Comparar commits
123 Commits
| Autor | SHA1 | Data | |
|---|---|---|---|
 gemeinl
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6398803efe | ||
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robintibor
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c32ac5b0d5 | ||
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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robintibor
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thodoris
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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robintibor
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Lukas Alexander Wilhelm Gemein
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gemeinl
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Lukas gemein
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robintibor
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Lukas gemein
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Lukas gemein
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Lukas gemein
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Lukas gemein
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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robintibor
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robintibor
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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robintibor
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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Kay Gregor Hartmann
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kahartma
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kahartma
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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Robin Tibor Schirrmeister
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robintibor
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robintibor
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robintibor
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robintibor
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robintibor
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Robin Tibor Schirrmeister
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@@ -1,4 +0,0 @@
|
||||
# Sphinx build info version 1
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||||
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
|
||||
config: 832f1c3dea2d710628d6890dafbf90d6
|
||||
tags: 645f666f9bcd5a90fca523b33c5a78b7
|
||||
@@ -0,0 +1,31 @@
|
||||
BSD 3-Clause License
|
||||
|
||||
Copyright (c) 2017 Robin Tibor Schirrmeister
|
||||
All rights reserved.
|
||||
|
||||
Redistribution and use in source and binary forms, with or without modification,
|
||||
are permitted provided that the following conditions are met:
|
||||
|
||||
1. Redistributions of source code must retain the above copyright notice,
|
||||
this list of conditions and the following disclaimer.
|
||||
|
||||
2. Redistributions in binary form must reproduce the above copyright notice,
|
||||
this list of conditions and the following disclaimer in the documentation
|
||||
and/or other materials provided with the distribution.
|
||||
|
||||
3. Neither the name of the copyright holder nor the names of its contributors
|
||||
may be used to endorse or promote products derived from this software
|
||||
without specific prior written permission.
|
||||
|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
|
||||
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A P
|
||||
ARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER
|
||||
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
||||
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
||||
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
||||
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
|
||||
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
|
||||
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
|
||||
WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGE.
|
||||
@@ -0,0 +1,5 @@
|
||||
include README.rst
|
||||
include LICENSE.txt
|
||||
|
||||
recursive-include docs *.ipynb *.rst conf.py Makefile
|
||||
recursive-exclude docs *checkpoint.ipynb
|
||||
@@ -0,0 +1,58 @@
|
||||
Note: The old braindecode repository has been moved to
|
||||
https://github.com/robintibor/braindevel.
|
||||
|
||||
Braindecode
|
||||
===========
|
||||
|
||||
A deep learning toolbox to decode raw time-domain EEG.
|
||||
|
||||
For EEG researchers that want to want to work with deep learning and
|
||||
deep learning researchers that want to work with EEG data.
|
||||
For now focussed on convolutional networks.
|
||||
|
||||
|
||||
Installation
|
||||
============
|
||||
|
||||
1. Install pytorch from http://pytorch.org/ (you don't need to install torchvision).
|
||||
|
||||
2. Install numpy (necessary for resamply installation to work), e.g.:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install numpy
|
||||
|
||||
3. Install braindecode via pip:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
pip install braindecode
|
||||
|
||||
|
||||
|
||||
Documentation
|
||||
=============
|
||||
|
||||
Documentation is online under https://robintibor.github.io/braindecode/
|
||||
|
||||
|
||||
Citing
|
||||
======
|
||||
If you use this code in a scientific publication, please cite us as:
|
||||
|
||||
.. code-block:: bibtex
|
||||
|
||||
@article {HBM:HBM23730,
|
||||
author = {Schirrmeister, Robin Tibor and Springenberg, Jost Tobias and Fiederer,
|
||||
Lukas Dominique Josef and Glasstetter, Martin and Eggensperger, Katharina and Tangermann, Michael and
|
||||
Hutter, Frank and Burgard, Wolfram and Ball, Tonio},
|
||||
title = {Deep learning with convolutional neural networks for EEG decoding and visualization},
|
||||
journal = {Human Brain Mapping},
|
||||
issn = {1097-0193},
|
||||
url = {http://dx.doi.org/10.1002/hbm.23730},
|
||||
doi = {10.1002/hbm.23730},
|
||||
month = {aug},
|
||||
year = {2017},
|
||||
keywords = {electroencephalography, EEG analysis, machine learning, end-to-end learning, brain–machine interface,
|
||||
brain–computer interface, model interpretability, brain mapping},
|
||||
}
|
||||
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@@ -1,841 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Read and Decode BBCI Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This tutorial shows how to read and decode BBCI data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup logging to see outputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"import sys\n",
|
||||
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
|
||||
" level=logging.DEBUG, stream=sys.stdout)\n",
|
||||
"log = logging.getLogger()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and preprocess data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First set the filename and the sensors you want to load. If you set\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"load_sensor_names=None\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"or just remove the parameter from the function call, all sensors will be loaded."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=3451320\n",
|
||||
" Range : 0 ... 3451319 = 0.000 ... 6902.638 secs\n",
|
||||
"Ready.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.datasets.bbci import BBCIDataset\n",
|
||||
"train_filename = '/home/schirrmr/data/BBCI-without-last-runs/BhNoMoSc1S001R01_ds10_1-12.BBCI.mat'\n",
|
||||
"cnt = BBCIDataset(train_filename, load_sensor_names=['C3', 'CPz', 'C4']).load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Preprocessing on continous data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First remove the stimulus channel, than apply any preprocessing you like. There are some very few directions available from Braindecode, such as resample_cnt. But you can apply any function on the chan x time matrix of the mne raw object (`cnt` in the code) by calling `mne_apply` with two arguments:\n",
|
||||
"\n",
|
||||
"1. Your function (2d-array-> 2darray), that transforms the channel x timesteps data array\n",
|
||||
"2. the Raw data object from mne itself"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:56:32,890 WARNING : This is not causal, uses future data....\n",
|
||||
"2017-11-03 17:56:32,891 INFO : Resampling from 500.000000 to 250.000000 Hz.\n",
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=1725660\n",
|
||||
" Range : 0 ... 1725659 = 0.000 ... 6902.636 secs\n",
|
||||
"Ready.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.mne_ext.signalproc import resample_cnt, mne_apply\n",
|
||||
"from braindecode.datautil.signalproc import exponential_running_standardize\n",
|
||||
"# Remove stimulus channel\n",
|
||||
"cnt = cnt.drop_channels(['STI 014'])\n",
|
||||
"cnt = resample_cnt(cnt, 250)\n",
|
||||
"# mne apply will apply the function to the data (a 2d-numpy-array)\n",
|
||||
"# have to transpose data back and forth, since\n",
|
||||
"# exponential_running_standardize expects time x chans order\n",
|
||||
"# while mne object has chans x time order\n",
|
||||
"cnt = mne_apply(lambda a: exponential_running_standardize(\n",
|
||||
" a.T, init_block_size=1000,factor_new=0.001, eps=1e-4).T,\n",
|
||||
" cnt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Transform to epoched dataset "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Braindecode supplies the `create_signal_target_from_raw_mne` function, which will transform the mne raw object into a `SignalAndTarget` object for use in Braindecode.\n",
|
||||
"`name_to_code` should be an `OrderedDict` that maps class names to either one or a list of marker codes for that class."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:56:34,795 INFO : Trial per class:\n",
|
||||
"Counter({'Feet': 225, 'Right': 224, 'Rest': 224, 'Left': 224})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.datautil.trial_segment import create_signal_target_from_raw_mne\n",
|
||||
"from collections import OrderedDict\n",
|
||||
"# can also give lists of marker codes in case a class has multiple marker codes...\n",
|
||||
"name_to_code = OrderedDict([('Right', 1), ('Left', 2), ('Rest', 3), ('Feet', 4)])\n",
|
||||
"segment_ival_ms = [-500,4000]\n",
|
||||
"\n",
|
||||
"train_set = create_signal_target_from_raw_mne(cnt, name_to_code, segment_ival_ms)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Same for test set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=617090\n",
|
||||
" Range : 0 ... 617089 = 0.000 ... 1234.178 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:56:35,707 WARNING : This is not causal, uses future data....\n",
|
||||
"2017-11-03 17:56:35,708 INFO : Resampling from 500.000000 to 250.000000 Hz.\n",
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=308545\n",
|
||||
" Range : 0 ... 308544 = 0.000 ... 1234.176 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:56:36,026 INFO : Trial per class:\n",
|
||||
"Counter({'Feet': 40, 'Left': 40, 'Rest': 40, 'Right': 40})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_filename = '/home/schirrmr/data/BBCI-only-last-runs/BhNoMoSc1S001R13_ds10_1-2BBCI.mat'\n",
|
||||
"cnt = BBCIDataset(test_filename, load_sensor_names=['C3', 'CPz', 'C4']).load()\n",
|
||||
"# Remove stimulus channel\n",
|
||||
"cnt = cnt.drop_channels(['STI 014'])\n",
|
||||
"cnt = resample_cnt(cnt, 250)\n",
|
||||
"cnt = mne_apply(lambda a: exponential_running_standardize(\n",
|
||||
" a.T, init_block_size=1000,factor_new=0.001, eps=1e-4).T,\n",
|
||||
" cnt)\n",
|
||||
"test_set = create_signal_target_from_raw_mne(cnt, name_to_code, segment_ival_ms)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<div class=\"alert alert-info\">\n",
|
||||
"\n",
|
||||
"In case of start and stop markers, provide a `name_to_stop_codes` dictionary (same as for the start codes in this example) as a final argument to `create_signal_target_from_raw_mne`. See [Read and Decode BBCI Data with Start-Stop-Markers Tutorial](BBCI_Data_Start_Stop.html)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Split off a validation set."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.splitters import split_into_two_sets\n",
|
||||
"\n",
|
||||
"train_set, valid_set = split_into_two_sets(train_set, first_set_fraction=0.8)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"from braindecode.models.util import to_dense_prediction_model\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = True\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = 800\n",
|
||||
"in_chans = 3\n",
|
||||
"n_classes = 4\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes, input_time_length=input_time_length,\n",
|
||||
" final_conv_length=30).create_network()\n",
|
||||
"to_dense_prediction_model(model)\n",
|
||||
"\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup optimizer and iterator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"267 predictions per input/trial\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from torch import optim\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"optimizer = optim.Adam(model.parameters())\n",
|
||||
"\n",
|
||||
"from braindecode.torch_ext.util import np_to_var\n",
|
||||
"# determine output size\n",
|
||||
"test_input = np_to_var(np.ones((2, 3, input_time_length, 1), dtype=np.float32))\n",
|
||||
"if cuda:\n",
|
||||
" test_input = test_input.cuda()\n",
|
||||
"out = model(test_input)\n",
|
||||
"n_preds_per_input = out.cpu().data.numpy().shape[2]\n",
|
||||
"print(\"{:d} predictions per input/trial\".format(n_preds_per_input))\n",
|
||||
"\n",
|
||||
"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
|
||||
"iterator = CropsFromTrialsIterator(batch_size=32,input_time_length=input_time_length,\n",
|
||||
" n_preds_per_input=n_preds_per_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup Monitors, Loss function, Stop Criteria"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.experiments.experiment import Experiment\n",
|
||||
"from braindecode.experiments.monitors import RuntimeMonitor, LossMonitor, CroppedTrialMisclassMonitor, MisclassMonitor\n",
|
||||
"from braindecode.experiments.stopcriteria import MaxEpochs\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"import torch as th\n",
|
||||
"from braindecode.torch_ext.modules import Expression\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"loss_function = lambda preds, targets: F.nll_loss(th.mean(preds, dim=2).squeeze(), targets)\n",
|
||||
"\n",
|
||||
"model_constraint = None\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='sample_misclass'),\n",
|
||||
" CroppedTrialMisclassMonitor(input_time_length), RuntimeMonitor(),]\n",
|
||||
"stop_criterion = MaxEpochs(20)\n",
|
||||
"exp = Experiment(model, train_set, valid_set, test_set, iterator, loss_function, optimizer, model_constraint,\n",
|
||||
" monitors, stop_criterion, remember_best_column='valid_misclass',\n",
|
||||
" run_after_early_stop=True, batch_modifier=None, cuda=cuda)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:56:39,458 INFO : Run until first stop...\n",
|
||||
"2017-11-03 17:56:40,298 INFO : Epoch 0\n",
|
||||
"2017-11-03 17:56:40,299 INFO : train_loss 7.89184\n",
|
||||
"2017-11-03 17:56:40,300 INFO : valid_loss 7.72731\n",
|
||||
"2017-11-03 17:56:40,301 INFO : test_loss 7.75617\n",
|
||||
"2017-11-03 17:56:40,303 INFO : train_sample_misclass 0.75013\n",
|
||||
"2017-11-03 17:56:40,304 INFO : valid_sample_misclass 0.74856\n",
|
||||
"2017-11-03 17:56:40,305 INFO : test_sample_misclass 0.75115\n",
|
||||
"2017-11-03 17:56:40,306 INFO : train_misclass 0.75070\n",
|
||||
"2017-11-03 17:56:40,308 INFO : valid_misclass 0.74860\n",
|
||||
"2017-11-03 17:56:40,309 INFO : test_misclass 0.75000\n",
|
||||
"2017-11-03 17:56:40,310 INFO : runtime 0.00000\n",
|
||||
"2017-11-03 17:56:40,311 INFO : \n",
|
||||
"2017-11-03 17:56:40,313 INFO : New best valid_misclass: 0.748603\n",
|
||||
"2017-11-03 17:56:40,314 INFO : \n",
|
||||
"2017-11-03 17:56:41,475 INFO : Time only for training updates: 0.94s\n",
|
||||
"2017-11-03 17:56:42,262 INFO : Epoch 1\n",
|
||||
"2017-11-03 17:56:42,263 INFO : train_loss 0.79550\n",
|
||||
"2017-11-03 17:56:42,264 INFO : valid_loss 0.79273\n",
|
||||
"2017-11-03 17:56:42,265 INFO : test_loss 0.83673\n",
|
||||
"2017-11-03 17:56:42,266 INFO : train_sample_misclass 0.36961\n",
|
||||
"2017-11-03 17:56:42,267 INFO : valid_sample_misclass 0.37374\n",
|
||||
"2017-11-03 17:56:42,268 INFO : test_sample_misclass 0.42969\n",
|
||||
"2017-11-03 17:56:42,268 INFO : train_misclass 0.30223\n",
|
||||
"2017-11-03 17:56:42,269 INFO : valid_misclass 0.26257\n",
|
||||
"2017-11-03 17:56:42,270 INFO : test_misclass 0.33750\n",
|
||||
"2017-11-03 17:56:42,271 INFO : runtime 2.01650\n",
|
||||
"2017-11-03 17:56:42,271 INFO : \n",
|
||||
"2017-11-03 17:56:42,274 INFO : New best valid_misclass: 0.262570\n",
|
||||
"2017-11-03 17:56:42,274 INFO : \n",
|
||||
"2017-11-03 17:56:43,423 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:44,209 INFO : Epoch 2\n",
|
||||
"2017-11-03 17:56:44,211 INFO : train_loss 0.68180\n",
|
||||
"2017-11-03 17:56:44,212 INFO : valid_loss 0.65888\n",
|
||||
"2017-11-03 17:56:44,213 INFO : test_loss 0.74155\n",
|
||||
"2017-11-03 17:56:44,214 INFO : train_sample_misclass 0.29325\n",
|
||||
"2017-11-03 17:56:44,215 INFO : valid_sample_misclass 0.30236\n",
|
||||
"2017-11-03 17:56:44,216 INFO : test_sample_misclass 0.37317\n",
|
||||
"2017-11-03 17:56:44,217 INFO : train_misclass 0.22563\n",
|
||||
"2017-11-03 17:56:44,218 INFO : valid_misclass 0.21229\n",
|
||||
"2017-11-03 17:56:44,219 INFO : test_misclass 0.29375\n",
|
||||
"2017-11-03 17:56:44,220 INFO : runtime 1.94802\n",
|
||||
"2017-11-03 17:56:44,221 INFO : \n",
|
||||
"2017-11-03 17:56:44,223 INFO : New best valid_misclass: 0.212291\n",
|
||||
"2017-11-03 17:56:44,224 INFO : \n",
|
||||
"2017-11-03 17:56:45,377 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:46,152 INFO : Epoch 3\n",
|
||||
"2017-11-03 17:56:46,153 INFO : train_loss 0.65244\n",
|
||||
"2017-11-03 17:56:46,154 INFO : valid_loss 0.68508\n",
|
||||
"2017-11-03 17:56:46,155 INFO : test_loss 0.81607\n",
|
||||
"2017-11-03 17:56:46,156 INFO : train_sample_misclass 0.27737\n",
|
||||
"2017-11-03 17:56:46,157 INFO : valid_sample_misclass 0.32108\n",
|
||||
"2017-11-03 17:56:46,157 INFO : test_sample_misclass 0.43695\n",
|
||||
"2017-11-03 17:56:46,158 INFO : train_misclass 0.20195\n",
|
||||
"2017-11-03 17:56:46,159 INFO : valid_misclass 0.25140\n",
|
||||
"2017-11-03 17:56:46,160 INFO : test_misclass 0.36250\n",
|
||||
"2017-11-03 17:56:46,160 INFO : runtime 1.95389\n",
|
||||
"2017-11-03 17:56:46,161 INFO : \n",
|
||||
"2017-11-03 17:56:47,319 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:48,106 INFO : Epoch 4\n",
|
||||
"2017-11-03 17:56:48,107 INFO : train_loss 0.59123\n",
|
||||
"2017-11-03 17:56:48,108 INFO : valid_loss 0.57887\n",
|
||||
"2017-11-03 17:56:48,109 INFO : test_loss 0.71750\n",
|
||||
"2017-11-03 17:56:48,110 INFO : train_sample_misclass 0.25470\n",
|
||||
"2017-11-03 17:56:48,110 INFO : valid_sample_misclass 0.25364\n",
|
||||
"2017-11-03 17:56:48,111 INFO : test_sample_misclass 0.35189\n",
|
||||
"2017-11-03 17:56:48,112 INFO : train_misclass 0.20474\n",
|
||||
"2017-11-03 17:56:48,113 INFO : valid_misclass 0.20670\n",
|
||||
"2017-11-03 17:56:48,113 INFO : test_misclass 0.30625\n",
|
||||
"2017-11-03 17:56:48,114 INFO : runtime 1.94214\n",
|
||||
"2017-11-03 17:56:48,115 INFO : \n",
|
||||
"2017-11-03 17:56:48,117 INFO : New best valid_misclass: 0.206704\n",
|
||||
"2017-11-03 17:56:48,118 INFO : \n",
|
||||
"2017-11-03 17:56:49,274 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:50,058 INFO : Epoch 5\n",
|
||||
"2017-11-03 17:56:50,060 INFO : train_loss 0.76997\n",
|
||||
"2017-11-03 17:56:50,060 INFO : valid_loss 0.72850\n",
|
||||
"2017-11-03 17:56:50,061 INFO : test_loss 0.83681\n",
|
||||
"2017-11-03 17:56:50,062 INFO : train_sample_misclass 0.33041\n",
|
||||
"2017-11-03 17:56:50,063 INFO : valid_sample_misclass 0.32293\n",
|
||||
"2017-11-03 17:56:50,063 INFO : test_sample_misclass 0.39756\n",
|
||||
"2017-11-03 17:56:50,064 INFO : train_misclass 0.25487\n",
|
||||
"2017-11-03 17:56:50,065 INFO : valid_misclass 0.29050\n",
|
||||
"2017-11-03 17:56:50,066 INFO : test_misclass 0.34375\n",
|
||||
"2017-11-03 17:56:50,066 INFO : runtime 1.95470\n",
|
||||
"2017-11-03 17:56:50,067 INFO : \n",
|
||||
"2017-11-03 17:56:51,213 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:51,992 INFO : Epoch 6\n",
|
||||
"2017-11-03 17:56:51,994 INFO : train_loss 0.61221\n",
|
||||
"2017-11-03 17:56:51,995 INFO : valid_loss 0.62389\n",
|
||||
"2017-11-03 17:56:51,996 INFO : test_loss 0.72417\n",
|
||||
"2017-11-03 17:56:51,996 INFO : train_sample_misclass 0.26388\n",
|
||||
"2017-11-03 17:56:51,997 INFO : valid_sample_misclass 0.28059\n",
|
||||
"2017-11-03 17:56:51,998 INFO : test_sample_misclass 0.34581\n",
|
||||
"2017-11-03 17:56:51,999 INFO : train_misclass 0.21031\n",
|
||||
"2017-11-03 17:56:51,999 INFO : valid_misclass 0.22346\n",
|
||||
"2017-11-03 17:56:52,000 INFO : test_misclass 0.30625\n",
|
||||
"2017-11-03 17:56:52,001 INFO : runtime 1.93918\n",
|
||||
"2017-11-03 17:56:52,002 INFO : \n",
|
||||
"2017-11-03 17:56:53,152 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:53,932 INFO : Epoch 7\n",
|
||||
"2017-11-03 17:56:53,933 INFO : train_loss 0.57982\n",
|
||||
"2017-11-03 17:56:53,934 INFO : valid_loss 0.60830\n",
|
||||
"2017-11-03 17:56:53,936 INFO : test_loss 0.73615\n",
|
||||
"2017-11-03 17:56:53,937 INFO : train_sample_misclass 0.25238\n",
|
||||
"2017-11-03 17:56:53,938 INFO : valid_sample_misclass 0.30217\n",
|
||||
"2017-11-03 17:56:53,939 INFO : test_sample_misclass 0.36385\n",
|
||||
"2017-11-03 17:56:53,941 INFO : train_misclass 0.17827\n",
|
||||
"2017-11-03 17:56:53,942 INFO : valid_misclass 0.21788\n",
|
||||
"2017-11-03 17:56:53,943 INFO : test_misclass 0.31875\n",
|
||||
"2017-11-03 17:56:53,944 INFO : runtime 1.93903\n",
|
||||
"2017-11-03 17:56:53,946 INFO : \n",
|
||||
"2017-11-03 17:56:55,088 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:55,866 INFO : Epoch 8\n",
|
||||
"2017-11-03 17:56:55,867 INFO : train_loss 0.53394\n",
|
||||
"2017-11-03 17:56:55,868 INFO : valid_loss 0.54075\n",
|
||||
"2017-11-03 17:56:55,868 INFO : test_loss 0.69350\n",
|
||||
"2017-11-03 17:56:55,869 INFO : train_sample_misclass 0.22455\n",
|
||||
"2017-11-03 17:56:55,870 INFO : valid_sample_misclass 0.24399\n",
|
||||
"2017-11-03 17:56:55,871 INFO : test_sample_misclass 0.35034\n",
|
||||
"2017-11-03 17:56:55,872 INFO : train_misclass 0.15460\n",
|
||||
"2017-11-03 17:56:55,872 INFO : valid_misclass 0.14525\n",
|
||||
"2017-11-03 17:56:55,873 INFO : test_misclass 0.21250\n",
|
||||
"2017-11-03 17:56:55,874 INFO : runtime 1.93581\n",
|
||||
"2017-11-03 17:56:55,875 INFO : \n",
|
||||
"2017-11-03 17:56:55,877 INFO : New best valid_misclass: 0.145251\n",
|
||||
"2017-11-03 17:56:55,878 INFO : \n",
|
||||
"2017-11-03 17:56:57,025 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:57,808 INFO : Epoch 9\n",
|
||||
"2017-11-03 17:56:57,809 INFO : train_loss 0.52253\n",
|
||||
"2017-11-03 17:56:57,810 INFO : valid_loss 0.55157\n",
|
||||
"2017-11-03 17:56:57,811 INFO : test_loss 0.66259\n",
|
||||
"2017-11-03 17:56:57,812 INFO : train_sample_misclass 0.20662\n",
|
||||
"2017-11-03 17:56:57,813 INFO : valid_sample_misclass 0.23934\n",
|
||||
"2017-11-03 17:56:57,813 INFO : test_sample_misclass 0.31630\n",
|
||||
"2017-11-03 17:56:57,814 INFO : train_misclass 0.12535\n",
|
||||
"2017-11-03 17:56:57,815 INFO : valid_misclass 0.15084\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:56:57,816 INFO : test_misclass 0.19375\n",
|
||||
"2017-11-03 17:56:57,816 INFO : runtime 1.93780\n",
|
||||
"2017-11-03 17:56:57,817 INFO : \n",
|
||||
"2017-11-03 17:56:58,966 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:56:59,747 INFO : Epoch 10\n",
|
||||
"2017-11-03 17:56:59,749 INFO : train_loss 0.63435\n",
|
||||
"2017-11-03 17:56:59,749 INFO : valid_loss 0.54363\n",
|
||||
"2017-11-03 17:56:59,750 INFO : test_loss 0.66703\n",
|
||||
"2017-11-03 17:56:59,751 INFO : train_sample_misclass 0.23284\n",
|
||||
"2017-11-03 17:56:59,752 INFO : valid_sample_misclass 0.25216\n",
|
||||
"2017-11-03 17:56:59,752 INFO : test_sample_misclass 0.31337\n",
|
||||
"2017-11-03 17:56:59,753 INFO : train_misclass 0.19081\n",
|
||||
"2017-11-03 17:56:59,754 INFO : valid_misclass 0.16760\n",
|
||||
"2017-11-03 17:56:59,755 INFO : test_misclass 0.21875\n",
|
||||
"2017-11-03 17:56:59,755 INFO : runtime 1.94030\n",
|
||||
"2017-11-03 17:56:59,756 INFO : \n",
|
||||
"2017-11-03 17:57:00,903 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:01,683 INFO : Epoch 11\n",
|
||||
"2017-11-03 17:57:01,684 INFO : train_loss 0.50823\n",
|
||||
"2017-11-03 17:57:01,685 INFO : valid_loss 0.57940\n",
|
||||
"2017-11-03 17:57:01,686 INFO : test_loss 0.65863\n",
|
||||
"2017-11-03 17:57:01,686 INFO : train_sample_misclass 0.21697\n",
|
||||
"2017-11-03 17:57:01,687 INFO : valid_sample_misclass 0.26513\n",
|
||||
"2017-11-03 17:57:01,688 INFO : test_sample_misclass 0.33127\n",
|
||||
"2017-11-03 17:57:01,689 INFO : train_misclass 0.13510\n",
|
||||
"2017-11-03 17:57:01,689 INFO : valid_misclass 0.18994\n",
|
||||
"2017-11-03 17:57:01,690 INFO : test_misclass 0.24375\n",
|
||||
"2017-11-03 17:57:01,691 INFO : runtime 1.93741\n",
|
||||
"2017-11-03 17:57:01,692 INFO : \n",
|
||||
"2017-11-03 17:57:02,839 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:03,619 INFO : Epoch 12\n",
|
||||
"2017-11-03 17:57:03,621 INFO : train_loss 0.51300\n",
|
||||
"2017-11-03 17:57:03,622 INFO : valid_loss 0.54631\n",
|
||||
"2017-11-03 17:57:03,623 INFO : test_loss 0.64827\n",
|
||||
"2017-11-03 17:57:03,624 INFO : train_sample_misclass 0.20628\n",
|
||||
"2017-11-03 17:57:03,624 INFO : valid_sample_misclass 0.23820\n",
|
||||
"2017-11-03 17:57:03,625 INFO : test_sample_misclass 0.32021\n",
|
||||
"2017-11-03 17:57:03,626 INFO : train_misclass 0.13649\n",
|
||||
"2017-11-03 17:57:03,627 INFO : valid_misclass 0.15642\n",
|
||||
"2017-11-03 17:57:03,628 INFO : test_misclass 0.24375\n",
|
||||
"2017-11-03 17:57:03,628 INFO : runtime 1.93566\n",
|
||||
"2017-11-03 17:57:03,629 INFO : \n",
|
||||
"2017-11-03 17:57:04,777 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:05,568 INFO : Epoch 13\n",
|
||||
"2017-11-03 17:57:05,569 INFO : train_loss 0.53439\n",
|
||||
"2017-11-03 17:57:05,570 INFO : valid_loss 0.57364\n",
|
||||
"2017-11-03 17:57:05,571 INFO : test_loss 0.75146\n",
|
||||
"2017-11-03 17:57:05,572 INFO : train_sample_misclass 0.22174\n",
|
||||
"2017-11-03 17:57:05,573 INFO : valid_sample_misclass 0.25399\n",
|
||||
"2017-11-03 17:57:05,573 INFO : test_sample_misclass 0.36589\n",
|
||||
"2017-11-03 17:57:05,574 INFO : train_misclass 0.14903\n",
|
||||
"2017-11-03 17:57:05,575 INFO : valid_misclass 0.16201\n",
|
||||
"2017-11-03 17:57:05,576 INFO : test_misclass 0.27500\n",
|
||||
"2017-11-03 17:57:05,576 INFO : runtime 1.93826\n",
|
||||
"2017-11-03 17:57:05,577 INFO : \n",
|
||||
"2017-11-03 17:57:06,724 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:07,510 INFO : Epoch 14\n",
|
||||
"2017-11-03 17:57:07,511 INFO : train_loss 0.49450\n",
|
||||
"2017-11-03 17:57:07,512 INFO : valid_loss 0.50904\n",
|
||||
"2017-11-03 17:57:07,513 INFO : test_loss 0.59711\n",
|
||||
"2017-11-03 17:57:07,514 INFO : train_sample_misclass 0.19409\n",
|
||||
"2017-11-03 17:57:07,514 INFO : valid_sample_misclass 0.23406\n",
|
||||
"2017-11-03 17:57:07,515 INFO : test_sample_misclass 0.29099\n",
|
||||
"2017-11-03 17:57:07,516 INFO : train_misclass 0.11421\n",
|
||||
"2017-11-03 17:57:07,517 INFO : valid_misclass 0.15084\n",
|
||||
"2017-11-03 17:57:07,517 INFO : test_misclass 0.21875\n",
|
||||
"2017-11-03 17:57:07,518 INFO : runtime 1.94703\n",
|
||||
"2017-11-03 17:57:07,519 INFO : \n",
|
||||
"2017-11-03 17:57:08,667 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:09,447 INFO : Epoch 15\n",
|
||||
"2017-11-03 17:57:09,448 INFO : train_loss 0.55814\n",
|
||||
"2017-11-03 17:57:09,449 INFO : valid_loss 0.52381\n",
|
||||
"2017-11-03 17:57:09,450 INFO : test_loss 0.63100\n",
|
||||
"2017-11-03 17:57:09,450 INFO : train_sample_misclass 0.19992\n",
|
||||
"2017-11-03 17:57:09,451 INFO : valid_sample_misclass 0.22669\n",
|
||||
"2017-11-03 17:57:09,452 INFO : test_sample_misclass 0.30698\n",
|
||||
"2017-11-03 17:57:09,453 INFO : train_misclass 0.14624\n",
|
||||
"2017-11-03 17:57:09,453 INFO : valid_misclass 0.16201\n",
|
||||
"2017-11-03 17:57:09,454 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:57:09,455 INFO : runtime 1.94305\n",
|
||||
"2017-11-03 17:57:09,456 INFO : \n",
|
||||
"2017-11-03 17:57:10,604 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:11,388 INFO : Epoch 16\n",
|
||||
"2017-11-03 17:57:11,389 INFO : train_loss 0.54798\n",
|
||||
"2017-11-03 17:57:11,390 INFO : valid_loss 0.60483\n",
|
||||
"2017-11-03 17:57:11,391 INFO : test_loss 0.70932\n",
|
||||
"2017-11-03 17:57:11,392 INFO : train_sample_misclass 0.23531\n",
|
||||
"2017-11-03 17:57:11,393 INFO : valid_sample_misclass 0.26090\n",
|
||||
"2017-11-03 17:57:11,394 INFO : test_sample_misclass 0.35440\n",
|
||||
"2017-11-03 17:57:11,396 INFO : train_misclass 0.19359\n",
|
||||
"2017-11-03 17:57:11,397 INFO : valid_misclass 0.22905\n",
|
||||
"2017-11-03 17:57:11,398 INFO : test_misclass 0.32500\n",
|
||||
"2017-11-03 17:57:11,399 INFO : runtime 1.93673\n",
|
||||
"2017-11-03 17:57:11,399 INFO : \n",
|
||||
"2017-11-03 17:57:12,550 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:13,342 INFO : Epoch 17\n",
|
||||
"2017-11-03 17:57:13,343 INFO : train_loss 0.57735\n",
|
||||
"2017-11-03 17:57:13,344 INFO : valid_loss 0.60624\n",
|
||||
"2017-11-03 17:57:13,344 INFO : test_loss 0.71293\n",
|
||||
"2017-11-03 17:57:13,345 INFO : train_sample_misclass 0.23263\n",
|
||||
"2017-11-03 17:57:13,346 INFO : valid_sample_misclass 0.25866\n",
|
||||
"2017-11-03 17:57:13,347 INFO : test_sample_misclass 0.35750\n",
|
||||
"2017-11-03 17:57:13,347 INFO : train_misclass 0.18524\n",
|
||||
"2017-11-03 17:57:13,348 INFO : valid_misclass 0.19553\n",
|
||||
"2017-11-03 17:57:13,349 INFO : test_misclass 0.29375\n",
|
||||
"2017-11-03 17:57:13,350 INFO : runtime 1.94563\n",
|
||||
"2017-11-03 17:57:13,350 INFO : \n",
|
||||
"2017-11-03 17:57:14,500 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:15,288 INFO : Epoch 18\n",
|
||||
"2017-11-03 17:57:15,289 INFO : train_loss 0.46534\n",
|
||||
"2017-11-03 17:57:15,290 INFO : valid_loss 0.52169\n",
|
||||
"2017-11-03 17:57:15,291 INFO : test_loss 0.64741\n",
|
||||
"2017-11-03 17:57:15,292 INFO : train_sample_misclass 0.19295\n",
|
||||
"2017-11-03 17:57:15,292 INFO : valid_sample_misclass 0.23086\n",
|
||||
"2017-11-03 17:57:15,293 INFO : test_sample_misclass 0.33224\n",
|
||||
"2017-11-03 17:57:15,294 INFO : train_misclass 0.11978\n",
|
||||
"2017-11-03 17:57:15,295 INFO : valid_misclass 0.18436\n",
|
||||
"2017-11-03 17:57:15,295 INFO : test_misclass 0.24375\n",
|
||||
"2017-11-03 17:57:15,296 INFO : runtime 1.95093\n",
|
||||
"2017-11-03 17:57:15,297 INFO : \n",
|
||||
"2017-11-03 17:57:16,445 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:17,230 INFO : Epoch 19\n",
|
||||
"2017-11-03 17:57:17,232 INFO : train_loss 0.47752\n",
|
||||
"2017-11-03 17:57:17,232 INFO : valid_loss 0.55626\n",
|
||||
"2017-11-03 17:57:17,233 INFO : test_loss 0.68759\n",
|
||||
"2017-11-03 17:57:17,234 INFO : train_sample_misclass 0.20194\n",
|
||||
"2017-11-03 17:57:17,235 INFO : valid_sample_misclass 0.24273\n",
|
||||
"2017-11-03 17:57:17,236 INFO : test_sample_misclass 0.35215\n",
|
||||
"2017-11-03 17:57:17,236 INFO : train_misclass 0.13928\n",
|
||||
"2017-11-03 17:57:17,237 INFO : valid_misclass 0.16201\n",
|
||||
"2017-11-03 17:57:17,238 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:57:17,239 INFO : runtime 1.94504\n",
|
||||
"2017-11-03 17:57:17,240 INFO : \n",
|
||||
"2017-11-03 17:57:18,387 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:57:19,174 INFO : Epoch 20\n",
|
||||
"2017-11-03 17:57:19,175 INFO : train_loss 0.46220\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:57:19,176 INFO : valid_loss 0.54947\n",
|
||||
"2017-11-03 17:57:19,177 INFO : test_loss 0.62923\n",
|
||||
"2017-11-03 17:57:19,178 INFO : train_sample_misclass 0.18707\n",
|
||||
"2017-11-03 17:57:19,179 INFO : valid_sample_misclass 0.24050\n",
|
||||
"2017-11-03 17:57:19,179 INFO : test_sample_misclass 0.29137\n",
|
||||
"2017-11-03 17:57:19,180 INFO : train_misclass 0.10864\n",
|
||||
"2017-11-03 17:57:19,181 INFO : valid_misclass 0.18436\n",
|
||||
"2017-11-03 17:57:19,182 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:57:19,182 INFO : runtime 1.94147\n",
|
||||
"2017-11-03 17:57:19,183 INFO : \n",
|
||||
"2017-11-03 17:57:19,184 INFO : Setup for second stop...\n",
|
||||
"2017-11-03 17:57:19,188 INFO : Train loss to reach 0.53394\n",
|
||||
"2017-11-03 17:57:19,189 INFO : Run until second stop...\n",
|
||||
"2017-11-03 17:57:20,151 INFO : Epoch 9\n",
|
||||
"2017-11-03 17:57:20,153 INFO : train_loss 0.53530\n",
|
||||
"2017-11-03 17:57:20,154 INFO : valid_loss 0.54075\n",
|
||||
"2017-11-03 17:57:20,154 INFO : test_loss 0.69350\n",
|
||||
"2017-11-03 17:57:20,155 INFO : train_sample_misclass 0.22843\n",
|
||||
"2017-11-03 17:57:20,156 INFO : valid_sample_misclass 0.24399\n",
|
||||
"2017-11-03 17:57:20,157 INFO : test_sample_misclass 0.35034\n",
|
||||
"2017-11-03 17:57:20,157 INFO : train_misclass 0.15273\n",
|
||||
"2017-11-03 17:57:20,158 INFO : valid_misclass 0.14525\n",
|
||||
"2017-11-03 17:57:20,159 INFO : test_misclass 0.21250\n",
|
||||
"2017-11-03 17:57:20,160 INFO : runtime 0.80763\n",
|
||||
"2017-11-03 17:57:20,161 INFO : \n",
|
||||
"2017-11-03 17:57:21,603 INFO : Time only for training updates: 1.16s\n",
|
||||
"2017-11-03 17:57:22,533 INFO : Epoch 10\n",
|
||||
"2017-11-03 17:57:22,534 INFO : train_loss 0.54183\n",
|
||||
"2017-11-03 17:57:22,535 INFO : valid_loss 0.50682\n",
|
||||
"2017-11-03 17:57:22,536 INFO : test_loss 0.64068\n",
|
||||
"2017-11-03 17:57:22,537 INFO : train_sample_misclass 0.22782\n",
|
||||
"2017-11-03 17:57:22,537 INFO : valid_sample_misclass 0.23420\n",
|
||||
"2017-11-03 17:57:22,538 INFO : test_sample_misclass 0.31642\n",
|
||||
"2017-11-03 17:57:22,539 INFO : train_misclass 0.18283\n",
|
||||
"2017-11-03 17:57:22,540 INFO : valid_misclass 0.15642\n",
|
||||
"2017-11-03 17:57:22,540 INFO : test_misclass 0.24375\n",
|
||||
"2017-11-03 17:57:22,541 INFO : runtime 2.40818\n",
|
||||
"2017-11-03 17:57:22,542 INFO : \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We arrive at ca. 80% accuracy."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you want to do trialwise decoding instead of cropped decoding, perform the following changes:\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Change:\n",
|
||||
"```python\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = 800\n",
|
||||
"in_chans = 3\n",
|
||||
"n_classes = 4\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes, input_time_length=input_time_length,\n",
|
||||
" final_conv_length=30).create_network()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"to:\n",
|
||||
"```python\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = train_set.X.shape[2]\n",
|
||||
"in_chans = 3\n",
|
||||
"n_classes = 4\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes, input_time_length=input_time_length,\n",
|
||||
" final_conv_length='auto').create_network()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Remove:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"to_dense_prediction_model(model)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Remove:\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from braindecode.torch_ext.util import np_to_var\n",
|
||||
"# determine output size\n",
|
||||
"test_input = np_to_var(np.ones((2, 3, input_time_length, 1), dtype=np.float32))\n",
|
||||
"if cuda:\n",
|
||||
" test_input = test_input.cuda()\n",
|
||||
"out = model(test_input)\n",
|
||||
"n_preds_per_input = out.cpu().data.numpy().shape[2]\n",
|
||||
"print(\"{:d} predictions per input/trial\".format(n_preds_per_input))\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Change:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
|
||||
"iterator = CropsFromTrialsIterator(batch_size=32,input_time_length=input_time_length,\n",
|
||||
" n_preds_per_input=n_preds_per_input)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"to:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"from braindecode.datautil.iterators import BalancedBatchSizeIterator\n",
|
||||
"iterator = BalancedBatchSizeIterator(batch_size=32)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Change:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"loss_function = lambda preds, targets: F.nll_loss(th.mean(preds, dim=2)[:,:,0], targets)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"to:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"loss_function = F.nll_loss\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Change:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='sample_misclass'),\n",
|
||||
" CroppedTrialMisclassMonitor(input_time_length), RuntimeMonitor(),]\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"to:\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='misclass'), \n",
|
||||
" RuntimeMonitor(),]\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Resulting code can be seen at [BBCI Data Epoched](BBCI_Data_Epoched.html)."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,899 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Read and Decode BBCI Data with Start-Stop-Markers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This tutorial shows how to read and decode BBCI data with start and stop markers. The data loading part is more complicated and it is advised to read the other tutorials before."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup logging to see outputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"import sys\n",
|
||||
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
|
||||
" level=logging.DEBUG, stream=sys.stdout)\n",
|
||||
"log = logging.getLogger()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and preprocess data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is a bit more complicated than before since we have to add breaks etc. Here I now opt to add breaks do all preprocessings per run and only later combine them together."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from braindecode.datautil.splitters import concatenate_sets\n",
|
||||
"from braindecode.datautil.trial_segment import create_signal_target_from_raw_mne, add_breaks\n",
|
||||
"from braindecode.datasets.bbci import load_bbci_sets_from_folder\n",
|
||||
"from copy import deepcopy\n",
|
||||
"from braindecode.mne_ext.signalproc import resample_cnt, mne_apply\n",
|
||||
"from braindecode.datautil.signalproc import lowpass_cnt\n",
|
||||
"from braindecode.datautil.signalproc import exponential_running_standardize\n",
|
||||
"\n",
|
||||
"def create_cnts(folder, runs,):\n",
|
||||
" # Load data\n",
|
||||
" cnts = load_bbci_sets_from_folder(folder, runs)\n",
|
||||
" \n",
|
||||
" # Now do some preprocessings:\n",
|
||||
" # Resampling to 250 Hz, lowpass below 38, eponential standardization\n",
|
||||
" \n",
|
||||
" new_cnts = []\n",
|
||||
" for cnt in cnts:\n",
|
||||
" # Only take some channels \n",
|
||||
" #cnt = cnt.drop_channels(['STI 014']) # This would remove stimulus channel\n",
|
||||
" cnt = cnt.pick_channels(['C3', 'CPz', 'C4'])\n",
|
||||
" log.info(\"Preprocessing....\")\n",
|
||||
" cnt = mne_apply(lambda a: lowpass_cnt(a, 38,cnt.info['sfreq'], axis=1), cnt)\n",
|
||||
" cnt = resample_cnt(cnt, 250)\n",
|
||||
" # mne apply will apply the function to the data (a 2d-numpy-array)\n",
|
||||
" # have to transpose data back and forth, since\n",
|
||||
" # exponential_running_standardize expects time x chans order\n",
|
||||
" # while mne object has chans x time order\n",
|
||||
" cnt = mne_apply(lambda a: exponential_running_standardize(\n",
|
||||
" a.T, init_block_size=1000,factor_new=0.001, eps=1e-4).T,\n",
|
||||
" cnt)\n",
|
||||
" new_cnts.append(cnt)\n",
|
||||
" return new_cnts"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:16,149 INFO : Loading /home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R01-8/AnLaNBD1S001R01_1-1_250Hz.BBCI.mat\n",
|
||||
"Creating RawArray with float64 data, n_channels=64, n_times=151350\n",
|
||||
" Range : 0 ... 151349 = 0.000 ... 605.396 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:54:17,481 INFO : Loading /home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R01-8/AnLaNBD1S001R02_1-1_250Hz.BBCI.mat\n",
|
||||
"Creating RawArray with float64 data, n_channels=64, n_times=153500\n",
|
||||
" Range : 0 ... 153499 = 0.000 ... 613.996 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:54:18,843 INFO : Loading /home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R01-8/AnLaNBD1S001R03_1-1_250Hz.BBCI.mat\n",
|
||||
"Creating RawArray with float64 data, n_channels=64, n_times=180700\n",
|
||||
" Range : 0 ... 180699 = 0.000 ... 722.796 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:54:20,609 INFO : Preprocessing....\n",
|
||||
"2017-11-03 17:54:20,626 INFO : Just copying data, no resampling, since new sampling rate same.\n",
|
||||
"2017-11-03 17:54:20,801 INFO : Preprocessing....\n",
|
||||
"2017-11-03 17:54:20,809 INFO : Just copying data, no resampling, since new sampling rate same.\n",
|
||||
"2017-11-03 17:54:21,018 INFO : Preprocessing....\n",
|
||||
"2017-11-03 17:54:21,026 INFO : Just copying data, no resampling, since new sampling rate same.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from collections import OrderedDict\n",
|
||||
"\n",
|
||||
"train_runs = [1,2,3]\n",
|
||||
"train_cnts = create_cnts('/home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R01-8/', \n",
|
||||
" train_runs,)\n",
|
||||
"\n",
|
||||
"name_to_start_code = OrderedDict([('Right Hand', 1), ('Feet', 4),\n",
|
||||
" ('Rotation', 8), ('Words', [10])])\n",
|
||||
"\n",
|
||||
"name_to_stop_code = OrderedDict([('Right Hand', [20,21,22,23,24,28,30]),\n",
|
||||
" ('Feet', [20,21,22,23,24,28,30]),\n",
|
||||
" ('Rotation', [20,21,22,23,24,28,30]), \n",
|
||||
" ('Words', [20,21,22,23,24,28,30])])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:21,179 INFO : Loading /home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R09-10/AnLaNBD1S001R09_1-1_250Hz.BBCI.mat\n",
|
||||
"Creating RawArray with float64 data, n_channels=64, n_times=152050\n",
|
||||
" Range : 0 ... 152049 = 0.000 ... 608.196 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:54:22,618 INFO : Loading /home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R09-10/AnLaNBD1S001R10_1-1_250Hz.BBCI.mat\n",
|
||||
"Creating RawArray with float64 data, n_channels=64, n_times=151100\n",
