1_channel_CNN_single_session.ipynb

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      "name": "1_channel_CNN_single_session.ipynb",
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    {
      "cell_type": "code",
      "metadata": {
        "id": "xp2AHUNY-Hqo",
        "colab_type": "code",
        "outputId": "a578903e-4a8c-4e90-ca4a-dac403adb49a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tue Oct 29 15:24:40 2019       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 430.50       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   32C    P8    27W / 149W |      0MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JUXsTgf9J24R",
        "colab_type": "text"
      },
      "source": [
        "**Initialize**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDc7jvxeJ24S",
        "colab_type": "code",
        "outputId": "e6a9e4be-b9b2-433a-e5e1-0758e3635e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import scipy.io\n",
        "from scipy import stats\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.utils import resample\n",
        "from sklearn.utils import class_weight\n",
        "from sklearn.model_selection import KFold\n",
        "import time\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "read_file=scipy.io.loadmat('1channel_input_PRRB.mat') # Read data\n",
        "\n",
        "Input =np.array(read_file['mag_Input_data']) # total_Input_data\n",
        "\n",
        "X = Input[:,0:450]\n",
        "Y = Input[:,-1]\n",
        "\n",
        "# number of class\n",
        "n_classes= 42\n",
        "# number of features\n",
        "n_features=X.shape[1]\n",
        "\n",
        "print(X.shape, Y.shape)\n",
        "\n",
        "print(n_features)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
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          },
          "metadata": {
            "tags": []
          }
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        {
          "output_type": "stream",
          "text": [
            "(2100, 450) (2100,)\n",
            "450\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDvjJ0iBJ24V",
        "colab_type": "text"
      },
      "source": [
        "**Set Target Subject**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y77v4jDdJ24W",
        "colab_type": "code",
        "outputId": "9ac27376-b29f-43a3-f58f-8eb15180bf2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "### Target ID: ?(1-42) \n",
        "\n",
        "ID = 1\n",
        "print('ID',ID)\n",
        "\n",
        "Y[np.where(Y!=ID)] = 0\n",
        "   "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ID 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u18ca18CJ24Y",
        "colab_type": "code",
        "outputId": "2476bf9b-cf11-4c36-9fcb-7ab63ac0765c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(np.unique(Y, return_counts=True)) # Check whether target has 1, while others are 0\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([0., 1.]), array([2050,   50]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6HoS344fJ24a",
        "colab_type": "text"
      },
      "source": [
        "**Onehot Encoding + Data spliting and suffle**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msbUOLyqJ24b",
        "colab_type": "code",
        "outputId": "ad60cbbe-c31a-412e-8c54-4addacef14b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "enc = OneHotEncoder()\n",
        "# randomly shuffle data before training and testing\n",
        "\n",
        "randIndx = np.arange(X.shape[0])\n",
        "np.random.seed(111)\n",
        "np.random.shuffle(randIndx)\n",
        "\n",
        "# 60% for training and 40% for testing\n",
        "trainSamples=np.floor(X.shape[0]*0.6).astype(int)\n",
        "testSamples=np.floor(X.shape[0]*0.4).astype(int) \n",
        "\n",
        "print(trainSamples)\n",
        "print(testSamples)\n",
        "\n",
        "X = X[randIndx,:] \n",
        " \n",
        "Y =enc.fit_transform(Y[randIndx].reshape(-1,1)).toarray()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1260\n",
            "840\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/_encoders.py:415: FutureWarning: The handling of integer data will change in version 0.22. Currently, the categories are determined based on the range [0, max(values)], while in the future they will be determined based on the unique values.\n",
            "If you want the future behaviour and silence this warning, you can specify \"categories='auto'\".\n",
            "In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGsRmnv0J24d",
        "colab_type": "code",
        "outputId": "035bcb51-9e92-4bb2-c6d0-165c761c42c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(np.unique(Y[:,1], return_counts=True))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(array([0., 1.]), array([2050,   50]))\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TxS_qxPtJ24f",
        "colab_type": "code",
        "outputId": "fe33aaac-7f5a-487e-c227-314efc13e7dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Assign training and testing set\n",
        "nChannels=1\n",
        "\n",
