text_ktrain_nbsvm_cmet_O.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/737b90dddef04c03ba86fc9fb614c45c\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import comet_ml at the top of your file\n",
    "from comet_ml import Experiment\n",
    "experiment = Experiment(\n",
    "            project_name=\"music-genre-multiclass-classification\",\n",
    "            workspace=\"wodenwang820118\",\n",
    "        )\n",
    "import comet_ml\n",
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(level=logging.INFO)\n",
    "LOGGER = logging.getLogger(\"comet_ml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import argparse\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# the api for grid search the model hyperparameters\n",
    "from comet_ml import Experiment\n",
    "import json\n",
    "\n",
    "os.environ[\"GRPC_FAIL_FAST\"] = \"use_caller\"\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tensorflow 2.7 \n",
    "import tensorflow as tf \n",
    "from tensorflow.keras.layers import Input,Flatten,Dense,Dropout,BatchNormalization\n",
    "from tensorflow.keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_features = pd.read_csv('train_features.csv')\n",
    "df_train_labels = pd.read_csv('train_labels.csv')\n",
    "\n",
    "df_valid_features = pd.read_csv('valid_features.csv')\n",
    "df_valid_labels = pd.read_csv('valid_labels.csv')\n",
    "\n",
    "df_test_features = pd.read_csv('test_features.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def str_remove_comma(string):\n",
    "    str = string.split(\", \")\n",
    "    return str\n",
    "\n",
    "def list_to_string(list):\n",
    "    str = \" \"\n",
    "    return str.join(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = df_train_features['tags'].apply(lambda x : list_to_string(str_remove_comma(x)))\n",
    "y_train = df_train_labels['genre'].to_numpy()\n",
    "\n",
    "x_test = df_valid_features['tags'].apply(lambda x : list_to_string(str_remove_comma(x)))\n",
    "y_test = df_valid_labels['genre'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = np.array(x_train)\n",
    "x_test = np.array(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import class_weight\n",
    "class_weight = class_weight.compute_class_weight(class_weight='balanced', classes=np.unique(df_train_labels['genre']),y=df_train_labels['genre'])\n",
    "class_weight = {i :class_weight[i] for i in range(8)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ktrain\n",
    "from ktrain import text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TextGenreClassifier:\n",
    "    def __init__(self,x_train,y_train,x_text,y_text,class_weight,experiment):\n",
    "        self.x_train = x_train\n",
    "        self.y_train = y_train\n",
    "        self.x_test = x_text\n",
    "        self.y_test = y_text\n",
    "        self.class_weight = class_weight\n",
    "        self.experiment = experiment\n",
    "    \n",
    "    def build_model(self):\n",
    "        trn, val, preproc = text.texts_from_array(\n",
    "            x_train=self.x_train, y_train=self.y_train,\n",
    "            x_test=self.x_test, y_test=self.y_test,\n",
    "            class_names = np.unique(df_train_labels['genre']),\n",
    "            ngram_range=self.experiment.get_parameter(\"ngram_range\"), \n",
    "            maxlen=self.experiment.get_parameter(\"maxlen\"), \n",
    "            preprocess_mode='standard'\n",
    "        )\n",
    "        model = text.text_classifier('nbsvm', train_data=trn, preproc=preproc, metrics=['accuracy'])\n",
    "\n",
    "        return model, trn, val\n",
    "    \n",
    "    def train_model(self):\n",
    "        # Train the model\n",
    "        model, trn, val = self.build_model()\n",
    "        learner = ktrain.get_learner(\n",
    "            model,\n",
    "            train_data=trn,\n",
    "            val_data=val,\n",
    "            batch_size=self.experiment.get_parameter(\"batch_size\"))\n",
    "        learner.fit_onecycle(\n",
    "            lr=0.001,\n",
    "            epochs=self.experiment.get_parameter(\"epochs\"),\n",
    "            class_weight=self.class_weight\n",
    "        )\n",
    "        return learner, val\n",
    "    \n",
    "    def evaluate_model(self):\n",
    "        # Evaluate the model\n",
    "        learner, val = self.train_model()\n",
    "        score = learner.evaluate(test_data=val)\n",
    "        LOGGER.info(f\"{ score }\")\n",
    "    \n",
    "    def grid_search(self, config_dict):\n",
    "        opt = comet_ml.Optimizer(config_dict)\n",
    "        for self.experiment in opt.get_experiments(project_name=\"music-genre-multiclass-classification\"):\n",
    "            self.build_model()\n",
    "            self.train_model()\n",
    "            self.evaluate_model()\n",
    "            self.experiment.end()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "textGenreClassifier = TextGenreClassifier(x_train,y_train,x_test,y_test,class_weight,experiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: COMET_OPTIMIZER_ID=4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO: Using optimizer config: {'algorithm': 'bayes', 'configSpaceSize': 12, 'endTime': None, 'id': '4ee2cc9feea940f0a54fbe66f1ee8c5c', 'lastUpdateTime': None, 'maxCombo': 10, 'name': 'Optimize Music Classification Network', 'parameters': {'batch_size': {'type': 'discrete', 'values': [8, 16, 32]}, 'epochs': {'type': 'discrete', 'values': [8, 9, 10, 11]}, 'maxlen': {'type': 'discrete', 'values': [150]}, 'ngram_range': {'type': 'discrete', 'values': [1]}}, 'predictor': None, 'spec': {'gridSize': 10, 'maxCombo': 10, 'metric': 'loss', 'minSampleSize': 100, 'objective': 'minimize', 'retryAssignLimit': 0, 'retryLimit': 1000}, 'startTime': 26900017498, 'state': {'mode': None, 'seed': None, 'sequence': [], 'sequence_i': 0, 'sequence_pid': None, 'sequence_retry': 0, 'sequence_retry_count': 0}, 'status': 'running', 'suggestion_count': 0, 'trials': 1, 'version': '2.0.1'}\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/737b90dddef04c03ba86fc9fb614c45c\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/aeabecbd85a748b59f509e3f188fd3e2\n",
      "\n"
     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "480/480 [==============================] - 2s 3ms/step - loss: 1.9036 - accuracy: 0.3408 - val_loss: 1.5164 - val_accuracy: 0.5400\n",
      "Epoch 2/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.6420 - accuracy: 0.4700 - val_loss: 1.3382 - val_accuracy: 0.5844\n",
      "Epoch 3/9\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.4767 - accuracy: 0.5156 - val_loss: 1.2696 - val_accuracy: 0.5867\n",
      "Epoch 4/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.3494 - accuracy: 0.5460 - val_loss: 1.2343 - val_accuracy: 0.5911\n",
      "Epoch 5/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.2476 - accuracy: 0.5705 - val_loss: 1.2205 - val_accuracy: 0.5911\n",
      "Epoch 6/9\n",
      "480/480 [==============================] - 2s 5ms/step - loss: 1.1717 - accuracy: 0.5891 - val_loss: 1.2249 - val_accuracy: 0.5733\n",
      "Epoch 7/9\n",
      "480/480 [==============================] - 2s 5ms/step - loss: 1.1247 - accuracy: 0.5992 - val_loss: 1.2149 - val_accuracy: 0.5733\n",
      "Epoch 8/9\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.0954 - accuracy: 0.6059 - val_loss: 1.2149 - val_accuracy: 0.5756\n",
      "Epoch 9/9\n",
      "480/480 [==============================] - 2s 5ms/step - loss: 1.0796 - accuracy: 0.6131 - val_loss: 1.2170 - val_accuracy: 0.5756\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "480/480 [==============================] - 3s 6ms/step - loss: 1.8744 - accuracy: 0.3661 - val_loss: 1.5033 - val_accuracy: 0.5467\n",
      "Epoch 2/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.6322 - accuracy: 0.4725 - val_loss: 1.3347 - val_accuracy: 0.5844\n",
      "Epoch 3/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.4701 - accuracy: 0.5188 - val_loss: 1.2643 - val_accuracy: 0.5933\n",
      "Epoch 4/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.3450 - accuracy: 0.5474 - val_loss: 1.2328 - val_accuracy: 0.5889\n",
      "Epoch 5/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.2451 - accuracy: 0.5694 - val_loss: 1.2215 - val_accuracy: 0.5889\n",
      "Epoch 6/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.1700 - accuracy: 0.5864 - val_loss: 1.2178 - val_accuracy: 0.5822\n",