|
||||
" Range : 0 ... 151099 = 0.000 ... 604.396 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:54:24,103 INFO : Preprocessing....\n",
|
||||
"2017-11-03 17:54:24,112 INFO : Just copying data, no resampling, since new sampling rate same.\n",
|
||||
"2017-11-03 17:54:24,277 INFO : Preprocessing....\n",
|
||||
"2017-11-03 17:54:24,284 INFO : Just copying data, no resampling, since new sampling rate same.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_runs = [9,10]\n",
|
||||
"test_cnts = create_cnts('/home/schirrmr/data/robot-hall/AnLa/AnLaNBD1R09-10/', test_runs,)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We already create the model now, since we need to know the receptive field size for properly cutting out the data to predict. We need to cut out data starting at -receptive_field_size samples before the first sample we want to predict."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"132 predictions per input/trial\n",
|
||||
"Receptive field: 518/2072.00 (samples/ms)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"from braindecode.models.util import to_dense_prediction_model\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = True\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = 650\n",
|
||||
"in_chans = train_cnts[0].get_data().shape[0]\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=5, input_time_length=input_time_length,\n",
|
||||
" final_conv_length=29).create_network()\n",
|
||||
"to_dense_prediction_model(model)\n",
|
||||
"\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()\n",
|
||||
"from braindecode.torch_ext.util import np_to_var\n",
|
||||
"import numpy as np\n",
|
||||
"# determine output size\n",
|
||||
"test_input = np_to_var(np.ones((2, in_chans, input_time_length, 1), dtype=np.float32))\n",
|
||||
"if cuda:\n",
|
||||
" test_input = test_input.cuda()\n",
|
||||
"out = model(test_input)\n",
|
||||
"n_preds_per_input = out.cpu().data.numpy().shape[2]\n",
|
||||
"print(\"{:d} predictions per input/trial\".format(n_preds_per_input))\n",
|
||||
"n_receptive_field = input_time_length - n_preds_per_input\n",
|
||||
"receptive_field_ms = n_receptive_field * 1000.0 / train_cnts[0].info['sfreq']\n",
|
||||
"print(\"Receptive field: {:d}/{:.2f} (samples/ms)\".format(n_receptive_field,\n",
|
||||
" receptive_field_ms))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create SignalAndTarget Sets"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:27,739 INFO : Trial per class:\n",
|
||||
"Counter({'Break': 72, 'Right Hand': 29, 'Words': 21, 'Feet': 19, 'Rotation': 4})\n",
|
||||
"2017-11-03 17:54:27,766 INFO : Trial per class:\n",
|
||||
"Counter({'Break': 80, 'Feet': 31, 'Words': 26, 'Right Hand': 18, 'Rotation': 6})\n",
|
||||
"2017-11-03 17:54:27,796 INFO : Trial per class:\n",
|
||||
"Counter({'Break': 95, 'Feet': 38, 'Words': 29, 'Right Hand': 22, 'Rotation': 7})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.datautil.trial_segment import create_signal_target_with_breaks_from_mne\n",
|
||||
"\n",
|
||||
"break_start_offset_ms = 1000\n",
|
||||
"break_stop_offset_ms = -500\n",
|
||||
"\n",
|
||||
"train_sets = [create_signal_target_with_breaks_from_mne(\n",
|
||||
" cnt, name_to_start_code, [0,0], \n",
|
||||
" name_to_stop_code, min_break_length_ms=1000, max_break_length_ms=10000,\n",
|
||||
" break_epoch_ival_ms=[500,-500],\n",
|
||||
" prepad_trials_to_n_samples=input_time_length) \n",
|
||||
" for cnt in train_cnts]\n",
|
||||
"train_set = concatenate_sets(train_sets)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:27,856 INFO : Trial per class:\n",
|
||||
"Counter({'Break': 76, 'Feet': 24, 'Right Hand': 24, 'Words': 19, 'Rotation': 10})\n",
|
||||
"2017-11-03 17:54:27,884 INFO : Trial per class:\n",
|
||||
"Counter({'Break': 80, 'Feet': 30, 'Right Hand': 22, 'Words': 21, 'Rotation': 8})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_sets = [create_signal_target_with_breaks_from_mne(\n",
|
||||
" cnt, name_to_start_code, [0,0], \n",
|
||||
" name_to_stop_code, min_break_length_ms=1000, max_break_length_ms=10000,\n",
|
||||
" break_epoch_ival_ms=[500,-500],\n",
|
||||
" prepad_trials_to_n_samples=input_time_length) \n",
|
||||
" for cnt in test_cnts]\n",
|
||||
"test_set = concatenate_sets(test_sets)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.splitters import split_into_two_sets\n",
|
||||
"\n",
|
||||
"train_set, valid_set = split_into_two_sets(train_set, first_set_fraction=0.8)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup optimizer and iterator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torch import optim\n",
|
||||
"\n",
|
||||
"optimizer = optim.Adam(model.parameters())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
|
||||
"iterator = CropsFromTrialsIterator(batch_size=32,input_time_length=input_time_length,\n",
|
||||
" n_preds_per_input=n_preds_per_input)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup Monitors, Loss function, Stop Criteria"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.experiments.experiment import Experiment\n",
|
||||
"from braindecode.experiments.monitors import RuntimeMonitor, LossMonitor, CroppedTrialMisclassMonitor, MisclassMonitor\n",
|
||||
"from braindecode.experiments.stopcriteria import MaxEpochs\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"import torch as th\n",
|
||||
"from braindecode.torch_ext.modules import Expression\n",
|
||||
"from braindecode.torch_ext.losses import log_categorical_crossentropy\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"loss_function = log_categorical_crossentropy\n",
|
||||
"\n",
|
||||
"model_constraint = None\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='sample_misclass'),\n",
|
||||
" CroppedTrialMisclassMonitor(input_time_length), RuntimeMonitor(),]\n",
|
||||
"stop_criterion = MaxEpochs(20)\n",
|
||||
"exp = Experiment(model, train_set, valid_set, test_set, iterator, loss_function, optimizer, model_constraint,\n",
|
||||
" monitors, stop_criterion, remember_best_column='valid_misclass',\n",
|
||||
" run_after_early_stop=True, batch_modifier=None, cuda=cuda)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:28,100 INFO : Run until first stop...\n",
|
||||
"2017-11-03 17:54:29,008 INFO : Epoch 0\n",
|
||||
"2017-11-03 17:54:29,011 INFO : train_loss 6.69229\n",
|
||||
"2017-11-03 17:54:29,012 INFO : valid_loss 6.46568\n",
|
||||
"2017-11-03 17:54:29,013 INFO : test_loss 7.03033\n",
|
||||
"2017-11-03 17:54:29,014 INFO : train_sample_misclass 0.82260\n",
|
||||
"2017-11-03 17:54:29,015 INFO : valid_sample_misclass 0.80963\n",
|
||||
"2017-11-03 17:54:29,016 INFO : test_sample_misclass 0.84529\n",
|
||||
"2017-11-03 17:54:29,017 INFO : train_misclass 0.84673\n",
|
||||
"2017-11-03 17:54:29,018 INFO : valid_misclass 0.83838\n",
|
||||
"2017-11-03 17:54:29,019 INFO : test_misclass 0.87261\n",
|
||||
"2017-11-03 17:54:29,020 INFO : runtime 0.00000\n",
|
||||
"2017-11-03 17:54:29,021 INFO : \n",
|
||||
"2017-11-03 17:54:29,023 INFO : New best valid_misclass: 0.838384\n",
|
||||
"2017-11-03 17:54:29,024 INFO : \n",
|
||||
"2017-11-03 17:54:29,883 INFO : Time only for training updates: 0.76s\n",
|
||||
"2017-11-03 17:54:30,662 INFO : Epoch 1\n",
|
||||
"2017-11-03 17:54:30,663 INFO : train_loss 1.11019\n",
|
||||
"2017-11-03 17:54:30,664 INFO : valid_loss 1.27928\n",
|
||||
"2017-11-03 17:54:30,665 INFO : test_loss 1.36444\n",
|
||||
"2017-11-03 17:54:30,666 INFO : train_sample_misclass 0.42905\n",
|
||||
"2017-11-03 17:54:30,667 INFO : valid_sample_misclass 0.49247\n",
|
||||
"2017-11-03 17:54:30,668 INFO : test_sample_misclass 0.53468\n",
|
||||
"2017-11-03 17:54:30,669 INFO : train_misclass 0.35427\n",
|
||||
"2017-11-03 17:54:30,670 INFO : valid_misclass 0.44444\n",
|
||||
"2017-11-03 17:54:30,671 INFO : test_misclass 0.45541\n",
|
||||
"2017-11-03 17:54:30,672 INFO : runtime 1.78367\n",
|
||||
"2017-11-03 17:54:30,673 INFO : \n",
|
||||
"2017-11-03 17:54:30,675 INFO : New best valid_misclass: 0.444444\n",
|
||||
"2017-11-03 17:54:30,676 INFO : \n",
|
||||
"2017-11-03 17:54:31,506 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:32,301 INFO : Epoch 2\n",
|
||||
"2017-11-03 17:54:32,304 INFO : train_loss 0.89880\n",
|
||||
"2017-11-03 17:54:32,305 INFO : valid_loss 1.17010\n",
|
||||
"2017-11-03 17:54:32,306 INFO : test_loss 1.30903\n",
|
||||
"2017-11-03 17:54:32,307 INFO : train_sample_misclass 0.34776\n",
|
||||
"2017-11-03 17:54:32,308 INFO : valid_sample_misclass 0.41259\n",
|
||||
"2017-11-03 17:54:32,309 INFO : test_sample_misclass 0.50234\n",
|
||||
"2017-11-03 17:54:32,310 INFO : train_misclass 0.28643\n",
|
||||
"2017-11-03 17:54:32,311 INFO : valid_misclass 0.40404\n",
|
||||
"2017-11-03 17:54:32,312 INFO : test_misclass 0.46815\n",
|
||||
"2017-11-03 17:54:32,313 INFO : runtime 1.62281\n",
|
||||
"2017-11-03 17:54:32,314 INFO : \n",
|
||||
"2017-11-03 17:54:32,317 INFO : New best valid_misclass: 0.404040\n",
|
||||
"2017-11-03 17:54:32,318 INFO : \n",
|
||||
"2017-11-03 17:54:33,151 INFO : Time only for training updates: 0.73s\n",
|
||||
"2017-11-03 17:54:33,950 INFO : Epoch 3\n",
|
||||
"2017-11-03 17:54:33,952 INFO : train_loss 0.81194\n",
|
||||
"2017-11-03 17:54:33,953 INFO : valid_loss 1.23906\n",
|
||||
"2017-11-03 17:54:33,954 INFO : test_loss 1.32349\n",
|
||||
"2017-11-03 17:54:33,955 INFO : train_sample_misclass 0.31683\n",
|
||||
"2017-11-03 17:54:33,956 INFO : valid_sample_misclass 0.44712\n",
|
||||
"2017-11-03 17:54:33,957 INFO : test_sample_misclass 0.49383\n",
|
||||
"2017-11-03 17:54:33,957 INFO : train_misclass 0.25628\n",
|
||||
"2017-11-03 17:54:33,958 INFO : valid_misclass 0.43434\n",
|
||||
"2017-11-03 17:54:33,959 INFO : test_misclass 0.45860\n",
|
||||
"2017-11-03 17:54:33,960 INFO : runtime 1.64646\n",
|
||||
"2017-11-03 17:54:33,960 INFO : \n",
|
||||
"2017-11-03 17:54:34,776 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:35,547 INFO : Epoch 4\n",
|
||||
"2017-11-03 17:54:35,549 INFO : train_loss 0.73879\n",
|
||||
"2017-11-03 17:54:35,549 INFO : valid_loss 1.21058\n",
|
||||
"2017-11-03 17:54:35,550 INFO : test_loss 1.31367\n",
|
||||
"2017-11-03 17:54:35,551 INFO : train_sample_misclass 0.27924\n",
|
||||
"2017-11-03 17:54:35,552 INFO : valid_sample_misclass 0.42925\n",
|
||||
"2017-11-03 17:54:35,553 INFO : test_sample_misclass 0.47731\n",
|
||||
"2017-11-03 17:54:35,553 INFO : train_misclass 0.23618\n",
|
||||
"2017-11-03 17:54:35,554 INFO : valid_misclass 0.39394\n",
|
||||
"2017-11-03 17:54:35,555 INFO : test_misclass 0.44586\n",
|
||||
"2017-11-03 17:54:35,556 INFO : runtime 1.62406\n",
|
||||
"2017-11-03 17:54:35,556 INFO : \n",
|
||||
"2017-11-03 17:54:35,559 INFO : New best valid_misclass: 0.393939\n",
|
||||
"2017-11-03 17:54:35,559 INFO : \n",
|
||||
"2017-11-03 17:54:36,368 INFO : Time only for training updates: 0.71s\n",
|
||||
"2017-11-03 17:54:37,117 INFO : Epoch 5\n",
|
||||
"2017-11-03 17:54:37,118 INFO : train_loss 0.69937\n",
|
||||
"2017-11-03 17:54:37,119 INFO : valid_loss 1.20308\n",
|
||||
"2017-11-03 17:54:37,120 INFO : test_loss 1.31024\n",
|
||||
"2017-11-03 17:54:37,121 INFO : train_sample_misclass 0.26148\n",
|
||||
"2017-11-03 17:54:37,121 INFO : valid_sample_misclass 0.42965\n",
|
||||
"2017-11-03 17:54:37,122 INFO : test_sample_misclass 0.46976\n",
|
||||
"2017-11-03 17:54:37,123 INFO : train_misclass 0.23116\n",
|
||||
"2017-11-03 17:54:37,124 INFO : valid_misclass 0.41414\n",
|
||||
"2017-11-03 17:54:37,124 INFO : test_misclass 0.46497\n",
|
||||
"2017-11-03 17:54:37,125 INFO : runtime 1.59298\n",
|
||||
"2017-11-03 17:54:37,126 INFO : \n",
|
||||
"2017-11-03 17:54:37,940 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:38,703 INFO : Epoch 6\n",
|
||||
"2017-11-03 17:54:38,704 INFO : train_loss 0.68692\n",
|
||||
"2017-11-03 17:54:38,705 INFO : valid_loss 1.21140\n",
|
||||
"2017-11-03 17:54:38,706 INFO : test_loss 1.37866\n",
|
||||
"2017-11-03 17:54:38,707 INFO : train_sample_misclass 0.26337\n",
|
||||
"2017-11-03 17:54:38,707 INFO : valid_sample_misclass 0.42246\n",
|
||||
"2017-11-03 17:54:38,708 INFO : test_sample_misclass 0.47892\n",
|
||||
"2017-11-03 17:54:38,709 INFO : train_misclass 0.22613\n",
|
||||
"2017-11-03 17:54:38,710 INFO : valid_misclass 0.41414\n",
|
||||
"2017-11-03 17:54:38,710 INFO : test_misclass 0.43631\n",
|
||||
"2017-11-03 17:54:38,711 INFO : runtime 1.57037\n",
|
||||
"2017-11-03 17:54:38,712 INFO : \n",
|
||||
"2017-11-03 17:54:39,529 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:40,288 INFO : Epoch 7\n",
|
||||
"2017-11-03 17:54:40,290 INFO : train_loss 0.64330\n",
|
||||
"2017-11-03 17:54:40,291 INFO : valid_loss 1.26310\n",
|
||||
"2017-11-03 17:54:40,291 INFO : test_loss 1.33326\n",
|
||||
"2017-11-03 17:54:40,292 INFO : train_sample_misclass 0.23914\n",
|
||||
"2017-11-03 17:54:40,293 INFO : valid_sample_misclass 0.43057\n",
|
||||
"2017-11-03 17:54:40,294 INFO : test_sample_misclass 0.45974\n",
|
||||
"2017-11-03 17:54:40,295 INFO : train_misclass 0.19347\n",
|
||||
"2017-11-03 17:54:40,295 INFO : valid_misclass 0.37374\n",
|
||||
"2017-11-03 17:54:40,296 INFO : test_misclass 0.43312\n",
|
||||
"2017-11-03 17:54:40,297 INFO : runtime 1.58912\n",
|
||||
"2017-11-03 17:54:40,298 INFO : \n",
|
||||
"2017-11-03 17:54:40,300 INFO : New best valid_misclass: 0.373737\n",
|
||||
"2017-11-03 17:54:40,301 INFO : \n",
|
||||
"2017-11-03 17:54:41,126 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:41,882 INFO : Epoch 8\n",
|
||||
"2017-11-03 17:54:41,884 INFO : train_loss 0.63941\n",
|
||||
"2017-11-03 17:54:41,885 INFO : valid_loss 1.26822\n",
|
||||
"2017-11-03 17:54:41,885 INFO : test_loss 1.37385\n",
|
||||
"2017-11-03 17:54:41,886 INFO : train_sample_misclass 0.24020\n",
|
||||
"2017-11-03 17:54:41,887 INFO : valid_sample_misclass 0.43108\n",
|
||||
"2017-11-03 17:54:41,888 INFO : test_sample_misclass 0.47164\n",
|
||||
"2017-11-03 17:54:41,888 INFO : train_misclass 0.20352\n",
|
||||
"2017-11-03 17:54:41,889 INFO : valid_misclass 0.44444\n",
|
||||
"2017-11-03 17:54:41,890 INFO : test_misclass 0.42675\n",
|
||||
"2017-11-03 17:54:41,891 INFO : runtime 1.59709\n",
|
||||
"2017-11-03 17:54:41,891 INFO : \n",
|
||||
"2017-11-03 17:54:42,704 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:43,453 INFO : Epoch 9\n",
|
||||
"2017-11-03 17:54:43,455 INFO : train_loss 0.61007\n",
|
||||
"2017-11-03 17:54:43,456 INFO : valid_loss 1.22956\n",
|
||||
"2017-11-03 17:54:43,456 INFO : test_loss 1.40305\n",
|
||||
"2017-11-03 17:54:43,457 INFO : train_sample_misclass 0.22529\n",
|
||||
"2017-11-03 17:54:43,458 INFO : valid_sample_misclass 0.43356\n",
|
||||
"2017-11-03 17:54:43,459 INFO : test_sample_misclass 0.48479\n",
|
||||
"2017-11-03 17:54:43,459 INFO : train_misclass 0.19849\n",
|
||||
"2017-11-03 17:54:43,460 INFO : valid_misclass 0.43434\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:54:43,461 INFO : test_misclass 0.46497\n",
|
||||
"2017-11-03 17:54:43,462 INFO : runtime 1.57837\n",
|
||||
"2017-11-03 17:54:43,462 INFO : \n",
|
||||
"2017-11-03 17:54:44,271 INFO : Time only for training updates: 0.71s\n",
|
||||
"2017-11-03 17:54:45,020 INFO : Epoch 10\n",
|
||||
"2017-11-03 17:54:45,021 INFO : train_loss 0.64797\n",
|
||||
"2017-11-03 17:54:45,022 INFO : valid_loss 1.28604\n",
|
||||
"2017-11-03 17:54:45,023 INFO : test_loss 1.41996\n",
|
||||
"2017-11-03 17:54:45,023 INFO : train_sample_misclass 0.24798\n",
|
||||
"2017-11-03 17:54:45,024 INFO : valid_sample_misclass 0.44050\n",
|
||||
"2017-11-03 17:54:45,025 INFO : test_sample_misclass 0.47793\n",
|
||||
"2017-11-03 17:54:45,026 INFO : train_misclass 0.23869\n",
|
||||
"2017-11-03 17:54:45,027 INFO : valid_misclass 0.41414\n",
|
||||
"2017-11-03 17:54:45,027 INFO : test_misclass 0.44904\n",
|
||||
"2017-11-03 17:54:45,028 INFO : runtime 1.56668\n",
|
||||
"2017-11-03 17:54:45,029 INFO : \n",
|
||||
"2017-11-03 17:54:45,847 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:46,639 INFO : Epoch 11\n",
|
||||
"2017-11-03 17:54:46,640 INFO : train_loss 0.60028\n",
|
||||
"2017-11-03 17:54:46,641 INFO : valid_loss 1.19866\n",
|
||||
"2017-11-03 17:54:46,642 INFO : test_loss 1.51705\n",
|
||||
"2017-11-03 17:54:46,642 INFO : train_sample_misclass 0.22617\n",
|
||||
"2017-11-03 17:54:46,643 INFO : valid_sample_misclass 0.43046\n",
|
||||
"2017-11-03 17:54:46,644 INFO : test_sample_misclass 0.50496\n",
|
||||
"2017-11-03 17:54:46,645 INFO : train_misclass 0.18593\n",
|
||||
"2017-11-03 17:54:46,646 INFO : valid_misclass 0.34343\n",
|
||||
"2017-11-03 17:54:46,646 INFO : test_misclass 0.47134\n",
|
||||
"2017-11-03 17:54:46,647 INFO : runtime 1.57587\n",
|
||||
"2017-11-03 17:54:46,648 INFO : \n",
|
||||
"2017-11-03 17:54:46,650 INFO : New best valid_misclass: 0.343434\n",
|
||||
"2017-11-03 17:54:46,651 INFO : \n",
|
||||
"2017-11-03 17:54:47,467 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:48,218 INFO : Epoch 12\n",
|
||||
"2017-11-03 17:54:48,219 INFO : train_loss 0.57952\n",
|
||||
"2017-11-03 17:54:48,220 INFO : valid_loss 1.23002\n",
|
||||
"2017-11-03 17:54:48,221 INFO : test_loss 1.39561\n",
|
||||
"2017-11-03 17:54:48,222 INFO : train_sample_misclass 0.21030\n",
|
||||
"2017-11-03 17:54:48,222 INFO : valid_sample_misclass 0.42522\n",
|
||||
"2017-11-03 17:54:48,223 INFO : test_sample_misclass 0.45922\n",
|
||||
"2017-11-03 17:54:48,224 INFO : train_misclass 0.17337\n",
|
||||
"2017-11-03 17:54:48,225 INFO : valid_misclass 0.39394\n",
|
||||
"2017-11-03 17:54:48,225 INFO : test_misclass 0.42038\n",
|
||||
"2017-11-03 17:54:48,226 INFO : runtime 1.61997\n",
|
||||
"2017-11-03 17:54:48,227 INFO : \n",
|
||||
"2017-11-03 17:54:49,066 INFO : Time only for training updates: 0.74s\n",
|
||||
"2017-11-03 17:54:49,828 INFO : Epoch 13\n",
|
||||
"2017-11-03 17:54:49,829 INFO : train_loss 0.56454\n",
|
||||
"2017-11-03 17:54:49,830 INFO : valid_loss 1.30951\n",
|
||||
"2017-11-03 17:54:49,831 INFO : test_loss 1.35587\n",
|
||||
"2017-11-03 17:54:49,832 INFO : train_sample_misclass 0.20067\n",
|
||||
"2017-11-03 17:54:49,833 INFO : valid_sample_misclass 0.43243\n",
|
||||
"2017-11-03 17:54:49,833 INFO : test_sample_misclass 0.45508\n",
|
||||
"2017-11-03 17:54:49,834 INFO : train_misclass 0.17839\n",
|
||||
"2017-11-03 17:54:49,835 INFO : valid_misclass 0.40404\n",
|
||||
"2017-11-03 17:54:49,836 INFO : test_misclass 0.41083\n",
|
||||
"2017-11-03 17:54:49,836 INFO : runtime 1.59916\n",
|
||||
"2017-11-03 17:54:49,837 INFO : \n",
|
||||
"2017-11-03 17:54:50,655 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:51,412 INFO : Epoch 14\n",
|
||||
"2017-11-03 17:54:51,413 INFO : train_loss 0.62104\n",
|
||||
"2017-11-03 17:54:51,414 INFO : valid_loss 1.40254\n",
|
||||
"2017-11-03 17:54:51,415 INFO : test_loss 1.41643\n",
|
||||
"2017-11-03 17:54:51,415 INFO : train_sample_misclass 0.23836\n",
|
||||
"2017-11-03 17:54:51,416 INFO : valid_sample_misclass 0.45438\n",
|
||||
"2017-11-03 17:54:51,417 INFO : test_sample_misclass 0.46678\n",
|
||||
"2017-11-03 17:54:51,418 INFO : train_misclass 0.23367\n",
|
||||
"2017-11-03 17:54:51,419 INFO : valid_misclass 0.39394\n",
|
||||
"2017-11-03 17:54:51,419 INFO : test_misclass 0.39172\n",
|
||||
"2017-11-03 17:54:51,420 INFO : runtime 1.58891\n",
|
||||
"2017-11-03 17:54:51,421 INFO : \n",
|
||||
"2017-11-03 17:54:52,235 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:53,001 INFO : Epoch 15\n",
|
||||
"2017-11-03 17:54:53,002 INFO : train_loss 0.55985\n",
|
||||
"2017-11-03 17:54:53,003 INFO : valid_loss 1.38133\n",
|
||||
"2017-11-03 17:54:53,004 INFO : test_loss 1.49168\n",
|
||||
"2017-11-03 17:54:53,005 INFO : train_sample_misclass 0.19814\n",
|
||||
"2017-11-03 17:54:53,006 INFO : valid_sample_misclass 0.44258\n",
|
||||
"2017-11-03 17:54:53,006 INFO : test_sample_misclass 0.47665\n",
|
||||
"2017-11-03 17:54:53,007 INFO : train_misclass 0.16332\n",
|
||||
"2017-11-03 17:54:53,008 INFO : valid_misclass 0.43434\n",
|
||||
"2017-11-03 17:54:53,009 INFO : test_misclass 0.43312\n",
|
||||
"2017-11-03 17:54:53,009 INFO : runtime 1.57987\n",
|
||||
"2017-11-03 17:54:53,010 INFO : \n",
|
||||
"2017-11-03 17:54:53,824 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:54,592 INFO : Epoch 16\n",
|
||||
"2017-11-03 17:54:54,593 INFO : train_loss 0.52848\n",
|
||||
"2017-11-03 17:54:54,594 INFO : valid_loss 1.33559\n",
|
||||
"2017-11-03 17:54:54,595 INFO : test_loss 1.51660\n",
|
||||
"2017-11-03 17:54:54,596 INFO : train_sample_misclass 0.18632\n",
|
||||
"2017-11-03 17:54:54,596 INFO : valid_sample_misclass 0.46433\n",
|
||||
"2017-11-03 17:54:54,597 INFO : test_sample_misclass 0.48484\n",
|
||||
"2017-11-03 17:54:54,598 INFO : train_misclass 0.15578\n",
|
||||
"2017-11-03 17:54:54,599 INFO : valid_misclass 0.42424\n",
|
||||
"2017-11-03 17:54:54,599 INFO : test_misclass 0.43949\n",
|
||||
"2017-11-03 17:54:54,600 INFO : runtime 1.58907\n",
|
||||
"2017-11-03 17:54:54,601 INFO : \n",
|
||||
"2017-11-03 17:54:55,417 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:56,169 INFO : Epoch 17\n",
|
||||
"2017-11-03 17:54:56,171 INFO : train_loss 0.53104\n",
|
||||
"2017-11-03 17:54:56,171 INFO : valid_loss 1.27837\n",
|
||||
"2017-11-03 17:54:56,172 INFO : test_loss 1.56129\n",
|
||||
"2017-11-03 17:54:56,173 INFO : train_sample_misclass 0.19424\n",
|
||||
"2017-11-03 17:54:56,174 INFO : valid_sample_misclass 0.43299\n",
|
||||
"2017-11-03 17:54:56,175 INFO : test_sample_misclass 0.48812\n",
|
||||
"2017-11-03 17:54:56,175 INFO : train_misclass 0.16583\n",
|
||||
"2017-11-03 17:54:56,176 INFO : valid_misclass 0.39394\n",
|
||||
"2017-11-03 17:54:56,177 INFO : test_misclass 0.44904\n",
|
||||
"2017-11-03 17:54:56,178 INFO : runtime 1.59308\n",
|
||||
"2017-11-03 17:54:56,178 INFO : \n",
|
||||
"2017-11-03 17:54:56,988 INFO : Time only for training updates: 0.71s\n",
|
||||
"2017-11-03 17:54:57,764 INFO : Epoch 18\n",
|
||||
"2017-11-03 17:54:57,766 INFO : train_loss 0.52136\n",
|
||||
"2017-11-03 17:54:57,767 INFO : valid_loss 1.35125\n",
|
||||
"2017-11-03 17:54:57,767 INFO : test_loss 1.46087\n",
|
||||
"2017-11-03 17:54:57,768 INFO : train_sample_misclass 0.18336\n",
|
||||
"2017-11-03 17:54:57,769 INFO : valid_sample_misclass 0.44739\n",
|
||||
"2017-11-03 17:54:57,770 INFO : test_sample_misclass 0.48096\n",
|
||||
"2017-11-03 17:54:57,770 INFO : train_misclass 0.16332\n",
|
||||
"2017-11-03 17:54:57,771 INFO : valid_misclass 0.39394\n",
|
||||
"2017-11-03 17:54:57,772 INFO : test_misclass 0.42675\n",
|
||||
"2017-11-03 17:54:57,773 INFO : runtime 1.57133\n",
|
||||
"2017-11-03 17:54:57,774 INFO : \n",
|
||||
"2017-11-03 17:54:58,595 INFO : Time only for training updates: 0.72s\n",
|
||||
"2017-11-03 17:54:59,356 INFO : Epoch 19\n",
|
||||
"2017-11-03 17:54:59,357 INFO : train_loss 0.52737\n",
|
||||
"2017-11-03 17:54:59,358 INFO : valid_loss 1.33823\n",
|
||||
"2017-11-03 17:54:59,359 INFO : test_loss 1.56256\n",
|
||||
"2017-11-03 17:54:59,360 INFO : train_sample_misclass 0.19466\n",
|
||||
"2017-11-03 17:54:59,360 INFO : valid_sample_misclass 0.44720\n",
|
||||
"2017-11-03 17:54:59,361 INFO : test_sample_misclass 0.47450\n",
|
||||
"2017-11-03 17:54:59,362 INFO : train_misclass 0.15578\n",
|
||||
"2017-11-03 17:54:59,363 INFO : valid_misclass 0.42424\n",
|
||||
"2017-11-03 17:54:59,364 INFO : test_misclass 0.42675\n",
|
||||
"2017-11-03 17:54:59,364 INFO : runtime 1.60626\n",
|
||||
"2017-11-03 17:54:59,365 INFO : \n",
|
||||
"2017-11-03 17:55:00,184 INFO : Time only for training updates: 0.72s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:55:00,944 INFO : Epoch 20\n",
|
||||
"2017-11-03 17:55:00,945 INFO : train_loss 0.50380\n",
|
||||
"2017-11-03 17:55:00,946 INFO : valid_loss 1.28838\n",
|
||||
"2017-11-03 17:55:00,947 INFO : test_loss 1.54519\n",
|
||||
"2017-11-03 17:55:00,947 INFO : train_sample_misclass 0.17429\n",
|
||||
"2017-11-03 17:55:00,948 INFO : valid_sample_misclass 0.43744\n",
|
||||
"2017-11-03 17:55:00,949 INFO : test_sample_misclass 0.48286\n",
|
||||
"2017-11-03 17:55:00,950 INFO : train_misclass 0.17085\n",
|
||||
"2017-11-03 17:55:00,950 INFO : valid_misclass 0.38384\n",
|
||||
"2017-11-03 17:55:00,951 INFO : test_misclass 0.42994\n",
|
||||
"2017-11-03 17:55:00,952 INFO : runtime 1.58936\n",
|
||||
"2017-11-03 17:55:00,953 INFO : \n",
|
||||
"2017-11-03 17:55:00,954 INFO : Setup for second stop...\n",
|
||||
"2017-11-03 17:55:00,957 INFO : Train loss to reach 0.60028\n",
|
||||
"2017-11-03 17:55:00,958 INFO : Run until second stop...\n",
|
||||
"2017-11-03 17:55:01,805 INFO : Epoch 12\n",
|
||||
"2017-11-03 17:55:01,806 INFO : train_loss 0.71588\n",
|
||||
"2017-11-03 17:55:01,807 INFO : valid_loss 1.19866\n",
|
||||
"2017-11-03 17:55:01,808 INFO : test_loss 1.51705\n",
|
||||
"2017-11-03 17:55:01,808 INFO : train_sample_misclass 0.26564\n",
|
||||
"2017-11-03 17:55:01,809 INFO : valid_sample_misclass 0.43046\n",
|
||||
"2017-11-03 17:55:01,810 INFO : test_sample_misclass 0.50496\n",
|
||||
"2017-11-03 17:55:01,811 INFO : train_misclass 0.21730\n",
|
||||
"2017-11-03 17:55:01,812 INFO : valid_misclass 0.34343\n",
|
||||
"2017-11-03 17:55:01,812 INFO : test_misclass 0.47134\n",
|
||||
"2017-11-03 17:55:01,813 INFO : runtime 0.77324\n",
|
||||
"2017-11-03 17:55:01,814 INFO : \n",
|
||||
"2017-11-03 17:55:02,820 INFO : Time only for training updates: 0.89s\n",
|
||||
"2017-11-03 17:55:03,681 INFO : Epoch 13\n",
|
||||
"2017-11-03 17:55:03,683 INFO : train_loss 0.71408\n",
|
||||
"2017-11-03 17:55:03,684 INFO : valid_loss 1.02489\n",
|
||||
"2017-11-03 17:55:03,684 INFO : test_loss 1.36414\n",
|
||||
"2017-11-03 17:55:03,685 INFO : train_sample_misclass 0.26404\n",
|
||||
"2017-11-03 17:55:03,686 INFO : valid_sample_misclass 0.38184\n",
|
||||
"2017-11-03 17:55:03,687 INFO : test_sample_misclass 0.47608\n",
|
||||
"2017-11-03 17:55:03,688 INFO : train_misclass 0.21328\n",
|
||||
"2017-11-03 17:55:03,688 INFO : valid_misclass 0.35354\n",
|
||||
"2017-11-03 17:55:03,689 INFO : test_misclass 0.42357\n",
|
||||
"2017-11-03 17:55:03,690 INFO : runtime 1.86268\n",
|
||||
"2017-11-03 17:55:03,691 INFO : \n",
|
||||
"2017-11-03 17:55:04,693 INFO : Time only for training updates: 0.88s\n",
|
||||
"2017-11-03 17:55:05,529 INFO : Epoch 14\n",
|
||||
"2017-11-03 17:55:05,531 INFO : train_loss 0.68211\n",
|
||||
"2017-11-03 17:55:05,532 INFO : valid_loss 0.92467\n",
|
||||
"2017-11-03 17:55:05,532 INFO : test_loss 1.41786\n",
|
||||
"2017-11-03 17:55:05,533 INFO : train_sample_misclass 0.25519\n",
|
||||
"2017-11-03 17:55:05,534 INFO : valid_sample_misclass 0.34189\n",
|
||||
"2017-11-03 17:55:05,535 INFO : test_sample_misclass 0.48768\n",
|
||||
"2017-11-03 17:55:05,536 INFO : train_misclass 0.22133\n",
|
||||
"2017-11-03 17:55:05,537 INFO : valid_misclass 0.28283\n",
|
||||
"2017-11-03 17:55:05,539 INFO : test_misclass 0.45860\n",
|
||||
"2017-11-03 17:55:05,540 INFO : runtime 1.87288\n",
|
||||
"2017-11-03 17:55:05,541 INFO : \n",
|
||||
"2017-11-03 17:55:06,539 INFO : Time only for training updates: 0.88s\n",
|
||||
"2017-11-03 17:55:07,379 INFO : Epoch 15\n",
|
||||
"2017-11-03 17:55:07,381 INFO : train_loss 0.65594\n",
|
||||
"2017-11-03 17:55:07,382 INFO : valid_loss 0.87031\n",
|
||||
"2017-11-03 17:55:07,383 INFO : test_loss 1.45573\n",
|
||||
"2017-11-03 17:55:07,385 INFO : train_sample_misclass 0.23964\n",
|
||||
"2017-11-03 17:55:07,386 INFO : valid_sample_misclass 0.33582\n",
|
||||
"2017-11-03 17:55:07,387 INFO : test_sample_misclass 0.49530\n",
|
||||
"2017-11-03 17:55:07,388 INFO : train_misclass 0.22938\n",
|
||||
"2017-11-03 17:55:07,389 INFO : valid_misclass 0.33333\n",
|
||||
"2017-11-03 17:55:07,390 INFO : test_misclass 0.50637\n",
|
||||
"2017-11-03 17:55:07,392 INFO : runtime 1.84665\n",
|
||||
"2017-11-03 17:55:07,393 INFO : \n",
|
||||
"2017-11-03 17:55:08,391 INFO : Time only for training updates: 0.88s\n",
|
||||
"2017-11-03 17:55:09,225 INFO : Epoch 16\n",
|
||||
"2017-11-03 17:55:09,227 INFO : train_loss 0.67155\n",
|
||||
"2017-11-03 17:55:09,228 INFO : valid_loss 0.88822\n",
|
||||
"2017-11-03 17:55:09,229 INFO : test_loss 1.46725\n",
|
||||
"2017-11-03 17:55:09,231 INFO : train_sample_misclass 0.25023\n",
|
||||
"2017-11-03 17:55:09,232 INFO : valid_sample_misclass 0.35430\n",
|
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"2017-11-03 17:55:09,233 INFO : test_sample_misclass 0.51942\n",
|
||||
"2017-11-03 17:55:09,234 INFO : train_misclass 0.22736\n",
|
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"2017-11-03 17:55:09,235 INFO : valid_misclass 0.30303\n",
|
||||
"2017-11-03 17:55:09,237 INFO : test_misclass 0.47134\n",
|
||||
"2017-11-03 17:55:09,238 INFO : runtime 1.85138\n",
|
||||
"2017-11-03 17:55:09,239 INFO : \n",
|
||||
"2017-11-03 17:55:10,282 INFO : Time only for training updates: 0.92s\n",
|
||||
"2017-11-03 17:55:11,169 INFO : Epoch 17\n",
|
||||
"2017-11-03 17:55:11,171 INFO : train_loss 0.65888\n",
|
||||
"2017-11-03 17:55:11,172 INFO : valid_loss 0.85140\n",
|
||||
"2017-11-03 17:55:11,174 INFO : test_loss 1.38991\n",
|
||||
"2017-11-03 17:55:11,175 INFO : train_sample_misclass 0.24352\n",
|
||||
"2017-11-03 17:55:11,176 INFO : valid_sample_misclass 0.32228\n",
|
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"2017-11-03 17:55:11,177 INFO : test_sample_misclass 0.47197\n",
|
||||
"2017-11-03 17:55:11,179 INFO : train_misclass 0.23139\n",
|
||||
"2017-11-03 17:55:11,180 INFO : valid_misclass 0.28283\n",
|
||||
"2017-11-03 17:55:11,181 INFO : test_misclass 0.44904\n",
|
||||
"2017-11-03 17:55:11,182 INFO : runtime 1.89099\n",
|
||||
"2017-11-03 17:55:11,183 INFO : \n",
|
||||
"2017-11-03 17:55:12,185 INFO : Time only for training updates: 0.88s\n",
|
||||
"2017-11-03 17:55:13,031 INFO : Epoch 18\n",
|
||||
"2017-11-03 17:55:13,033 INFO : train_loss 0.60783\n",
|
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"2017-11-03 17:55:13,034 INFO : valid_loss 0.75969\n",
|
||||
"2017-11-03 17:55:13,035 INFO : test_loss 1.40295\n",
|
||||
"2017-11-03 17:55:13,037 INFO : train_sample_misclass 0.22382\n",
|
||||
"2017-11-03 17:55:13,038 INFO : valid_sample_misclass 0.29660\n",
|
||||
"2017-11-03 17:55:13,039 INFO : test_sample_misclass 0.48256\n",
|
||||
"2017-11-03 17:55:13,040 INFO : train_misclass 0.18511\n",
|
||||
"2017-11-03 17:55:13,042 INFO : valid_misclass 0.27273\n",
|
||||
"2017-11-03 17:55:13,043 INFO : test_misclass 0.45860\n",
|
||||
"2017-11-03 17:55:13,044 INFO : runtime 1.90315\n",
|
||||
"2017-11-03 17:55:13,045 INFO : \n",
|
||||
"2017-11-03 17:55:14,056 INFO : Time only for training updates: 0.89s\n",
|
||||
"2017-11-03 17:55:14,953 INFO : Epoch 19\n",
|
||||
"2017-11-03 17:55:14,956 INFO : train_loss 0.61093\n",
|
||||
"2017-11-03 17:55:14,959 INFO : valid_loss 0.78444\n",
|
||||
"2017-11-03 17:55:14,960 INFO : test_loss 1.37623\n",
|
||||
"2017-11-03 17:55:14,963 INFO : train_sample_misclass 0.22366\n",
|
||||
"2017-11-03 17:55:14,965 INFO : valid_sample_misclass 0.30558\n",
|
||||
"2017-11-03 17:55:14,966 INFO : test_sample_misclass 0.47023\n",
|
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"2017-11-03 17:55:14,969 INFO : train_misclass 0.18712\n",
|
||||
"2017-11-03 17:55:14,970 INFO : valid_misclass 0.24242\n",
|
||||
"2017-11-03 17:55:14,973 INFO : test_misclass 0.43312\n",
|
||||
"2017-11-03 17:55:14,975 INFO : runtime 1.87216\n",
|
||||
"2017-11-03 17:55:14,977 INFO : \n",
|
||||
"2017-11-03 17:55:15,992 INFO : Time only for training updates: 0.89s\n",
|
||||
"2017-11-03 17:55:16,858 INFO : Epoch 20\n",
|
||||
"2017-11-03 17:55:16,860 INFO : train_loss 0.59346\n",
|
||||
"2017-11-03 17:55:16,861 INFO : valid_loss 0.71784\n",
|
||||
"2017-11-03 17:55:16,861 INFO : test_loss 1.45776\n",
|
||||
"2017-11-03 17:55:16,862 INFO : train_sample_misclass 0.21111\n",
|
||||
"2017-11-03 17:55:16,863 INFO : valid_sample_misclass 0.26341\n",
|
||||
"2017-11-03 17:55:16,864 INFO : test_sample_misclass 0.49474\n",
|
||||
"2017-11-03 17:55:16,865 INFO : train_misclass 0.20322\n",
|
||||
"2017-11-03 17:55:16,865 INFO : valid_misclass 0.27273\n",
|
||||
"2017-11-03 17:55:16,866 INFO : test_misclass 0.46815\n",
|
||||
"2017-11-03 17:55:16,867 INFO : runtime 1.93566\n",
|
||||
"2017-11-03 17:55:16,868 INFO : \n",
|
||||
"2017-11-03 17:55:17,876 INFO : Time only for training updates: 0.89s\n",
|
||||
"2017-11-03 17:55:18,753 INFO : Epoch 21\n",
|
||||
"2017-11-03 17:55:18,755 INFO : train_loss 0.60215\n",
|
||||
"2017-11-03 17:55:18,756 INFO : valid_loss 0.75351\n",
|
||||
"2017-11-03 17:55:18,756 INFO : test_loss 1.50636\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:55:18,757 INFO : train_sample_misclass 0.21574\n",
|
||||
"2017-11-03 17:55:18,758 INFO : valid_sample_misclass 0.28888\n",
|
||||
"2017-11-03 17:55:18,759 INFO : test_sample_misclass 0.51284\n",
|
||||
"2017-11-03 17:55:18,760 INFO : train_misclass 0.19517\n",
|
||||
"2017-11-03 17:55:18,760 INFO : valid_misclass 0.28283\n",
|
||||
"2017-11-03 17:55:18,761 INFO : test_misclass 0.48089\n",
|
||||
"2017-11-03 17:55:18,762 INFO : runtime 1.88302\n",
|
||||
"2017-11-03 17:55:18,763 INFO : \n",
|
||||
"2017-11-03 17:55:19,771 INFO : Time only for training updates: 0.89s\n",
|
||||
"2017-11-03 17:55:20,627 INFO : Epoch 22\n",
|
||||
"2017-11-03 17:55:20,628 INFO : train_loss 0.65365\n",
|
||||
"2017-11-03 17:55:20,629 INFO : valid_loss 0.82897\n",
|
||||
"2017-11-03 17:55:20,629 INFO : test_loss 1.40076\n",
|
||||
"2017-11-03 17:55:20,630 INFO : train_sample_misclass 0.25455\n",
|
||||
"2017-11-03 17:55:20,631 INFO : valid_sample_misclass 0.33560\n",
|
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"2017-11-03 17:55:20,632 INFO : test_sample_misclass 0.48546\n",
|
||||
"2017-11-03 17:55:20,632 INFO : train_misclass 0.24145\n",
|
||||
"2017-11-03 17:55:20,633 INFO : valid_misclass 0.30303\n",
|
||||
"2017-11-03 17:55:20,634 INFO : test_misclass 0.46178\n",
|
||||
"2017-11-03 17:55:20,635 INFO : runtime 1.89486\n",
|
||||
"2017-11-03 17:55:20,636 INFO : \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We arrive at about 54% accuracy. With only 3 sensors and 3 training runs, we cannot get much better :)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,715 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Read and Decode BBCI Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This tutorial shows how to read and decode BBCI data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup logging to see outputs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"import sys\n",
|
||||
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
|
||||
" level=logging.DEBUG, stream=sys.stdout)\n",
|
||||
"log = logging.getLogger()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load and preprocess data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First set the filename and the sensors you want to load. If you set\n",
|
||||
"\n",
|
||||
"```python\n",
|
||||
"load_sensor_names=None\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"or just remove the parameter from the function call, all sensors will be loaded."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=3451320\n",
|
||||
" Range : 0 ... 3451319 = 0.000 ... 6902.638 secs\n",
|
||||
"Ready.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.datasets.bbci import BBCIDataset\n",
|
||||
"train_filename = '/home/schirrmr/data/BBCI-without-last-runs/BhNoMoSc1S001R01_ds10_1-12.BBCI.mat'\n",
|
||||
"cnt = BBCIDataset(train_filename, load_sensor_names=['C3', 'CPz', 'C4']).load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Preprocessing on continous data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"First remove the stimulus channel, than apply any preprocessing you like. There are some very few directions available from Braindecode, such as resample_cnt. But you can apply any function on the chan x time matrix of the mne raw object (`cnt` in the code) by calling `mne_apply` with two arguments:\n",
|
||||
"\n",
|
||||
"1. Your function (2d-array-> 2darray), that transforms the channel x timesteps data array\n",
|
||||
"2. the Raw data object from mne itself"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:59:09,513 WARNING : This is not causal, uses future data....\n",
|
||||
"2017-11-03 17:59:09,514 INFO : Resampling from 500.000000 to 250.000000 Hz.\n",
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=1725660\n",
|
||||
" Range : 0 ... 1725659 = 0.000 ... 6902.636 secs\n",
|
||||
"Ready.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.mne_ext.signalproc import resample_cnt, mne_apply\n",
|
||||
"from braindecode.datautil.signalproc import exponential_running_standardize\n",
|
||||
"# Remove stimulus channel\n",
|
||||
"cnt = cnt.drop_channels(['STI 014'])\n",
|
||||
"cnt = resample_cnt(cnt, 250)\n",
|
||||
"# mne apply will apply the function to the data (a 2d-numpy-array)\n",
|
||||
"# have to transpose data back and forth, since\n",
|
||||
"# exponential_running_standardize expects time x chans order\n",
|
||||
"# while mne object has chans x time order\n",
|
||||
"cnt = mne_apply(lambda a: exponential_running_standardize(\n",
|
||||
" a.T, init_block_size=1000,factor_new=0.001, eps=1e-4).T,\n",
|
||||
" cnt)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Transform to epoched dataset "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Braindecode supplies the `create_signal_target_from_raw_mne` function, which will transform the mne raw object into a `SignalAndTarget` object for use in Braindecode.\n",
|
||||
"`name_to_code` should be an `OrderedDict` that maps class names to either one or a list of marker codes for that class."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:59:11,376 INFO : Trial per class:\n",
|
||||
"Counter({'Feet': 225, 'Right': 224, 'Rest': 224, 'Left': 224})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.datautil.trial_segment import create_signal_target_from_raw_mne\n",
|
||||
"from collections import OrderedDict\n",
|
||||
"# can also give lists of marker codes in case a class has multiple marker codes...\n",
|
||||
"name_to_code = OrderedDict([('Right', 1), ('Left', 2), ('Rest', 3), ('Feet', 4)])\n",
|
||||
"segment_ival_ms = [-500,4000]\n",
|
||||
"\n",
|
||||
"train_set = create_signal_target_from_raw_mne(cnt, name_to_code, segment_ival_ms)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Same for test set"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=617090\n",
|
||||
" Range : 0 ... 617089 = 0.000 ... 1234.178 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:59:12,305 WARNING : This is not causal, uses future data....\n",
|
||||
"2017-11-03 17:59:12,306 INFO : Resampling from 500.000000 to 250.000000 Hz.\n",
|
||||
"Creating RawArray with float64 data, n_channels=3, n_times=308545\n",
|
||||
" Range : 0 ... 308544 = 0.000 ... 1234.176 secs\n",
|
||||
"Ready.\n",
|
||||
"2017-11-03 17:59:12,653 INFO : Trial per class:\n",
|
||||
"Counter({'Feet': 40, 'Left': 40, 'Rest': 40, 'Right': 40})\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_filename = '/home/schirrmr/data/BBCI-only-last-runs/BhNoMoSc1S001R13_ds10_1-2BBCI.mat'\n",
|
||||
"cnt = BBCIDataset(test_filename, load_sensor_names=['C3', 'CPz', 'C4']).load()\n",
|
||||
"# Remove stimulus channel\n",
|
||||
"cnt = cnt.drop_channels(['STI 014'])\n",
|
||||
"cnt = resample_cnt(cnt, 250)\n",
|
||||
"cnt = mne_apply(lambda a: exponential_running_standardize(\n",
|
||||
" a.T, init_block_size=1000,factor_new=0.001, eps=1e-4).T,\n",
|
||||
" cnt)\n",
|
||||
"test_set = create_signal_target_from_raw_mne(cnt, name_to_code, segment_ival_ms)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<div class=\"alert alert-info\">\n",
|
||||
"\n",
|
||||
"In case of start and stop markers, provide a `name_to_stop_codes` dictionary (same as for the start codes in this example) as a final argument to `create_signal_target_from_raw_mne`. See [Read and Decode BBCI Data with Start-Stop-Markers Tutorial](BBCI_Data_Start_Stop.html)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Split off a validation set."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.splitters import split_into_two_sets\n",
|
||||
"\n",
|
||||
"train_set, valid_set = split_into_two_sets(train_set, first_set_fraction=0.8)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = True\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = train_set.X.shape[2]\n",
|
||||
"in_chans = 3\n",
|
||||
"n_classes = 4\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes, input_time_length=input_time_length,\n",
|
||||
" final_conv_length='auto').create_network()\n",
|
||||
"\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup optimizer and iterator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torch import optim\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"optimizer = optim.Adam(model.parameters())\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from braindecode.datautil.iterators import BalancedBatchSizeIterator\n",
|
||||
"iterator = BalancedBatchSizeIterator(batch_size=32)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup Monitors, Loss function, Stop Criteria"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.experiments.experiment import Experiment\n",
|
||||
"from braindecode.experiments.monitors import RuntimeMonitor, LossMonitor, CroppedTrialMisclassMonitor, MisclassMonitor\n",
|
||||
"from braindecode.experiments.stopcriteria import MaxEpochs\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"import torch as th\n",
|
||||
"from braindecode.torch_ext.modules import Expression\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"loss_function = F.nll_loss\n",
|
||||
"\n",
|
||||
"model_constraint = None\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='misclass'), \n",
|
||||
" RuntimeMonitor(),]\n",
|
||||
"stop_criterion = MaxEpochs(20)\n",
|
||||
"exp = Experiment(model, train_set, valid_set, test_set, iterator, loss_function, optimizer, model_constraint,\n",
|
||||
" monitors, stop_criterion, remember_best_column='valid_misclass',\n",
|
||||
" run_after_early_stop=True, batch_modifier=None, cuda=cuda)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:59:15,468 INFO : Run until first stop...\n",
|
||||
"2017-11-03 17:59:15,979 INFO : Epoch 0\n",
|
||||
"2017-11-03 17:59:15,981 INFO : train_loss 3.82544\n",
|
||||
"2017-11-03 17:59:15,982 INFO : valid_loss 3.72206\n",
|
||||
"2017-11-03 17:59:15,982 INFO : test_loss 3.90394\n",
|
||||
"2017-11-03 17:59:15,983 INFO : train_misclass 0.76184\n",
|
||||
"2017-11-03 17:59:15,984 INFO : valid_misclass 0.76536\n",
|
||||
"2017-11-03 17:59:15,985 INFO : test_misclass 0.75625\n",
|
||||
"2017-11-03 17:59:15,985 INFO : runtime 0.00000\n",
|
||||
"2017-11-03 17:59:15,986 INFO : \n",
|
||||
"2017-11-03 17:59:15,988 INFO : New best valid_misclass: 0.765363\n",
|
||||
"2017-11-03 17:59:15,989 INFO : \n",
|
||||
"2017-11-03 17:59:16,564 INFO : Time only for training updates: 0.57s\n",
|
||||
"2017-11-03 17:59:16,671 INFO : Epoch 1\n",
|
||||
"2017-11-03 17:59:16,672 INFO : train_loss 2.65436\n",
|
||||
"2017-11-03 17:59:16,673 INFO : valid_loss 2.88156\n",