        "train_data, train_target = np.reshape(X[:trainSamples,:],[trainSamples,n_features,nChannels]), Y[:trainSamples,:]\n",
        "\n",
        "test_data, test_target = np.reshape(X[trainSamples:,:],[testSamples,n_features,nChannels]), Y[trainSamples:,:]\n",
        "\n",
        "\n",
        "\n",
        "print(train_data.shape,train_target.shape)\n",
        "print(test_data.shape,test_target.shape)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1260, 450, 1) (1260, 2)\n",
            "(840, 450, 1) (840, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUtU-KBHJ24i",
        "colab_type": "code",
        "outputId": "c678c8ef-df48-46bd-b8e4-594ebb3eb007",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Check # of 1 and 0 in training and testing set\n",
        "\n",
        "unique, counts = np.unique(train_target[:,1], return_counts=True)\n",
        "print('Train set dictionary: ',dict(zip(unique, counts)))\n",
        "\n",
        "unique, counts = np.unique(test_target[:,1], return_counts=True)\n",
        "print('Test set dictionary: ',dict(zip(unique, counts)))\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train set dictionary:  {0.0: 1230, 1.0: 30}\n",
            "Test set dictionary:  {0.0: 820, 1.0: 20}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhBqwSUDJ24l",
        "colab_type": "text"
      },
      "source": [
        "**Network Structure**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfw5VYBdJ24m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Model:\n",
        "\n",
        "    def __init__(self, sess, name):\n",
        "\n",
        "        self.sess = sess\n",
        "\n",
        "        self.name = name\n",
        "\n",
        "        self._build_net()\n",
        "\n",
        "    def _build_net(self):\n",
        "\n",
        "        with tf.variable_scope(self.name):\n",
        "\n",
        "            # dropout (keep_prob) rate  0.7~0.5 on training, but should be 1 for testing\n",
        "\n",
        "            self.training = tf.placeholder(tf.bool)\n",
        "    \n",
        "            # input place holders\n",
        "            self.X = tf.placeholder(tf.float32, [None, 450, 1]) \n",
        "            self.Y = tf.placeholder(tf.float32, [None, 2])\n",
        "            self.class_weights = tf.placeholder(tf.float32) # For weighted loss\n",
        "            self.learning_rate = tf.placeholder(tf.float32)\n",
        "            \n",
        "            # Convolutional Layer #1, length: 450x1 -> 271x50  ((I-F)/S) + 1 \n",
        "            W1 = tf.Variable(tf.random_normal([180, 1, 50], stddev=0.01))\n",
        "            conv1 = tf.nn.conv1d(self.X, W1, stride=1, padding='VALID')\n",
        "            relu1 = tf.nn.relu(conv1) # max(0,x)\n",
        "            dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, training=self.training)\n",
        "            \n",
        "            # Convolutional Layer #2, length: 271x50 -> 92x70 \n",
        "            W2 = tf.Variable(tf.random_normal([180, 50, 70], stddev=0.01))\n",
        "            conv2 = tf.nn.conv1d(dropout1, W2, stride=1, padding='VALID')\n",
        "            relu2 = tf.nn.relu(conv2)                                    \n",
        "            dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, training=self.training)\n",
        "            \n",
        "            # FC 1, length: 32x70  -> 2 output\n",
        "            FC1 = tf.reshape(dropout2, [-1, 92*70])         \n",
        "            FC_weight = tf.get_variable(\"FC_weight\", shape=[92*70, 2], initializer=tf.contrib.layers.xavier_initializer())\n",
        "            logits = tf.matmul(FC1,FC_weight)\n",
        "\n",
        "    \n",
        "        # define cost/loss & optimizer   \n",
        "        reg_losses = tf.nn.l2_loss(W1) + tf.nn.l2_loss(W2)  + tf.nn.l2_loss(FC_weight)\n",
        "        reg_constant = 0.005  # Choose an appropriate one.\n",
        "    \n",
        "        self.cost = tf.reduce_mean(tf.nn.weighted_cross_entropy_with_logits(self.Y, logits, self.class_weights)) \n",
        "        self.cost = self.cost + reg_constant * reg_losses\n",
        "        \n",
        "        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost) \n",
        "#         self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate).minimize(self.cost)    \n",
        "        \n",
        "        self.sigmoid_out = tf.nn.sigmoid(logits) \n",
        "        self.predict = tf.cast(self.sigmoid_out[:,1], tf.float32)\n",
        "                    \n",
        "        self.weight1 = W1\n",
        "        self.weight2 = W2\n",
        "        \n",
        "    def get_predict(self, x_test, training=False):          \n",
        "\n",
        "        return self.sess.run(self.predict, feed_dict={self.X: x_test, self.training: training})\n",
        "    \n",
        "\n",
        "    def get_cost(self, x_valid, y_valid, weights, training=False):\n",
        "        \n",
        "        return self.sess.run(self.cost, feed_dict={self.X: x_valid, \n",
        "                                                   self.Y: y_valid, self.class_weights: weights, self.training: training})\n",
