09_eeg_use_case.ipynb

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       "    Copyright (C) 2019-2021 Scientific IT Services of ETH Zurich,\n",
       "    <p>\n",
       "    Contributing Authors:\n",
       "    Dr. Tarun Chadha,\n",
       "    Dr. Franziska Oschmann,\n",
       "    Dr. Mikolaj Rybinski,\n",
       "    Dr. Uwe Schmitt.\n",
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   "source": [
    "# IGNORE THIS CELL WHICH CUSTOMIZES LAYOUT AND STYLING OF THE NOTEBOOK !\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings = lambda *a, **kw: None\n",
    "from IPython.core.display import HTML; HTML(open(\"custom.html\", \"r\").read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 9: Use case - prediction of arm movements\n",
    "\n",
    "<center>\n",
    "<figure>\n",
    "<table><tr>\n",
    "<td> <img src=\"./images/eeg_cap.png\" style=\"width: 400px;\"/> </td>\n",
    "<td> <img src=\"./images/arm_movement.png\" style=\"width: 400px;\"/> </td>\n",
    "</tr></table>\n",
    "<figcaption>Setup of an EEG-experiment.</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<figure>\n",
    "    <img src=\"./images/eeg_electrode_numbering.jpg\" width=35%/> \n",
    "    <figcaption>Arrangement of electrodes on head.</figcaption>\n",
    "</figure>\n",
    "</center>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This data contains EEG recordings of one subject performing **grasp-and-lift (GAL)** trials. \n",
    "\n",
    "There is **1 subject** in total, **8 series** of trials for this subject, and approximately **30 trials** within each series. The number of trials varies for each series.\n",
    "\n",
    "For each **GAL**, you are tasked to detect 6 events:\n",
    "\n",
    "- HandStart\n",
    "- FirstDigitTouch\n",
    "- BothStartLoadPhase\n",
    "- LiftOff\n",
    "- Replace\n",
    "- BothReleased\n",
    "\n",
    "These events always occur in the same order. In this dataset, there are two files for the subject + series combination:\n",
    "\n",
    "the ***_data.csv** files contain the raw 32 channels EEG data (sampling rate 500Hz)\n",
    "the ***_events.csv** files contains the ground truth frame-wise labels for all events\n",
    "\n",
    "\n",
    "Detailed information about the data can be found here:\n",
    "Luciw MD, Jarocka E, Edin BB (2014) Multi-channel EEG recordings during 3,936 grasp and lift trials with varying weight and friction. Scientific Data 1:140047. www.nature.com/articles/sdata201447\n",
    "\n",
    "*Description from https://www.kaggle.com/c/grasp-and-lift-eeg-detection/data*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "<figure>\n",
    "    <img src=\"./images/eeg_signal_preprocessing.png\" title=\"made at imgflip.com\" width=75%/> \n",
    "    <figcaption>Preprocessing steps for EEG-signals.</figcaption>\n",
    "</figure>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can be found in: `/data/eeg_use_case` and contains:\n",
    "\n",
    "- 8 series of recorded EEG data\n",
    "\n",
    "- 8 series of events of arm movements\n",
    "\n",
    "Load the EEG data and the events:\n",
    "- combine all EEG series in one array (size: (total number of time series, number of channels))\n",
    "\n",
    "- combine all events in one array (size: (total number of time series, number of different arm movement))\n",
    "\n",
    "- pay attention to the order of the series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>Filter strings with the lambda-operator</strong>  \n",
    "     The lambda-operator allows to build hidden functions, which are basically functions without a name. These hidden      functions have any number of parameters, execute an expression and return the value of this expression. The lambda operator can be applied in the following way to filter the filenames:\n",
    "  \n",
    "     all_data_files = list(filter(lambda x: '_data' in x, os.listdir(path)))\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(file_names, path):\n",
    "    # read the csv file and drop the id column\n",
    "    dfs = []\n",
    "    for f in file_names:\n",
    "        df = pd.read_csv(path + f).drop('id', axis = 1)\n",
    "        dfs.append(df)\n",
    "    return dfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define path and list of all data and event files\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "path = 'data/eeg_use_case/' \n",