|
||||
"2017-11-03 17:59:16,674 INFO : test_loss 3.03544\n",
|
||||
"2017-11-03 17:59:16,675 INFO : train_misclass 0.72006\n",
|
||||
"2017-11-03 17:59:16,675 INFO : valid_misclass 0.73184\n",
|
||||
"2017-11-03 17:59:16,676 INFO : test_misclass 0.71250\n",
|
||||
"2017-11-03 17:59:16,677 INFO : runtime 1.09594\n",
|
||||
"2017-11-03 17:59:16,678 INFO : \n",
|
||||
"2017-11-03 17:59:16,681 INFO : New best valid_misclass: 0.731844\n",
|
||||
"2017-11-03 17:59:16,682 INFO : \n",
|
||||
"2017-11-03 17:59:17,228 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:17,336 INFO : Epoch 2\n",
|
||||
"2017-11-03 17:59:17,338 INFO : train_loss 0.60706\n",
|
||||
"2017-11-03 17:59:17,339 INFO : valid_loss 0.61918\n",
|
||||
"2017-11-03 17:59:17,339 INFO : test_loss 0.81560\n",
|
||||
"2017-11-03 17:59:17,340 INFO : train_misclass 0.24791\n",
|
||||
"2017-11-03 17:59:17,341 INFO : valid_misclass 0.27933\n",
|
||||
"2017-11-03 17:59:17,342 INFO : test_misclass 0.33750\n",
|
||||
"2017-11-03 17:59:17,343 INFO : runtime 0.66460\n",
|
||||
"2017-11-03 17:59:17,344 INFO : \n",
|
||||
"2017-11-03 17:59:17,346 INFO : New best valid_misclass: 0.279330\n",
|
||||
"2017-11-03 17:59:17,347 INFO : \n",
|
||||
"2017-11-03 17:59:17,893 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:17,998 INFO : Epoch 3\n",
|
||||
"2017-11-03 17:59:17,999 INFO : train_loss 0.58638\n",
|
||||
"2017-11-03 17:59:18,000 INFO : valid_loss 0.62992\n",
|
||||
"2017-11-03 17:59:18,000 INFO : test_loss 0.85668\n",
|
||||
"2017-11-03 17:59:18,001 INFO : train_misclass 0.23677\n",
|
||||
"2017-11-03 17:59:18,002 INFO : valid_misclass 0.27374\n",
|
||||
"2017-11-03 17:59:18,003 INFO : test_misclass 0.38750\n",
|
||||
"2017-11-03 17:59:18,003 INFO : runtime 0.66507\n",
|
||||
"2017-11-03 17:59:18,004 INFO : \n",
|
||||
"2017-11-03 17:59:18,006 INFO : New best valid_misclass: 0.273743\n",
|
||||
"2017-11-03 17:59:18,007 INFO : \n",
|
||||
"2017-11-03 17:59:18,552 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:18,664 INFO : Epoch 4\n",
|
||||
"2017-11-03 17:59:18,666 INFO : train_loss 0.48614\n",
|
||||
"2017-11-03 17:59:18,666 INFO : valid_loss 0.56710\n",
|
||||
"2017-11-03 17:59:18,667 INFO : test_loss 0.76815\n",
|
||||
"2017-11-03 17:59:18,668 INFO : train_misclass 0.19638\n",
|
||||
"2017-11-03 17:59:18,669 INFO : valid_misclass 0.26816\n",
|
||||
"2017-11-03 17:59:18,669 INFO : test_misclass 0.31875\n",
|
||||
"2017-11-03 17:59:18,670 INFO : runtime 0.65807\n",
|
||||
"2017-11-03 17:59:18,671 INFO : \n",
|
||||
"2017-11-03 17:59:18,673 INFO : New best valid_misclass: 0.268156\n",
|
||||
"2017-11-03 17:59:18,674 INFO : \n",
|
||||
"2017-11-03 17:59:19,219 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:19,327 INFO : Epoch 5\n",
|
||||
"2017-11-03 17:59:19,329 INFO : train_loss 0.42408\n",
|
||||
"2017-11-03 17:59:19,329 INFO : valid_loss 0.52337\n",
|
||||
"2017-11-03 17:59:19,330 INFO : test_loss 0.79153\n",
|
||||
"2017-11-03 17:59:19,331 INFO : train_misclass 0.14485\n",
|
||||
"2017-11-03 17:59:19,332 INFO : valid_misclass 0.22905\n",
|
||||
"2017-11-03 17:59:19,333 INFO : test_misclass 0.36250\n",
|
||||
"2017-11-03 17:59:19,333 INFO : runtime 0.66796\n",
|
||||
"2017-11-03 17:59:19,334 INFO : \n",
|
||||
"2017-11-03 17:59:19,336 INFO : New best valid_misclass: 0.229050\n",
|
||||
"2017-11-03 17:59:19,337 INFO : \n",
|
||||
"2017-11-03 17:59:19,884 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:19,992 INFO : Epoch 6\n",
|
||||
"2017-11-03 17:59:19,993 INFO : train_loss 0.35395\n",
|
||||
"2017-11-03 17:59:19,994 INFO : valid_loss 0.51115\n",
|
||||
"2017-11-03 17:59:19,995 INFO : test_loss 0.72994\n",
|
||||
"2017-11-03 17:59:19,996 INFO : train_misclass 0.11003\n",
|
||||
"2017-11-03 17:59:19,996 INFO : valid_misclass 0.20112\n",
|
||||
"2017-11-03 17:59:19,997 INFO : test_misclass 0.27500\n",
|
||||
"2017-11-03 17:59:19,998 INFO : runtime 0.66456\n",
|
||||
"2017-11-03 17:59:19,999 INFO : \n",
|
||||
"2017-11-03 17:59:20,001 INFO : New best valid_misclass: 0.201117\n",
|
||||
"2017-11-03 17:59:20,002 INFO : \n",
|
||||
"2017-11-03 17:59:20,548 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:20,656 INFO : Epoch 7\n",
|
||||
"2017-11-03 17:59:20,657 INFO : train_loss 0.34276\n",
|
||||
"2017-11-03 17:59:20,658 INFO : valid_loss 0.57949\n",
|
||||
"2017-11-03 17:59:20,659 INFO : test_loss 0.79834\n",
|
||||
"2017-11-03 17:59:20,659 INFO : train_misclass 0.11978\n",
|
||||
"2017-11-03 17:59:20,660 INFO : valid_misclass 0.25140\n",
|
||||
"2017-11-03 17:59:20,661 INFO : test_misclass 0.32500\n",
|
||||
"2017-11-03 17:59:20,662 INFO : runtime 0.66413\n",
|
||||
"2017-11-03 17:59:20,662 INFO : \n",
|
||||
"2017-11-03 17:59:21,209 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:21,318 INFO : Epoch 8\n",
|
||||
"2017-11-03 17:59:21,319 INFO : train_loss 0.34212\n",
|
||||
"2017-11-03 17:59:21,320 INFO : valid_loss 0.45382\n",
|
||||
"2017-11-03 17:59:21,321 INFO : test_loss 0.68899\n",
|
||||
"2017-11-03 17:59:21,322 INFO : train_misclass 0.09610\n",
|
||||
"2017-11-03 17:59:21,323 INFO : valid_misclass 0.19553\n",
|
||||
"2017-11-03 17:59:21,323 INFO : test_misclass 0.27500\n",
|
||||
"2017-11-03 17:59:21,324 INFO : runtime 0.66085\n",
|
||||
"2017-11-03 17:59:21,325 INFO : \n",
|
||||
"2017-11-03 17:59:21,327 INFO : New best valid_misclass: 0.195531\n",
|
||||
"2017-11-03 17:59:21,328 INFO : \n",
|
||||
"2017-11-03 17:59:21,874 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:21,981 INFO : Epoch 9\n",
|
||||
"2017-11-03 17:59:21,982 INFO : train_loss 0.26619\n",
|
||||
"2017-11-03 17:59:21,983 INFO : valid_loss 0.48593\n",
|
||||
"2017-11-03 17:59:21,983 INFO : test_loss 0.70376\n",
|
||||
"2017-11-03 17:59:21,984 INFO : train_misclass 0.07242\n",
|
||||
"2017-11-03 17:59:21,985 INFO : valid_misclass 0.18436\n",
|
||||
"2017-11-03 17:59:21,986 INFO : test_misclass 0.26250\n",
|
||||
"2017-11-03 17:59:21,987 INFO : runtime 0.66527\n",
|
||||
"2017-11-03 17:59:21,987 INFO : \n",
|
||||
"2017-11-03 17:59:21,990 INFO : New best valid_misclass: 0.184358\n",
|
||||
"2017-11-03 17:59:21,990 INFO : \n",
|
||||
"2017-11-03 17:59:22,536 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:22,644 INFO : Epoch 10\n",
|
||||
"2017-11-03 17:59:22,645 INFO : train_loss 0.26250\n",
|
||||
"2017-11-03 17:59:22,646 INFO : valid_loss 0.47718\n",
|
||||
"2017-11-03 17:59:22,647 INFO : test_loss 0.71324\n",
|
||||
"2017-11-03 17:59:22,648 INFO : train_misclass 0.08914\n",
|
||||
"2017-11-03 17:59:22,649 INFO : valid_misclass 0.18994\n",
|
||||
"2017-11-03 17:59:22,649 INFO : test_misclass 0.28125\n",
|
||||
"2017-11-03 17:59:22,650 INFO : runtime 0.66180\n",
|
||||
"2017-11-03 17:59:22,651 INFO : \n",
|
||||
"2017-11-03 17:59:23,198 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:23,309 INFO : Epoch 11\n",
|
||||
"2017-11-03 17:59:23,311 INFO : train_loss 0.21297\n",
|
||||
"2017-11-03 17:59:23,312 INFO : valid_loss 0.48684\n",
|
||||
"2017-11-03 17:59:23,312 INFO : test_loss 0.74883\n",
|
||||
"2017-11-03 17:59:23,313 INFO : train_misclass 0.06546\n",
|
||||
"2017-11-03 17:59:23,314 INFO : valid_misclass 0.20670\n",
|
||||
"2017-11-03 17:59:23,315 INFO : test_misclass 0.31250\n",
|
||||
"2017-11-03 17:59:23,315 INFO : runtime 0.66222\n",
|
||||
"2017-11-03 17:59:23,316 INFO : \n",
|
||||
"2017-11-03 17:59:23,864 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:23,973 INFO : Epoch 12\n",
|
||||
"2017-11-03 17:59:23,974 INFO : train_loss 0.21066\n",
|
||||
"2017-11-03 17:59:23,975 INFO : valid_loss 0.49149\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:59:23,976 INFO : test_loss 0.78449\n",
|
||||
"2017-11-03 17:59:23,977 INFO : train_misclass 0.05710\n",
|
||||
"2017-11-03 17:59:23,977 INFO : valid_misclass 0.18994\n",
|
||||
"2017-11-03 17:59:23,978 INFO : test_misclass 0.30000\n",
|
||||
"2017-11-03 17:59:23,979 INFO : runtime 0.66576\n",
|
||||
"2017-11-03 17:59:23,980 INFO : \n",
|
||||
"2017-11-03 17:59:24,527 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:24,636 INFO : Epoch 13\n",
|
||||
"2017-11-03 17:59:24,637 INFO : train_loss 0.22913\n",
|
||||
"2017-11-03 17:59:24,638 INFO : valid_loss 0.52561\n",
|
||||
"2017-11-03 17:59:24,639 INFO : test_loss 0.68987\n",
|
||||
"2017-11-03 17:59:24,639 INFO : train_misclass 0.06407\n",
|
||||
"2017-11-03 17:59:24,640 INFO : valid_misclass 0.18436\n",
|
||||
"2017-11-03 17:59:24,641 INFO : test_misclass 0.26250\n",
|
||||
"2017-11-03 17:59:24,642 INFO : runtime 0.66327\n",
|
||||
"2017-11-03 17:59:24,642 INFO : \n",
|
||||
"2017-11-03 17:59:24,645 INFO : New best valid_misclass: 0.184358\n",
|
||||
"2017-11-03 17:59:24,645 INFO : \n",
|
||||
"2017-11-03 17:59:25,191 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:25,303 INFO : Epoch 14\n",
|
||||
"2017-11-03 17:59:25,304 INFO : train_loss 0.29666\n",
|
||||
"2017-11-03 17:59:25,305 INFO : valid_loss 0.62789\n",
|
||||
"2017-11-03 17:59:25,306 INFO : test_loss 0.88607\n",
|
||||
"2017-11-03 17:59:25,306 INFO : train_misclass 0.09610\n",
|
||||
"2017-11-03 17:59:25,307 INFO : valid_misclass 0.26257\n",
|
||||
"2017-11-03 17:59:25,308 INFO : test_misclass 0.31875\n",
|
||||
"2017-11-03 17:59:25,309 INFO : runtime 0.66325\n",
|
||||
"2017-11-03 17:59:25,309 INFO : \n",
|
||||
"2017-11-03 17:59:25,857 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:25,965 INFO : Epoch 15\n",
|
||||
"2017-11-03 17:59:25,966 INFO : train_loss 0.20635\n",
|
||||
"2017-11-03 17:59:25,967 INFO : valid_loss 0.51987\n",
|
||||
"2017-11-03 17:59:25,968 INFO : test_loss 0.77714\n",
|
||||
"2017-11-03 17:59:25,969 INFO : train_misclass 0.07103\n",
|
||||
"2017-11-03 17:59:25,970 INFO : valid_misclass 0.20670\n",
|
||||
"2017-11-03 17:59:25,970 INFO : test_misclass 0.31250\n",
|
||||
"2017-11-03 17:59:25,971 INFO : runtime 0.66714\n",
|
||||
"2017-11-03 17:59:25,972 INFO : \n",
|
||||
"2017-11-03 17:59:26,519 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:26,631 INFO : Epoch 16\n",
|
||||
"2017-11-03 17:59:26,632 INFO : train_loss 0.18682\n",
|
||||
"2017-11-03 17:59:26,633 INFO : valid_loss 0.47353\n",
|
||||
"2017-11-03 17:59:26,634 INFO : test_loss 0.71371\n",
|
||||
"2017-11-03 17:59:26,635 INFO : train_misclass 0.05432\n",
|
||||
"2017-11-03 17:59:26,636 INFO : valid_misclass 0.19553\n",
|
||||
"2017-11-03 17:59:26,637 INFO : test_misclass 0.26250\n",
|
||||
"2017-11-03 17:59:26,637 INFO : runtime 0.66127\n",
|
||||
"2017-11-03 17:59:26,638 INFO : \n",
|
||||
"2017-11-03 17:59:27,185 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:27,293 INFO : Epoch 17\n",
|
||||
"2017-11-03 17:59:27,295 INFO : train_loss 0.15229\n",
|
||||
"2017-11-03 17:59:27,296 INFO : valid_loss 0.51995\n",
|
||||
"2017-11-03 17:59:27,296 INFO : test_loss 0.70597\n",
|
||||
"2017-11-03 17:59:27,297 INFO : train_misclass 0.04178\n",
|
||||
"2017-11-03 17:59:27,298 INFO : valid_misclass 0.21229\n",
|
||||
"2017-11-03 17:59:27,299 INFO : test_misclass 0.30000\n",
|
||||
"2017-11-03 17:59:27,300 INFO : runtime 0.66622\n",
|
||||
"2017-11-03 17:59:27,301 INFO : \n",
|
||||
"2017-11-03 17:59:27,849 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:27,954 INFO : Epoch 18\n",
|
||||
"2017-11-03 17:59:27,955 INFO : train_loss 0.19083\n",
|
||||
"2017-11-03 17:59:27,956 INFO : valid_loss 0.50357\n",
|
||||
"2017-11-03 17:59:27,957 INFO : test_loss 0.67131\n",
|
||||
"2017-11-03 17:59:27,957 INFO : train_misclass 0.04735\n",
|
||||
"2017-11-03 17:59:27,958 INFO : valid_misclass 0.19553\n",
|
||||
"2017-11-03 17:59:27,959 INFO : test_misclass 0.26250\n",
|
||||
"2017-11-03 17:59:27,960 INFO : runtime 0.66379\n",
|
||||
"2017-11-03 17:59:27,960 INFO : \n",
|
||||
"2017-11-03 17:59:28,508 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:28,616 INFO : Epoch 19\n",
|
||||
"2017-11-03 17:59:28,617 INFO : train_loss 0.14053\n",
|
||||
"2017-11-03 17:59:28,618 INFO : valid_loss 0.53879\n",
|
||||
"2017-11-03 17:59:28,619 INFO : test_loss 0.74517\n",
|
||||
"2017-11-03 17:59:28,619 INFO : train_misclass 0.03343\n",
|
||||
"2017-11-03 17:59:28,620 INFO : valid_misclass 0.20670\n",
|
||||
"2017-11-03 17:59:28,621 INFO : test_misclass 0.25625\n",
|
||||
"2017-11-03 17:59:28,622 INFO : runtime 0.65849\n",
|
||||
"2017-11-03 17:59:28,622 INFO : \n",
|
||||
"2017-11-03 17:59:29,169 INFO : Time only for training updates: 0.55s\n",
|
||||
"2017-11-03 17:59:29,279 INFO : Epoch 20\n",
|
||||
"2017-11-03 17:59:29,280 INFO : train_loss 0.22331\n",
|
||||
"2017-11-03 17:59:29,281 INFO : valid_loss 0.49784\n",
|
||||
"2017-11-03 17:59:29,282 INFO : test_loss 0.70844\n",
|
||||
"2017-11-03 17:59:29,283 INFO : train_misclass 0.06964\n",
|
||||
"2017-11-03 17:59:29,283 INFO : valid_misclass 0.16760\n",
|
||||
"2017-11-03 17:59:29,284 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:59:29,285 INFO : runtime 0.66110\n",
|
||||
"2017-11-03 17:59:29,286 INFO : \n",
|
||||
"2017-11-03 17:59:29,290 INFO : New best valid_misclass: 0.167598\n",
|
||||
"2017-11-03 17:59:29,290 INFO : \n",
|
||||
"2017-11-03 17:59:29,291 INFO : Setup for second stop...\n",
|
||||
"2017-11-03 17:59:29,295 INFO : Train loss to reach 0.22331\n",
|
||||
"2017-11-03 17:59:29,296 INFO : Run until second stop...\n",
|
||||
"2017-11-03 17:59:29,410 INFO : Epoch 21\n",
|
||||
"2017-11-03 17:59:29,412 INFO : train_loss 0.27810\n",
|
||||
"2017-11-03 17:59:29,412 INFO : valid_loss 0.49784\n",
|
||||
"2017-11-03 17:59:29,413 INFO : test_loss 0.70844\n",
|
||||
"2017-11-03 17:59:29,414 INFO : train_misclass 0.08919\n",
|
||||
"2017-11-03 17:59:29,415 INFO : valid_misclass 0.16760\n",
|
||||
"2017-11-03 17:59:29,415 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:59:29,416 INFO : runtime 0.13360\n",
|
||||
"2017-11-03 17:59:29,417 INFO : \n",
|
||||
"2017-11-03 17:59:29,958 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:30,085 INFO : Epoch 22\n",
|
||||
"2017-11-03 17:59:30,087 INFO : train_loss 0.23934\n",
|
||||
"2017-11-03 17:59:30,088 INFO : valid_loss 0.45107\n",
|
||||
"2017-11-03 17:59:30,088 INFO : test_loss 0.86186\n",
|
||||
"2017-11-03 17:59:30,089 INFO : train_misclass 0.09142\n",
|
||||
"2017-11-03 17:59:30,090 INFO : valid_misclass 0.20670\n",
|
||||
"2017-11-03 17:59:30,091 INFO : test_misclass 0.28750\n",
|
||||
"2017-11-03 17:59:30,091 INFO : runtime 0.65554\n",
|
||||
"2017-11-03 17:59:30,092 INFO : \n",
|
||||
"2017-11-03 17:59:30,630 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:30,754 INFO : Epoch 23\n",
|
||||
"2017-11-03 17:59:30,755 INFO : train_loss 0.23411\n",
|
||||
"2017-11-03 17:59:30,756 INFO : valid_loss 0.33481\n",
|
||||
"2017-11-03 17:59:30,757 INFO : test_loss 0.72723\n",
|
||||
"2017-11-03 17:59:30,758 INFO : train_misclass 0.06132\n",
|
||||
"2017-11-03 17:59:30,759 INFO : valid_misclass 0.13408\n",
|
||||
"2017-11-03 17:59:30,760 INFO : test_misclass 0.31250\n",
|
||||
"2017-11-03 17:59:30,761 INFO : runtime 0.67196\n",
|
||||
"2017-11-03 17:59:30,762 INFO : \n",
|
||||
"2017-11-03 17:59:31,304 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:31,429 INFO : Epoch 24\n",
|
||||
"2017-11-03 17:59:31,431 INFO : train_loss 0.24864\n",
|
||||
"2017-11-03 17:59:31,432 INFO : valid_loss 0.30050\n",
|
||||
"2017-11-03 17:59:31,432 INFO : test_loss 0.63026\n",
|
||||
"2017-11-03 17:59:31,433 INFO : train_misclass 0.08250\n",
|
||||
"2017-11-03 17:59:31,434 INFO : valid_misclass 0.11173\n",
|
||||
"2017-11-03 17:59:31,435 INFO : test_misclass 0.23750\n",
|
||||
"2017-11-03 17:59:31,435 INFO : runtime 0.67471\n",
|
||||
"2017-11-03 17:59:31,437 INFO : \n",
|
||||
"2017-11-03 17:59:31,976 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:32,101 INFO : Epoch 25\n",
|
||||
"2017-11-03 17:59:32,102 INFO : train_loss 0.16780\n",
|
||||
"2017-11-03 17:59:32,103 INFO : valid_loss 0.26910\n",
|
||||
"2017-11-03 17:59:32,104 INFO : test_loss 0.76967\n",
|
||||
"2017-11-03 17:59:32,105 INFO : train_misclass 0.05017\n",
|
||||
"2017-11-03 17:59:32,105 INFO : valid_misclass 0.09497\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-11-03 17:59:32,106 INFO : test_misclass 0.29375\n",
|
||||
"2017-11-03 17:59:32,107 INFO : runtime 0.67182\n",
|
||||
"2017-11-03 17:59:32,108 INFO : \n",
|
||||
"2017-11-03 17:59:32,648 INFO : Time only for training updates: 0.54s\n",
|
||||
"2017-11-03 17:59:32,772 INFO : Epoch 26\n",
|
||||
"2017-11-03 17:59:32,773 INFO : train_loss 0.13316\n",
|
||||
"2017-11-03 17:59:32,774 INFO : valid_loss 0.21726\n",
|
||||
"2017-11-03 17:59:32,775 INFO : test_loss 0.72940\n",
|
||||
"2017-11-03 17:59:32,776 INFO : train_misclass 0.04571\n",
|
||||
"2017-11-03 17:59:32,776 INFO : valid_misclass 0.08380\n",
|
||||
"2017-11-03 17:59:32,777 INFO : test_misclass 0.27500\n",
|
||||
"2017-11-03 17:59:32,778 INFO : runtime 0.67151\n",
|
||||
"2017-11-03 17:59:32,779 INFO : \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"exp.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We arrive around 26%, exact value depending on stars :))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"celltoolbar": "Edit 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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,982 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Cropped Decoding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we will use cropped decoding. Cropped decoding means the ConvNet is trained on time windows/time crops within the trials. We will explain this visually by comparing trialwise to cropped decoding.\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Trialwise Decoding | Cropped Decoding\n",
|
||||
"- | - \n",
|
||||
"![Trialwise Decoding](./trialwise_explanation.png \"Trialwise Decoding\") | ![Cropped Decoding](./cropped_explanation.png \"Cropped Decoding\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"On the left, you see trialwise decoding:\n",
|
||||
"\n",
|
||||
"1. A complete trial is pushed through the network\n",
|
||||
"2. The network produces a prediction\n",
|
||||
"3. The prediction is compared to the target (label) for that trial to compute the loss\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"On the right, you see cropped decoding:\n",
|
||||
"\n",
|
||||
"1. Instead of a complete trial, windows within the trial, here called *crops*, are pushed through the network\n",
|
||||
"2. For computational efficiency, multiple neighbouring crops are pushed through the network simultaneously (these neighbouring crops are called a *supercrop*)\n",
|
||||
"3. Therefore, the network produces multiple predictions (one per crop in the supercrop)\n",
|
||||
"4. The individual crop predictions are averaged before computing the loss function\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Notes:\n",
|
||||
"\n",
|
||||
"* The network architecture implicitly defines the crop size (it is the receptive field size, i.e., the number of timesteps the network uses to make a single prediction)\n",
|
||||
"* The supercrop size is a user-defined hyperparameter, called `input_time_length` in Braindecode. It mostly affects runtime (larger supercrop sizes should be faster). As a rule of thumb, you can set it to two times the crop size.\n",
|
||||
"* Crop size and supercrop size together define how many predictions the network makes per supercrop: $\\mathrm{\\#supercrop}-\\mathrm{\\#crop}+1=\\mathrm{\\#predictions}$"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For cropped decoding, the above training setup is mathematically identical to sampling crops in your dataset, pushing them through the network and training directly on the individual crops. At the same time, the above training setup is much faster as it avoids redundant computations by using dilated convolutions, see our paper [Deep learning with convolutional neural networks for EEG decoding and visualization](https://arxiv.org/abs/1703.05051). However, the two setups are only mathematically identical in case (1) your network does not use any padding and (2) your loss function leads to the same gradients when using the averaged output. The first is true for our shallow and deep ConvNet models and the second is true for the log-softmax outputs and negative log likelihood loss that is typically used for classification in PyTorch."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Most of the code for cropped decoding is identical to the [Trialwise Decoding Tutorial](Trialwise_Decoding.html), differences are explained in the text."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Enable logging"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"import importlib\n",
|
||||
"importlib.reload(logging) # see https://stackoverflow.com/a/21475297/1469195\n",
|
||||
"log = logging.getLogger()\n",
|
||||
"log.setLevel('INFO')\n",
|
||||
"import sys\n",
|
||||
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
|
||||
" level=logging.INFO, stream=sys.stdout)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import mne\n",
|
||||
"from mne.io import concatenate_raws\n",
|
||||
"\n",
|
||||
"# 5,6,7,10,13,14 are codes for executed and imagined hands/feet\n",
|
||||
"subject_id = 22\n",
|
||||
"event_codes = [5,6,9,10,13,14]\n",
|
||||
"#event_codes = [3,4,5,6,7,8,9,10,11,12,13,14]\n",
|
||||
"\n",
|
||||
"# This will download the files if you don't have them yet,\n",
|
||||
"# and then return the paths to the files.\n",
|
||||
"physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)\n",
|
||||
"\n",
|
||||
"# Load each of the files\n",
|
||||
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto', verbose='WARNING')\n",
|
||||
" for path in physionet_paths]\n",
|
||||
"\n",
|
||||
"# Concatenate them\n",
|
||||
"raw = concatenate_raws(parts)\n",
|
||||
"\n",
|
||||
"# Find the events in this dataset\n",
|
||||
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
|
||||
"\n",
|
||||
"# Use only EEG channels\n",
|
||||
"eeg_channel_inds = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
|
||||
" exclude='bads')\n",
|
||||
"\n",
|
||||
"# Extract trials, only using EEG channels\n",
|
||||
"epoched = mne.Epochs(raw, events, dict(hands_or_left=2, feet_or_right=3), tmin=1, tmax=4.1, proj=False, picks=eeg_channel_inds,\n",
|
||||
" baseline=None, preload=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Convert data to Braindecode format"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"# Convert data from volt to millivolt\n",
|
||||
"# Pytorch expects float32 for input and int64 for labels.\n",
|
||||
"X = (epoched.get_data() * 1e6).astype(np.float32)\n",
|
||||
"y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.signal_target import SignalAndTarget\n",
|
||||
"\n",
|
||||
"train_set = SignalAndTarget(X[:40], y=y[:40])\n",
|
||||
"valid_set = SignalAndTarget(X[40:70], y=y[40:70])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<div class=\"alert alert-info\">\n",
|
||||
"\n",
|
||||
"As in the trialwise decoding tutorial, we will use the Braindecode model class directly to perform the training in a few lines of code. If you instead want to use your own training loop, have a look at the [Cropped Manual Training Loop Tutorial](./Cropped_Manual_Training_Loop.html).\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For cropped decoding, we now transform the model into a model that outputs a dense time series of predictions.\n",
|
||||
"For this, we manually set the length of the final convolution layer to some length that makes the receptive field of the ConvNet smaller than the number of samples in a trial (see `final_conv_length=12` in the model definition). "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = False\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"n_classes = 2\n",
|
||||
"in_chans = train_set.X.shape[1]\n",
|
||||
"\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes,\n",
|
||||
" input_time_length=None,\n",
|
||||
" final_conv_length=12)\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we supply `cropped=True` to our compile function "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.torch_ext.optimizers import AdamW\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"#optimizer = AdamW(model.parameters(), lr=1*0.01, weight_decay=0.5*0.001) # these are good values for the deep model\n",
|
||||
"optimizer = AdamW(model.parameters(), lr=0.0625 * 0.01, weight_decay=0)\n",
|
||||
"model.compile(loss=F.nll_loss, optimizer=optimizer, iterator_seed=1, cropped=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run the training"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For fitting, we must supply the super crop size. Here, we it to 450 by setting `input_time_length = 450`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2018-08-08 11:49:48,805 INFO : Run until first stop...\n",
|
||||
"2018-08-08 11:49:52,805 INFO : Time only for training updates: 3.99s\n",
|
||||
"2018-08-08 11:49:54,825 INFO : Epoch 0\n",
|
||||
"2018-08-08 11:49:54,826 INFO : train_loss 4.16812\n",
|
||||
"2018-08-08 11:49:54,830 INFO : valid_loss 3.39068\n",
|
||||
"2018-08-08 11:49:54,830 INFO : train_misclass 0.50000\n",
|
||||
"2018-08-08 11:49:54,831 INFO : valid_misclass 0.53333\n",
|
||||
"2018-08-08 11:49:54,833 INFO : runtime 0.00000\n",
|
||||
"2018-08-08 11:49:54,835 INFO : \n",
|
||||
"2018-08-08 11:49:59,395 INFO : Time only for training updates: 4.55s\n",
|
||||
"2018-08-08 11:50:03,372 INFO : Epoch 1\n",
|
||||
"2018-08-08 11:50:03,379 INFO : train_loss 3.04388\n",
|
||||
"2018-08-08 11:50:03,384 INFO : valid_loss 2.38477\n",
|
||||
"2018-08-08 11:50:03,389 INFO : train_misclass 0.52500\n",
|
||||
"2018-08-08 11:50:03,394 INFO : valid_misclass 0.53333\n",
|
||||
"2018-08-08 11:50:03,398 INFO : runtime 6.59494\n",
|
||||
"2018-08-08 11:50:03,403 INFO : \n",
|
||||
"2018-08-08 11:50:09,317 INFO : Time only for training updates: 5.90s\n",
|
||||
"2018-08-08 11:50:13,263 INFO : Epoch 2\n",
|
||||
"2018-08-08 11:50:13,268 INFO : train_loss 2.06639\n",
|
||||
"2018-08-08 11:50:13,273 INFO : valid_loss 1.61044\n",
|
||||
"2018-08-08 11:50:13,277 INFO : train_misclass 0.47500\n",
|
||||
"2018-08-08 11:50:13,281 INFO : valid_misclass 0.50000\n",
|
||||
"2018-08-08 11:50:13,285 INFO : runtime 9.91786\n",
|
||||
"2018-08-08 11:50:13,289 INFO : \n",
|
||||
"2018-08-08 11:50:19,343 INFO : Time only for training updates: 6.03s\n",
|
||||
"2018-08-08 11:50:23,245 INFO : Epoch 3\n",
|
||||
"2018-08-08 11:50:23,251 INFO : train_loss 1.41936\n",
|
||||
"2018-08-08 11:50:23,255 INFO : valid_loss 1.18789\n",
|
||||
"2018-08-08 11:50:23,259 INFO : train_misclass 0.47500\n",
|
||||
"2018-08-08 11:50:23,263 INFO : valid_misclass 0.50000\n",
|
||||
"2018-08-08 11:50:23,267 INFO : runtime 10.02883\n",
|
||||
"2018-08-08 11:50:23,271 INFO : \n",
|
||||
"2018-08-08 11:50:29,225 INFO : Time only for training updates: 5.94s\n",
|
||||
"2018-08-08 11:50:33,218 INFO : Epoch 4\n",
|
||||
"2018-08-08 11:50:33,223 INFO : train_loss 1.00751\n",
|
||||
"2018-08-08 11:50:33,227 INFO : valid_loss 0.95534\n",
|
||||
"2018-08-08 11:50:33,232 INFO : train_misclass 0.40000\n",
|
||||
"2018-08-08 11:50:33,236 INFO : valid_misclass 0.43333\n",
|
||||
"2018-08-08 11:50:33,242 INFO : runtime 9.88115\n",
|
||||
"2018-08-08 11:50:33,248 INFO : \n",
|
||||
"2018-08-08 11:50:39,206 INFO : Time only for training updates: 5.94s\n",
|
||||
"2018-08-08 11:50:43,233 INFO : Epoch 5\n",
|
||||
"2018-08-08 11:50:43,237 INFO : train_loss 0.76304\n",
|
||||
"2018-08-08 11:50:43,240 INFO : valid_loss 0.83303\n",
|
||||
"2018-08-08 11:50:43,243 INFO : train_misclass 0.32500\n",
|
||||
"2018-08-08 11:50:43,247 INFO : valid_misclass 0.43333\n",
|
||||
"2018-08-08 11:50:43,250 INFO : runtime 9.97928\n",
|
||||
"2018-08-08 11:50:43,255 INFO : \n",
|
||||
"2018-08-08 11:50:49,142 INFO : Time only for training updates: 5.87s\n",
|
||||
"2018-08-08 11:50:53,124 INFO : Epoch 6\n",
|
||||
"2018-08-08 11:50:53,130 INFO : train_loss 0.61960\n",
|
||||
"2018-08-08 11:50:53,134 INFO : valid_loss 0.78367\n",
|
||||
"2018-08-08 11:50:53,139 INFO : train_misclass 0.30000\n",
|
||||
"2018-08-08 11:50:53,143 INFO : valid_misclass 0.43333\n",
|
||||
"2018-08-08 11:50:53,148 INFO : runtime 9.93809\n",
|
||||
"2018-08-08 11:50:53,152 INFO : \n",
|
||||
"2018-08-08 11:50:59,114 INFO : Time only for training updates: 5.95s\n",
|
||||
"2018-08-08 11:51:03,191 INFO : Epoch 7\n",
|
||||
"2018-08-08 11:51:03,197 INFO : train_loss 0.51544\n",
|
||||
"2018-08-08 11:51:03,202 INFO : valid_loss 0.75722\n",
|
||||
"2018-08-08 11:51:03,207 INFO : train_misclass 0.25000\n",
|
||||
"2018-08-08 11:51:03,211 INFO : valid_misclass 0.36667\n",
|
||||
"2018-08-08 11:51:03,215 INFO : runtime 9.97154\n",
|
||||
"2018-08-08 11:51:03,220 INFO : \n",
|
||||
"2018-08-08 11:51:09,159 INFO : Time only for training updates: 5.92s\n",
|
||||
"2018-08-08 11:51:13,178 INFO : Epoch 8\n",
|
||||
"2018-08-08 11:51:13,183 INFO : train_loss 0.41659\n",
|
||||
"2018-08-08 11:51:13,187 INFO : valid_loss 0.70797\n",
|
||||
"2018-08-08 11:51:13,191 INFO : train_misclass 0.20000\n",
|
||||
"2018-08-08 11:51:13,194 INFO : valid_misclass 0.33333\n",
|
||||
"2018-08-08 11:51:13,197 INFO : runtime 10.04526\n",
|
||||
"2018-08-08 11:51:13,201 INFO : \n",
|
||||
"2018-08-08 11:51:19,142 INFO : Time only for training updates: 5.93s\n",
|
||||
"2018-08-08 11:51:23,173 INFO : Epoch 9\n",
|
||||
"2018-08-08 11:51:23,195 INFO : train_loss 0.33328\n",
|
||||
"2018-08-08 11:51:23,197 INFO : valid_loss 0.65720\n",
|
||||
"2018-08-08 11:51:23,198 INFO : train_misclass 0.17500\n",
|
||||
"2018-08-08 11:51:23,199 INFO : valid_misclass 0.30000\n",
|
||||
"2018-08-08 11:51:23,200 INFO : runtime 9.98219\n",
|
||||
"2018-08-08 11:51:23,201 INFO : \n",
|
||||
"2018-08-08 11:51:29,120 INFO : Time only for training updates: 5.89s\n",
|
||||
"2018-08-08 11:51:32,990 INFO : Epoch 10\n",
|
||||
"2018-08-08 11:51:32,995 INFO : train_loss 0.27232\n",
|
||||
"2018-08-08 11:51:32,999 INFO : valid_loss 0.62543\n",
|
||||
"2018-08-08 11:51:33,004 INFO : train_misclass 0.15000\n",
|
||||
"2018-08-08 11:51:33,008 INFO : valid_misclass 0.30000\n",
|
||||
"2018-08-08 11:51:33,012 INFO : runtime 9.97608\n",
|
||||
"2018-08-08 11:51:33,015 INFO : \n",
|
||||
"2018-08-08 11:51:38,980 INFO : Time only for training updates: 5.95s\n",
|
||||
"2018-08-08 11:51:42,894 INFO : Epoch 11\n",
|
||||
"2018-08-08 11:51:42,899 INFO : train_loss 0.22401\n",
|
||||
"2018-08-08 11:51:42,903 INFO : valid_loss 0.59923\n",
|
||||
"2018-08-08 11:51:42,907 INFO : train_misclass 0.07500\n",
|
||||
"2018-08-08 11:51:42,911 INFO : valid_misclass 0.30000\n",
|
||||
"2018-08-08 11:51:42,914 INFO : runtime 9.86210\n",
|
||||
"2018-08-08 11:51:42,918 INFO : \n",
|
||||
"2018-08-08 11:51:48,798 INFO : Time only for training updates: 5.87s\n",
|
||||
"2018-08-08 11:51:52,831 INFO : Epoch 12\n",
|
||||
"2018-08-08 11:51:52,835 INFO : train_loss 0.18465\n",
|
||||
"2018-08-08 11:51:52,839 INFO : valid_loss 0.56904\n",
|
||||
"2018-08-08 11:51:52,843 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:51:52,847 INFO : valid_misclass 0.26667\n",
|
||||
"2018-08-08 11:51:52,850 INFO : runtime 9.81899\n",
|
||||
"2018-08-08 11:51:52,854 INFO : \n",
|
||||
"2018-08-08 11:51:58,798 INFO : Time only for training updates: 5.93s\n",
|
||||
"2018-08-08 11:52:02,766 INFO : Epoch 13\n",
|
||||
"2018-08-08 11:52:02,771 INFO : train_loss 0.15289\n",
|
||||
"2018-08-08 11:52:02,775 INFO : valid_loss 0.53387\n",
|
||||
"2018-08-08 11:52:02,780 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:02,784 INFO : valid_misclass 0.23333\n",
|
||||
"2018-08-08 11:52:02,788 INFO : runtime 9.99905\n",
|
||||
"2018-08-08 11:52:02,793 INFO : \n",
|
||||
"2018-08-08 11:52:08,751 INFO : Time only for training updates: 5.94s\n",
|
||||
"2018-08-08 11:52:12,614 INFO : Epoch 14\n",
|
||||
"2018-08-08 11:52:12,620 INFO : train_loss 0.12854\n",
|
||||
"2018-08-08 11:52:12,627 INFO : valid_loss 0.49827\n",
|
||||
"2018-08-08 11:52:12,633 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:12,637 INFO : valid_misclass 0.20000\n",
|
||||
"2018-08-08 11:52:12,642 INFO : runtime 9.95297\n",
|
||||
"2018-08-08 11:52:12,644 INFO : \n",
|
||||
"2018-08-08 11:52:18,474 INFO : Time only for training updates: 5.82s\n",
|
||||
"2018-08-08 11:52:21,438 INFO : Epoch 15\n",
|
||||
"2018-08-08 11:52:21,439 INFO : train_loss 0.11070\n",
|
||||
"2018-08-08 11:52:21,440 INFO : valid_loss 0.46770\n",
|
||||
"2018-08-08 11:52:21,441 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:21,443 INFO : valid_misclass 0.16667\n",
|
||||
"2018-08-08 11:52:21,444 INFO : runtime 9.72386\n",
|
||||
"2018-08-08 11:52:21,445 INFO : \n",
|
||||
"2018-08-08 11:52:24,518 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:52:26,554 INFO : Epoch 16\n",
|
||||
"2018-08-08 11:52:26,556 INFO : train_loss 0.09734\n",
|
||||
"2018-08-08 11:52:26,557 INFO : valid_loss 0.44107\n",
|
||||
"2018-08-08 11:52:26,558 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:26,559 INFO : valid_misclass 0.16667\n",
|
||||
"2018-08-08 11:52:26,560 INFO : runtime 6.04018\n",
|
||||
"2018-08-08 11:52:26,561 INFO : \n",
|
||||
"2018-08-08 11:52:29,640 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:52:31,673 INFO : Epoch 17\n",
|
||||
"2018-08-08 11:52:31,674 INFO : train_loss 0.08650\n",
|
||||
"2018-08-08 11:52:31,678 INFO : valid_loss 0.41685\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2018-08-08 11:52:31,680 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:31,681 INFO : valid_misclass 0.16667\n",
|
||||
"2018-08-08 11:52:31,682 INFO : runtime 5.12189\n",
|
||||
"2018-08-08 11:52:31,683 INFO : \n",
|
||||
"2018-08-08 11:52:34,754 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:52:36,786 INFO : Epoch 18\n",
|
||||
"2018-08-08 11:52:36,787 INFO : train_loss 0.07784\n",
|
||||
"2018-08-08 11:52:36,791 INFO : valid_loss 0.39502\n",
|
||||
"2018-08-08 11:52:36,792 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:36,793 INFO : valid_misclass 0.13333\n",
|
||||
"2018-08-08 11:52:36,794 INFO : runtime 5.11526\n",
|
||||
"2018-08-08 11:52:36,796 INFO : \n",
|
||||
"2018-08-08 11:52:39,876 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:52:41,910 INFO : Epoch 19\n",
|
||||
"2018-08-08 11:52:41,911 INFO : train_loss 0.07096\n",
|
||||
"2018-08-08 11:52:41,915 INFO : valid_loss 0.37620\n",
|
||||
"2018-08-08 11:52:41,916 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:41,920 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:52:41,921 INFO : runtime 5.12150\n",
|
||||
"2018-08-08 11:52:41,923 INFO : \n",
|
||||
"2018-08-08 11:52:45,000 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:52:47,032 INFO : Epoch 20\n",
|
||||
"2018-08-08 11:52:47,033 INFO : train_loss 0.06583\n",
|
||||
"2018-08-08 11:52:47,037 INFO : valid_loss 0.36116\n",
|
||||
"2018-08-08 11:52:47,038 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:47,040 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:52:47,042 INFO : runtime 5.12370\n",
|
||||
"2018-08-08 11:52:47,045 INFO : \n",
|
||||
"2018-08-08 11:52:50,116 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:52:52,148 INFO : Epoch 21\n",
|
||||
"2018-08-08 11:52:52,149 INFO : train_loss 0.06186\n",
|
||||
"2018-08-08 11:52:52,151 INFO : valid_loss 0.34902\n",
|
||||
"2018-08-08 11:52:52,152 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:52,153 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:52:52,154 INFO : runtime 5.11570\n",
|
||||
"2018-08-08 11:52:52,156 INFO : \n",
|
||||
"2018-08-08 11:52:55,236 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:52:57,268 INFO : Epoch 22\n",
|
||||
"2018-08-08 11:52:57,269 INFO : train_loss 0.05873\n",
|
||||
"2018-08-08 11:52:57,273 INFO : valid_loss 0.33921\n",
|
||||
"2018-08-08 11:52:57,274 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:52:57,278 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:52:57,279 INFO : runtime 5.12058\n",
|
||||
"2018-08-08 11:52:57,282 INFO : \n",
|
||||
"2018-08-08 11:53:00,444 INFO : Time only for training updates: 3.15s\n",
|
||||
"2018-08-08 11:53:02,601 INFO : Epoch 23\n",
|
||||
"2018-08-08 11:53:02,602 INFO : train_loss 0.05626\n",
|
||||
"2018-08-08 11:53:02,606 INFO : valid_loss 0.33130\n",
|
||||
"2018-08-08 11:53:02,607 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:02,607 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:02,610 INFO : runtime 5.20754\n",
|
||||
"2018-08-08 11:53:02,612 INFO : \n",
|
||||
"2018-08-08 11:53:05,691 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:53:07,723 INFO : Epoch 24\n",
|
||||
"2018-08-08 11:53:07,724 INFO : train_loss 0.05439\n",
|
||||
"2018-08-08 11:53:07,727 INFO : valid_loss 0.32496\n",
|
||||
"2018-08-08 11:53:07,728 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:07,729 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:07,730 INFO : runtime 5.24707\n",
|
||||
"2018-08-08 11:53:07,731 INFO : \n",
|
||||
"2018-08-08 11:53:10,805 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:53:12,832 INFO : Epoch 25\n",
|
||||
"2018-08-08 11:53:12,833 INFO : train_loss 0.05298\n",
|
||||
"2018-08-08 11:53:12,835 INFO : valid_loss 0.31987\n",
|
||||
"2018-08-08 11:53:12,836 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:12,837 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:12,838 INFO : runtime 5.11422\n",
|
||||
"2018-08-08 11:53:12,839 INFO : \n",
|
||||
"2018-08-08 11:53:15,915 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:53:17,942 INFO : Epoch 26\n",
|
||||
"2018-08-08 11:53:17,943 INFO : train_loss 0.05193\n",
|
||||
"2018-08-08 11:53:17,947 INFO : valid_loss 0.31576\n",
|
||||
"2018-08-08 11:53:17,948 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:17,949 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:17,950 INFO : runtime 5.10983\n",
|
||||
"2018-08-08 11:53:17,951 INFO : \n",
|
||||
"2018-08-08 11:53:21,025 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:53:23,051 INFO : Epoch 27\n",
|
||||
"2018-08-08 11:53:23,052 INFO : train_loss 0.05121\n",
|
||||
"2018-08-08 11:53:23,055 INFO : valid_loss 0.31244\n",
|
||||
"2018-08-08 11:53:23,056 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:23,057 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:23,058 INFO : runtime 5.10984\n",
|
||||
"2018-08-08 11:53:23,060 INFO : \n",
|
||||
"2018-08-08 11:53:26,138 INFO : Time only for training updates: 3.07s\n",
|
||||
"2018-08-08 11:53:28,171 INFO : Epoch 28\n",
|
||||
"2018-08-08 11:53:28,172 INFO : train_loss 0.05073\n",
|
||||
"2018-08-08 11:53:28,175 INFO : valid_loss 0.30970\n",
|
||||
"2018-08-08 11:53:28,176 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:28,177 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:28,179 INFO : runtime 5.11324\n",
|
||||
"2018-08-08 11:53:28,180 INFO : \n",
|
||||
"2018-08-08 11:53:31,252 INFO : Time only for training updates: 3.06s\n",
|
||||
"2018-08-08 11:53:33,283 INFO : Epoch 29\n",
|
||||
"2018-08-08 11:53:33,284 INFO : train_loss 0.05041\n",
|
||||
"2018-08-08 11:53:33,287 INFO : valid_loss 0.30740\n",
|
||||
"2018-08-08 11:53:33,288 INFO : train_misclass 0.00000\n",
|
||||
"2018-08-08 11:53:33,289 INFO : valid_misclass 0.10000\n",
|
||||
"2018-08-08 11:53:33,290 INFO : runtime 5.11368\n",
|
||||
"2018-08-08 11:53:33,292 INFO : \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<braindecode.experiments.experiment.Experiment at 0x7f75b82e8940>"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"input_time_length = 450\n",
|
||||
"model.fit(train_set.X, train_set.y, epochs=30, batch_size=64, scheduler='cosine',\n",
|
||||
" input_time_length=input_time_length,\n",
|
||||
" validation_data=(valid_set.X, valid_set.y),)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>train_loss</th>\n",
|
||||
" <th>valid_loss</th>\n",
|
||||
" <th>train_misclass</th>\n",
|
||||
" <th>valid_misclass</th>\n",
|
||||
" <th>runtime</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>4.168118</td>\n",
|
||||
" <td>3.390682</td>\n",
|
||||
" <td>0.500</td>\n",
|
||||
" <td>0.533333</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>3.043880</td>\n",
|
||||
" <td>2.384769</td>\n",
|
||||
" <td>0.525</td>\n",
|
||||
" <td>0.533333</td>\n",
|
||||
" <td>6.594939</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>2.066392</td>\n",
|
||||
" <td>1.610444</td>\n",
|
||||
" <td>0.475</td>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>9.917859</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>1.419359</td>\n",
|
||||
" <td>1.187893</td>\n",
|
||||
" <td>0.475</td>\n",
|
||||
" <td>0.500000</td>\n",
|
||||
" <td>10.028832</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>1.007514</td>\n",
|
||||
" <td>0.955341</td>\n",
|
||||
" <td>0.400</td>\n",
|
||||
" <td>0.433333</td>\n",
|
||||
" <td>9.881147</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>0.763041</td>\n",
|
||||
" <td>0.833030</td>\n",
|
||||
" <td>0.325</td>\n",
|
||||
" <td>0.433333</td>\n",
|
||||
" <td>9.979284</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>6</th>\n",
|
||||
" <td>0.619604</td>\n",
|
||||
" <td>0.783671</td>\n",
|
||||
" <td>0.300</td>\n",
|
||||
" <td>0.433333</td>\n",
|
||||
" <td>9.938087</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>7</th>\n",
|
||||
" <td>0.515436</td>\n",
|
||||
" <td>0.757223</td>\n",
|
||||
" <td>0.250</td>\n",
|
||||
" <td>0.366667</td>\n",
|
||||
" <td>9.971540</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>8</th>\n",
|
||||
" <td>0.416595</td>\n",
|
||||
" <td>0.707971</td>\n",
|
||||
" <td>0.200</td>\n",
|
||||
" <td>0.333333</td>\n",
|
||||
" <td>10.045257</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>9</th>\n",
|
||||
" <td>0.333283</td>\n",
|
||||
" <td>0.657200</td>\n",
|
||||
" <td>0.175</td>\n",
|
||||
" <td>0.300000</td>\n",
|
||||
" <td>9.982185</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>10</th>\n",
|
||||
" <td>0.272317</td>\n",
|
||||
" <td>0.625433</td>\n",
|
||||
" <td>0.150</td>\n",
|
||||
" <td>0.300000</td>\n",
|
||||
" <td>9.976075</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>11</th>\n",
|
||||
" <td>0.224010</td>\n",
|
||||
" <td>0.599227</td>\n",
|
||||
" <td>0.075</td>\n",
|
||||
" <td>0.300000</td>\n",
|
||||
" <td>9.862099</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>12</th>\n",
|
||||
" <td>0.184646</td>\n",
|
||||
" <td>0.569042</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.266667</td>\n",
|
||||
" <td>9.818995</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>13</th>\n",
|
||||
" <td>0.152886</td>\n",
|
||||
" <td>0.533874</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.233333</td>\n",
|
||||
" <td>9.999050</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>14</th>\n",
|
||||
" <td>0.128538</td>\n",
|
||||
" <td>0.498271</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.200000</td>\n",
|
||||
" <td>9.952966</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15</th>\n",
|
||||
" <td>0.110699</td>\n",
|
||||
" <td>0.467696</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.166667</td>\n",
|
||||
