Neural_Network-Accelerometer-Features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  This notebook shows an example of neural networks applied to time series classification using Tensorflow. The task here is to classify 6 different types of activities (walking, walking upstairs, walking downstairs, sitting, standing, laying) based on x, y and z accelerometer signals. We extract features from x, y and z signals (statistical features, geometrical features, frequency domain features...) and feed them into a 2-layer Neural Network with softmax activation. We add L2 regularization to prevent overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: MacOSX\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score\n",
    "from sklearn import preprocessing\n",
    "import plot_confusion_matrix as pcm\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the raw accelerometer signals of the 3 available axis (x, y and z). Each sample of the data set is a 2.56s window of an activity being performed recorded at a 50Hz rate which makes 128 readings per sample per axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train_x_raw shape : (7352, 128)\n",
      "X_test_x_raw shape : (2947, 128)\n",
      "X_train_y_raw shape : (7352, 128)\n",
      "X_test_y_raw shape : (2947, 128)\n",
      "X_train_z_raw shape : (7352, 128)\n",
      "X_test_z_raw shape : (2947, 128)\n"
     ]
    }
   ],
   "source": [
    "os.chdir('data')\n",
    "# Raw signals\n",
    "# X axis\n",
    "X_train_x_raw = np.loadtxt('X_x_train.txt')\n",
    "X_test_x_raw = np.loadtxt('X_x_test.txt')\n",
    "# Y axis\n",
    "X_train_y_raw = np.loadtxt('X_y_train.txt')\n",
    "X_test_y_raw = np.loadtxt('X_y_test.txt')\n",
    "# Z axis\n",
    "X_train_z_raw = np.loadtxt('X_z_train.txt')\n",
    "X_test_z_raw = np.loadtxt('X_z_test.txt')\n",
    "\n",
    "print(\"X_train_x_raw shape : {}\".format(X_train_x_raw.shape))\n",
    "print(\"X_test_x_raw shape : {}\".format(X_test_x_raw.shape))\n",
    "print(\"X_train_y_raw shape : {}\".format(X_train_y_raw.shape))\n",
    "print(\"X_test_y_raw shape : {}\".format(X_test_y_raw.shape))\n",
    "print(\"X_train_z_raw shape : {}\".format(X_train_z_raw.shape))\n",
    "print(\"X_test_z_raw shape : {}\".format(X_test_z_raw.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Feature extraction helper functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As explained at the beginning of this notebook, we are not going to use the raw signals as input for our model but we are going to extract features from the signals. The following functions help us build the feature vectors out of the raw signals we have loaded above.\n",
    "These functions help us extract statistical and geometrical features from raw signals and jerk signals (acceleration first derivative), frequency domain features from raw signals and jerk signals\n",
    "For each sample we extract the following features:\n",
    "  - **x,y and z raw signals** : mean, max, min, standard deviation, skewness, kurtosis, interquartile range, median absolute deviation, area under curve, area under squared curve\n",
    "  - **x,y and z jerk signals (first derivative)** : mean, max, min, standard deviation, skewness, kurtosis, interquartile range, median absolute deviation, area under curve, area under squared curve\n",
    "  - **x,y and z raw signals Discrete Fourrier Transform**: mean, max, min, standard deviation, skewness, kurtosis, interquartile range, median absolute deviation, area under curve, area under squared curve, weighted mean frequency, 5 first DFT coefficients, 5 first local maxima of DFT coefficients and their corresponding frequencies.\n",
    "  - **x,y and z jerk signals Discrete Fourrier Transform**: mean, max, min, standard deviation, skewness, kurtosis, interquartile range, median absolute deviation, area under curve, area under squared curve, weighted mean frequency, 5 first DFT coefficients, 5 first local maxima of DFT coefficients and their corresponding frequencies.\n",