      "Epoch 7/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.1224 - accuracy: 0.6004 - val_loss: 1.2161 - val_accuracy: 0.5711\n",
      "Epoch 8/9\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.0939 - accuracy: 0.6086 - val_loss: 1.2169 - val_accuracy: 0.5667\n",
      "Epoch 9/9\n",
      "480/480 [==============================] - 2s 3ms/step - loss: 1.0780 - accuracy: 0.6145 - val_loss: 1.2157 - val_accuracy: 0.5689\n"
     ]
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      "COMET INFO: [[ 4  3  5 16  0  4  1 22]\n",
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     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.33      0.07      0.12        55\n",
      "           1       0.17      0.07      0.10        45\n",
      "           2       0.59      0.66      0.62        64\n",
      "           3       0.29      0.36      0.32        44\n",
      "           4       0.76      0.67      0.71        66\n",
      "           5       0.76      1.00      0.86        74\n",
      "           6       0.63      0.73      0.67        44\n",
      "           7       0.48      0.71      0.57        58\n",
      "\n",
      "    accuracy                           0.57       450\n",
      "   macro avg       0.50      0.53      0.50       450\n",
      "weighted avg       0.53      0.57      0.53       450\n",
      "\n"
     ]
    },
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     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/aeabecbd85a748b59f509e3f188fd3e2\n",
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      "COMET INFO:     validate_batch_loss [54]     : (1.1911046504974365, 1.5086524486541748)\n",
      "COMET INFO:   Others:\n",
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      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 480\n",
      "COMET INFO:   Uploads:\n",
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      "COMET INFO: ---------------------------\n",
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      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/ed9aaca6993a463384e68cb19654bfcc\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.8973 - accuracy: 0.3280 - val_loss: 1.5278 - val_accuracy: 0.5067\n",
      "Epoch 2/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.6564 - accuracy: 0.4557 - val_loss: 1.3575 - val_accuracy: 0.5667\n",
      "Epoch 3/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.5006 - accuracy: 0.5048 - val_loss: 1.2854 - val_accuracy: 0.5889\n",
      "Epoch 4/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.3784 - accuracy: 0.5374 - val_loss: 1.2390 - val_accuracy: 0.5778\n",
      "Epoch 5/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.2796 - accuracy: 0.5612 - val_loss: 1.2290 - val_accuracy: 0.5778\n",
      "Epoch 6/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1978 - accuracy: 0.5817 - val_loss: 1.2194 - val_accuracy: 0.5733\n",
      "Epoch 7/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1334 - accuracy: 0.5946 - val_loss: 1.2246 - val_accuracy: 0.5689\n",
      "Epoch 8/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.0900 - accuracy: 0.6094 - val_loss: 1.2208 - val_accuracy: 0.5622\n",
      "Epoch 9/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.0611 - accuracy: 0.6184 - val_loss: 1.2247 - val_accuracy: 0.5600\n",
      "Epoch 10/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.0419 - accuracy: 0.6274 - val_loss: 1.2228 - val_accuracy: 0.5578\n",
      "Epoch 11/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.0312 - accuracy: 0.6301 - val_loss: 1.2276 - val_accuracy: 0.5533\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/11\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.8824 - accuracy: 0.3322 - val_loss: 1.5221 - val_accuracy: 0.5111\n",
      "Epoch 2/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.6532 - accuracy: 0.4553 - val_loss: 1.3546 - val_accuracy: 0.5733\n",
      "Epoch 3/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.4984 - accuracy: 0.5113 - val_loss: 1.2679 - val_accuracy: 0.5867\n",
      "Epoch 4/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.3765 - accuracy: 0.5405 - val_loss: 1.2295 - val_accuracy: 0.5800\n",
      "Epoch 5/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.2783 - accuracy: 0.5641 - val_loss: 1.2144 - val_accuracy: 0.5822\n",
      "Epoch 6/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.1965 - accuracy: 0.5793 - val_loss: 1.2099 - val_accuracy: 0.5711\n",
      "Epoch 7/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.1324 - accuracy: 0.5964 - val_loss: 1.2078 - val_accuracy: 0.5733\n",
      "Epoch 8/11\n",
      "480/480 [==============================] - 2s 3ms/step - loss: 1.0890 - accuracy: 0.6099 - val_loss: 1.2154 - val_accuracy: 0.5622\n",
      "Epoch 9/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.0600 - accuracy: 0.6200 - val_loss: 1.2139 - val_accuracy: 0.5600\n",
      "Epoch 10/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.0409 - accuracy: 0.6245 - val_loss: 1.2201 - val_accuracy: 0.5578\n",
      "Epoch 11/11\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.0301 - accuracy: 0.6293 - val_loss: 1.2196 - val_accuracy: 0.5578\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.27      0.07      0.11        55\n",
      "           1       0.14      0.07      0.09        45\n",
      "           2       0.59      0.64      0.61        64\n",
      "           3       0.27      0.34      0.30        44\n",
      "           4       0.76      0.67      0.71        66\n",
      "           5       0.76      1.00      0.87        74\n",
      "           6       0.62      0.73      0.67        44\n",
      "           7       0.47      0.66      0.55        58\n",
      "\n",
      "    accuracy                           0.56       450\n",
      "   macro avg       0.48      0.52      0.49       450\n",
      "weighted avg       0.51      0.56      0.52       450\n",
      "\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 4  3  5 15  0  5  1 22]\n",
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      " [ 1  3  3  7  1  2  3 38]]\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/ed9aaca6993a463384e68cb19654bfcc\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [22]                : (0.32795000076293945, 0.6301119923591614)\n",
      "COMET INFO:     batch_accuracy [1056]        : (0.14772726595401764, 0.8125)\n",
      "COMET INFO:     batch_loss [1056]            : (0.7097311019897461, 2.0760719776153564)\n",
      "COMET INFO:     epoch_duration [22]          : (1.3280000000013388, 2.2810000000026776)\n",
      "COMET INFO:     loss [22]                    : (1.0301498174667358, 1.8973019123077393)\n",
      "COMET INFO:     val_accuracy [22]            : (0.5066666603088379, 0.5888888835906982)\n",
      "COMET INFO:     val_loss [22]                : (1.207776665687561, 1.5278329849243164)\n",
      "COMET INFO:     validate_batch_accuracy [66] : (0.375, 0.5982142686843872)\n",
      "COMET INFO:     validate_batch_loss [66]     : (1.1849650144577026, 1.5284926891326904)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 2\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.0301498174667358\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 16, \"epochs\": 11, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 764444eb41c19c59015b05b0bf189762aa1e642a\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
      "COMET INFO:     Adam_epsilon       : 1e-07\n",
      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 16\n",
      "COMET INFO:     epochs             : 11\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 480\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/9d3e0496001148e9913b5c79e08389b1\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "480/480 [==============================] - 3s 6ms/step - loss: 1.8851 - accuracy: 0.3507 - val_loss: 1.5028 - val_accuracy: 0.5222\n",
      "Epoch 2/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.6264 - accuracy: 0.4694 - val_loss: 1.3361 - val_accuracy: 0.5711\n",
      "Epoch 3/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.4569 - accuracy: 0.5194 - val_loss: 1.2613 - val_accuracy: 0.5933\n",
      "Epoch 4/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.3288 - accuracy: 0.5481 - val_loss: 1.2255 - val_accuracy: 0.5978\n",