" <td>9.723856</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>16</th>\n",
|
||||
" <td>0.097337</td>\n",
|
||||
" <td>0.441067</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.166667</td>\n",
|
||||
" <td>6.040177</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>17</th>\n",
|
||||
" <td>0.086503</td>\n",
|
||||
" <td>0.416848</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.166667</td>\n",
|
||||
" <td>5.121891</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>18</th>\n",
|
||||
" <td>0.077835</td>\n",
|
||||
" <td>0.395015</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.133333</td>\n",
|
||||
" <td>5.115263</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>19</th>\n",
|
||||
" <td>0.070961</td>\n",
|
||||
" <td>0.376196</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.121497</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>20</th>\n",
|
||||
" <td>0.065832</td>\n",
|
||||
" <td>0.361156</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.123702</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>0.061856</td>\n",
|
||||
" <td>0.349016</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.115699</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>22</th>\n",
|
||||
" <td>0.058733</td>\n",
|
||||
" <td>0.339215</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.120580</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>23</th>\n",
|
||||
" <td>0.056258</td>\n",
|
||||
" <td>0.331300</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.207545</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>24</th>\n",
|
||||
" <td>0.054392</td>\n",
|
||||
" <td>0.324961</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.247069</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25</th>\n",
|
||||
" <td>0.052977</td>\n",
|
||||
" <td>0.319866</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.114219</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>26</th>\n",
|
||||
" <td>0.051933</td>\n",
|
||||
" <td>0.315760</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.109835</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>27</th>\n",
|
||||
" <td>0.051210</td>\n",
|
||||
" <td>0.312438</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.109844</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>28</th>\n",
|
||||
" <td>0.050732</td>\n",
|
||||
" <td>0.309702</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.113238</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>29</th>\n",
|
||||
" <td>0.050408</td>\n",
|
||||
" <td>0.307397</td>\n",
|
||||
" <td>0.000</td>\n",
|
||||
" <td>0.100000</td>\n",
|
||||
" <td>5.113682</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" train_loss valid_loss train_misclass valid_misclass runtime\n",
|
||||
"0 4.168118 3.390682 0.500 0.533333 0.000000\n",
|
||||
"1 3.043880 2.384769 0.525 0.533333 6.594939\n",
|
||||
"2 2.066392 1.610444 0.475 0.500000 9.917859\n",
|
||||
"3 1.419359 1.187893 0.475 0.500000 10.028832\n",
|
||||
"4 1.007514 0.955341 0.400 0.433333 9.881147\n",
|
||||
"5 0.763041 0.833030 0.325 0.433333 9.979284\n",
|
||||
"6 0.619604 0.783671 0.300 0.433333 9.938087\n",
|
||||
"7 0.515436 0.757223 0.250 0.366667 9.971540\n",
|
||||
"8 0.416595 0.707971 0.200 0.333333 10.045257\n",
|
||||
"9 0.333283 0.657200 0.175 0.300000 9.982185\n",
|
||||
"10 0.272317 0.625433 0.150 0.300000 9.976075\n",
|
||||
"11 0.224010 0.599227 0.075 0.300000 9.862099\n",
|
||||
"12 0.184646 0.569042 0.000 0.266667 9.818995\n",
|
||||
"13 0.152886 0.533874 0.000 0.233333 9.999050\n",
|
||||
"14 0.128538 0.498271 0.000 0.200000 9.952966\n",
|
||||
"15 0.110699 0.467696 0.000 0.166667 9.723856\n",
|
||||
"16 0.097337 0.441067 0.000 0.166667 6.040177\n",
|
||||
"17 0.086503 0.416848 0.000 0.166667 5.121891\n",
|
||||
"18 0.077835 0.395015 0.000 0.133333 5.115263\n",
|
||||
"19 0.070961 0.376196 0.000 0.100000 5.121497\n",
|
||||
"20 0.065832 0.361156 0.000 0.100000 5.123702\n",
|
||||
"21 0.061856 0.349016 0.000 0.100000 5.115699\n",
|
||||
"22 0.058733 0.339215 0.000 0.100000 5.120580\n",
|
||||
"23 0.056258 0.331300 0.000 0.100000 5.207545\n",
|
||||
"24 0.054392 0.324961 0.000 0.100000 5.247069\n",
|
||||
"25 0.052977 0.319866 0.000 0.100000 5.114219\n",
|
||||
"26 0.051933 0.315760 0.000 0.100000 5.109835\n",
|
||||
"27 0.051210 0.312438 0.000 0.100000 5.109844\n",
|
||||
"28 0.050732 0.309702 0.000 0.100000 5.113238\n",
|
||||
"29 0.050408 0.307397 0.000 0.100000 5.113682"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.epochs_df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Eventually, we arrive at 90% accuracy, so 27 from 30 trials are correctly predicted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'loss': 0.4325282573699951,\n",
|
||||
" 'misclass': 0.09999999999999998,\n",
|
||||
" 'runtime': 0.0004916191101074219}"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"test_set = SignalAndTarget(X[70:], y=y[70:])\n",
|
||||
"\n",
|
||||
"model.evaluate(test_set.X, test_set.y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model.predict(test_set.X)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dataset references\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" This dataset was created and contributed to PhysioNet by the developers of the [BCI2000](http://www.schalklab.org/research/bci2000) instrumentation system, which they used in making these recordings. The system is described in:\n",
|
||||
" \n",
|
||||
" Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE TBME 51(6):1034-1043.\n",
|
||||
"\n",
|
||||
"[PhysioBank](https://physionet.org/physiobank/) is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community and further described in:\n",
|
||||
"\n",
|
||||
" Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,804 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Using the Experiment Class"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Braindecode provides a convenience `Experiment` class, which removes the necessity to write your own training loop. It expects a training, a validation and a test set and trains as follows:\n",
|
||||
"\n",
|
||||
"1. Train on training set until a given stop criterion is fulfilled\n",
|
||||
"2. Reset to the best epoch, i.e. reset parameters of the model and the optimizer to the state at the best epoch (\"best\" according to a given criterion) \n",
|
||||
"3. Continue training on the combined training + validation set until the loss on the validation set is as low as it was on the best epoch for the training set. (or until the ConvNet was trained twice as many epochs as the best epoch to prevent infinite training)\n",
|
||||
"\n",
|
||||
"<div class='alert alert-warning'>\n",
|
||||
"\n",
|
||||
"It is not necessary to use the Experiment class to use the remaning functionality of Braindecode. Feel free to ignore it :)\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Removing orphaned offset at the beginning of the file.\n",
|
||||
"179 events found\n",
|
||||
"Events id: [1 2 3]\n",
|
||||
"90 matching events found\n",
|
||||
"Loading data for 90 events and 497 original time points ...\n",
|
||||
"0 bad epochs dropped\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import mne\n",
|
||||
"from mne.io import concatenate_raws\n",
|
||||
"\n",
|
||||
"# 5,6,7,10,13,14 are codes for executed and imagined hands/feet\n",
|
||||
"subject_id = 1\n",
|
||||
"event_codes = [5,6,9,10,13,14]\n",
|
||||
"\n",
|
||||
"# This will download the files if you don't have them yet,\n",
|
||||
"# and then return the paths to the files.\n",
|
||||
"physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)\n",
|
||||
"\n",
|
||||
"# Load each of the files\n",
|
||||
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto', verbose='WARNING')\n",
|
||||
" for path in physionet_paths]\n",
|
||||
"\n",
|
||||
"# Concatenate them\n",
|
||||
"raw = concatenate_raws(parts)\n",
|
||||
"\n",
|
||||
"# Find the events in this dataset\n",
|
||||
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
|
||||
"\n",
|
||||
"# Use only EEG channels\n",
|
||||
"eeg_channel_inds = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
|
||||
" exclude='bads')\n",
|
||||
"\n",
|
||||
"# Extract trials, only using EEG channels\n",
|
||||
"epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=eeg_channel_inds,\n",
|
||||
" baseline=None, preload=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Convert data to Braindecode Format"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from braindecode.datautil.signal_target import SignalAndTarget\n",
|
||||
"from braindecode.datautil.splitters import split_into_two_sets\n",
|
||||
"# Convert data from volt to millivolt\n",
|
||||
"# Pytorch expects float32 for input and int64 for labels.\n",
|
||||
"X = (epoched.get_data() * 1e6).astype(np.float32)\n",
|
||||
"y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1\n",
|
||||
"\n",
|
||||
"train_set = SignalAndTarget(X[:60], y=y[:60])\n",
|
||||
"test_set = SignalAndTarget(X[60:], y=y[60:])\n",
|
||||
"\n",
|
||||
"train_set, valid_set = split_into_two_sets(train_set, first_set_fraction=0.8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"from braindecode.models.util import to_dense_prediction_model\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = False\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"\n",
|
||||
"# This will determine how many crops are processed in parallel\n",
|
||||
"input_time_length = 450\n",
|
||||
"n_classes = 2\n",
|
||||
"in_chans = train_set.X.shape[1]\n",
|
||||
"# final_conv_length determines the size of the receptive field of the ConvNet\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes, input_time_length=input_time_length,\n",
|
||||
" final_conv_length=12).create_network()\n",
|
||||
"to_dense_prediction_model(model)\n",
|
||||
"\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torch import optim\n",
|
||||
"\n",
|
||||
"optimizer = optim.Adam(model.parameters())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"187 predictions per input/trial\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.torch_ext.util import np_to_var\n",
|
||||
"# determine output size\n",
|
||||
"test_input = np_to_var(np.ones((2, in_chans, input_time_length, 1), dtype=np.float32))\n",
|
||||
"if cuda:\n",
|
||||
" test_input = test_input.cuda()\n",
|
||||
"out = model(test_input)\n",
|
||||
"n_preds_per_input = out.cpu().data.numpy().shape[2]\n",
|
||||
"print(\"{:d} predictions per input/trial\".format(n_preds_per_input))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now we need to setup everything for the experiment: Iterator, loss function, monitors and stop criterion."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.experiments.experiment import Experiment\n",
|
||||
"from braindecode.datautil.iterators import CropsFromTrialsIterator\n",
|
||||
"from braindecode.experiments.monitors import RuntimeMonitor, LossMonitor, CroppedTrialMisclassMonitor, MisclassMonitor\n",
|
||||
"from braindecode.experiments.stopcriteria import MaxEpochs\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"import torch as th\n",
|
||||
"from braindecode.torch_ext.modules import Expression\n",
|
||||
"# Iterator is used to iterate over datasets both for training\n",
|
||||
"# and evaluation\n",
|
||||
"iterator = CropsFromTrialsIterator(batch_size=32,input_time_length=input_time_length,\n",
|
||||
" n_preds_per_input=n_preds_per_input)\n",
|
||||
"\n",
|
||||
"# Loss function takes predictions as they come out of the network and the targets\n",
|
||||
"# and returns a loss\n",
|
||||
"loss_function = lambda preds, targets: F.nll_loss(th.mean(preds, dim=2, keepdim=False), targets)\n",
|
||||
"\n",
|
||||
"# Could be used to apply some constraint on the models, then should be object\n",
|
||||
"# with apply method that accepts a module\n",
|
||||
"model_constraint = None\n",
|
||||
"# Monitors log the training progress\n",
|
||||
"monitors = [LossMonitor(), MisclassMonitor(col_suffix='sample_misclass'),\n",
|
||||
" CroppedTrialMisclassMonitor(input_time_length), RuntimeMonitor(),]\n",
|
||||
"# Stop criterion determines when the first stop happens\n",
|
||||
"stop_criterion = MaxEpochs(20)\n",
|
||||
"exp = Experiment(model, train_set, valid_set, test_set, iterator, loss_function, optimizer, model_constraint,\n",
|
||||
" monitors, stop_criterion, remember_best_column='valid_misclass',\n",
|
||||
" run_after_early_stop=True, batch_modifier=None, cuda=cuda)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Run experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-10-18 14:35:26,018 INFO : Run until first stop...\n",
|
||||
"2017-10-18 14:35:27,899 INFO : Epoch 0\n",
|
||||
"2017-10-18 14:35:27,904 INFO : train_loss 0.86930\n",
|
||||
"2017-10-18 14:35:27,908 INFO : valid_loss 0.74838\n",
|
||||
"2017-10-18 14:35:27,909 INFO : test_loss 0.69756\n",
|
||||
"2017-10-18 14:35:27,910 INFO : train_sample_misclass 0.53894\n",
|
||||
"2017-10-18 14:35:27,912 INFO : valid_sample_misclass 0.47103\n",
|
||||
"2017-10-18 14:35:27,913 INFO : test_sample_misclass 0.44251\n",
|
||||
"2017-10-18 14:35:27,914 INFO : train_misclass 0.60417\n",
|
||||
"2017-10-18 14:35:27,915 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:35:27,916 INFO : test_misclass 0.40000\n",
|
||||
"2017-10-18 14:35:27,917 INFO : runtime 0.00000\n",
|
||||
"2017-10-18 14:35:27,919 INFO : \n",
|
||||
"2017-10-18 14:35:27,922 INFO : New best valid_misclass: 0.500000\n",
|
||||
"2017-10-18 14:35:27,924 INFO : \n",
|
||||
"2017-10-18 14:35:30,830 INFO : Time only for training updates: 2.87s\n",
|
||||
"2017-10-18 14:35:32,663 INFO : Epoch 1\n",
|
||||
"2017-10-18 14:35:32,667 INFO : train_loss 2.33626\n",
|
||||
"2017-10-18 14:35:32,671 INFO : valid_loss 2.31771\n",
|
||||
"2017-10-18 14:35:32,674 INFO : test_loss 2.14077\n",
|
||||
"2017-10-18 14:35:32,677 INFO : train_sample_misclass 0.48279\n",
|
||||
"2017-10-18 14:35:32,681 INFO : valid_sample_misclass 0.50000\n",
|
||||
"2017-10-18 14:35:32,684 INFO : test_sample_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:32,687 INFO : train_misclass 0.50000\n",
|
||||
"2017-10-18 14:35:32,688 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:35:32,690 INFO : test_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:32,691 INFO : runtime 4.81331\n",
|
||||
"2017-10-18 14:35:32,694 INFO : \n",
|
||||
"2017-10-18 14:35:32,697 INFO : New best valid_misclass: 0.500000\n",
|
||||
"2017-10-18 14:35:32,698 INFO : \n",
|
||||
"2017-10-18 14:35:35,105 INFO : Time only for training updates: 2.39s\n",
|
||||
"2017-10-18 14:35:37,198 INFO : Epoch 2\n",
|
||||
"2017-10-18 14:35:37,203 INFO : train_loss 0.59815\n",
|
||||
"2017-10-18 14:35:37,206 INFO : valid_loss 0.78503\n",
|
||||
"2017-10-18 14:35:37,208 INFO : test_loss 0.70060\n",
|
||||
"2017-10-18 14:35:37,209 INFO : train_sample_misclass 0.33918\n",
|
||||
"2017-10-18 14:35:37,210 INFO : valid_sample_misclass 0.47995\n",
|
||||
"2017-10-18 14:35:37,212 INFO : test_sample_misclass 0.41996\n",
|
||||
"2017-10-18 14:35:37,213 INFO : train_misclass 0.22917\n",
|
||||
"2017-10-18 14:35:37,214 INFO : valid_misclass 0.41667\n",
|
||||
"2017-10-18 14:35:37,215 INFO : test_misclass 0.43333\n",
|
||||
"2017-10-18 14:35:37,217 INFO : runtime 4.29139\n",
|
||||
"2017-10-18 14:35:37,218 INFO : \n",
|
||||
"2017-10-18 14:35:37,221 INFO : New best valid_misclass: 0.416667\n",
|
||||
"2017-10-18 14:35:37,222 INFO : \n",
|
||||
"2017-10-18 14:35:39,655 INFO : Time only for training updates: 2.42s\n",
|
||||
"2017-10-18 14:35:41,575 INFO : Epoch 3\n",
|
||||
"2017-10-18 14:35:41,577 INFO : train_loss 0.53122\n",
|
||||
"2017-10-18 14:35:41,578 INFO : valid_loss 0.87284\n",
|
||||
"2017-10-18 14:35:41,579 INFO : test_loss 0.69774\n",
|
||||
"2017-10-18 14:35:41,580 INFO : train_sample_misclass 0.24588\n",
|
||||
"2017-10-18 14:35:41,581 INFO : valid_sample_misclass 0.55548\n",
|
||||
"2017-10-18 14:35:41,582 INFO : test_sample_misclass 0.42709\n",
|
||||
"2017-10-18 14:35:41,583 INFO : train_misclass 0.22917\n",
|
||||
"2017-10-18 14:35:41,585 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:35:41,586 INFO : test_misclass 0.36667\n",
|
||||
"2017-10-18 14:35:41,587 INFO : runtime 4.53691\n",
|
||||
"2017-10-18 14:35:41,588 INFO : \n",
|
||||
"2017-10-18 14:35:44,007 INFO : Time only for training updates: 2.40s\n",
|
||||
"2017-10-18 14:35:45,935 INFO : Epoch 4\n",
|
||||
"2017-10-18 14:35:45,939 INFO : train_loss 0.51574\n",
|
||||
"2017-10-18 14:35:45,943 INFO : valid_loss 1.10377\n",
|
||||
"2017-10-18 14:35:45,946 INFO : test_loss 0.80095\n",
|
||||
"2017-10-18 14:35:45,949 INFO : train_sample_misclass 0.25869\n",
|
||||
"2017-10-18 14:35:45,952 INFO : valid_sample_misclass 0.60094\n",
|
||||
"2017-10-18 14:35:45,956 INFO : test_sample_misclass 0.42727\n",
|
||||
"2017-10-18 14:35:45,959 INFO : train_misclass 0.27083\n",
|
||||
"2017-10-18 14:35:45,960 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:35:45,961 INFO : test_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:45,966 INFO : runtime 4.35103\n",
|
||||
"2017-10-18 14:35:45,967 INFO : \n",
|
||||
"2017-10-18 14:35:48,389 INFO : Time only for training updates: 2.40s\n",
|
||||
"2017-10-18 14:35:50,406 INFO : Epoch 5\n",
|
||||
"2017-10-18 14:35:50,417 INFO : train_loss 0.52613\n",
|
||||
"2017-10-18 14:35:50,419 INFO : valid_loss 1.22322\n",
|
||||
"2017-10-18 14:35:50,421 INFO : test_loss 0.86285\n",
|
||||
"2017-10-18 14:35:50,423 INFO : train_sample_misclass 0.27635\n",
|
||||
"2017-10-18 14:35:50,425 INFO : valid_sample_misclass 0.58445\n",
|
||||
"2017-10-18 14:35:50,426 INFO : test_sample_misclass 0.43841\n",
|
||||
"2017-10-18 14:35:50,428 INFO : train_misclass 0.33333\n",
|
||||
"2017-10-18 14:35:50,430 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:35:50,432 INFO : test_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:50,433 INFO : runtime 4.38171\n",
|
||||
"2017-10-18 14:35:50,435 INFO : \n",
|
||||
"2017-10-18 14:35:53,064 INFO : Time only for training updates: 2.61s\n",
|
||||
"2017-10-18 14:35:55,069 INFO : Epoch 6\n",
|
||||
"2017-10-18 14:35:55,074 INFO : train_loss 0.54906\n",
|
||||
"2017-10-18 14:35:55,077 INFO : valid_loss 1.23573\n",
|
||||
"2017-10-18 14:35:55,080 INFO : test_loss 0.92210\n",
|
||||
"2017-10-18 14:35:55,082 INFO : train_sample_misclass 0.28816\n",
|
||||
"2017-10-18 14:35:55,083 INFO : valid_sample_misclass 0.53810\n",
|
||||
"2017-10-18 14:35:55,084 INFO : test_sample_misclass 0.45205\n",
|
||||
"2017-10-18 14:35:55,085 INFO : train_misclass 0.33333\n",
|
||||
"2017-10-18 14:35:55,086 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:35:55,087 INFO : test_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:55,089 INFO : runtime 4.67268\n",
|
||||
"2017-10-18 14:35:55,090 INFO : \n",
|
||||
"2017-10-18 14:35:57,534 INFO : Time only for training updates: 2.42s\n",
|
||||
"2017-10-18 14:35:59,286 INFO : Epoch 7\n",
|
||||
"2017-10-18 14:35:59,287 INFO : train_loss 0.59627\n",
|
||||
"2017-10-18 14:35:59,289 INFO : valid_loss 1.16602\n",
|
||||
"2017-10-18 14:35:59,290 INFO : test_loss 0.95655\n",
|
||||
"2017-10-18 14:35:59,291 INFO : train_sample_misclass 0.32253\n",
|
||||
"2017-10-18 14:35:59,292 INFO : valid_sample_misclass 0.53053\n",
|
||||
"2017-10-18 14:35:59,293 INFO : test_sample_misclass 0.46346\n",
|
||||
"2017-10-18 14:35:59,294 INFO : train_misclass 0.37500\n",
|
||||
"2017-10-18 14:35:59,295 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:35:59,296 INFO : test_misclass 0.46667\n",
|
||||
"2017-10-18 14:35:59,297 INFO : runtime 4.47253\n",
|
||||
"2017-10-18 14:35:59,298 INFO : \n",
|
||||
"2017-10-18 14:36:01,422 INFO : Time only for training updates: 2.11s\n",
|
||||
"2017-10-18 14:36:02,798 INFO : Epoch 8\n",
|
||||
"2017-10-18 14:36:02,800 INFO : train_loss 0.40527\n",
|
||||
"2017-10-18 14:36:02,801 INFO : valid_loss 0.95821\n",
|
||||
"2017-10-18 14:36:02,802 INFO : test_loss 0.79267\n",
|
||||
"2017-10-18 14:36:02,802 INFO : train_sample_misclass 0.19112\n",
|
||||
"2017-10-18 14:36:02,803 INFO : valid_sample_misclass 0.52607\n",
|
||||
"2017-10-18 14:36:02,804 INFO : test_sample_misclass 0.43351\n",
|
||||
"2017-10-18 14:36:02,805 INFO : train_misclass 0.16667\n",
|
||||
"2017-10-18 14:36:02,806 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:02,807 INFO : test_misclass 0.40000\n",
|
||||
"2017-10-18 14:36:02,808 INFO : runtime 3.88520\n",
|
||||
"2017-10-18 14:36:02,809 INFO : \n",
|
||||
"2017-10-18 14:36:04,816 INFO : Time only for training updates: 2.00s\n",
|
||||
"2017-10-18 14:36:06,168 INFO : Epoch 9\n",
|
||||
"2017-10-18 14:36:06,169 INFO : train_loss 0.30290\n",
|
||||
"2017-10-18 14:36:06,171 INFO : valid_loss 0.89595\n",
|
||||
"2017-10-18 14:36:06,172 INFO : test_loss 0.75205\n",
|
||||
"2017-10-18 14:36:06,172 INFO : train_sample_misclass 0.10968\n",
|
||||
"2017-10-18 14:36:06,173 INFO : valid_sample_misclass 0.56684\n",
|
||||
"2017-10-18 14:36:06,174 INFO : test_sample_misclass 0.43119\n",
|
||||
"2017-10-18 14:36:06,175 INFO : train_misclass 0.08333\n",
|
||||
"2017-10-18 14:36:06,176 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:06,177 INFO : test_misclass 0.36667\n",
|
||||
"2017-10-18 14:36:06,178 INFO : runtime 3.39363\n",
|
||||
"2017-10-18 14:36:06,179 INFO : \n",
|
||||
"2017-10-18 14:36:08,220 INFO : Time only for training updates: 2.03s\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-10-18 14:36:09,672 INFO : Epoch 10\n",
|
||||
"2017-10-18 14:36:09,674 INFO : train_loss 0.25623\n",
|
||||
"2017-10-18 14:36:09,675 INFO : valid_loss 0.89555\n",
|
||||
"2017-10-18 14:36:09,676 INFO : test_loss 0.75208\n",
|
||||
"2017-10-18 14:36:09,678 INFO : train_sample_misclass 0.06088\n",
|
||||
"2017-10-18 14:36:09,679 INFO : valid_sample_misclass 0.55637\n",
|
||||
"2017-10-18 14:36:09,680 INFO : test_sample_misclass 0.41765\n",
|
||||
"2017-10-18 14:36:09,681 INFO : train_misclass 0.02083\n",
|
||||
"2017-10-18 14:36:09,682 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:09,683 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:09,684 INFO : runtime 3.40419\n",
|
||||
"2017-10-18 14:36:09,685 INFO : \n",
|
||||
"2017-10-18 14:36:11,601 INFO : Time only for training updates: 1.90s\n",
|
||||
"2017-10-18 14:36:12,948 INFO : Epoch 11\n",
|
||||
"2017-10-18 14:36:12,950 INFO : train_loss 0.24021\n",
|
||||
"2017-10-18 14:36:12,951 INFO : valid_loss 0.90580\n",
|
||||
"2017-10-18 14:36:12,953 INFO : test_loss 0.75648\n",
|
||||
"2017-10-18 14:36:12,954 INFO : train_sample_misclass 0.06205\n",
|
||||
"2017-10-18 14:36:12,956 INFO : valid_sample_misclass 0.54256\n",
|
||||
"2017-10-18 14:36:12,957 INFO : test_sample_misclass 0.39804\n",
|
||||
"2017-10-18 14:36:12,958 INFO : train_misclass 0.02083\n",
|
||||
"2017-10-18 14:36:12,960 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:12,961 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:12,963 INFO : runtime 3.38414\n",
|
||||
"2017-10-18 14:36:12,964 INFO : \n",
|
||||
"2017-10-18 14:36:14,862 INFO : Time only for training updates: 1.88s\n",
|
||||
"2017-10-18 14:36:16,237 INFO : Epoch 12\n",
|
||||
"2017-10-18 14:36:16,238 INFO : train_loss 0.20491\n",
|
||||
"2017-10-18 14:36:16,240 INFO : valid_loss 0.90257\n",
|
||||
"2017-10-18 14:36:16,241 INFO : test_loss 0.75600\n",
|
||||
"2017-10-18 14:36:16,242 INFO : train_sample_misclass 0.05041\n",
|
||||
"2017-10-18 14:36:16,244 INFO : valid_sample_misclass 0.54100\n",
|
||||
"2017-10-18 14:36:16,245 INFO : test_sample_misclass 0.39501\n",
|
||||
"2017-10-18 14:36:16,247 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:16,248 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:16,250 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:16,251 INFO : runtime 3.26028\n",
|
||||
"2017-10-18 14:36:16,253 INFO : \n",
|
||||
"2017-10-18 14:36:18,224 INFO : Time only for training updates: 1.95s\n",
|
||||
"2017-10-18 14:36:19,583 INFO : Epoch 13\n",
|
||||
"2017-10-18 14:36:19,584 INFO : train_loss 0.17244\n",
|
||||
"2017-10-18 14:36:19,585 INFO : valid_loss 0.90562\n",
|
||||
"2017-10-18 14:36:19,588 INFO : test_loss 0.76539\n",
|
||||
"2017-10-18 14:36:19,590 INFO : train_sample_misclass 0.03793\n",
|
||||
"2017-10-18 14:36:19,592 INFO : valid_sample_misclass 0.56194\n",
|
||||
"2017-10-18 14:36:19,594 INFO : test_sample_misclass 0.38752\n",
|
||||
"2017-10-18 14:36:19,595 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:19,596 INFO : valid_misclass 0.66667\n",
|
||||
"2017-10-18 14:36:19,600 INFO : test_misclass 0.36667\n",
|
||||
"2017-10-18 14:36:19,601 INFO : runtime 3.35971\n",
|
||||
"2017-10-18 14:36:19,602 INFO : \n",
|
||||
"2017-10-18 14:36:21,657 INFO : Time only for training updates: 2.04s\n",
|
||||
"2017-10-18 14:36:23,308 INFO : Epoch 14\n",
|
||||
"2017-10-18 14:36:23,310 INFO : train_loss 0.15207\n",
|
||||
"2017-10-18 14:36:23,311 INFO : valid_loss 0.90600\n",
|
||||
"2017-10-18 14:36:23,312 INFO : test_loss 0.76428\n",
|
||||
"2017-10-18 14:36:23,313 INFO : train_sample_misclass 0.02969\n",
|
||||
"2017-10-18 14:36:23,315 INFO : valid_sample_misclass 0.54924\n",
|
||||
"2017-10-18 14:36:23,316 INFO : test_sample_misclass 0.36551\n",
|
||||
"2017-10-18 14:36:23,317 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:23,318 INFO : valid_misclass 0.58333\n",
|
||||
"2017-10-18 14:36:23,319 INFO : test_misclass 0.36667\n",
|
||||
"2017-10-18 14:36:23,320 INFO : runtime 3.43644\n",
|
||||
"2017-10-18 14:36:23,322 INFO : \n",
|
||||
"2017-10-18 14:36:25,291 INFO : Time only for training updates: 1.95s\n",
|
||||
"2017-10-18 14:36:26,659 INFO : Epoch 15\n",
|
||||
"2017-10-18 14:36:26,660 INFO : train_loss 0.14998\n",
|
||||
"2017-10-18 14:36:26,662 INFO : valid_loss 0.96410\n",
|
||||
"2017-10-18 14:36:26,663 INFO : test_loss 0.81256\n",
|
||||
"2017-10-18 14:36:26,665 INFO : train_sample_misclass 0.04027\n",
|
||||
"2017-10-18 14:36:26,666 INFO : valid_sample_misclass 0.48061\n",
|
||||
"2017-10-18 14:36:26,668 INFO : test_sample_misclass 0.35811\n",
|
||||
"2017-10-18 14:36:26,669 INFO : train_misclass 0.02083\n",
|
||||
"2017-10-18 14:36:26,671 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:36:26,672 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:26,674 INFO : runtime 3.63001\n",
|
||||
"2017-10-18 14:36:26,675 INFO : \n",
|
||||
"2017-10-18 14:36:28,567 INFO : Time only for training updates: 1.88s\n",
|
||||
"2017-10-18 14:36:29,934 INFO : Epoch 16\n",
|
||||
"2017-10-18 14:36:29,935 INFO : train_loss 0.10915\n",
|
||||
"2017-10-18 14:36:29,936 INFO : valid_loss 0.91489\n",
|
||||
"2017-10-18 14:36:29,937 INFO : test_loss 0.80608\n",
|
||||
"2017-10-18 14:36:29,938 INFO : train_sample_misclass 0.01588\n",
|
||||
"2017-10-18 14:36:29,939 INFO : valid_sample_misclass 0.54234\n",
|
||||
"2017-10-18 14:36:29,940 INFO : test_sample_misclass 0.34537\n",
|
||||
"2017-10-18 14:36:29,941 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:29,942 INFO : valid_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:29,943 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:29,943 INFO : runtime 3.27961\n",
|
||||
"2017-10-18 14:36:29,944 INFO : \n",
|
||||
"2017-10-18 14:36:29,946 INFO : New best valid_misclass: 0.333333\n",
|
||||
"2017-10-18 14:36:29,947 INFO : \n",
|
||||
"2017-10-18 14:36:31,906 INFO : Time only for training updates: 1.95s\n",
|
||||
"2017-10-18 14:36:33,248 INFO : Epoch 17\n",
|
||||
"2017-10-18 14:36:33,249 INFO : train_loss 0.15003\n",
|
||||
"2017-10-18 14:36:33,250 INFO : valid_loss 0.91323\n",
|
||||
"2017-10-18 14:36:33,251 INFO : test_loss 0.81002\n",
|
||||
"2017-10-18 14:36:33,252 INFO : train_sample_misclass 0.05403\n",
|
||||
"2017-10-18 14:36:33,253 INFO : valid_sample_misclass 0.51337\n",
|
||||
"2017-10-18 14:36:33,254 INFO : test_sample_misclass 0.34403\n",
|
||||
"2017-10-18 14:36:33,255 INFO : train_misclass 0.04167\n",
|
||||
"2017-10-18 14:36:33,256 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:36:33,256 INFO : test_misclass 0.23333\n",
|
||||
"2017-10-18 14:36:33,257 INFO : runtime 3.33830\n",
|
||||
"2017-10-18 14:36:33,258 INFO : \n",
|
||||
"2017-10-18 14:36:35,205 INFO : Time only for training updates: 1.94s\n",
|
||||
"2017-10-18 14:36:36,576 INFO : Epoch 18\n",
|
||||
"2017-10-18 14:36:36,577 INFO : train_loss 0.09315\n",
|
||||
"2017-10-18 14:36:36,578 INFO : valid_loss 0.89064\n",
|
||||
"2017-10-18 14:36:36,580 INFO : test_loss 0.77360\n",
|
||||
"2017-10-18 14:36:36,581 INFO : train_sample_misclass 0.01025\n",
|
||||
"2017-10-18 14:36:36,583 INFO : valid_sample_misclass 0.49666\n",
|
||||
"2017-10-18 14:36:36,584 INFO : test_sample_misclass 0.30873\n",
|
||||
"2017-10-18 14:36:36,586 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:36,587 INFO : valid_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:36,588 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:36,589 INFO : runtime 3.30138\n",
|
||||
"2017-10-18 14:36:36,590 INFO : \n",
|
||||
"2017-10-18 14:36:36,593 INFO : New best valid_misclass: 0.333333\n",
|
||||
"2017-10-18 14:36:36,594 INFO : \n",
|
||||
"2017-10-18 14:36:38,535 INFO : Time only for training updates: 1.93s\n",
|
||||
"2017-10-18 14:36:39,895 INFO : Epoch 19\n",
|
||||
"2017-10-18 14:36:39,896 INFO : train_loss 0.09408\n",
|
||||
"2017-10-18 14:36:39,897 INFO : valid_loss 0.96349\n",
|
||||
"2017-10-18 14:36:39,898 INFO : test_loss 0.84831\n",
|
||||
"2017-10-18 14:36:39,899 INFO : train_sample_misclass 0.02078\n",
|
||||
"2017-10-18 14:36:39,900 INFO : valid_sample_misclass 0.49042\n",
|
||||
"2017-10-18 14:36:39,901 INFO : test_sample_misclass 0.33137\n",
|
||||
"2017-10-18 14:36:39,901 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:39,902 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:36:39,903 INFO : test_misclass 0.30000\n",
|
||||
"2017-10-18 14:36:39,904 INFO : runtime 3.32518\n",
|
||||
"2017-10-18 14:36:39,905 INFO : \n",
|
||||
"2017-10-18 14:36:41,863 INFO : Time only for training updates: 1.95s\n",
|
||||
"2017-10-18 14:36:43,294 INFO : Epoch 20\n",
|
||||
"2017-10-18 14:36:43,295 INFO : train_loss 0.06822\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-10-18 14:36:43,296 INFO : valid_loss 1.05598\n",
|
||||
"2017-10-18 14:36:43,297 INFO : test_loss 0.88760\n",
|
||||
"2017-10-18 14:36:43,298 INFO : train_sample_misclass 0.00847\n",
|
||||
"2017-10-18 14:36:43,300 INFO : valid_sample_misclass 0.51448\n",
|
||||
"2017-10-18 14:36:43,301 INFO : test_sample_misclass 0.33066\n",
|
||||
"2017-10-18 14:36:43,302 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:43,303 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:36:43,305 INFO : test_misclass 0.36667\n",
|
||||
"2017-10-18 14:36:43,306 INFO : runtime 3.33038\n",
|
||||
"2017-10-18 14:36:43,307 INFO : \n",
|
||||
"2017-10-18 14:36:43,308 INFO : Setup for second stop...\n",
|
||||
"2017-10-18 14:36:43,314 INFO : Train loss to reach 0.09315\n",
|
||||
"2017-10-18 14:36:43,315 INFO : Run until second stop...\n",
|
||||
"2017-10-18 14:36:44,773 INFO : Epoch 19\n",
|
||||
"2017-10-18 14:36:44,774 INFO : train_loss 0.25265\n",
|
||||
"2017-10-18 14:36:44,776 INFO : valid_loss 0.89064\n",
|
||||
"2017-10-18 14:36:44,777 INFO : test_loss 0.77360\n",
|
||||
"2017-10-18 14:36:44,778 INFO : train_sample_misclass 0.10753\n",
|
||||
"2017-10-18 14:36:44,779 INFO : valid_sample_misclass 0.49666\n",
|
||||
"2017-10-18 14:36:44,780 INFO : test_sample_misclass 0.30873\n",
|
||||
"2017-10-18 14:36:44,781 INFO : train_misclass 0.06667\n",
|
||||
"2017-10-18 14:36:44,782 INFO : valid_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:44,783 INFO : test_misclass 0.33333\n",
|
||||
"2017-10-18 14:36:44,784 INFO : runtime 1.45227\n",
|
||||
"2017-10-18 14:36:44,785 INFO : \n",
|
||||
"2017-10-18 14:36:47,428 INFO : Time only for training updates: 2.62s\n",
|
||||
"2017-10-18 14:36:49,022 INFO : Epoch 20\n",
|
||||
"2017-10-18 14:36:49,024 INFO : train_loss 0.21080\n",
|
||||
"2017-10-18 14:36:49,025 INFO : valid_loss 0.68103\n",
|
||||
"2017-10-18 14:36:49,026 INFO : test_loss 0.73509\n",
|
||||
"2017-10-18 14:36:49,028 INFO : train_sample_misclass 0.08324\n",
|
||||
"2017-10-18 14:36:49,028 INFO : valid_sample_misclass 0.34915\n",
|
||||
"2017-10-18 14:36:49,029 INFO : test_sample_misclass 0.31025\n",
|
||||
"2017-10-18 14:36:49,030 INFO : train_misclass 0.10000\n",
|
||||
"2017-10-18 14:36:49,031 INFO : valid_misclass 0.50000\n",
|
||||
"2017-10-18 14:36:49,032 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:36:49,033 INFO : runtime 4.11350\n",
|
||||
"2017-10-18 14:36:49,033 INFO : \n",
|
||||
"2017-10-18 14:36:51,467 INFO : Time only for training updates: 2.42s\n",
|
||||
"2017-10-18 14:36:53,069 INFO : Epoch 21\n",
|
||||
"2017-10-18 14:36:53,071 INFO : train_loss 0.18816\n",
|
||||
"2017-10-18 14:36:53,072 INFO : valid_loss 0.46174\n",
|
||||
"2017-10-18 14:36:53,073 INFO : test_loss 0.66965\n",
|
||||
"2017-10-18 14:36:53,074 INFO : train_sample_misclass 0.07406\n",
|
||||
"2017-10-18 14:36:53,075 INFO : valid_sample_misclass 0.24643\n",
|
||||
"2017-10-18 14:36:53,076 INFO : test_sample_misclass 0.28066\n",
|
||||
"2017-10-18 14:36:53,077 INFO : train_misclass 0.05000\n",
|
||||
"2017-10-18 14:36:53,077 INFO : valid_misclass 0.16667\n",
|
||||
"2017-10-18 14:36:53,078 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:36:53,079 INFO : runtime 4.03852\n",
|
||||
"2017-10-18 14:36:53,080 INFO : \n",
|
||||
"2017-10-18 14:36:55,529 INFO : Time only for training updates: 2.44s\n",
|
||||
"2017-10-18 14:36:57,074 INFO : Epoch 22\n",
|
||||
"2017-10-18 14:36:57,076 INFO : train_loss 0.13663\n",
|
||||
"2017-10-18 14:36:57,077 INFO : valid_loss 0.28806\n",
|
||||
"2017-10-18 14:36:57,078 INFO : test_loss 0.63336\n",
|
||||
"2017-10-18 14:36:57,079 INFO : train_sample_misclass 0.02567\n",
|
||||
"2017-10-18 14:36:57,080 INFO : valid_sample_misclass 0.06796\n",
|
||||
"2017-10-18 14:36:57,081 INFO : test_sample_misclass 0.25677\n",
|
||||
"2017-10-18 14:36:57,082 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:57,083 INFO : valid_misclass 0.00000\n",
|
||||
"2017-10-18 14:36:57,084 INFO : test_misclass 0.23333\n",
|
||||
"2017-10-18 14:36:57,085 INFO : runtime 4.06202\n",
|
||||
"2017-10-18 14:36:57,086 INFO : \n",
|
||||
"2017-10-18 14:36:59,625 INFO : Time only for training updates: 2.53s\n",
|
||||
"2017-10-18 14:37:01,160 INFO : Epoch 23\n",
|
||||
"2017-10-18 14:37:01,161 INFO : train_loss 0.18469\n",
|
||||
"2017-10-18 14:37:01,162 INFO : valid_loss 0.33505\n",
|
||||
"2017-10-18 14:37:01,163 INFO : test_loss 0.74569\n",
|
||||
"2017-10-18 14:37:01,164 INFO : train_sample_misclass 0.07469\n",
|
||||
"2017-10-18 14:37:01,165 INFO : valid_sample_misclass 0.15575\n",
|
||||
"2017-10-18 14:37:01,166 INFO : test_sample_misclass 0.29652\n",
|
||||
"2017-10-18 14:37:01,167 INFO : train_misclass 0.05000\n",
|
||||
"2017-10-18 14:37:01,168 INFO : valid_misclass 0.08333\n",
|
||||
"2017-10-18 14:37:01,168 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:37:01,169 INFO : runtime 4.09383\n",
|
||||
"2017-10-18 14:37:01,170 INFO : \n",
|
||||
"2017-10-18 14:37:03,695 INFO : Time only for training updates: 2.51s\n",
|
||||
"2017-10-18 14:37:05,297 INFO : Epoch 24\n",
|
||||
"2017-10-18 14:37:05,299 INFO : train_loss 0.12348\n",
|
||||
"2017-10-18 14:37:05,300 INFO : valid_loss 0.28360\n",
|
||||
"2017-10-18 14:37:05,301 INFO : test_loss 0.78044\n",
|
||||
"2017-10-18 14:37:05,302 INFO : train_sample_misclass 0.03632\n",
|
||||
"2017-10-18 14:37:05,303 INFO : valid_sample_misclass 0.10495\n",
|
||||
"2017-10-18 14:37:05,304 INFO : test_sample_misclass 0.28529\n",
|
||||
"2017-10-18 14:37:05,305 INFO : train_misclass 0.03333\n",
|
||||
"2017-10-18 14:37:05,306 INFO : valid_misclass 0.08333\n",
|
||||
"2017-10-18 14:37:05,307 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:37:05,308 INFO : runtime 4.06927\n",
|
||||
"2017-10-18 14:37:05,310 INFO : \n",
|
||||
"2017-10-18 14:37:07,772 INFO : Time only for training updates: 2.44s\n",
|
||||
"2017-10-18 14:37:09,352 INFO : Epoch 25\n",
|
||||
"2017-10-18 14:37:09,354 INFO : train_loss 0.14714\n",
|
||||
"2017-10-18 14:37:09,355 INFO : valid_loss 0.19252\n",
|
||||
"2017-10-18 14:37:09,356 INFO : test_loss 0.77621\n",
|
||||
"2017-10-18 14:37:09,357 INFO : train_sample_misclass 0.04635\n",
|
||||
"2017-10-18 14:37:09,359 INFO : valid_sample_misclass 0.05660\n",
|
||||
"2017-10-18 14:37:09,360 INFO : test_sample_misclass 0.26863\n",
|
||||
"2017-10-18 14:37:09,362 INFO : train_misclass 0.05000\n",
|
||||
"2017-10-18 14:37:09,363 INFO : valid_misclass 0.08333\n",
|
||||
"2017-10-18 14:37:09,364 INFO : test_misclass 0.23333\n",
|
||||
"2017-10-18 14:37:09,366 INFO : runtime 4.08017\n",
|
||||
"2017-10-18 14:37:09,367 INFO : \n",
|
||||
"2017-10-18 14:37:11,754 INFO : Time only for training updates: 2.37s\n",
|
||||
"2017-10-18 14:37:13,257 INFO : Epoch 26\n",
|
||||
"2017-10-18 14:37:13,259 INFO : train_loss 0.33872\n",
|
||||
"2017-10-18 14:37:13,260 INFO : valid_loss 0.33738\n",
|
||||
"2017-10-18 14:37:13,262 INFO : test_loss 0.75610\n",
|
||||
"2017-10-18 14:37:13,264 INFO : train_sample_misclass 0.16430\n",
|
||||
"2017-10-18 14:37:13,265 INFO : valid_sample_misclass 0.16689\n",
|
||||
"2017-10-18 14:37:13,266 INFO : test_sample_misclass 0.27763\n",
|
||||
"2017-10-18 14:37:13,267 INFO : train_misclass 0.11667\n",
|
||||
"2017-10-18 14:37:13,269 INFO : valid_misclass 0.16667\n",
|
||||
"2017-10-18 14:37:13,270 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:37:13,271 INFO : runtime 3.98591\n",
|
||||
"2017-10-18 14:37:13,272 INFO : \n",
|
||||
"2017-10-18 14:37:15,774 INFO : Time only for training updates: 2.48s\n",
|
||||
"2017-10-18 14:37:17,314 INFO : Epoch 27\n",
|
||||
"2017-10-18 14:37:17,315 INFO : train_loss 0.12509\n",
|
||||
"2017-10-18 14:37:17,316 INFO : valid_loss 0.14952\n",
|
||||
"2017-10-18 14:37:17,317 INFO : test_loss 0.60954\n",
|
||||
"2017-10-18 14:37:17,318 INFO : train_sample_misclass 0.03111\n",
|
||||
"2017-10-18 14:37:17,318 INFO : valid_sample_misclass 0.02718\n",
|
||||
"2017-10-18 14:37:17,319 INFO : test_sample_misclass 0.23351\n",
|
||||
"2017-10-18 14:37:17,320 INFO : train_misclass 0.01667\n",
|
||||
"2017-10-18 14:37:17,321 INFO : valid_misclass 0.00000\n",
|
||||
"2017-10-18 14:37:17,321 INFO : test_misclass 0.16667\n",
|
||||
"2017-10-18 14:37:17,322 INFO : runtime 4.01630\n",
|
||||
"2017-10-18 14:37:17,323 INFO : \n",
|
||||
"2017-10-18 14:37:19,768 INFO : Time only for training updates: 2.43s\n",
|
||||
"2017-10-18 14:37:21,331 INFO : Epoch 28\n",
|
||||
"2017-10-18 14:37:21,333 INFO : train_loss 0.09141\n",
|
||||
"2017-10-18 14:37:21,334 INFO : valid_loss 0.15032\n",
|
||||
"2017-10-18 14:37:21,335 INFO : test_loss 0.64890\n",
|
||||
"2017-10-18 14:37:21,336 INFO : train_sample_misclass 0.01239\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"2017-10-18 14:37:21,337 INFO : valid_sample_misclass 0.03387\n",
|
||||
"2017-10-18 14:37:21,338 INFO : test_sample_misclass 0.25856\n",
|
||||
"2017-10-18 14:37:21,339 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:37:21,339 INFO : valid_misclass 0.00000\n",
|
||||
"2017-10-18 14:37:21,340 INFO : test_misclass 0.26667\n",
|
||||
"2017-10-18 14:37:21,341 INFO : runtime 3.99343\n",
|
||||
"2017-10-18 14:37:21,342 INFO : \n",
|
||||
"2017-10-18 14:37:23,868 INFO : Time only for training updates: 2.51s\n",
|
||||
"2017-10-18 14:37:25,405 INFO : Epoch 29\n",
|
||||
"2017-10-18 14:37:25,406 INFO : train_loss 0.12664\n",
|
||||
"2017-10-18 14:37:25,407 INFO : valid_loss 0.21718\n",
|
||||
"2017-10-18 14:37:25,408 INFO : test_loss 0.74796\n",
|
||||
"2017-10-18 14:37:25,409 INFO : train_sample_misclass 0.04274\n",
|
||||
"2017-10-18 14:37:25,410 INFO : valid_sample_misclass 0.08623\n",
|
||||
"2017-10-18 14:37:25,411 INFO : test_sample_misclass 0.26381\n",
|
||||
"2017-10-18 14:37:25,411 INFO : train_misclass 0.03333\n",
|
||||
"2017-10-18 14:37:25,412 INFO : valid_misclass 0.08333\n",
|
||||
"2017-10-18 14:37:25,413 INFO : test_misclass 0.23333\n",
|
||||
"2017-10-18 14:37:25,414 INFO : runtime 4.09971\n",
|
||||
"2017-10-18 14:37:25,415 INFO : \n",
|
||||
"2017-10-18 14:37:27,883 INFO : Time only for training updates: 2.46s\n",
|
||||
"2017-10-18 14:37:29,539 INFO : Epoch 30\n",
|
||||
"2017-10-18 14:37:29,541 INFO : train_loss 0.07408\n",
|
||||
"2017-10-18 14:37:29,542 INFO : valid_loss 0.15273\n",
|
||||
"2017-10-18 14:37:29,543 INFO : test_loss 0.67137\n",
|
||||
"2017-10-18 14:37:29,544 INFO : train_sample_misclass 0.01497\n",
|
||||
"2017-10-18 14:37:29,546 INFO : valid_sample_misclass 0.06061\n",
|
||||
"2017-10-18 14:37:29,547 INFO : test_sample_misclass 0.23984\n",
|
||||
"2017-10-18 14:37:29,548 INFO : train_misclass 0.01667\n",
|
||||
"2017-10-18 14:37:29,549 INFO : valid_misclass 0.08333\n",
|
||||
"2017-10-18 14:37:29,550 INFO : test_misclass 0.20000\n",
|
||||
"2017-10-18 14:37:29,551 INFO : runtime 4.01253\n",
|
||||
"2017-10-18 14:37:29,552 INFO : \n",
|
||||
"2017-10-18 14:37:32,114 INFO : Time only for training updates: 2.55s\n",
|
||||
"2017-10-18 14:37:33,748 INFO : Epoch 31\n",
|
||||
"2017-10-18 14:37:33,750 INFO : train_loss 0.05036\n",
|
||||
"2017-10-18 14:37:33,751 INFO : valid_loss 0.09104\n",
|
||||
"2017-10-18 14:37:33,752 INFO : test_loss 0.66771\n",
|
||||
"2017-10-18 14:37:33,753 INFO : train_sample_misclass 0.00744\n",
|
||||
"2017-10-18 14:37:33,755 INFO : valid_sample_misclass 0.02317\n",
|
||||
"2017-10-18 14:37:33,756 INFO : test_sample_misclass 0.20677\n",
|
||||
"2017-10-18 14:37:33,757 INFO : train_misclass 0.00000\n",
|
||||
"2017-10-18 14:37:33,758 INFO : valid_misclass 0.00000\n",
|
||||
"2017-10-18 14:37:33,759 INFO : test_misclass 0.20000\n",
|
||||
"2017-10-18 14:37:33,760 INFO : runtime 4.23093\n",
|
||||
"2017-10-18 14:37:33,761 INFO : \n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# need to setup python logging before to be able to see anything\n",
|
||||
"import logging\n",
|
||||
"import sys\n",
|
||||
"logging.basicConfig(format='%(asctime)s %(levelname)s : %(message)s',\n",
|
||||
" level=logging.DEBUG, stream=sys.stdout)\n",
|
||||
"exp.run()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this case, we arrive at 80.0% accuracy, the training stops after the validation loss decreases below the training loss at the best epoch of 0.09315."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dataset References\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" This dataset was created and contributed to PhysioNet by the developers of the [BCI2000](http://www.schalklab.org/research/bci2000) instrumentation system, which they used in making these recordings. The system is described in:\n",
|
||||
" \n",
|
||||
" Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE TBME 51(6):1034-1043.\n",
|
||||
"\n",
|
||||
"[PhysioBank](https://physionet.org/physiobank/) is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community and further described in:\n",
|
||||
"\n",
|
||||
" Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220."
