diff --git a/09_eeg_use_case.ipynb b/09_eeg_use_case.ipynb
index 7ffcfea89b6de29279658d60bc9074fab1818795..eaf08b5dd20b6a1f5a9297efbd122a22c94f4f90 100644
--- a/09_eeg_use_case.ipynb
+++ b/09_eeg_use_case.ipynb
@@ -2,41 +2,32 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 22,
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
- " from numpy.core.umath_tests import inner1d\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import numpy as np\n",
"import os\n",
- "import glob\n",
+ "# import glob\n",
"import pandas as pd\n",
- "from scipy.signal import resample, butter, lfilter\n",
+ "# from scipy.signal import resample, butter, lfilter\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from tqdm import tqdm\n",
- "from itertools import islice\n",
+ "# from itertools import islice\n",
"\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
"from sklearn.linear_model import LogisticRegression\n",
- "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier, VotingClassifier, AdaBoostClassifier\n",
"from sklearn.metrics import precision_recall_fscore_support, roc_auc_score, confusion_matrix\n",
"from sklearn.model_selection import train_test_split\n",
- "from sklearn.preprocessing import StandardScaler\n",
+ "# from sklearn.preprocessing import StandardScaler\n",
"\n",
- "from multiprocessing import Pool\n",
- "from multiprocessing.pool import ThreadPool\n",
+ "# from multiprocessing import Pool\n",
+ "# from multiprocessing.pool import ThreadPool\n",
"\n",
- "import time"
+ "# import time"
]
},
{
@@ -87,23 +78,31 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Load data"
+ "<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": "code",
- "execution_count": 2,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "train_path = '../ml-use-case-eeg/train/' #'../ml-use-case-eeg/train/' #'data/eeg_use_case/train/'\n",
- "test_path = '../ml-use-case-eeg/test/' #'../ml-use-case-eeg/test/' #'data/eeg_use_case/test/'\n",
- "all_files = os.listdir(train_path)"
+ "### Load data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Task 1: Load the trainig and test data sets and ... the order of the sessions."
]
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -120,88 +119,79 @@
" ix = np.where([subj + '_' in a and series in a for a in events])[0][0]\n",
" events_filt.append(events[ix])\n",
"\n",
- " return data_filt, events_filt"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "# all_data_files = glob.glob('data/eeg_use_case/train/*data.csv')\n",
- "# all_event_files = glob.glob('data/eeg_use_case/train/*events.csv')"
+ " return data_filt, events_filt\n",
+ "\n",
+ "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)\n",
+ " df = df.drop('id', axis = 1)\n",
+ " dfs.append(df)\n",
+ " #all_dfs = pd.concat(dfs)\n",
+ " all_dfs = dfs\n",
+ " return all_dfs"
]
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
- "all_data_files = list(filter(lambda x: '_data' in x, os.listdir(train_path)))\n",
- "all_event_files = list(filter(lambda x: '_events' in x, os.listdir(train_path)))"
+ "# define path and list all data and event files\n",
+ "path = '../ml-use-case-eeg/train/' \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)))"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
+ "# sort data and event file names\n",
"data_filt, events_filt = filter_data(all_data_files, all_event_files, subj='subj1')"
]
},
{
"cell_type": "code",
- "execution_count": 7,
- "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)\n",
- " df = df.drop('id', axis = 1)\n",
- " dfs.append(df)\n",
- " #all_dfs = pd.concat(dfs)\n",
- " all_dfs = dfs\n",
- " return all_dfs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
- "# get channel names\n",
- "channels = pd.read_csv(train_path + all_data_files[0]).columns[1:]"
