Healthcare_ADM_with_Keras_Horovod.ipynb

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  "metadata": {
    "colab": {
      "name": "Healthcare-ADM-with-Keras-Horovod.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOG2nC52KGgrigtQTH3VZnk",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MuhammadShifa/Anamoly-Detection-Model-ADM-in-Health-Care-Insurance-/blob/main/Healthcare_ADM_with_Keras_Horovod.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zkV83f-nS7nS",
        "outputId": "40b06cb1-822e-470b-ed89-8295de387922"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZmHThlSgILm"
      },
      "source": [
        "!pip install horovod[tensorflow,keras,pytorch,mxnet,spark]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KNVZGar3VMRG"
      },
      "source": [
        "#### Load the training dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "LnQ3HXc7Lfuo",
        "outputId": "5acdb767-fda9-480e-de4c-98d72e1b95be"
      },
      "source": [
        "# import all the necessary packages\r\n",
        "\r\n",
        "import horovod.keras as hvd\r\n",
        "import tensorflow as tf\r\n",
        "import keras\r\n",
        "from keras.models import Sequential\r\n",
        "from keras.layers import Dense, BatchNormalization, Dropout\r\n",
        "from keras import backend as k\r\n",
        "import math\r\n",
        "import pandas as pd\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "from sklearn.preprocessing import OneHotEncoder\r\n",
        "\r\n",
        "# Load the dataset for training and validation\r\n",
        "train_dataset = pd.read_csv('/content/drive/My Drive/ADM Datasets/train_data.csv')\r\n",
        "print(train_dataset.shape)\r\n",
        "\r\n",
        "# Print first 10 value of data frame\r\n",
        "train_dataset.head(10)\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(558211, 27)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Provider</th>\n",
              "      <th>PotentialFraud</th>\n",
              "      <th>InscClaimAmtReimbursed</th>\n",
              "      <th>DeductibleAmtPaid</th>\n",
              "      <th>AdmitDays</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Race</th>\n",
              "      <th>RenalDiseaseIndicator</th>\n",
              "      <th>NoOfMonths_PartACov</th>\n",
              "      <th>NoOfMonths_PartBCov</th>\n",
              "      <th>ChronicCond_Alzheimer</th>\n",
              "      <th>ChronicCond_Heartfailure</th>\n",
              "      <th>ChronicCond_KidneyDisease</th>\n",
              "      <th>ChronicCond_Cancer</th>\n",
              "      <th>ChronicCond_ObstrPulmonary</th>\n",
              "      <th>ChronicCond_Depression</th>\n",
              "      <th>ChronicCond_Diabetes</th>\n",
              "      <th>ChronicCond_IschemicHeart</th>\n",
              "      <th>ChronicCond_Osteoporasis</th>\n",
              "      <th>ChronicCond_rheumatoidarthritis</th>\n",
              "      <th>ChronicCond_stroke</th>\n",
              "      <th>IPAnnualReimbursementAmt</th>\n",
              "      <th>IPAnnualDeductibleAmt</th>\n",
              "      <th>OPAnnualReimbursementAmt</th>\n",
              "      <th>OPAnnualDeductibleAmt</th>\n",
              "      <th>Age</th>\n",
              "      <th>IsDead</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>300</td>\n",
              "      <td>110</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>700</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>720</td>\n",
              "      <td>10</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>900</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1380</td>\n",
              "      <td>370</td>\n",
              "      <td>76.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>500</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2020</td>\n",
              "      <td>1068</td>\n",
              "      <td>6700</td>\n",
              "      <td>2700</td>\n",
              "      <td>74.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>36000</td>\n",
              "      <td>1068.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>36000</td>\n",
              "      <td>1068</td>\n",
              "      <td>3520</td>\n",
              "      <td>140</td>\n",
              "      <td>69.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>12000</td>\n",