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.6.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,567 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"nbsphinx": "hidden"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%load_ext autoreload\n",
|
||||
"%autoreload 2\n",
|
||||
"import os\n",
|
||||
"os.sys.path.insert(0, '/home/schirrmr/braindecode/code/braindecode/')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Trialwise Decoding"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"In this example, we will use a convolutional neural network on the [Physiobank EEG Motor Movement/Imagery Dataset](https://www.physionet.org/physiobank/database/eegmmidb/) to decode two classes:\n",
|
||||
"\n",
|
||||
"1. Executed and imagined opening and closing of both hands\n",
|
||||
"2. Executed and imagined opening and closing of both feet\n",
|
||||
"\n",
|
||||
"<div class=\"alert alert-warning\">\n",
|
||||
"\n",
|
||||
"We use only one subject (with 90 trials) in this tutorial for demonstration purposes. A more interesting decoding task with many more trials would be to do cross-subject decoding on the same dataset.\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can load and preprocess your EEG dataset in any way, Braindecode only expects a 3darray (trials, channels, timesteps) of input signals `X` and a vector of labels `y` later (see below). In this tutorial, we will use the [MNE](https://www.martinos.org/mne/stable/index.html) library to load an EEG motor imagery/motor execution dataset. For a tutorial from MNE using Common Spatial Patterns to decode this data, see [here](http://martinos.org/mne/stable/auto_examples/decoding/plot_decoding_csp_eeg.html). For another library useful for loading EEG data, take a look at [Neo IO](https://pythonhosted.org/neo/io.html)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Removing orphaned offset at the beginning of the file.\n",
|
||||
"179 events found\n",
|
||||
"Events id: [1 2 3]\n",
|
||||
"90 matching events found\n",
|
||||
"Loading data for 90 events and 497 original time points ...\n",
|
||||
"0 bad epochs dropped\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import mne\n",
|
||||
"from mne.io import concatenate_raws\n",
|
||||
"\n",
|
||||
"# 5,6,7,10,13,14 are codes for executed and imagined hands/feet\n",
|
||||
"subject_id = 1\n",
|
||||
"event_codes = [5,6,9,10,13,14]\n",
|
||||
"\n",
|
||||
"# This will download the files if you don't have them yet,\n",
|
||||
"# and then return the paths to the files.\n",
|
||||
"physionet_paths = mne.datasets.eegbci.load_data(subject_id, event_codes)\n",
|
||||
"\n",
|
||||
"# Load each of the files\n",
|
||||
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto', verbose='WARNING')\n",
|
||||
" for path in physionet_paths]\n",
|
||||
"\n",
|
||||
"# Concatenate them\n",
|
||||
"raw = concatenate_raws(parts)\n",
|
||||
"\n",
|
||||
"# Find the events in this dataset\n",
|
||||
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
|
||||
"\n",
|
||||
"# Use only EEG channels\n",
|
||||
"eeg_channel_inds = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
|
||||
" exclude='bads')\n",
|
||||
"\n",
|
||||
"# Extract trials, only using EEG channels\n",
|
||||
"epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=eeg_channel_inds,\n",
|
||||
" baseline=None, preload=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Convert data to Braindecode Format"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Braindecode has a minimalistic ```SignalAndTarget``` class, with attributes `X` for the signal and `y` for the labels. `X` should have these dimensions: trials x channels x timesteps. `y` should have one label per trial."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"# Convert data from volt to millivolt\n",
|
||||
"# Pytorch expects float32 for input and int64 for labels.\n",
|
||||
"X = (epoched.get_data() * 1e6).astype(np.float32)\n",
|
||||
"y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We use the first 60 trials for training and the last 30 for testing."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.datautil.signal_target import SignalAndTarget\n",
|
||||
"\n",
|
||||
"train_set = SignalAndTarget(X[:60], y=y[:60])\n",
|
||||
"test_set = SignalAndTarget(X[60:], y=y[60:])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create the model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Braindecode comes with some predefined convolutional neural network architectures for raw time-domain EEG. Here, we use the shallow ConvNet model from [Deep learning with convolutional neural networks for EEG decoding and visualization](https://arxiv.org/abs/1703.05051)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from braindecode.models.shallow_fbcsp import ShallowFBCSPNet\n",
|
||||
"from torch import nn\n",
|
||||
"from braindecode.torch_ext.util import set_random_seeds\n",
|
||||
"\n",
|
||||
"# Set if you want to use GPU\n",
|
||||
"# You can also use torch.cuda.is_available() to determine if cuda is available on your machine.\n",
|
||||
"cuda = False\n",
|
||||
"set_random_seeds(seed=20170629, cuda=cuda)\n",
|
||||
"n_classes = 2\n",
|
||||
"in_chans = train_set.X.shape[1]\n",
|
||||
"# final_conv_length = auto ensures we only get a single output in the time dimension\n",
|
||||
"model = ShallowFBCSPNet(in_chans=in_chans, n_classes=n_classes,\n",
|
||||
" input_time_length=train_set.X.shape[2],\n",
|
||||
" final_conv_length='auto').create_network()\n",
|
||||
"if cuda:\n",
|
||||
" model.cuda()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We use [Adam](https://arxiv.org/abs/1412.6980) to optimize the parameters of our network."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torch import optim\n",
|
||||
"\n",
|
||||
"optimizer = optim.Adam(model.parameters())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Training loop"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is a conventional mini-batch stochastic gradient descent training loop:\n",
|
||||
"\n",
|
||||
"1. Get randomly shuffled batches of trials\n",
|
||||
"2. Compute outputs, loss and gradients on the batches of trials\n",
|
||||
"3. Update your model\n",
|
||||
"4. After iterating through all batches of your dataset, report some statistics like mean accuracy and mean loss."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Epoch 0\n",
|
||||
"Train Loss: 1.17760\n",
|
||||
"Train Accuracy: 51.7%\n",
|
||||
"Test Loss: 1.26024\n",
|
||||
"Test Accuracy: 53.3%\n",
|
||||
"Epoch 1\n",
|
||||
"Train Loss: 0.70688\n",
|
||||
"Train Accuracy: 63.3%\n",
|
||||
"Test Loss: 0.93679\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 2\n",
|
||||
"Train Loss: 0.39426\n",
|
||||
"Train Accuracy: 86.7%\n",
|
||||
"Test Loss: 0.65984\n",
|
||||
"Test Accuracy: 66.7%\n",
|
||||
"Epoch 3\n",
|
||||
"Train Loss: 0.33593\n",
|
||||
"Train Accuracy: 90.0%\n",
|
||||
"Test Loss: 0.65626\n",
|
||||
"Test Accuracy: 63.3%\n",
|
||||
"Epoch 4\n",
|
||||
"Train Loss: 0.27905\n",
|
||||
"Train Accuracy: 96.7%\n",
|
||||
"Test Loss: 0.61044\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 5\n",
|
||||
"Train Loss: 0.27319\n",
|
||||
"Train Accuracy: 96.7%\n",
|
||||
"Test Loss: 0.59499\n",
|
||||
"Test Accuracy: 66.7%\n",
|
||||
"Epoch 6\n",
|
||||
"Train Loss: 0.26624\n",
|
||||
"Train Accuracy: 88.3%\n",
|
||||
"Test Loss: 0.61360\n",
|
||||
"Test Accuracy: 70.0%\n",
|
||||
"Epoch 7\n",
|
||||
"Train Loss: 0.23338\n",
|
||||
"Train Accuracy: 91.7%\n",
|
||||
"Test Loss: 0.65626\n",
|
||||
"Test Accuracy: 73.3%\n",
|
||||
"Epoch 8\n",
|
||||
"Train Loss: 0.21903\n",
|
||||
"Train Accuracy: 90.0%\n",
|
||||
"Test Loss: 0.68762\n",
|
||||
"Test Accuracy: 63.3%\n",
|
||||
"Epoch 9\n",
|
||||
"Train Loss: 0.18902\n",
|
||||
"Train Accuracy: 96.7%\n",
|
||||
"Test Loss: 0.67174\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 10\n",
|
||||
"Train Loss: 0.17774\n",
|
||||
"Train Accuracy: 96.7%\n",
|
||||
"Test Loss: 0.68129\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 11\n",
|
||||
"Train Loss: 0.16396\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.69190\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 12\n",
|
||||
"Train Loss: 0.15179\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.69641\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 13\n",
|
||||
"Train Loss: 0.15035\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.70514\n",
|
||||
"Test Accuracy: 60.0%\n",
|
||||
"Epoch 14\n",
|
||||
"Train Loss: 0.14255\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.74239\n",
|
||||
"Test Accuracy: 53.3%\n",
|
||||
"Epoch 15\n",
|
||||
"Train Loss: 0.13597\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.77918\n",
|
||||
"Test Accuracy: 53.3%\n",
|
||||
"Epoch 16\n",
|
||||
"Train Loss: 0.13680\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.84159\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 17\n",
|
||||
"Train Loss: 0.12191\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.87802\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 18\n",
|
||||
"Train Loss: 0.10604\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.90945\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 19\n",
|
||||
"Train Loss: 0.10445\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.93717\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 20\n",
|
||||
"Train Loss: 0.10497\n",
|
||||
"Train Accuracy: 98.3%\n",
|
||||
"Test Loss: 0.95502\n",
|
||||
"Test Accuracy: 60.0%\n",
|
||||
"Epoch 21\n",
|
||||
"Train Loss: 0.10946\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.99562\n",
|
||||
"Test Accuracy: 60.0%\n",
|
||||
"Epoch 22\n",
|
||||
"Train Loss: 0.05721\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.84824\n",
|
||||
"Test Accuracy: 60.0%\n",
|
||||
"Epoch 23\n",
|
||||
"Train Loss: 0.04464\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.78784\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 24\n",
|
||||
"Train Loss: 0.04181\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.78525\n",
|
||||
"Test Accuracy: 56.7%\n",
|
||||
"Epoch 25\n",
|
||||
"Train Loss: 0.03730\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.75642\n",
|
||||
"Test Accuracy: 63.3%\n",
|
||||
"Epoch 26\n",
|
||||
"Train Loss: 0.03549\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.71706\n",
|
||||
"Test Accuracy: 66.7%\n",
|
||||
"Epoch 27\n",
|
||||
"Train Loss: 0.03415\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.69795\n",
|
||||
"Test Accuracy: 66.7%\n",
|
||||
"Epoch 28\n",
|
||||
"Train Loss: 0.03205\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.69323\n",
|
||||
"Test Accuracy: 66.7%\n",
|
||||
"Epoch 29\n",
|
||||
"Train Loss: 0.02805\n",
|
||||
"Train Accuracy: 100.0%\n",
|
||||
"Test Loss: 0.69241\n",
|
||||
"Test Accuracy: 63.3%\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from braindecode.torch_ext.util import np_to_var, var_to_np\n",
|
||||
"from braindecode.datautil.iterators import get_balanced_batches\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"from numpy.random import RandomState\n",
|
||||
"rng = RandomState((2017,6,30))\n",
|
||||
"for i_epoch in range(30):\n",
|
||||
" i_trials_in_batch = get_balanced_batches(len(train_set.X), rng, shuffle=True,\n",
|
||||
" batch_size=30)\n",
|
||||
" # Set model to training mode\n",
|
||||
" model.train()\n",
|
||||
" for i_trials in i_trials_in_batch:\n",
|
||||
" # Have to add empty fourth dimension to X\n",
|
||||
" batch_X = train_set.X[i_trials][:,:,:,None]\n",
|
||||
" batch_y = train_set.y[i_trials]\n",
|
||||
" net_in = np_to_var(batch_X)\n",
|
||||
" if cuda:\n",
|
||||
" net_in = net_in.cuda()\n",
|
||||
" net_target = np_to_var(batch_y)\n",
|
||||
" if cuda:\n",
|
||||
" net_target = net_target.cuda()\n",
|
||||
" # Remove gradients of last backward pass from all parameters \n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" # Compute outputs of the network\n",
|
||||
" outputs = model(net_in)\n",
|
||||
" # Compute the loss\n",
|
||||
" loss = F.nll_loss(outputs, net_target)\n",
|
||||
" # Do the backpropagation\n",
|
||||
" loss.backward()\n",
|
||||
" # Update parameters with the optimizer\n",
|
||||
" optimizer.step()\n",
|
||||
" \n",
|
||||
" # Print some statistics each epoch\n",
|
||||
" model.eval()\n",
|
||||
" print(\"Epoch {:d}\".format(i_epoch))\n",
|
||||
" for setname, dataset in (('Train', train_set), ('Test', test_set)):\n",
|
||||
" # Here, we will use the entire dataset at once, which is still possible\n",
|
||||
" # for such smaller datasets. Otherwise we would have to use batches.\n",
|
||||
" net_in = np_to_var(dataset.X[:,:,:,None])\n",
|
||||
" if cuda:\n",
|
||||
" net_in = net_in.cuda()\n",
|
||||
" net_target = np_to_var(dataset.y)\n",
|
||||
" if cuda:\n",
|
||||
" net_target = net_target.cuda()\n",
|
||||
" outputs = model(net_in)\n",
|
||||
" loss = F.nll_loss(outputs, net_target)\n",
|
||||
" print(\"{:6s} Loss: {:.5f}\".format(\n",
|
||||
" setname, float(var_to_np(loss))))\n",
|
||||
" predicted_labels = np.argmax(var_to_np(outputs), axis=1)\n",
|
||||
" accuracy = np.mean(dataset.y == predicted_labels)\n",
|
||||
" print(\"{:6s} Accuracy: {:.1f}%\".format(\n",
|
||||
" setname, accuracy * 100))\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Eventually, we arrive at 63.3% accuracy, so 19 from 30 trials are correctly predicted. In the [Cropped Decoding Tutorial](./Cropped_Decoding.html), we can learn how to achieve higher accuracies using cropped training."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<div class=\"alert alert-info\">\n",
|
||||
"\n",
|
||||
"If you want to try cross-subject decoding, changing the loading code to the following will perform cross-subject decoding on imagined left vs right hand closing, with 50 training and 5 test subjects (Warning, might be very slow):\n",
|
||||
"\n",
|
||||
"</div>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import mne\n",
|
||||
"import numpy as np\n",
|
||||
"from mne.io import concatenate_raws\n",
|
||||
"from braindecode.datautil.signal_target import SignalAndTarget\n",
|
||||
"\n",
|
||||
"physionet_paths = [ mne.datasets.eegbci.load_data(sub_id,[4,8,12,]) for sub_id in range(1,51)]\n",
|
||||
"physionet_paths = np.concatenate(physionet_paths)\n",
|
||||
"parts = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto')\n",
|
||||
" for path in physionet_paths] \n",
|
||||
"\n",
|
||||
"raw = concatenate_raws(parts)\n",
|
||||
"\n",
|
||||
"picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,\n",
|
||||
" exclude='bads')\n",
|
||||
"\n",
|
||||
"events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')\n",
|
||||
"\n",
|
||||
"# Read epochs (train will be done only between 1 and 2s)\n",
|
||||
"# Testing will be done with a running classifier\n",
|
||||
"epoched = mne.Epochs(raw, events, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=picks,\n",
|
||||
" baseline=None, preload=True)\n",
|
||||
"\n",
|
||||
"physionet_paths_test = [mne.datasets.eegbci.load_data(sub_id,[4,8,12,]) for sub_id in range(51,56)]\n",
|
||||
"physionet_paths_test = np.concatenate(physionet_paths_test)\n",
|
||||
"parts_test = [mne.io.read_raw_edf(path, preload=True,stim_channel='auto')\n",
|
||||
" for path in physionet_paths_test]\n",
|
||||
"raw_test = concatenate_raws(parts_test)\n",
|
||||
"\n",
|
||||
"picks_test = mne.pick_types(raw_test.info, meg=False, eeg=True, stim=False, eog=False,\n",
|
||||
" exclude='bads')\n",
|
||||
"\n",
|
||||
"events_test = mne.find_events(raw_test, shortest_event=0, stim_channel='STI 014')\n",
|
||||
"\n",
|
||||
"# Read epochs (train will be done only between 1 and 2s)\n",
|
||||
"# Testing will be done with a running classifier\n",
|
||||
"epoched_test = mne.Epochs(raw_test, events_test, dict(hands=2, feet=3), tmin=1, tmax=4.1, proj=False, picks=picks_test,\n",
|
||||
" baseline=None, preload=True)\n",
|
||||
"\n",
|
||||
"train_X = (epoched.get_data() * 1e6).astype(np.float32)\n",
|
||||
"train_y = (epoched.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1\n",
|
||||
"test_X = (epoched_test.get_data() * 1e6).astype(np.float32)\n",
|
||||
"test_y = (epoched_test.events[:,2] - 2).astype(np.int64) #2,3 -> 0,1\n",
|
||||
"train_set = SignalAndTarget(train_X, y=train_y)\n",
|
||||
"test_set = SignalAndTarget(test_X, y=test_y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Dataset References\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
" This dataset was created and contributed to PhysioNet by the developers of the [BCI2000](http://www.schalklab.org/research/bci2000) instrumentation system, which they used in making these recordings. The system is described in:\n",
|
||||
" \n",
|
||||
" Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R. (2004) BCI2000: A General-Purpose Brain-Computer Interface (BCI) System. IEEE TBME 51(6):1034-1043.\n",
|
||||
"\n",
|
||||
"[PhysioBank](https://physionet.org/physiobank/) is a large and growing archive of well-characterized digital recordings of physiologic signals and related data for use by the biomedical research community and further described in:\n",
|
||||
"\n",
|
||||
" Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
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|
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|
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|
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},
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"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
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||||
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|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.contentstable p.biglink {
|
||||
line-height: 150%;
|
||||
}
|
||||
|
||||
a.biglink {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
span.linkdescr {
|
||||
font-style: italic;
|
||||
padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
table.indextable td {
|
||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
table.indextable ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
|
||||
|
||||
table.indextable > tbody > tr > td > ul {
|
||||
padding-left: 0em;
|
||||
}
|
||||
|
||||
table.indextable tr.pcap {
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
table.indextable tr.cap {
|
||||
margin-top: 10px;
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
|
||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
/* -- domain module index --------------------------------------------------- */
|
||||
|
||||
table.modindextable td {
|
||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px 7px 0 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px 7px 0 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.admonition dl {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd p {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dt:target, .highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
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|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
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|
||||
|
||||
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|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
padding: 5px 0px;
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
margin-left: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
padding: 0 0.5em 0 0.5em;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
div.code-block-caption + div > div.highlight > pre {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
padding: 1em 1em 0;
|
||||
}
|
||||
|
||||
div.literal-block-wrapper div.highlight {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: relative;
|
||||
left: 0px;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
Arquivo binário não exibido.
|
Antes Largura: | Altura: | Tamanho: 756 B |
Arquivo binário não exibido.
|
Antes Largura: | Altura: | Tamanho: 829 B |
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|
Antes Largura: | Altura: | Tamanho: 641 B |
@@ -1,2 +0,0 @@
|
||||
.fa:before{-webkit-font-smoothing:antialiased}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;content:""}.clearfix:after{clear:both}@font-face{font-family:FontAwesome;font-weight:normal;font-style:normal;src:url("../font/fontawesome_webfont.eot");src:url("../font/fontawesome_webfont.eot?#iefix") format("embedded-opentype"),url("../font/fontawesome_webfont.woff") format("woff"),url("../font/fontawesome_webfont.ttf") format("truetype"),url("../font/fontawesome_webfont.svg#FontAwesome") format("svg")}.fa:before{display:inline-block;font-family:FontAwesome;font-style:normal;font-weight:normal;line-height:1;text-decoration:inherit}a .fa{display:inline-block;text-decoration:inherit}li .fa{display:inline-block}li .fa-large:before,li .fa-large:before{width:1.875em}ul.fas{list-style-type:none;margin-left:2em;text-indent:-0.8em}ul.fas li .fa{width:0.8em}ul.fas li .fa-large:before,ul.fas li .fa-large:before{vertical-align:baseline}.fa-book:before{content:""}.icon-book:before{content:""}.fa-caret-down:before{content:""}.icon-caret-down:before{content:""}.fa-caret-up:before{content:""}.icon-caret-up:before{content:""}.fa-caret-left:before{content:""}.icon-caret-left:before{content:""}.fa-caret-right:before{content:""}.icon-caret-right:before{content:""}.rst-versions{position:fixed;bottom:0;left:0;width:300px;color:#fcfcfc;background:#1f1d1d;border-top:solid 10px #343131;font-family:"Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;z-index:400}.rst-versions a{color:#2980B9;text-decoration:none}.rst-versions .rst-badge-small{display:none}.rst-versions .rst-current-version{padding:12px;background-color:#272525;display:block;text-align:right;font-size:90%;cursor:pointer;color:#27AE60;*zoom:1}.rst-versions .rst-current-version:before,.rst-versions .rst-current-version:after{display:table;content:""}.rst-versions .rst-current-version:after{clear:both}.rst-versions .rst-current-version .fa{color:#fcfcfc}.rst-versions .rst-current-version .fa-book{float:left}.rst-versions .rst-current-version .icon-book{float:left}.rst-versions .rst-current-version.rst-out-of-date{background-color:#E74C3C;color:#fff}.rst-versions .rst-current-version.rst-active-old-version{background-color:#F1C40F;color:#000}.rst-versions.shift-up .rst-other-versions{display:block}.rst-versions .rst-other-versions{font-size:90%;padding:12px;color:gray;display:none}.rst-versions .rst-other-versions hr{display:block;height:1px;border:0;margin:20px 0;padding:0;border-top:solid 1px #413d3d}.rst-versions .rst-other-versions dd{display:inline-block;margin:0}.rst-versions .rst-other-versions dd a{display:inline-block;padding:6px;color:#fcfcfc}.rst-versions.rst-badge{width:auto;bottom:20px;right:20px;left:auto;border:none;max-width:300px}.rst-versions.rst-badge .icon-book{float:none}.rst-versions.rst-badge .fa-book{float:none}.rst-versions.rst-badge.shift-up .rst-current-version{text-align:right}.rst-versions.rst-badge.shift-up .rst-current-version .fa-book{float:left}.rst-versions.rst-badge.shift-up .rst-current-version .icon-book{float:left}.rst-versions.rst-badge .rst-current-version{width:auto;height:30px;line-height:30px;padding:0 6px;display:block;text-align:center}@media screen and (max-width: 768px){.rst-versions{width:85%;display:none}.rst-versions.shift{display:block}}
|
||||
/*# sourceMappingURL=badge_only.css.map */
|
||||
Diff do arquivo suprimido porque uma ou mais linhas são muito longas
@@ -1,287 +0,0 @@
|
||||
/*
|
||||
* doctools.js
|
||||
* ~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/**
|
||||
* select a different prefix for underscore
|
||||
*/
|
||||
$u = _.noConflict();
|
||||
|
||||
/**
|
||||
* make the code below compatible with browsers without
|
||||
* an installed firebug like debugger
|
||||
if (!window.console || !console.firebug) {
|
||||
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
|
||||
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
|
||||
"profile", "profileEnd"];
|
||||
window.console = {};
|
||||
for (var i = 0; i < names.length; ++i)
|
||||
window.console[names[i]] = function() {};
|
||||
}
|
||||
*/
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*/
|
||||
jQuery.urldecode = function(x) {
|
||||
return decodeURIComponent(x).replace(/\+/g, ' ');
|
||||
};
|
||||
|
||||
/**
|
||||
* small helper function to urlencode strings
|
||||
*/
|
||||
jQuery.urlencode = encodeURIComponent;
|
||||
|
||||
/**
|
||||
* This function returns the parsed url parameters of the
|
||||
* current request. Multiple values per key are supported,
|
||||
* it will always return arrays of strings for the value parts.
|
||||
*/
|
||||
jQuery.getQueryParameters = function(s) {
|
||||
if (typeof s == 'undefined')
|
||||
s = document.location.search;
|
||||
var parts = s.substr(s.indexOf('?') + 1).split('&');
|
||||
var result = {};
|
||||
for (var i = 0; i < parts.length; i++) {
|
||||
var tmp = parts[i].split('=', 2);
|
||||
var key = jQuery.urldecode(tmp[0]);
|
||||
var value = jQuery.urldecode(tmp[1]);
|
||||
if (key in result)
|
||||
result[key].push(value);
|
||||
else
|
||||
result[key] = [value];
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/**
|
||||
* highlight a given string on a jquery object by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
jQuery.fn.highlightText = function(text, className) {
|
||||
function highlight(node) {
|
||||
if (node.nodeType == 3) {
|
||||
var val = node.nodeValue;
|
||||
var pos = val.toLowerCase().indexOf(text);
|
||||
if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) {
|
||||
var span = document.createElement("span");
|
||||
span.className = className;
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
}
|
||||
}
|
||||
else if (!jQuery(node).is("button, select, textarea")) {
|
||||
jQuery.each(node.childNodes, function() {
|
||||
highlight(this);
|
||||
});
|
||||
}
|
||||
}
|
||||
return this.each(function() {
|
||||
highlight(this);
|
||||
});
|
||||
};
|
||||
|
||||
/*
|
||||
* backward compatibility for jQuery.browser
|
||||
* This will be supported until firefox bug is fixed.
|
||||
*/
|
||||
if (!jQuery.browser) {
|
||||
jQuery.uaMatch = function(ua) {
|
||||
ua = ua.toLowerCase();
|
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(msie) ([\w.]+)/.exec(ua) ||
|
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
version: match[ 2 ] || "0"
|
||||
};
|
||||
};
|
||||
jQuery.browser = {};
|
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
var Documentation = {
|
||||
|
||||
init : function() {
|
||||
this.fixFirefoxAnchorBug();
|
||||
this.highlightSearchWords();
|
||||
this.initIndexTable();
|
||||
|
||||
},
|
||||
|
||||
/**
|
||||
* i18n support
|
||||
*/
|
||||
TRANSLATIONS : {},
|
||||
PLURAL_EXPR : function(n) { return n == 1 ? 0 : 1; },
|
||||
LOCALE : 'unknown',
|
||||
|
||||
// gettext and ngettext don't access this so that the functions
|
||||
// can safely bound to a different name (_ = Documentation.gettext)
|
||||
gettext : function(string) {
|
||||
var translated = Documentation.TRANSLATIONS[string];
|
||||
if (typeof translated == 'undefined')
|
||||
return string;
|
||||
return (typeof translated == 'string') ? translated : translated[0];
|
||||
},
|
||||
|
||||
ngettext : function(singular, plural, n) {
|
||||
var translated = Documentation.TRANSLATIONS[singular];
|
||||
if (typeof translated == 'undefined')
|
||||
return (n == 1) ? singular : plural;
|
||||
return translated[Documentation.PLURALEXPR(n)];
|
||||
},
|
||||
|
||||
addTranslations : function(catalog) {
|
||||
for (var key in catalog.messages)
|
||||
this.TRANSLATIONS[key] = catalog.messages[key];
|
||||
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
|
||||
this.LOCALE = catalog.locale;
|
||||
},
|
||||
|
||||
/**
|
||||
* add context elements like header anchor links
|
||||
*/
|
||||
addContextElements : function() {
|
||||
$('div[id] > :header:first').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this headline')).
|
||||
appendTo(this);
|
||||
});
|
||||
$('dt[id]').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this definition')).
|
||||
appendTo(this);
|
||||
});
|
||||
},
|
||||
|
||||
/**
|
||||
* workaround a firefox stupidity
|
||||
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
|
||||
*/
|
||||
fixFirefoxAnchorBug : function() {
|
||||
if (document.location.hash)
|
||||
window.setTimeout(function() {
|
||||
document.location.href += '';
|
||||
}, 10);
|
||||
},
|
||||
|
||||
/**
|
||||
* highlight the search words provided in the url in the text
|
||||
*/
|
||||
highlightSearchWords : function() {
|
||||
var params = $.getQueryParameters();
|
||||
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
|
||||
if (terms.length) {
|
||||
var body = $('div.body');
|
||||
if (!body.length) {
|
||||
body = $('body');
|
||||
}
|
||||
window.setTimeout(function() {
|
||||
$.each(terms, function() {
|
||||
body.highlightText(this.toLowerCase(), 'highlighted');
|
||||
});
|
||||
}, 10);
|
||||
$('<p class="highlight-link"><a href="javascript:Documentation.' +
|
||||
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
|
||||
.appendTo($('#searchbox'));
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* init the domain index toggle buttons
|
||||
*/
|
||||
initIndexTable : function() {
|
||||
var togglers = $('img.toggler').click(function() {
|
||||
var src = $(this).attr('src');
|
||||
var idnum = $(this).attr('id').substr(7);
|
||||
$('tr.cg-' + idnum).toggle();
|
||||
if (src.substr(-9) == 'minus.png')
|
||||
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
|
||||
else
|
||||
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
|
||||
}).css('display', '');
|
||||
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
|
||||
togglers.click();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to hide the search marks again
|
||||
*/
|
||||
hideSearchWords : function() {
|
||||
$('#searchbox .highlight-link').fadeOut(300);
|
||||
$('span.highlighted').removeClass('highlighted');
|
||||
},
|
||||
|
||||
/**
|
||||
* make the url absolute
|
||||
*/
|
||||
makeURL : function(relativeURL) {
|
||||
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
|
||||
},
|
||||
|
||||
/**
|
||||
* get the current relative url
|
||||
*/
|
||||
getCurrentURL : function() {
|
||||
var path = document.location.pathname;
|
||||
var parts = path.split(/\//);
|
||||
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
|
||||
if (this == '..')
|
||||
parts.pop();
|
||||
});
|
||||
var url = parts.join('/');
|
||||
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
|
||||
},
|
||||
|
||||
initOnKeyListeners: function() {
|
||||
$(document).keyup(function(event) {
|
||||
var activeElementType = document.activeElement.tagName;
|
||||
// don't navigate when in search box or textarea
|
||||
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') {
|
||||
switch (event.keyCode) {
|
||||
case 37: // left
|
||||
var prevHref = $('link[rel="prev"]').prop('href');
|
||||
if (prevHref) {
|
||||
window.location.href = prevHref;
|
||||
return false;
|
||||
}
|
||||
case 39: // right
|
||||
var nextHref = $('link[rel="next"]').prop('href');
|
||||
if (nextHref) {
|
||||
window.location.href = nextHref;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
// quick alias for translations
|
||||
_ = Documentation.gettext;
|
||||
|
||||
$(document).ready(function() {
|
||||
Documentation.init();
|
||||
});
|
||||
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|
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@@ -1,685 +0,0 @@
|
||||
<?xml version="1.0" standalone="no"?>
|
||||
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd" >
|
||||
<svg xmlns="http://www.w3.org/2000/svg">
|
||||
<metadata></metadata>
|
||||
<defs>
|
||||
<font id="fontawesomeregular" horiz-adv-x="1536" >
|
||||
<font-face units-per-em="1792" ascent="1536" descent="-256" />
|
||||
<missing-glyph horiz-adv-x="448" />
|
||||
<glyph unicode=" " horiz-adv-x="448" />
|
||||
<glyph unicode="	" horiz-adv-x="448" />
|
||||
<glyph unicode=" " horiz-adv-x="448" />
|
||||
<glyph unicode="¨" horiz-adv-x="1792" />
|
||||
<glyph unicode="©" horiz-adv-x="1792" />
|
||||
<glyph unicode="®" horiz-adv-x="1792" />
|
||||
<glyph unicode="´" horiz-adv-x="1792" />
|
||||
<glyph unicode="Æ" horiz-adv-x="1792" />
|
||||
<glyph unicode="Ø" horiz-adv-x="1792" />
|
||||
<glyph unicode=" " horiz-adv-x="768" />
|
||||
<glyph unicode=" " horiz-adv-x="1537" />
|
||||
<glyph unicode=" " horiz-adv-x="768" />
|
||||
<glyph unicode=" " horiz-adv-x="1537" />
|
||||
<glyph unicode=" " horiz-adv-x="512" />
|
||||
<glyph unicode=" " horiz-adv-x="384" />
|
||||
<glyph unicode=" " horiz-adv-x="256" />
|
||||
<glyph unicode=" " horiz-adv-x="256" />
|
||||
<glyph unicode=" " horiz-adv-x="192" />
|
||||
<glyph unicode=" " horiz-adv-x="307" />
|
||||
<glyph unicode=" " horiz-adv-x="85" />
|
||||
<glyph unicode=" " horiz-adv-x="307" />
|
||||
<glyph unicode=" " horiz-adv-x="384" />
|
||||
<glyph unicode="™" horiz-adv-x="1792" />
|
||||
<glyph unicode="∞" horiz-adv-x="1792" />
|
||||
<glyph unicode="≠" horiz-adv-x="1792" />
|
||||
<glyph unicode="◼" horiz-adv-x="500" d="M0 0z" />
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||||
<glyph unicode="" horiz-adv-x="1792" d="M1699 1350q0 -35 -43 -78l-632 -632v-768h320q26 0 45 -19t19 -45t-19 -45t-45 -19h-896q-26 0 -45 19t-19 45t19 45t45 19h320v768l-632 632q-43 43 -43 78q0 23 18 36.5t38 17.5t43 4h1408q23 0 43 -4t38 -17.5t18 -36.5z" />
|
||||
<glyph unicode="" d="M1536 1312v-1120q0 -50 -34 -89t-86 -60.5t-103.5 -32t-96.5 -10.5t-96.5 10.5t-103.5 32t-86 60.5t-34 89t34 89t86 60.5t103.5 32t96.5 10.5q105 0 192 -39v537l-768 -237v-709q0 -50 -34 -89t-86 -60.5t-103.5 -32t-96.5 -10.5t-96.5 10.5t-103.5 32t-86 60.5t-34 89 t34 89t86 60.5t103.5 32t96.5 10.5q105 0 192 -39v967q0 31 19 56.5t49 35.5l832 256q12 4 28 4q40 0 68 -28t28 -68z" />
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||||
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||||
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||||
<glyph unicode="" horiz-adv-x="1792" d="M896 -128q-26 0 -44 18l-624 602q-10 8 -27.5 26t-55.5 65.5t-68 97.5t-53.5 121t-23.5 138q0 220 127 344t351 124q62 0 126.5 -21.5t120 -58t95.5 -68.5t76 -68q36 36 76 68t95.5 68.5t120 58t126.5 21.5q224 0 351 -124t127 -344q0 -221 -229 -450l-623 -600 q-18 -18 -44 -18z" />
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<glyph unicode="" horiz-adv-x="1664" d="M1664 889q0 -22 -26 -48l-363 -354l86 -500q1 -7 1 -20q0 -21 -10.5 -35.5t-30.5 -14.5q-19 0 -40 12l-449 236l-449 -236q-22 -12 -40 -12q-21 0 -31.5 14.5t-10.5 35.5q0 6 2 20l86 500l-364 354q-25 27 -25 48q0 37 56 46l502 73l225 455q19 41 49 41t49 -41l225 -455 l502 -73q56 -9 56 -46z" />
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|
||||
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||||
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|
||||
link.prepend(expand);
|
||||
});
|
||||
};
|
||||
|
||||
nav.reset = function () {
|
||||
// Get anchor from URL and open up nested nav
|
||||
var anchor = encodeURI(window.location.hash);
|
||||
if (anchor) {
|
||||
try {
|
||||
var link = $('.wy-menu-vertical')
|
||||
.find('[href="' + anchor + '"]');
|
||||
// If we didn't find a link, it may be because we clicked on
|
||||
// something that is not in the sidebar (eg: when using
|
||||
// sphinxcontrib.httpdomain it generates headerlinks but those
|
||||
// aren't picked up and placed in the toctree). So let's find
|
||||
// the closest header in the document and try with that one.
|
||||
if (link.length === 0) {
|
||||
var doc_link = $('.document a[href="' + anchor + '"]');
|
||||
var closest_section = doc_link.closest('div.section');
|
||||
// Try again with the closest section entry.
|
||||
link = $('.wy-menu-vertical')
|
||||
.find('[href="#' + closest_section.attr("id") + '"]');
|
||||
|
||||
}
|
||||
$('.wy-menu-vertical li.toctree-l1 li.current')
|
||||
.removeClass('current');
|
||||
link.closest('li.toctree-l2').addClass('current');
|
||||
link.closest('li.toctree-l3').addClass('current');
|
||||
link.closest('li.toctree-l4').addClass('current');
|
||||
}
|
||||
catch (err) {
|
||||
console.log("Error expanding nav for anchor", err);
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
nav.onScroll = function () {
|
||||
this.winScroll = false;
|
||||
var newWinPosition = this.win.scrollTop(),
|
||||
winBottom = newWinPosition + this.winHeight,
|
||||
navPosition = this.navBar.scrollTop(),
|
||||
newNavPosition = navPosition + (newWinPosition - this.winPosition);
|
||||
if (newWinPosition < 0 || winBottom > this.docHeight) {
|
||||
return;
|
||||
}
|
||||
this.navBar.scrollTop(newNavPosition);
|
||||
this.winPosition = newWinPosition;
|
||||
};
|
||||
|
||||
nav.onResize = function () {
|
||||
this.winResize = false;
|
||||
this.winHeight = this.win.height();
|
||||
this.docHeight = $(document).height();
|
||||
};
|
||||
|
||||
nav.hashChange = function () {
|
||||
this.linkScroll = true;
|
||||
this.win.one('hashchange', function () {
|
||||
this.linkScroll = false;
|
||||
});
|
||||
};
|
||||
|
||||
nav.toggleCurrent = function (elem) {
|
||||
var parent_li = elem.closest('li');
|
||||
parent_li.siblings('li.current').removeClass('current');
|
||||
parent_li.siblings().find('li.current').removeClass('current');
|
||||
parent_li.find('> ul li.current').removeClass('current');
|
||||
parent_li.toggleClass('current');
|
||||
}
|
||||
|
||||
return nav;
|
||||
};
|
||||
|
||||
module.exports.ThemeNav = ThemeNav();
|
||||
|
||||
if (typeof(window) != 'undefined') {
|
||||
window.SphinxRtdTheme = { StickyNav: module.exports.ThemeNav };
|
||||
}
|
||||
|
||||
},{"jquery":"jquery"}]},{},["sphinx-rtd-theme"]);
|
||||
Arquivo binário não exibido.
