Tensorflow LSTM Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, LSTM\n",
    "from keras.layers import Convolution2D, MaxPooling2D\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping,ReduceLROnPlateau,ModelCheckpoint\n",
    "from keras.utils import np_utils\n",
    "from sklearn import metrics \n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, auc, roc_curve, classification_report\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import itertools\n",
    "\n",
    "import os, fnmatch\n",
    "import librosa\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMPLE_RATE = 16000\n",
    "\n",
    "MAX_SOUND_CLIP_DURATION=12 # seconds\n",
    "\n",
    "def audio_norm(data):\n",
    "    max_data = np.max(data)\n",
    "    min_data = np.min(data)\n",
    "    data = (data-min_data)/(max_data-min_data+0.0001)\n",
    "    return data-0.5\n",
    "\n",
    "# get audio data without padding highest qualify audio\n",
    "def load_file_data_without_change(folder,file_names, duration=3, sr=16000):\n",
    "    input_length=sr*duration\n",
    "    # function to load files and extract features\n",
    "    # file_names = glob.glob(os.path.join(folder, '*.wav'))\n",
    "    data = []\n",
    "    for file_name in file_names:\n",
    "        try:\n",
    "            sound_file=folder+file_name\n",
    "            print (\"load file \",sound_file)\n",
    "            # use kaiser_fast technique for faster extraction\n",
    "            X, sr = librosa.load( sound_file, res_type='kaiser_fast') \n",
    "            dur = librosa.get_duration(y=X, sr=sr)\n",
    "            # extract normalized mfcc feature from data\n",
    "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sr, n_mfcc=40).T,axis=0) \n",
    "        except Exception as e:\n",
    "            print(\"Error encountered while parsing file: \", file)\n",
    "        feature = np.array(mfccs).reshape([-1,1])\n",
    "        data.append(feature)\n",
    "    return data\n",
    "\n",
    "\n",
    "# get audio data with a fix padding may also chop off some file\n",
    "def load_file_data (folder, file_names, duration=12, sr=16000):\n",
    "    input_length=sr*duration\n",
    "    # function to load files and extract features\n",
    "    # file_names = glob.glob(os.path.join(folder, '*.wav'))\n",
    "    data = []\n",
    "    for file_name in file_names:\n",
    "        try:\n",
    "            sound_file=folder+file_name\n",
    "            print (\"load file \",sound_file)\n",
    "            # use kaiser_fast technique for faster extraction\n",
    "            X, sr = librosa.load( sound_file, sr=sr, duration=duration,res_type='kaiser_fast') \n",
    "            dur = librosa.get_duration(y=X, sr=sr)\n",
    "            # pad audio file same duration\n",
    "            if (round(dur) < duration):\n",
    "                print (\"fixing audio lenght :\", file_name)\n",
    "                y = librosa.util.fix_length(X, input_length)                \n",
    "            #normalized raw audio \n",
    "            # y = audio_norm(y)            \n",
    "            # extract normalized mfcc feature from data\n",
    "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sr, n_mfcc=40).T,axis=0)             \n",
    "        except Exception as e:\n",
    "            print(\"Error encountered while parsing file: \", file)        \n",
    "        feature = np.array(mfccs).reshape([-1,1])\n",
    "        data.append(feature)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'artifact': 0, 'murmur': 1, 'normal': 2}\n",
      " \n",
      "{0: 'artifact', 1: 'murmur', 2: 'normal'}\n"
     ]
    }
   ],
   "source": [
    "# simple encoding of categories, limited to 3 types\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "\n",
    "# Map label text to integer\n",
    "CLASSES = ['artifact','murmur','normal']\n",
    "# {'artifact': 0, 'murmur': 1, 'normal': 3}\n",
    "NB_CLASSES=len(CLASSES)\n",
    "\n",
    "# Map integer value to text labels\n",
    "label_to_int = {k:v for v,k in enumerate(CLASSES)}\n",
    "print (label_to_int)\n",
    "print (\" \")\n",
    "# map integer to label text\n",
    "int_to_label = {v:k for k,v in label_to_int.items()}\n",
    "print(int_to_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fixing audio lenght : Aunlabelledtest__201108011116.wav\n",
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      "fixing audio lenght : Aunlabelledtest__201108222257.wav\n"
     ]
    }
   ],
   "source": [
    "# load dataset-a, keep them separate for testing purpose\n",
    "\n",
    "INPUT_DIR = \"data\"\n",
    "A_folder=INPUT_DIR+'/set_a/'\n",
    "# set-a\n",
    "A_artifact_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_a'), 'artifact*.wav')\n",
    "A_artifact_sounds = load_file_data(folder=A_folder,file_names=A_artifact_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "A_artifact_labels = [0 for items in A_artifact_files]\n",
    "\n",
    "A_normal_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_a'), 'normal*.wav')\n",
    "A_normal_sounds = load_file_data(folder=A_folder,file_names=A_normal_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "A_normal_labels = [2 for items in A_normal_sounds]\n",
    "\n",
    "A_extrahls_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_a'), 'extrahls*.wav')\n",
