n",
        "\n",
        "    return model\n",
        "##==============================================================================\n",
        "## Función para entrenar un modelo\n",
        "##==============================================================================\n",
        "def entrena_modelo(model, ruta_entrenamiento, ruta_validacion, csv_target, epochs=20, loss='mean_squared_error', batch=100, optim=opt, epochs_save= 10, ext = '.jpg'):\n",
        "    '''\n",
        "    ENTRADA\n",
        "    model: Modelo creado con la función crea_modelo\n",
        "    ruta_entrenamiento: String con la ruta de la carpeta con el conjunto de entrenamiento\n",
        "    ruta_validacion: String  con la ruta de la carpeta con el conjunto de validacion\n",
        "    csv_target: pandas dataframe cuya primer columna es el id de la imagen\n",
        "    y el resto de las columnas son las cantidades objetivo\n",
        "    epochs: Entero, número de épocas.\n",
        "    loss: String función de pérdida\n",
        "    batch: Tamaño del bloque de entrenamiento\n",
        "    optim: Objeto que representa algún método para optimizar\n",
        "    epochs_save: Entero que representa cada cuantas épocas se guarda el modelo\n",
        "    ext: String con la extensión de los archivos (imágenes)s\n",
        "    SALIDA\n",
        "    modelo entrenado\n",
        "    historia: Objeto con la historia del entrenamiento\n",
        "    '''\n",
        "\n",
        "    #Compila el modelo\n",
        "    model.compile(loss = loss, optimizer = optim)\n",
        "\n",
        "    #listas con las rutas de los archivos\n",
        "    arch_entrena = pre.lista_archivos(ruta_entrenamiento, ext)\n",
        "    arch_valida = pre.lista_archivos(ruta_validacion, ext)\n",
        "\n",
        "    #entrena\n",
        "    archivo_modelo = 'modelo-{epoch:02d}.hdf5' #nombre del archivo con el checkpoint del modelo\n",
        "    checkpoint = ModelCheckpoint(archivo_modelo, save_best_only=True, period = epochs_save)\n",
        "    inicio = time.ctime()\n",
        "    historia = model.fit_generator(generator = pre.generador(arch_entrena, csv_target, batch)\n",
        "        ,steps_per_epoch = int(len(arch_entrena) / batch), epochs = epochs\n",
        "        ,callbacks = [checkpoint], verbose = 1, validation_data = pre.generador(arch_valida, csv_target, batch)\n",
        "        ,validation_steps = int(len(arch_valida) / batch))\n",
        "    fin = time.ctime()\n",
        "\n",
        "    print 'Inicio ' + inicio + ' Fin ' + fin\n",
        "\n",
        "    return model, historia\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "N8GR4d0KK_5D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model = crea_modelo()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZdR7CAEpLS_j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ruta_csv = '../all/training_solutions_rev1.csv'\n",
        "ruta_imagenes_entrena = '../all/images_15000_BW_training/'\n",
        "ruta_imagenes_valida = '../all/images_15000_BW_validation/'\n",
        "\n",
        "csv_target = pd.read_csv(ruta_csv)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "eZguM9gNLHdb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "epochs=20\n",
        "loss='mean_squared_error'\n",
        "batch=100\n",
        "optim=opt\n",
        "epochs_save= 10\n",
        "ext = '.jpg'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1rm1E7KZQNUS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        ""
      ]
    },
    {
      "metadata": {
        "id": "2Ofz0qmbLaFS",
        "colab_type": "code",
        "outputId": "0747a38f-74a7-4c89-a5bd-b1296656424d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "cell_type": "code",
      "source": [
        "model, historia = entrena_modelo(model, ruta_imagenes_entrena, ruta_imagenes_valida, csv_target, batch = batch, epochs = epochs)\n",
        "model.save('modelo.hdf5')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "135/135 [==============================] - 350s 3s/step - loss: 0.0273 - val_loss: 0.1221\n",
            "Epoch 2/20\n",
            "135/135 [==============================] - 213s 2s/step - loss: 0.0066 - val_loss: 0.0522\n",
