Attempt 8 MFCC on NeuroLex Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lucaslyon/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import librosa\n",
    "#import librosa.display\n",
    "import numpy as np\n",
    "#import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "#from matplotlib.pyplot import specgram\n",
    "import pandas as pd\n",
    "import os\n",
    "import keras\n",
    "from keras.preprocessing import sequence\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Embedding\n",
    "from keras.layers import LSTM\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.utils import to_categorical\n",
    "from keras.layers import Input, Flatten, Dropout, Activation\n",
    "from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "angryFiles = os.listdir('emotion_train_set/angry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['379_4.wav', '441_3.json', '845_2.wav', '1986_5.wav', '210_3.json']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "angryFiles[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "disgustFiles = os.listdir('emotion_train_set/disgust')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "fearFiles = os.listdir('emotion_train_set/fear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "happyFiles = os.listdir('emotion_train_set/happy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "neutralFiles = os.listdir('emotion_train_set/neutral')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "sadFiles = os.listdir('emotion_train_set/sad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "surpriseFiles = os.listdir('emotion_train_set/surprise')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "angryFiles.sort()\n",
    "disgustFiles.sort()\n",
    "fearFiles.sort()\n",
    "happyFiles.sort()\n",
    "neutralFiles.sort()\n",
    "sadFiles.sort()\n",
    "surpriseFiles.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NoBackendError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNoBackendError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-90ca2d1980e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mbookmark\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mangryFilesList\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msampling_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlibrosa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mangryFilesDir\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mduration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0msample_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampling_rate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     mfccs = np.mean(librosa.feature.mfcc(y=X, \n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/librosa/core/audio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(path, sr, mono, offset, duration, dtype, res_type)\u001b[0m\n\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 112\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0maudioread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudio_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrealpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    113\u001b[0m         \u001b[0msr_native\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamplerate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mn_channels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/audioread/__init__.py\u001b[0m in \u001b[0;36maudio_open\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;31m# All backends failed!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m     \u001b[0;32mraise\u001b[0m \u001b[0mNoBackendError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNoBackendError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "angryFilesList = os.listdir('emotion_train_set/angry')\n",
    "angryFilesList.sort()\n",
    "angryFilesDir = 'emotion_train_set/angry/'\n",
    "df = pd.DataFrame(columns=['feature'])\n",
    "bookmark=0\n",
    "for file in angryFilesList:\n",
    "    X, sampling_rate = librosa.load(angryFilesDir+file, duration=0.5)\n",
    "    sample_rate = np.array(sampling_rate)\n",
    "    mfccs = np.mean(librosa.feature.mfcc(y=X, \n",
    "                                        sr=sample_rate, \n",
    "                                        n_mfcc=13),\n",
    "                    axis=0)\n",
    "    feature = mfccs\n",
    "    df.loc[bookmark] = [feature]\n",
    "    bookmark+=1\n",
    "\n",
    "angryDf = pd.DataFrame(df['feature'].values.tolist())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "angryDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "angryJSONFiles = os.listdir('emotion_train_set/angry')\n",
    "angryJSONFiles.sort()\n",
    "angryData=[]\n",
    "for file in angryJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/angry/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            angryData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "disgustJSONFiles = os.listdir('emotion_train_set/disgust')\n",
    "disgustJSONFiles.sort()\n",
    "disgustData=[]\n",
    "for file in disgustJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/disgust/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            disgustData.append(d['features'])\n",
    "    except: pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fearJSONFiles = os.listdir('emotion_train_set/fear')\n",
    "fearJSONFiles.sort()\n",
    "fearData=[]\n",
    "for file in fearJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/fear/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            fearData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "happyJSONFiles = os.listdir('emotion_train_set/happy')\n",
    "happyJSONFiles.sort()\n",
    "happyData=[]\n",
    "for file in happyJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/happy/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            happyData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "neutralJSONFiles = os.listdir('emotion_train_set/neutral')\n",
    "neutralJSONFiles.sort()\n",
    "neutralData=[]\n",
    "for file in neutralJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/neutral/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            neutralData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sadJSONFiles = os.listdir('emotion_train_set/sad')\n",
    "sadJSONFiles.sort()\n",
    "sadData=[]\n",
    "for file in sadJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/sad/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            sadData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "surpriseJSONFiles = os.listdir('emotion_train_set/surprise')\n",
    "surpriseJSONFiles.sort()\n",
    "surpriseData=[]\n",
    "for file in surpriseJSONFiles:\n",
    "    try:\n",
    "        with open('emotion_train_set/surprise/'+file) as json_data:\n",
    "            d = json.load(json_data)\n",
    "            surpriseData.append(d['features'])\n",