    "A_extrahls_sounds = load_file_data(folder=A_folder,file_names=A_extrahls_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "A_extrahls_labels = [1 for items in A_extrahls_sounds]\n",
    "\n",
    "A_murmur_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_a'), 'murmur*.wav')\n",
    "A_murmur_sounds = load_file_data(folder=A_folder,file_names=A_murmur_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "A_murmur_labels = [1 for items in A_murmur_files]\n",
    "\n",
    "# test files\n",
    "A_unlabelledtest_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_a'), 'Aunlabelledtest*.wav')\n",
    "A_unlabelledtest_sounds = load_file_data(folder=A_folder,file_names=A_unlabelledtest_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "A_unlabelledtest_labels = [-1 for items in A_unlabelledtest_sounds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "load file  data/set_b/Bunlabelledtest__245_1309200438094_C.wav\n",
      "fixing audio lenght : Bunlabelledtest__245_1309200438094_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__248_1309201683806_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__248_1309201683806_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__248_1309201683806_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__254_1309350589009_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__254_1309350589009_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__255_1309351210897_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__255_1309351210897_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__259_1309352630271_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__259_1309352630271_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__260_1309353164458_A.wav\n",
      "fixing audio lenght : Bunlabelledtest__260_1309353164458_A.wav\n",
      "load file  data/set_b/Bunlabelledtest__262_1309355283807_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__262_1309355283807_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__264_1309356143724_A.wav\n",
      "fixing audio lenght : Bunlabelledtest__264_1309356143724_A.wav\n",
      "load file  data/set_b/Bunlabelledtest__268_1309368960960_A.wav\n",
      "fixing audio lenght : Bunlabelledtest__268_1309368960960_A.wav\n",
      "load file  data/set_b/Bunlabelledtest__268_1309368960960_E.wav\n",
      "fixing audio lenght : Bunlabelledtest__268_1309368960960_E.wav\n",
      "load file  data/set_b/Bunlabelledtest__270_1309369533040_D.wav\n",
      "fixing audio lenght : Bunlabelledtest__270_1309369533040_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__271_1309369876160_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__272_1309370164386_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__272_1309370164386_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__274_1311075637574_C.wav\n",
      "fixing audio lenght : Bunlabelledtest__274_1311075637574_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__278_1311163365896_C.wav\n",
      "fixing audio lenght : Bunlabelledtest__278_1311163365896_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__278_1311163365896_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__280_1311165195344_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__280_1311165195344_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__281_1311165683454_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__282_1311166081161_D.wav\n",
      "fixing audio lenght : Bunlabelledtest__282_1311166081161_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__284_1311168471850_C.wav\n",
      "fixing audio lenght : Bunlabelledtest__284_1311168471850_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__286_1311170606028_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__286_1311170606028_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__287_1311170903290_D.wav\n",
      "fixing audio lenght : Bunlabelledtest__287_1311170903290_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__288_1311164615284_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__288_1311164615284_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__290_1311182875320_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__290_1311182875320_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__293_1311680805936_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__293_1311680805936_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__293_1311680805936_B2.wav\n",
      "fixing audio lenght : Bunlabelledtest__293_1311680805936_B2.wav\n",
      "load file  data/set_b/Bunlabelledtest__294_1311681084248_D.wav\n",
      "fixing audio lenght : Bunlabelledtest__294_1311681084248_D.wav\n",
      "load file  data/set_b/Bunlabelledtest__296_1311682952647_A.wav\n",
      "fixing audio lenght : Bunlabelledtest__296_1311682952647_A.wav\n",
      "load file  data/set_b/Bunlabelledtest__296_1311682952647_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__296_1311682952647_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__300_1311772096120_B.wav\n",
      "fixing audio lenght : Bunlabelledtest__300_1311772096120_B.wav\n",
      "load file  data/set_b/Bunlabelledtest__300_1311772096120_C.wav\n",
      "fixing audio lenght : Bunlabelledtest__300_1311772096120_C.wav\n",