              "      <td>1068.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>95000</td>\n",
              "      <td>2136</td>\n",
              "      <td>2270</td>\n",
              "      <td>900</td>\n",
              "      <td>72.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5000</td>\n",
              "      <td>1068</td>\n",
              "      <td>370</td>\n",
              "      <td>70</td>\n",
              "      <td>91.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>320</td>\n",
              "      <td>80</td>\n",
              "      <td>90.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>630</td>\n",
              "      <td>130</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>PRV51001</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1590</td>\n",
              "      <td>1070</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Provider  PotentialFraud  ...   Age  IsDead\n",
              "0  PRV51001               0  ...  80.0     0.0\n",
              "1  PRV51001               0  ...  67.0     0.0\n",
              "2  PRV51001               0  ...  76.0     0.0\n",
              "3  PRV51001               0  ...  74.0     0.0\n",
              "4  PRV51001               0  ...  69.0     0.0\n",
              "5  PRV51001               0  ...  72.0     0.0\n",
              "6  PRV51001               0  ...  91.0     0.0\n",
              "7  PRV51001               0  ...  90.0     0.0\n",
              "8  PRV51001               0  ...  57.0     0.0\n",
              "9  PRV51001               0  ...  98.0     0.0\n",
              "\n",
              "[10 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxKTWi_3Vbdv"
      },
      "source": [
        "#### Horovod Step 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEsvDbLcL4K7"
      },
      "source": [
        "# 1. Horovod: intialize horovod library\r\n",
        "hvd.init()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXFfGlQKVi20"
      },
      "source": [
        "#### Horovod Step 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOZXIUEyL97d",
        "outputId": "0cf24439-4dbf-4639-e4b3-dd75edac603a"
      },
      "source": [
        "# 2. Horovod: Pin a GPU to each worker\r\n",
        "# Horovod: pin GPU to be used to process local rank (one GPU per process), this code because we have tf v2\r\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\r\n",
        "for gpu in gpus:\r\n",
        "  tf.config.experimental.set_memory_growth(gpu, True)\r\n",
        "if gpus:\r\n",
        "  tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()],'GPU')\r\n",
        "print(\"The gpus is: \",gpus)\r\n",
        "print(\"The local rank is: \", hvd.local_rank())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The gpus is:  [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n",
            "The local rank is:  0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q61ks5BxVrJk"
      },
      "source": [
        "#### Preprocessing and mlp model creation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmzYGMTIMSat",
        "outputId": "42f798d4-dfa3-413e-eaab-3e592f22833d"
      },
      "source": [
        "# Droping the Duplicate row \r\n",
        "train_dataset = train_dataset.drop_duplicates(subset = None,inplace= False )\r\n",
        "\r\n",
        "#Changing pandas dataframe into input and output numpy array\r\n",
        "input = train_dataset.iloc[:,2:28].values.astype('float32')\r\n",
        "output = train_dataset.iloc[:,1:2].values.astype('float32')\r\n",
        "\r\n",
        "# Scaled the input with MinMaxScaler\r\n",
        "input_scaler = MinMaxScaler(feature_range=(0, 1))\r\n",
        "scaled_input = input_scaler.fit_transform(input)\r\n",
        "\r\n",
        "# one hot encoding the output of training dataset\r\n",
        "output_encoder = OneHotEncoder()\r\n",
        "encoded_output = output_encoder.fit_transform(output).toarray()\r\n",
        "\r\n",
        "print(scaled_input.shape)\r\n",
        "print(encoded_output.shape)\r\n",
        "\r\n",
        "input_dim = scaled_input.shape[-1]\r\n",
        "print(input_dim)\r\n",
        "\r\n",
        "#Split into train test set\r\n",
        "X_train,X_test,y_train,y_test = train_test_split(scaled_input, encoded_output, test_size = 0.25)\r\n",
        "\r\n",
        "\r\n",
        "# Define Model parameter\r\n",
        "\r\n",
        "batch_size = 32\r\n",