    "  - **x,y and z correlation coefficients**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.stats as st\n",
    "from scipy.fftpack import fft, fftfreq \n",
    "from scipy.signal import argrelextrema\n",
    "import operator\n",
    "\n",
    "def stat_area_features(x, Te=1.0):\n",
    "\n",
    "    mean_ts = np.mean(x, axis=1).reshape(-1,1) # mean\n",
    "    max_ts = np.amax(x, axis=1).reshape(-1,1) # max\n",
    "    min_ts = np.amin(x, axis=1).reshape(-1,1) # min\n",
    "    std_ts = np.std(x, axis=1).reshape(-1,1) # std\n",
    "    skew_ts = st.skew(x, axis=1).reshape(-1,1) # skew\n",
    "    kurtosis_ts = st.kurtosis(x, axis=1).reshape(-1,1) # kurtosis \n",
    "    iqr_ts = st.iqr(x, axis=1).reshape(-1,1) # interquartile rante\n",
    "    mad_ts = np.median(np.sort(abs(x - np.median(x, axis=1).reshape(-1,1)),\n",
    "                               axis=1), axis=1).reshape(-1,1) # median absolute deviation\n",
    "    area_ts = np.trapz(x, axis=1, dx=Te).reshape(-1,1) # area under curve\n",
    "    sq_area_ts = np.trapz(x ** 2, axis=1, dx=Te).reshape(-1,1) # area under curve ** 2\n",
    "\n",
    "    return np.concatenate((mean_ts,max_ts,min_ts,std_ts,skew_ts,kurtosis_ts,\n",
    "                           iqr_ts,mad_ts,area_ts,sq_area_ts), axis=1)\n",
    "\n",
    "def frequency_domain_features(x, Te=1.0):\n",
    "\n",
    "    # As the DFT coefficients and their corresponding frequencies are symetrical arrays\n",
    "    # with respect to the middle of the array we need to know if the number of readings \n",
    "    # in x is even or odd to then split the arrays...\n",
    "    if x.shape[1]%2 == 0:\n",
    "        N = int(x.shape[1]/2)\n",
    "    else:\n",
    "        N = int(x.shape[1]/2) - 1\n",
    "    xf = np.repeat(fftfreq(x.shape[1],d=Te)[:N].reshape(1,-1), x.shape[0], axis=0) # frequencies\n",
    "    dft = np.abs(fft(x, axis=1))[:,:N] # DFT coefficients   \n",
    "    \n",
    "    # statistical and area features\n",
    "    dft_features = stat_area_features(dft, Te=1.0)\n",
    "    # weighted mean frequency\n",
    "    dft_weighted_mean_f = np.average(xf, axis=1, weights=dft).reshape(-1,1)\n",
    "    # 5 first DFT coefficients \n",
    "    dft_first_coef = dft[:,:5]    \n",
    "    # 5 first local maxima of DFT coefficients and their corresponding frequencies\n",
    "    dft_max_coef = np.zeros((x.shape[0],5))\n",
    "    dft_max_coef_f = np.zeros((x.shape[0],5))\n",
    "    for row in range(x.shape[0]):\n",
    "        # finds all local maximas indexes\n",
    "        extrema_ind = argrelextrema(dft[row,:], np.greater, axis=0) \n",
    "        # makes a list of tuples (DFT_i, f_i) of all the local maxima\n",
    "        # and keeps the 5 biggest...\n",
    "        extrema_row = sorted([(dft[row,:][j],xf[row,j]) for j in extrema_ind[0]],\n",
    "                             key=operator.itemgetter(0), reverse=True)[:5] \n",
    "        for i, ext in enumerate(extrema_row):\n",
    "            dft_max_coef[row,i] = ext[0]\n",
    "            dft_max_coef_f[row,i] = ext[1]    \n",
    "    \n",
    "    return np.concatenate((dft_features,dft_weighted_mean_f,dft_first_coef,\n",
    "                           dft_max_coef,dft_max_coef_f), axis=1)\n",
    "\n",
    "def make_feature_vector(x,y,z, Te=1.0):\n",
    "\n",
    "    # Raw signals :  stat and area features\n",
    "    features_xt = stat_area_features(x, Te=Te)\n",
    "    features_yt = stat_area_features(y, Te=Te)\n",
    "    features_zt = stat_area_features(z, Te=Te)\n",
    "    \n",
    "    # Jerk signals :  stat and area features\n",
    "    features_xt_jerk = stat_area_features((x[:,1:]-x[:,:-1])/Te, Te=Te)\n",
    "    features_yt_jerk = stat_area_features((y[:,1:]-y[:,:-1])/Te, Te=Te)\n",
    "    features_zt_jerk = stat_area_features((z[:,1:]-z[:,:-1])/Te, Te=Te) \n",
    "    \n",