|
Antes Largura: | Altura: | Tamanho: 90 B |
Arquivo binário não exibido.
|
Antes Largura: | Altura: | Tamanho: 90 B |
@@ -1,69 +0,0 @@
|
||||
.highlight .hll { background-color: #ffffcc }
|
||||
.highlight { background: #eeffcc; }
|
||||
.highlight .c { color: #408090; font-style: italic } /* Comment */
|
||||
.highlight .err { border: 1px solid #FF0000 } /* Error */
|
||||
.highlight .k { color: #007020; font-weight: bold } /* Keyword */
|
||||
.highlight .o { color: #666666 } /* Operator */
|
||||
.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
|
||||
.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
|
||||
.highlight .cp { color: #007020 } /* Comment.Preproc */
|
||||
.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
|
||||
.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
|
||||
.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
|
||||
.highlight .gd { color: #A00000 } /* Generic.Deleted */
|
||||
.highlight .ge { font-style: italic } /* Generic.Emph */
|
||||
.highlight .gr { color: #FF0000 } /* Generic.Error */
|
||||
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
|
||||
.highlight .gi { color: #00A000 } /* Generic.Inserted */
|
||||
.highlight .go { color: #333333 } /* Generic.Output */
|
||||
.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
|
||||
.highlight .gs { font-weight: bold } /* Generic.Strong */
|
||||
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
|
||||
.highlight .gt { color: #0044DD } /* Generic.Traceback */
|
||||
.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
|
||||
.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
|
||||
.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
|
||||
.highlight .kp { color: #007020 } /* Keyword.Pseudo */
|
||||
.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
|
||||
.highlight .kt { color: #902000 } /* Keyword.Type */
|
||||
.highlight .m { color: #208050 } /* Literal.Number */
|
||||
.highlight .s { color: #4070a0 } /* Literal.String */
|
||||
.highlight .na { color: #4070a0 } /* Name.Attribute */
|
||||
.highlight .nb { color: #007020 } /* Name.Builtin */
|
||||
.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
|
||||
.highlight .no { color: #60add5 } /* Name.Constant */
|
||||
.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
|
||||
.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
|
||||
.highlight .ne { color: #007020 } /* Name.Exception */
|
||||
.highlight .nf { color: #06287e } /* Name.Function */
|
||||
.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
|
||||
.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
|
||||
.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
|
||||
.highlight .nv { color: #bb60d5 } /* Name.Variable */
|
||||
.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
|
||||
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
|
||||
.highlight .mb { color: #208050 } /* Literal.Number.Bin */
|
||||
.highlight .mf { color: #208050 } /* Literal.Number.Float */
|
||||
.highlight .mh { color: #208050 } /* Literal.Number.Hex */
|
||||
.highlight .mi { color: #208050 } /* Literal.Number.Integer */
|
||||
.highlight .mo { color: #208050 } /* Literal.Number.Oct */
|
||||
.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
|
||||
.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
|
||||
.highlight .sc { color: #4070a0 } /* Literal.String.Char */
|
||||
.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
|
||||
.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
|
||||
.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
|
||||
.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
|
||||
.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
|
||||
.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
|
||||
.highlight .sx { color: #c65d09 } /* Literal.String.Other */
|
||||
.highlight .sr { color: #235388 } /* Literal.String.Regex */
|
||||
.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
|
||||
.highlight .ss { color: #517918 } /* Literal.String.Symbol */
|
||||
.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
|
||||
.highlight .fm { color: #06287e } /* Name.Function.Magic */
|
||||
.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
|
||||
.highlight .vg { color: #bb60d5 } /* Name.Variable.Global */
|
||||
.highlight .vi { color: #bb60d5 } /* Name.Variable.Instance */
|
||||
.highlight .vm { color: #bb60d5 } /* Name.Variable.Magic */
|
||||
.highlight .il { color: #208050 } /* Literal.Number.Integer.Long */
|
||||
@@ -1,758 +0,0 @@
|
||||
/*
|
||||
* searchtools.js_t
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for the full-text search.
|
||||
*
|
||||
* :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
|
||||
/* Non-minified version JS is _stemmer.js if file is provided */
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* Simple result scoring code.
|
||||
*/
|
||||
var Scorer = {
|
||||
// Implement the following function to further tweak the score for each result
|
||||
// The function takes a result array [filename, title, anchor, descr, score]
|
||||
// and returns the new score.
|
||||
/*
|
||||
score: function(result) {
|
||||
return result[4];
|
||||
},
|
||||
*/
|
||||
|
||||
// query matches the full name of an object
|
||||
objNameMatch: 11,
|
||||
// or matches in the last dotted part of the object name
|
||||
objPartialMatch: 6,
|
||||
// Additive scores depending on the priority of the object
|
||||
objPrio: {0: 15, // used to be importantResults
|
||||
1: 5, // used to be objectResults
|
||||
2: -5}, // used to be unimportantResults
|
||||
// Used when the priority is not in the mapping.
|
||||
objPrioDefault: 0,
|
||||
|
||||
// query found in title
|
||||
title: 15,
|
||||
// query found in terms
|
||||
term: 5
|
||||
};
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() {
|
||||
var result = {};
|
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
|
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
|
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
|
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
|
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
|
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
|
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
|
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
|
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
|
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
|
||||
var i, j, start, end;
|
||||
for (i = 0; i < singles.length; i++) {
|
||||
result[singles[i]] = true;
|
||||
}
|
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
|
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
|
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
|
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
|
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
|
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
|
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
|
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
|
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
|
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
|
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
|
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
|
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
|
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
|
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
|
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
|
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
|
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
|
||||
[4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
|
||||
[4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
|
||||
[4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
|
||||
[4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
|
||||
[5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
|
||||
[6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
|
||||
[6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
|
||||
[6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
|
||||
[6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
|
||||
[7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
|
||||
[7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
|
||||
[8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
|
||||
[8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
|
||||
[8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
|
||||
[10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
|
||||
[11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
|
||||
[12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
|
||||
[12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
|
||||
[12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
|
||||
[19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
|
||||
[42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
|
||||
[42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
|
||||
[43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
|
||||
[43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
|
||||
[43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
|
||||
[43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
|
||||
[44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
|
||||
[57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
|
||||
[64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
|
||||
[65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
|
||||
[65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
|
||||
for (i = 0; i < ranges.length; i++) {
|
||||
start = ranges[i][0];
|
||||
end = ranges[i][1];
|
||||
for (j = start; j <= end; j++) {
|
||||
result[j] = true;
|
||||
}
|
||||
}
|
||||
return result;
|
||||
})();
|
||||
|
||||
function splitQuery(query) {
|
||||
var result = [];
|
||||
var start = -1;
|
||||
for (var i = 0; i < query.length; i++) {
|
||||
if (splitChars[query.charCodeAt(i)]) {
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start, i));
|
||||
start = -1;
|
||||
}
|
||||
} else if (start === -1) {
|
||||
start = i;
|
||||
}
|
||||
}
|
||||
if (start !== -1) {
|
||||
result.push(query.slice(start));
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
/**
|
||||
* Search Module
|
||||
*/
|
||||
var Search = {
|
||||
|
||||
_index : null,
|
||||
_queued_query : null,
|
||||
_pulse_status : -1,
|
||||
|
||||
init : function() {
|
||||
var params = $.getQueryParameters();
|
||||
if (params.q) {
|
||||
var query = params.q[0];
|
||||
$('input[name="q"]')[0].value = query;
|
||||
this.performSearch(query);
|
||||
}
|
||||
},
|
||||
|
||||
loadIndex : function(url) {
|
||||
$.ajax({type: "GET", url: url, data: null,
|
||||
dataType: "script", cache: true,
|
||||
complete: function(jqxhr, textstatus) {
|
||||
if (textstatus != "success") {
|
||||
document.getElementById("searchindexloader").src = url;
|
||||
}
|
||||
}});
|
||||
},
|
||||
|
||||
setIndex : function(index) {
|
||||
var q;
|
||||
this._index = index;
|
||||
if ((q = this._queued_query) !== null) {
|
||||
this._queued_query = null;
|
||||
Search.query(q);
|
||||
}
|
||||
},
|
||||
|
||||
hasIndex : function() {
|
||||
return this._index !== null;
|
||||
},
|
||||
|
||||
deferQuery : function(query) {
|
||||
this._queued_query = query;
|
||||
},
|
||||
|
||||
stopPulse : function() {
|
||||
this._pulse_status = 0;
|
||||
},
|
||||
|
||||
startPulse : function() {
|
||||
if (this._pulse_status >= 0)
|
||||
return;
|
||||
function pulse() {
|
||||
var i;
|
||||
Search._pulse_status = (Search._pulse_status + 1) % 4;
|
||||
var dotString = '';
|
||||
for (i = 0; i < Search._pulse_status; i++)
|
||||
dotString += '.';
|
||||
Search.dots.text(dotString);
|
||||
if (Search._pulse_status > -1)
|
||||
window.setTimeout(pulse, 500);
|
||||
}
|
||||
pulse();
|
||||
},
|
||||
|
||||
/**
|
||||
* perform a search for something (or wait until index is loaded)
|
||||
*/
|
||||
performSearch : function(query) {
|
||||
// create the required interface elements
|
||||
this.out = $('#search-results');
|
||||
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
|
||||
this.dots = $('<span></span>').appendTo(this.title);
|
||||
this.status = $('<p style="display: none"></p>').appendTo(this.out);
|
||||
this.output = $('<ul class="search"/>').appendTo(this.out);
|
||||
|
||||
$('#search-progress').text(_('Preparing search...'));
|
||||
this.startPulse();
|
||||
|
||||
// index already loaded, the browser was quick!
|
||||
if (this.hasIndex())
|
||||
this.query(query);
|
||||
else
|
||||
this.deferQuery(query);
|
||||
},
|
||||
|
||||
/**
|
||||
* execute search (requires search index to be loaded)
|
||||
*/
|
||||
query : function(query) {
|
||||
var i;
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
|
||||
|
||||
// stem the searchterms and add them to the correct list
|
||||
var stemmer = new Stemmer();
|
||||
var searchterms = [];
|
||||
var excluded = [];
|
||||
var hlterms = [];
|
||||
var tmp = splitQuery(query);
|
||||
var objectterms = [];
|
||||
for (i = 0; i < tmp.length; i++) {
|
||||
if (tmp[i] !== "") {
|
||||
objectterms.push(tmp[i].toLowerCase());
|
||||
}
|
||||
|
||||
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
|
||||
tmp[i] === "") {
|
||||
// skip this "word"
|
||||
continue;
|
||||
}
|
||||
// stem the word
|
||||
var word = stemmer.stemWord(tmp[i].toLowerCase());
|
||||
// prevent stemmer from cutting word smaller than two chars
|
||||
if(word.length < 3 && tmp[i].length >= 3) {
|
||||
word = tmp[i];
|
||||
}
|
||||
var toAppend;
|
||||
// select the correct list
|
||||
if (word[0] == '-') {
|
||||
toAppend = excluded;
|
||||
word = word.substr(1);
|
||||
}
|
||||
else {
|
||||
toAppend = searchterms;
|
||||
hlterms.push(tmp[i].toLowerCase());
|
||||
}
|
||||
// only add if not already in the list
|
||||
if (!$u.contains(toAppend, word))
|
||||
toAppend.push(word);
|
||||
}
|
||||
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
|
||||
|
||||
// console.debug('SEARCH: searching for:');
|
||||
// console.info('required: ', searchterms);
|
||||
// console.info('excluded: ', excluded);
|
||||
|
||||
// prepare search
|
||||
var terms = this._index.terms;
|
||||
var titleterms = this._index.titleterms;
|
||||
|
||||
// array of [filename, title, anchor, descr, score]
|
||||
var results = [];
|
||||
$('#search-progress').empty();
|
||||
|
||||
// lookup as object
|
||||
for (i = 0; i < objectterms.length; i++) {
|
||||
var others = [].concat(objectterms.slice(0, i),
|
||||
objectterms.slice(i+1, objectterms.length));
|
||||
results = results.concat(this.performObjectSearch(objectterms[i], others));
|
||||
}
|
||||
|
||||
// lookup as search terms in fulltext
|
||||
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
|
||||
|
||||
// let the scorer override scores with a custom scoring function
|
||||
if (Scorer.score) {
|
||||
for (i = 0; i < results.length; i++)
|
||||
results[i][4] = Scorer.score(results[i]);
|
||||
}
|
||||
|
||||
// now sort the results by score (in opposite order of appearance, since the
|
||||
// display function below uses pop() to retrieve items) and then
|
||||
// alphabetically
|
||||
results.sort(function(a, b) {
|
||||
var left = a[4];
|
||||
var right = b[4];
|
||||
if (left > right) {
|
||||
return 1;
|
||||
} else if (left < right) {
|
||||
return -1;
|
||||
} else {
|
||||
// same score: sort alphabetically
|
||||
left = a[1].toLowerCase();
|
||||
right = b[1].toLowerCase();
|
||||
return (left > right) ? -1 : ((left < right) ? 1 : 0);
|
||||
}
|
||||
});
|
||||
|
||||
// for debugging
|
||||
//Search.lastresults = results.slice(); // a copy
|
||||
//console.info('search results:', Search.lastresults);
|
||||
|
||||
// print the results
|
||||
var resultCount = results.length;
|
||||
function displayNextItem() {
|
||||
// results left, load the summary and display it
|
||||
if (results.length) {
|
||||
var item = results.pop();
|
||||
var listItem = $('<li style="display:none"></li>');
|
||||
if (DOCUMENTATION_OPTIONS.FILE_SUFFIX === '') {
|
||||
// dirhtml builder
|
||||
var dirname = item[0] + '/';
|
||||
if (dirname.match(/\/index\/$/)) {
|
||||
dirname = dirname.substring(0, dirname.length-6);
|
||||
} else if (dirname == 'index/') {
|
||||
dirname = '';
|
||||
}
|
||||
listItem.append($('<a/>').attr('href',
|
||||
DOCUMENTATION_OPTIONS.URL_ROOT + dirname +
|
||||
highlightstring + item[2]).html(item[1]));
|
||||
} else {
|
||||
// normal html builders
|
||||
listItem.append($('<a/>').attr('href',
|
||||
item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX +
|
||||
highlightstring + item[2]).html(item[1]));
|
||||
}
|
||||
if (item[3]) {
|
||||
listItem.append($('<span> (' + item[3] + ')</span>'));
|
||||
Search.output.append(listItem);
|
||||
listItem.slideDown(5, function() {
|
||||
displayNextItem();
|
||||
});
|
||||
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
|
||||
var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
|
||||
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
|
||||
dataType: "text",
|
||||
complete: function(jqxhr, textstatus) {
|
||||
var data = jqxhr.responseText;
|
||||
if (data !== '' && data !== undefined) {
|
||||
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
|
||||
}
|
||||
Search.output.append(listItem);
|
||||
listItem.slideDown(5, function() {
|
||||
displayNextItem();
|
||||
});
|
||||
}});
|
||||
} else {
|
||||
// no source available, just display title
|
||||
Search.output.append(listItem);
|
||||
listItem.slideDown(5, function() {
|
||||
displayNextItem();
|
||||
});
|
||||
}
|
||||
}
|
||||
// search finished, update title and status message
|
||||
else {
|
||||
Search.stopPulse();
|
||||
Search.title.text(_('Search Results'));
|
||||
if (!resultCount)
|
||||
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
|
||||
else
|
||||
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
|
||||
Search.status.fadeIn(500);
|
||||
}
|
||||
}
|
||||
displayNextItem();
|
||||
},
|
||||
|
||||
/**
|
||||
* search for object names
|
||||
*/
|
||||
performObjectSearch : function(object, otherterms) {
|
||||
var filenames = this._index.filenames;
|
||||
var docnames = this._index.docnames;
|
||||
var objects = this._index.objects;
|
||||
var objnames = this._index.objnames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i;
|
||||
var results = [];
|
||||
|
||||
for (var prefix in objects) {
|
||||
for (var name in objects[prefix]) {
|
||||
var fullname = (prefix ? prefix + '.' : '') + name;
|
||||
if (fullname.toLowerCase().indexOf(object) > -1) {
|
||||
var score = 0;
|
||||
var parts = fullname.split('.');
|
||||
// check for different match types: exact matches of full name or
|
||||
// "last name" (i.e. last dotted part)
|
||||
if (fullname == object || parts[parts.length - 1] == object) {
|
||||
score += Scorer.objNameMatch;
|
||||
// matches in last name
|
||||
} else if (parts[parts.length - 1].indexOf(object) > -1) {
|
||||
score += Scorer.objPartialMatch;
|
||||
}
|
||||
var match = objects[prefix][name];
|
||||
var objname = objnames[match[1]][2];
|
||||
var title = titles[match[0]];
|
||||
// If more than one term searched for, we require other words to be
|
||||
// found in the name/title/description
|
||||
if (otherterms.length > 0) {
|
||||
var haystack = (prefix + ' ' + name + ' ' +
|
||||
objname + ' ' + title).toLowerCase();
|
||||
var allfound = true;
|
||||
for (i = 0; i < otherterms.length; i++) {
|
||||
if (haystack.indexOf(otherterms[i]) == -1) {
|
||||
allfound = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!allfound) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
var descr = objname + _(', in ') + title;
|
||||
|
||||
var anchor = match[3];
|
||||
if (anchor === '')
|
||||
anchor = fullname;
|
||||
else if (anchor == '-')
|
||||
anchor = objnames[match[1]][1] + '-' + fullname;
|
||||
// add custom score for some objects according to scorer
|
||||
if (Scorer.objPrio.hasOwnProperty(match[2])) {
|
||||
score += Scorer.objPrio[match[2]];
|
||||
} else {
|
||||
score += Scorer.objPrioDefault;
|
||||
}
|
||||
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* search for full-text terms in the index
|
||||
*/
|
||||
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
|
||||
var docnames = this._index.docnames;
|
||||
var filenames = this._index.filenames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i, j, file;
|
||||
var fileMap = {};
|
||||
var scoreMap = {};
|
||||
var results = [];
|
||||
|
||||
// perform the search on the required terms
|
||||
for (i = 0; i < searchterms.length; i++) {
|
||||
var word = searchterms[i];
|
||||
var files = [];
|
||||
var _o = [
|
||||
{files: terms[word], score: Scorer.term},
|
||||
{files: titleterms[word], score: Scorer.title}
|
||||
];
|
||||
|
||||
// no match but word was a required one
|
||||
if ($u.every(_o, function(o){return o.files === undefined;})) {
|
||||
break;
|
||||
}
|
||||
// found search word in contents
|
||||
$u.each(_o, function(o) {
|
||||
var _files = o.files;
|
||||
if (_files === undefined)
|
||||
return
|
||||
|
||||
if (_files.length === undefined)
|
||||
_files = [_files];
|
||||
files = files.concat(_files);
|
||||
|
||||
// set score for the word in each file to Scorer.term
|
||||
for (j = 0; j < _files.length; j++) {
|
||||
file = _files[j];
|
||||
if (!(file in scoreMap))
|
||||
scoreMap[file] = {}
|
||||
scoreMap[file][word] = o.score;
|
||||
}
|
||||
});
|
||||
|
||||
// create the mapping
|
||||
for (j = 0; j < files.length; j++) {
|
||||
file = files[j];
|
||||
if (file in fileMap)
|
||||
fileMap[file].push(word);
|
||||
else
|
||||
fileMap[file] = [word];
|
||||
}
|
||||
}
|
||||
|
||||
// now check if the files don't contain excluded terms
|
||||
for (file in fileMap) {
|
||||
var valid = true;
|
||||
|
||||
// check if all requirements are matched
|
||||
if (fileMap[file].length != searchterms.length)
|
||||
continue;
|
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
for (i = 0; i < excluded.length; i++) {
|
||||
if (terms[excluded[i]] == file ||
|
||||
titleterms[excluded[i]] == file ||
|
||||
$u.contains(terms[excluded[i]] || [], file) ||
|
||||
$u.contains(titleterms[excluded[i]] || [], file)) {
|
||||
valid = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// if we have still a valid result we can add it to the result list
|
||||
if (valid) {
|
||||
// select one (max) score for the file.
|
||||
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
|
||||
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
|
||||
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
|
||||
}
|
||||
}
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to return a node containing the
|
||||
* search summary for a given text. keywords is a list
|
||||
* of stemmed words, hlwords is the list of normal, unstemmed
|
||||
* words. the first one is used to find the occurrence, the
|
||||
* latter for highlighting it.
|
||||
*/
|
||||
makeSearchSummary : function(text, keywords, hlwords) {
|
||||
var textLower = text.toLowerCase();
|
||||
var start = 0;
|
||||
$.each(keywords, function() {
|
||||
var i = textLower.indexOf(this.toLowerCase());
|
||||
if (i > -1)
|
||||
start = i;
|
||||
});
|
||||
start = Math.max(start - 120, 0);
|
||||
var excerpt = ((start > 0) ? '...' : '') +
|
||||
$.trim(text.substr(start, 240)) +
|
||||
((start + 240 - text.length) ? '...' : '');
|
||||
var rv = $('<div class="context"></div>').text(excerpt);
|
||||
$.each(hlwords, function() {
|
||||
rv = rv.highlightText(this, 'highlighted');
|
||||
});
|
||||
return rv;
|
||||
}
|
||||
};
|
||||
|
||||
$(document).ready(function() {
|
||||
Search.init();
|
||||
});
|
||||
@@ -1,999 +0,0 @@
|
||||
// Underscore.js 1.3.1
|
||||
// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
|
||||
// Underscore is freely distributable under the MIT license.
|
||||
// Portions of Underscore are inspired or borrowed from Prototype,
|
||||
// Oliver Steele's Functional, and John Resig's Micro-Templating.
|
||||
// For all details and documentation:
|
||||
// http://documentcloud.github.com/underscore
|
||||
|
||||
(function() {
|
||||
|
||||
// Baseline setup
|
||||
// --------------
|
||||
|
||||
// Establish the root object, `window` in the browser, or `global` on the server.
|
||||
var root = this;
|
||||
|
||||
// Save the previous value of the `_` variable.
|
||||
var previousUnderscore = root._;
|
||||
|
||||
// Establish the object that gets returned to break out of a loop iteration.
|
||||
var breaker = {};
|
||||
|
||||
// Save bytes in the minified (but not gzipped) version:
|
||||
var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
|
||||
|
||||
// Create quick reference variables for speed access to core prototypes.
|
||||
var slice = ArrayProto.slice,
|
||||
unshift = ArrayProto.unshift,
|
||||
toString = ObjProto.toString,
|
||||
hasOwnProperty = ObjProto.hasOwnProperty;
|
||||
|
||||
// All **ECMAScript 5** native function implementations that we hope to use
|
||||
// are declared here.
|
||||
var
|
||||
nativeForEach = ArrayProto.forEach,
|
||||
nativeMap = ArrayProto.map,
|
||||
nativeReduce = ArrayProto.reduce,
|
||||
nativeReduceRight = ArrayProto.reduceRight,
|
||||
nativeFilter = ArrayProto.filter,
|
||||
nativeEvery = ArrayProto.every,
|
||||
nativeSome = ArrayProto.some,
|
||||
nativeIndexOf = ArrayProto.indexOf,
|
||||
nativeLastIndexOf = ArrayProto.lastIndexOf,
|
||||
nativeIsArray = Array.isArray,
|
||||
nativeKeys = Object.keys,
|
||||
nativeBind = FuncProto.bind;
|
||||
|
||||
// Create a safe reference to the Underscore object for use below.
|
||||
var _ = function(obj) { return new wrapper(obj); };
|
||||
|
||||
// Export the Underscore object for **Node.js**, with
|
||||
// backwards-compatibility for the old `require()` API. If we're in
|
||||
// the browser, add `_` as a global object via a string identifier,
|
||||
// for Closure Compiler "advanced" mode.
|
||||
if (typeof exports !== 'undefined') {
|
||||
if (typeof module !== 'undefined' && module.exports) {
|
||||
exports = module.exports = _;
|
||||
}
|
||||
exports._ = _;
|
||||
} else {
|
||||
root['_'] = _;
|
||||
}
|
||||
|
||||
// Current version.
|
||||
_.VERSION = '1.3.1';
|
||||
|
||||
// Collection Functions
|
||||
// --------------------
|
||||
|
||||
// The cornerstone, an `each` implementation, aka `forEach`.
|
||||
// Handles objects with the built-in `forEach`, arrays, and raw objects.
|
||||
// Delegates to **ECMAScript 5**'s native `forEach` if available.
|
||||
var each = _.each = _.forEach = function(obj, iterator, context) {
|
||||
if (obj == null) return;
|
||||
if (nativeForEach && obj.forEach === nativeForEach) {
|
||||
obj.forEach(iterator, context);
|
||||
} else if (obj.length === +obj.length) {
|
||||
for (var i = 0, l = obj.length; i < l; i++) {
|
||||
if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
|
||||
}
|
||||
} else {
|
||||
for (var key in obj) {
|
||||
if (_.has(obj, key)) {
|
||||
if (iterator.call(context, obj[key], key, obj) === breaker) return;
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
// Return the results of applying the iterator to each element.
|
||||
// Delegates to **ECMAScript 5**'s native `map` if available.
|
||||
_.map = _.collect = function(obj, iterator, context) {
|
||||
var results = [];
|
||||
if (obj == null) return results;
|
||||
if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
|
||||
each(obj, function(value, index, list) {
|
||||
results[results.length] = iterator.call(context, value, index, list);
|
||||
});
|
||||
if (obj.length === +obj.length) results.length = obj.length;
|
||||
return results;
|
||||
};
|
||||
|
||||
// **Reduce** builds up a single result from a list of values, aka `inject`,
|
||||
// or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
|
||||
_.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
|
||||
var initial = arguments.length > 2;
|
||||
if (obj == null) obj = [];
|
||||
if (nativeReduce && obj.reduce === nativeReduce) {
|
||||
if (context) iterator = _.bind(iterator, context);
|
||||
return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
|
||||
}
|
||||
each(obj, function(value, index, list) {
|
||||
if (!initial) {
|
||||
memo = value;
|
||||
initial = true;
|
||||
} else {
|
||||
memo = iterator.call(context, memo, value, index, list);
|
||||
}
|
||||
});
|
||||
if (!initial) throw new TypeError('Reduce of empty array with no initial value');
|
||||
return memo;
|
||||
};
|
||||
|
||||
// The right-associative version of reduce, also known as `foldr`.
|
||||
// Delegates to **ECMAScript 5**'s native `reduceRight` if available.
|
||||
_.reduceRight = _.foldr = function(obj, iterator, memo, context) {
|
||||
var initial = arguments.length > 2;
|
||||
if (obj == null) obj = [];
|
||||
if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
|
||||
if (context) iterator = _.bind(iterator, context);
|
||||
return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
|
||||
}
|
||||
var reversed = _.toArray(obj).reverse();
|
||||
if (context && !initial) iterator = _.bind(iterator, context);
|
||||
return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
|
||||
};
|
||||
|
||||
// Return the first value which passes a truth test. Aliased as `detect`.
|
||||
_.find = _.detect = function(obj, iterator, context) {
|
||||
var result;
|
||||
any(obj, function(value, index, list) {
|
||||
if (iterator.call(context, value, index, list)) {
|
||||
result = value;
|
||||
return true;
|
||||
}
|
||||
});
|
||||
return result;
|
||||
};
|
||||
|
||||
// Return all the elements that pass a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `filter` if available.
|
||||
// Aliased as `select`.
|
||||
_.filter = _.select = function(obj, iterator, context) {
|
||||
var results = [];
|
||||
if (obj == null) return results;
|
||||
if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
|
||||
each(obj, function(value, index, list) {
|
||||
if (iterator.call(context, value, index, list)) results[results.length] = value;
|
||||
});
|
||||
return results;
|
||||
};
|
||||
|
||||
// Return all the elements for which a truth test fails.
|
||||
_.reject = function(obj, iterator, context) {
|
||||
var results = [];
|
||||
if (obj == null) return results;
|
||||
each(obj, function(value, index, list) {
|
||||
if (!iterator.call(context, value, index, list)) results[results.length] = value;
|
||||
});
|
||||
return results;
|
||||
};
|
||||
|
||||
// Determine whether all of the elements match a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `every` if available.
|
||||
// Aliased as `all`.
|
||||
_.every = _.all = function(obj, iterator, context) {
|
||||
var result = true;
|
||||
if (obj == null) return result;
|
||||
if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
|
||||
each(obj, function(value, index, list) {
|
||||
if (!(result = result && iterator.call(context, value, index, list))) return breaker;
|
||||
});
|
||||
return result;
|
||||
};
|
||||
|
||||
// Determine if at least one element in the object matches a truth test.
|
||||
// Delegates to **ECMAScript 5**'s native `some` if available.
|
||||
// Aliased as `any`.
|
||||
var any = _.some = _.any = function(obj, iterator, context) {
|
||||
iterator || (iterator = _.identity);
|
||||
var result = false;
|
||||
if (obj == null) return result;
|
||||
if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
|
||||
each(obj, function(value, index, list) {
|
||||
if (result || (result = iterator.call(context, value, index, list))) return breaker;
|
||||
});
|
||||
return !!result;
|
||||
};
|
||||
|
||||
// Determine if a given value is included in the array or object using `===`.
|
||||
// Aliased as `contains`.
|
||||
_.include = _.contains = function(obj, target) {
|
||||
var found = false;
|
||||
if (obj == null) return found;
|
||||
if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
|
||||
found = any(obj, function(value) {
|
||||
return value === target;
|
||||
});
|
||||
return found;
|
||||
};
|
||||
|
||||
// Invoke a method (with arguments) on every item in a collection.
|
||||
_.invoke = function(obj, method) {
|
||||
var args = slice.call(arguments, 2);
|
||||
return _.map(obj, function(value) {
|
||||
return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
|
||||
});
|
||||
};
|
||||
|
||||
// Convenience version of a common use case of `map`: fetching a property.
|
||||
_.pluck = function(obj, key) {
|
||||
return _.map(obj, function(value){ return value[key]; });
|
||||
};
|
||||
|
||||
// Return the maximum element or (element-based computation).
|
||||
_.max = function(obj, iterator, context) {
|
||||
if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
|
||||
if (!iterator && _.isEmpty(obj)) return -Infinity;
|
||||
var result = {computed : -Infinity};
|
||||
each(obj, function(value, index, list) {
|
||||
var computed = iterator ? iterator.call(context, value, index, list) : value;
|
||||
computed >= result.computed && (result = {value : value, computed : computed});
|
||||
});
|
||||
return result.value;
|
||||
};
|
||||
|
||||
// Return the minimum element (or element-based computation).
|
||||
_.min = function(obj, iterator, context) {
|
||||
if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
|
||||
if (!iterator && _.isEmpty(obj)) return Infinity;
|
||||
var result = {computed : Infinity};
|
||||
each(obj, function(value, index, list) {
|
||||
var computed = iterator ? iterator.call(context, value, index, list) : value;
|
||||
computed < result.computed && (result = {value : value, computed : computed});
|
||||
});
|
||||
return result.value;
|
||||
};
|
||||
|
||||
// Shuffle an array.
|
||||
_.shuffle = function(obj) {
|
||||
var shuffled = [], rand;
|
||||
each(obj, function(value, index, list) {
|
||||
if (index == 0) {
|
||||
shuffled[0] = value;
|
||||
} else {
|
||||
rand = Math.floor(Math.random() * (index + 1));
|
||||
shuffled[index] = shuffled[rand];
|
||||
shuffled[rand] = value;
|
||||
}
|
||||
});
|
||||
return shuffled;
|
||||
};
|
||||
|
||||
// Sort the object's values by a criterion produced by an iterator.
|
||||
_.sortBy = function(obj, iterator, context) {
|
||||
return _.pluck(_.map(obj, function(value, index, list) {
|
||||
return {
|
||||
value : value,
|
||||
criteria : iterator.call(context, value, index, list)
|
||||
};
|
||||
}).sort(function(left, right) {
|
||||
var a = left.criteria, b = right.criteria;
|
||||
return a < b ? -1 : a > b ? 1 : 0;
|
||||
}), 'value');
|
||||
};
|
||||
|
||||
// Groups the object's values by a criterion. Pass either a string attribute
|
||||
// to group by, or a function that returns the criterion.
|
||||
_.groupBy = function(obj, val) {
|
||||
var result = {};
|
||||
var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
|
||||
each(obj, function(value, index) {
|
||||
var key = iterator(value, index);
|
||||
(result[key] || (result[key] = [])).push(value);
|
||||
});
|
||||
return result;
|
||||
};
|
||||
|
||||
// Use a comparator function to figure out at what index an object should
|
||||
// be inserted so as to maintain order. Uses binary search.
|
||||
_.sortedIndex = function(array, obj, iterator) {
|
||||
iterator || (iterator = _.identity);
|
||||
var low = 0, high = array.length;
|
||||
while (low < high) {
|
||||
var mid = (low + high) >> 1;
|
||||
iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
|
||||
}
|
||||
return low;
|
||||
};
|
||||
|
||||
// Safely convert anything iterable into a real, live array.
|
||||
_.toArray = function(iterable) {
|
||||
if (!iterable) return [];
|
||||
if (iterable.toArray) return iterable.toArray();
|
||||
if (_.isArray(iterable)) return slice.call(iterable);
|
||||
if (_.isArguments(iterable)) return slice.call(iterable);
|
||||
return _.values(iterable);
|
||||
};
|
||||
|
||||
// Return the number of elements in an object.
|
||||
_.size = function(obj) {
|
||||
return _.toArray(obj).length;
|
||||
};
|
||||
|
||||
// Array Functions
|
||||
// ---------------
|
||||
|
||||
// Get the first element of an array. Passing **n** will return the first N
|
||||
// values in the array. Aliased as `head`. The **guard** check allows it to work
|
||||
// with `_.map`.
|
||||
_.first = _.head = function(array, n, guard) {
|
||||
return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
|
||||
};
|
||||
|
||||
// Returns everything but the last entry of the array. Especcialy useful on
|
||||
// the arguments object. Passing **n** will return all the values in
|
||||
// the array, excluding the last N. The **guard** check allows it to work with
|
||||
// `_.map`.
|
||||
_.initial = function(array, n, guard) {
|
||||
return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
|
||||
};
|
||||
|
||||
// Get the last element of an array. Passing **n** will return the last N
|
||||
// values in the array. The **guard** check allows it to work with `_.map`.
|
||||
_.last = function(array, n, guard) {
|
||||
if ((n != null) && !guard) {
|
||||
return slice.call(array, Math.max(array.length - n, 0));
|
||||
} else {
|
||||
return array[array.length - 1];
|
||||
}
|
||||
};
|
||||
|
||||
// Returns everything but the first entry of the array. Aliased as `tail`.
|
||||
// Especially useful on the arguments object. Passing an **index** will return
|
||||
// the rest of the values in the array from that index onward. The **guard**
|
||||
// check allows it to work with `_.map`.
|
||||
_.rest = _.tail = function(array, index, guard) {
|
||||
return slice.call(array, (index == null) || guard ? 1 : index);
|
||||
};
|
||||
|
||||
// Trim out all falsy values from an array.
|
||||
_.compact = function(array) {
|
||||
return _.filter(array, function(value){ return !!value; });
|
||||
};
|
||||
|
||||
// Return a completely flattened version of an array.
|
||||
_.flatten = function(array, shallow) {
|
||||
return _.reduce(array, function(memo, value) {
|
||||
if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
|
||||
memo[memo.length] = value;
|
||||
return memo;
|
||||
}, []);
|
||||
};
|
||||
|
||||
// Return a version of the array that does not contain the specified value(s).
|
||||
_.without = function(array) {
|
||||
return _.difference(array, slice.call(arguments, 1));
|
||||
};
|
||||
|
||||
// Produce a duplicate-free version of the array. If the array has already
|
||||
// been sorted, you have the option of using a faster algorithm.
|
||||
// Aliased as `unique`.
|
||||
_.uniq = _.unique = function(array, isSorted, iterator) {
|
||||
var initial = iterator ? _.map(array, iterator) : array;
|
||||
var result = [];
|
||||
_.reduce(initial, function(memo, el, i) {
|
||||
if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
|
||||
memo[memo.length] = el;
|
||||
result[result.length] = array[i];
|
||||
}
|
||||
return memo;
|
||||
}, []);
|
||||
return result;
|
||||
};
|
||||
|
||||
// Produce an array that contains the union: each distinct element from all of
|
||||
// the passed-in arrays.
|
||||
_.union = function() {
|
||||
return _.uniq(_.flatten(arguments, true));
|
||||
};
|
||||
|
||||
// Produce an array that contains every item shared between all the
|
||||
// passed-in arrays. (Aliased as "intersect" for back-compat.)
|
||||
_.intersection = _.intersect = function(array) {
|
||||
var rest = slice.call(arguments, 1);
|
||||
return _.filter(_.uniq(array), function(item) {
|
||||
return _.every(rest, function(other) {
|
||||
return _.indexOf(other, item) >= 0;
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
// Take the difference between one array and a number of other arrays.
|
||||
// Only the elements present in just the first array will remain.
|
||||
_.difference = function(array) {
|
||||
var rest = _.flatten(slice.call(arguments, 1));
|
||||
return _.filter(array, function(value){ return !_.include(rest, value); });
|
||||
};
|
||||
|
||||
// Zip together multiple lists into a single array -- elements that share
|
||||
// an index go together.
|
||||
_.zip = function() {
|
||||
var args = slice.call(arguments);
|
||||
var length = _.max(_.pluck(args, 'length'));
|
||||
var results = new Array(length);
|
||||
for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
|
||||
return results;
|
||||
};
|
||||
|
||||
// If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
|
||||
// we need this function. Return the position of the first occurrence of an
|
||||
// item in an array, or -1 if the item is not included in the array.
|
||||
// Delegates to **ECMAScript 5**'s native `indexOf` if available.
|
||||
// If the array is large and already in sort order, pass `true`
|
||||
// for **isSorted** to use binary search.
|
||||
_.indexOf = function(array, item, isSorted) {
|
||||
if (array == null) return -1;
|
||||
var i, l;
|
||||
if (isSorted) {
|
||||
i = _.sortedIndex(array, item);
|
||||
return array[i] === item ? i : -1;
|
||||
}
|
||||
if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
|
||||
for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
|
||||
return -1;
|
||||
};
|
||||
|
||||
// Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
|
||||
_.lastIndexOf = function(array, item) {
|
||||
if (array == null) return -1;
|
||||
if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
|
||||
var i = array.length;
|
||||
while (i--) if (i in array && array[i] === item) return i;
|
||||
return -1;
|
||||
};
|
||||
|
||||
// Generate an integer Array containing an arithmetic progression. A port of
|
||||
// the native Python `range()` function. See
|
||||
// [the Python documentation](http://docs.python.org/library/functions.html#range).
|
||||
_.range = function(start, stop, step) {
|
||||
if (arguments.length <= 1) {
|
||||
stop = start || 0;
|
||||
start = 0;
|
||||
}
|
||||
step = arguments[2] || 1;
|
||||
|
||||
var len = Math.max(Math.ceil((stop - start) / step), 0);
|
||||
var idx = 0;
|
||||
var range = new Array(len);
|
||||
|
||||
while(idx < len) {
|
||||
range[idx++] = start;
|
||||
start += step;
|
||||
}
|
||||
|
||||
return range;
|
||||
};
|
||||
|
||||
// Function (ahem) Functions
|
||||
// ------------------
|
||||
|
||||
// Reusable constructor function for prototype setting.
|
||||
var ctor = function(){};
|
||||
|
||||
// Create a function bound to a given object (assigning `this`, and arguments,
|
||||
// optionally). Binding with arguments is also known as `curry`.
|
||||
// Delegates to **ECMAScript 5**'s native `Function.bind` if available.
|
||||
// We check for `func.bind` first, to fail fast when `func` is undefined.
|
||||
_.bind = function bind(func, context) {
|
||||
var bound, args;
|
||||
if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
|
||||
if (!_.isFunction(func)) throw new TypeError;
|
||||
args = slice.call(arguments, 2);
|
||||
return bound = function() {
|
||||
if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
|
||||
ctor.prototype = func.prototype;
|
||||
var self = new ctor;
|
||||
var result = func.apply(self, args.concat(slice.call(arguments)));
|
||||
if (Object(result) === result) return result;
|
||||
return self;
|
||||
};
|
||||
};
|
||||
|
||||
// Bind all of an object's methods to that object. Useful for ensuring that
|
||||
// all callbacks defined on an object belong to it.
|
||||
_.bindAll = function(obj) {
|
||||
var funcs = slice.call(arguments, 1);
|
||||
if (funcs.length == 0) funcs = _.functions(obj);
|
||||
each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
|
||||
return obj;
|
||||
};
|
||||
|
||||
// Memoize an expensive function by storing its results.
|
||||
_.memoize = function(func, hasher) {
|
||||
var memo = {};
|
||||
hasher || (hasher = _.identity);
|
||||
return function() {
|
||||
var key = hasher.apply(this, arguments);
|
||||
return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
|
||||
};
|
||||
};
|
||||
|
||||
// Delays a function for the given number of milliseconds, and then calls
|
||||
// it with the arguments supplied.
|
||||
_.delay = function(func, wait) {
|
||||
var args = slice.call(arguments, 2);
|
||||
return setTimeout(function(){ return func.apply(func, args); }, wait);
|
||||
};
|
||||
|
||||
// Defers a function, scheduling it to run after the current call stack has
|
||||
// cleared.
|
||||
_.defer = function(func) {
|
||||
return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
|
||||
};
|
||||
|
||||
// Returns a function, that, when invoked, will only be triggered at most once
|
||||
// during a given window of time.
|
||||
_.throttle = function(func, wait) {
|
||||
var context, args, timeout, throttling, more;
|
||||
var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
|
||||
return function() {
|
||||
context = this; args = arguments;
|
||||
var later = function() {
|
||||
timeout = null;
|
||||
if (more) func.apply(context, args);
|
||||
whenDone();
|
||||
};
|
||||
if (!timeout) timeout = setTimeout(later, wait);
|
||||
if (throttling) {
|
||||
more = true;
|
||||
} else {
|
||||
func.apply(context, args);
|
||||
}
|
||||
whenDone();
|
||||
throttling = true;
|
||||
};
|
||||
};
|
||||
|
||||
// Returns a function, that, as long as it continues to be invoked, will not
|
||||
// be triggered. The function will be called after it stops being called for
|
||||
// N milliseconds.
|
||||
_.debounce = function(func, wait) {
|
||||
var timeout;
|
||||
return function() {
|
||||
var context = this, args = arguments;
|
||||
var later = function() {
|
||||
timeout = null;
|
||||
func.apply(context, args);
|
||||
};
|
||||
clearTimeout(timeout);
|
||||
timeout = setTimeout(later, wait);
|
||||
};
|
||||
};
|
||||
|
||||
// Returns a function that will be executed at most one time, no matter how
|
||||
// often you call it. Useful for lazy initialization.
|
||||
_.once = function(func) {
|
||||
var ran = false, memo;
|
||||
return function() {
|
||||
if (ran) return memo;
|
||||
ran = true;
|
||||
return memo = func.apply(this, arguments);
|
||||
};
|
||||
};
|
||||
|
||||
// Returns the first function passed as an argument to the second,
|
||||
// allowing you to adjust arguments, run code before and after, and
|
||||
// conditionally execute the original function.
|
||||
_.wrap = function(func, wrapper) {
|
||||
return function() {
|
||||
var args = [func].concat(slice.call(arguments, 0));
|
||||
return wrapper.apply(this, args);
|
||||
};
|
||||
};
|
||||
|
||||
// Returns a function that is the composition of a list of functions, each
|
||||
// consuming the return value of the function that follows.
|
||||
_.compose = function() {
|
||||
var funcs = arguments;
|
||||
return function() {
|
||||
var args = arguments;
|
||||
for (var i = funcs.length - 1; i >= 0; i--) {
|
||||
args = [funcs[i].apply(this, args)];
|
||||
}
|
||||
return args[0];
|
||||
};
|
||||
};
|
||||
|
||||
// Returns a function that will only be executed after being called N times.
|
||||
_.after = function(times, func) {
|
||||
if (times <= 0) return func();
|
||||
return function() {
|
||||
if (--times < 1) { return func.apply(this, arguments); }
|
||||
};
|
||||
};
|
||||
|
||||
// Object Functions
|
||||
// ----------------
|
||||
|
||||
// Retrieve the names of an object's properties.
|
||||
// Delegates to **ECMAScript 5**'s native `Object.keys`
|
||||
_.keys = nativeKeys || function(obj) {
|
||||
if (obj !== Object(obj)) throw new TypeError('Invalid object');
|
||||
var keys = [];
|
||||
for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
|
||||
return keys;
|
||||
};
|
||||
|
||||
// Retrieve the values of an object's properties.
|
||||
_.values = function(obj) {
|
||||
return _.map(obj, _.identity);
|
||||
};
|
||||
|
||||
// Return a sorted list of the function names available on the object.
|
||||
// Aliased as `methods`
|
||||
_.functions = _.methods = function(obj) {
|
||||
var names = [];
|
||||
for (var key in obj) {
|
||||
if (_.isFunction(obj[key])) names.push(key);
|
||||
}
|
||||
return names.sort();
|
||||
};
|
||||
|
||||
// Extend a given object with all the properties in passed-in object(s).
|
||||
_.extend = function(obj) {
|
||||
each(slice.call(arguments, 1), function(source) {
|
||||
for (var prop in source) {
|
||||
obj[prop] = source[prop];
|
||||
}
|
||||
});
|
||||
return obj;
|
||||
};
|
||||
|
||||
// Fill in a given object with default properties.
|
||||
_.defaults = function(obj) {
|
||||
each(slice.call(arguments, 1), function(source) {
|
||||
for (var prop in source) {
|
||||
if (obj[prop] == null) obj[prop] = source[prop];
|
||||
}
|
||||
});
|
||||
return obj;
|
||||
};
|
||||
|
||||
// Create a (shallow-cloned) duplicate of an object.
|
||||
_.clone = function(obj) {
|
||||
if (!_.isObject(obj)) return obj;
|
||||
return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
|
||||
};
|
||||
|
||||
// Invokes interceptor with the obj, and then returns obj.
|
||||
// The primary purpose of this method is to "tap into" a method chain, in
|
||||
// order to perform operations on intermediate results within the chain.
|
||||
_.tap = function(obj, interceptor) {
|
||||
interceptor(obj);
|
||||
return obj;
|
||||
};
|
||||
|
||||
// Internal recursive comparison function.
|
||||
function eq(a, b, stack) {
|
||||
// Identical objects are equal. `0 === -0`, but they aren't identical.
|
||||
// See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
|
||||
if (a === b) return a !== 0 || 1 / a == 1 / b;
|
||||
// A strict comparison is necessary because `null == undefined`.
|
||||
if (a == null || b == null) return a === b;
|
||||
// Unwrap any wrapped objects.
|
||||
if (a._chain) a = a._wrapped;
|
||||
if (b._chain) b = b._wrapped;
|
||||
// Invoke a custom `isEqual` method if one is provided.