+ "# load all data and event files\n",
+ "all_data = np.concatenate(load_data(data_filt, path))\n",
+ "all_events = np.concatenate(load_data(events_filt, path))"
]
},
{
- "cell_type": "code",
- "execution_count": 9,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "all_data = np.concatenate(load_data(data_filt, train_path))\n",
- "all_events = np.concatenate(load_data(events_filt, train_path))"
+ "### Visualization"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Visualization"
+ "### Feature extraction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Feature extraction"
+ "Task .. : Extract time-dependend features.\n",
+ "\n",
+ "Single steps:\n",
+ "- define sliding window of length 500 (datapoints)\n",
+ "- compute the average power per window (power: square of the signal)\n",
+ "- three consecutive windows predict the event in the following time step\n",
+ "- the window slides with a step size of 2 throught the dataset"
]
},
{
@@ -210,23 +200,30 @@
"source": [
"<center>\n",
"<figure>\n",
- " <img src=\"./images/eeg_signal_preprocessing.png\" title=\"made at imgflip.com\" width=75%/> \n",
+ " <img src=\"./images/time_window.001.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": [
+ "#### Generate windows"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 697 ms, sys: 22.7 ms, total: 720 ms\n",
- "Wall time: 718 ms\n"
+ "CPU times: user 857 ms, sys: 40.8 ms, total: 898 ms\n",
+ "Wall time: 899 ms\n"
]
}
],
@@ -239,4201 +236,113 @@
"ix_start = np.arange(0, num_win*step_size - win_size*num_feat, step_size)\n",
"ix_end = ix_start + 500\n",
"\n",
- "#all_data_resh = np.array([all_data[start:end, :] for start, end in zip(ix_start, ix_end)])\n",
- "\n",
- "#all_data[:(num_win*win_size), :].reshape(num_win, win_size, 32)\n",
- "\n",
- "#ix_events = np.arange(500, num_win*win_size + 1, 500)\n",
- "all_events_resh = np.array([all_events[end + 1501, :] for end in ix_end])\n",
- "#all_events[ix_events, :]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([ 500, 502, 504, ..., 1419886, 1419888, 1419890])"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "ix_end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [],
- "source": [
- "# def sliding_window(seq, n):\n",
- "# #returns an iterator for a sliding window\n",
- "# it = iter(seq)\n",
- "# result = tuple(islice(it, n))\n",
- "# if len(result) == n:\n",
- "# yield result\n",
- "# for elem in it:\n",
- "# result = result[1:] + (elem,)\n",
- "# yield result"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Extract specific channels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [],
- "source": [
- "#select channel\n",
- "ix_c3 = np.where(channels == 'C3')[0][0]\n",
- "ix_c4 = np.where(channels == 'C4')[0][0]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [],
- "source": [
- "#red_data = all_data#[:, [ix_c3, ix_c4]]"
+ "all_events_resh = np.array([all_events[end + 1501, :] for end in ix_end])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "##### Apply a high-pass filter "
+ "#### Compute the mean power per time window"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
- "def butter_bandpass(fs, lowcut, highcut, order = 5):\n",
- " nyq = 0.5 * fs\n",
- " low = lowcut / nyq\n",
- " high = highcut / nyq\n",
- " b, a = butter(order, [low, high], btype='band')\n",
- " return b, a\n",
+ "# def butter_bandpass(fs, lowcut, highcut, order = 5):\n",
+ "# nyq = 0.5 * fs\n",
+ "# low = lowcut / nyq\n",
+ "# high = highcut / nyq\n",
+ "# b, a = butter(order, [low, high], btype='band')\n",
+ "# return b, a\n",
"\n",
- "def mean_pow(y):\n",
- " return np.mean(y**2, axis = 0)\n",
+ "# def butter_bandpass_filter(data):\n",
+ "# b, a = butter_bandpass(fs = 500, lowcut = 0, highcut = 50)\n",
+ "# y = lfilter(b, a, data, axis = 0)\n",
"\n",