      "Epoch 5/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.2298 - accuracy: 0.5703 - val_loss: 1.2073 - val_accuracy: 0.5978\n",
      "Epoch 6/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1653 - accuracy: 0.5883 - val_loss: 1.2093 - val_accuracy: 0.5867\n",
      "Epoch 7/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1278 - accuracy: 0.5991 - val_loss: 1.2137 - val_accuracy: 0.5822\n",
      "Epoch 8/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1079 - accuracy: 0.6042 - val_loss: 1.2130 - val_accuracy: 0.5800\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.8680 - accuracy: 0.3474 - val_loss: 1.4979 - val_accuracy: 0.5333\n",
      "Epoch 2/8\n",
      "480/480 [==============================] - 2s 5ms/step - loss: 1.6180 - accuracy: 0.4713 - val_loss: 1.3357 - val_accuracy: 0.5978\n",
      "Epoch 3/8\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.4534 - accuracy: 0.5221 - val_loss: 1.2551 - val_accuracy: 0.6000\n",
      "Epoch 4/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.3268 - accuracy: 0.5524 - val_loss: 1.2234 - val_accuracy: 0.5956\n",
      "Epoch 5/8\n",
      "480/480 [==============================] - 1s 3ms/step - loss: 1.2283 - accuracy: 0.5740 - val_loss: 1.2199 - val_accuracy: 0.5778\n",
      "Epoch 6/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1647 - accuracy: 0.5860 - val_loss: 1.2121 - val_accuracy: 0.5711\n",
      "Epoch 7/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1272 - accuracy: 0.5949 - val_loss: 1.2156 - val_accuracy: 0.5756\n",
      "Epoch 8/8\n",
      "480/480 [==============================] - 2s 4ms/step - loss: 1.1077 - accuracy: 0.6025 - val_loss: 1.2136 - val_accuracy: 0.5711\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.30      0.05      0.09        55\n",
      "           1       0.16      0.07      0.09        45\n",
      "           2       0.61      0.69      0.65        64\n",
      "           3       0.29      0.36      0.32        44\n",
      "           4       0.73      0.67      0.70        66\n",
      "           5       0.74      0.99      0.85        74\n",
      "           6       0.62      0.75      0.68        44\n",
      "           7       0.49      0.71      0.58        58\n",
      "\n",
      "    accuracy                           0.57       450\n",
      "   macro avg       0.49      0.54      0.50       450\n",
      "weighted avg       0.52      0.57      0.53       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 3  4  5 16  0  4  1 22]\n",
      " [ 1  3  9  6  2  5  7 12]\n",
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      " [ 1  2  3  5  1  3  2 41]]\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/9d3e0496001148e9913b5c79e08389b1\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [16]                : (0.34735608100891113, 0.6041938066482544)\n",
      "COMET INFO:     batch_accuracy [768]         : (0.125, 0.75)\n",
      "COMET INFO:     batch_loss [768]             : (1.0352727174758911, 2.403183698654175)\n",
      "COMET INFO:     epoch_duration [16]          : (1.2810000000026776, 2.936999999998079)\n",
      "COMET INFO:     loss [16]                    : (1.1076984405517578, 1.8851109743118286)\n",
      "COMET INFO:     val_accuracy [16]            : (0.5222222208976746, 0.6000000238418579)\n",
      "COMET INFO:     val_loss [16]                : (1.2073041200637817, 1.5027642250061035)\n",
      "COMET INFO:     validate_batch_accuracy [48] : (0.375, 0.6101190447807312)\n",
      "COMET INFO:     validate_batch_loss [48]     : (1.1825987100601196, 1.503770112991333)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 3\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.1076984405517578\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 16, \"epochs\": 8, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : c67b566d5fbcb2fe417d90fe51e77dae27432eb1\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
      "COMET INFO:     Adam_epsilon       : 1e-07\n",
      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 16\n",
      "COMET INFO:     epochs             : 8\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 480\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
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      "COMET INFO:     model graph              : 1\n",
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      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/7e552f99ed674fca923ac6d766cbc241\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "240/240 [==============================] - 2s 6ms/step - loss: 1.9492 - accuracy: 0.3117 - val_loss: 1.5978 - val_accuracy: 0.4933\n",
      "Epoch 2/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.7082 - accuracy: 0.4483 - val_loss: 1.4003 - val_accuracy: 0.5489\n",
      "Epoch 3/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.5542 - accuracy: 0.4924 - val_loss: 1.3088 - val_accuracy: 0.5778\n",
      "Epoch 4/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.4312 - accuracy: 0.5292 - val_loss: 1.2617 - val_accuracy: 0.5844\n",
      "Epoch 5/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.3311 - accuracy: 0.5512 - val_loss: 1.2359 - val_accuracy: 0.5844\n",
      "Epoch 6/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2569 - accuracy: 0.5702 - val_loss: 1.2262 - val_accuracy: 0.5889\n",
      "Epoch 7/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2104 - accuracy: 0.5830 - val_loss: 1.2181 - val_accuracy: 0.5822\n",
      "Epoch 8/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1823 - accuracy: 0.5892 - val_loss: 1.2170 - val_accuracy: 0.5756\n",
      "Epoch 9/9\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.1673 - accuracy: 0.5927 - val_loss: 1.2178 - val_accuracy: 0.5756\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "240/240 [==============================] - 3s 10ms/step - loss: 1.9335 - accuracy: 0.3360 - val_loss: 1.6096 - val_accuracy: 0.4889\n",
      "Epoch 2/9\n",
      "240/240 [==============================] - 2s 8ms/step - loss: 1.6935 - accuracy: 0.4538 - val_loss: 1.4077 - val_accuracy: 0.5511\n",
      "Epoch 3/9\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.5450 - accuracy: 0.4960 - val_loss: 1.3156 - val_accuracy: 0.5844\n",
      "Epoch 4/9\n",
      "240/240 [==============================] - 2s 7ms/step - loss: 1.4256 - accuracy: 0.5264 - val_loss: 1.2611 - val_accuracy: 0.5867\n",
      "Epoch 5/9\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.3275 - accuracy: 0.5530 - val_loss: 1.2431 - val_accuracy: 0.5889\n",
      "Epoch 6/9\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.2543 - accuracy: 0.5692 - val_loss: 1.2271 - val_accuracy: 0.5956\n",
      "Epoch 7/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2084 - accuracy: 0.5797 - val_loss: 1.2202 - val_accuracy: 0.5933\n",
      "Epoch 8/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1801 - accuracy: 0.5880 - val_loss: 1.2188 - val_accuracy: 0.5800\n",
      "Epoch 9/9\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1655 - accuracy: 0.5906 - val_loss: 1.2167 - val_accuracy: 0.5800\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.05      0.10        55\n",
      "           1       0.21      0.07      0.10        45\n",
      "           2       0.56      0.67      0.61        64\n",
      "           3       0.31      0.39      0.35        44\n",
      "           4       0.74      0.70      0.72        66\n",
      "           5       0.76      1.00      0.86        74\n",
      "           6       0.66      0.75      0.70        44\n",
      "           7       0.48      0.72      0.58        58\n",
      "\n",
      "    accuracy                           0.58       450\n",
      "   macro avg       0.51      0.54      0.50       450\n",
      "weighted avg       0.54      0.58      0.53       450\n",
      "\n"
     ]
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     "name": "stderr",
     "output_type": "stream",
     "text": [
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      " [ 1  1  3  5  1  3  2 42]]\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/7e552f99ed674fca923ac6d766cbc241\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [18]                : (0.3116697072982788, 0.5927324891090393)\n",
      "COMET INFO:     batch_accuracy [432]         : (0.09375, 0.6875)\n",