    "\n",
    "all_data_files = list(filter(lambda x: '_data' in x, os.listdir(path)))\n",
    "all_event_files = list(filter(lambda x: '_events' in x, os.listdir(path)))\n",
    "\n",
    "all_data_sort = np.sort(all_data_files)\n",
    "all_event_sort = np.sort(all_event_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load all data and event files\n",
    "all_data = np.concatenate(load_data(all_data_sort, path))\n",
    "all_events = np.concatenate(load_data(all_event_sort, path))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "\n",
    "Visualize the EEG-data and events and pay attention to:\n",
    "- the EEG traces (plt.plot())\n",
    "- the number of detected arm movements (plt.hist())\n",
    "\n",
    "What do you observe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 504x720 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 598,
       "width": 466
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = pd.read_csv(path + all_data_sort[0]).columns[1:]\n",
    "\n",
    "ix = np.arange(len(columns))[::8]\n",
    "labels = columns[::8]\n",
    "start = np.where(all_events == 1)[0][0]\n",
    "\n",
    "plt.figure(figsize = (7,10))\n",
    "plt.subplots_adjust(hspace = 0.3)\n",
    "\n",
    "cols = ['C0', 'C1', 'C2', 'C3']\n",
    "for i, ch in enumerate(ix):\n",
    "    ax = plt.subplot(5,1,i+1)\n",
    "    ax.plot(all_data[(start-500):(start+3500), ch], linewidth = 1.5, color = cols[i], label = labels[i])\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xticks([])\n",
    "    ax.legend(loc='upper left', bbox_to_anchor= (0, 1.1), fontsize = 14)\n",
    "    ax.set_ylim(-500,3000)\n",
    "    \n",
    "ax = plt.subplot(5,1,5)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.plot(all_events[(start-500):(start+3500)], linewidth = 2)\n",
    "ax.set_xticks(np.arange(0,4100,1000))\n",
    "ax.set_xticklabels(['0', '2', '4', '6', '8'], fontsize = 14)\n",
    "ax.set_xlabel('Time [sec]', fontsize = 14)\n",
    "ax.set_ylim(0.1,1)\n",
    "lgd = ax.legend(['1', '2', '3', '4', '5', '6'],\n",
    "               loc='lower left', bbox_to_anchor= (0.85, 0.1), ncol=2, \n",
    "                borderaxespad=0, frameon=True, fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 6 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 432,
       "width": 622
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,7))\n",
    "plt.subplots_adjust(wspace = 0.5)\n",
    "plt.subplots_adjust(hspace = 0.5)\n",
    "for i, e in enumerate(all_events.T):\n",
    "    plt.subplot(2,3,i+1)\n",
    "    plt.hist(e, [0, 0.5, 1, 1.5])\n",
    "    plt.xticks([0.25, 1.25], ['no event', 'event'], fontsize = 14)\n",
    "    plt.yticks([500000, 1000000], [r'$5 \\cdot 10^{5}$', r'$1 \\cdot 10^{6}$'], fontsize = 14) \n",
    "    plt.title('movement ' + str(i+1), fontsize = 14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The purpose of the feature extraction is to extract time-dependent features from the EEG data. To do so, a sliding window containing **500 datapoints** each is used. **Three consecutive time windows** each predict the event in the following time step.\n",
    "\n",
    "Extract time-dependend features from the EEG-data:\n",
    "\n",
    "- define the start and end points of a sliding window with a length of **500 datapoints** and a **step size of 2**\n",
    "- loop through those start and end points\n",
    "- per iteration:\n",
    "    - take **three consecutive time windows** (window_1 = data[start:end,:], window_2 = data[start+500:end+500,:],\n",
    "    window_3 = data[start+1000:end+1000,:])\n",
    "    - compute the **average power** per window (power: square of the signal)\n",
    "    - combine the three arrays containing the average power to one array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.46 ms, sys: 2.75 ms, total: 5.22 ms\n",
      "Wall time: 4.24 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "win_size = 500\n",
    "step_size = 2\n",
    "num_feat = 3\n",
    "num_win = int((all_data.shape[0] - (win_size * num_feat))/step_size)\n",
    "ix_start = np.arange(0, num_win*step_size - win_size*num_feat, step_size)\n",
    "ix_end = ix_start + 500"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute the mean power per time window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_pow(y):\n",