- "def butter_bandpass_filter(data):\n",
- " b, a = butter_bandpass(fs = 500, lowcut = 0, highcut = 50)\n",
- " y = lfilter(b, a, data, axis = 0)\n",
- "\n",
- " filt_mean_pow = mean_pow(y)\n",
- " return filt_mean_pow"
+ "# filt_mean_pow = mean_pow(y)\n",
+ "# return filt_mean_pow"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "#win_size = 500\n",
- "# iter_window = sliding_window(red_data, win_size)"
+ "def mean_pow(y):\n",
+ " return np.mean(y**2, axis = 0)"
]
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
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- "\n",
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- ]
- },
- {
- "name": "stderr",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "CPU times: user 2min 8s, sys: 621 ms, total: 2min 9s\n",
- "Wall time: 2min 9s\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n",
- "\n",
- "\n",
- "100%|██████████| 709696/709696 [02:23<00:00, 5479.83it/s]\u001b[A\u001b[A\u001b[A"
+ "CPU times: user 2min 44s, sys: 1.51 s, total: 2min 45s\n",
+ "Wall time: 2min 45s\n"
]
}
],
"source": [
"%%time\n",
"\n",
- "# n_jobs = 1\n",
- "# with Pool(n_jobs) as p:\n",
- "# filt_data = p.map(butter_bandpass_filter, iter_window)\n",
- "\n",
"pbar = tqdm(total = len(ix_start))\n",
"filt_data = []\n",
- "#for window in all_data_resh:\n",
"for start, end in zip(ix_start, ix_end):\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",
" filt_data.append(np.hstack([pow_1, pow_2, pow_3]))\n",
" \n",
- "# window = all_data[start:end, :]\n",
- "# filt_data.append(mean_pow(window))\n",
- " \n",
- " #window = np.vstack(window)\n",
- " #filt_data.append(butter_bandpass_filter(window))\n",
- " \n",
" pbar.update(1)\n",
" \n",
- "filt_data = np.array(filt_data)\n",
- "#scale = StandardScaler()\n",
- "#filt_data = scale.fit_transform(filt_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {},
- "outputs": [],
- "source": [
- "#filt_data = np.array(filt_data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "##### Compute the mean power per time window"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {},
- "outputs": [],
- "source": [
- "# all_mean_power = []\n",
- "\n",
- "# for window in iter_window:\n",
- "# window = np.vstack(window)\n",
- "# all_mean_power.append(np.mean(window**2, axis = 0))\n",
- "# all_mean_power = np.vstack(all_mean_power)"
+ "filt_data = np.array(filt_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "##### Analysis of features"
+ "#### Dimensionality reduction"
]
},
{
"cell_type": "code",
- "execution_count": 50,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
- "pca = PCA(n_components=2)\n",
+ "pca = PCA(n_components=10)\n",
"filt_data_red = pca.fit_transform(filt_data)"
]
},
{
"cell_type": "code",
- "execution_count": 51,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(709696, 96)"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "filt_data.shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 52,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "<matplotlib.collections.PathCollection at 0x2b8adc2940f0>"
+ "<matplotlib.collections.PathCollection at 0x2ba7365b2320>"
]
},
- "execution_count": 52,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
@@ -4451,9 +360,6 @@
}
],
"source": [
- "# reshape events acc to sliding window\n",
- "#all_events_resh = all_events[int(win_size/2) -1 : int(-win_size/2), :]\n",
- "\n",
"plt.scatter(filt_data_red[all_events_resh[:,0] == 1, 0], filt_data_red[all_events_resh[:,0] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,1] == 1, 0], filt_data_red[all_events_resh[:,1] == 1, 1])\n",
"plt.scatter(filt_data_red[all_events_resh[:,2] == 1, 0], filt_data_red[all_events_resh[:,2] == 1, 1])\n",
@@ -4466,102 +372,98 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Modeling"
+ "### Modeling"