      "COMET INFO:     batch_loss [432]             : (0.9175338745117188, 2.408168315887451)\n",
      "COMET INFO:     epoch_duration [18]          : (0.875, 2.9060000000026776)\n",
      "COMET INFO:     loss [18]                    : (1.1654733419418335, 1.949246883392334)\n",
      "COMET INFO:     val_accuracy [18]            : (0.4888888895511627, 0.5955555438995361)\n",
      "COMET INFO:     val_loss [18]                : (1.2167482376098633, 1.6095777750015259)\n",
      "COMET INFO:     validate_batch_accuracy [36] : (0.4375, 0.5994318127632141)\n",
      "COMET INFO:     validate_batch_loss [36]     : (1.1964690685272217, 1.598164439201355)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 4\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.1654733419418335\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 32, \"epochs\": 9, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 7ad63f51470c1d52e853dbdb797d5051ab1e75a0\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
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      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 32\n",
      "COMET INFO:     epochs             : 9\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 240\n",
      "COMET INFO:   Uploads:\n",
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      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/30bb2c5186344e1c993ded2f958a7db9\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "960/960 [==============================] - 11s 11ms/step - loss: 1.8358 - accuracy: 0.3688 - val_loss: 1.4436 - val_accuracy: 0.5467\n",
      "Epoch 2/8\n",
      "960/960 [==============================] - 10s 11ms/step - loss: 1.5594 - accuracy: 0.4909 - val_loss: 1.2898 - val_accuracy: 0.5911\n",
      "Epoch 3/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.3829 - accuracy: 0.5366 - val_loss: 1.2272 - val_accuracy: 0.5822\n",
      "Epoch 4/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.2509 - accuracy: 0.5656 - val_loss: 1.2180 - val_accuracy: 0.5778\n",
      "Epoch 5/8\n",
      "960/960 [==============================] - 10s 11ms/step - loss: 1.1497 - accuracy: 0.5888 - val_loss: 1.2145 - val_accuracy: 0.5800\n",
      "Epoch 6/8\n",
      "960/960 [==============================] - 9s 9ms/step - loss: 1.0838 - accuracy: 0.6099 - val_loss: 1.2304 - val_accuracy: 0.5578\n",
      "Epoch 7/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.0452 - accuracy: 0.6215 - val_loss: 1.2267 - val_accuracy: 0.5511\n",
      "Epoch 8/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.0244 - accuracy: 0.6296 - val_loss: 1.2274 - val_accuracy: 0.5533\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "960/960 [==============================] - 11s 11ms/step - loss: 1.8352 - accuracy: 0.3833 - val_loss: 1.4386 - val_accuracy: 0.5533\n",
      "Epoch 2/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.5537 - accuracy: 0.4940 - val_loss: 1.2871 - val_accuracy: 0.5867\n",
      "Epoch 3/8\n",
      "960/960 [==============================] - 10s 10ms/step - loss: 1.3773 - accuracy: 0.5439 - val_loss: 1.2288 - val_accuracy: 0.5889\n",
      "Epoch 4/8\n",
      "960/960 [==============================] - 10s 11ms/step - loss: 1.2474 - accuracy: 0.5675 - val_loss: 1.2150 - val_accuracy: 0.5733\n",
      "Epoch 5/8\n",
      "960/960 [==============================] - 7s 7ms/step - loss: 1.1481 - accuracy: 0.5918 - val_loss: 1.2090 - val_accuracy: 0.5778\n",
      "Epoch 6/8\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.0823 - accuracy: 0.6098 - val_loss: 1.2217 - val_accuracy: 0.5600\n",
      "Epoch 7/8\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.0437 - accuracy: 0.6231 - val_loss: 1.2277 - val_accuracy: 0.5533\n",
      "Epoch 8/8\n",
      "960/960 [==============================] - 9s 9ms/step - loss: 1.0230 - accuracy: 0.6306 - val_loss: 1.2277 - val_accuracy: 0.5489\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 4  3  5 15  0  6  1 21]\n",
      " [ 1  3  8  6  1  9  7 10]\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.07      0.12        55\n",
      "           1       0.14      0.07      0.09        45\n",
      "           2       0.58      0.62      0.60        64\n",
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      "           6       0.60      0.70      0.65        44\n",
      "           7       0.49      0.66      0.56        58\n",
      "\n",
      "    accuracy                           0.55       450\n",
      "   macro avg       0.48      0.51      0.48       450\n",
      "weighted avg       0.51      0.55      0.51       450\n",
      "\n"
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     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/30bb2c5186344e1c993ded2f958a7db9\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [16]                : (0.36884605884552, 0.6306329965591431)\n",
      "COMET INFO:     batch_accuracy [1536]        : (0.125, 1.0)\n",
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      "COMET INFO:     epoch_duration [16]          : (3.610000000000582, 10.625)\n",
      "COMET INFO:     loss [16]                    : (1.0230273008346558, 1.8357616662979126)\n",
      "COMET INFO:     val_accuracy [16]            : (0.54666668176651, 0.5911111235618591)\n",
      "COMET INFO:     val_loss [16]                : (1.2090415954589844, 1.4435937404632568)\n",
      "COMET INFO:     validate_batch_accuracy [96] : (0.375, 0.6006097793579102)\n",
      "COMET INFO:     validate_batch_loss [96]     : (1.177718162536621, 1.5018268823623657)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 5\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.0230273008346558\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 8, \"epochs\": 8, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 0f6d8d18031109281aa66db9074e076100a95fd0\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
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      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 8\n",
      "COMET INFO:     epochs             : 8\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 960\n",
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      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/a209acbdf97949f89cfb129941f96a50\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "960/960 [==============================] - 9s 9ms/step - loss: 1.8603 - accuracy: 0.3743 - val_loss: 1.4632 - val_accuracy: 0.5511\n",
      "Epoch 2/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.5790 - accuracy: 0.4762 - val_loss: 1.3086 - val_accuracy: 0.6044\n",
      "Epoch 3/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.4033 - accuracy: 0.5318 - val_loss: 1.2420 - val_accuracy: 0.5911\n",
      "Epoch 4/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.2709 - accuracy: 0.5620 - val_loss: 1.2293 - val_accuracy: 0.5800\n",
      "Epoch 5/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.1685 - accuracy: 0.5840 - val_loss: 1.2169 - val_accuracy: 0.5644\n",
      "Epoch 6/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.0915 - accuracy: 0.6071 - val_loss: 1.2325 - val_accuracy: 0.5600\n",
      "Epoch 7/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0437 - accuracy: 0.6222 - val_loss: 1.2360 - val_accuracy: 0.5578\n",
      "Epoch 8/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0136 - accuracy: 0.6353 - val_loss: 1.2415 - val_accuracy: 0.5578\n",
      "Epoch 9/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9972 - accuracy: 0.6401 - val_loss: 1.2386 - val_accuracy: 0.5578\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.8259 - accuracy: 0.3784 - val_loss: 1.4395 - val_accuracy: 0.5489\n",
      "Epoch 2/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.5687 - accuracy: 0.4872 - val_loss: 1.2988 - val_accuracy: 0.5778\n",
      "Epoch 3/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.3961 - accuracy: 0.5301 - val_loss: 1.2422 - val_accuracy: 0.5822\n",
      "Epoch 4/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.2671 - accuracy: 0.5646 - val_loss: 1.2180 - val_accuracy: 0.5756\n",
      "Epoch 5/9\n",
      "960/960 [==============================] - 5s 5ms/step - loss: 1.1654 - accuracy: 0.5864 - val_loss: 1.2091 - val_accuracy: 0.5800\n",
      "Epoch 6/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0891 - accuracy: 0.6067 - val_loss: 1.2282 - val_accuracy: 0.5644\n",