|
||||
if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
|
||||
if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
|
||||
// Compare `[[Class]]` names.
|
||||
var className = toString.call(a);
|
||||
if (className != toString.call(b)) return false;
|
||||
switch (className) {
|
||||
// Strings, numbers, dates, and booleans are compared by value.
|
||||
case '[object String]':
|
||||
// Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
|
||||
// equivalent to `new String("5")`.
|
||||
return a == String(b);
|
||||
case '[object Number]':
|
||||
// `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
|
||||
// other numeric values.
|
||||
return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
|
||||
case '[object Date]':
|
||||
case '[object Boolean]':
|
||||
// Coerce dates and booleans to numeric primitive values. Dates are compared by their
|
||||
// millisecond representations. Note that invalid dates with millisecond representations
|
||||
// of `NaN` are not equivalent.
|
||||
return +a == +b;
|
||||
// RegExps are compared by their source patterns and flags.
|
||||
case '[object RegExp]':
|
||||
return a.source == b.source &&
|
||||
a.global == b.global &&
|
||||
a.multiline == b.multiline &&
|
||||
a.ignoreCase == b.ignoreCase;
|
||||
}
|
||||
if (typeof a != 'object' || typeof b != 'object') return false;
|
||||
// Assume equality for cyclic structures. The algorithm for detecting cyclic
|
||||
// structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
|
||||
var length = stack.length;
|
||||
while (length--) {
|
||||
// Linear search. Performance is inversely proportional to the number of
|
||||
// unique nested structures.
|
||||
if (stack[length] == a) return true;
|
||||
}
|
||||
// Add the first object to the stack of traversed objects.
|
||||
stack.push(a);
|
||||
var size = 0, result = true;
|
||||
// Recursively compare objects and arrays.
|
||||
if (className == '[object Array]') {
|
||||
// Compare array lengths to determine if a deep comparison is necessary.
|
||||
size = a.length;
|
||||
result = size == b.length;
|
||||
if (result) {
|
||||
// Deep compare the contents, ignoring non-numeric properties.
|
||||
while (size--) {
|
||||
// Ensure commutative equality for sparse arrays.
|
||||
if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Objects with different constructors are not equivalent.
|
||||
if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
|
||||
// Deep compare objects.
|
||||
for (var key in a) {
|
||||
if (_.has(a, key)) {
|
||||
// Count the expected number of properties.
|
||||
size++;
|
||||
// Deep compare each member.
|
||||
if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
|
||||
}
|
||||
}
|
||||
// Ensure that both objects contain the same number of properties.
|
||||
if (result) {
|
||||
for (key in b) {
|
||||
if (_.has(b, key) && !(size--)) break;
|
||||
}
|
||||
result = !size;
|
||||
}
|
||||
}
|
||||
// Remove the first object from the stack of traversed objects.
|
||||
stack.pop();
|
||||
return result;
|
||||
}
|
||||
|
||||
// Perform a deep comparison to check if two objects are equal.
|
||||
_.isEqual = function(a, b) {
|
||||
return eq(a, b, []);
|
||||
};
|
||||
|
||||
// Is a given array, string, or object empty?
|
||||
// An "empty" object has no enumerable own-properties.
|
||||
_.isEmpty = function(obj) {
|
||||
if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
|
||||
for (var key in obj) if (_.has(obj, key)) return false;
|
||||
return true;
|
||||
};
|
||||
|
||||
// Is a given value a DOM element?
|
||||
_.isElement = function(obj) {
|
||||
return !!(obj && obj.nodeType == 1);
|
||||
};
|
||||
|
||||
// Is a given value an array?
|
||||
// Delegates to ECMA5's native Array.isArray
|
||||
_.isArray = nativeIsArray || function(obj) {
|
||||
return toString.call(obj) == '[object Array]';
|
||||
};
|
||||
|
||||
// Is a given variable an object?
|
||||
_.isObject = function(obj) {
|
||||
return obj === Object(obj);
|
||||
};
|
||||
|
||||
// Is a given variable an arguments object?
|
||||
_.isArguments = function(obj) {
|
||||
return toString.call(obj) == '[object Arguments]';
|
||||
};
|
||||
if (!_.isArguments(arguments)) {
|
||||
_.isArguments = function(obj) {
|
||||
return !!(obj && _.has(obj, 'callee'));
|
||||
};
|
||||
}
|
||||
|
||||
// Is a given value a function?
|
||||
_.isFunction = function(obj) {
|
||||
return toString.call(obj) == '[object Function]';
|
||||
};
|
||||
|
||||
// Is a given value a string?
|
||||
_.isString = function(obj) {
|
||||
return toString.call(obj) == '[object String]';
|
||||
};
|
||||
|
||||
// Is a given value a number?
|
||||
_.isNumber = function(obj) {
|
||||
return toString.call(obj) == '[object Number]';
|
||||
};
|
||||
|
||||
// Is the given value `NaN`?
|
||||
_.isNaN = function(obj) {
|
||||
// `NaN` is the only value for which `===` is not reflexive.
|
||||
return obj !== obj;
|
||||
};
|
||||
|
||||
// Is a given value a boolean?
|
||||
_.isBoolean = function(obj) {
|
||||
return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
|
||||
};
|
||||
|
||||
// Is a given value a date?
|
||||
_.isDate = function(obj) {
|
||||
return toString.call(obj) == '[object Date]';
|
||||
};
|
||||
|
||||
// Is the given value a regular expression?
|
||||
_.isRegExp = function(obj) {
|
||||
return toString.call(obj) == '[object RegExp]';
|
||||
};
|
||||
|
||||
// Is a given value equal to null?
|
||||
_.isNull = function(obj) {
|
||||
return obj === null;
|
||||
};
|
||||
|
||||
// Is a given variable undefined?
|
||||
_.isUndefined = function(obj) {
|
||||
return obj === void 0;
|
||||
};
|
||||
|
||||
// Has own property?
|
||||
_.has = function(obj, key) {
|
||||
return hasOwnProperty.call(obj, key);
|
||||
};
|
||||
|
||||
// Utility Functions
|
||||
// -----------------
|
||||
|
||||
// Run Underscore.js in *noConflict* mode, returning the `_` variable to its
|
||||
// previous owner. Returns a reference to the Underscore object.
|
||||
_.noConflict = function() {
|
||||
root._ = previousUnderscore;
|
||||
return this;
|
||||
};
|
||||
|
||||
// Keep the identity function around for default iterators.
|
||||
_.identity = function(value) {
|
||||
return value;
|
||||
};
|
||||
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||||
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|
||||
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Antes Largura: | Altura: | Tamanho: 214 B |
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|
Antes Largura: | Altura: | Tamanho: 203 B |
@@ -1,808 +0,0 @@
|
||||
/*
|
||||
* websupport.js
|
||||
* ~~~~~~~~~~~~~
|
||||
*
|
||||
* sphinx.websupport utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
(function($) {
|
||||
$.fn.autogrow = function() {
|
||||
return this.each(function() {
|
||||
var textarea = this;
|
||||
|
||||
$.fn.autogrow.resize(textarea);
|
||||
|
||||
$(textarea)
|
||||
.focus(function() {
|
||||
textarea.interval = setInterval(function() {
|
||||
$.fn.autogrow.resize(textarea);
|
||||
}, 500);
|
||||
})
|
||||
.blur(function() {
|
||||
clearInterval(textarea.interval);
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
$.fn.autogrow.resize = function(textarea) {
|
||||
var lineHeight = parseInt($(textarea).css('line-height'), 10);
|
||||
var lines = textarea.value.split('\n');
|
||||
var columns = textarea.cols;
|
||||
var lineCount = 0;
|
||||
$.each(lines, function() {
|
||||
lineCount += Math.ceil(this.length / columns) || 1;
|
||||
});
|
||||
var height = lineHeight * (lineCount + 1);
|
||||
$(textarea).css('height', height);
|
||||
};
|
||||
})(jQuery);
|
||||
|
||||
(function($) {
|
||||
var comp, by;
|
||||
|
||||
function init() {
|
||||
initEvents();
|
||||
initComparator();
|
||||
}
|
||||
|
||||
function initEvents() {
|
||||
$(document).on("click", 'a.comment-close', function(event) {
|
||||
event.preventDefault();
|
||||
hide($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.vote', function(event) {
|
||||
event.preventDefault();
|
||||
handleVote($(this));
|
||||
});
|
||||
$(document).on("click", 'a.reply', function(event) {
|
||||
event.preventDefault();
|
||||
openReply($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.close-reply', function(event) {
|
||||
event.preventDefault();
|
||||
closeReply($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.sort-option', function(event) {
|
||||
event.preventDefault();
|
||||
handleReSort($(this));
|
||||
});
|
||||
$(document).on("click", 'a.show-proposal', function(event) {
|
||||
event.preventDefault();
|
||||
showProposal($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.hide-proposal', function(event) {
|
||||
event.preventDefault();
|
||||
hideProposal($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.show-propose-change', function(event) {
|
||||
event.preventDefault();
|
||||
showProposeChange($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.hide-propose-change', function(event) {
|
||||
event.preventDefault();
|
||||
hideProposeChange($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.accept-comment', function(event) {
|
||||
event.preventDefault();
|
||||
acceptComment($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.delete-comment', function(event) {
|
||||
event.preventDefault();
|
||||
deleteComment($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.comment-markup', function(event) {
|
||||
event.preventDefault();
|
||||
toggleCommentMarkupBox($(this).attr('id').substring(2));
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Set comp, which is a comparator function used for sorting and
|
||||
* inserting comments into the list.
|
||||
*/
|
||||
function setComparator() {
|
||||
// If the first three letters are "asc", sort in ascending order
|
||||
// and remove the prefix.
|
||||
if (by.substring(0,3) == 'asc') {
|
||||
var i = by.substring(3);
|
||||
comp = function(a, b) { return a[i] - b[i]; };
|
||||
} else {
|
||||
// Otherwise sort in descending order.
|
||||
comp = function(a, b) { return b[by] - a[by]; };
|
||||
}
|
||||
|
||||
// Reset link styles and format the selected sort option.
|
||||
$('a.sel').attr('href', '#').removeClass('sel');
|
||||
$('a.by' + by).removeAttr('href').addClass('sel');
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a comp function. If the user has preferences stored in
|
||||
* the sortBy cookie, use those, otherwise use the default.
|
||||
*/
|
||||
function initComparator() {
|
||||
by = 'rating'; // Default to sort by rating.
|
||||
// If the sortBy cookie is set, use that instead.
|
||||
if (document.cookie.length > 0) {
|
||||
var start = document.cookie.indexOf('sortBy=');
|
||||
if (start != -1) {
|
||||
start = start + 7;
|
||||
var end = document.cookie.indexOf(";", start);
|
||||
if (end == -1) {
|
||||
end = document.cookie.length;
|
||||
by = unescape(document.cookie.substring(start, end));
|
||||
}
|
||||
}
|
||||
}
|
||||
setComparator();
|
||||
}
|
||||
|
||||
/**
|
||||
* Show a comment div.
|
||||
*/
|
||||
function show(id) {
|
||||
$('#ao' + id).hide();
|
||||
$('#ah' + id).show();
|
||||
var context = $.extend({id: id}, opts);
|
||||
var popup = $(renderTemplate(popupTemplate, context)).hide();
|
||||
popup.find('textarea[name="proposal"]').hide();
|
||||
popup.find('a.by' + by).addClass('sel');
|
||||
var form = popup.find('#cf' + id);
|
||||
form.submit(function(event) {
|
||||
event.preventDefault();
|
||||
addComment(form);
|
||||
});
|
||||
$('#s' + id).after(popup);
|
||||
popup.slideDown('fast', function() {
|
||||
getComments(id);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Hide a comment div.
|
||||
*/
|
||||
function hide(id) {
|
||||
$('#ah' + id).hide();
|
||||
$('#ao' + id).show();
|
||||
var div = $('#sc' + id);
|
||||
div.slideUp('fast', function() {
|
||||
div.remove();
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform an ajax request to get comments for a node
|
||||
* and insert the comments into the comments tree.
|
||||
*/
|
||||
function getComments(id) {
|
||||
$.ajax({
|
||||
type: 'GET',
|
||||
url: opts.getCommentsURL,
|
||||
data: {node: id},
|
||||
success: function(data, textStatus, request) {
|
||||
var ul = $('#cl' + id);
|
||||
var speed = 100;
|
||||
$('#cf' + id)
|
||||
.find('textarea[name="proposal"]')
|
||||
.data('source', data.source);
|
||||
|
||||
if (data.comments.length === 0) {
|
||||
ul.html('<li>No comments yet.</li>');
|
||||
ul.data('empty', true);
|
||||
} else {
|
||||
// If there are comments, sort them and put them in the list.
|
||||
var comments = sortComments(data.comments);
|
||||
speed = data.comments.length * 100;
|
||||
appendComments(comments, ul);
|
||||
ul.data('empty', false);
|
||||
}
|
||||
$('#cn' + id).slideUp(speed + 200);
|
||||
ul.slideDown(speed);
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem retrieving the comments.');
|
||||
},
|
||||
dataType: 'json'
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a comment via ajax and insert the comment into the comment tree.
|
||||
*/
|
||||
function addComment(form) {
|
||||
var node_id = form.find('input[name="node"]').val();
|
||||
var parent_id = form.find('input[name="parent"]').val();
|
||||
var text = form.find('textarea[name="comment"]').val();
|
||||
var proposal = form.find('textarea[name="proposal"]').val();
|
||||
|
||||
if (text == '') {
|
||||
showError('Please enter a comment.');
|
||||
return;
|
||||
}
|
||||
|
||||
// Disable the form that is being submitted.
|
||||
form.find('textarea,input').attr('disabled', 'disabled');
|
||||
|
||||
// Send the comment to the server.
|
||||
$.ajax({
|
||||
type: "POST",
|
||||
url: opts.addCommentURL,
|
||||
dataType: 'json',
|
||||
data: {
|
||||
node: node_id,
|
||||
parent: parent_id,
|
||||
text: text,
|
||||
proposal: proposal
|
||||
},
|
||||
success: function(data, textStatus, error) {
|
||||
// Reset the form.
|
||||
if (node_id) {
|
||||
hideProposeChange(node_id);
|
||||
}
|
||||
form.find('textarea')
|
||||
.val('')
|
||||
.add(form.find('input'))
|
||||
.removeAttr('disabled');
|
||||
var ul = $('#cl' + (node_id || parent_id));
|
||||
if (ul.data('empty')) {
|
||||
$(ul).empty();
|
||||
ul.data('empty', false);
|
||||
}
|
||||
insertComment(data.comment);
|
||||
var ao = $('#ao' + node_id);
|
||||
ao.find('img').attr({'src': opts.commentBrightImage});
|
||||
if (node_id) {
|
||||
// if this was a "root" comment, remove the commenting box
|
||||
// (the user can get it back by reopening the comment popup)
|
||||
$('#ca' + node_id).slideUp();
|
||||
}
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
form.find('textarea,input').removeAttr('disabled');
|
||||
showError('Oops, there was a problem adding the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Recursively append comments to the main comment list and children
|
||||
* lists, creating the comment tree.
|
||||
*/
|
||||
function appendComments(comments, ul) {
|
||||
$.each(comments, function() {
|
||||
var div = createCommentDiv(this);
|
||||
ul.append($(document.createElement('li')).html(div));
|
||||
appendComments(this.children, div.find('ul.comment-children'));
|
||||
// To avoid stagnating data, don't store the comments children in data.
|
||||
this.children = null;
|
||||
div.data('comment', this);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* After adding a new comment, it must be inserted in the correct
|
||||
* location in the comment tree.
|
||||
*/
|
||||
function insertComment(comment) {
|
||||
var div = createCommentDiv(comment);
|
||||
|
||||
// To avoid stagnating data, don't store the comments children in data.
|
||||
comment.children = null;
|
||||
div.data('comment', comment);
|
||||
|
||||
var ul = $('#cl' + (comment.node || comment.parent));
|
||||
var siblings = getChildren(ul);
|
||||
|
||||
var li = $(document.createElement('li'));
|
||||
li.hide();
|
||||
|
||||
// Determine where in the parents children list to insert this comment.
|
||||
for(i=0; i < siblings.length; i++) {
|
||||
if (comp(comment, siblings[i]) <= 0) {
|
||||
$('#cd' + siblings[i].id)
|
||||
.parent()
|
||||
.before(li.html(div));
|
||||
li.slideDown('fast');
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// If we get here, this comment rates lower than all the others,
|
||||
// or it is the only comment in the list.
|
||||
ul.append(li.html(div));
|
||||
li.slideDown('fast');
|
||||
}
|
||||
|
||||
function acceptComment(id) {
|
||||
$.ajax({
|
||||
type: 'POST',
|
||||
url: opts.acceptCommentURL,
|
||||
data: {id: id},
|
||||
success: function(data, textStatus, request) {
|
||||
$('#cm' + id).fadeOut('fast');
|
||||
$('#cd' + id).removeClass('moderate');
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem accepting the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function deleteComment(id) {
|
||||
$.ajax({
|
||||
type: 'POST',
|
||||
url: opts.deleteCommentURL,
|
||||
data: {id: id},
|
||||
success: function(data, textStatus, request) {
|
||||
var div = $('#cd' + id);
|
||||
if (data == 'delete') {
|
||||
// Moderator mode: remove the comment and all children immediately
|
||||
div.slideUp('fast', function() {
|
||||
div.remove();
|
||||
});
|
||||
return;
|
||||
}
|
||||
// User mode: only mark the comment as deleted
|
||||
div
|
||||
.find('span.user-id:first')
|
||||
.text('[deleted]').end()
|
||||
.find('div.comment-text:first')
|
||||
.text('[deleted]').end()
|
||||
.find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
|
||||
', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
|
||||
.remove();
|
||||
var comment = div.data('comment');
|
||||
comment.username = '[deleted]';
|
||||
comment.text = '[deleted]';
|
||||
div.data('comment', comment);
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem deleting the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function showProposal(id) {
|
||||
$('#sp' + id).hide();
|
||||
$('#hp' + id).show();
|
||||
$('#pr' + id).slideDown('fast');
|
||||
}
|
||||
|
||||
function hideProposal(id) {
|
||||
$('#hp' + id).hide();
|
||||
$('#sp' + id).show();
|
||||
$('#pr' + id).slideUp('fast');
|
||||
}
|
||||
|
||||
function showProposeChange(id) {
|
||||
$('#pc' + id).hide();
|
||||
$('#hc' + id).show();
|
||||
var textarea = $('#pt' + id);
|
||||
textarea.val(textarea.data('source'));
|
||||
$.fn.autogrow.resize(textarea[0]);
|
||||
textarea.slideDown('fast');
|
||||
}
|
||||
|
||||
function hideProposeChange(id) {
|
||||
$('#hc' + id).hide();
|
||||
$('#pc' + id).show();
|
||||
var textarea = $('#pt' + id);
|
||||
textarea.val('').removeAttr('disabled');
|
||||
textarea.slideUp('fast');
|
||||
}
|
||||
|
||||
function toggleCommentMarkupBox(id) {
|
||||
$('#mb' + id).toggle();
|
||||
}
|
||||
|
||||
/** Handle when the user clicks on a sort by link. */
|
||||
function handleReSort(link) {
|
||||
var classes = link.attr('class').split(/\s+/);
|
||||
for (var i=0; i<classes.length; i++) {
|
||||
if (classes[i] != 'sort-option') {
|
||||
by = classes[i].substring(2);
|
||||
}
|
||||
}
|
||||
setComparator();
|
||||
// Save/update the sortBy cookie.
|
||||
var expiration = new Date();
|
||||
expiration.setDate(expiration.getDate() + 365);
|
||||
document.cookie= 'sortBy=' + escape(by) +
|
||||
';expires=' + expiration.toUTCString();
|
||||
$('ul.comment-ul').each(function(index, ul) {
|
||||
var comments = getChildren($(ul), true);
|
||||
comments = sortComments(comments);
|
||||
appendComments(comments, $(ul).empty());
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Function to process a vote when a user clicks an arrow.
|
||||
*/
|
||||
function handleVote(link) {
|
||||
if (!opts.voting) {
|
||||
showError("You'll need to login to vote.");
|
||||
return;
|
||||
}
|
||||
|
||||
var id = link.attr('id');
|
||||
if (!id) {
|
||||
// Didn't click on one of the voting arrows.
|
||||
return;
|
||||
}
|
||||
// If it is an unvote, the new vote value is 0,
|
||||
// Otherwise it's 1 for an upvote, or -1 for a downvote.
|
||||
var value = 0;
|
||||
if (id.charAt(1) != 'u') {
|
||||
value = id.charAt(0) == 'u' ? 1 : -1;
|
||||
}
|
||||
// The data to be sent to the server.
|
||||
var d = {
|
||||
comment_id: id.substring(2),
|
||||
value: value
|
||||
};
|
||||
|
||||
// Swap the vote and unvote links.
|
||||
link.hide();
|
||||
$('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
|
||||
.show();
|
||||
|
||||
// The div the comment is displayed in.
|
||||
var div = $('div#cd' + d.comment_id);
|
||||
var data = div.data('comment');
|
||||
|
||||
// If this is not an unvote, and the other vote arrow has
|
||||
// already been pressed, unpress it.
|
||||
if ((d.value !== 0) && (data.vote === d.value * -1)) {
|
||||
$('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
|
||||
$('#' + (d.value == 1 ? 'd' : 'u') + 'v' + d.comment_id).show();
|
||||
}
|
||||
|
||||
// Update the comments rating in the local data.
|
||||
data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
|
||||
data.vote = d.value;
|
||||
div.data('comment', data);
|
||||
|
||||
// Change the rating text.
|
||||
div.find('.rating:first')
|
||||
.text(data.rating + ' point' + (data.rating == 1 ? '' : 's'));
|
||||
|
||||
// Send the vote information to the server.
|
||||
$.ajax({
|
||||
type: "POST",
|
||||
url: opts.processVoteURL,
|
||||
data: d,
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem casting that vote.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Open a reply form used to reply to an existing comment.
|
||||
*/
|
||||
function openReply(id) {
|
||||
// Swap out the reply link for the hide link
|
||||
$('#rl' + id).hide();
|
||||
$('#cr' + id).show();
|
||||
|
||||
// Add the reply li to the children ul.
|
||||
var div = $(renderTemplate(replyTemplate, {id: id})).hide();
|
||||
$('#cl' + id)
|
||||
.prepend(div)
|
||||
// Setup the submit handler for the reply form.
|
||||
.find('#rf' + id)
|
||||
.submit(function(event) {
|
||||
event.preventDefault();
|
||||
addComment($('#rf' + id));
|
||||
closeReply(id);
|
||||
})
|
||||
.find('input[type=button]')
|
||||
.click(function() {
|
||||
closeReply(id);
|
||||
});
|
||||
div.slideDown('fast', function() {
|
||||
$('#rf' + id).find('textarea').focus();
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Close the reply form opened with openReply.
|
||||
*/
|
||||
function closeReply(id) {
|
||||
// Remove the reply div from the DOM.
|
||||
$('#rd' + id).slideUp('fast', function() {
|
||||
$(this).remove();
|
||||
});
|
||||
|
||||
// Swap out the hide link for the reply link
|
||||
$('#cr' + id).hide();
|
||||
$('#rl' + id).show();
|
||||
}
|
||||
|
||||
/**
|
||||
* Recursively sort a tree of comments using the comp comparator.
|
||||
*/
|
||||
function sortComments(comments) {
|
||||
comments.sort(comp);
|
||||
$.each(comments, function() {
|
||||
this.children = sortComments(this.children);
|
||||
});
|
||||
return comments;
|
||||
}
|
||||
|
||||
/**
|
||||
* Get the children comments from a ul. If recursive is true,
|
||||
* recursively include childrens' children.
|
||||
*/
|
||||
function getChildren(ul, recursive) {
|
||||
var children = [];
|
||||
ul.children().children("[id^='cd']")
|
||||
.each(function() {
|
||||
var comment = $(this).data('comment');
|
||||
if (recursive)
|
||||
comment.children = getChildren($(this).find('#cl' + comment.id), true);
|
||||
children.push(comment);
|
||||
});
|
||||
return children;
|
||||
}
|
||||
|
||||
/** Create a div to display a comment in. */
|
||||
function createCommentDiv(comment) {
|
||||
if (!comment.displayed && !opts.moderator) {
|
||||
return $('<div class="moderate">Thank you! Your comment will show up '
|
||||
+ 'once it is has been approved by a moderator.</div>');
|
||||
}
|
||||
// Prettify the comment rating.
|
||||
comment.pretty_rating = comment.rating + ' point' +
|
||||
(comment.rating == 1 ? '' : 's');
|
||||
// Make a class (for displaying not yet moderated comments differently)
|
||||
comment.css_class = comment.displayed ? '' : ' moderate';
|
||||
// Create a div for this comment.
|
||||
var context = $.extend({}, opts, comment);
|
||||
var div = $(renderTemplate(commentTemplate, context));
|
||||
|
||||
// If the user has voted on this comment, highlight the correct arrow.
|
||||
if (comment.vote) {
|
||||
var direction = (comment.vote == 1) ? 'u' : 'd';
|
||||
div.find('#' + direction + 'v' + comment.id).hide();
|
||||
div.find('#' + direction + 'u' + comment.id).show();
|
||||
}
|
||||
|
||||
if (opts.moderator || comment.text != '[deleted]') {
|
||||
div.find('a.reply').show();
|
||||
if (comment.proposal_diff)
|
||||
div.find('#sp' + comment.id).show();
|
||||
if (opts.moderator && !comment.displayed)
|
||||
div.find('#cm' + comment.id).show();
|
||||
if (opts.moderator || (opts.username == comment.username))
|
||||
div.find('#dc' + comment.id).show();
|
||||
}
|
||||
return div;
|
||||
}
|
||||
|
||||
/**
|
||||
* A simple template renderer. Placeholders such as <%id%> are replaced
|
||||
* by context['id'] with items being escaped. Placeholders such as <#id#>
|
||||
* are not escaped.
|
||||
*/
|
||||
function renderTemplate(template, context) {
|
||||
var esc = $(document.createElement('div'));
|
||||
|
||||
function handle(ph, escape) {
|
||||
var cur = context;
|
||||
$.each(ph.split('.'), function() {
|
||||
cur = cur[this];
|
||||
});
|
||||
return escape ? esc.text(cur || "").html() : cur;
|
||||
}
|
||||
|
||||
return template.replace(/<([%#])([\w\.]*)\1>/g, function() {
|
||||
return handle(arguments[2], arguments[1] == '%' ? true : false);
|
||||
});
|
||||
}
|
||||
|
||||
/** Flash an error message briefly. */
|
||||
function showError(message) {
|
||||
$(document.createElement('div')).attr({'class': 'popup-error'})
|
||||
.append($(document.createElement('div'))
|
||||
.attr({'class': 'error-message'}).text(message))
|
||||
.appendTo('body')
|
||||
.fadeIn("slow")
|
||||
.delay(2000)
|
||||
.fadeOut("slow");
|
||||
}
|
||||
|
||||
/** Add a link the user uses to open the comments popup. */
|
||||
$.fn.comment = function() {
|
||||
return this.each(function() {
|
||||
var id = $(this).attr('id').substring(1);
|
||||
var count = COMMENT_METADATA[id];
|
||||
var title = count + ' comment' + (count == 1 ? '' : 's');
|
||||
var image = count > 0 ? opts.commentBrightImage : opts.commentImage;
|
||||
var addcls = count == 0 ? ' nocomment' : '';
|
||||
$(this)
|
||||
.append(
|
||||
$(document.createElement('a')).attr({
|
||||
href: '#',
|
||||
'class': 'sphinx-comment-open' + addcls,
|
||||
id: 'ao' + id
|
||||
})
|
||||
.append($(document.createElement('img')).attr({
|
||||
src: image,
|
||||
alt: 'comment',
|
||||
title: title
|
||||
}))
|
||||
.click(function(event) {
|
||||
event.preventDefault();
|
||||
show($(this).attr('id').substring(2));
|
||||
})
|
||||
)
|
||||
.append(
|
||||
$(document.createElement('a')).attr({
|
||||
href: '#',
|
||||
'class': 'sphinx-comment-close hidden',
|
||||
id: 'ah' + id
|
||||
})
|
||||
.append($(document.createElement('img')).attr({
|
||||
src: opts.closeCommentImage,
|
||||
alt: 'close',
|
||||
title: 'close'
|
||||
}))
|
||||
.click(function(event) {
|
||||
event.preventDefault();
|
||||
hide($(this).attr('id').substring(2));
|
||||
})
|
||||
);
|
||||
});
|
||||
};
|
||||
|
||||
var opts = {
|
||||
processVoteURL: '/_process_vote',
|
||||
addCommentURL: '/_add_comment',
|
||||
getCommentsURL: '/_get_comments',
|
||||
acceptCommentURL: '/_accept_comment',
|
||||
deleteCommentURL: '/_delete_comment',
|
||||
commentImage: '/static/_static/comment.png',
|
||||
closeCommentImage: '/static/_static/comment-close.png',
|
||||
loadingImage: '/static/_static/ajax-loader.gif',
|
||||
commentBrightImage: '/static/_static/comment-bright.png',
|
||||
upArrow: '/static/_static/up.png',
|
||||
downArrow: '/static/_static/down.png',
|
||||
upArrowPressed: '/static/_static/up-pressed.png',
|
||||
downArrowPressed: '/static/_static/down-pressed.png',
|
||||
voting: false,
|
||||
moderator: false
|
||||
};
|
||||
|
||||
if (typeof COMMENT_OPTIONS != "undefined") {
|
||||
opts = jQuery.extend(opts, COMMENT_OPTIONS);
|
||||
}
|
||||
|
||||
var popupTemplate = '\
|
||||
<div class="sphinx-comments" id="sc<%id%>">\
|
||||
<p class="sort-options">\
|
||||
Sort by:\
|
||||
<a href="#" class="sort-option byrating">best rated</a>\
|
||||
<a href="#" class="sort-option byascage">newest</a>\
|
||||
<a href="#" class="sort-option byage">oldest</a>\
|
||||
</p>\
|
||||
<div class="comment-header">Comments</div>\
|
||||
<div class="comment-loading" id="cn<%id%>">\
|
||||
loading comments... <img src="<%loadingImage%>" alt="" /></div>\
|
||||
<ul id="cl<%id%>" class="comment-ul"></ul>\
|
||||
<div id="ca<%id%>">\
|
||||
<p class="add-a-comment">Add a comment\
|
||||
(<a href="#" class="comment-markup" id="ab<%id%>">markup</a>):</p>\
|
||||
<div class="comment-markup-box" id="mb<%id%>">\
|
||||
reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
|
||||
<code>``code``</code>, \
|
||||
code blocks: <code>::</code> and an indented block after blank line</div>\
|
||||
<form method="post" id="cf<%id%>" class="comment-form" action="">\
|
||||
<textarea name="comment" cols="80"></textarea>\
|
||||
<p class="propose-button">\
|
||||
<a href="#" id="pc<%id%>" class="show-propose-change">\
|
||||
Propose a change ▹\
|
||||
</a>\
|
||||
<a href="#" id="hc<%id%>" class="hide-propose-change">\
|
||||
Propose a change ▿\
|
||||
</a>\
|
||||
</p>\
|
||||
<textarea name="proposal" id="pt<%id%>" cols="80"\
|
||||
spellcheck="false"></textarea>\
|
||||
<input type="submit" value="Add comment" />\
|
||||
<input type="hidden" name="node" value="<%id%>" />\
|
||||
<input type="hidden" name="parent" value="" />\
|
||||
</form>\
|
||||
</div>\
|
||||
</div>';
|
||||
|
||||
var commentTemplate = '\
|
||||
<div id="cd<%id%>" class="sphinx-comment<%css_class%>">\
|
||||
<div class="vote">\
|
||||
<div class="arrow">\
|
||||
<a href="#" id="uv<%id%>" class="vote" title="vote up">\
|
||||
<img src="<%upArrow%>" />\
|
||||
</a>\
|
||||
<a href="#" id="uu<%id%>" class="un vote" title="vote up">\
|
||||
<img src="<%upArrowPressed%>" />\
|
||||
</a>\
|
||||
</div>\
|
||||
<div class="arrow">\
|
||||
<a href="#" id="dv<%id%>" class="vote" title="vote down">\
|
||||
<img src="<%downArrow%>" id="da<%id%>" />\
|
||||
</a>\
|
||||
<a href="#" id="du<%id%>" class="un vote" title="vote down">\
|
||||
<img src="<%downArrowPressed%>" />\
|
||||
</a>\
|
||||
</div>\
|
||||
</div>\
|
||||
<div class="comment-content">\
|
||||
<p class="tagline comment">\
|
||||
<span class="user-id"><%username%></span>\
|
||||
<span class="rating"><%pretty_rating%></span>\
|
||||
<span class="delta"><%time.delta%></span>\
|
||||
</p>\
|
||||
<div class="comment-text comment"><#text#></div>\
|
||||
<p class="comment-opts comment">\
|
||||
<a href="#" class="reply hidden" id="rl<%id%>">reply ▹</a>\
|
||||
<a href="#" class="close-reply" id="cr<%id%>">reply ▿</a>\
|
||||
<a href="#" id="sp<%id%>" class="show-proposal">proposal ▹</a>\
|
||||
<a href="#" id="hp<%id%>" class="hide-proposal">proposal ▿</a>\
|
||||
<a href="#" id="dc<%id%>" class="delete-comment hidden">delete</a>\
|
||||
<span id="cm<%id%>" class="moderation hidden">\
|
||||
<a href="#" id="ac<%id%>" class="accept-comment">accept</a>\
|
||||
</span>\
|
||||
</p>\
|
||||
<pre class="proposal" id="pr<%id%>">\
|
||||
<#proposal_diff#>\
|
||||
</pre>\
|
||||
<ul class="comment-children" id="cl<%id%>"></ul>\
|
||||
</div>\
|
||||
<div class="clearleft"></div>\
|
||||
</div>\
|
||||
</div>';
|
||||
|
||||
var replyTemplate = '\
|
||||
<li>\
|
||||
<div class="reply-div" id="rd<%id%>">\
|
||||
<form id="rf<%id%>">\
|
||||
<textarea name="comment" cols="80"></textarea>\
|
||||
<input type="submit" value="Add reply" />\
|
||||
<input type="button" value="Cancel" />\
|
||||
<input type="hidden" name="parent" value="<%id%>" />\
|
||||
<input type="hidden" name="node" value="" />\
|
||||
</form>\
|
||||
</div>\
|
||||
</li>';
|
||||
|
||||
$(document).ready(function() {
|
||||
init();
|
||||
});
|
||||
})(jQuery);
|
||||
|
||||
$(document).ready(function() {
|
||||
// add comment anchors for all paragraphs that are commentable
|
||||
$('.sphinx-has-comment').comment();
|
||||
|
||||
// highlight search words in search results
|
||||
$("div.context").each(function() {
|
||||
var params = $.getQueryParameters();
|
||||
var terms = (params.q) ? params.q[0].split(/\s+/) : [];
|
||||
var result = $(this);
|
||||
$.each(terms, function() {
|
||||
result.highlightText(this.toLowerCase(), 'highlighted');
|
||||
});
|
||||
});
|
||||
|
||||
// directly open comment window if requested
|
||||
var anchor = document.location.hash;
|
||||
if (anchor.substring(0, 9) == '#comment-') {
|
||||
$('#ao' + anchor.substring(9)).click();
|
||||
document.location.hash = '#s' + anchor.substring(9);
|
||||
}
|
||||
});
|
||||
@@ -0,0 +1 @@
|
||||
from braindecode.version import __version__
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Loader code for some datasets.
|
||||
"""
|
||||
@@ -0,0 +1,399 @@
|
||||
import logging
|
||||
import re
|
||||
from glob import glob
|
||||
import os.path
|
||||
|
||||
import numpy as np
|
||||
import h5py
|
||||
import mne
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BBCIDataset(object):
|
||||
"""
|
||||
Loader class for files created by saving BBCI files in matlab (make
|
||||
sure to save with '-v7.3' in matlab, see
|
||||
https://de.mathworks.com/help/matlab/import_export/mat-file-versions.html#buk6i87
|
||||
)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
filename: str
|
||||
load_sensor_names: list of str, optional
|
||||
Also speeds up loading if you only load some sensors.
|
||||
None means load all sensors.
|
||||
check_class_names: bool, optional
|
||||
check if the class names are part of some known class names at
|
||||
Translational NeuroTechnology Lab, AG Ball, Freiburg, Germany.
|
||||
"""
|
||||
def __init__(self, filename, load_sensor_names=None, check_class_names=False):
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def load(self):
|
||||
cnt = self._load_continuous_signal()
|
||||
cnt = self._add_markers(cnt)
|
||||
return cnt
|
||||
|
||||
def _load_continuous_signal(self):
|
||||
wanted_chan_inds, wanted_sensor_names = self._determine_sensors()
|
||||
fs = self._determine_samplingrate()
|
||||
with h5py.File(self.filename, 'r') as h5file:
|
||||
samples = int(h5file['nfo']['T'][0, 0])
|
||||
cnt_signal_shape = (samples, len(wanted_chan_inds))
|
||||
continuous_signal = np.ones(cnt_signal_shape,
|
||||
dtype=np.float32) * np.nan
|
||||
for chan_ind_arr, chan_ind_set in enumerate(wanted_chan_inds):
|
||||
# + 1 because matlab/this hdf5-naming logic
|
||||
# has 1-based indexing
|
||||
# i.e ch1,ch2,....
|
||||
chan_set_name = 'ch' + str(chan_ind_set + 1)
|
||||
# first 0 to unpack into vector, before it is 1xN matrix
|
||||
chan_signal = h5file[chan_set_name][
|
||||
:].squeeze() # already load into memory
|
||||
continuous_signal[:, chan_ind_arr] = chan_signal
|
||||
assert not np.any(
|
||||
np.isnan(continuous_signal)), "No NaNs expected in signal"
|
||||
|
||||
# Assume we cant know channel type here automatically
|
||||
ch_types = ['misc'] * len(wanted_chan_inds)
|
||||
info = mne.create_info(ch_names=wanted_sensor_names, sfreq=fs,
|
||||
ch_types=ch_types)
|
||||
|
||||
cnt = mne.io.RawArray(continuous_signal.T, info)
|
||||
return cnt
|
||||
|
||||
def _determine_sensors(self):
|
||||
all_sensor_names = self.get_all_sensors(self.filename, pattern=None)
|
||||
if self.load_sensor_names is None:
|
||||
|
||||
# if no sensor names given, take all EEG-chans
|
||||
eeg_sensor_names = all_sensor_names
|
||||
eeg_sensor_names = filter(lambda s: not s.startswith('BIP'),
|
||||
eeg_sensor_names)
|
||||
eeg_sensor_names = filter(lambda s: not s.startswith('E'),
|
||||
eeg_sensor_names)
|
||||
eeg_sensor_names = filter(lambda s: not s.startswith('Microphone'),
|
||||
eeg_sensor_names)
|
||||
eeg_sensor_names = filter(lambda s: not s.startswith('Breath'),
|
||||
eeg_sensor_names)
|
||||
eeg_sensor_names = filter(lambda s: not s.startswith('GSR'),
|
||||
eeg_sensor_names)
|
||||
eeg_sensor_names = list(eeg_sensor_names)
|
||||
assert (len(eeg_sensor_names) == 128 or
|
||||
len(eeg_sensor_names) == 64 or
|
||||
len(eeg_sensor_names) == 32 or
|
||||
len(eeg_sensor_names) == 16), (
|
||||
"Recheck this code if you have different sensors...")
|
||||
self.load_sensor_names = eeg_sensor_names
|
||||
chan_inds = self._determine_chan_inds(all_sensor_names,
|
||||
self.load_sensor_names)
|
||||
return chan_inds, self.load_sensor_names
|
||||
|
||||
def _determine_samplingrate(self):
|
||||
with h5py.File(self.filename, 'r') as h5file:
|
||||
fs = h5file['nfo']['fs'][0, 0]
|
||||
assert isinstance(fs, int) or fs.is_integer()
|
||||
fs = int(fs)
|
||||
return fs
|
||||
|
||||
@staticmethod
|
||||
def _determine_chan_inds(all_sensor_names, sensor_names):
|
||||
assert sensor_names is not None
|
||||
chan_inds = [all_sensor_names.index(s) for s in sensor_names]
|
||||
assert len(chan_inds) == len(sensor_names), ("All"
|
||||
"sensors should be there.")
|
||||
assert len(set(chan_inds)) == len(chan_inds), ("No"
|
||||
"duplicated sensors wanted.")
|
||||
return chan_inds
|
||||
|
||||
@staticmethod
|
||||
def get_all_sensors(filename, pattern=None):
|
||||
"""
|
||||
Get all sensors that exist in the given file.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
filename: str
|
||||
pattern: str, optional
|
||||
Only return those sensor names that match the given pattern.
|
||||
|
||||
Returns
|
||||
-------
|
||||
sensor_names: list of str
|
||||
Sensor names that match the pattern or all sensor names in the file.
|
||||
"""
|
||||
with h5py.File(filename, 'r') as h5file:
|
||||
clab_set = h5file['nfo']['clab'][:].squeeze()
|
||||
all_sensor_names = [''.join(chr(c) for c in h5file[obj_ref]) for
|
||||
obj_ref in clab_set]
|
||||
if pattern is not None:
|
||||
all_sensor_names = filter(
|
||||
lambda sname: re.search(pattern, sname),
|
||||
all_sensor_names)
|
||||
return all_sensor_names
|
||||
|
||||
def _add_markers(self, cnt):
|
||||
with h5py.File(self.filename, 'r') as h5file:
|
||||
event_times_in_ms = h5file['mrk']['time'][:].squeeze()
|
||||
event_classes = h5file['mrk']['event']['desc'][:].squeeze().astype(np.int64)
|
||||
|
||||
# Check whether class names known and correct order
|
||||
class_name_set = h5file['nfo']['className'][:].squeeze()
|
||||
all_class_names = [''.join(chr(c) for c in h5file[obj_ref])
|
||||
for obj_ref in class_name_set]
|
||||
|
||||
if self.check_class_names:
|
||||
_check_class_names(
|
||||
all_class_names, event_times_in_ms, event_classes,)
|
||||
|
||||
event_times_in_samples = event_times_in_ms * cnt.info['sfreq'] / 1000.0
|
||||
event_times_in_samples = np.uint32(np.round(event_times_in_samples))
|
||||
|
||||
# Check if there are markers at the same time
|
||||
previous_i_sample = -1
|
||||
for i_event, (i_sample, id_class) in enumerate(
|
||||
zip(event_times_in_samples, event_classes)):
|
||||
if i_sample == previous_i_sample:
|
||||
log.warning("Same sample has at least two markers.\n"
|
||||
"{:d}: ({:.0f} and {:.0f}).\n".format(i_sample,
|
||||
event_classes[
|
||||
i_event - 1],
|
||||
event_classes[
|
||||
i_event]) +
|
||||
"Marker codes will be summed.")
|
||||
previous_i_sample = i_sample
|
||||
|
||||
# Now create stim chan
|
||||
stim_chan = np.zeros_like(cnt.get_data()[0])
|
||||
for i_sample, id_class in zip(event_times_in_samples, event_classes):
|
||||
stim_chan[i_sample] += id_class
|
||||
info = mne.create_info(ch_names=['STI 014'],
|
||||
sfreq=cnt.info['sfreq'],
|
||||
ch_types=['stim'])
|
||||
stim_cnt = mne.io.RawArray(stim_chan[None], info, verbose='WARNING')
|
||||
cnt = cnt.add_channels([stim_cnt])
|
||||
event_arr = [event_times_in_samples,
|
||||
[0] * len(event_times_in_samples),
|
||||
event_classes]
|
||||
cnt.info['events'] = np.array(event_arr).T
|
||||
return cnt
|
||||
|
||||
|
||||
def _check_class_names(all_class_names, event_times_in_ms, event_classes, ):
|
||||
"""
|
||||
Checks if the class names are part of some known class names used in
|
||||
translational neurotechnology lab, AG Ball, Freiburg.
|
||||
|
||||
Logs warning in case class names are not known.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
all_class_names: list of str
|
||||
event_times_in_ms: list of number
|
||||
event_classes: list of number
|
||||
|
||||
"""
|
||||
if all_class_names == ['Right Hand', 'Left Hand', 'Rest', 'Feet']:
|
||||
pass
|
||||
elif ((all_class_names == ['1', '10', '11', '111', '12', '13',
|
||||
'150',
|
||||
'2', '20', '22', '3', '30', '33', '4',
|
||||
'40', '44', '99']) or
|
||||
(all_class_names == ['1', '10', '11', '12', '13', '150',
|
||||
'2', '20', '22', '3', '30', '33',
|
||||
'4', '40', '44', '99']) or
|
||||
(all_class_names == ['1', '2', '3', '4'])):
|
||||
pass # Semantic classes
|
||||
elif all_class_names == ['Rest', 'Feet', 'Left Hand', 'Right Hand']:
|
||||
# Have to swap from
|
||||
# ['Rest', 'Feet', 'Left Hand', 'Right Hand']
|
||||
# to
|
||||
# ['Right Hand', 'Left Hand', 'Rest', 'Feet']
|
||||
right_mask = event_classes == 4
|
||||
left_mask = event_classes == 3
|
||||
rest_mask = event_classes == 1
|
||||
feet_mask = event_classes == 2
|
||||
event_classes[right_mask] = 1
|
||||
event_classes[left_mask] = 2
|
||||
event_classes[rest_mask] = 3
|
||||
event_classes[feet_mask] = 4
|
||||
log.warn("Swapped class names {:s}... might cause problems...".format(
|
||||
all_class_names))
|
||||
elif all_class_names == ['Right Hand Start', 'Left Hand Start',
|
||||
'Rest Start', 'Feet Start',
|
||||
'Right Hand End',
|
||||
'Left Hand End', 'Rest End', 'Feet End']:
|
||||
pass
|
||||
elif all_class_names == ['Right Hand', 'Left Hand', 'Rest',
|
||||
'Feet', 'Face', 'Navigation', 'Music',
|
||||
'Rotation',
|
||||
'Subtraction', 'Words']:
|
||||
pass # robot hall 10 class decoding
|
||||
elif (all_class_names == ['RightHand', 'Feet', 'Rotation', 'Words',
|
||||
'\x00\x00', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'RightHand_End', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'Feet_End', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'Rotation_End', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'Words_End'] or
|
||||
all_class_names == ['RightHand', 'Feet', 'Rotation',
|
||||
'Words',
|
||||
'Rest', '\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'RightHand_End', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', 'Feet_End',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', 'Rotation_End',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'Words_End', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'\x00\x00',
|
||||
'\x00\x00', '\x00\x00',
|
||||
'Rest_End']):
|
||||
pass # weird stuff when we recorded cursor in robot hall
|
||||
# on 2016-09-14 and 2016-09-16 :D
|
||||
|
||||
elif (all_class_names == ['0004', '0016', '0032', '0056', '0064',
|
||||
'0088', '0095', '0120']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0056', '0088', '0120']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0016', '0032', '0048', '0056',
|
||||
'0064', '0080', '0088', '0095', '0120']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0016', '0056', '0088', '0120',
|
||||
'__']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0056', '0088', '0120', '__']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0032', '0048', '0056', '0064',
|
||||
'0080', '0088', '0095', '0120', '__']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0056', '0080', '0088', '0096',
|
||||
'0120', '__']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0032', '0056', '0064', '0080',
|
||||
'0088', '0095', '0120']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0032', '0048', '0056', '0064',
|
||||
'0080', '0088', '0095', '0120']):
|
||||
pass
|
||||
elif (all_class_names == ['0004', '0016', '0032', '0048', '0056',
|
||||
'0064', '0080', '0088', '0095', '0096',
|
||||
'0120']):
|
||||
pass
|
||||
elif (all_class_names == ['4', '16', '32', '56', '64', '88', '95',
|
||||
'120']):
|
||||
pass
|
||||
elif (all_class_names == ['4', '56', '88', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['4', '16', '32', '48', '56',
|
||||
'64', '80', '88', '95', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['0', '4', '56', '88', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['0', '4', '16', '56', '88', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['0', '4', '32', '48', '56', '64', '80',
|
||||
'88', '95', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['0', '4', '56', '80', '88', '96', '120']):
|
||||
pass
|
||||
elif (all_class_names == ['4', '32', '56', '64', '80', '88', '95',
|
||||
'120']):
|
||||
pass
|
||||
elif (all_class_names == ['One', 'Two', 'Three', 'Four']):
|
||||
pass
|
||||
elif (
|
||||
all_class_names == ['1', '10', '11', '12', '2', '20', '3', '30',
|
||||
'4', '40']):
|
||||
pass
|
||||
elif (
|
||||
all_class_names == ['1', '10', '12', '13', '2', '20', '3', '30',
|
||||
'4', '40']):
|
||||
pass
|
||||
elif (
|
||||
all_class_names == ['1', '10', '13', '2', '20', '3', '30', '4',
|
||||
'40', '99']):
|
||||
pass
|
||||
elif (all_class_names == ['1', '10', '11', '14', '18', '20', '21',
|
||||
'24', '251', '252', '28', '30', '4',
|
||||
'8']):
|
||||
pass
|
||||
elif (all_class_names == ['1', '10', '11', '14', '18', '20', '21',
|
||||
'24', '252', '253', '28', '30', '4',
|
||||
'8']):
|
||||
pass
|
||||
elif len(event_times_in_ms) == len(all_class_names):
|
||||
pass # weird neuroone(?) logic where class names have event classes
|
||||
elif (all_class_names == ['Right_hand_stimulus_onset',
|
||||
'Feet_stimulus_onset',
|
||||
'Rotation_stimulus_onset',
|
||||
'Words_stimulus_onset',
|
||||
'Right_hand_stimulus_offset',
|
||||
'Feet_stimulus_offset',
|
||||
'Rotation_stimulus_offset',
|
||||
'Words_stimulus_offset']):
|
||||
pass
|
||||
else:
|
||||
# remove this whole if else stuffs?
|
||||
log.warn("Unknown class names {:s}".format(
|
||||
all_class_names))
|
||||
|
||||
|
||||
def load_bbci_sets_from_folder(folder, runs='all'):
|
||||
"""
|
||||
Load bbci datasets from files in given folder.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
folder: str
|
||||
Folder with .BBCI.mat files inside
|
||||
runs: list of int
|
||||
If you only want to load specific runs.
|
||||
Assumes filenames with such kind of part: S001R02 for Run 2.
|
||||
Tries to match this regex: ``'S[0-9]{3,3}R[0-9]{2,2}_'``.
