Experiment5_RandomSearch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M_LY1NAEY1ru"
   },
   "source": [
    "# Utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "HcHw4Om8XvLm",
    "outputId": "b17f45dd-4cae-4cdd-8f6a-f426bdbd2b80"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from keras import backend as K\n",
    "\n",
    "class utility:\n",
    "\n",
    "    def read_CSV(self, filename):\n",
    "        df = pd.read_csv(filename)\n",
    "        return df\n",
    "\n",
    "    def get_text_label(self, df):\n",
    "        texts = []  # list of text samples\n",
    "        labels = []  # list of label ids\n",
    "        for index, row in df.iterrows():\n",
    "            if isinstance(row['text_cleaned'], float):\n",
    "                texts.append(str(row['text_cleaned']))\n",
    "            else:\n",
    "                texts.append(row['text_cleaned'])\n",
    "\n",
    "            labels.append(row['target'])\n",
    "\n",
    "        return texts, labels\n",
    "\n",
    "    def tokenize_texts(self, texts):\n",
    "        tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=5000)\n",
    "        tokenizer.fit_on_texts(texts)\n",
    "\n",
    "        return tokenizer\n",
    "\n",
    "    def padding_texts(self, texts, maxlen):\n",
    "\n",
    "        texts = tf.keras.preprocessing.sequence.pad_sequences(texts, padding='post', maxlen=maxlen)\n",
    "\n",
    "        return texts\n",
    "\n",
    "    def get_testing_metric(self, y_test, y_pred):\n",
    "        accuracyScore = accuracy_score(y_test, y_pred)\n",
    "        precisionScore = precision_score(y_test, y_pred)\n",
    "        recallScore = recall_score(y_test, y_test)\n",
    "        f1Score = f1_score(y_test, y_pred)\n",
    "        return accuracyScore, precisionScore, recallScore, f1Score\n",
    "\n",
    "    def write_df_csv(self, df, out_path):\n",
    "        df.to_csv(out_path, index=False)\n",
    "\n",
    "    def create_embedding_matrix(self, filepath, word_index, embedding_dim):\n",
    "        vocab_size = len(word_index) + 1  # Adding again 1 because of reserved 0 index\n",
    "        embedding_matrix = np.zeros((vocab_size, embedding_dim))\n",
    "\n",
    "        with open(filepath, encoding=\"utf8\") as f:\n",
    "            for line in f:\n",
    "                word, *vector = line.split()\n",
    "                if word in word_index:\n",
    "                    idx = word_index[word]\n",
    "                    embedding_matrix[idx] = np.array(\n",
    "                        vector, dtype=np.float32)[:embedding_dim]\n",
    "\n",
    "        return embedding_matrix\n",
    "\n",
    "    def get_max_length_of_sentences(self, texts):\n",
    "        maxlength = 0\n",
    "        for text in texts:\n",
    "            if (len(text.split()) > maxlength):\n",
    "                maxlength = len(text.split())\n",
    "\n",
    "        return maxlength\n",
    "\n",
    "    def get_training_val_data(self, textsTraining, labelsTraining, train_index, val_index, glovePath):\n",
    "        textsTraining, textsTesting = np.asarray(textsTraining)[train_index], np.asarray(textsTraining)[val_index]\n",
    "        y_train, y_val = np.asarray(labelsTraining)[train_index], np.asarray(labelsTraining)[val_index]\n",
    "\n",
    "        # Tokenize words\n",
    "        tokenizer = self.tokenize_texts(textsTraining)\n",
    "        X_train = tokenizer.texts_to_sequences(textsTraining)\n",
    "        X_val = tokenizer.texts_to_sequences(textsTesting)\n",
    "\n",
    "        # Adding 1 because of reserved 0 index\n",
    "        vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "        # get maxlen\n",
    "        maxlen = self.get_max_length_of_sentences(textsTraining)\n",
    "\n",
    "        # Pad sequences with zeros\n",
    "        X_train = self.padding_texts(X_train, maxlen)\n",
    "        X_val = self.padding_texts(X_val, maxlen)\n",
    "\n",
    "        embedding_matrix = []\n",
    "        embedding_matrix.append(self.create_embedding_matrix(glovePath[0], tokenizer.word_index, 50))\n",
    "        embedding_matrix.append(self.create_embedding_matrix(glovePath[1], tokenizer.word_index, 100))\n",
    "        embedding_matrix.append(self.create_embedding_matrix(glovePath[2], tokenizer.word_index, 200))\n",
    "        embedding_matrix.append(self.create_embedding_matrix(glovePath[3], tokenizer.word_index, 300))\n",
    "\n",
    "        return X_train, X_val, y_train, y_val, vocab_size, maxlen, embedding_matrix\n",
    "\n",
    "    def Average(self, list):\n",
    "        return sum(list) / len(list)\n",
    "\n",
    "    def recall(self, y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "        recall = true_positives / (possible_positives + K.epsilon())\n",
    "        return recall\n",