            "Epoch 3/20\n",
            "135/135 [==============================] - 223s 2s/step - loss: 0.0153 - val_loss: 0.0574\n",
            "Epoch 4/20\n",
            "135/135 [==============================] - 219s 2s/step - loss: 0.0056 - val_loss: 0.0463\n",
            "Epoch 5/20\n",
            "135/135 [==============================] - 225s 2s/step - loss: 0.0054 - val_loss: 0.0439\n",
            "Epoch 6/20\n",
            "135/135 [==============================] - 222s 2s/step - loss: 0.0054 - val_loss: 0.0425\n",
            "Epoch 7/20\n",
            "135/135 [==============================] - 220s 2s/step - loss: 0.0054 - val_loss: 0.0409\n",
            "Epoch 8/20\n",
            "135/135 [==============================] - 224s 2s/step - loss: 0.0054 - val_loss: 0.0412\n",
            "Epoch 9/20\n",
            "135/135 [==============================] - 222s 2s/step - loss: 0.0053 - val_loss: 0.0407\n",
            "Epoch 10/20\n",
            "135/135 [==============================] - 221s 2s/step - loss: 0.0053 - val_loss: 0.0398\n",
            "Epoch 11/20\n",
            "135/135 [==============================] - 264s 2s/step - loss: 0.0051 - val_loss: 0.0400\n",
            "Epoch 12/20\n",
            "135/135 [==============================] - 224s 2s/step - loss: 0.0049 - val_loss: 0.0397\n",
            "Epoch 13/20\n",
            "135/135 [==============================] - 223s 2s/step - loss: 0.0047 - val_loss: 0.0392\n",
            "Epoch 14/20\n",
            "135/135 [==============================] - 227s 2s/step - loss: 0.0047 - val_loss: 0.0387\n",
            "Epoch 15/20\n",
            "135/135 [==============================] - 220s 2s/step - loss: 0.0047 - val_loss: 0.0383\n",
            "Epoch 16/20\n",
            "135/135 [==============================] - 224s 2s/step - loss: 0.0047 - val_loss: 0.0394\n",
            "Epoch 17/20\n",
            "135/135 [==============================] - 224s 2s/step - loss: 0.0047 - val_loss: 0.0386\n",
            "Epoch 18/20\n",
            "135/135 [==============================] - 221s 2s/step - loss: 0.0047 - val_loss: 0.0383\n",
            "Epoch 19/20\n",
            "135/135 [==============================] - 221s 2s/step - loss: 0.0047 - val_loss: 0.0375\n",
            "Epoch 20/20\n",
            "135/135 [==============================] - 225s 2s/step - loss: 0.0047 - val_loss: 0.0385\n",
            "Inicio Sat Nov 17 06:46:38 2018 Fin Sat Nov 17 08:04:58 2018\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "I-YQb_Yqj90r",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def carga_imagenes_arreglo(ruta, model, num_imagenes = 100, ext = '.jpg'):\n",
        "    '''\n",
        "    ENTRADA\n",
        "    ruta: String con la ruta de la carpeta que contiene las imagenes\n",
        "\n",
        "    model: Modelo entrenado\n",
        "\n",
        "    num_imagenes: Entero que indica la cantidad de imagenes que se tomaran\n",
        "    de la carpeta. si num_imagenes = '' entonces obtiene todos los archivos\n",
        "\n",
        "    ext: String con la extension de las imagenes\n",
        "\n",
        "    SALIDA\n",
        "    Arreglo de numpy con las imagenes\n",
        "    '''\n",
        "\n",
        "    #Obtiene la ruta de cada archivo en la carpeta\n",
        "    lista_arch = pre.lista_archivos(ruta, ext)\n",
        "\n",
        "    #Selecciona los archivos a utilizar\n",
        "    if num_imagenes != '':\n",
        "        lista_arch = pre.np.random.choice(lista_arch, size = num_imagenes, replace = False)\n",
        "\n",
        "    #para almacenar cada imagen y cada id\n",
        "    x_test = []\n",
        "    ids = []\n",
        "\n",
        "    for arch in lista_arch:\n",
        "        #Abre la imagen\n",
        "        imagen = pre.Image.open(arch)\n",
        "\n",
        "        #determina si es una imagen a color\n",
        "        if 'BW' in arch:\n",
        "            col = False\n",
        "        else:\n",
        "            col = True\n",
        "\n",
        "        #Convierte la imagen en un arreglo numerico\n",
        "        arreglo = pre.imagen_a_arreglo(imagen, col)\n",