        "num_classes = 2\r\n",
        "\r\n",
        "# Horovod: adjust number of epochs based on number of gpu\r\n",
        "epochs = int(math.ceil(20.0 / hvd.size())) \r\n",
        "# Neural network\r\n",
        "model = Sequential()\r\n",
        "model.add(Dense(36, input_dim=input_dim, activation='relu'))\r\n",
        "model.add(Dense(18, activation='relu'))\r\n",
        "model.add(BatchNormalization())\r\n",
        "model.add(Dropout(0.25))\r\n",
        "model.add(Dense(2, activation='softmax'))\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(534843, 25)\n",
            "(534843, 2)\n",
            "25\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fp6dYCwwV3Yy"
      },
      "source": [
        "#### Horovod Step 3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ylX6gVlPMZM8",
        "outputId": "a5cf8444-3ce3-438b-b1a2-56d46ab6724a"
      },
      "source": [
        "# 3. Horovod: wrap the optimizer\r\n",
        "# Adjust the learning rate based on the number of gpu\r\n",
        "opt = keras.optimizers.Adam(0.001 * hvd.size())\r\n",
        "model.compile(loss=keras.losses.binary_crossentropy,\r\n",
        "              optimizer=opt,\r\n",
        "              metrics=['accuracy'])\r\n",
        "\r\n",
        "# Horovod: add Horovod Distributed Optimizer.\r\n",
        "opt = hvd.DistributedOptimizer(opt)\r\n",
        "print(\"The hvd distributed optimizer is: \", opt)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The hvd distributed optimizer is:  <horovod._keras.Adam object at 0x7fc348f2bc18>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2xii3y_WBUR"
      },
      "source": [
        "#### Horovod Step 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRwMljYZMgyl",
        "outputId": "148f10e1-1bcc-4972-ddd5-4357ef7ff244"
      },
      "source": [
        "# 4. Horovod: Synchronize state accross workers\r\n",
        "\r\n",
        "callbacks = [\r\n",
        "    # Horovod: broadcast initial variable states from rank 0 to all other processes.\r\n",
        "    # This is necessary to ensure consistent initialization of all workers when\r\n",
        "    # training is started with random weights or restored from a checkpoint.\r\n",
        "    hvd.callbacks.BroadcastGlobalVariablesCallback(0),\r\n",
        "]\r\n",
        "\r\n",
        "print(\"The callbacks is: \", callbacks)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The callbacks is:  [<horovod.keras.callbacks.BroadcastGlobalVariablesCallback object at 0x7fc348fb17f0>]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9GQutlBmWGut"
      },
      "source": [
        "#### Horovod Step 5"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4VZqMeAMob5"
      },
      "source": [
        "# 5. Horovod: Checkpoint on the first worker\r\n",
        "# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.\r\n",
        "if hvd.rank() == 0:\r\n",
        "    callbacks.append(keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0ym6I_cmWRCY"
      },
      "source": [
        "#### Model training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgOfOiECMw9-",
        "outputId": "8e703958-5362-46ce-f9e8-20a2a8b0e8e2"
      },
      "source": [
        "history = model.fit(X_train, y_train,\r\n",
        "          batch_size=batch_size,\r\n",
        "          callbacks=callbacks,\r\n",
        "          epochs=epochs,\r\n",
        "          verbose=1 if hvd.rank() == 0 else 0,\r\n",
        "          validation_data=(X_test, y_test))\r\n",
        "model.save('/content/drive/My Drive/ADM Datasets/mlp_horovod_model.h5')\r\n",
        "print('Congrats! Model Saved')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "    1/12536 [..............................] - ETA: 2:47:56 - loss: 0.7323 - accuracy: 0.5938WARNING:tensorflow:5 out of the last 5 calls to <function _make_broadcast_group_fn.<locals>.broadcast_group at 0x7fc3b5c14d08> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "WARNING:tensorflow:6 out of the last 6 calls to <function _make_broadcast_group_fn.<locals>.broadcast_group at 0x7fc3b5c14d08> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "    2/12536 [..............................] - ETA: 40:29 - loss: 0.7300 - accuracy: 0.5703  WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0037s vs `on_train_batch_end` time: 0.0330s). Check your callbacks.\n",