    "\n",
    "    def precision(self, y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
    "        return precision\n",
    "\n",
    "    def f1_score(self, y_true, y_pred):\n",
    "        precision = self.precision(y_true, y_pred)\n",
    "        recall = self.recall(y_true, y_pred)\n",
    "        return 2*((precision*recall)/(precision+recall+K.epsilon()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3i6SWB1wYk2Z"
   },
   "source": [
    "# Finite State Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CDgY_SANYjWK"
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "def FSM():\n",
    "    fsm = {}\n",
    "    fsm[0] = {'src': 0, 'dst': 1, 'layer': 'embedding_layer', 'next_path': [1]}\n",
    "    fsm[1] = {'src': 1, 'dst': 2, 'layer': 'convolutional_layer', 'next_path': [2, 4]}\n",
    "    fsm[2] = {'src': 2, 'dst': 3, 'layer': 'maxpooling_layer', 'next_path': [3]}    \n",
    "    fsm[3] = {'src': 3, 'dst': 2, 'layer': 'convolutional_layer', 'next_path': [2, 4]}\n",
    "    fsm[4] = {'src': 2, 'dst': 4, 'layer': 'global_maxpooling_layer', 'next_path': [5]}\n",
    "    fsm[5] = {'src': 4, 'dst': 5, 'layer': 'dense_layer', 'next_path': [6, 7]}\n",
    "    fsm[6] = {'src': 5, 'dst': 5, 'layer': 'dense_layer', 'next_path': [6, 7]}    \n",
    "    fsm[7] = {'src': 5, 'dst': 6, 'layer': 'dropout_layer', 'next_path': [8]}\n",
    "    fsm[8] = {'src': 6, 'dst': 7, 'layer': 'output_layer', 'next_path': []}\n",
    "\n",
    "    return fsm\n",
    "\n",
    "def addConvLayer(idx, hyperparameters):\n",
    "    hyperparameters['num_filters' + str(idx)] = random.randint(32, 512)\n",
    "    hyperparameters['kernel_size' + str(idx)] = random.randint(1, 5)\n",
    "    hyperparameters['conv_activation_func' + str(idx)] = random.choice(['relu', 'softmax', 'elu', 'selu',\n",
    "                                                'softplus', 'softsign', 'tanh',\n",
    "                                                'sigmoid', 'hard_sigmoid', 'linear'])\n",
    "    hyperparameters['conv_init_mode' + str(idx)] = random.choice([\n",
    "                                          'zeros',\n",
    "                                          'ones',\n",
    "                                          'uniform',\n",
    "                                          'normal',\n",
    "                                          'glorot_normal',\n",
    "                                          'glorot_uniform',\n",
    "                                          'he_normal',\n",
    "                                          'he_uniform',\n",
    "                                          'lecun_normal',\n",
    "                                          'lecun_uniform'])\n",
    "    hyperparameters['conv_weight_constraint' + str(idx)] = random.randint(1, 5)\n",
    "\n",
    "\n",
    "def addDenseLayer(idx, hyperparameters):\n",
    "    hyperparameters['neurons' + str(idx)] = random.randint(1, 30)\n",
    "    hyperparameters['dense_activation_func' + str(idx)] = random.choice(['relu', 'softmax', 'elu', 'selu',\n",
    "                                                'softplus', 'softsign', 'tanh',\n",
    "                                                'sigmoid', 'hard_sigmoid', 'linear'])\n",
    "    hyperparameters['dense_init_mode' + str(idx)] = random.choice([\n",
    "                                          'zeros',\n",
    "                                          'ones',\n",
    "                                          'uniform',\n",
    "                                          'normal',\n",
    "                                          'glorot_normal',\n",
    "                                          'glorot_uniform',\n",
    "                                          'he_normal',\n",
    "                                          'he_uniform',\n",
    "                                          'lecun_normal',\n",
    "                                          'lecun_uniform'])\n",
    "    hyperparameters['dense_weight_constraint' + str(idx)] = random.randint(1, 5)\n",
    "\n",
    "\n",
    "def addMaxPoolingLayer(idx, hyperparameters):\n",
    "    hyperparameters['pool_size' + str(idx)] = random.choice([5])\n",
    "\n",
    "def addDropoutLayer(idx, hyperparameters):\n",
    "    hyperparameters['dropout_rate' + str(idx)] = random.choice([0.2])\n",
    "\n",
    "def getLayerSize(layer, conv_idx, dense_idx, dropout_idx, maxpooling_idx):\n",
    "    if layer == 'convolutional_layer':\n",
    "        conv_idx += 1\n",
    "    elif layer == 'dense_layer':\n",
    "        dense_idx += 1\n",
    "    elif layer == 'dropout_layer':\n",
    "        dropout_idx += 1\n",
    "    elif layer == 'maxpooling_layer':\n",
    "        maxpooling_idx += 1\n",
    "    return conv_idx, dense_idx, dropout_idx, maxpooling_idx\n",
    "\n",