    "    # Raw signals : frequency domain features \n",
    "    features_xf = frequency_domain_features(x, Te=1/Te)\n",
    "    features_yf = frequency_domain_features(y, Te=1/Te)\n",
    "    features_zf = frequency_domain_features(z, Te=1/Te)\n",
    "    \n",
    "    # Jerk signals : frequency domain features \n",
    "    features_xf_jerk = frequency_domain_features((x[:,1:]-x[:,:-1])/Te, Te=1/Te)\n",
    "    features_yf_jerk = frequency_domain_features((y[:,1:]-y[:,:-1])/Te, Te=1/Te)\n",
    "    features_zf_jerk = frequency_domain_features((z[:,1:]-z[:,:-1])/Te, Te=1/Te)\n",
    "    \n",
    "    # Raw signals correlation coefficient between axis\n",
    "    cor = np.empty((x.shape[0],3))\n",
    "    for row in range(x.shape[0]):\n",
    "        xyz_matrix = np.concatenate((x[row,:].reshape(1,-1),y[row,:].reshape(1,-1),\n",
    "                                     z[row,:].reshape(1,-1)), axis=0)\n",
    "        cor[row,0] = np.corrcoef(xyz_matrix)[0,1]\n",
    "        cor[row,1] = np.corrcoef(xyz_matrix)[0,2]\n",
    "        cor[row,2] = np.corrcoef(xyz_matrix)[1,2]\n",
    "    \n",
    "    return np.concatenate((features_xt, features_yt, features_zt,\n",
    "                           features_xt_jerk, features_yt_jerk, features_zt_jerk,\n",
    "                           features_xf, features_yf, features_zf,\n",
    "                           features_xf_jerk, features_yf_jerk, features_zf_jerk,\n",
    "                           cor), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape : (7352, 219)\n",
      "X_test shape: (2947, 219)\n"
     ]
    }
   ],
   "source": [
    "X_train = make_feature_vector(X_train_x_raw, X_train_y_raw, X_train_z_raw, Te=1/50)\n",
    "X_test = make_feature_vector(X_test_x_raw, X_test_y_raw, X_test_z_raw, Te=1/50)\n",
    "\n",
    "print(\"X_train shape : {}\".format(X_train.shape))\n",
    "print(\"X_test shape: {}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We scale the features (standard scaler i.e. each feature column has a zero mean and one standard deviation). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(X_train)\n",
    "X_train = scaler.transform(X_train) \n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train shape : (7352, 6)\n",
      "y_test shape : (2947, 6)\n"
     ]
    }
   ],
   "source": [
    "y_train = np.loadtxt('y_train.txt') - 1\n",
    "y_train = (np.arange(np.unique(y_train).shape[0]) == y_train[:, None]).astype(int)\n",
    "\n",
    "y_test = np.loadtxt('y_test.txt') - 1\n",
    "y_test = (np.arange(np.unique(y_test).shape[0]) == y_test[:, None]).astype(int)\n",
    "\n",
    "print(\"y_train shape : {}\".format(y_train.shape))\n",
    "print(\"y_test shape : {}\".format(y_test.shape))\n",
    "\n",
    "label_names = ['Walking', 'Walking upstairs', 'Walking downstairs', 'Sitting', 'Standing', 'Laying']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Storing number of features and number of labels for later use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_features = X_train.shape[1]\n",
    "n_labels = y_train.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network and training parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "training_epoch = 1500\n",
    "batch_size = 100\n",
    "display_step = 100\n",
    "num_layer_1 = n_features\n",
    "num_layer_2 = n_features \n",
    "l = 1.0e-4 # L2 regularization coefficient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Tensorflow graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Neural Network input and output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, n_features])\n",
    "y = tf.placeholder(tf.float32, shape=[None, n_labels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Here we build the actual network with two layers: the first layer a n_features number of neurons with softmax activation and a second layer with num_layer_2 neurons and no activation function since we are going to apply softmax to the output of this second layer when defining the neural network cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 1st hidden layer\n",