            "12536/12536 [==============================] - 55s 4ms/step - loss: 0.6642 - accuracy: 0.6257 - val_loss: 0.6562 - val_accuracy: 0.6339\n",
            "Epoch 2/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6560 - accuracy: 0.6339 - val_loss: 0.6572 - val_accuracy: 0.6339\n",
            "Epoch 3/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6567 - accuracy: 0.6320 - val_loss: 0.6563 - val_accuracy: 0.6339\n",
            "Epoch 4/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6556 - accuracy: 0.6345 - val_loss: 0.6562 - val_accuracy: 0.6339\n",
            "Epoch 5/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6565 - accuracy: 0.6325 - val_loss: 0.6560 - val_accuracy: 0.6339\n",
            "Epoch 6/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6556 - accuracy: 0.6343 - val_loss: 0.6559 - val_accuracy: 0.6339\n",
            "Epoch 7/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6558 - accuracy: 0.6336 - val_loss: 0.6563 - val_accuracy: 0.6339\n",
            "Epoch 8/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6562 - accuracy: 0.6325 - val_loss: 0.6559 - val_accuracy: 0.6339\n",
            "Epoch 9/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6559 - accuracy: 0.6329 - val_loss: 0.6558 - val_accuracy: 0.6339\n",
            "Epoch 10/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6549 - accuracy: 0.6352 - val_loss: 0.6561 - val_accuracy: 0.6339\n",
            "Epoch 11/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6563 - accuracy: 0.6324 - val_loss: 0.6557 - val_accuracy: 0.6339\n",
            "Epoch 12/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6556 - accuracy: 0.6335 - val_loss: 0.6558 - val_accuracy: 0.6339\n",
            "Epoch 13/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6546 - accuracy: 0.6354 - val_loss: 0.6560 - val_accuracy: 0.6338\n",
            "Epoch 14/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6556 - accuracy: 0.6333 - val_loss: 0.6558 - val_accuracy: 0.6339\n",
            "Epoch 15/20\n",
            "12536/12536 [==============================] - 54s 4ms/step - loss: 0.6556 - accuracy: 0.6336 - val_loss: 0.6559 - val_accuracy: 0.6339\n",
            "Epoch 16/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6549 - accuracy: 0.6346 - val_loss: 0.6557 - val_accuracy: 0.6338\n",
            "Epoch 17/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6556 - accuracy: 0.6329 - val_loss: 0.6560 - val_accuracy: 0.6339\n",
            "Epoch 18/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6552 - accuracy: 0.6337 - val_loss: 0.6557 - val_accuracy: 0.6339\n",
            "Epoch 19/20\n",
            "12536/12536 [==============================] - 53s 4ms/step - loss: 0.6548 - accuracy: 0.6347 - val_loss: 0.6559 - val_accuracy: 0.6338\n",
            "Epoch 20/20\n",
            "12536/12536 [==============================] - 52s 4ms/step - loss: 0.6555 - accuracy: 0.6329 - val_loss: 0.6561 - val_accuracy: 0.6338\n",
            "Congrats! Model Saved\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fHcOGtrWYpR"
      },
      "source": [
        "#### Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLduSvg7Rrve",
        "outputId": "2909a525-d652-4a2f-f7ac-ba172e5ee535"
      },
      "source": [
        "loss_train, acc_train = model.evaluate(X_train, y_train, verbose = 0)\r\n",
        "loss_val, acc_val = model.evaluate(X_test, y_test, verbose = 0)\r\n",
        "print(\"The train loss is: {0} and the train accuracy is: {1}\".format(loss_train, acc_train))\r\n",
        "print(\"The validation loss is: {0} and the validation accuracy is: {1}\".format(loss_val, acc_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The train loss is: 0.6545116305351257 and the train accuracy is: 0.6353120803833008\n",
            "The validation loss is: 0.6561300754547119 and the validation accuracy is: 0.6337997913360596\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xf2sxt01WnQ5"
      },
      "source": [
        "#### Accuracy, Precision, Recall, F1 Score, roc auc Curve and confusion marix"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xp2Fc08BWehd",
        "outputId": "7d14054e-949c-4230-de6a-9d82d348354c"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\r\n",
        "from sklearn.metrics import precision_score\r\n",
        "from sklearn.metrics import recall_score\r\n",
        "from sklearn.metrics import f1_score\r\n",
        "from sklearn.metrics import cohen_kappa_score\r\n",