    "\n",
    "def addLayerToolboxes(max_conv_idx, max_dense_idx, max_dropout_idx, max_maxpooling_idx, hyperparameters):\n",
    "    idx = 0\n",
    "    while idx < max_conv_idx:\n",
    "        idx += 1\n",
    "        addConvLayer(idx, hyperparameters)\n",
    "\n",
    "    idx = 0\n",
    "    while idx < max_dense_idx:\n",
    "        idx += 1\n",
    "        addDenseLayer(idx, hyperparameters)\n",
    "\n",
    "    idx = 0\n",
    "    while idx < max_maxpooling_idx:\n",
    "        idx += 1\n",
    "        addMaxPoolingLayer(idx, hyperparameters)\n",
    "\n",
    "    idx = 0\n",
    "    while idx < max_dropout_idx:\n",
    "        idx += 1\n",
    "        addDropoutLayer(idx, hyperparameters)\n",
    "\n",
    "\n",
    "def generateFSM(hyperparameters):\n",
    "    fsm = FSM()\n",
    "\n",
    "    idx = conv_idx = dense_idx = dropout_idx = maxpooling_idx = 0\n",
    "    path = [fsm[idx]['layer']]\n",
    "    while len(fsm[idx]['next_path']) != 0:\n",
    "        idx = random.choice(fsm[idx]['next_path'])\n",
    "        layer = fsm[idx]['layer']\n",
    "        path.append(layer)\n",
    "        conv_idx, dense_idx, dropout_idx, maxpooling_idx = getLayerSize(layer, conv_idx, dense_idx, dropout_idx,\n",
    "                                                                        maxpooling_idx)\n",
    "\n",
    "    addLayerToolboxes(conv_idx, dense_idx, dropout_idx, maxpooling_idx, hyperparameters)\n",
    "    hyperparameters['layers'] = path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jMvyO9qEYx91"
   },
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "USzyKDK0XT6w"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "class CNN:\n",
    "\n",
    "    def cnn_model(self, vocab_size, maxlen, embedding_matrix, indiv, path):\n",
    "        model = tf.keras.models.Sequential()\n",
    "        conv_idx = dense_idx = dropout_idx = maxpooling_idx = 0\n",
    "        for layer in path:\n",
    "            if layer == 'embedding_layer':\n",
    "                model.add(\n",
    "                    tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=indiv['output_dim'],\n",
    "                                     weights=[embedding_matrix], input_length=maxlen, trainable=True))\n",
    "            elif layer == 'convolutional_layer':\n",
    "                conv_idx += 1\n",
    "                model.add(tf.keras.layers.Conv1D(indiv['num_filters' + str(conv_idx)], indiv['kernel_size' + str(conv_idx)],\n",
    "                                        kernel_initializer=indiv['conv_init_mode' + str(conv_idx)],\n",
    "                                        activation=indiv['conv_activation_func' + str(conv_idx)],\n",
    "                                        kernel_constraint=tf.keras.constraints.max_norm(indiv['conv_weight_constraint' + str(conv_idx)]),\n",
    "                                        data_format='channels_first'))\n",
    "            elif layer == 'dense_layer':\n",
    "                dense_idx += 1\n",
    "                model.add(tf.keras.layers.Dense(indiv['neurons' + str(dense_idx)],\n",
    "                                       kernel_initializer=indiv['dense_init_mode' + str(dense_idx)],\n",
    "                                       activation=indiv['dense_activation_func' + str(dense_idx)],\n",
    "                                       kernel_constraint=tf.keras.constraints.max_norm(indiv['dense_weight_constraint' + str(dense_idx)])))\n",
    "            elif layer == 'dropout_layer':\n",
    "                dropout_idx += 1\n",
    "                model.add(tf.keras.layers.Dropout(indiv['dropout_rate' + str(dropout_idx)]))\n",
    "            elif layer == 'maxpooling_layer':\n",
    "                maxpooling_idx += 1\n",
    "                model.add(tf.keras.layers.MaxPooling1D(indiv['pool_size' + str(maxpooling_idx)]))\n",
    "            elif layer == 'global_maxpooling_layer':\n",
    "                model.add(tf.keras.layers.GlobalMaxPooling1D())\n",
    "            elif layer == 'output_layer':\n",
    "                model.add(tf.keras.layers.Dense(1, kernel_initializer=indiv['output_init_mode'], activation='sigmoid'))\n",
    "\n",
    "        if indiv['optimizer'] == 'sgd':\n",
    "            opt = tf.keras.optimizers.SGD(lr=indiv['learning_rate'], momentum=indiv['momentum'], decay=0.0,\n",
    "                                 nesterov=False)\n",
    "        elif indiv['optimizer'] == 'rmsprop':\n",
    "            opt = tf.keras.optimizers.RMSprop(lr=indiv['learning_rate'], rho=0.9, epsilon=None, decay=0.0)\n",
    "        elif indiv['optimizer'] == 'adagrad':\n",
    "            opt = tf.keras.optimizers.Adagrad(lr=indiv['learning_rate'], epsilon=None, decay=0.0)\n",
    "        elif indiv['optimizer'] == 'adadelta':\n",