        "\n",
        "\n",
        "    def train(self, x_data, y_data, weights, learning, training=True):\n",
        "\n",
        "        return self.sess.run(self.optimizer, feed_dict={\n",
        "             self.X: x_data, self.Y: y_data, self.class_weights: weights, self.learning_rate: learning, self.training: training})\n",
        "    \n",
        "    def plot_feature(self, x_data, y_data, training=False):\n",
        "        \n",
        "        return self.sess.run(self.weight1, feed_dict={self.X: x_data, self.Y: y_data, self.training: training})\n",
        "    \n",
        "    def plot_feature2(self, x_data, y_data, training=False):\n",
        "        \n",
        "        return self.sess.run(self.weight2, feed_dict={self.X: x_data, self.Y: y_data, self.training: training})\n",
        "    \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hFRGqJsJ24o",
        "colab_type": "text"
      },
      "source": [
        "**Main**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "7MCVaLqOJ24p",
        "colab_type": "code",
        "outputId": "d77896a0-95b5-4a76-ef33-bcf0d859d523",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        }
      },
      "source": [
        "### hyper parameters\n",
        "\n",
        "learning_rate = 0.0001\n",
        "training_epochs = 30\n",
        "batch_size = 126 # Mini-batch size: total: 1260(train)/840(test)  \n",
        "num_cv = 10 #  Number of fold cross validation\n",
        "bag_size = 1134 # Each bagging size\n",
        "ear_epoch = 0\n",
        "\n",
        "valid_cost = np.zeros((training_epochs))\n",
        "train_cost = np.zeros((training_epochs))\n",
        "\n",
        "# initialize\n",
        "sess = tf.Session()\n",
        "\n",
        "models = []\n",
        "num_models = 3 # Number of models for bagging\n",
        "\n",
        "for m in range(num_models):\n",
        "    print('Number of Model+Bagging {}'.format(m+1))\n",
        "    models.append(Model(sess, \"model\" + str(m)))    \n",
        "\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "#saver = tf.train.Saver()\n",
        "\n",
        "start_time = time.time() # time starts!\n",
        "\n",
        "print('Learning Started!')\n",
        "\n",
        "# train my model\n",
        "for epoch in range(training_epochs):\n",
        "         \n",
        "    total_batch = int(bag_size / batch_size)\n",
        "    valid_cost_cv = 0\n",
        "    \n",
        "    cv = KFold(n_splits=num_cv, shuffle=True)\n",
        "    for train_index, test_index in cv.split(train_data):\n",
        "        cv_train_data, cv_validate_data = train_data[train_index], train_data[test_index]\n",
        "        cv_train_target, cv_validate_target = train_target[train_index], train_target[test_index]    \n",
        "                    \n",
        "        for m_idx, m in enumerate(models):\n",
        "            bag_index = np.random.choice(len(cv_train_target), bag_size, replace=True) # Random sampling with replacement\n",
        "            #print(bag_index)\n",
        "            bag_cv_train_data = cv_train_data[bag_index,:]\n",
        "            bag_cv_train_target = cv_train_target[bag_index,:] \n",
        "\n",
        "            for i in range(total_batch):\n",
        "\n",
        "                batch_xs, batch_ys = bag_cv_train_data[batch_size*i:batch_size*(i+1),:], bag_cv_train_target[batch_size*i:batch_size*(i+1)]                \n",
        "                pos_weight = (n_classes - 1) # Increase Sensitivity(FN down) and Decrease Specifity (FP up)\n",
        "\n",
        "                _ = m.train(batch_xs, batch_ys, pos_weight, learning_rate)    \n",
        "                \n",
        "                pos_weight = 1\n",
        "#                 train_cost[epoch] += m.get_cost(batch_xs, batch_ys, pos_weight) / (num_cv * num_models * total_batch) #train_cost\n",
        "            \n",
        "            \n",
        "            valid_cost_cv += m.get_cost(cv_validate_data, cv_validate_target, pos_weight)  #valid_cost \n",
        "            \n",
        "    valid_cost[epoch] = valid_cost_cv / (num_cv * num_models) # valid_cost for cross validation\n",
        "    \n",
        "    if ((epoch+1)%5) == 0:\n",
        "        print('Epoch:', '%04d' % (epoch + 1), 'Validating_cost =', '{:.5f}'.format(valid_cost[epoch]))\n",
        "#         print('Epoch:', '%04d' % (epoch + 1), 'Training_cost =', '{:.5f}'.format(train_cost[epoch]))\n",
        "           \n",
        "            \n",
        "    if valid_cost[epoch] < 0.01:\n",
        "        print('Early Stopping, Epoch: ', epoch+1)\n",
        "        ear_epoch = epoch+1\n",
        "        break\n",
        "        \n",
        "        \n",
        "print('Learning Finished!')\n",
        "\n",
        "\n",
        "end_time = time.time() # time ends!\n",
        "\n",
        "elapsed = end_time - start_time\n",
        "\n",
        "print('Execution time:{:.2f} seconds'.format(elapsed))\n",
        "print('Last validation error:{:.3f} '.format(valid_cost[-1]))\n",