        "from sklearn.metrics import roc_auc_score\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "\r\n",
        "# predict probabilities for test set\r\n",
        "yhat_probs = model.predict(X_test, verbose=0)\r\n",
        "# predict crisp classes for test set\r\n",
        "yhat_classes = model.predict_classes(X_test, verbose=0)\r\n",
        "\r\n",
        "print(yhat_probs) # 2-D\r\n",
        "print(yhat_classes) # 1-D\r\n",
        "\r\n",
        "# reduce to 1d array\r\n",
        "yhat_probs = yhat_probs[:, 0]\r\n",
        "y_test_1D = output_encoder.inverse_transform(y_test) # Need to be in 1D\r\n",
        "\r\n",
        "print(yhat_probs)\r\n",
        "print(yhat_classes)\r\n",
        "print()\r\n",
        "\r\n",
        "\r\n",
        "# accuracy: (tp + tn) / (p + n)\r\n",
        "accuracy = accuracy_score(y_test_1D, yhat_classes)\r\n",
        "print('Accuracy: %f' % accuracy)\r\n",
        "# precision tp / (tp + fp)\r\n",
        "precision = precision_score(y_test_1D, yhat_classes)\r\n",
        "print('Precision: %f' % precision)\r\n",
        "# recall: tp / (tp + fn)\r\n",
        "recall = recall_score(y_test_1D, yhat_classes)\r\n",
        "print('Recall: %f' % recall)\r\n",
        "# f1: 2 tp / (2 tp + fp + fn)\r\n",
        "f1 = f1_score(y_test_1D, yhat_classes)\r\n",
        "print('F1 score: %f' % f1)\r\n",
        " \r\n",
        "# kappa\r\n",
        "kappa = cohen_kappa_score(y_test_1D, yhat_classes)\r\n",
        "print('Cohens kappa: %f' % kappa)\r\n",
        "# ROC AUC\r\n",
        "auc = roc_auc_score(y_test_1D, yhat_probs)\r\n",
        "print('ROC AUC: %f' % auc)\r\n",
        "# confusion matrix\r\n",
        "matrix = confusion_matrix(y_test_1D, yhat_classes)\r\n",
        "print(matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[[0.72752726 0.27247274]\n",
            " [0.7776309  0.2223691 ]\n",
            " [0.7793836  0.22061636]\n",
            " ...\n",
            " [0.3717535  0.6282465 ]\n",
            " [0.76869774 0.2313022 ]\n",
            " [0.718872   0.28112802]]\n",
            "[0 0 0 ... 1 0 0]\n",
            "[0.72752726 0.7776309  0.7793836  ... 0.3717535  0.76869774 0.718872  ]\n",
            "[0 0 0 ... 1 0 0]\n",
            "\n",
            "Accuracy: 0.633800\n",
            "Precision: 0.570021\n",
            "Recall: 0.110314\n",
            "F1 score: 0.184854\n",
            "Cohens kappa: 0.071519\n",
            "ROC AUC: 0.455852\n",
            "[[79194  4188]\n",
            " [44777  5552]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0SRlJycW1qG"
      },
      "source": [
        "#### Visualization of training loss and accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "JyCwBUwEXBdb",
        "outputId": "c8bfa06c-0d78-4e86-980a-324aaf66fdec"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# plot loss during training\r\n",
        "epoch_range = range(1,epochs+1)\r\n",
        "plt.plot(epoch_range,history.history['loss'], label='Training loss')\r\n",
        "plt.plot(epoch_range,history.history['val_loss'], label='validation loss')\r\n",
        "plt.title('Training and Validation loss')\r\n",
        "plt.xlabel('Epochs')\r\n",
        "plt.ylabel('Loss')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# plot Accuracy during training\r\n",
        "plt.plot(epoch_range,history.history['accuracy'], label='Training Accuracy')\r\n",
        "plt.plot(epoch_range,history.history['val_accuracy'], label='validation Accuracy')\r\n",
        "plt.title('Training and Validation Accuracy')\r\n",
        "plt.xlabel('Epochs')\r\n",
        "plt.ylabel('Accuracy')\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZF5Xc_SkXi27"
      },
      "source": [
        "## Making Prediction on Saved Horovod MLP Model "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6G7w2tDjX5a7"
      },
      "source": [
        "#### Load the saved classifier or model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "muC9ssCIX4Cr"
      },
      "source": [
        "from keras.models import load_model\r\n",
        "#Load the saved Model and do prediction\r\n",
        "\r\n",
        "classifier = load_model('/content/drive/My Drive/ADM Datasets/mlp_horovod_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3uYCSbaEXrek"
      },
      "source": [
        "#### Prediction on test dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8fu8hj9Xbtq",