    "    except: pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'angryData' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-4bf1e224063a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mangryDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mangryData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mdisgustDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdisgustData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mfearDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfearData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mhappyDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhappyData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mneutralDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneutralData\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'angryData' is not defined"
     ]
    }
   ],
   "source": [
    "angryDf = pd.DataFrame(angryData)\n",
    "disgustDf = pd.DataFrame(disgustData)\n",
    "fearDf = pd.DataFrame(fearData)\n",
    "happyDf = pd.DataFrame(happyData)\n",
    "neutralDf = pd.DataFrame(neutralData)\n",
    "sadDf = pd.DataFrame(sadData)\n",
    "surpriseDf = pd.DataFrame(surpriseData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "angryDf['label'] = 'angry'\n",
    "disgustDf['label'] = 'disgust'\n",
    "fearDf['label'] = 'fear'\n",
    "happyDf['label'] = 'happy'\n",
    "neutralDf['label'] = 'neutral'\n",
    "sadDf['label'] = 'sad'\n",
    "surpriseDf['label'] = 'surprise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'angryDf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-7a2c2066eff2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0memotionsDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mangryDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdisgustDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfearDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhappyDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneutralDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msadDf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msurpriseDf\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'angryDf' is not defined"
     ]
    }
   ],
   "source": [
    "emotionsDf = pd.concat([angryDf, disgustDf, fearDf, happyDf, neutralDf, sadDf, surpriseDf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'emotionsDf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-f57ed80458b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mshuffledEmotionsDf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0memotionsDf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'emotionsDf' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "shuffledEmotionsDf = shuffle(emotionsDf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'shuffledEmotionsDf' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-30ac1642ba26>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdivider\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffledEmotionsDf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.85\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffledEmotionsDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdivider\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffledEmotionsDf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mdivider\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'shuffledEmotionsDf' is not defined"
     ]
    }
   ],
   "source": [
    "divider = np.random.rand(len(shuffledEmotionsDf)) < 0.85\n",
    "train = shuffledEmotionsDf[divider]\n",
    "test = shuffledEmotionsDf[~divider]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-52be56145d8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtrainlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"
     ]
    }
   ],
   "source": [
    "trainfeatures = train.iloc[:, :-1]\n",
    "trainlabel = train.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "testfeatures = test.iloc[:, :-1]\n",
    "testlabel = test.iloc[:, -1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trainfeatures' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-6ca34be26671>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtestfeatures\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trainfeatures' is not defined"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "X_train = np.array(trainfeatures)\n",
    "y_train = np.array(trainlabel)\n",
    "X_test = np.array(testfeatures)\n",
    "y_test = np.array(testlabel)\n",
    "\n",
    "lb = LabelEncoder()\n",
    "\n",
    "y_train = np_utils.to_categorical(lb.fit_transform(y_train))\n",
    "y_test = np_utils.to_categorical(lb.fit_transform(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5707, 272)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv1D(128, 5,padding='same',\n",
    "                 input_shape=(272,1)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv1D(128, 5,padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.1))\n",
    "model.add(MaxPooling1D(pool_size=(8)))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Conv1D(128, 5,padding='same',))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(7))\n",
    "model.add(Activation('softmax'))\n",
    "opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "plot_model(model, to_file='Attempt8Architecture.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pad sequences\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'X_train' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-57701a41a9f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Pad sequences'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mx_traincnn\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mx_testcnn\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
     ]
    }
   ],
   "source": [
    "print('Pad sequences')\n",
    "x_traincnn =np.expand_dims(X_train, axis=2)\n",
    "x_testcnn= np.expand_dims(X_test, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.losses import mean_squared_error\n",
    "model.compile(loss=mean_squared_error, optimizer=opt,metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnnhistory=model.fit(x_traincnn, y_train, batch_size=32, epochs=100, validation_data=(x_testcnn, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(cnnhistory.history['acc'])\n",
    "plt.plot(cnnhistory.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.save(\"Attempt8Acc.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot training & validation loss values\n",
    "plt.plot(cnnhistory.history['loss'])\n",
    "plt.plot(cnnhistory.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.savefig(\"Attempt8Loss.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "score = model.evaluate(x_testcnn, y_test, verbose=0)\n",
    "print(\"test loss:\", score[0])\n",
    "print(\"test accuracy:\", score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "model.save('full_model_v8.h5') #saving\n",
    "#model = load_model('full_model.h5') #loading"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}