        "#saver.save(sess, 'SingleSession\\Model\\m'+str(ID)) # Model Save\n",
        "\n",
        "\n",
        "pos_weight = 1 # No weighted loss because no more training"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of Model+Bagging 1\n",
            "WARNING:tensorflow:From <ipython-input-9-adfafba3492f>:29: dropout (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.dropout instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:271: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "Number of Model+Bagging 2\n",
            "Number of Model+Bagging 3\n",
            "Learning Started!\n",
            "Epoch: 0005 Validating_cost = 0.55108\n",
            "Epoch: 0010 Validating_cost = 0.35880\n",
            "Epoch: 0015 Validating_cost = 0.26503\n",
            "Epoch: 0020 Validating_cost = 0.21081\n",
            "Epoch: 0025 Validating_cost = 0.18045\n",
            "Epoch: 0030 Validating_cost = 0.16540\n",
            "Learning Finished!\n",
            "Execution time:244.36 seconds\n",
            "Last validation error:0.165 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WvqGKXPJ24r",
        "colab_type": "text"
      },
      "source": [
        "**Epoch vs Cost plot**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NIzEZHOJ24s",
        "colab_type": "code",
        "outputId": "8bb74439-4b07-463c-c1fa-4f13c01c1ec0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "fig = plt.figure()\n",
        "ax = plt.subplot(111)\n",
        "\n",
        "if ear_epoch == 0: # No early stop\n",
        "    x_axis = np.linspace(1.0, training_epochs, num=training_epochs)\n",
        "#     ax.plot(x_axis, train_cost, 'b', label='Training')\n",
        "    ax.plot(x_axis, valid_cost,'b', label='Validation')\n",
        "else: # Yes early stop\n",
        "    x_axis = np.linspace(1.0, ear_epoch, num=ear_epoch)\n",
        "#     ax.plot(x_axis, train_cost[:ear_epoch], 'b', label='Training')\n",
        "    ax.plot(x_axis, valid_cost[:ear_epoch],'b', label='Validation')\n",
        "\n",
        "ax.legend()\n",
        "plt.title('Epoch vs Cost')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Cost')\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qyVRV4y5AJFPVrg1PPw3XXx8Gs5s2Dbp2hZycEBQ5OdtuNWvGWqqkOQWBSIyq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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EbC6hKWlJ24u",
        "colab_type": "text"
      },
      "source": [
        "**Final Result of train set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "slbApn3UJ24u",
        "colab_type": "code",
        "outputId": "7393ff43-55c4-479b-9fd8-096ffa535184",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        }
      },
      "source": [
        "thres = np.arange(0, 1.0001, 0.0001)\n",
        "thres_matrix_train = np.zeros((len(train_target),len(thres)))\n",
        "total_matrix_train = np.zeros((len(thres),4)) #acc,far,frr,recall(order of column)\n",
        "    \n",
        "test_logit2_temp = np.zeros((len(train_target), num_models))\n",
        "train_cost2 = 0\n",
        "\n",
        "for m3_idx, m3 in enumerate(models):\n",
        "    test_logit2_temp[:,m3_idx] =  m3.get_predict(train_data)\n",
        "\n",
        "    if test_logit2_temp[np.where(test_logit2_temp< 0)] < 0:\n",
        "        print('Error')\n",
        "    else:\n",
        "        print('Okay, No minus values')       \n",
        "\n",
        "    train_cost2 += m3.get_cost(train_data,train_target,pos_weight) / num_models\n",
        "\n",
        "print('Train set Cost:{:.3f}'.format(train_cost2))\n",
        "\n",
        "test_logit2 = np.mean(test_logit2_temp, axis=1)\n",
        "\n",
        "for j in range(len(thres)):\n",
        "    for i in range(len(train_target)):\n",
        "        if test_logit2[i] < thres[j]:\n",
        "            thres_matrix_train[i,j] = 0\n",
        "        else:\n",
        "            thres_matrix_train[i,j] = 1\n",
        "\n",
        "\n",
        "unique, counts = np.unique(train_target[:,1], return_counts=True)\n",
        "print('Train set dictionary: ',dict(zip(unique, counts)))\n",
        "\n",
        "for i in range(len(thres)):\n",
        "\n",
        "    equal_logit2 = np.equal(thres_matrix_train[:,i], train_target[:,1]) ##Train\n",
        "    unique2, True_pos_neg_train = np.unique(thres_matrix_train[np.where(equal_logit2==True),i], return_counts=True)\n",
        "    unique3, False_pos_neg_train = np.unique(thres_matrix_train[np.where(equal_logit2==False),i], return_counts=True) \n",
        "\n",
        "    if np.shape(True_pos_neg_train)==(1,):\n",
        "        if unique2[0] == 0:\n",
        "            True_pos_neg_train = [True_pos_neg_train[0], 0]\n",
        "        else:\n",
        "            True_pos_neg_train = [0, True_pos_neg_train[0]]\n",
        "\n",
        "    if np.shape(False_pos_neg_train)==(1,):\n",
        "        if unique3[0] == 0:\n",
        "            False_pos_neg_train = [False_pos_neg_train[0], 0]\n",
        "        else:\n",
        "            False_pos_neg_train = [0, False_pos_neg_train[0]]\n",
        "   \n",
        "    if np.shape(True_pos_neg_train)==(0,):\n",