]
},
{
"cell_type": "code",
- "execution_count": 53,
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# split of the data\n",
+ "X_train, X_test, y_train, y_test = train_test_split(filt_data_red, all_events_resh,\\\n",
+ " test_size = 0.33, shuffle = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "training of the data\n"
+ "0.5015816548374611\n",
+ "[[227893 2]\n",
+ " [ 6285 20]]\n",
+ "0.5021839332037699\n",
+ "[[227838 9]\n",
+ " [ 6325 28]]\n",
+ "0.5020450496683306\n",
+ "[[227849 1]\n",
+ " [ 6324 26]]\n",
+ "0.5004700720777185\n",
+ "[[227818 0]\n",
+ " [ 6376 6]]\n",
+ "0.5048009615038914\n",
+ "[[227503 195]\n",
+ " [ 6434 68]]\n",
+ "0.5064005635338957\n",
+ "[[227395 311]\n",
+ " [ 6402 92]]\n",
+ "CPU times: user 5min 52s, sys: 402 ms, total: 5min 53s\n",
+ "Wall time: 5min 54s\n"
]
}
],
"source": [
- "# training, transition into binary classification problem\n",
- "print('training of the data')\n",
- "X_train, X_test, y_train, y_test = train_test_split(filt_data, all_events_resh,\\\n",
- " test_size = 0.33, shuffle = True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 54,
- "metadata": {},
- "outputs": [],
- "source": [
- "# %%time\n",
- "# gbc = GradientBoostingClassifier()\n",
- "\n",
- "# all_pred = []\n",
- "# all_labels = []\n",
- "# for i in range(6):\n",
- "\n",
- "# gbc.fit(X_train, y_train[:,i])\n",
- "# y_pred = gbc.predict(X_test)\n",
- "\n",
- "# all_pred.append(y_pred)\n",
- "# all_labels.append(y_test[:,i])\n",
- "# print(roc_auc_score(y_test[:,i], y_pred))\n",
- "# print(confusion_matrix(y_test[:,i], y_pred))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "metadata": {},
- "outputs": [],
- "source": [
- "# %%time\n",
- "# abc = AdaBoostClassifier()\n",
+ "%%time\n",
+ "abc = AdaBoostClassifier()\n",
"\n",
- "# all_pred = []\n",
- "# all_labels = []\n",
- "# for i in range(6):\n",
+ "all_pred = []\n",
+ "all_labels = []\n",
+ "for i in range(6):\n",
"\n",
- "# abc.fit(X_train, y_train[:,i])\n",
- "# y_pred = abc.predict(X_test)\n",
+ " abc.fit(X_train, y_train[:,i])\n",
+ " y_pred = abc.predict(X_test)\n",
"\n",
- "# all_pred.append(y_pred)\n",
- "# all_labels.append(y_test[:,i])\n",
- "# print(roc_auc_score(y_test[:,i], y_pred))\n",
- "# print(confusion_matrix(y_test[:,i], y_pred))"
+ " all_pred.append(y_pred)\n",
+ " all_labels.append(y_test[:,i])\n",
+ " print(roc_auc_score(y_test[:,i], y_pred))\n",
+ " print(confusion_matrix(y_test[:,i], y_pred))"
]
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "0.7463346727811783\n",
- "[[161712 66093]\n",
- " [ 1389 5006]]\n",
- "0.7253640701194027\n",
- "[[156626 71169]\n",
- " [ 1517 4888]]\n",
- "0.7273466702521886\n",
- "[[159181 68665]\n",
- " [ 1550 4804]]\n",
- "0.7406915298355794\n",
- "[[157298 70412]\n",
- " [ 1359 5131]]\n",
- "0.9017967429048931\n",
- "[[199076 28703]\n",
- " [ 452 5969]]\n",
- "0.8739729959818299\n",
- "[[192261 35503]\n",
- " [ 619 5817]]\n",
- "CPU times: user 9min 59s, sys: 967 µs, total: 9min 59s\n",
- "Wall time: 9min 59s\n"
+ "0.5712988484634542\n",
+ "[[ 89209 138686]\n",
+ " [ 1569 4736]]\n",
+ "0.5357646342979414\n",
+ "[[ 70740 157107]\n",
+ " [ 1518 4835]]\n",
+ "0.5398906933868289\n",
+ "[[ 64717 163133]\n",
+ " [ 1297 5053]]\n",
+ "0.5683459761359975\n",
+ "[[ 62697 165121]\n",
+ " [ 884 5498]]\n",
+ "0.6282825974068698\n",
+ "[[ 63077 164621]\n",
+ " [ 133 6369]]\n",
+ "0.6396764403905532\n",
+ "[[ 68905 158801]\n",
+ " [ 151 6343]]\n",
+ "CPU times: user 16 s, sys: 48.9 ms, total: 16.1 s\n",
+ "Wall time: 16.1 s\n"
]
}
],
@@ -4584,7 +486,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
@@ -4599,9 +501,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.8700681448145394\n",
- "[[227633 172]\n",
- " [ 1657 4738]]\n"
+ "0.7815740181416588\n",
+ "[[227810 85]\n",