    "    return np.mean(y**2, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 10s, sys: 2.4 s, total: 1min 13s\n",
      "Wall time: 1min 13s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "data_filt = []\n",
    "for start, end in zip(ix_start, ix_end):\n",
    "    \n",
    "    pow_1 = mean_pow(all_data[start:end, :])\n",
    "    pow_2 = mean_pow(all_data[start+500:end+500, :])\n",
    "    pow_3 = mean_pow(all_data[start+1000:end+1000, :])\n",
    "    data_filt.append(np.hstack([pow_1, pow_2, pow_3]))\n",
    "    \n",
    "data_filt = np.array(data_filt)\n",
    "events_filt = np.array([all_events[end + 1501, :] for end in ix_end])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise section\n",
    "\n",
    "1. Split the data into a train and test dataset.\n",
    "\n",
    "\n",
    "2. Define a pipeline which includes:\n",
    "    - PCA to reduce the data to 10 dimensions\n",
    "    - Scaling of the data\n",
    "    - a classifier (LogisticRegression)\n",
    "\n",
    "\n",
    "3. Choose an appropriate parametrization of the classifier according to the <strong>imbalance</strong> of the data (see lecture 6).\n",
    "\n",
    "\n",
    "4. Transfer the multi-class classification problem into a one-vs-rest classification (start with only one arm movement).\n",
    "\n",
    "\n",
    "5. Use cross-validation to test the model performance (cv = 5).\n",
    "<br>(hint: use cross_val_predict to evaluate the model performance using the test dataset)\n",
    "\n",
    "\n",
    "6. Use the ROC-AUC curve and the confusion matrix for the evaluation of the model.\n",
    "\n",
    "\n",
    "7. Visualize the model performance by plotting the true and predicted hand movements.\n",
    "\n",
    "\n",
    "8. Once you evaluated the model performance, make predictions based on the test dataset.\n",
    "<br>(hint: you have to train your pipeline first)\n",
    "<br>\n",
    "<br>\n",
    "9. Repeat the above named steps for another classifier (Random Forest) and compare the results. \n",
    "\n",
    "\n",
    "10. Once your training works, train classifiers for all different arm movements.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>ROC (Receiver Operating Characteristics) curve</strong>  \n",
    "    <p>A classifier can produce four different types of results:</p>\n",
    "    <p>- <strong>true positive</strong> (arm movement was observed and predicted)</p>\n",
    "    <p>- <strong>true negative</strong> (arm movement was not observed and not predicted)</p>\n",
    "    <p>- <strong>false positive</strong> (arm movement was not observed but predicted)</p>\n",
    "    <p>- <strong>false negative</strong> (arm movement was observed but not predicted)</p>\n",
    "    <p>\n",
    "        <figure>\n",
    "        <img src=\"./images/evaluation-measures-for-roc.png\" title=\"made at imgflip.com\" width=50%/>\n",
    "        </figure>\n",
    "    </p>\n",
    "    <p>\n",
    "    These four possible outcomes also determine the following values:</p>\n",
    "    <p>- <strong>recall/sensitivity</strong>: true positive rate (should be high) </p>\n",
    "    <p>- <strong>specificity</strong>: true negative rate (should be low) </p>\n",
    "    <p>- <strong>precision</strong>: positive predictive value </p> \n",
    "    <br>\n",
    "    <p> <strong>f1-score</strong> = $\\frac{precision \\cdot recall}{precision + recall}$</p>\n",
    "    <br>\n",
    "    <p>The <strong>ROC curve</strong> plots the sensitivity against (1 - specificity):</p>\n",
    "    <p>\n",
    "        <figure>\n",
    "        <img src=\"./images/a-roc-curve-connecting-points.png\" title=\"made at imgflip.com\" width=30%/>\n",
    "        </figure>\n",
    "    </p>\n",
    "    <p>\n",
    "    <p> As the sensitivity should be high and the specificity should be low the ROC-curve for different classifier performances looks as follows:\n",
    "    </p>\n",
    "    <p>\n",
    "        <center>\n",
    "        <figure>\n",
    "        <table><tr>\n",
    "        <td> <img src=\"./images/a-roc-curve-of-a-random-classifier.png\" style=\"width: 400px;\"/> </td>\n",
    "        <td> <img src=\"./images/a-roc-curve-of-a-perfect-classifier.png\" style=\"width: 400px;\"/> </td>\n",
    "        </tr></table>\n",
    "        </figure>\n",
    "        </center>\n",