        "        True_pos_neg_train = [0, 0]\n",
        "        \n",
        "    if np.shape(False_pos_neg_train)==(0,):\n",
        "        False_pos_neg_train = [0, 0]\n",
        " \n",
        "    Recall_train= True_pos_neg_train[1]/(False_pos_neg_train[0]+True_pos_neg_train[1])\n",
        "    Specific_train = True_pos_neg_train[0] /(True_pos_neg_train[0]+False_pos_neg_train[1])\n",
        "\n",
        "    ACC_train = (True_pos_neg_train[0]+True_pos_neg_train[1]) / (len(train_target))\n",
        "    FAR_train = 1 - Specific_train\n",
        "    FRR_train = 1 - Recall_train\n",
        "\n",
        "    total_matrix_train[i,:] = ACC_train, FAR_train, FRR_train, Recall_train\n",
        "    \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
            "  # This is added back by InteractiveShellApp.init_path()\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Okay, No minus values\n",
            "Okay, No minus values\n",
            "Okay, No minus values\n",
            "Train set Cost:0.159\n",
            "Train set dictionary:  {0.0: 1230, 1.0: 30}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "npbkZAkTJ24w",
        "colab_type": "code",
        "outputId": "be849888-7efe-47ce-9044-e689e4d09df5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "source": [
        "fig2 = plt.figure()\n",
        "ax = plt.subplot(111)\n",
        "ax.plot(thres, total_matrix_train[:,1], 'b', label='FAR')\n",
        "ax.plot(thres, total_matrix_train[:,2], 'r', label='FRR')\n",
        "ax.legend()\n",
        "plt.title('FAR vs FRR (Train)')\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Err Rate')\n",
        "plt.show()\n",
        "\n",
        "EER_line = thres[::-1]\n",
        "fig3 = plt.figure()\n",
        "ax2 = plt.subplot(111)\n",
        "ax2.plot(total_matrix_train[:,1], total_matrix_train[:,3],'r',label='ROC')\n",
        "ax2.plot(thres, EER_line, 'b',label='EER Line')\n",
        "ax2.legend()\n",
        "plt.title('ROC Curve (Train)')\n",
        "plt.xlabel('FAR')\n",
        "plt.ylabel('TPR')\n",
        "plt.show()\n",
        "\n",
        "EER_loc_train = np.argmin(abs(total_matrix_train[:,1] - total_matrix_train[:,2]))\n",
        "# print('EER threshold: ', thres[EER_loc_train])\n",
        "print('EER: {:.3f} '.format((total_matrix_train[EER_loc_train,1]+total_matrix_train[EER_loc_train,2])/2))\n",
        "print('FRR at EER: {:.3f} '.format(total_matrix_train[EER_loc_train,2]))\n",
        "print('FAR at EER: {:.3f} '.format(total_matrix_train[EER_loc_train,1]))\n",
        "print('Accuracy at EER: {:.3f} '.format(total_matrix_train[EER_loc_train,0]))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MBCoRiEhJqKqCjRtjv4wSgYhIsWpuhqOPjv0yqUwEqhoSkZLQ3Bx711FIaSKoqAiN8U1N\nSUciItKObdtiH2cIUpwIQKUCESlyKhHEJzNdpRKBiBQ1JYL4ZEoEajAWkaKmRBAfJQIRKQlKBPFR\n1ZCIlAQlgvioRCAiJUGJID5KBCJSEpQI4qOqIREpCUoE8VGJQESK3pYtsGyZEkFclAhEpOg9+2xY\nZr6wYpTKRKCqIREpeo2NYTl5cuyXSmUiUIlARIpeJhFoYpp4VFWF5TvvJBuHiEibMolAU1XGI5MI\nNmxINg4RkTZlhkdWIohH375hZNd33006EhGRNqhqKF5m0L+/EoGIFLElS8JS3Ufjo0QgIkUt072x\npib2SykRiIgUo0wbQZ8+sV8qtYmgqgrWr086ChGRNpRLryEzm2hmL5rZUjOb3sYxnzKzF8xskZn9\nIs54sg0aBG+/XairiYh0UgETQWzN0WbWE7gR+ChQD8w3s9nu/kLWMWOAfweOcve1ZrZHXPG0NmgQ\nvP56oa4mItJJmaqhEu81dDiw1N1fdfetwN1A62elvwDc6O5rAdx9dYzx7EAlAhEpWg0N8MMfhnWz\n2C8XZyIYBizPel8fbcu2H7CfmT1hZvPMbGKuE5nZNDNbYGYL1qxZ0y3BKRGISNH62c9gzRoYMaIg\nl8s7EZhZvxiu3wsYA0wApgK3mNng1ge5+83uXuvutUOHDu2WCw8aFIaYcO+W04mIdJ+NG8Py+ecL\ncrkOE4GZfcjMXgCWRO8PMbMf5nHuFcDwrPc10bZs9cBsd29099eAlwiJIXaDBoU5H9SFVESKTqZ9\noLKyIJfLp0RwHXAS0ADg7v8Ajsnjc/OBMWY22sz6AGcAs1sd81tCaQAzqyZUFb2aV+RdNGhQWKp6\nSESKTqbHUAGeKoY8q4bcfXmrTc15fKYJuACYCywG7nH3RWZ2lZlNig6bCzREJY4/A19z94a8o+8C\nJQIRKVqNjaHbaAEaiiG/7qPLzexDgJtZb+DfCF/sHXL3OcCcVtuuzFp34CvRq6B23z0sGwqSdkRE\nOqGxsSDdRjPyKRGcB5xP6PGzAhgHfDnOoAqhujos33wz2ThERHbwla/AT35SkAfJMvJJOe9z989k\nbzCzo4An4gmpMDKdj5QIRKSozJ4d6q4vvLBgl8ynRPCDPLeVlCFDwlKJQESKSmMjTJgAX/1qwS7Z\nZonAzI4EPgQMNbPsOvyBQGGasmNUWRlGIFUiEJGikmkoLqD2qob6AAOiY6qytr8DnB5nUIVSXR0e\n3hMRKRpNTcWTCNz9UeBRM7vd3ctyeLbqapUIRKTIFFmJIGOjmX0XOACoyGx09+Nii6pAhgxR91ER\nKTLr1hW06yjk11j8c8LwEqOB/wHqCE8NlzwlAhEpKuvWhWWBZ83KJxEMcfefAI3u/qi7/ytQ8qUB\nCA+VKRGISNHIJIBDDinoZfMpf0SDXvCGmZ0KrAR2jy+kwhkyJCTgpqaCl8RERHaWGWxuwICCXjaf\nr7//NbNBwFcJzw8MBC6JNaoCyTxLsG5dy5PGIiKJyQw2V+Bfph1ezd0fiFbfBj4CYGb94wyqUDKJ\noKFBiUBEikAB5ynO1m4bgZkNM7PaaBhpzGwPM/sm8HJBootZdiIQEUlcpmqoWBKBmV0MLCRUB80z\ns3MJo45WAh8sTHjxUiIQkaJy/fVhWURVQ9MIA869ZWYjCLOHHeXuTxUmtPhpKGoRKSpz54blwQcX\n9LLtVQ1tdve3ANx9GfBiOSUBgL32CssVrSfQFBFJQnMzfPGLMHJkQS/bXomgxsy+n/V+r+z37n5R\nfGEVRr9+YTjq18tyAA0RKTkJDC8B7SeCr7V6X1algYyRI5UIRKRIFFsicPc7ChlIUkaOhF//Ouko\nRERI7OnWvCavL2fDhoXl2rXJxiEiKbd1K2zenEiJIPWJYOLEsHzuuWTjEJGUGz8+LPv1K/ilO3qg\nrKeZlcVwEm054ICwXLw42ThEJOVeeQUGDoRp0wp+6XYTgbs3A1MLFEsihg0LVXJqMBaRRG3bBuee\nG7oyFlg+rRJPmNkMYBbwbmajuz8dW1QF1LMnjBoFS5cmHYmIpFoCU1Rm5JMIxkXLq7K2OWUyJwHA\nmDHwclmMniQiJSuhrqPQQSIwsx7Aj9z9ngLFk4hRo2DevKSjEJHU2rYtvBKaGKWjNoJtwGUFiiUx\nI0aE7qPvvtvxsSIi3W7LlrBMqESQT/fRP5rZpWY23Mx2z7xij6yAhg8Py+XLk41DRFLqxRfDcuvW\nRC6fTzlkSrQ8P2ubA+/t/nCSMWJEWC5bBvvvn2wsIpJCmQlpamsTuXw+M5SNLkQgScqUCJYtSzYO\nEUmp5uaw7Nkzkcu3NzHNZVnrn2y175txBlVoNTVQWQmLFiUdiYikUrEmAuCMrPV/b7VvYj4nN7OJ\nZvaimS01s+ntHPcvZuZmlki5qFcvOPRQmD8/iauLSOoVcSKwNtZzvd/5w2Y9gRuBk4GxwFQzG5vj\nuCrg34C/dxhtjA47DJ5+umXKUBGRginiROBtrOd6n8vhwFJ3f9XdtwJ3A5NzHPcN4Bpgcx7njM1h\nh8GmTfDCC0lGISKptG1bWPZIZhzQ9q56iJm9Y2brgYOj9cz7g/I49zAgu0NmfbRtOzP7ADDc3X/X\n3onMbJqZLTCzBWvWrMnj0p132GFhOXt2LKcXEWlbwiWC9iamiTWi6Knla4GzOzrW3W8Gbgaora3N\npzTSafvuG5aZ7rwiIgVTxFVDXbUCGJ71viballEFHAg8YmZ1wBHA7KQajHv0gBNP1HDUIpKAMk4E\n84ExZjbazPoQeiFtr3hx97fdvdrdR7n7KGAeMMndF8QYU7vGjg2JIFNdJyJSEOWaCNy9CbgAmAss\nBu5x90VmdpWZTYrrul3x/vfDxo0aakJECqxY2wi6g7vPAea02nZlG8dOiDOWfIyOnqFetChMai8i\nUhArV4ZluZUISlHmy1+zlYlIQd13X1jutlsil1ciyLLPPuEpY405JCIFVVER5s0dNqzjY2OgRJAl\nM23lww8nHYmIpEpjY2JJAJQIdjJsGCxYAB7L0woiIjk0NSU2OxkoEezkIx8J3UfVTiAiBZPgfMWg\nRLCT004LyyefTDYOEUkRJYLictBB0LevEoGIFNDTTysRFJM+fcLcBL/8ZdKRiEhqDBoEa9cmdnkl\nghz23z90IX3llaQjEZFUaG6GD3wgscsrEeRw3nlhecklycYhIimhNoLiM358GHfo/vth9eqkoxGR\nsqfuo8XpuuvC8vbbEw1DRNJAJYLidNJJ8J73wC23JB2JiJS15mYlgmJ26qmwdCmsWpV0JCJStqZP\nD8v+/RMLQYmgHWefHZYae0hEYvPGG2GZ6aWSACWCdhx+eHiu4IYbko5ERMpWYyO8733hWYKEKBG0\no29fOPro8NDfli1JRyMiZampKdH2AVAi6ND554e/pz/8IelIRKQsNTYm2nUUlAg69NGPhqWGnBCR\nWBRBiSDZNFQCqqrCzGV33gk//GGiDfsiUsoWL4bbbtt5spMXXkh0UhpQIsjLOefAFVfAjBlw+eVJ\nRyMiJemWW8KTqrl+TX7844WPJ4uqhvIwfTrsuSc8+mjSkYhIydqyBaqrYcOGnV/XX59oaEoEeTAL\nD5c9+CDU1ycdjYiUpCJoFG6LEkGePvaxHZciIp1SBI3CbVEiyNPkyXDiibBwYWgrEBHplITHE2qP\nEkEnzJoVehFdeGGo1hMRyZuqhsrD4MEtw1N/85vJxiIiJWT27PBLUomgPHzuc2H5rW/B1q3JxiIi\nJeLZZ8Py6quTjaMNSgSd1Ls3fP/7Yf3ee5ONRURKRGNjWE6enGwcbVAi2AXnnhuWd9yRbBwiUiIy\nU1GaJR1JTrEmAjObaGYvmtlSM5ueY/9XzOwFM3vWzP5kZiPjjKe7VFbCscfCQw/B2rVJRyMiRa+I\newxBjInAzHoCNwInA2OBqWY2ttVhzwC17n4w8CvgO3HF090uvTQsL7oo2ThEpAQUcY8hiHesocOB\npe7+KoCZ3Q1MBl7IHODuf846fh5wZozxdKtTT4UPfQh+9jM4/fSirfoTkSSsWhXGrs8MMPf880Vd\nIogzEQwDlme9rwfGt3P8OcCDuXaY2TRgGsCIESO6K74uMYNf/AJGjYLLLoNJk4q2+k9ECu0b3wjD\nFWc74IBkYslDUTQWm9mZQC3w3Vz73f1md69199qhQ4cWNrh2jBwJZ50FL70E3/520tGISNHYsAFq\nauCVV1pef/970lG1Kc5EsAIYnvW+Jtq2AzM7AfgPYJK7l9yEkDfdFAYUvOIKeO65pKMRkaLQ2Bh6\nlbz3vS2vIp7MJM5EMB8YY2ajzawPcAYwO/sAMzsU+DEhCayOMZbYVFTAww+H9YMPhrfeSjYeESkC\nRd443FpsicDdm4ALgLnAYuAed19kZleZ2aTosO8CA4BfmtlCM5vdxumK2kEHhSeNAQ48cOcJiEQk\nZYq8u2hrsaYsd58DzGm17cqs9RPivH4hTZ8Of/wj/OlPMHYs/PjHcMwxSUclIoko4iGncymKxuJy\n8dBDcOSRsGRJeODsH/9IOiIRKYht2+DTnw5fAEceCX/5ixJBWvXqBX/9a0vngCOPbBliRETK2Pr1\nMHNmaCQcOBDGj4d//deko8qbEkEMDj8cpkyBTZtCryIRKXOZX3wXXABz54bXF76QbEydoEQQk7vu\nCsuLLoK6ukRDEZG4ZRJBCVUHZVMiiEnv3nDffWH9wguTjUVEYpZJBCXUZTSbEkGMJk0KVYUPPBCq\niUSkTJV4iaA001cJueQSOOOMMCbR66+HB9BEJEH19bBmTfeeM1P/q0QguXzqU/CDH8ATT8ANN8Dl\nlycdkUiKrV8fhnuIqzvfwIHxnDdm5iX2GGxtba0vWLAg6TA6xR123x3WrSu5J89FysvKlTBsGHz5\ny3Diid177spKOO64ov0PbmZPuXttrn3FGXGZMYNp0+A734GvfKVlzmMRKbBMSaC2VpOIZFFjcYH8\n7//CgAGhmujJJ5OORiSlSrx3T1yUCAqkd++WUUozDx3++tcaoE6koEq8d09clAgK6LDD4NZbw0RF\nP/1pmOLy8MNh1qykIxNJCSWCnFQ+KrBzzgmvDRvgM5+B2bND99Jbb4Xf/17TXYp0aN06+PrXYePG\nzn/2zTfDUlVDO9CfRkIGDAhPHq9dC6eeGoawrqqCxYth+PCOPy+SWo8+GvpiV1fv2i/7ffaB97+/\n++MqYUoECdttN3jkkTA+1Z13wogRcMIJoWfbJZfoh4vITjLVOw8/HGaFki7T10wR6NMH7rgDjjoK\nfvnLUDr44x/hsstCYqipCaWGCRNg8GDYay/o1w/69k06cpEEqJ6/2ykRFJFp08Jrw4bQmPzss7B6\ndWhH+Otfdz5+jz1CEjnttNAT6YgjYP/9Cx+3SEEpEXQ7JYIiNGDAjiOWrl8fnj3YvBmWLQuJYvly\n2Lo1DF1x000t8x4MHQqf/zycey6MGZNM/CKx0rMA3U5/kiWgqgqOP77t/Q0NsGhRqE76+c/DE8zf\n+Q585CMwdWqYQa9//8LFW3Lcw1SDUhq2bg1LlQi6jcYaKjPu8NRT8MUvwtNPt2yfOjU8xHb88eqi\nupMjj4R585KOQjqroSEM4iV50VhDKWIWhlF56iloaoLrr4d77gnTqc6cGYbBrq4Or0suCe0Lqf+/\ntGhRSAYnn5x0JJKvmhr9w+1GKhGkxJtvws03wyuvwLvv7vg0c6bLat++oRrp6KOTizMRlZWhUeY7\n30k6EpHYqEQgVFfDFVe0vL/6avjzn2HJktAr6cEH4Y034Ec/gr33Dq+TTgrDYGRPprPbbvCe9xQ+\n/lg1Nqq+WVJNiSCl9tknvAD+7//CcuHCMNTF8uUhOSxYEBJGLmahh9L48aEkMWZMGDdp5Mgdj6uo\nCNfp0ye+e+kSd2huViKQVFMikO3GjYMZM8J6c3N4kn/16pb9TU1hus0tW8IwL3/9K7z6apjr41e/\nav/chxwSksRhh7W8iqKKt6kpLNUVUVJM//olp549w2RL+aqvh+eea+nZB+HH9rJloT1i69ZQFTV7\ndsv+UaPCK1u/fmEmwWHDwvMUFRWh/aL1cbvkb38LrejZ9HCSiBKBdI+amvDK5aKLwnLbttAm8dxz\nISm8/HLLD3IIyeKll0J7Res+DL16hSSRsffeMGhQy/v+/UOjd8+eYdC+3XYLCeW447I+d9ZZ4aK5\ntK7TEkkR9RqSotPUFEYahttEi2EAAAkbSURBVDDMxuOPt7wHeOst+Oc/W95v2RLaNRobYdWqHUsl\n0PJj/7WmGv4x8BhuPfj7DBnSsr/ZerGxz2B69w7JpE+fMKbT3nvv+j1UVobc0p3PbAwdGhr9Zdf1\n6JHe52jUa0hKSq9eLV94xx3XuSqq5uYwFMfKlTB/Pjz/fNje1AT9f9DEtv5VLF5Tja/e8XOZaqzN\nm7vnHqQ4DRoUxujKpV8/+NCHYODA8L5Hj1C63H//UE25114wZEhI8uVGiUDKSs+eoZpozJgcYy39\npJHTPtGb02bk/mymcOwOS5e2NB901rZtUFcXSirdZevWkKh2NSYJY3StWJF7NJHm5jCe1223tWxr\n6+9v9913rKaElqrKQYN2LEmahSrT6urQzjVgQBg5u9hGDo41EZjZROAGoCdwq7t/u9X+vsCdwAeB\nBmCKu9fFGZOkWGNju72DMlUGZrDffl27lIbJL32bN4eJolauDA9hrlgROkVkV1NCmFxq1SpYsyYM\n69Lc3LKv9bEZRxwRhp0/5hiYODH57tWxJQIz6wncCHwUqAfmm9lsd38h67BzgLXuvq+ZnQFcA0yJ\nKyZJOT04Jp1QUQGHHhpeu6qpKSSIurrQ5XrJktBR4skn4XvfCy+APfeEKVNC9dPHPgZjx3bLLeQt\nzhLB4cBSd38VwMzuBiYD2YlgMvD1aP1XwAwzM4+jBfu221r+1CWdNm9WIpCC6tUrfLnvtVd4f/zx\ncP75YX3ZsjBi8PPPh2lrb7ghbJ8+PfR4y36iP+PKK0PC6PY4u/+U2w0Dlme9rwfGt3WMuzeZ2dvA\nEODN7IPMbBowDWDEiBG7Fs2QIYVPs1JcDjwwjJkhUgRGjAgjAkP4jdrYGEoO114bBlbNZbfd4oml\nJBqL3f1m4GYI3Ud36SSTJ4eXiEiRMQvtBPvt1zLJVCH1iPHcK4DhWe9rom05jzGzXsAgQqOxiIgU\nSJyJYD4wxsxGm1kf4AxgdqtjZgNnReunAw/H0j4gIiJtiq1qKKrzvwCYS+g+epu7LzKzq4AF7j4b\n+Alwl5ktBd4iJAsRESmgWNsI3H0OMKfVtiuz1jcDn4wzBhERaV+cVUMiIlIClAhERFJOiUBEJOWU\nCEREUq7k5iMwszXA67v48WpaPbWcArrndNA9p0NX7nmkuw/NtaPkEkFXmNmCtiZmKFe653TQPadD\nXPesqiERkZRTIhARSbm0JYKbkw4gAbrndNA9p0Ms95yqNgIREdlZ2koEIiLSihKBiEjKlWUiMLOJ\nZvaimS01s+k59vc1s1nR/r+b2ajCR9m98rjnr5jZC2b2rJn9ycxGJhFnd+ronrOO+xczczMr+a6G\n+dyzmX0q+rteZGa/KHSM3S2Pf9sjzOzPZvZM9O/7lCTi7C5mdpuZrTaz59vYb2b2/ejP41kz+0CX\nL+ruZfUiDHn9CvBeoA/wD2Bsq2O+DNwUrZ8BzEo67gLc80eAftH6l9Jwz9FxVcBjwDygNum4C/D3\nPAZ4Btgter9H0nEX4J5vBr4UrY8F6pKOu4v3fAzwAeD5NvafAjwIGHAE8PeuXrMcSwSHA0vd/VV3\n3wrcDbSeo3IycEe0/ivgeDOzAsbY3Tq8Z3f/s7tvjN7OI8wYV8ry+XsG+AZwDbC5kMHFJJ97/gJw\no7uvBXD31QWOsbvlc88ODIzWBwErCxhft3P3xwjzs7RlMnCnB/OAwWa2V1euWY6JYBiwPOt9fbQt\n5zHu3gS8DQwpSHTxyOees51D+EVRyjq856jIPNzdf1fIwGKUz9/zfsB+ZvaEmc0zs4kFiy4e+dzz\n14EzzayeMP/JhYUJLTGd/f/eoZKYvF66j5mdCdQCxyYdS5zMrAdwLXB2wqEUWi9C9dAEQqnvMTM7\nyN3XJRpVvKYCt7v798zsSMKshwe6+7akAysV5VgiWAEMz3pfE23LeYyZ9SIUJxsKEl088rlnzOwE\n4D+ASe6+pUCxxaWje64CDgQeMbM6Ql3q7BJvMM7n77kemO3uje7+GvASITGUqnzu+RzgHgB3/xtQ\nQRicrVzl9f+9M8oxEcwHxpjZaDPrQ2gMnt3qmNnAWdH66cDDHrXClKgO79nMDgV+TEgCpV5vDB3c\ns7u/7e7V7j7K3UcR2kUmufuCZMLtFvn82/4toTSAmVUTqopeLWSQ3Syfe14GHA9gZu8nJII1BY2y\nsGYDn4t6Dx0BvO3ub3TlhGVXNeTuTWZ2ATCX0OPgNndfZGZXAQvcfTbwE0LxcSmhUeaM5CLuujzv\n+bvAAOCXUbv4MneflFjQXZTnPZeVPO95LnCimb0ANANfc/eSLe3mec9fBW4xs0sIDcdnl/IPOzOb\nSUjm1VG7x38DvQHc/SZCO8gpwFJgI/D5Ll+zhP+8RESkG5Rj1ZCIiHSCEoGISMopEYiIpJwSgYhI\nyikRiIiknBKBpIaZDTGzhdFrlZmtiNbXRd0tu/t6E8zsgU5+5pFcD72Z2dlmNqP7ohNpoUQgqeHu\nDe4+zt3HATcB10Xr44AOhyOInkIXKTtKBCJBTzO7JRrD//dmVgnbf6Ffb2YLgH8zs6Fm9mszmx+9\njoqOOzartPGMmVVF5x1gZr8ysyVm9vPMKLdmdnx03HPR+PN9WwdkZp83s5fM7EngqAL9OUgKKRGI\nBGMIwzcfAKwD/iVrXx93r3X37wE3EEoSh0XH3BodcylwflTC+DCwKdp+KHAxYZz89wJHmVkFcDsw\nxd0PIjzh/6XsYKJhhf+HkACOjj4vEgslApHgNXdfGK0/BYzK2jcra/0EYIaZLSSM+TLQzAYATwDX\nmtlFwOBoeHOAJ929PhoJc2F03vdF13spOuYOwmQk2cYDj7j7mmgc/lmIxER1niJB9miszUBl1vt3\ns9Z7AEe4e+uJbr5tZr8jjAHzhJmd1MZ59X9Oio5KBCKd83uyJj4xs3HRch93f87dryGMmLl/O+d4\nERhlZvtG7z8LPNrqmL8Dx0Y9nXoDn+yuGxBpTYlApHMuAmqjScNfAM6Ltl9sZs+b2bNAI+3MABeV\nJj5PGAn2OUKPpZtaHfMGYeatvxGqnRZ3942IZGj0URGRlFOJQEQk5ZQIRERSTolARCTllAhERFJO\niUBEJOWUCEREUk6JQEQk5f4fvkcdBiPXOEgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xmdUAbgbO3dC+7j7G3fPdPb9x48bRByeSIWrVggsvDCWaW7eG44+HAw6A//wn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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "EER: 0.029 \n",
            "FRR at EER: 0.033 \n",
            "FAR at EER: 0.025 \n",
            "Accuracy at EER: 0.975 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mtv2qUIyJ24z",
        "colab_type": "text"
      },
      "source": [
        "**Final Result of test set**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lw7L1AEBJ240",
        "colab_type": "code",
        "outputId": "80268dd9-4720-4b26-e7ee-e3d32121b552",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        }
      },
      "source": [
        "thres = np.arange(0, 1.0001, 0.0001)\n",
        "\n",
        "start_time_test = time.time() # time starts!\n",
        "\n",
        "thres_matrix_test = np.zeros((len(test_target),len(thres)))\n",
        "total_matrix_test = np.zeros((len(thres),4)) #acc,far,frr,recall(order of column)\n",
        "    \n",
        "test_logit_temp = np.zeros((len(test_target), num_models))\n",
        "test_cost = 0\n",
        "\n",
        "for m4_idx, m4 in enumerate(models):\n",
        "    test_logit_temp[:,m4_idx] =  m4.get_predict(test_data)\n",
        "\n",
        "    if test_logit_temp[np.where(test_logit_temp< 0)] < 0:\n",
        "        print('Error')\n",
        "    else:\n",
        "        print('Okay, No minus values')       \n",
        "\n",
        "    test_cost += m4.get_cost(test_data,test_target,pos_weight) / num_models\n",
        "\n",
        "print('Test set Cost: {:.3f}'.format(test_cost))\n",
        "\n",
        "test_logit = np.mean(test_logit_temp, axis=1)\n",
        "\n",
        "for j in range(len(thres)):\n",
        "    for i in range(len(test_target)):\n",
        "        if test_logit[i] < thres[j]:\n",
        "            thres_matrix_test[i,j] = 0\n",
        "        else:\n",
        "            thres_matrix_test[i,j] = 1\n",
        "\n",
        "\n",
        "unique, counts = np.unique(test_target[:,1], return_counts=True)\n",
        "print('Test set dictionary: ',dict(zip(unique, counts)))\n",
        "\n",
        "\n",
        "for i in range(len(thres)):\n",
        "\n",
        "    equal_logit = np.equal(thres_matrix_test[:,i], test_target[:,1]) ##Test\n",
        "    unique2, True_pos_neg_test = np.unique(thres_matrix_test[np.where(equal_logit==True),i], return_counts=True)\n",
        "    unique3, False_pos_neg_test = np.unique(thres_matrix_test[np.where(equal_logit==False),i], return_counts=True) \n",
        "\n",
        "    if np.shape(True_pos_neg_test)==(1,):\n",
        "        if unique2[0] == 0:\n",
        "            True_pos_neg_test = [True_pos_neg_test[0], 0]\n",
        "        else:\n",
        "            True_pos_neg_test = [0, True_pos_neg_test[0]]\n",
        "\n",
        "    if np.shape(False_pos_neg_test)==(1,):\n",
        "        if unique3[0] == 0:\n",
        "            False_pos_neg_test = [False_pos_neg_test[0], 0]\n",
        "        else:\n",
        "            False_pos_neg_test = [0, False_pos_neg_test[0]]\n",
        "   \n",