+ " [ 2752 3553]]\n"
]
},
{
@@ -4616,17 +518,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.8513977080512853\n",
- "[[227596 199]\n",
- " [ 1898 4507]]\n"
+ "0.7835624299156497\n",
+ "[[227845 2]\n",
+ " [ 2750 3603]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
- " np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
@@ -4635,17 +535,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.8528683729746489\n",
- "[[227604 242]\n",
- " [ 1863 4491]]\n"
+ "0.7865354330708662\n",
+ "[[227850 0]\n",
+ " [ 2711 3639]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
- " np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
@@ -4654,9 +552,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.8637231356700219\n",
- "[[227539 171]\n",
- " [ 1764 4726]]\n"
+ "0.8096969591358668\n",
+ "[[227817 1]\n",
+ " [ 2429 3953]]\n"
]
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{
@@ -4671,25 +569,29 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "0.9430318406614016\n",
- "[[224672 3107]\n",
- " [ 644 5777]]\n",
- "0.9318073940741972\n",
- "[[225684 2080]\n",
- " [ 819 5617]]\n",
- "CPU times: user 15min 24s, sys: 3.72 s, total: 15min 28s\n",
- "Wall time: 15min 28s\n"
+ "0.9251682147781864\n",
+ "[[227589 109]\n",
+ " [ 970 5532]]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/linear_model/base.py:340: RuntimeWarning: overflow encountered in exp\n",
- " np.exp(prob, prob)\n",
"/cluster/apps/python/3.6.1/x86_64/lib64/python3.6/site-packages/sklearn/preprocessing/label.py:151: DeprecationWarning: The truth value of an empty array is ambiguous. Returning False, but in future this will result in an error. Use `array.size > 0` to check that an array is not empty.\n",
" if diff:\n"
]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0.9337942375789381\n",
+ "[[227605 101]\n",
+ " [ 857 5637]]\n",
+ "CPU times: user 2min 6s, sys: 346 ms, total: 2min 6s\n",
+ "Wall time: 2min 6s\n"
+ ]
}
],
"source": [
@@ -4715,9 +617,790 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {},
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+ {
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>700</th>\n",
+ " <td>subj10_series1_700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>800</th>\n",
+ " <td>subj10_series1_800</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>900</th>\n",
+ " <td>subj10_series1_900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1000</th>\n",
+ " <td>subj10_series1_1000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1100</th>\n",
+ " <td>subj10_series1_1100</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1200</th>\n",
+ " <td>subj10_series1_1200</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1300</th>\n",
+ " <td>subj10_series1_1300</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1400</th>\n",
+ " <td>subj10_series1_1400</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1500</th>\n",
+ " <td>subj10_series1_1500</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1600</th>\n",
+ " <td>subj10_series1_1600</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1700</th>\n",
+ " <td>subj10_series1_1700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1800</th>\n",
+ " <td>subj10_series1_1800</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1900</th>\n",
+ " <td>subj10_series1_1900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2000</th>\n",
+ " <td>subj10_series1_2000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2100</th>\n",
+ " <td>subj10_series1_2100</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2200</th>\n",
+ " <td>subj10_series1_2200</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2300</th>\n",
+ " <td>subj10_series1_2300</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2400</th>\n",