        "\n",
        "        #redimensiona el arreglo para considerar el numero de canales\n",
        "        if col:\n",
        "            arreglo.shape = (3, arreglo.shape[0], arreglo.shape[1])\n",
        "        else:\n",
        "            arreglo.shape = (1, arreglo.shape[0], arreglo.shape[1])\n",
        "        \n",
        "        arreglo = arreglo / 255.0\n",
        "        \n",
        "        #agrega el arreglo a x_test\n",
        "        x_test.append(arreglo)\n",
        "\n",
        "        #cierra la imagen\n",
        "        imagen.close()\n",
        "\n",
        "        #obtiene el Id de la imagen\n",
        "        id_imagen = arch.split('/')[-1].split('.')[0]\n",
        "        ids.append(id_imagen)\n",
        "\n",
        "    #convierte x_test en un numpy array\n",
        "    x_test = pre.np.array(x_test)\n",
        "    ids = pre.np.array(ids)\n",
        "    \n",
        "    #hace las predicciones\n",
        "    pred = model.predict(x_test)\n",
        "\n",
        "    return [pred, ids]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SoXGhvwRkEKx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "ruta_img = '../all/images_training_10000_BW/'\n",
        "\n",
        "pred = carga_imagenes_arreglo(ruta_img, model, 100)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kfHQPfko7170",
        "colab_type": "code",
        "outputId": "5f3727bd-c8b1-40bc-bf3a-840c71bb0264",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2012
        }
      },
      "cell_type": "code",
      "source": [
        "columnas = ['Class1.1', 'Class1.2', 'Class1.3', 'Class2.2', 'Class2.2', 'Class3.1', \n",
        "    'Class3.2', 'Class4.1', 'Class4.2', 'Class5.1', 'Class5.2', 'Class5.3', 'Class5.4',\n",
        "    'Class6.1', 'Class6.2', 'Class7.1', 'Class7.2', 'Class7.3', 'Class8.1',\t'Class8.2',\t\n",
        "    'Class8.3',\t'Class8.4', 'Class8.5',\t'Class8.6',\t'Class8.7',\t'Class9.1',\t'Class9.2',\n",
        "    'Class9.3',\t'Class10.1', 'Class10.2', 'Class10.3', 'Class11.1', 'Class11.2',\n",
        "    'Class11.3', 'Class11.4', 'Class11.5', 'Class11.6']\n",
        "data_frame = pd.DataFrame(data = pred[0], index= pred[1], columns = columnas)\n",
        "data_frame.to_csv('predicciones.csv', index= False)\n",
        "data_frame"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Class1.1</th>\n",
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              "      <th>Class11.5</th>\n",
              "      <th>Class11.6</th>\n",
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              "      <td>0.102825</td>\n",
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              "    <tr>\n",
              "      <th>310133</th>\n",
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              "    <tr>\n",
              "      <th>980808</th>\n",
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              "    <tr>\n",
              "      <th>120632</th>\n",
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              "    <tr>\n",
              "      <th>727697</th>\n",
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              "    <tr>\n",
              "      <th>195617</th>\n",
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              "    <tr>\n",
              "      <th>482930</th>\n",
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              "    <tr>\n",
              "      <th>546305</th>\n",
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              "      <td>0.163241</td>\n",
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              "    <tr>\n",
              "      <th>354787</th>\n",
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              "      <td>0.291658</td>\n",
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              "    <tr>\n",
              "      <th>339097</th>\n",
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              "    <tr>\n",
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              "      <th>360142</th>\n",
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              "      <td>0.177947</td>\n",