      "load file  data/set_b/Bunlabelledtest__300_1311772096120_D.wav\n",
      "fixing audio lenght : Bunlabelledtest__300_1311772096120_D.wav\n"
     ]
    }
   ],
   "source": [
    "# load dataset-b, keep them separate for testing purpose \n",
    "B_folder=INPUT_DIR+'/set_b/'\n",
    "# set-b\n",
    "B_normal_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_b'), 'normal*.wav')  # include noisy files\n",
    "B_normal_sounds = load_file_data(folder=B_folder,file_names=B_normal_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "B_normal_labels = [2 for items in B_normal_sounds]\n",
    "\n",
    "B_murmur_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_b'), 'murmur*.wav')  # include noisy files\n",
    "B_murmur_sounds = load_file_data(folder=B_folder,file_names=B_murmur_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "B_murmur_labels = [1 for items in B_murmur_files]\n",
    "\n",
    "B_extrastole_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_b'), 'extrastole*.wav')\n",
    "B_extrastole_sounds = load_file_data(folder=B_folder,file_names=B_extrastole_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "B_extrastole_labels = [1 for items in B_extrastole_files]\n",
    "\n",
    "#test files\n",
    "B_unlabelledtest_files = fnmatch.filter(os.listdir(INPUT_DIR+'/set_b'), 'Bunlabelledtest*.wav')\n",
    "B_unlabelledtest_sounds = load_file_data(folder=B_folder,file_names=B_unlabelledtest_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "B_unlabelledtest_labels = [-1 for items in B_unlabelledtest_sounds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data records:  585\n",
      "Testing data records:  247\n",
      "Total records:  832\n"
     ]
    }
   ],
   "source": [
    "# Concatenate train and test data\n",
    "x_data = np.concatenate((A_artifact_sounds, A_normal_sounds,A_extrahls_sounds,A_murmur_sounds, \n",
    "                         B_normal_sounds,B_murmur_sounds,B_extrastole_sounds))\n",
    "\n",
    "y_data = np.concatenate((A_artifact_labels, A_normal_labels,A_extrahls_labels,A_murmur_labels,\n",
    "                         B_normal_labels,B_murmur_labels,B_extrastole_labels))\n",
    "\n",
    "test_x = np.concatenate((A_unlabelledtest_sounds,B_unlabelledtest_sounds))\n",
    "test_y = np.concatenate((A_unlabelledtest_labels,B_unlabelledtest_labels))\n",
    "\n",
    "print (\"Training data records: \",len(x_data))\n",
    "print (\"Testing data records: \",len(test_x))\n",
    "print(\"Total records: \", len(x_data) + len(test_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_test_split train data\n",
    "X_train, X_val, y_train, y_val = train_test_split(x_data, y_data, test_size=0.20, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-Hot encoding for classes\n",
    "y_train = np.array(keras.utils.to_categorical(y_train, len(CLASSES)))\n",
    "y_val = np.array(keras.utils.to_categorical(y_val, len(CLASSES)))\n",
    "test_y = np.array(keras.utils.to_categorical(test_y, len(CLASSES)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info of the dataset\n",
      "\n",
      "label shape:  (585,)\n",
      "data size of the array: : 585\n",
      "length of one array element in bytes:  4\n",
      "total bytes consumed by the elements of the array:  2340\n",
      "0\n",
      "\n",
      "audio data shape:  (468, 40, 1)\n",
      "data size of the array: : 18720\n",
      "length of one array element in bytes:  4\n",
      "total bytes consumed by the elements of the array:  74880\n",
      "\n",
      "training data shape:  (468, 40, 1)\n",
      "training label shape:  (468, 3)\n",
      "\n",
      "validation data shape:  (117, 40, 1)\n",
      "validation label shape:  (117, 3)\n",
      "\n",
      "test data shape:  (247, 40, 1)\n",
      "test label shape:  (247, 3)\n"
     ]
    }
   ],
   "source": [
    "print('info of the dataset')\n",
    "print()\n",
    "\n",
    "print (\"label shape: \", y_data.shape)\n",
    "print (\"data size of the array: : %s\" % y_data.size)\n",
    "print (\"length of one array element in bytes: \", y_data.itemsize)\n",
    "print (\"total bytes consumed by the elements of the array: \", y_data.nbytes)\n",
    "print (y_data[1])\n",
    "print ()\n",
    "\n",
    "print (\"audio data shape: \", X_train.shape)\n",
    "print (\"data size of the array: : %s\" % X_train.size)\n",
    "print (\"length of one array element in bytes: \", X_train.itemsize)\n",
    "print (\"total bytes consumed by the elements of the array: \", X_train.nbytes)\n",
    "\n",
    "print ()\n",
    "print (\"training data shape: \", X_train.shape)\n",
    "print (\"training label shape: \", y_train.shape)\n",
    "print (\"\")\n",
    "print (\"validation data shape: \", X_val.shape)\n",
    "print (\"validation label shape: \", y_val.shape)\n",
    "print ()\n",
    "print (\"test data shape: \", test_x.shape)\n",
    "print (\"test label shape: \", test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 40, 64)            16896     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 40, 64)            0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 32)                12416     \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 3)                 99        \n",
      "=================================================================\n",