        "outputId": "4e2c37d6-3711-4fbe-b16a-0529b88c16c6"
      },
      "source": [
        "# Now make prediction on saved Model/classifier for test dataset\r\n",
        "# load the dataset for testing the model\r\n",
        "\r\n",
        "pred_dataset = pd.read_csv('/content/drive/My Drive/ADM Datasets/test_data.csv')\r\n",
        "\r\n",
        "#Changing pandas dataframe to numpy array\r\n",
        "pred_input = pred_dataset.iloc[:,2:28].values.astype('float32')\r\n",
        "pred_output = pred_dataset.iloc[:,1:2].values.astype('float32')\r\n",
        "\r\n",
        "# Scaled the input and output for prediction\r\n",
        "pred_input_scaled = input_scaler.fit_transform(pred_input)\r\n",
        "pred_output_endcoded = output_encoder.fit_transform(pred_output).toarray()\r\n",
        "\r\n",
        "# evaluate the loss and accuracy for the prediction dataset\r\n",
        "pred_loss, pred_acc = classifier.evaluate(pred_input_scaled, pred_output_endcoded, verbose =0)\r\n",
        "print(\"The Predicted dataset loss is: {0} and Accuracy is: {1}\".format(pred_loss, pred_acc))\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Predicted dataset loss is: 0.658100426197052 and Accuracy is: 0.6299123167991638\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2PcXzweY-BV",
        "outputId": "73383d85-3912-470c-e38b-0826c43a56c0"
      },
      "source": [
        "# Prediction on input data\r\n",
        "\r\n",
        "prediction = classifier.predict(pred_input_scaled)\r\n",
        "\r\n",
        "# Inverse transform the predicted and output encoded value\r\n",
        "inv_predict = output_encoder.inverse_transform(prediction).astype('int64')\r\n",
        "inv_actual = output_encoder.inverse_transform(pred_output_endcoded).astype('int64')\r\n",
        "for j in range(0,len(pred_output)):\r\n",
        "        print(\"Pred={0} Act={1}\\n\".format(inv_predict[j], inv_actual[j]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[1] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
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            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[1] Act=[1]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[1] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
            "\n",
            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[1] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
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            "Pred=[0] Act=[0]\n",
            "\n",
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            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IdVO2cmOcJZw"
      },
      "source": [
        "## Tying all together, the complete code is listed below [Training Part]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k1RaxFxYc0s-"
      },
      "source": [
        "# import all the necessary packages\r\n",
        "\r\n",
        "import horovod.keras as hvd\r\n",
        "import tensorflow as tf\r\n",
        "import keras\r\n",
        "from keras.models import Sequential\r\n",
        "from keras.layers import Dense, BatchNormalization, Dropout\r\n",
        "from keras import backend as k\r\n",
        "import math\r\n",
        "import pandas as pd\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "from sklearn.preprocessing import MinMaxScaler\r\n",
        "from sklearn.preprocessing import OneHotEncoder\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "from sklearn.metrics import precision_score\r\n",
        "from sklearn.metrics import recall_score\r\n",
        "from sklearn.metrics import f1_score\r\n",
        "from sklearn.metrics import cohen_kappa_score\r\n",
        "from sklearn.metrics import roc_auc_score\r\n",
        "from sklearn.metrics import confusion_matrix\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "# Load the dataset for training and validation\r\n",
        "train_dataset = pd.read_csv('/content/drive/My Drive/ADM Datasets/train_data.csv')\r\n",
        "print(train_dataset.shape)\r\n",
        "\r\n",
        "# Print first 10 value of data frame\r\n",
        "train_dataset.head(10)\r\n",
        "# 1. Horovod: intialize horovod library\r\n",
        "hvd.init()\r\n",
        "# 2. Horovod: Pin a GPU to each worker\r\n",
        "# Horovod: pin GPU to be used to process local rank (one GPU per process), this code because we have tf v2\r\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\r\n",