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>396333</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "      <td>0.460063</td>\n",
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              "      <td>0.188623</td>\n",
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              "    <tr>\n",
              "      <th>416106</th>\n",
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              "    <tr>\n",
              "      <th>291196</th>\n",
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              "    <tr>\n",
              "      <th>641659</th>\n",
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              "    <tr>\n",
              "      <th>103348</th>\n",
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              "    <tr>\n",
              "      <th>540158</th>\n",
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              "    <tr>\n",
              "      <th>861215</th>\n",
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              "      <td>0.109750</td>\n",
              "      <td>0.104723</td>\n",
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              "    <tr>\n",
              "      <th>557119</th>\n",
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              "    <tr>\n",
              "      <th>940527</th>\n",
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              "    <tr>\n",
              "      <th>105334</th>\n",
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              "    <tr>\n",
              "      <th>541355</th>\n",
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              "      <td>0.273883</td>\n",
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              "    <tr>\n",
              "      <th>489188</th>\n",
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              "    <tr>\n",
              "      <th>759338</th>\n",
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              "    <tr>\n",
              "      <th>822399</th>\n",
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              "    <tr>\n",
              "      <th>318223</th>\n",
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              "    <tr>\n",
              "      <th>987215</th>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>522047</th>\n",
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              "    <tr>\n",
              "      <th>257384</th>\n",
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              "      <td>0.668249</td>\n",
              "      <td>0.003175</td>\n",
              "      <td>0.287790</td>\n",
              "      <td>0.283297</td>\n",
              "      <td>0.343321</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.155416</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.155482</td>\n",
              "      <td>0.083017</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.261186</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 37 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Class1.1  Class1.2  Class1.3  Class2.2  Class2.2  Class3.1  Class3.2  \\\n",
              "353190  0.384702  0.551285  0.040400  0.000000  0.632470  0.093098  0.527895   \n",
              "455182  0.362959  0.678278  0.027182  0.280312  0.373666  0.149259  0.238099   \n",
              "853016  0.763992  0.150099  0.000000  0.000000  0.207011  0.000000  0.126678   \n",
              "204336  0.771447  0.214365  0.008202  0.000000  0.188269  0.003681  0.122339   \n",
              "534803  0.034258  0.503111  0.000000  0.000000  0.241154  0.590364  0.000000   \n",
              "738274  0.509445  0.501255  0.000508  0.000000  0.523579  0.000000  0.488197   \n",
              "431391  0.620737  0.373048  0.000000  0.082880  0.247755  0.000000  0.275140   \n",
              "229431  0.324550  0.592347  0.003547  0.000000  0.519573  0.242310  0.292874   \n",
              "481202  0.000000  0.386929  0.086073  0.000000  0.500971  0.100867  0.174351   \n",
              "453455  0.285734  0.743007  0.006261  0.000000  0.700431  0.370383  0.357914   \n",
              "512022  0.416538  0.617475  0.000000  0.000000  0.479160  0.212715  0.288542   \n",