      "Epoch 7/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0418 - accuracy: 0.6218 - val_loss: 1.2252 - val_accuracy: 0.5578\n",
      "Epoch 8/9\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0125 - accuracy: 0.6331 - val_loss: 1.2352 - val_accuracy: 0.5489\n",
      "Epoch 9/9\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 0.9959 - accuracy: 0.6362 - val_loss: 1.2357 - val_accuracy: 0.5556\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 4  3  5 16  0  6  1 20]\n",
      " [ 1  3  8  6  1 10  7  9]\n",
      " [ 5  1 40 10  4  1  0  3]\n",
      " [ 3  4  5 16  7  3  2  4]\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.07      0.11        55\n",
      "           1       0.14      0.07      0.09        45\n",
      "           2       0.60      0.62      0.61        64\n",
      "           3       0.28      0.36      0.32        44\n",
      "           4       0.75      0.67      0.70        66\n",
      "           5       0.73      0.99      0.84        74\n",
      "           6       0.61      0.70      0.65        44\n",
      "           7       0.50      0.67      0.57        58\n",
      "\n",
      "    accuracy                           0.56       450\n",
      "   macro avg       0.48      0.52      0.49       450\n",
      "weighted avg       0.51      0.56      0.52       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/a209acbdf97949f89cfb129941f96a50\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [18]                 : (0.3743162155151367, 0.6401406526565552)\n",
      "COMET INFO:     batch_accuracy [1728]         : (0.125, 1.0)\n",
      "COMET INFO:     batch_loss [1728]             : (0.5164788961410522, 2.376208782196045)\n",
      "COMET INFO:     epoch_duration [18]           : (2.6560000000026776, 8.796999999998661)\n",
      "COMET INFO:     loss [18]                     : (0.9958898425102234, 1.8602776527404785)\n",
      "COMET INFO:     val_accuracy [18]             : (0.5488888621330261, 0.6044444441795349)\n",
      "COMET INFO:     val_loss [18]                 : (1.209072232246399, 1.463248610496521)\n",
      "COMET INFO:     validate_batch_accuracy [108] : (0.375, 0.6169354915618896)\n",
      "COMET INFO:     validate_batch_loss [108]     : (1.1827412843704224, 1.5108890533447266)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 6\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 0.9958898425102234\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 8, \"epochs\": 9, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 32acbd5ee67e6686bc2c31b93031691a6aaaa4d6\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
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      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 8\n",
      "COMET INFO:     epochs             : 9\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 960\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/3ed418bf8a1047b0a0731e9cab2efe71\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/11\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.8568 - accuracy: 0.3571 - val_loss: 1.4621 - val_accuracy: 0.5511\n",
      "Epoch 2/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.5958 - accuracy: 0.4777 - val_loss: 1.3081 - val_accuracy: 0.5911\n",
      "Epoch 3/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.4277 - accuracy: 0.5266 - val_loss: 1.2496 - val_accuracy: 0.6022\n",
      "Epoch 4/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.3000 - accuracy: 0.5574 - val_loss: 1.2115 - val_accuracy: 0.5956\n",
      "Epoch 5/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.1986 - accuracy: 0.5798 - val_loss: 1.2136 - val_accuracy: 0.5756\n",
      "Epoch 6/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.1180 - accuracy: 0.6004 - val_loss: 1.2100 - val_accuracy: 0.5644\n",
      "Epoch 7/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.0535 - accuracy: 0.6190 - val_loss: 1.2350 - val_accuracy: 0.5511\n",
      "Epoch 8/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.0104 - accuracy: 0.6296 - val_loss: 1.2324 - val_accuracy: 0.5511\n",
      "Epoch 9/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 0.9816 - accuracy: 0.6397 - val_loss: 1.2505 - val_accuracy: 0.5511\n",
      "Epoch 10/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 0.9627 - accuracy: 0.6461 - val_loss: 1.2527 - val_accuracy: 0.5444\n",
      "Epoch 11/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 0.9513 - accuracy: 0.6512 - val_loss: 1.2511 - val_accuracy: 0.5422\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/11\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.8420 - accuracy: 0.3747 - val_loss: 1.4671 - val_accuracy: 0.5333\n",
      "Epoch 2/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.5912 - accuracy: 0.4807 - val_loss: 1.3051 - val_accuracy: 0.5956\n",
      "Epoch 3/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.4255 - accuracy: 0.5294 - val_loss: 1.2411 - val_accuracy: 0.5800\n",
      "Epoch 4/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.2995 - accuracy: 0.5557 - val_loss: 1.2168 - val_accuracy: 0.5911\n",
      "Epoch 5/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.1996 - accuracy: 0.5794 - val_loss: 1.2017 - val_accuracy: 0.5867\n",
      "Epoch 6/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.1179 - accuracy: 0.5965 - val_loss: 1.2116 - val_accuracy: 0.5689\n",
      "Epoch 7/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.0537 - accuracy: 0.6196 - val_loss: 1.2233 - val_accuracy: 0.5600\n",
      "Epoch 8/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 1.0106 - accuracy: 0.6308 - val_loss: 1.2302 - val_accuracy: 0.5533\n",
      "Epoch 9/11\n",
      "960/960 [==============================] - 3s 4ms/step - loss: 0.9820 - accuracy: 0.6433 - val_loss: 1.2528 - val_accuracy: 0.5467\n",
      "Epoch 10/11\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9630 - accuracy: 0.6486 - val_loss: 1.2557 - val_accuracy: 0.5444\n",
      "Epoch 11/11\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9516 - accuracy: 0.6510 - val_loss: 1.2550 - val_accuracy: 0.5489\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 4  3  5 15  0  7  1 20]\n",
      " [ 0  5  7  6  1 10  7  9]\n",
      " [ 6  1 39  9  5  1  0  3]\n",
      " [ 2  4  5 17  6  4  2  4]\n",
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.25      0.07      0.11        55\n",
      "           1       0.21      0.11      0.14        45\n",
      "           2       0.60      0.61      0.60        64\n",
      "           3       0.30      0.39      0.34        44\n",
      "           4       0.74      0.64      0.68        66\n",
      "           5       0.70      0.97      0.81        74\n",
      "           6       0.58      0.68      0.62        44\n",
      "           7       0.50      0.66      0.57        58\n",
      "\n",
      "    accuracy                           0.55       450\n",
      "   macro avg       0.48      0.52      0.49       450\n",
      "weighted avg       0.51      0.55      0.52       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/3ed418bf8a1047b0a0731e9cab2efe71\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [22]                 : (0.3571242392063141, 0.6512112617492676)\n",
      "COMET INFO:     batch_accuracy [2112]         : (0.16532258689403534, 1.0)\n",
      "COMET INFO:     batch_loss [2112]             : (0.7552825212478638, 2.566906213760376)\n",
      "COMET INFO:     epoch_duration [22]           : (3.3280000000013388, 3.9530000000013388)\n",
      "COMET INFO:     loss [22]                     : (0.951273500919342, 1.8568252325057983)\n",
      "COMET INFO:     val_accuracy [22]             : (0.5333333611488342, 0.602222204208374)\n",
      "COMET INFO:     val_loss [22]                 : (1.2016838788986206, 1.4670593738555908)\n",
      "COMET INFO:     validate_batch_accuracy [132] : (0.375, 0.6129032373428345)\n",
      "COMET INFO:     validate_batch_loss [132]     : (1.1667006015777588, 1.530494213104248)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 7\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 0.951273500919342\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 8, \"epochs\": 11, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 92b954db6fc275e3e729fc7c319af4318c7f5862\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
      "COMET INFO:     Adam_epsilon       : 1e-07\n",