    "W_1 = tf.Variable(tf.random_normal([num_layer_1, num_layer_2], stddev=0.01))\n",
    "b_1 = tf.Variable(tf.zeros([num_layer_2]))\n",
    "y_1 = tf.nn.softmax(tf.matmul(x, W_1) + b_1)\n",
    "\n",
    "# Output layer\n",
    "W_2 = tf.Variable(tf.random_normal([num_layer_2, n_labels], stddev=0.01))\n",
    "b_2 = tf.Variable(tf.zeros([n_labels]))\n",
    "y_pred = tf.matmul(y_1, W_2) + b_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Cost and optimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# L2 regularization coefficient: we make it a placeholder so we can set it to zero when evaluating training cost\n",
    "lam = tf.placeholder(tf.float32)\n",
    "# Cost function with L2 regularization\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_pred) + \n",
    "                      lam * (tf.nn.l2_loss(W_1) + tf.nn.l2_loss(W_2)))\n",
    "# optimizer\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n",
    "# prediction\n",
    "prediction = tf.nn.softmax(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paramters:\n",
      "learning rate = 0.001\n",
      "training_epoch = 1500\n",
      "batch_size = 100\n",
      "-- Network structure -- \n",
      "num_layer_1 = 219\n",
      "num_layer_2 = 219\n",
      "-- Regularization --\n",
      "L2 regularization = 0.0001\n",
      "\n",
      "\n",
      "Epoch n°    0. cost = 1.74488133.\n",
      "Epoch n°  100. cost = 0.36772378.\n",
      "Epoch n°  200. cost = 0.35364649.\n",
      "Epoch n°  300. cost = 0.35161251.\n",
      "Epoch n°  400. cost = 0.34900558.\n",
      "Epoch n°  500. cost = 0.34680990.\n",
      "Epoch n°  600. cost = 0.32722991.\n",
      "Epoch n°  700. cost = 0.30552918.\n",
      "Epoch n°  800. cost = 0.29736346.\n",
      "Epoch n°  900. cost = 0.29787096.\n",
      "Epoch n° 1000. cost = 0.29212622.\n",
      "Epoch n° 1100. cost = 0.29017006.\n",
      "Epoch n° 1200. cost = 0.29240596.\n",
      "Epoch n° 1300. cost = 0.28893587.\n",
      "Epoch n° 1400. cost = 0.28782980.\n",
      "Training over...\n",
      "\n",
      "\n",
      "Training set report\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "           Walking       1.00      1.00      1.00      1226\n",
      "  Walking upstairs       1.00      1.00      1.00      1073\n",
      "Walking downstairs       1.00      1.00      1.00       986\n",
      "           Sitting       0.71      0.53      0.61      1286\n",
      "          Standing       0.71      0.81      0.76      1374\n",
      "            Laying       0.85      0.94      0.89      1407\n",
      "\n",
      "       avg / total       0.87      0.87      0.87      7352\n",
      "\n",
      "Test set report\n",
      "                    precision    recall  f1-score   support\n",
      "\n",
      "           Walking       0.90      0.99      0.94       496\n",
      "  Walking upstairs       0.91      0.89      0.90       471\n",
      "Walking downstairs       0.98      0.89      0.94       420\n",
      "           Sitting       0.68      0.48      0.56       491\n",
      "          Standing       0.68      0.82      0.74       532\n",
      "            Laying       0.83      0.88      0.86       537\n",
      "\n",
      "       avg / total       0.82      0.82      0.82      2947\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python3.5/site-packages/matplotlib/tight_layout.py:222: UserWarning: tight_layout : falling back to Agg renderer\n",
      "  warnings.warn(\"tight_layout : falling back to Agg renderer\")\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()\n",
    "sess.run(init_op)\n",
    "\n",
    "cost_log = []\n",
    "\n",
    "print(\"Paramters:\")\n",
    "print(\"learning rate = {}\".format(learning_rate))\n",
    "print(\"training_epoch = {}\".format(training_epoch))\n",
    "print(\"batch_size = {}\".format(batch_size))\n",