              "527725  0.305391  0.698517  0.000000  0.100839  0.560254  0.294548  0.270293   \n",
              "694563  0.337574  0.689650  0.007360  0.056681  0.602923  0.141534  0.501943   \n",
              "619240  0.405463  0.294917  0.025619  0.013340  0.160292  0.137111  0.046315   \n",
              "370446  0.514303  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
              "562140  0.550674  0.383551  0.013901  0.079428  0.245967  0.066789  0.164507   \n",
              "775244  0.567151  0.444963  0.013464  0.000000  0.421625  0.056723  0.391718   \n",
              "347573  0.702575  0.273419  0.028154  0.000000  0.310157  0.000000  0.297236   \n",
              "102602  0.517639  0.561565  0.065788  0.000000  0.547252  0.206874  0.307530   \n",
              "310133  0.738646  0.169275  0.131916  0.000000  0.249212  0.000000  0.259950   \n",
              "980808  0.464400  0.608179  0.002631  0.000000  0.549786  0.129225  0.411176   \n",
              "120632  0.159447  0.733380  0.000627  0.097375  0.581903  0.307751  0.285468   \n",
              "727697  0.211202  0.709090  0.000000  0.000000  0.805911  0.000000  0.637691   \n",
              "195617  0.172595  0.816933  0.003057  0.516764  0.311710  0.028503  0.270346   \n",
              "482930  0.304319  0.692313  0.025670  0.729259  0.000000  0.043648  0.000000   \n",
              "546305  0.613973  0.351847  0.023827  0.000000  0.352567  0.030912  0.306002   \n",
              "354787  0.153083  0.777638  0.000000  0.000000  0.627547  0.000000  0.688016   \n",
              "339097  0.512135  0.468015  0.028159  0.000000  0.524575  0.119974  0.449855   \n",
              "951273  0.384422  0.645428  0.033915  0.000000  0.567808  0.226650  0.378058   \n",
              "360142  0.305928  0.619717  0.048611  0.000000  0.538864  0.133289  0.360298   \n",
              "...          ...       ...       ...       ...       ...       ...       ...   \n",
              "396333  0.270705  0.682978  0.018934  0.000000  0.729259  0.382832  0.398135   \n",
              "371832  0.702026  0.246606  0.038548  0.000000  0.309511  0.000000  0.275796   \n",
              "782896  0.000000  0.972370  0.000000  0.133912  0.714036  0.740521  0.115552   \n",
              "611341  0.222527  0.000000  0.174952  0.000000  0.000000  0.000000  0.000000   \n",
              "977774  0.413655  0.512854  0.000000  0.000000  0.468315  0.223697  0.247893   \n",
              "230943  0.020628  0.761456  0.000000  0.000000  0.612354  0.125966  0.364268   \n",
              "729065  0.017677  0.408478  0.000000  0.000000  0.475190  0.441562  0.077282   \n",
              "487962  0.524649  0.444761  0.020613  0.027579  0.398968  0.000000  0.388138   \n",
              "729808  0.406185  0.622774  0.024977  0.000000  0.569832  0.122737  0.460063   \n",
              "416106  0.468372  0.534045  0.010730  0.000000  0.505374  0.163099  0.366003   \n",
              "291196  0.405162  0.597223  0.011881  0.112820  0.446166  0.224885  0.248836   \n",
              "641659  0.626402  0.357662  0.075091  0.000000  0.417178  0.037709  0.332593   \n",
              "103348  0.235327  0.739283  0.000000  0.460876  0.325767  0.372431  0.000000   \n",
              "540158  0.517842  0.381617  0.036445  0.000000  0.427259  0.000000  0.503512   \n",
              "861215  0.501194  0.496020  0.043192  0.000000  0.549655  0.243669  0.336172   \n",
              "557119  0.259013  0.711236  0.000000  0.000000  0.681388  0.158586  0.494379   \n",
              "940527  0.584357  0.388781  0.000000  0.026656  0.306564  0.059591  0.199846   \n",
              "105334  0.431535  0.570088  0.013704  0.237325  0.299895  0.146473  0.167609   \n",
              "541355  0.366604  0.584168  0.048843  0.000000  0.553405  0.137976  0.448131   \n",
              "489188  0.211012  0.738045  0.012797  0.471244  0.192323  0.340860  0.000000   \n",
              "759338  0.393096  0.627524  0.019352  0.000000  0.652087  0.342991  0.374517   \n",
              "822399  0.667031  0.332130  0.001159  0.000000  0.411883  0.240780  0.119817   \n",
              "318223  0.233084  0.753429  0.037684  0.000000  0.860299  0.212559  0.679025   \n",