      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 8\n",
      "COMET INFO:     epochs             : 11\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 960\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/41d868bbbdbc4fde953b906cac84f11a\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.9033 - accuracy: 0.3423 - val_loss: 1.5494 - val_accuracy: 0.5133\n",
      "Epoch 2/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.6835 - accuracy: 0.4553 - val_loss: 1.3799 - val_accuracy: 0.5667\n",
      "Epoch 3/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.5273 - accuracy: 0.5031 - val_loss: 1.2928 - val_accuracy: 0.5911\n",
      "Epoch 4/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.4038 - accuracy: 0.5307 - val_loss: 1.2435 - val_accuracy: 0.5933\n",
      "Epoch 5/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.3083 - accuracy: 0.5567 - val_loss: 1.2287 - val_accuracy: 0.5911\n",
      "Epoch 6/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2464 - accuracy: 0.5722 - val_loss: 1.2174 - val_accuracy: 0.5889\n",
      "Epoch 7/8\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.2108 - accuracy: 0.5800 - val_loss: 1.2106 - val_accuracy: 0.5889\n",
      "Epoch 8/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1925 - accuracy: 0.5856 - val_loss: 1.2095 - val_accuracy: 0.5844\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/8\n",
      "240/240 [==============================] - 1s 5ms/step - loss: 1.8927 - accuracy: 0.3311 - val_loss: 1.5493 - val_accuracy: 0.5089\n",
      "Epoch 2/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.6852 - accuracy: 0.4483 - val_loss: 1.3734 - val_accuracy: 0.5689\n",
      "Epoch 3/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.5309 - accuracy: 0.4962 - val_loss: 1.2898 - val_accuracy: 0.5956\n",
      "Epoch 4/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.4065 - accuracy: 0.5336 - val_loss: 1.2472 - val_accuracy: 0.5911\n",
      "Epoch 5/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.3103 - accuracy: 0.5565 - val_loss: 1.2265 - val_accuracy: 0.5911\n",
      "Epoch 6/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2480 - accuracy: 0.5714 - val_loss: 1.2138 - val_accuracy: 0.5867\n",
      "Epoch 7/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2119 - accuracy: 0.5798 - val_loss: 1.2159 - val_accuracy: 0.5844\n",
      "Epoch 8/8\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1935 - accuracy: 0.5850 - val_loss: 1.2165 - val_accuracy: 0.5867\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 3  3  7 16  0  2  1 23]\n",
      " [ 0  1 11  6  2  7  5 13]\n",
      " [ 0  0 45 11  5  0  0  3]\n",
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     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.05      0.10        55\n",
      "           1       0.07      0.02      0.03        45\n",
      "           2       0.56      0.70      0.62        64\n",
      "           3       0.29      0.36      0.32        44\n",
      "           4       0.73      0.70      0.71        66\n",
      "           5       0.80      1.00      0.89        74\n",
      "           6       0.70      0.80      0.74        44\n",
      "           7       0.49      0.76      0.60        58\n",
      "\n",
      "    accuracy                           0.59       450\n",
      "   macro avg       0.52      0.55      0.50       450\n",
      "weighted avg       0.55      0.59      0.54       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/41d868bbbdbc4fde953b906cac84f11a\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [16]                : (0.3310757875442505, 0.5855691432952881)\n",
      "COMET INFO:     batch_accuracy [384]         : (0.15625, 0.71875)\n",
      "COMET INFO:     batch_loss [384]             : (0.9097651243209839, 2.3208110332489014)\n",
      "COMET INFO:     epoch_duration [16]          : (0.8280000000013388, 1.4059999999954016)\n",
      "COMET INFO:     loss [16]                    : (1.1924649477005005, 1.9033044576644897)\n",
      "COMET INFO:     val_accuracy [16]            : (0.5088889002799988, 0.5955555438995361)\n",
      "COMET INFO:     val_loss [16]                : (1.2095346450805664, 1.549403429031372)\n",
      "COMET INFO:     validate_batch_accuracy [32] : (0.46875, 0.6079545617103577)\n",
      "COMET INFO:     validate_batch_loss [32]     : (1.1880053281784058, 1.5386614799499512)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 8\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.1924649477005005\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 32, \"epochs\": 8, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : c18a021d18ea6d82e48ba8ad0eff84b01089adee\n",
      "COMET INFO:     optimizer_process      : 25196\n",
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      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
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      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 32\n",
      "COMET INFO:     epochs             : 8\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 240\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/6c87f748690c459895d52b458dfbd261\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/10\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.8504 - accuracy: 0.3746 - val_loss: 1.4521 - val_accuracy: 0.5467\n",
      "Epoch 2/10\n",
      "960/960 [==============================] - 4s 4ms/step - loss: 1.5851 - accuracy: 0.4827 - val_loss: 1.2981 - val_accuracy: 0.5933\n",
      "Epoch 3/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.4147 - accuracy: 0.5290 - val_loss: 1.2431 - val_accuracy: 0.5867\n",
      "Epoch 4/10\n",
      "960/960 [==============================] - 8s 8ms/step - loss: 1.2855 - accuracy: 0.5590 - val_loss: 1.2277 - val_accuracy: 0.5822\n",
      "Epoch 5/10\n",
      "960/960 [==============================] - 9s 10ms/step - loss: 1.1836 - accuracy: 0.5817 - val_loss: 1.2167 - val_accuracy: 0.5733\n",
      "Epoch 6/10\n",
      "960/960 [==============================] - 9s 9ms/step - loss: 1.1031 - accuracy: 0.6012 - val_loss: 1.2231 - val_accuracy: 0.5644\n",
      "Epoch 7/10\n",
      "960/960 [==============================] - 5s 5ms/step - loss: 1.0462 - accuracy: 0.6205 - val_loss: 1.2303 - val_accuracy: 0.5578\n",
      "Epoch 8/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0096 - accuracy: 0.6352 - val_loss: 1.2321 - val_accuracy: 0.5556\n",
      "Epoch 9/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9862 - accuracy: 0.6407 - val_loss: 1.2360 - val_accuracy: 0.5489\n",
      "Epoch 10/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9727 - accuracy: 0.6431 - val_loss: 1.2421 - val_accuracy: 0.5489\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.8384 - accuracy: 0.3586 - val_loss: 1.4615 - val_accuracy: 0.5400\n",
      "Epoch 2/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.5829 - accuracy: 0.4786 - val_loss: 1.3083 - val_accuracy: 0.5889\n",
      "Epoch 3/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.4116 - accuracy: 0.5283 - val_loss: 1.2469 - val_accuracy: 0.5800\n",
      "Epoch 4/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.2838 - accuracy: 0.5602 - val_loss: 1.2086 - val_accuracy: 0.5867\n",
      "Epoch 5/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.1819 - accuracy: 0.5793 - val_loss: 1.2590 - val_accuracy: 0.5600\n",
      "Epoch 6/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.1016 - accuracy: 0.6037 - val_loss: 1.2394 - val_accuracy: 0.5556\n",
      "Epoch 7/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0449 - accuracy: 0.6192 - val_loss: 1.2332 - val_accuracy: 0.5556\n",
      "Epoch 8/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 1.0087 - accuracy: 0.6326 - val_loss: 1.2359 - val_accuracy: 0.5578\n",
      "Epoch 9/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9854 - accuracy: 0.6421 - val_loss: 1.2483 - val_accuracy: 0.5556\n",
      "Epoch 10/10\n",
      "960/960 [==============================] - 3s 3ms/step - loss: 0.9720 - accuracy: 0.6450 - val_loss: 1.2474 - val_accuracy: 0.5556\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 5  3  4 15  0  7  1 20]\n",
      " [ 1  4  8  6  1  9  7  9]\n",
      " [ 6  1 39  9  5  1  0  3]\n",
      " [ 2  4  5 16  7  3  3  4]\n",
      " [ 1  6  4  0 44  5  6  0]\n",
      " [ 0  0  0  0  0 73  1  0]\n",
      " [ 1  2  2  3  2  2 30  2]\n",