        "for gpu in gpus:\r\n",
        "  tf.config.experimental.set_memory_growth(gpu, True)\r\n",
        "if gpus:\r\n",
        "  tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()],'GPU')\r\n",
        "print(\"The gpus is: \",gpus)\r\n",
        "print(\"The local rank is: \", hvd.local_rank())\r\n",
        "# Droping the Duplicate row \r\n",
        "train_dataset = train_dataset.drop_duplicates(subset = None,inplace= False )\r\n",
        "\r\n",
        "#Changing pandas dataframe into input and output numpy array\r\n",
        "input = train_dataset.iloc[:,2:28].values.astype('float32')\r\n",
        "output = train_dataset.iloc[:,1:2].values.astype('float32')\r\n",
        "\r\n",
        "# Scaled the input with MinMaxScaler\r\n",
        "input_scaler = MinMaxScaler(feature_range=(0, 1))\r\n",
        "scaled_input = input_scaler.fit_transform(input)\r\n",
        "\r\n",
        "# one hot encoding the output of training dataset\r\n",
        "output_encoder = OneHotEncoder()\r\n",
        "encoded_output = output_encoder.fit_transform(output).toarray()\r\n",
        "\r\n",
        "print(scaled_input.shape)\r\n",
        "print(encoded_output.shape)\r\n",
        "\r\n",
        "input_dim = scaled_input.shape[-1]\r\n",
        "print(input_dim)\r\n",
        "\r\n",
        "#Split into train test set\r\n",
        "X_train,X_test,y_train,y_test = train_test_split(scaled_input, encoded_output, test_size = 0.25)\r\n",
        "\r\n",
        "\r\n",
        "# Define Model parameter\r\n",
        "\r\n",
        "batch_size = 32\r\n",
        "num_classes = 2\r\n",
        "\r\n",
        "# Horovod: adjust number of epochs based on number of gpu\r\n",
        "epochs = int(math.ceil(20.0 / hvd.size())) \r\n",
        "# Neural network\r\n",
        "model = Sequential()\r\n",
        "model.add(Dense(36, input_dim=input_dim, activation='relu'))\r\n",
        "model.add(Dense(18, activation='relu'))\r\n",
        "model.add(BatchNormalization())\r\n",
        "model.add(Dropout(0.25))\r\n",
        "model.add(Dense(2, activation='softmax'))\r\n",
        "\r\n",
        "# 3. Horovod: wrap the optimizer\r\n",
        "# Adjust the learning rate based on the number of gpu\r\n",
        "opt = keras.optimizers.Adam(0.001 * hvd.size())\r\n",
        "model.compile(loss=keras.losses.binary_crossentropy,\r\n",
        "              optimizer=opt,\r\n",
        "              metrics=['accuracy'])\r\n",
        "\r\n",
        "# Horovod: add Horovod Distributed Optimizer.\r\n",
        "opt = hvd.DistributedOptimizer(opt)\r\n",
        "\r\n",
        "\r\n",
        "# 4. Horovod: Synchronize state accross workers\r\n",
        "callbacks = [\r\n",
        "    # Horovod: broadcast initial variable states from rank 0 to all other processes.\r\n",
        "    # This is necessary to ensure consistent initialization of all workers when\r\n",
        "    # training is started with random weights or restored from a checkpoint.\r\n",
        "    hvd.callbacks.BroadcastGlobalVariablesCallback(0),\r\n",
        "]\r\n",
        "\r\n",
        "# 5. Horovod: Checkpoint on the first worker\r\n",
        "# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.\r\n",
        "if hvd.rank() == 0:\r\n",
        "    callbacks.append(keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))\r\n",
        "\r\n",
        "history = model.fit(X_train, y_train,\r\n",
        "          batch_size=batch_size,\r\n",
        "          callbacks=callbacks,\r\n",
        "          epochs=epochs,\r\n",
        "          verbose=1 if hvd.rank() == 0 else 0,\r\n",
        "          validation_data=(X_test, y_test))\r\n",
        "model.save('/content/drive/My Drive/ADM Datasets/mlp_horovod_model.h5')\r\n",
        "print('Congrats! Model Saved')\r\n",
        "\r\n",
        "loss_train, acc_train = model.evaluate(X_train, y_train, verbose = 0)\r\n",
        "loss_val, acc_val = model.evaluate(X_test, y_test, verbose = 0)\r\n",
        "print(\"The train loss is: {0} and the train accuracy is: {1}\".format(loss_train, acc_train))\r\n",
        "print(\"The validation loss is: {0} and the validation accuracy is: {1}\".format(loss_val, acc_val))\r\n",
        "\r\n",
        "# predict probabilities for test set\r\n",
        "yhat_probs = model.predict(X_test, verbose=0)\r\n",
        "# predict crisp classes for test set\r\n",