    "print(\"-- Network structure -- \")\n",
    "print(\"num_layer_1 = {}\".format(num_layer_1))\n",
    "print(\"num_layer_2 = {}\".format(num_layer_2))\n",
    "print(\"-- Regularization --\")\n",
    "print(\"L2 regularization = {}\".format(l))\n",
    "print(\"\\n\")\n",
    "\n",
    "for epoch in range(training_epoch):\n",
    "    avg_cost = 0.\n",
    "    num_batch = X_train.shape[0] // batch_size\n",
    "    \n",
    "    # We first shuffle the training data\n",
    "    shuffle = np.random.permutation(X_train.shape[0])\n",
    "    X_train_s = X_train[shuffle, :]\n",
    "    y_train_s = y_train[shuffle, :]\n",
    "    \n",
    "    for i in range(num_batch):\n",
    "        batch_x = X_train_s[int(i*batch_size):int((i+1)*batch_size), :]\n",
    "        batch_y = y_train_s[int(i*batch_size):int((i+1)*batch_size), :]\n",
    "        train = sess.run(optimizer, feed_dict={x: batch_x, y: batch_y, lam: l})\n",
    "        c =  sess.run(cost, feed_dict={x: batch_x, y: batch_y, lam: 0.})        \n",
    "        avg_cost += c/num_batch\n",
    "        \n",
    "    if epoch % display_step == 0:\n",
    "        print(\"Epoch n° {:4d}. cost = {:.8f}.\".format(epoch, avg_cost))\n",
    "        \n",
    "    cost_log.append(avg_cost)\n",
    "    \n",
    "\n",
    "print(\"Training over...\")\n",
    "print(\"\\n\")\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(range(len(cost_log)), cost_log)\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"cost\")\n",
    "plt.title(\"Training set cost\")\n",
    "\n",
    "\n",
    "y_train_pred = sess.run(tf.argmax(prediction,1), feed_dict={x: X_train})\n",
    "print('Training set report')\n",
    "print(classification_report(np.argmax(y_train, axis=1), y_train_pred, target_names=label_names))\n",
    "\n",
    "\n",
    "y_test_pred = sess.run(tf.argmax(prediction,1), feed_dict={x: X_test})\n",
    "print('Test set report')\n",
    "print(classification_report(np.argmax(y_test, axis=1), y_test_pred, target_names=label_names))\n",
    "\n",
    "plt.figure(2)\n",
    "pcm.plot_confusion_matrix(confusion_matrix(np.argmax(y_train, axis=1), y_train_pred), label_names)\n",
    "plt.figure(3)\n",
    "pcm.plot_confusion_matrix(confusion_matrix(np.argmax(y_test, axis=1), y_test_pred), label_names)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training set cost per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bOZrZoZs5mtmhG9zGAOJDDCAAAABIVgwgPsQpWAAAAEhWDCA+xABibvLkyV4v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\nQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('images/nnf_c.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix: training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ygoGBAY4ePYpTp06hS5cuimP79+9Hbm4uDh48CFvbfx/vGhERUaLrCoKAXr16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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/nnf_train.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion matrix: test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XLpVtgimKInJycpCfn4979+7BxMRENqz7v6RSKSwtLZGamiors7GxQUFBgULv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kBDQ4CxvjbepWfL7NfQqQJcqlli2+mHYtlfccnQEAR0a1EDW0/9M0Vtr5Zfgohw6/HrkrugEiKV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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/nnf_test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We achieve good results here with metrics similar to what we do in the notebook about SVM and features ([here](https://github.com/jeandeducla/ML-Time-Series/blob/master/SVM-Accelerometer.ipynb)). There is still a little problem with the \"sitting\" label recall (\"sitting\" is confused with \"standing\"). The results are much better than the ones we have in the notebooks using raw features and neural networks ([here](https://github.com/jeandeducla/ML-Time-Series/blob/master/Neural_Network-Accelerometer-Raw.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}