    "            opt = tf.keras.optimizers.Adadelta(lr=indiv['learning_rate'], rho=0.95, epsilon=None, decay=0.0)\n",
    "        elif indiv['optimizer'] == 'adam':\n",
    "            opt = tf.keras.optimizers.Adam(lr=indiv['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=None,\n",
    "                                  decay=0.0, amsgrad=False)\n",
    "        elif indiv['optimizer'] == 'adamax':\n",
    "            opt = tf.keras.optimizers.Adamax(lr=indiv['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=None,\n",
    "                                    decay=0.0)\n",
    "        elif indiv['optimizer'] == 'nadam':\n",
    "            opt = tf.keras.optimizers.Nadam(lr=indiv['learning_rate'], beta_1=0.9, beta_2=0.999, epsilon=None,\n",
    "                                   schedule_decay=0.004)\n",
    "\n",
    "        util = utility()\n",
    "        model.compile(optimizer=opt, loss='binary_crossentropy', metrics=[util.f1_score])\n",
    "\n",
    "        return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Gal0h7YmYvNp"
   },
   "source": [
    "# Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "_VjbiI2WX6UO",
    "outputId": "71028c90-8308-417b-c307-6f2ca8a0ed4d"
   },
   "outputs": [],
   "source": [
    "training_path = 'trainPreprocessed.csv'\n",
    "root_path = '/lab/dbms/fatyanosa'\n",
    "datasetPath = '{}/Dataset/Disaster Tweets/'.format(root_path)\n",
    "resultsPath = '{}/Server1/Disaster Tweets/Paper GA-CNN/Results/'.format(root_path)\n",
    "archPath = '{}/Server1/Disaster Tweets/Paper GA-CNN/Architecture/'.format(root_path)\n",
    "testing_name = \"Experiment5_RandomSearch\"\n",
    "glovePath = ['{}/Glove/glove.6B.50d.txt'.format(root_path),\n",
    "             '{}/Glove/glove.6B.100d.txt'.format(root_path),\n",
    "             '{}/Glove/glove.6B.200d.txt'.format(root_path),\n",
    "             '{}/Glove/glove.6B.300d.txt'.format(root_path)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AMUrUZl-ZXh8"
   },
   "source": [
    "# Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "U8c9Yn-RXmdF",
    "outputId": "da368f39-ab6c-4f9e-cf4a-fdc1ef012313"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "450\n",
      "451\n",
      "452\n",
      "453\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "454\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "455\n",
      "456\n",
      "457\n",
      "458\n",
      "459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "460\n",
      "461\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "462\n",
      "463\n",
      "464\n",
      "465\n",
      "466\n",
      "467\n",
      "468\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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     "output_type": "stream",
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      "532\n"
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    },
    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "568\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "569\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "571\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "581\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "583\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "584\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "590\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "597\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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      "598\n",
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    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "602\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "609\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "621\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "624\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "625\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "629\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "635\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "645\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "646\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "654\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "659\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "660\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "662\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "663\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "670\n",
      "671\n",
      "672\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "673\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "674\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "675\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "684\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "685\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "686\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "691\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "694\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "695\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "715\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "717\n",