      " [ 1  3  3  6  1  2  3 39]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.29      0.09      0.14        55\n",
      "           1       0.17      0.09      0.12        45\n",
      "           2       0.60      0.61      0.60        64\n",
      "           3       0.29      0.36      0.32        44\n",
      "           4       0.73      0.67      0.70        66\n",
      "           5       0.72      0.99      0.83        74\n",
      "           6       0.59      0.68      0.63        44\n",
      "           7       0.51      0.67      0.58        58\n",
      "\n",
      "    accuracy                           0.56       450\n",
      "   macro avg       0.49      0.52      0.49       450\n",
      "weighted avg       0.52      0.56      0.52       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/6c87f748690c459895d52b458dfbd261\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [20]                 : (0.35855692625045776, 0.6449596285820007)\n",
      "COMET INFO:     batch_accuracy [1920]         : (0.0, 0.875)\n",
      "COMET INFO:     batch_loss [1920]             : (0.7269760370254517, 2.4091172218322754)\n",
      "COMET INFO:     epoch_duration [20]           : (2.6089999999967404, 9.18699999999808)\n",
      "COMET INFO:     loss [20]                     : (0.9720174670219421, 1.8503860235214233)\n",
      "COMET INFO:     val_accuracy [20]             : (0.5400000214576721, 0.5933333039283752)\n",
      "COMET INFO:     val_loss [20]                 : (1.208624243736267, 1.4614750146865845)\n",
      "COMET INFO:     validate_batch_accuracy [120] : (0.375, 0.6048387289047241)\n",
      "COMET INFO:     validate_batch_loss [120]     : (1.1726475954055786, 1.509983777999878)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 9\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 0.9720174670219421\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 8, \"epochs\": 10, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 8f12f32ae4f8460c81dfcf139c737a7231b733a8\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
      "COMET INFO:     Adam_epsilon       : 1e-07\n",
      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 8\n",
      "COMET INFO:     epochs             : 10\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 960\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: All files uploaded, waiting for confirmation they have been all received\n",
      "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
      "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/fca20699d34d47fb9c55a4deda549430\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/10\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.9182 - accuracy: 0.3303 - val_loss: 1.5794 - val_accuracy: 0.5200\n",
      "Epoch 2/10\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.7111 - accuracy: 0.4427 - val_loss: 1.4101 - val_accuracy: 0.5667\n",
      "Epoch 3/10\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.5637 - accuracy: 0.4905 - val_loss: 1.3135 - val_accuracy: 0.5889\n",
      "Epoch 4/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.4436 - accuracy: 0.5229 - val_loss: 1.2677 - val_accuracy: 0.5889\n",
      "Epoch 5/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.3454 - accuracy: 0.5468 - val_loss: 1.2215 - val_accuracy: 0.6000\n",
      "Epoch 6/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.2657 - accuracy: 0.5689 - val_loss: 1.2181 - val_accuracy: 0.5844\n",
      "Epoch 7/10\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.2100 - accuracy: 0.5817 - val_loss: 1.2194 - val_accuracy: 0.5756\n",
      "Epoch 8/10\n",
      "240/240 [==============================] - 1s 4ms/step - loss: 1.1742 - accuracy: 0.5900 - val_loss: 1.2155 - val_accuracy: 0.5756\n",
      "Epoch 9/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.1516 - accuracy: 0.5944 - val_loss: 1.2145 - val_accuracy: 0.5733\n",
      "Epoch 10/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.1395 - accuracy: 0.5970 - val_loss: 1.2156 - val_accuracy: 0.5711\n",
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n",
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n",
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/10\n",
      "240/240 [==============================] - 2s 7ms/step - loss: 1.9167 - accuracy: 0.3277 - val_loss: 1.5705 - val_accuracy: 0.5267\n",
      "Epoch 2/10\n",
      "240/240 [==============================] - 3s 14ms/step - loss: 1.7029 - accuracy: 0.4454 - val_loss: 1.3899 - val_accuracy: 0.5667\n",
      "Epoch 3/10\n",
      "240/240 [==============================] - 3s 12ms/step - loss: 1.5583 - accuracy: 0.4918 - val_loss: 1.3083 - val_accuracy: 0.5778\n",
      "Epoch 4/10\n",
      "240/240 [==============================] - 3s 13ms/step - loss: 1.4407 - accuracy: 0.5247 - val_loss: 1.2523 - val_accuracy: 0.5911\n",
      "Epoch 5/10\n",
      "240/240 [==============================] - 3s 12ms/step - loss: 1.3440 - accuracy: 0.5457 - val_loss: 1.2276 - val_accuracy: 0.5933\n",
      "Epoch 6/10\n",
      "240/240 [==============================] - 3s 11ms/step - loss: 1.2653 - accuracy: 0.5675 - val_loss: 1.2210 - val_accuracy: 0.5844\n",
      "Epoch 7/10\n",
      "240/240 [==============================] - 3s 11ms/step - loss: 1.2102 - accuracy: 0.5814 - val_loss: 1.2133 - val_accuracy: 0.5933\n",
      "Epoch 8/10\n",
      "240/240 [==============================] - 3s 11ms/step - loss: 1.1744 - accuracy: 0.5871 - val_loss: 1.2101 - val_accuracy: 0.5822\n",
      "Epoch 9/10\n",
      "240/240 [==============================] - 3s 11ms/step - loss: 1.1518 - accuracy: 0.5929 - val_loss: 1.2103 - val_accuracy: 0.5867\n",
      "Epoch 10/10\n",
      "240/240 [==============================] - 3s 11ms/step - loss: 1.1398 - accuracy: 0.5961 - val_loss: 1.2092 - val_accuracy: 0.5867\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: [[ 4  3  5 16  0  4  1 22]\n",
      " [ 0  3  8  7  2  6  7 12]\n",
      " [ 2  1 44 11  3  0  0  3]\n",
      " [ 1  4  9 17  6  3  2  2]\n",
      " [ 1  4  2  0 46  5  6  2]\n",
      " [ 0  0  0  0  0 74  0  0]\n",
      " [ 0  2  2  2  2  0 33  3]\n",
      " [ 1  1  3  4  1  3  2 43]]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.44      0.07      0.12        55\n",
      "           1       0.17      0.07      0.10        45\n",
      "           2       0.60      0.69      0.64        64\n",
      "           3       0.30      0.39      0.34        44\n",
      "           4       0.77      0.70      0.73        66\n",
      "           5       0.78      1.00      0.88        74\n",
      "           6       0.65      0.75      0.69        44\n",
      "           7       0.49      0.74      0.59        58\n",
      "\n",
      "    accuracy                           0.59       450\n",
      "   macro avg       0.52      0.55      0.51       450\n",
      "weighted avg       0.55      0.59      0.54       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Comet.ml Experiment Summary\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO:   Data:\n",
      "COMET INFO:     display_summary_level : 1\n",
      "COMET INFO:     url                   : https://www.comet.ml/wodenwang820118/music-genre-multiclass-classification/fca20699d34d47fb9c55a4deda549430\n",
      "COMET INFO:   Metrics [count] (min, max):\n",
      "COMET INFO:     accuracy [20]                : (0.3276894986629486, 0.5970304608345032)\n",
      "COMET INFO:     batch_accuracy [480]         : (0.125, 0.6875)\n",
      "COMET INFO:     batch_loss [480]             : (0.9605534672737122, 2.1322851181030273)\n",
      "COMET INFO:     epoch_duration [20]          : (0.7350000000005821, 3.4530000000013388)\n",
      "COMET INFO:     loss [20]                    : (1.139471173286438, 1.918200969696045)\n",
      "COMET INFO:     val_accuracy [20]            : (0.5199999809265137, 0.6000000238418579)\n",
      "COMET INFO:     val_loss [20]                : (1.209211826324463, 1.579359531402588)\n",
      "COMET INFO:     validate_batch_accuracy [40] : (0.46875, 0.6107954382896423)\n",
      "COMET INFO:     validate_batch_loss [40]     : (1.1901921033859253, 1.567911148071289)\n",
      "COMET INFO:   Others:\n",
      "COMET INFO:     optimizer_count        : 10\n",
      "COMET INFO:     optimizer_id           : 4ee2cc9feea940f0a54fbe66f1ee8c5c\n",
      "COMET INFO:     optimizer_metric       : loss\n",