              "987215  0.566648  0.421785  0.020638  0.000000  0.392970  0.197474  0.224356   \n",
              "224368  0.271324  0.725154  0.023835  0.511397  0.175957  0.095964  0.085169   \n",
              "522047  0.397605  0.582596  0.016704  0.000000  0.550700  0.320614  0.287280   \n",
              "807436  0.688293  0.266340  0.017160  0.000000  0.268188  0.005331  0.205955   \n",
              "257384  0.674750  0.270363  0.056137  0.000000  0.285423  0.029339  0.195111   \n",
              "273431  0.694128  0.316867  0.073747  0.000000  0.303685  0.000000  0.360751   \n",
              "829951  0.112170  0.668249  0.003175  0.287790  0.283297  0.343321  0.000000   \n",
              "\n",
              "        Class4.1  Class4.2  Class5.1    ...      Class9.3  Class10.1  \\\n",
              "353190  0.438172  0.214000  0.104207    ...      0.000000   0.033135   \n",
              "455182  0.250156  0.119324  0.038494    ...      0.212111   0.117414   \n",
              "853016  0.036145  0.019696  0.000000    ...      0.000000   0.000000   \n",
              "204336  0.073763  0.084707  0.000000    ...      0.000000   0.000000   \n",
              "534803  0.212508  0.000000  0.000000    ...      0.000000   0.000000   \n",
              "738274  0.112588  0.363839  0.060079    ...      0.000000   0.057042   \n",
              "431391  0.065558  0.129782  0.000000    ...      0.000000   0.033999   \n",
              "229431  0.337521  0.242406  0.239242    ...      0.000000   0.127415   \n",
              "481202  0.000000  0.380372  0.000000    ...      0.000000   0.000000   \n",
              "453455  0.349997  0.345516  0.259667    ...      0.000000   0.155887   \n",
              "512022  0.389830  0.116944  0.053084    ...      0.000000   0.134506   \n",
              "527725  0.261556  0.292947  0.112981    ...      0.000000   0.000000   \n",
              "694563  0.272376  0.315218  0.254550    ...      0.047548   0.152618   \n",
              "619240  0.071419  0.112332  0.000000    ...      0.000000   0.078608   \n",
              "370446  0.000000  0.000000  0.000000    ...      0.000000   0.000000   \n",
              "562140  0.000000  0.214330  0.000000    ...      0.000000   0.000000   \n",
              "775244  0.231401  0.187227  0.181876    ...      0.000000   0.045775   \n",
              "347573  0.078834  0.180167  0.000000    ...      0.000000   0.000000   \n",
              "102602  0.368287  0.134679  0.069173    ...      0.000000   0.102825   \n",
              "310133  0.000000  0.399326  0.000000    ...      0.000000   0.000000   \n",
              "980808  0.421446  0.121476  0.012438    ...      0.000000   0.103724   \n",
              "120632  0.175410  0.348929  0.029560    ...      0.123173   0.000000   \n",
              "727697  0.311048  0.361370  0.264230    ...      0.000000   0.000000   \n",
              "195617  0.043084  0.222889  0.015466    ...      0.268988   0.000000   \n",
              "482930  0.000000  0.000000  0.219911    ...      0.339972   0.000000   \n",
              "546305  0.312866  0.032905  0.034062    ...      0.000000   0.085259   \n",
              "354787  0.382252  0.269328  0.210736    ...      0.000000   0.291658   \n",
              "339097  0.316329  0.186448  0.132058    ...      0.000000   0.104135   \n",
              "951273  0.223123  0.351286  0.174954    ...      0.000000   0.139989   \n",
              "360142  0.315206  0.263025  0.142110    ...      0.000000   0.177947   \n",
              "...          ...       ...       ...    ...           ...        ...   \n",
              "396333  0.550805  0.191756  0.000000    ...      0.000000   0.230410   \n",
              "371832  0.082364  0.145607  0.000000    ...      0.000000   0.000000   \n",
              "782896  0.646642  0.032646  0.000000    ...      0.000000   0.022161   \n",
              "611341  0.000000  0.118548  0.000000    ...      0.000000   0.000000   \n",
              "977774  0.203051  0.237990  0.042646    ...      0.000000   0.053094   \n",
              "230943  0.991826  0.000000  0.097879    ...      0.000000   0.403872   \n",
              "729065  0.435875  0.000000  0.030994    ...      0.000000   0.017910   \n",