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    },
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "720\n",
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    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "734\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "735\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "736\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "739\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "741\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "749\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "758\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "759\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "767\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "769\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "773\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "774\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "775\n",
      "776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "778\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "787\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "798\n",
      "799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "800\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "802\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "811\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "812\n",
      "813\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
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     "output_type": "stream",
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      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "824\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "827\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "833\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "836\n",
      "837\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "838\n",
      "839\n",
      "840\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "841\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "842\n",
      "843\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "844\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "845\n",
      "846\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "847\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "851\n",
      "852\n",
      "853\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "854\n",
      "855\n",
      "856\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "857\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "858\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "862\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "864\n",
      "865\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "866\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "868\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "869\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "871\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "872\n",
      "873\n",
      "874\n",
      "875\n",
      "876\n",
      "877\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "879\n",
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     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "884\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "887\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/lab/dbms/fatyanosa/.userprogram/anaconda3/envs/GA-CNN/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "899\n"
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    }
   ],
   "source": [
    "import time\n",
    "import os\n",
    "import csv\n",
    "import tensorflow as tf\n",
    "import random\n",
    "\n",
    "# object class\n",
    "util = utility()\n",
    "cnn = CNN()\n",
    "\n",
    "# Read data\n",
    "df = util.read_CSV(datasetPath + training_path)\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "dfTraining, dfVal = train_test_split(df, test_size = 0.3, random_state=42)\n",
    "\n",
    "# get texts and labels\n",
    "textsTraining, y_train = util.get_text_label(dfTraining)\n",
    "textsVal, y_val = util.get_text_label(dfVal)\n",
    "\n",
    "# Tokenize words\n",
    "tokenizer = util.tokenize_texts(textsTraining)\n",
    "X_train = tokenizer.texts_to_sequences(textsTraining)\n",
    "X_val = tokenizer.texts_to_sequences(textsVal)\n",
    "\n",
    "# Adding 1 because of reserved 0 index\n",
    "vocab_size = len(tokenizer.word_index) + 1\n",
    "\n",
    "# get maxlen\n",
    "maxlen = util.get_max_length_of_sentences(textsTraining)\n",
    "\n",
    "# Pad sequences with zeros\n",
    "X_train = util.padding_texts(X_train, maxlen)\n",
    "X_val = util.padding_texts(X_val, maxlen)\n",