      "Total params: 29,411\n",
      "Trainable params: 29,411\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 00017: early stopping\n"
     ]
    }
   ],
   "source": [
    "epochs=100\n",
    "batch_size=20 \n",
    "hidden_size=64\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(hidden_size, return_sequences=True, input_shape = (40,1)))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(32, return_sequences=False))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(len(CLASSES), activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['acc','mse', 'mae'])\n",
    "model.summary()\n",
    "\n",
    "early_stopping = keras.callbacks.EarlyStopping(monitor='loss', mode='min', verbose=1, patience=5)\n",
    "weights = ModelCheckpoint(filepath='models/LSTMbeatclassification.h5')\n",
    "    \n",
    "history=model.fit(X_train, y_train, \n",
    "                  batch_size=batch_size, \n",
    "                  epochs=epochs,\n",
    "                  verbose=0,\n",
    "                  validation_data=(X_val, y_val),\n",
    "                  callbacks=[early_stopping, weights]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    \n",
    "    #model accuracy\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'val'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "    #model loss\n",
    "    plt.plot(history.history['loss'])\n",
    "    plt.plot(history.history['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'val'], loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model evaluation accuracy:  68 %\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(X_val, y_val, verbose=0)\n",
    "print (\"Model evaluation accuracy: \", round(scores[1]*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    artifact       0.89      1.00      0.94         8\n",
      "      murmur       0.73      0.19      0.30        43\n",
      "      normal       0.65      0.95      0.77        66\n",
      "\n",
      "    accuracy                           0.68       117\n",
      "   macro avg       0.76      0.71      0.67       117\n",
      "weighted avg       0.69      0.68      0.61       117\n",
      "\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict(X_val) # label scores \n",
    "classpreds = np.argmax(preds, axis=1) # predicted classes \n",
    "y_testclass = np.argmax(y_val, axis=1) # true classes\n",
    "# Classification Report\n",
    "print(classification_report(y_testclass, classpreds, target_names=CLASSES))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8  0  0]\n",
      " [ 1  8 34]\n",
      " [ 0  3 63]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "print(confusion_matrix(y_testclass, classpreds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ROC plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_classes = len(CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in range(n_classes):\n",
    "    fpr[i], tpr[i], _ = roc_curve(y_val[:, i], preds[:, i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curves\n",
    "fig, ax = plt.subplots(figsize=(16, 10))\n",
    "ax.plot([0, 1], [0, 1], 'k--')\n",
    "ax.set_xlim([0.0, 1.0])\n",
    "ax.set_ylim([0.0, 1.05])\n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_title('ROC Curve for Each Class')\n",
    "for i in range(n_classes):\n",
    "    ax.plot(fpr[i], tpr[i], linewidth=3, label='ROC curve (area = %0.2f) for %s' % (roc_auc[i], CLASSES[i]))\n",
    "ax.legend(loc=\"best\", fontsize='x-large')\n",
    "ax.grid(alpha=.4)\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction test return : 2 - normal\n",
      "\n",
      "All predicted labels:  [0 2 2 1 2 1 1 2 2 2 1 0 2 2 2 1 0 1 1 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
      " 1 0 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "# Example predict on test data\n",
    "y_pred = model.predict_classes(test_x, batch_size=32)\n",
    "print (\"prediction test return :\",y_pred[1], \"-\", int_to_label[y_pred[1]])\n",
    "print()\n",
    "print('All predicted labels: ' , y_pred)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Try model audio in folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "load file  data/test/Bunlabelledtest__103_1305031931979_A.wav\n",
      "fixing audio lenght : Bunlabelledtest__103_1305031931979_A.wav\n",
      "load file  data/test/Bunlabelledtest__105_1305033453095_D.wav\n",
      "Testing record files:  2\n"
     ]
    }
   ],
   "source": [
    "folder_from = \"data\"\n",
    "test_folder=INPUT_DIR+'/test/'\n",
    "# test data\n",
    "test_files = fnmatch.filter(os.listdir(INPUT_DIR+'/test'), '*.wav')\n",
    "test_sounds = load_file_data(folder=test_folder,file_names=test_files, duration=MAX_SOUND_CLIP_DURATION)\n",
    "print('Testing record files: ', len(test_sounds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = np.array(test_sounds).reshape((len(test_sounds),-1,1))\n",
    "#test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction test return : 2 - normal\n",
      "prediction test return : 2 - normal\n"
     ]
    }
   ],
   "source": [
    "# Example predict on test data\n",
    "y_pred = model.predict_classes(test)\n",
    "for pred in y_pred:    \n",
    "    print (\"prediction test return :\",pred, \"-\", int_to_label[pred])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}