              "487962  0.126582  0.242971  0.000000    ...      0.034359   0.058151   \n",
              "729808  0.363515  0.250315  0.175101    ...      0.000000   0.188623   \n",
              "416106  0.219736  0.279697  0.063975    ...      0.000000   0.008278   \n",
              "291196  0.299109  0.146082  0.139607    ...      0.012271   0.102581   \n",
              "641659  0.306673  0.133287  0.040337    ...      0.000000   0.223715   \n",
              "103348  0.349473  0.000000  0.000000    ...      0.198135   0.045392   \n",
              "540158  0.028827  0.369380  0.000000    ...      0.000000   0.150648   \n",
              "861215  0.341349  0.191970  0.000000    ...      0.000000   0.109750   \n",
              "557119  0.448316  0.234697  0.184727    ...      0.000000   0.096919   \n",
              "940527  0.167694  0.073275  0.000000    ...      0.000000   0.028377   \n",
              "105334  0.149711  0.113006  0.072975    ...      0.134472   0.029117   \n",
              "541355  0.376854  0.168189  0.000000    ...      0.000000   0.273883   \n",
              "489188  0.212338  0.000000  0.049108    ...      0.190125   0.000000   \n",
              "759338  0.524814  0.127410  0.078694    ...      0.000000   0.259745   \n",
              "822399  0.284821  0.051911  0.014000    ...      0.000000   0.000000   \n",
              "318223  0.477348  0.396856  0.148886    ...      0.000000   0.216516   \n",
              "987215  0.199611  0.194405  0.078029    ...      0.000000   0.009056   \n",
              "224368  0.158760  0.000000  0.143683    ...      0.279773   0.092114   \n",
              "522047  0.249606  0.294939  0.080312    ...      0.000000   0.066049   \n",
              "807436  0.035748  0.176038  0.000000    ...      0.000000   0.000000   \n",
              "257384  0.183779  0.056677  0.000000    ...      0.000000   0.059576   \n",
              "273431  0.284774  0.023251  0.037358    ...      0.001517   0.151842   \n",
              "829951  0.155416  0.000000  0.000000    ...      0.000000   0.000000   \n",
              "\n",
              "        Class10.2  Class10.3  Class11.1  Class11.2  Class11.3  Class11.4  \\\n",
              "353190   0.380118   0.011786   0.000000   0.261720   0.057452   0.000000   \n",
              "455182   0.068240   0.000000   0.000000   0.103754   0.000000   0.000000   \n",
              "853016   0.028306   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "204336   0.038966   0.000000   0.000000   0.043055   0.000000   0.000000   \n",
              "534803   0.076402   0.000000   0.000000   0.171935   0.000000   0.000000   \n",
              "738274   0.002591   0.002382   0.016047   0.000000   0.000000   0.000000   \n",
              "431391   0.012484   0.000000   0.000000   0.002997   0.000000   0.000000   \n",
              "229431   0.240435   0.000000   0.000000   0.146411   0.072452   0.028711   \n",
              "481202   0.118171   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "453455   0.138266   0.034075   0.012442   0.120261   0.020998   0.030082   \n",
              "512022   0.156040   0.030059   0.031883   0.195932   0.000000   0.019889   \n",
              "527725   0.188124   0.057138   0.000000   0.227330   0.010976   0.019611   \n",
              "694563   0.046888   0.063548   0.012516   0.000000   0.053034   0.055628   \n",
              "619240   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "370446   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "562140   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "775244   0.146471   0.001562   0.000000   0.107098   0.000000   0.000000   \n",
              "347573   0.054641   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "102602   0.210759   0.042181   0.000000   0.302142   0.000000   0.000000   \n",
              "310133   0.000000   0.000000   0.001978   0.000000   0.000000   0.000000   \n",
              "980808   0.237174   0.091053   0.000000   0.192918   0.068298   0.000000   \n",
              "120632   0.049322   0.099914   0.000000   0.201542   0.025741   0.000000   \n",
              "727697   0.185571   0.211142   0.000000   0.182570   0.092030   0.000000   \n",