    "\n",
    "tempF1Score=0\n",
    "for i in range(0, 900):\n",
    "    print(i)\n",
    "    then = time.time()\n",
    "    text = ''\n",
    "    hyperparameters = {}\n",
    "    \n",
    "    # Optimized hyperparameters\n",
    "    hyperparameters['epochs'] = random.randint(1, 100)\n",
    "    hyperparameters['batch_size'] = random.randint(32, 256)\n",
    "    hyperparameters['optimizer'] = random.choice(['sgd', 'rmsprop', 'adagrad', 'adadelta', 'adam', 'adamax', 'nadam'])\n",
    "    hyperparameters['learning_rate'] = random.uniform(1e-4, 1e-2)\n",
    "    hyperparameters['momentum'] = random.choice([0.9])\n",
    "    hyperparameters['output_init_mode'] = random.choice(['glorot_uniform'])\n",
    "    hyperparameters['output_dim'] = random.choice([100])\n",
    "    \n",
    "    generateFSM(hyperparameters)\n",
    "\n",
    "#     print(hyperparameters)\n",
    "\n",
    "    if int(hyperparameters['output_dim']) == 50:\n",
    "        glove = glovePath[0]\n",
    "    elif int(hyperparameters['output_dim']) == 100:\n",
    "        glove = glovePath[1]\n",
    "    elif int(hyperparameters['output_dim']) == 200:\n",
    "        glove = glovePath[2]\n",
    "    elif int(hyperparameters['output_dim']) == 300:\n",
    "        glove = glovePath[3]\n",
    "\n",
    "    embedding_matrix = util.create_embedding_matrix(glove, tokenizer.word_index, int(hyperparameters['output_dim']))\n",
    "\n",
    "    model = cnn.cnn_model(vocab_size, maxlen, embedding_matrix, hyperparameters, hyperparameters['layers'])\n",
    "\n",
    "    #early stopping\n",
    "    callbacks = [tf.keras.callbacks.EarlyStopping(monitor='val_f1_score', mode='max', verbose=False, patience=10)]\n",
    "\n",
    "    #save the best model\n",
    "    callbacks += [tf.keras.callbacks.ModelCheckpoint(\"{}{}.h5\".format(archPath, testing_name), monitor='val_f1_score', mode='max', verbose=False, save_best_only=True)]\n",
    "    \n",
    "    # class_weight = {0: 0.25,\n",
    "    #                 1: 0.75}\n",
    "    \n",
    "#     model.summary()\n",
    "    y_train = np.uint8(y_train)\n",
    "    y_val = np.uint8(y_val)\n",
    "    model.fit(X_train, y_train, epochs=hyperparameters['epochs'], verbose=False, validation_data=(X_val, y_val),\n",
    "              batch_size=hyperparameters['batch_size'], callbacks=callbacks)\n",
    "\n",
    "    dependencies = {\n",
    "    'f1_score': util.f1_score\n",
    "    }\n",
    "\n",
    "    # load the saved model\n",
    "    for x in range(0, 4):  # try 4 times\n",
    "        try:\n",
    "            # msg.send()\n",
    "            saved_model = tf.keras.models.load_model(\"{}{}.h5\".format(archPath, testing_name), custom_objects=dependencies)   \n",
    "            str_error = None\n",
    "        except Exception as e:\n",
    "            print('An error occurs when loading saved model.')\n",
    "            str_error = e\n",
    "            pass\n",
    "\n",
    "        if str_error:\n",
    "            sleep(2)  # wait for 2 seconds before trying to fetch the data again\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    y_pred = saved_model.predict_classes(X_val)\n",
    "    os.remove(\"{}{}.h5\".format(archPath, testing_name))   \n",
    "\n",
    "    # CNN metrics\n",
    "    accuracyScore, precisionScore, recallScore, f1Score = util.get_testing_metric(y_val, y_pred)\n",
    "\n",
    "    now = time.time()\n",
    "    diff = now - then\n",
    "    \n",
    "\n",
    "    # save testing data\n",
    "    f = open(resultsPath + testing_name + \".csv\", 'a')\n",
    "    text = \"{0},\".format('i')\n",
    "    for param in hyperparameters:\n",
    "        text += \"{0},\".format(param)\n",
    "    text +='accuracy,precision,recall,f1Score,time\\n'\n",
    "    text += \"{0}\".format(str(i + 1))\n",
    "    for value in hyperparameters.values():\n",
    "        text += \",{0}\".format(value)\n",
    "\n",
    "    text += \"{0},{1},{2},{3},{4},{5},{6}\".format(',',accuracyScore, precisionScore, recallScore, f1Score, diff, '\\n')\n",
    "    f.write(text)    \n",
    "    f.close()\n",
    "\n",
    "    # save testing data\n",
    "    f = open(resultsPath + testing_name + '_summary' + \".csv\", 'a')\n",
    "    text = \"{0},{1},{2},{3},{4},{5}\\n\".format(str(i + 1),accuracyScore, precisionScore, recallScore, f1Score, diff)\n",
    "    if f1Score > tempF1Score:\n",
    "        tempF1Score = f1Score\n",
    "        print(tempF1Score)\n",
    "        text += \"'Best F1Score is in iteration {0} with {1}\\n\".format(str(i + 1),tempF1Score)\n",
    "    f.write(text)    \n",
    "    f.close() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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     "data": {
      "application/javascript": [
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