    "    </p>\n",
    "    <p>\n",
    "    The metric <strong>'roc-auc'</strong> describes the area under the ROC-curve. Thus, the higher this values is the better is the performance of the classifier.\n",
    "    </p>\n",
    "    <p> All figures are from: https://classeval.wordpress.com/introduction/introduction-to-the-roc-receiver-operating-characteristics-plot/\n",
    "    </p>\n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "    <i class=\"fa fa-info-circle\"></i>&nbsp; <strong>One-vs-rest classification</strong>\n",
    "    <p> Multiclass classification can also be tranferred to multiple binary classification problems. One strategy is called One-vs-rest, where one classifier is trained per class. In our case this means that for each arm movement one classifier is trained by considering only the labels of the respective arm movement.\n",
    "    </p>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split of the data\n",
    "# from sklearn.model_selection import train_test_split\n",
    "# ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make pipeline\n",
    "# from sklearn.pipeline import make_pipeline\n",
    "# from sklearn.decomposition import PCA\n",
    "# from sklearn.preprocessing import StandardScaler\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "# p = make_pipeline(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training of model\n",
    "# from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "# from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "# preds = []\n",
    "# for i in range(#nr of arm movements):\n",
    "#     y_pred = cross_val_predict(...)\n",
    "#     preds.append(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# split of the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(data_filt, events_filt,\\\n",
    "                                         test_size = 0.33, shuffle = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pipeline with single classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "p_lr = make_pipeline(PCA(10),  StandardScaler(), LogisticRegression(class_weight = 'balanced', solver = 'lbfgs'))\n",
    "p_rf = make_pipeline(PCA(10),  StandardScaler(), RandomForestClassifier(class_weight = 'balanced', n_estimators = 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for arm movement number 1:\n",
      "confusion matrix: \n",
      "[[218386 244225]\n",
      " [  3973   8912]]\n",
      "roc-auc score: 0.5818648069870105\n",
      "\n",
      "Results for arm movement number 2:\n",
      "confusion matrix: \n",
      "[[174936 287630]\n",
      " [  4637   8293]]\n",
      "roc-auc score: 0.5097813376479718\n",
      "\n",
      "Results for arm movement number 3:\n",
      "confusion matrix: \n",
      "[[159938 302671]\n",
      " [  4337   8550]]\n",
      "roc-auc score: 0.504594855856682\n",
      "\n",
      "Results for arm movement number 4:\n",
      "confusion matrix: \n",
      "[[155437 307076]\n",
      " [  3248   9735]]\n",
      "roc-auc score: 0.5429486250708738\n",
      "\n",
      "Results for arm movement number 5:\n",
      "confusion matrix: \n",
      "[[235343 227177]\n",
      " [  1491  11485]]\n",
      "roc-auc score: 0.696961643702174\n",
      "\n",
      "Results for arm movement number 6:\n",
      "confusion matrix: \n",
      "[[241339 221083]\n",
      " [  1310  11764]]\n",
      "roc-auc score: 0.7108516020754958\n",
      "\n",
      "CPU times: user 3min 49s, sys: 29.7 s, total: 4min 19s\n",
      "Wall time: 1min 25s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "for i in range(6):\n",
    "    y_pred = cross_val_predict(p_lr, X_train, y_train[:,i], cv = 5)\n",
    "    print('Results for arm movement number ' + str(i+1) + ':')\n",
    "    print('confusion matrix: ')\n",
    "    print(confusion_matrix(y_train[:,i], y_pred))\n",
    "    print('roc-auc score: ' + str(roc_auc_score(y_train[:,i], y_pred)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for arm movement number 1:\n",
      "confusion matrix: \n",
      "[[107238 120422]\n",
      " [  1998   4542]]\n",
      "roc-auc score: 0.58276997647385\n",
      "\n",
      "Results for arm movement number 2:\n",
      "confusion matrix: \n",
      "[[ 85820 141885]\n",
      " [  2311   4184]]\n",
      "roc-auc score: 0.5105394949122397\n",
      "\n",