              "195617   0.000000   0.043988   0.000000   0.043172   0.000000   0.000000   \n",
              "482930   0.000000   0.000000   0.000000   0.000000   0.030768   0.000000   \n",
              "546305   0.163241   0.028403   0.000000   0.112025   0.000000   0.000000   \n",
              "354787   0.083998   0.010554   0.000000   0.174627   0.000000   0.000000   \n",
              "339097   0.145492   0.073873   0.000000   0.185688   0.043187   0.000000   \n",
              "951273   0.077623   0.007688   0.000000   0.103681   0.036506   0.020576   \n",
              "360142   0.201048   0.000000   0.000000   0.058541   0.084579   0.024177   \n",
              "...           ...        ...        ...        ...        ...        ...   \n",
              "396333   0.227213   0.081010   0.000000   0.329749   0.122356   0.000000   \n",
              "371832   0.000000   0.075955   0.030496   0.000000   0.000000   0.000000   \n",
              "782896   0.345369   0.341044   0.000000   0.697825   0.038324   0.000000   \n",
              "611341   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "977774   0.085897   0.018958   0.000000   0.128191   0.000000   0.000000   \n",
              "230943   0.163614   0.377993   0.000000   0.318067   0.306251   0.000000   \n",
              "729065   0.277163   0.000000   0.000000   0.443950   0.000000   0.000000   \n",
              "487962   0.035350   0.000428   0.028892   0.000000   0.000000   0.000000   \n",
              "729808   0.151108   0.000641   0.012230   0.000000   0.056148   0.027295   \n",
              "416106   0.139834   0.000000   0.000000   0.138840   0.000000   0.000000   \n",
              "291196   0.161429   0.027916   0.000000   0.164238   0.000000   0.000000   \n",
              "641659   0.067260   0.000000   0.000000   0.046972   0.016591   0.004498   \n",
              "103348   0.139674   0.143808   0.000000   0.386118   0.000000   0.000000   \n",
              "540158   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
              "861215   0.104723   0.136894   0.019531   0.215429   0.000000   0.000000   \n",
              "557119   0.279690   0.060147   0.020350   0.175774   0.109289   0.031051   \n",
              "940527   0.091997   0.000000   0.000000   0.068660   0.000000   0.000000   \n",
              "105334   0.059251   0.005815   0.000000   0.020748   0.000000   0.000000   \n",
              "541355   0.065616   0.026582   0.012364   0.000000   0.000564   0.024097   \n",
              "489188   0.071457   0.120984   0.000000   0.198314   0.000000   0.000000   \n",
              "759338   0.243852   0.066391   0.006992   0.299546   0.000000   0.058692   \n",
              "822399   0.208716   0.071736   0.000000   0.315146   0.000000   0.000000   \n",
              "318223   0.116316   0.137856   0.000000   0.098627   0.170209   0.043334   \n",
              "987215   0.143866   0.000000   0.000000   0.115833   0.008844   0.000000   \n",
              "224368   0.001852   0.019686   0.000000   0.000000   0.016988   0.000000   \n",
              "522047   0.161088   0.002403   0.000000   0.134949   0.057484   0.000000   \n",
              "807436   0.000000   0.029071   0.000000   0.000000   0.000000   0.000000   \n",
              "257384   0.040049   0.027632   0.000000   0.062025   0.000000   0.000000   \n",
              "273431   0.107028   0.006861   0.000000   0.016633   0.074373   0.002648   \n",
              "829951   0.155482   0.083017   0.000000   0.261186   0.000000   0.000000   \n",
              "\n",
              "        Class11.5  Class11.6  \n",
              "353190   0.000000   0.123850  \n",
              "455182   0.000000   0.159740  \n",
              "853016   0.000000   0.000000  \n",
              "204336   0.000000   0.000000  \n",
              "534803   0.000000   0.000000  \n",
              "738274   0.000000   0.052189  \n",
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              "...           ...        ...  \n",
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              "\n",
              "[100 rows x 37 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "7eIA2b3kuTXK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}