+ " <td>subj10_series1_2400</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2500</th>\n",
+ " <td>subj10_series1_2500</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2600</th>\n",
+ " <td>subj10_series1_2600</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2700</th>\n",
+ " <td>subj10_series1_2700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2800</th>\n",
+ " <td>subj10_series1_2800</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2900</th>\n",
+ " <td>subj10_series1_2900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259400</th>\n",
+ " <td>subj10_series1_259400</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259500</th>\n",
+ " <td>subj10_series1_259500</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259600</th>\n",
+ " <td>subj10_series1_259600</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259700</th>\n",
+ " <td>subj10_series1_259700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259800</th>\n",
+ " <td>subj10_series1_259800</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>259900</th>\n",
+ " <td>subj10_series1_259900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260000</th>\n",
+ " <td>subj10_series1_260000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260100</th>\n",
+ " <td>subj10_series1_260100</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260200</th>\n",
+ " <td>subj10_series1_260200</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260300</th>\n",
+ " <td>subj10_series1_260300</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260400</th>\n",
+ " <td>subj10_series1_260400</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260500</th>\n",
+ " <td>subj10_series1_260500</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260600</th>\n",
+ " <td>subj10_series1_260600</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260700</th>\n",
+ " <td>subj10_series1_260700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260800</th>\n",
+ " <td>subj10_series1_260800</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>260900</th>\n",
+ " <td>subj10_series1_260900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261000</th>\n",
+ " <td>subj10_series1_261000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261100</th>\n",
+ " <td>subj10_series1_261100</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261200</th>\n",
+ " <td>subj10_series1_261200</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261300</th>\n",
+ " <td>subj10_series1_261300</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261400</th>\n",
+ " <td>subj10_series1_261400</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261500</th>\n",
+ " <td>subj10_series1_261500</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261600</th>\n",
+ " <td>subj10_series1_261600</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261700</th>\n",
+ " <td>subj10_series1_261700</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261800</th>\n",
+ " <td>subj10_series1_261800</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>261900</th>\n",
+ " <td>subj10_series1_261900</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>262000</th>\n",
+ " <td>subj10_series1_262000</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>262100</th>\n",
+ " <td>subj10_series1_262100</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>262200</th>\n",
+ " <td>subj10_series1_262200</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>262300</th>\n",
+ " <td>subj10_series1_262300</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>2624 rows × 7 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " id HandStart FirstDigitTouch BothStartLoadPhase \\\n",
+ "0 subj10_series1_0 0 0 0 \n",
+ "100 subj10_series1_100 0 0 0 \n",
+ "200 subj10_series1_200 0 0 0 \n",