        "    if np.shape(True_pos_neg_test)==(0,):\n",
        "        True_pos_neg_test = [0, 0]\n",
        "        \n",
        "    if np.shape(False_pos_neg_test)==(0,):\n",
        "        False_pos_neg_test = [0, 0]\n",
        " \n",
        "    Recall_test= True_pos_neg_test[1]/(False_pos_neg_test[0]+True_pos_neg_test[1])\n",
        "    Specific_test = True_pos_neg_test[0] /(True_pos_neg_test[0]+False_pos_neg_test[1])\n",
        "\n",
        "    ACC_test = (True_pos_neg_test[0]+True_pos_neg_test[1]) / (len(test_target))\n",
        "    FAR_test = 1 - Specific_test\n",
        "    FRR_test = 1 - Recall_test\n",
        "\n",
        "    total_matrix_test[i,:] = ACC_test, FAR_test, FRR_test, Recall_test\n",
        "    \n",
        "end_time_test = time.time() # time ends! \n",
        "\n",
        "elapsed = end_time_test - start_time_test\n",
        "\n",
        "print('Execution Test time:{:.2f} seconds'.format(elapsed))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Okay, No minus values\n",
            "Okay, No minus values\n",
            "Okay, No minus values\n",
            "Test set Cost: 0.187\n",
            "Test set dictionary:  {0.0: 820, 1.0: 20}\n",
            "Execution Test time:7.29 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d39_aiGEJ242",
        "colab_type": "code",
        "outputId": "707846b1-f145-4846-d531-b69139f794eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "source": [
        "fig4 = plt.figure()\n",
        "ax = plt.subplot(111)\n",
        "ax.plot(thres, total_matrix_test[:,1], 'b', label='FAR')\n",
        "ax.plot(thres, total_matrix_test[:,2],'r', label='FRR')\n",
        "ax.legend()\n",
        "plt.title('FAR vs FRR (Test)')\n",
        "plt.xlabel('Threshold')\n",
        "plt.ylabel('Err Rate')\n",
        "plt.show()\n",
        "\n",
        "EER_line = thres[::-1]\n",
        "fig5 = plt.figure()\n",
        "ax2 = plt.subplot(111)\n",
        "ax2.plot(total_matrix_test[:,1], total_matrix_test[:,3],'r',label='ROC')\n",
        "ax2.plot(thres, EER_line, 'b',label='EER Line')\n",
        "ax2.legend()\n",
        "plt.title('ROC Curve (Test)')\n",
        "plt.xlabel('FAR')\n",
        "plt.ylabel('TPR')\n",
        "plt.show()\n",
        "\n",
        "EER_loc_test = np.argmin(abs(total_matrix_test[:,1] - total_matrix_test[:,2]))\n",
        "# print('EER threshold: ', thres[EER_loc_test])\n",
        "print('EER: {:.3f} '.format((total_matrix_test[EER_loc_test,1]+total_matrix_test[EER_loc_test,2])/2))\n",
        "print('FRR at EER: {:.3f} '.format(total_matrix_test[EER_loc_test,2]))\n",
        "print('FAR at EER: {:.3f} '.format(total_matrix_test[EER_loc_test,1]))\n",
        "print('Accuracy at EER: {:.3f} '.format(total_matrix_test[EER_loc_test,0]))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DRGQDqhpKTu/eYf3ee+nGISKyAVUNJaeQCFQiEJFMUdVQcgpVQyoRiEimKBEkRyUCEckk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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAQXrKTaKbctXC2gFvGFmi4E9gclmVuQUaBEp3q67wltvwe23h2J2rVrB6NEq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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "EER: 0.050 \n",
            "FRR at EER: 0.050 \n",
            "FAR at EER: 0.050 \n",
            "Accuracy at EER: 0.950 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4Rpf6biDPCO",
        "colab_type": "text"
      },
      "source": [
        "**Feature Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcjHYG1KhOrB",
        "colab_type": "code",
        "outputId": "26f7179c-e166-4ce7-d8d4-50e8271630b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "fig11= plt.figure(figsize=(10,10))\n",
        "for m4_idx, m4 in enumerate(models):\n",
        "    ax = fig11.add_subplot(num_models,1,m4_idx+1)\n",
        "    plot_1 = m4.plot_feature(train_data, train_target)\n",
        "    ax.plot(np.mean(plot_1, axis=2))\n",
        "    plt.title('Filter1_Model'+str(m4_idx+1))\n",
        "plt.tight_layout()\n",
        " \n",
        "        "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KTQQStNafaq018AxwdRjaelQ43d4a5N4Z5EiLmagIE212WU1PCCGEEGK8CEeAPAmoCPi6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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exO1K9ZGhOuT",
        "colab_type": "code",
        "outputId": "552a3bfa-6d17-4254-e4cf-c6ff6e9f8389",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "fig22= plt.figure(figsize=(10,10))\n",
        "for m4_idx, m4 in enumerate(models):\n",
        "    ax = fig22.add_subplot(num_models,1,m4_idx+1)\n",
        "    plot2 = m4.plot_feature2(train_data, train_target)\n",
        "    ax.plot(np.mean(np.mean(plot2, axis=2),axis=1))\n",
        "    plt.title('Filter2_Model'+str(m4_idx+1))\n",
        "plt.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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icFoKyqrIKChnX0EZa/fk8Yfz+tI9ytVbfvGQjjz0eTyb0goY0jkCcNVBP/J5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StFTj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\n",
            "text/plain": [
              "<Figure size 720x720 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}