      "Results for arm movement number 3:\n",
      "confusion matrix: \n",
      "[[ 78560 149102]\n",
      " [  2212   4326]]\n",
      "roc-auc score: 0.503371597290901\n",
      "\n",
      "Results for arm movement number 4:\n",
      "confusion matrix: \n",
      "[[ 76499 151224]\n",
      " [  1600   4877]]\n",
      "roc-auc score: 0.5444510551690052\n",
      "\n",
      "Results for arm movement number 5:\n",
      "confusion matrix: \n",
      "[[115536 112140]\n",
      " [   736   5788]]\n",
      "roc-auc score: 0.697321871090943\n",
      "\n",
      "Results for arm movement number 6:\n",
      "confusion matrix: \n",
      "[[118587 109187]\n",
      " [   631   5795]]\n",
      "roc-auc score: 0.7112198275415272\n",
      "\n",
      "CPU times: user 58.3 s, sys: 7.01 s, total: 1min 5s\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "preds_lr = []\n",
    "for i in range(6):\n",
    "    p_lr.fit(X_train, y_train[:,i])\n",
    "    y_pred = p_lr.predict(X_test)\n",
    "    preds_lr.append(y_pred)\n",
    "    print('Results for arm movement number ' + str(i+1) + ':')\n",
    "    print('confusion matrix: ')\n",
    "    print(confusion_matrix(y_test[:,i], y_pred))\n",
    "    print('roc-auc score: ' + str(roc_auc_score(y_test[:,i], y_pred)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for arm movement number 1:\n",
      "confusion matrix: \n",
      "[[462533     78]\n",
      " [   782  12103]]\n",
      "roc-auc score: 0.9695703330836672\n",
      "\n",
      "Results for arm movement number 2:\n",
      "confusion matrix: \n",
      "[[462473     93]\n",
      " [   850  12080]]\n",
      "roc-auc score: 0.9670301775944556\n",
      "\n",
      "Results for arm movement number 3:\n",
      "confusion matrix: \n",
      "[[462515     94]\n",
      " [   792  12095]]\n",
      "roc-auc score: 0.9691697610560872\n",
      "\n",
      "Results for arm movement number 4:\n",
      "confusion matrix: \n",
      "[[462434     79]\n",
      " [   797  12186]]\n",
      "roc-auc score: 0.9692206125539191\n",
      "\n",
      "Results for arm movement number 5:\n",
      "confusion matrix: \n",
      "[[462397    123]\n",
      " [   560  12416]]\n",
      "roc-auc score: 0.9782887343799201\n",
      "\n",
      "Results for arm movement number 6:\n",
      "confusion matrix: \n",
      "[[462303    119]\n",
      " [   533  12541]]\n",
      "roc-auc score: 0.979487361470148\n",
      "\n",
      "CPU times: user 7min 2s, sys: 23.5 s, total: 7min 25s\n",
      "Wall time: 5min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.model_selection import cross_val_score, cross_val_predict\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "for i in range(6):\n",
    "    y_pred = cross_val_predict(p_rf, X_train, y_train[:,i], cv = 5)\n",
    "    print('Results for arm movement number ' + str(i+1) + ':')\n",
    "    print('confusion matrix: ')\n",
    "    print(confusion_matrix(y_train[:,i], y_pred))\n",
    "    print('roc-auc score: ' + str(roc_auc_score(y_train[:,i], y_pred)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results for arm movement number 1:\n",
      "confusion matrix: \n",
      "[[227622     38]\n",
      " [   318   6222]]\n",
      "roc-auc score: 0.9756046156065661\n",
      "\n",
      "Results for arm movement number 2:\n",
      "confusion matrix: \n",
      "[[227661     44]\n",
      " [   327   6168]]\n",
      "roc-auc score: 0.9747301736024179\n",
      "\n",
      "Results for arm movement number 3:\n",
      "confusion matrix: \n",
      "[[227629     33]\n",
      " [   293   6245]]\n",
      "roc-auc score: 0.9775200600803725\n",
      "\n",
      "Results for arm movement number 4:\n",
      "confusion matrix: \n",
      "[[227683     40]\n",
      " [   276   6201]]\n",
      "roc-auc score: 0.9786060137414901\n",
      "\n",
      "Results for arm movement number 5:\n",
      "confusion matrix: \n",
      "[[227615     61]\n",
      " [   238   6286]]\n",
      "roc-auc score: 0.9816256943550609\n",
      "\n",
      "Results for arm movement number 6:\n",
      "confusion matrix: \n",
      "[[227734     40]\n",
      " [   219   6207]]\n",
      "roc-auc score: 0.9828720442725605\n",
      "\n",
      "CPU times: user 1min 52s, sys: 5.29 s, total: 1min 57s\n",
      "Wall time: 1min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",