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+ "500 subj10_series1_500 0 0 0 \n",
+ "600 subj10_series1_600 0 0 0 \n",
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+ "800 subj10_series1_800 0 0 0 \n",
+ "900 subj10_series1_900 0 0 0 \n",
+ "1000 subj10_series1_1000 0 0 0 \n",
+ "1100 subj10_series1_1100 0 0 0 \n",
+ "1200 subj10_series1_1200 0 0 0 \n",
+ "1300 subj10_series1_1300 0 0 0 \n",
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+ "1500 subj10_series1_1500 0 0 0 \n",
+ "1600 subj10_series1_1600 0 0 0 \n",
+ "1700 subj10_series1_1700 0 0 0 \n",
+ "1800 subj10_series1_1800 0 0 0 \n",
+ "1900 subj10_series1_1900 0 0 0 \n",
+ "2000 subj10_series1_2000 0 0 0 \n",
+ "2100 subj10_series1_2100 0 0 0 \n",
+ "2200 subj10_series1_2200 0 0 0 \n",
+ "2300 subj10_series1_2300 0 0 0 \n",
+ "2400 subj10_series1_2400 0 0 0 \n",
+ "2500 subj10_series1_2500 0 0 0 \n",
+ "2600 subj10_series1_2600 1 0 0 \n",
+ "2700 subj10_series1_2700 0 0 0 \n",
+ "2800 subj10_series1_2800 0 1 1 \n",
+ "2900 subj10_series1_2900 0 0 0 \n",
+ "... ... ... ... ... \n",
+ "259400 subj10_series1_259400 0 0 0 \n",
+ "259500 subj10_series1_259500 0 0 0 \n",
+ "259600 subj10_series1_259600 0 0 0 \n",
+ "259700 subj10_series1_259700 0 0 0 \n",
+ "259800 subj10_series1_259800 0 0 0 \n",
+ "259900 subj10_series1_259900 0 0 0 \n",
+ "260000 subj10_series1_260000 0 0 0 \n",
+ "260100 subj10_series1_260100 0 0 0 \n",
+ "260200 subj10_series1_260200 0 0 0 \n",
+ "260300 subj10_series1_260300 0 0 0 \n",
+ "260400 subj10_series1_260400 0 0 0 \n",
+ "260500 subj10_series1_260500 0 0 0 \n",
+ "260600 subj10_series1_260600 0 0 0 \n",
+ "260700 subj10_series1_260700 0 0 0 \n",
+ "260800 subj10_series1_260800 0 0 0 \n",
+ "260900 subj10_series1_260900 0 0 0 \n",
+ "261000 subj10_series1_261000 0 0 0 \n",
+ "261100 subj10_series1_261100 0 0 0 \n",
+ "261200 subj10_series1_261200 0 0 0 \n",
+ "261300 subj10_series1_261300 0 0 0 \n",
+ "261400 subj10_series1_261400 0 0 0 \n",
+ "261500 subj10_series1_261500 0 0 0 \n",
+ "261600 subj10_series1_261600 0 0 0 \n",
+ "261700 subj10_series1_261700 0 0 0 \n",
+ "261800 subj10_series1_261800 0 0 0 \n",
+ "261900 subj10_series1_261900 0 0 0 \n",
+ "262000 subj10_series1_262000 0 0 0 \n",
+ "262100 subj10_series1_262100 0 0 0 \n",
+ "262200 subj10_series1_262200 0 0 0 \n",
+ "262300 subj10_series1_262300 0 0 0 \n",
+ "\n",
+ " LiftOff Replace BothReleased \n",
+ "0 0 0 0 \n",
+ "100 0 0 0 \n",
+ "200 0 0 0 \n",
+ "300 0 0 0 \n",
+ "400 0 0 0 \n",
+ "500 0 0 0 \n",
+ "600 0 0 0 \n",
+ "700 0 0 0 \n",
+ "800 0 0 0 \n",
+ "900 0 0 0 \n",
+ "1000 0 0 0 \n",
+ "1100 0 0 0 \n",
+ "1200 0 0 0 \n",
+ "1300 0 0 0 \n",
+ "1400 0 0 0 \n",
+ "1500 0 0 0 \n",
+ "1600 0 0 0 \n",
+ "1700 0 0 0 \n",
+ "1800 0 0 0 \n",
+ "1900 0 0 0 \n",
+ "2000 0 0 0 \n",
+ "2100 0 0 0 \n",
+ "2200 0 0 0 \n",
+ "2300 0 0 0 \n",
+ "2400 0 0 0 \n",
+ "2500 0 0 0 \n",
+ "2600 0 0 0 \n",
+ "2700 0 0 0 \n",
+ "2800 0 0 0 \n",
+ "2900 1 0 0 \n",
+ "... ... ... ... \n",
+ "259400 0 0 0 \n",
+ "259500 0 0 0 \n",
+ "259600 0 0 0 \n",
+ "259700 0 0 0 \n",
+ "259800 0 0 0 \n",
+ "259900 0 0 0 \n",
+ "260000 0 0 0 \n",
+ "260100 0 0 0 \n",
+ "260200 0 0 0 \n",
+ "260300 0 0 0 \n",
+ "260400 0 0 0 \n",
+ "260500 0 0 0 \n",
+ "260600 0 0 0 \n",
+ "260700 0 0 0 \n",
+ "260800 0 0 0 \n",
+ "260900 0 1 0 \n",
+ "261000 0 1 0 \n",
+ "261100 0 0 1 \n",
+ "261200 0 0 0 \n",
+ "261300 0 0 0 \n",
+ "261400 0 0 0 \n",
+ "261500 0 0 0 \n",
+ "261600 0 0 0 \n",
+ "261700 0 0 0 \n",
+ "261800 0 0 0 \n",
+ "261900 0 0 0 \n",
+ "262000 0 0 0 \n",
+ "262100 0 0 0 \n",
+ "262200 0 0 0 \n",
+ "262300 0 0 0 \n",
+ "\n",
+ "[2624 rows x 7 columns]"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"pd.read_csv('../ml-use-case-eeg/train/subj10_series1_events.csv').loc[::100, :]"
]