        "yhat_classes = model.predict_classes(X_test, verbose=0)\r\n",
        "\r\n",
        "print(yhat_probs) # 2-D\r\n",
        "print(yhat_classes) # 1-D\r\n",
        "\r\n",
        "# reduce to 1d array\r\n",
        "yhat_probs = yhat_probs[:, 0]\r\n",
        "y_test_1D = output_encoder.inverse_transform(y_test) # Need to be in 1D\r\n",
        "\r\n",
        "print(yhat_probs)\r\n",
        "print(yhat_classes)\r\n",
        "print()\r\n",
        "\r\n",
        "\r\n",
        "# accuracy: (tp + tn) / (p + n)\r\n",
        "accuracy = accuracy_score(y_test_1D, yhat_classes)\r\n",
        "print('Accuracy: %f' % accuracy)\r\n",
        "# precision tp / (tp + fp)\r\n",
        "precision = precision_score(y_test_1D, yhat_classes)\r\n",
        "print('Precision: %f' % precision)\r\n",
        "# recall: tp / (tp + fn)\r\n",
        "recall = recall_score(y_test_1D, yhat_classes)\r\n",
        "print('Recall: %f' % recall)\r\n",
        "# f1: 2 tp / (2 tp + fp + fn)\r\n",
        "f1 = f1_score(y_test_1D, yhat_classes)\r\n",
        "print('F1 score: %f' % f1)\r\n",
        " \r\n",
        "# kappa\r\n",
        "kappa = cohen_kappa_score(y_test_1D, yhat_classes)\r\n",
        "print('Cohens kappa: %f' % kappa)\r\n",
        "# ROC AUC\r\n",
        "auc = roc_auc_score(y_test_1D, yhat_probs)\r\n",
        "print('ROC AUC: %f' % auc)\r\n",
        "# confusion matrix\r\n",
        "matrix = confusion_matrix(y_test_1D, yhat_classes)\r\n",
        "print(matrix)\r\n",
        "\r\n",
        "# plot loss during training\r\n",
        "epoch_range = range(1,epochs+1)\r\n",
        "plt.plot(epoch_range,history.history['loss'], label='Training loss')\r\n",
        "plt.plot(epoch_range,history.history['val_loss'], label='validation loss')\r\n",
        "plt.title('Training and Validation loss')\r\n",
        "plt.xlabel('Epochs')\r\n",
        "plt.ylabel('Loss')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n",
        "\r\n",
        "# plot Accuracy during training\r\n",
        "plt.plot(epoch_range,history.history['accuracy'], label='Training Accuracy')\r\n",
        "plt.plot(epoch_range,history.history['val_accuracy'], label='validation Accuracy')\r\n",
        "plt.title('Training and Validation Accuracy')\r\n",
        "plt.xlabel('Epochs')\r\n",
        "plt.ylabel('Accuracy')\r\n",
        "plt.legend()\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWZqMPvEep-8"
      },
      "source": [
        "## Tying all together, the complete code is listed below [Prediction Part]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hhI7LPKxe78J"
      },
      "source": [
        "from keras.models import load_model\r\n",
        "#Load the saved Model and do prediction\r\n",
        "\r\n",
        "classifier = load_model('/content/drive/My Drive/ADM Datasets/mlp_horovod_model.h5')\r\n",
        "# Now make prediction on saved Model/classifier for test dataset\r\n",
        "# load the dataset for testing the model\r\n",
        "\r\n",
        "pred_dataset = pd.read_csv('/content/drive/My Drive/ADM Datasets/test_data.csv')\r\n",
        "\r\n",
        "#Changing pandas dataframe to numpy array\r\n",
        "pred_input = pred_dataset.iloc[:,2:28].values.astype('float32')\r\n",
        "pred_output = pred_dataset.iloc[:,1:2].values.astype('float32')\r\n",
        "\r\n",
        "# Scaled the input and output for prediction\r\n",
        "pred_input_scaled = input_scaler.fit_transform(pred_input)\r\n",
        "pred_output_endcoded = output_encoder.fit_transform(pred_output).toarray()\r\n",
        "\r\n",
        "# evaluate the loss and accuracy for the prediction dataset\r\n",
        "pred_loss, pred_acc = classifier.evaluate(pred_input_scaled, pred_output_endcoded, verbose =0)\r\n",
        "print(\"The Predicted dataset loss is: {0} and Accuracy is: {1}\".format(pred_loss, pred_acc))\r\n",
        "\r\n",
        "# Prediction on input data\r\n",
        "\r\n",
        "prediction = classifier.predict(pred_input_scaled)\r\n",
        "inv_predict = output_encoder.inverse_transform(prediction).astype('int64')\r\n",
        "inv_actual = output_encoder.inverse_transform(pred_output_endcoded).astype('int64')\r\n",
        "for j in range(0,len(pred_output)):\r\n",
        "        print(\"Pred={0} Act={1}\\n\".format(inv_predict[j], inv_actual[j]))\r\n",
        "\r\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}