      "COMET INFO:     optimizer_metric_value : 1.139471173286438\n",
      "COMET INFO:     optimizer_objective    : minimum\n",
      "COMET INFO:     optimizer_parameters   : {\"batch_size\": 32, \"epochs\": 10, \"maxlen\": 150, \"ngram_range\": 1}\n",
      "COMET INFO:     optimizer_pid          : 2658f7c26698dfc2f02773bbe6ad504c4a0bdd4d\n",
      "COMET INFO:     optimizer_process      : 25196\n",
      "COMET INFO:     optimizer_trial        : 1\n",
      "COMET INFO:     optimizer_version      : 2.0.1\n",
      "COMET INFO:     trainable_params       : 44001\n",
      "COMET INFO:   Parameters:\n",
      "COMET INFO:     Adam_amsgrad       : False\n",
      "COMET INFO:     Adam_beta_1        : 0.8999999761581421\n",
      "COMET INFO:     Adam_beta_2        : 0.9990000128746033\n",
      "COMET INFO:     Adam_decay         : 0.0\n",
      "COMET INFO:     Adam_epsilon       : 1e-07\n",
      "COMET INFO:     Adam_learning_rate : 9.999999747378752e-05\n",
      "COMET INFO:     Adam_name          : Adam\n",
      "COMET INFO:     Optimizer          : Adam\n",
      "COMET INFO:     batch_size         : 32\n",
      "COMET INFO:     epochs             : 10\n",
      "COMET INFO:     maxlen             : 150\n",
      "COMET INFO:     ngram_range        : 1\n",
      "COMET INFO:     steps              : 240\n",
      "COMET INFO:   Uploads:\n",
      "COMET INFO:     environment details      : 1\n",
      "COMET INFO:     filename                 : 1\n",
      "COMET INFO:     git metadata             : 1\n",
      "COMET INFO:     git-patch (uncompressed) : 1 (113 bytes)\n",
      "COMET INFO:     installed packages       : 1\n",
      "COMET INFO:     model graph              : 1\n",
      "COMET INFO:     notebook                 : 1\n",
      "COMET INFO:     source_code              : 1\n",
      "COMET INFO: ---------------------------\n",
      "COMET INFO: Uploading 1 metrics, params and output messages\n",
      "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n",
      "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n",
      "COMET INFO: Still uploading 2 file(s), remaining 27.93 KB/28.27 KB\n",
      "COMET INFO: Optimizer search 4ee2cc9feea940f0a54fbe66f1ee8c5c has completed\n"
     ]
    }
   ],
   "source": [
    "textGenreClassifier.grid_search({\n",
    "    \"algorithm\": \"bayes\",\n",
    "    \"name\": \"Optimize Music Classification Network\",\n",
    "    \"spec\": {\"maxCombo\": 10, \"objective\": \"minimize\", \"metric\": \"loss\"},\n",
    "    \"parameters\": {\n",
    "        \"ngram_range\": {\"type\": \"discrete\", \"values\": [1]},\n",
    "        \"maxlen\": {\"type\": \"discrete\", \"values\": [150]},\n",
    "        \"epochs\": {\"type\": \"discrete\", \"values\": [8,9,10,11]},\n",
    "        \"batch_size\": {\"type\": \"discrete\", \"values\": [8,16,32]},\n",
    "    },\n",
    "    \"trials\": 1,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language: en\n",
      "Word Counts: 4888\n",
      "Nrows: 7678\n",
      "7678 train sequences\n",
      "train sequence lengths:\n",
      "\tmean : 80\n",
      "\t95percentile : 139\n",
      "\t99percentile : 193\n",
      "x_train shape: (7678,150)\n",
      "y_train shape: (7678, 8)\n",
      "Is Multi-Label? False\n",
      "450 test sequences\n",
      "test sequence lengths:\n",
      "\tmean : 74\n",
      "\t95percentile : 142\n",
      "\t99percentile : 180\n",
      "x_test shape: (450,150)\n",
      "y_test shape: (450, 8)\n",
      "task: text classification\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alex0\\AppData\\Roaming\\Python\\Python39\\site-packages\\ktrain\\utils.py:621: UserWarning: class_names argument was ignored, as they were extracted from string labels in dataset\n",
      "  if self.get_classes(): warnings.warn('class_names argument was ignored, as they were extracted from string labels in dataset')\n"
     ]
    }
   ],
   "source": [
    "trn2, val2, preproc2 = text.texts_from_array(\n",
    "    x_train=x_train, y_train=y_train,\n",
    "    x_test=x_test, y_test=y_test,\n",
    "    ngram_range=1, \n",
    "    maxlen=150,\n",
    "    class_names = np.unique(df_train_labels['genre']),\n",
    "    preprocess_mode='standard'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is Multi-Label? False\n",
      "compiling word ID features...\n",
      "maxlen is 150\n",
      "building document-term matrix... this may take a few moments...\n",
      "rows: 1-7678\n",
      "computing log-count ratios...\n",
      "done.\n"
     ]
    }
   ],
   "source": [
    "model2 = text.text_classifier('nbsvm', train_data=trn2, preproc=preproc2, metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = ktrain.get_learner(model2, train_data=trn2, val_data=val2, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 0.001...\n",
      "Epoch 1/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.9245 - accuracy: 0.3108 - val_loss: 1.5948 - val_accuracy: 0.5000\n",
      "Epoch 2/10\n",
      "240/240 [==============================] - 1s 2ms/step - loss: 1.7108 - accuracy: 0.4401 - val_loss: 1.4113 - val_accuracy: 0.5667\n",
      "Epoch 3/10\n",
      "240/240 [==============================] - 1s 2ms/step - loss: 1.5630 - accuracy: 0.4885 - val_loss: 1.3296 - val_accuracy: 0.5933\n",
      "Epoch 4/10\n",
      "240/240 [==============================] - 1s 2ms/step - loss: 1.4434 - accuracy: 0.5268 - val_loss: 1.2755 - val_accuracy: 0.5978\n",
      "Epoch 5/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.3453 - accuracy: 0.5469 - val_loss: 1.2456 - val_accuracy: 0.5933\n",
      "Epoch 6/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.2651 - accuracy: 0.5675 - val_loss: 1.2115 - val_accuracy: 0.5889\n",
      "Epoch 7/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.2101 - accuracy: 0.5797 - val_loss: 1.2147 - val_accuracy: 0.5822\n",
      "Epoch 8/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.1742 - accuracy: 0.5880 - val_loss: 1.2166 - val_accuracy: 0.5756\n",
      "Epoch 9/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.1515 - accuracy: 0.5927 - val_loss: 1.2168 - val_accuracy: 0.5689\n",
      "Epoch 10/10\n",
      "240/240 [==============================] - 1s 3ms/step - loss: 1.1395 - accuracy: 0.5952 - val_loss: 1.2174 - val_accuracy: 0.5689\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x256b5cc4070>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.fit_onecycle(lr=0.001,epochs=10, class_weight=class_weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(learner.history.history['accuracy'])\n",
    "plt.plot(learner.history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(learner.history.history['loss'])\n",
    "plt.plot(learner.history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'val'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.38      0.05      0.10        55\n",
      "           1       0.16      0.07      0.09        45\n",
      "           2       0.58      0.67      0.62        64\n",
      "           3       0.27      0.34      0.30        44\n",
      "           4       0.75      0.67      0.70        66\n",
      "           5       0.75      1.00      0.86        74\n",
      "           6       0.65      0.75      0.69        44\n",
      "           7       0.49      0.71      0.58        58\n",
      "\n",
      "    accuracy                           0.57       450\n",
      "   macro avg       0.50      0.53      0.49       450\n",
      "weighted avg       0.53      0.57      0.53       450\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 3,  3,  5, 16,  0,  4,  1, 23],\n",
       "       [ 0,  3,  9,  7,  2,  6,  7, 11],\n",
       "       [ 2,  1, 43, 11,  3,  1,  0,  3],\n",
       "       [ 1,  4,  8, 15,  7,  5,  2,  2],\n",
       "       [ 1,  4,  4,  0, 44,  5,  6,  2],\n",
       "       [ 0,  0,  0,  0,  0, 74,  0,  0],\n",
       "       [ 0,  2,  2,  3,  2,  0, 33,  2],\n",
       "       [ 1,  2,  3,  4,  1,  4,  2, 41]], dtype=int64)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learner.evaluate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = ktrain.get_predictor(model2, preproc=preproc2)\n",
    "predictor.save('text_only_nbsvm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "984944ce4eefbed249e2a3501e72ee0777f56e66d10a0a53283275062444400f"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}