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
bbci_mat_files = sorted(glob(os.path.join(folder, '*.BBCI.mat')))
|
||||
if runs != 'all':
|
||||
file_run_numbers = [int(re.search('S[0-9]{3,3}R[0-9]{2,2}_', f).group()[5:7])
|
||||
for f in bbci_mat_files]
|
||||
indices = [file_run_numbers.index(num) for num in runs]
|
||||
|
||||
wanted_files = np.array(bbci_mat_files)[indices]
|
||||
else:
|
||||
wanted_files = bbci_mat_files
|
||||
cnts = []
|
||||
for f in wanted_files:
|
||||
log.info("Loading {:s}".format(f))
|
||||
cnts.append(BBCIDataset(f).load())
|
||||
return cnts
|
||||
@@ -0,0 +1,79 @@
|
||||
import numpy as np
|
||||
import mne
|
||||
from scipy.io import loadmat
|
||||
|
||||
class BCICompetition4Set2A(object):
|
||||
def __init__(self, filename, load_sensor_names=None,
|
||||
labels_filename=None):
|
||||
assert load_sensor_names is None
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def load(self):
|
||||
cnt = self.extract_data()
|
||||
events, artifact_trial_mask = self.extract_events(cnt)
|
||||
cnt.info['events'] = events
|
||||
cnt.info['artifact_trial_mask'] = artifact_trial_mask
|
||||
return cnt
|
||||
|
||||
def extract_data(self):
|
||||
raw_edf = mne.io.read_raw_edf(self.filename, stim_channel='auto')
|
||||
raw_edf.load_data()
|
||||
# correct nan values
|
||||
|
||||
data = raw_edf.get_data()
|
||||
|
||||
# do not correct stimulus channel
|
||||
assert raw_edf.ch_names[-1] == 'STI 014'
|
||||
for i_chan in range(data.shape[0] - 1):
|
||||
# first set to nan, than replace nans by nanmean.
|
||||
this_chan = data[i_chan]
|
||||
data[i_chan] = np.where(this_chan == np.min(this_chan),
|
||||
np.nan, this_chan)
|
||||
mask = np.isnan(data[i_chan])
|
||||
chan_mean = np.nanmean(data[i_chan])
|
||||
data[i_chan, mask] = chan_mean
|
||||
gdf_events = raw_edf.find_edf_events()
|
||||
raw_edf = mne.io.RawArray(data, raw_edf.info, verbose='WARNING')
|
||||
# remember gdf events
|
||||
raw_edf.info['gdf_events'] = gdf_events
|
||||
return raw_edf
|
||||
|
||||
def extract_events(self, raw_edf):
|
||||
# all events
|
||||
events = np.array(list(zip(
|
||||
raw_edf.info['gdf_events'][1],
|
||||
raw_edf.info['gdf_events'][2])))
|
||||
|
||||
# only trial onset events
|
||||
trial_mask = [ev_code in [769, 770, 771, 772, 783]
|
||||
for ev_code in events[:,1]]
|
||||
trial_events = events[trial_mask]
|
||||
assert (len(trial_events) == 288), (
|
||||
"Got {:d} markers".format(len(trial_events)))
|
||||
# event markers 769,770 -> 1,2
|
||||
trial_events[:, 1] = trial_events[:, 1] - 768
|
||||
# possibly overwrite with markers from labels file
|
||||
if self.labels_filename is not None:
|
||||
classes = loadmat(self.labels_filename)['classlabel'].squeeze()
|
||||
trial_events[:, 1] = classes
|
||||
unique_classes = np.unique(trial_events[:, 1])
|
||||
assert np.array_equal([1, 2, 3 ,4], unique_classes), (
|
||||
"Expect 1,2,3,4 as class labels, got {:s}".format(
|
||||
str(unique_classes))
|
||||
)
|
||||
# now also create 0-1 vector for rejected trials
|
||||
trial_start_events = events[events[:, 1] == 768]
|
||||
assert len(trial_start_events) == len(trial_events)
|
||||
artifact_trial_mask = np.zeros(len(trial_events), dtype=np.uint8)
|
||||
artifact_events = events[events[:, 1] == 1023]
|
||||
for artifact_time in artifact_events[:, 0]:
|
||||
i_trial = trial_start_events[:, 0].tolist().index(artifact_time)
|
||||
artifact_trial_mask[i_trial] = 1
|
||||
|
||||
# mne expects events with 3 ints each:
|
||||
events = np.zeros((len(trial_events), 3), dtype=np.int32)
|
||||
events[:,0] = trial_events[:,0]
|
||||
events[:,2] = trial_events[:,1]
|
||||
|
||||
return events, artifact_trial_mask
|
||||
@@ -0,0 +1,45 @@
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
|
||||
class LazyDataset(ABC):
|
||||
""" Class implementing an abstract lazy data set. Custom lazy data sets
|
||||
have to override file_paths, X and y as well as the load_lazy function to
|
||||
load trials or crops. """
|
||||
def __init__(self):
|
||||
self.file_paths = "Not implemented: a list of all file paths"
|
||||
self.X = ("Not implemented: a list of empty ndarrays with number of "
|
||||
"samples as second dimension")
|
||||
self.y = "Not implemented: a list of all targets"
|
||||
|
||||
@abstractmethod
|
||||
def load_lazy(self, path, start_i, stop_i):
|
||||
""" Loading procedure that gets a file path, start and stop indices.
|
||||
Is supposed to return a trial / crop together with its target
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path: str
|
||||
file path
|
||||
start_i: int
|
||||
start index of signal crop
|
||||
stop_i: int
|
||||
stop index of signal crop
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def __len__(self):
|
||||
return len(self.X)
|
||||
|
||||
def __getitem__(self, idx):
|
||||
""" Returns a two-tuple of example, label """
|
||||
try:
|
||||
idx, start_i, stop_i = idx
|
||||
except (TypeError, ValueError):
|
||||
start_i = 0
|
||||
stop_i = None
|
||||
file_path = self.file_paths[idx]
|
||||
x = self.load_lazy(file_path, start_i, stop_i)
|
||||
|
||||
if x.ndim == 2:
|
||||
x = x[:, :, None]
|
||||
return x, self.y[idx]
|
||||
@@ -0,0 +1,41 @@
|
||||
import logging
|
||||
|
||||
from braindecode.mne_ext.signalproc import resample_cnt, \
|
||||
concatenate_raws_with_events
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MultipleSetLoader(object):
|
||||
"""
|
||||
Class to load multiple sets.
|
||||
|
||||
Resamples individual sets down to lowest common sampling frequency,
|
||||
if necessary.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
set_loaders: object with load method: () -> MNE Raw object
|
||||
The individual set loaders.
|
||||
"""
|
||||
|
||||
def __init__(self, set_loaders):
|
||||
self.set_loaders = set_loaders
|
||||
|
||||
def load(self):
|
||||
cnt = self.set_loaders[0].load()
|
||||
for loader in self.set_loaders[1:]:
|
||||
next_cnt = loader.load()
|
||||
# always sample down to lowest common denominator
|
||||
if next_cnt.fs > cnt.fs:
|
||||
log.warning("Next set has larger sampling rate ({:d}) "
|
||||
"than before ({:d}), resampling next set".format(
|
||||
next_cnt.fs, cnt.fs))
|
||||
next_cnt = resample_cnt(next_cnt, cnt.fs)
|
||||
if next_cnt.fs < cnt.fs:
|
||||
log.warning("Next set has smaller sampling rate ({:d}) "
|
||||
"than before ({:d}), resampling set so far".format(
|
||||
next_cnt.fs, cnt.fs))
|
||||
cnt = resample_cnt(cnt, next_cnt.fs)
|
||||
cnt = concatenate_raws_with_events(cnt, next_cnt)
|
||||
return cnt
|
||||
@@ -0,0 +1,223 @@
|
||||
import numpy as np
|
||||
import math
|
||||
|
||||
|
||||
CHANNEL_10_20_APPROX = ('angle',
|
||||
('Fpz',(0.000, 4.000)),
|
||||
('Fp1',(-3.500, 3.500)),
|
||||
('Fp2',(3.500, 3.500)),
|
||||
('AFp3h',(-1.000, 3.500)),
|
||||
('AFp4h',(1.000, 3.500)),
|
||||
('AF7',(-4.000, 3.000)),
|
||||
('AF3',(-2.000, 3.000)),
|
||||
('AFz',(0.000, 3.000)),
|
||||
('AF4',(2.000, 3.000)),
|
||||
('AF8',(4.000, 3.000)),
|
||||
('AFF5h',(-2.500, 2.500)),
|
||||
('AFF1',(-0.500, 2.500)),
|
||||
('AFF2',(0.500, 2.500)),
|
||||
('AFF6h',(2.500, 2.500)),
|
||||
('F7',(-4.000, 2.000)),
|
||||
('F5',(-3.000, 2.000)),
|
||||
('F3',(-2.000, 2.000)),
|
||||
('F1',(-1.000, 2.000)),
|
||||
('Fz',(0.000, 2.000)),
|
||||
('F2',(1.000, 2.000)),
|
||||
('F4',(2.000, 2.000)),
|
||||
('F6',(3.000, 2.000)),
|
||||
('F8',(4.000, 2.000)),
|
||||
('FFT7h',(-3.500, 1.500)),
|
||||
('FFC5h',(-2.500, 1.500)),
|
||||
('FFC3h',(-1.500, 1.500)),
|
||||
('FFC1h',(-0.500, 1.500)),
|
||||
('FFC2h',(0.500, 1.500)),
|
||||
('FFC4h',(1.500, 1.500)),
|
||||
('FFC6h',(2.500, 1.500)),
|
||||
('FFT8h',(3.500, 1.500)),
|
||||
('FT9',(-5.000, 1.000)),
|
||||
('FT7',(-4.000, 1.000)),
|
||||
('FC5',(-3.000, 1.000)),
|
||||
('FC3',(-2.000, 1.000)),
|
||||
('FC1',(-1.000, 1.000)),
|
||||
('FCz',(0.000, 1.000)),
|
||||
('FC2',(1.000, 1.000)),
|
||||
('FC4',(2.000, 1.000)),
|
||||
('FC6',(3.000, 1.000)),
|
||||
('FT8',(4.000, 1.000)),
|
||||
('FT10',(5.000, 1.000)),
|
||||
('FTT9h',(-4.500, 0.500)),
|
||||
('FTT7h',(-3.500, 0.500)),
|
||||
('FCC5h',(-2.500, 0.500)),
|
||||
('FCC3h',(-1.500, 0.500)),
|
||||
('FCC1h',(-0.500, 0.500)),
|
||||
('FCC2h',(0.500, 0.500)),
|
||||
('FCC4h',(1.500, 0.500)),
|
||||
('FCC6h',(2.500, 0.500)),
|
||||
('FTT8h',(3.500, 0.500)),
|
||||
('FTT10h',(4.500, 0.500)),
|
||||
('M1',(-5.000, 0.000)),
|
||||
# notsure if correct:
|
||||
('T9', (-4.500, 0.000)),
|
||||
('T7',(-4.000, 0.000)),
|
||||
('C5',(-3.000, 0.000)),
|
||||
('C3',(-2.000, 0.000)),
|
||||
('C1',(-1.000, 0.000)),
|
||||
('Cz',(0.000, 0.000)),
|
||||
('C2',(1.000, 0.000)),
|
||||
('C4',(2.000, 0.000)),
|
||||
('C6',(3.000, 0.000)),
|
||||
('T8',(4.000, 0.000)),
|
||||
('T10', (4.500, 0.000)),
|
||||
('M2',(5.000, 0.000)),
|
||||
('TTP7h',(-3.500, -0.500)),
|
||||
('CCP5h',(-2.500, -0.500)),
|
||||
('CCP3h',(-1.500, -0.500)),
|
||||
('CCP1h',(-0.500, -0.500)),
|
||||
('CCP2h',(0.500, -0.500)),
|
||||
('CCP4h',(1.500, -0.500)),
|
||||
('CCP6h',(2.500, -0.500)),
|
||||
('TTP8h',(3.500, -0.500)),
|
||||
('TP7',(-4.000, -1.000)),
|
||||
('CP5',(-3.000, -1.000)),
|
||||
('CP3',(-2.000, -1.000)),
|
||||
('CP1',(-1.000, -1.000)),
|
||||
('CPz',(0.000, -1.000)),
|
||||
('CP2',(1.000, -1.000)),
|
||||
('CP4',(2.000, -1.000)),
|
||||
('CP6',(3.000, -1.000)),
|
||||
('TP8',(4.000, -1.000)),
|
||||
('TPP9h',(-4.500, -1.500)),
|
||||
('TPP7h',(-3.500, -1.500)),
|
||||
('CPP5h',(-2.500, -1.500)),
|
||||
('CPP3h',(-1.500, -1.500)),
|
||||
('CPP1h',(-0.500, -1.500)),
|
||||
('CPP2h',(0.500, -1.500)),
|
||||
('CPP4h',(1.500, -1.500)),
|
||||
('CPP6h',(2.500, -1.500)),
|
||||
('TPP8h',(3.500, -1.500)),
|
||||
('TPP10h',(4.500, -1.500)),
|
||||
('P9',(-5.000, -2.000)),
|
||||
('P7',(-4.000, -2.000)),
|
||||
('P5',(-3.000, -2.000)),
|
||||
('P3',(-2.000, -2.000)),
|
||||
('P1',(-1.000, -2.000)),
|
||||
('Pz',(0.000, -2.000)),
|
||||
('P2',(1.000, -2.000)),
|
||||
('P4',(2.000, -2.000)),
|
||||
('P6',(3.000, -2.000)),
|
||||
('P8',(4.000, -2.000)),
|
||||
('P10',(5.000, -2.000)),
|
||||
('PPO9h',(-4.500, -2.500)),
|
||||
('PPO5h',(-3.000, -2.500)),
|
||||
('PPO1',(-0.650, -2.500)),
|
||||
('PPO2',(0.650, -2.500)),
|
||||
('PPO6h',(3.000, -2.500)),
|
||||
('PPO10h',(4.500, -2.500)),
|
||||
('PO9',(-5.000, -3.000)),
|
||||
('PO7',(-4.000, -3.000)),
|
||||
('PO5',(-3.000, -3.000)),
|
||||
('PO3',(-2.000, -3.000)),
|
||||
('PO1',(-1.000, -3.000)),
|
||||
('POz',(0.000, -3.000)),
|
||||
('PO2',(1.000, -3.000)),
|
||||
('PO4',(2.000, -3.000)),
|
||||
('PO6',(3.000, -3.000)),
|
||||
('PO8',(4.000, -3.000)),
|
||||
('PO10',(5.000, -3.000)),
|
||||
('POO9h',(-4.500, -3.250)),
|
||||
('POO3h',(-2.000, -3.250)),
|
||||
('POO4h',(2.000, -3.250)),
|
||||
('POO10h',(4.500, -3.250)),
|
||||
('O1',(-2.500, -3.750)),
|
||||
('Oz',(0.000, -3.750)),
|
||||
('O2',(2.500, -3.750)),
|
||||
('OI1h',(1.500, -4.250)),
|
||||
('OI2h',(-1.500, -4.250)),
|
||||
('I1',(1.000, -4.500)),
|
||||
('Iz',(0.000, -4.500)),
|
||||
('I2',(-1.000, -4.500)),
|
||||
)
|
||||
|
||||
|
||||
def get_channelpos(channame, chan_pos_list):
|
||||
if chan_pos_list[0] == 'angle':
|
||||
return get_channelpos_from_angle(channame, chan_pos_list[1:])
|
||||
elif chan_pos_list[0] == 'cartesian':
|
||||
channame = channame.lower()
|
||||
for name, coords in chan_pos_list[1:]:
|
||||
if name.lower() == channame:
|
||||
return coords[0], coords[1]
|
||||
return None
|
||||
else:
|
||||
raise ValueError("Unknown first element "
|
||||
"{:s} (should be type of positions)".format(
|
||||
chan_pos_list[0]))
|
||||
|
||||
|
||||
def get_channelpos_from_angle(channame, chan_pos_list=CHANNEL_10_20_APPROX):
|
||||
"""Return the x/y position of a channel.
|
||||
|
||||
This method calculates the stereographic projection of a channel
|
||||
from ``CHANNEL_10_20``, suitable for a scalp plot.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
channame : str
|
||||
Name of the channel, the search is case insensitive.
|
||||
|
||||
chan_pos_list=CHANNEL_10_20_APPROX,
|
||||
interpolation='bilinear'
|
||||
|
||||
Returns
|
||||
-------
|
||||
x, y : float or None
|
||||
The projected point on the plane if the point is known,
|
||||
otherwise ``None``
|
||||
|
||||
Examples
|
||||
--------
|
||||
|
||||
>>> plot.get_channelpos_from_angle('C2')
|
||||
(0.1720792096741632, 0.0)
|
||||
>>> # the channels are case insensitive
|
||||
>>> plot.get_channelpos_from_angle('c2')
|
||||
(0.1720792096741632, 0.0)
|
||||
>>> # lookup for an invalid channel
|
||||
>>> plot.get_channelpos_from_angle('foo')
|
||||
None
|
||||
|
||||
"""
|
||||
channame = channame.lower()
|
||||
for i in chan_pos_list:
|
||||
if i[0].lower() == channame:
|
||||
# convert the 90/4th angular position into x, y, z
|
||||
p = i[1]
|
||||
x, y = _convert_2d_angle_to_2d_coord(*p)
|
||||
return x, y
|
||||
return None
|
||||
|
||||
|
||||
def _convert_2d_angle_to_2d_coord(a,b):
|
||||
# convert the 90/4th angular position into x, y, z
|
||||
ea, eb = a * (90 / 4), b * (90 / 4)
|
||||
ea = ea * math.pi / 180
|
||||
eb = eb * math.pi / 180
|
||||
x = math.sin(ea) * math.cos(eb)
|
||||
y = math.sin(eb)
|
||||
z = math.cos(ea) * math.cos(eb)
|
||||
# Calculate the stereographic projection.
|
||||
# Given a unit sphere with radius ``r = 1`` and center at
|
||||
# the origin. Project the point ``p = (x, y, z)`` from the
|
||||
# sphere's South pole (0, 0, -1) on a plane on the sphere's
|
||||
# North pole (0, 0, 1).
|
||||
#
|
||||
# The formula is:
|
||||
#
|
||||
# P' = P * (2r / (r + z))
|
||||
#
|
||||
# We changed the values to move the point of projection
|
||||
# further below the south pole
|
||||
mu = 1 / (1.3 + z)
|
||||
x *= mu
|
||||
y *= mu
|
||||
return x, y
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Utilities for data manipulation.
|
||||
"""
|
||||
@@ -0,0 +1,330 @@
|
||||
import numpy as np
|
||||
from numpy.random import RandomState
|
||||
|
||||
|
||||
def get_balanced_batches(n_trials, rng, shuffle, n_batches=None,
|
||||
batch_size=None):
|
||||
"""Create indices for batches balanced in size
|
||||
(batches will have maximum size difference of 1).
|
||||
Supply either batch size or number of batches. Resulting batches
|
||||
will not have the given batch size but rather the next largest batch size
|
||||
that allows to split the set into balanced batches (maximum size difference 1).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n_trials : int
|
||||
Size of set.
|
||||
rng : RandomState
|
||||
shuffle : bool
|
||||
Whether to shuffle indices before splitting set.
|
||||
n_batches : int, optional
|
||||
batch_size : int, optional
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
assert batch_size is not None or n_batches is not None
|
||||
if n_batches is None:
|
||||
n_batches = int(np.round(n_trials / float(batch_size)))
|
||||
|
||||
if n_batches > 0:
|
||||
min_batch_size = n_trials // n_batches
|
||||
n_batches_with_extra_trial = n_trials % n_batches
|
||||
else:
|
||||
n_batches = 1
|
||||
min_batch_size = n_trials
|
||||
n_batches_with_extra_trial = 0
|
||||
assert n_batches_with_extra_trial < n_batches
|
||||
all_inds = np.array(range(n_trials))
|
||||
if shuffle:
|
||||
rng.shuffle(all_inds)
|
||||
i_start_trial = 0
|
||||
i_stop_trial = 0
|
||||
batches = []
|
||||
for i_batch in range(n_batches):
|
||||
i_stop_trial += min_batch_size
|
||||
if i_batch < n_batches_with_extra_trial:
|
||||
i_stop_trial += 1
|
||||
batch_inds = all_inds[range(i_start_trial, i_stop_trial)]
|
||||
batches.append(batch_inds)
|
||||
i_start_trial = i_stop_trial
|
||||
assert i_start_trial == n_trials
|
||||
return batches
|
||||
|
||||
|
||||
class BalancedBatchSizeIterator(object):
|
||||
"""
|
||||
Create batches of balanced size.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
batch_size: int
|
||||
Resulting batches will not necessarily have the given batch size
|
||||
but rather the next largest batch size that allows to split the set into
|
||||
balanced batches (maximum size difference 1).
|
||||
seed: int
|
||||
Random seed for initialization of `numpy.RandomState` random generator
|
||||
that shuffles the batches.
|
||||
"""
|
||||
def __init__(self, batch_size, seed=328774):
|
||||
self.batch_size = batch_size
|
||||
self.seed = seed
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
def get_batches(self, dataset, shuffle):
|
||||
n_trials = dataset.X.shape[0]
|
||||
batches = get_balanced_batches(n_trials,
|
||||
batch_size=self.batch_size,
|
||||
rng=self.rng,
|
||||
shuffle=shuffle)
|
||||
for batch_inds in batches:
|
||||
batch_X = dataset.X[batch_inds]
|
||||
batch_y = dataset.y[batch_inds]
|
||||
|
||||
# add empty fourth dimension if necessary
|
||||
if batch_X.ndim == 3:
|
||||
batch_X = batch_X[:, :, :, None]
|
||||
yield (batch_X, batch_y)
|
||||
|
||||
def reset_rng(self):
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
|
||||
class ClassBalancedBatchSizeIterator(object):
|
||||
"""
|
||||
Create batches of balanced size, that are also balanced per class, i.e.
|
||||
each class should be sampled roughly with the same frequency during
|
||||
training.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
batch_size: int
|
||||
Resulting batches will not necessarily have the given batch size
|
||||
but rather the next largest batch size that allows to split the set into
|
||||
balanced batches (maximum size difference 1).
|
||||
seed: int
|
||||
Random seed for initialization of `numpy.RandomState` random generator
|
||||
that shuffles the batches.
|
||||
"""
|
||||
|
||||
def __init__(self, batch_size, seed=328774):
|
||||
self.batch_size = batch_size
|
||||
self.seed = seed
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
def get_batches(self, dataset, shuffle):
|
||||
n_trials = dataset.X.shape[0]
|
||||
batches = get_balanced_batches(n_trials,
|
||||
batch_size=self.batch_size,
|
||||
rng=self.rng,
|
||||
shuffle=shuffle)
|
||||
if shuffle:
|
||||
n_classes = np.max(dataset.y) + 1
|
||||
class_probabilities = [np.mean(dataset.y == i_class)
|
||||
for i_class in range(n_classes)]
|
||||
class_probabilities = np.array(class_probabilities)
|
||||
# choose trials in inverse probability of class
|
||||
trial_probabilities = [1.0 / class_probabilities[y]
|
||||
for y in dataset.y]
|
||||
trial_probabilities = np.array(trial_probabilities) / np.sum(
|
||||
trial_probabilities)
|
||||
i_trial_to_balanced = self.rng.choice(n_trials, n_trials,
|
||||
p=trial_probabilities)
|
||||
|
||||
for batch_inds in batches:
|
||||
if shuffle:
|
||||
batch_inds = [i_trial_to_balanced[i_trial] for i_trial in
|
||||
batch_inds]
|
||||
batch_X = dataset.X[batch_inds]
|
||||
batch_y = dataset.y[batch_inds]
|
||||
|
||||
# add empty fourth dimension if necessary
|
||||
if batch_X.ndim == 3:
|
||||
batch_X = batch_X[:, :, :, None]
|
||||
yield (batch_X, batch_y)
|
||||
|
||||
def reset_rng(self):
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
|
||||
class CropsFromTrialsIterator(object):
|
||||
"""
|
||||
Iterator sampling crops out the trials so that each sample
|
||||
(after receptive size of the ConvNet) in each trial is predicted.
|
||||
|
||||
Predicting the given input batches can lead to some samples
|
||||
being predicted multiple times, if the receptive field size
|
||||
(input_time_length - n_preds_per_input + 1) is not a divisor
|
||||
of the trial length. :func:`compute_preds_per_trial_from_crops`
|
||||
can help with removing the overlapped predictions again for evaluation.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
batch_size: int
|
||||
input_time_length: int
|
||||
Input time length of the ConvNet, determines size of batches in
|
||||
3rd dimension.
|
||||
n_preds_per_input: int
|
||||
Number of predictions ConvNet makes per one input. Can be computed
|
||||
by making a forward pass with the given input time length, the
|
||||
output length in 3rd dimension is n_preds_per_input.
|
||||
seed: int
|
||||
Random seed for initialization of `numpy.RandomState` random generator
|
||||
that shuffles the batches.
|
||||
|
||||
See Also
|
||||
--------
|
||||
braindecode.experiments.monitors.compute_preds_per_trial_from_crops : Assigns predictions to trials, removes overlaps.
|
||||
"""
|
||||
def __init__(self, batch_size, input_time_length, n_preds_per_input,
|
||||
seed=(2017, 6, 28)):
|
||||
self.batch_size = batch_size
|
||||
self.input_time_length = input_time_length
|
||||
self.n_preds_per_input = n_preds_per_input
|
||||
self.seed = seed
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
def reset_rng(self):
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
def get_batches(self, dataset, shuffle):
|
||||
# start always at first predictable sample, so
|
||||
# start at end of receptive field
|
||||
n_receptive_field = self.input_time_length - self.n_preds_per_input + 1
|
||||
i_trial_starts = [n_receptive_field - 1] * len(dataset.X)
|
||||
i_trial_stops = [trial.shape[1] for trial in dataset.X]
|
||||
|
||||
# Check whether input lengths ok
|
||||
input_lens = i_trial_stops
|
||||
for i_trial, input_len in enumerate(input_lens):
|
||||
assert input_len >= self.input_time_length, (
|
||||
"Input length {:d} of trial {:d} is smaller than the "
|
||||
"input time length {:d}".format(input_len, i_trial,
|
||||
self.input_time_length))
|
||||
start_stop_blocks_per_trial = _compute_start_stop_block_inds(
|
||||
i_trial_starts, i_trial_stops, self.input_time_length,
|
||||
self.n_preds_per_input, check_preds_smaller_trial_len=True)
|
||||
for i_trial, trial_blocks in enumerate(start_stop_blocks_per_trial):
|
||||
assert trial_blocks[0][0] == 0
|
||||
assert trial_blocks[-1][1] == i_trial_stops[i_trial]
|
||||
|
||||
return self._yield_block_batches(dataset.X, dataset.y,
|
||||
start_stop_blocks_per_trial,
|
||||
shuffle=shuffle)
|
||||
|
||||
def _yield_block_batches(self, X, y, start_stop_blocks_per_trial, shuffle):
|
||||
# add trial nr to start stop blocks and flatten at same time
|
||||
i_trial_start_stop_block = [(i_trial, start, stop)
|
||||
for i_trial, block in
|
||||
enumerate(start_stop_blocks_per_trial)
|
||||
for (start, stop) in block]
|
||||
i_trial_start_stop_block = np.array(i_trial_start_stop_block)
|
||||
if i_trial_start_stop_block.ndim == 1:
|
||||
i_trial_start_stop_block = i_trial_start_stop_block[None,:]
|
||||
|
||||
blocks_per_batch = get_balanced_batches(len(i_trial_start_stop_block),
|
||||
batch_size=self.batch_size,
|
||||
rng=self.rng,
|
||||
shuffle=shuffle)
|
||||
for i_blocks in blocks_per_batch:
|
||||
start_stop_blocks = i_trial_start_stop_block[i_blocks]
|
||||
batch = _create_batch_from_i_trial_start_stop_blocks(
|
||||
X, y, start_stop_blocks, self.n_preds_per_input)
|
||||
yield batch
|
||||
|
||||
|
||||
def _compute_start_stop_block_inds(i_trial_starts, i_trial_stops,
|
||||
input_time_length, n_preds_per_input,
|
||||
check_preds_smaller_trial_len):
|
||||
"""
|
||||
Compute start stop block inds for all trials
|
||||
Parameters
|
||||
----------
|
||||
i_trial_starts: 1darray/list of int
|
||||
Indices of first samples to predict(!).
|
||||
i_trial_stops: 1darray/list of int
|
||||
Indices one past last sample to predict.
|
||||
input_time_length: int
|
||||
n_preds_per_input: int
|
||||
check_preds_smaller_trial_len: bool
|
||||
Check whether predictions fit inside trial
|
||||
Returns
|
||||
-------
|
||||
start_stop_blocks_per_trial: list of list of (int, int)
|
||||
Per trial, a list of 2-tuples indicating start and stop index
|
||||
of the inputs needed to predict entire trial.
|
||||
"""
|
||||
# create start stop indices for all batches still 2d trial -> start stop
|
||||
start_stop_blocks_per_trial = []
|
||||
for i_trial in range(len(i_trial_starts)):
|
||||
i_trial_start = i_trial_starts[i_trial]
|
||||
i_trial_stop = i_trial_stops[i_trial]
|
||||
start_stop_blocks = _get_start_stop_blocks_for_trial(
|
||||
i_trial_start, i_trial_stop, input_time_length,
|
||||
n_preds_per_input)
|
||||
|
||||
if check_preds_smaller_trial_len:
|
||||
# check that block is correct, all predicted samples together
|
||||
# should be the trial samples
|
||||
all_predicted_samples = [
|
||||
range(stop - n_preds_per_input,
|
||||
stop) for _,stop in start_stop_blocks]
|
||||
# this check takes about 50 ms in performance test
|
||||
# whereas loop itself takes only 5 ms.. deactivate it if not necessary
|
||||
assert np.array_equal(
|
||||
range(i_trial_starts[i_trial], i_trial_stops[i_trial]),
|
||||
np.unique(np.concatenate(all_predicted_samples)))
|
||||
|
||||
start_stop_blocks_per_trial.append(start_stop_blocks)
|
||||
return start_stop_blocks_per_trial
|
||||
|
||||
|
||||
def _get_start_stop_blocks_for_trial(i_trial_start, i_trial_stop,
|
||||
input_time_length, n_preds_per_input):
|
||||
|
||||
"""
|
||||
Compute start stop block inds for one trial
|
||||
Parameters
|
||||
----------
|
||||
i_trial_start: int
|
||||
Index of first sample to predict(!).
|
||||
i_trial_stops: 1daray/list of int
|
||||
Index one past last sample to predict.
|
||||
input_time_length: int
|
||||
n_preds_per_input: int
|
||||
Returns
|
||||
-------
|
||||
start_stop_blocks: list of (int, int)
|
||||
A list of 2-tuples indicating start and stop index
|
||||
of the inputs needed to predict entire trial.
|
||||
"""
|
||||
start_stop_blocks = []
|
||||
i_window_stop = i_trial_start # now when we add sample preds in loop,
|
||||
# first sample of trial corresponds to first prediction
|
||||
while i_window_stop < i_trial_stop:
|
||||
i_window_stop += n_preds_per_input
|
||||
i_adjusted_stop = min(i_window_stop, i_trial_stop)
|
||||
i_window_start = i_adjusted_stop - input_time_length
|
||||
start_stop_blocks.append((i_window_start, i_adjusted_stop))
|
||||
|
||||
return start_stop_blocks
|
||||
|
||||
|
||||
def _create_batch_from_i_trial_start_stop_blocks(X, y, i_trial_start_stop_block,
|
||||
n_preds_per_input=None):
|
||||
Xs = []
|
||||
ys = []
|
||||
for i_trial, start, stop in i_trial_start_stop_block:
|
||||
Xs.append(X[i_trial][:,start:stop])
|
||||
if not hasattr(y[i_trial], '__len__'):
|
||||
ys.append(y[i_trial])
|
||||
else:
|
||||
assert n_preds_per_input is not None
|
||||
ys.append(y[i_trial][stop-n_preds_per_input:stop])
|
||||
batch_X = np.array(Xs)
|
||||
batch_y = np.array(ys)
|
||||
# add empty fourth dimension if necessary
|
||||
if batch_X.ndim == 3:
|
||||
batch_X = batch_X[:,:,:, None]
|
||||
return batch_X, batch_y
|
||||
@@ -0,0 +1,123 @@
|
||||
from torch.utils.data import DataLoader
|
||||
from numpy.random import RandomState
|
||||
from functools import partial
|
||||
import torch as th
|
||||
import numpy as np
|
||||
|
||||
from braindecode.datautil.iterators import _compute_start_stop_block_inds, \
|
||||
get_balanced_batches
|
||||
|
||||
|
||||
def custom_collate(batch, rng_state=None):
|
||||
""" Puts each data field into a ndarray with outer dimension batch size.
|
||||
Taken and adapted from pytorch to return ndarrays instead of tensors:
|
||||
https://pytorch.org/docs/0.4.1/_modules/torch/utils/data/dataloader.html
|
||||
|
||||
this function is needed, since tensors require more system RAM which we
|
||||
want to decrease using lazy loading
|
||||
"""
|
||||
elem_type = type(batch[0])
|
||||
if elem_type.__module__ == 'numpy':
|
||||
if rng_state is not None:
|
||||
th.random.set_rng_state(rng_state)
|
||||
return np.stack([b for b in batch], 0)
|
||||
|
||||
elif isinstance(batch[0], tuple):
|
||||
transposed = zip(*batch)
|
||||
return [custom_collate(samples, rng_state) for samples in transposed]
|
||||
|
||||
|
||||
class LazyCropsFromTrialsIterator(object):
|
||||
""" This is basically the same code as CropsFromTrialsIterator adapted to
|
||||
work with lazy datasets. It uses pytorch DataLoader to load recordings
|
||||
from hdd with multiple threads when the data is actually needed. Reduces
|
||||
overall RAM requirements.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
input_time_length: int
|
||||
Input time length of the ConvNet, determines size of batches in
|
||||
3rd dimension.
|
||||
n_preds_per_input: int
|
||||
Number of predictions ConvNet makes per one input. Can be computed
|
||||
by making a forward pass with the given input time length, the
|
||||
output length in 3rd dimension is n_preds_per_input.
|
||||
batch_size: int
|
||||
seed: int
|
||||
Random seed for initialization of `numpy.RandomState` random generator
|
||||
that shuffles the batches.
|
||||
num_workers: int
|
||||
The number of workers to load crops in parallel
|
||||
collate_fn: func
|
||||
Merges a list of samples to form a mini-batch
|
||||
check_preds_smaller_trial_len: bool
|
||||
Checking validity of predictions and trial lengths. Disable to decrease
|
||||
runtime.
|
||||
"""
|
||||
def __init__(self, input_time_length, n_preds_per_input, batch_size,
|
||||
seed=328774, num_workers=0, collate_fn=custom_collate,
|
||||
check_preds_smaller_trial_len=True,
|
||||
reset_rng_after_each_batch=False):
|
||||
self.batch_size = batch_size
|
||||
self.seed = seed
|
||||
self.rng = RandomState(self.seed)
|
||||
self.input_time_length = input_time_length
|
||||
self.n_preds_per_input = n_preds_per_input
|
||||
self.num_workers = num_workers
|
||||
self.collate_fn = collate_fn
|
||||
self.check_preds_smaller_trial_len = check_preds_smaller_trial_len
|
||||
self.reset_rng_after_each_batch = reset_rng_after_each_batch
|
||||
|
||||
def reset_rng(self):
|
||||
self.rng = RandomState(self.seed)
|
||||
|
||||
def get_batches(self, dataset, shuffle):
|
||||
# in pytorch 1.0.0, internal random state is changed when using a
|
||||
# DataLoader, even if num_workers is 0. this did not happen in torch
|
||||
# 0.4.0 and breaks our equality tests of traditional and lazy loading
|
||||
# therefore, in the collate function of every batch, reset to the
|
||||
# random state before iterating through batches.
|
||||
if self.reset_rng_after_each_batch:
|
||||
random_state = th.random.get_rng_state()
|
||||
collate_fn = partial(self.collate_fn, rng_state=random_state)
|
||||
else:
|
||||
collate_fn = partial(self.collate_fn, rng_state=None)
|
||||
batch_indeces = self._get_batch_indeces(dataset=dataset,
|
||||
shuffle=shuffle)
|
||||
data_loader = DataLoader(dataset=dataset, batch_sampler=batch_indeces,
|
||||
num_workers=self.num_workers,
|
||||
pin_memory=False, collate_fn=collate_fn)
|
||||
return data_loader
|
||||
|
||||
def _get_batch_indeces(self, dataset, shuffle):
|
||||
# start always at first predictable sample, so
|
||||
# start at end of receptive field
|
||||
n_receptive_field = self.input_time_length - self.n_preds_per_input + 1
|
||||
i_trial_starts = [n_receptive_field - 1] * len(dataset.X)
|
||||
i_trial_stops = [trial.shape[1] for trial in dataset.X]
|
||||
|
||||
# Check whether input lengths ok
|
||||
input_lens = i_trial_stops
|
||||
for i_trial, input_len in enumerate(input_lens):
|
||||
assert input_len >= self.input_time_length, (
|
||||
"Input length {:d} of trial {:d} is smaller than the "
|
||||
"input time length {:d}".format(input_len, i_trial,
|
||||
self.input_time_length))
|
||||
|
||||
start_stop_blocks_per_trial = _compute_start_stop_block_inds(
|
||||
i_trial_starts, i_trial_stops, self.input_time_length,
|
||||
self.n_preds_per_input,
|
||||
check_preds_smaller_trial_len=self.check_preds_smaller_trial_len)
|
||||
for i_trial, trial_blocks in enumerate(start_stop_blocks_per_trial):
|
||||
assert trial_blocks[0][0] == 0
|
||||
assert trial_blocks[-1][1] == i_trial_stops[i_trial]
|
||||
|
||||
i_trial_start_stop_block = np.array([
|
||||
(i_trial, start, stop) for i_trial, block in
|
||||
enumerate(start_stop_blocks_per_trial) for start, stop in block])
|
||||
|
||||
batches = get_balanced_batches(
|
||||
n_trials=len(i_trial_start_stop_block), rng=self.rng,
|
||||
shuffle=shuffle, batch_size=self.batch_size)
|
||||
|
||||
return [i_trial_start_stop_block[batch_ind] for batch_ind in batches]
|
||||
@@ -0,0 +1,38 @@
|
||||
class SignalAndTarget(object):
|
||||
"""
|
||||
Simple data container class.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X: 3darray or list of 2darrays
|
||||
The input signal per trial.
|
||||
y: 1darray or list
|
||||
Labels for each trial.
|
||||
"""
|
||||
def __init__(self, X, y):
|
||||
assert len(X) == len(y)
|
||||
self.X = X
|
||||
self.y = y
|
||||
|
||||
|
||||
def apply_to_X_y(fn, *sets):
|
||||
"""
|
||||
Apply a function to all `X` and `y` attributes of all given sets.
|
||||
|
||||
Applies function to list of X arrays and to list of y arrays separately.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fn: function
|
||||
Function to apply
|
||||
sets: :class:`.SignalAndTarget` objects
|
||||
|
||||
Returns
|
||||
-------
|
||||
result_set: :class:`.SignalAndTarget`
|
||||
Dataset with X and y as the result of the
|
||||
application of the function.
|
||||
"""
|
||||
X = fn(*[s.X for s in sets])
|
||||
y = fn(*[s.y for s in sets])
|
||||
return SignalAndTarget(X,y)
|
||||
@@ -0,0 +1,222 @@
|
||||
import logging
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import scipy
|
||||
import scipy.signal
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def exponential_running_standardize(data, factor_new=0.001,
|
||||
init_block_size=None, eps=1e-4):
|
||||
"""
|
||||
Perform exponential running standardization.
|
||||
|
||||
Compute the exponental running mean :math:`m_t` at time `t` as
|
||||
:math:`m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}`.
|
||||
|
||||
Then, compute exponential running variance :math:`v_t` at time `t` as
|
||||
:math:`v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}`.
|
||||
|
||||
Finally, standardize the data point :math:`x_t` at time `t` as:
|
||||
:math:`x'_t=(x_t - m_t) / max(\sqrt{v_t}, eps)`.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2darray (time, channels)
|
||||
factor_new: float
|
||||
init_block_size: int
|
||||
Standardize data before to this index with regular standardization.
|
||||
eps: float
|
||||
Stabilizer for division by zero variance.
|
||||
|
||||
Returns
|
||||
-------
|
||||
standardized: 2darray (time, channels)
|
||||
Standardized data.
|
||||
"""
|
||||
df = pd.DataFrame(data)
|
||||
meaned = df.ewm(alpha=factor_new).mean()
|
||||
demeaned = df - meaned
|
||||
squared = demeaned * demeaned
|
||||
square_ewmed = squared.ewm(alpha=factor_new).mean()
|
||||
standardized = demeaned / np.maximum(eps, np.sqrt(np.array(square_ewmed)))
|
||||
standardized = np.array(standardized)
|
||||
if init_block_size is not None:
|
||||
other_axis = tuple(range(1, len(data.shape)))
|
||||
init_mean = np.mean(data[0:init_block_size], axis=other_axis,
|
||||
keepdims=True)
|
||||
init_std = np.std(data[0:init_block_size], axis=other_axis,
|
||||
keepdims=True)
|
||||
init_block_standardized = (data[0:init_block_size] - init_mean) / \
|
||||
np.maximum(eps, init_std)
|
||||
standardized[0:init_block_size] = init_block_standardized
|
||||
return standardized
|
||||
|
||||
|
||||
def exponential_running_demean(data, factor_new=0.001, init_block_size=None):
|
||||
"""
|
||||
Perform exponential running demeanining.
|
||||
|
||||
Compute the exponental running mean :math:`m_t` at time `t` as
|
||||
:math:`m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}`.
|
||||
|
||||
Deman the data point :math:`x_t` at time `t` as:
|
||||
:math:`x'_t=(x_t - m_t)`.
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2darray (time, channels)
|
||||
factor_new: float
|
||||
init_block_size: int
|
||||
Demean data before to this index with regular demeaning.
|
||||
|
||||
Returns
|
||||
-------
|
||||
demeaned: 2darray (time, channels)
|
||||
Demeaned data.
|
||||
"""
|
||||
df = pd.DataFrame(data)
|
||||
meaned = df.ewm(alpha=factor_new).mean()
|
||||
demeaned = df - meaned
|
||||
demeaned = np.array(demeaned)
|
||||
if init_block_size is not None:
|
||||
other_axis = tuple(range(1, len(data.shape)))
|
||||
init_mean = np.mean(data[0:init_block_size], axis=other_axis,
|
||||
keepdims=True)
|
||||
demeaned[0:init_block_size] = (data[0:init_block_size] - init_mean)
|
||||
return demeaned
|
||||
|
||||
|
||||
def highpass_cnt(data, low_cut_hz, fs, filt_order=3, axis=0):
|
||||
"""
|
||||
Highpass signal applying **causal** butterworth filter of given order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2d-array
|
||||
Time x channels
|
||||
low_cut_hz: float
|
||||
fs: float
|
||||
filt_order: int
|
||||
|
||||
Returns
|
||||
-------
|
||||
highpassed_data: 2d-array
|
||||
Data after applying highpass filter.
|
||||
"""
|
||||
if (low_cut_hz is None) or (low_cut_hz == 0):
|
||||
log.info("Not doing any highpass, since low 0 or None")
|
||||
return data.copy()
|
||||
b, a = scipy.signal.butter(filt_order, low_cut_hz / (fs / 2.0),
|
||||
btype='highpass')
|
||||
assert filter_is_stable(a)
|
||||
data_highpassed = scipy.signal.lfilter(b, a, data, axis=axis)
|
||||
return data_highpassed
|
||||
|
||||
|
||||
def lowpass_cnt(data, high_cut_hz, fs, filt_order=3, axis=0):
|
||||
"""
|
||||
Lowpass signal applying **causal** butterworth filter of given order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2d-array
|
||||
Time x channels
|
||||
high_cut_hz: float
|
||||
fs: float
|
||||
filt_order: int
|
||||
|
||||
Returns
|
||||
-------
|
||||
lowpassed_data: 2d-array
|
||||
Data after applying lowpass filter.
|
||||
"""
|
||||
if (high_cut_hz is None) or (high_cut_hz == fs / 2.0):
|
||||
log.info(
|
||||
"Not doing any lowpass, since high cut hz is None or nyquist freq.")
|
||||
return data.copy()
|
||||
b, a = scipy.signal.butter(filt_order, high_cut_hz / (fs / 2.0),
|
||||
btype='lowpass')
|
||||
assert filter_is_stable(a)
|
||||
data_lowpassed = scipy.signal.lfilter(b, a, data, axis=axis)
|
||||
return data_lowpassed
|
||||
|
||||
|
||||
def bandpass_cnt(data, low_cut_hz, high_cut_hz, fs, filt_order=3, axis=0,
|
||||
filtfilt=False):
|
||||
"""
|
||||
Bandpass signal applying **causal** butterworth filter of given order.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2d-array
|
||||
Time x channels
|
||||
low_cut_hz: float
|
||||
high_cut_hz: float
|
||||
fs: float
|
||||
filt_order: int
|
||||
filtfilt: bool
|
||||
Whether to use filtfilt instead of lfilter
|
||||
|
||||
Returns
|
||||
-------
|
||||
bandpassed_data: 2d-array
|
||||
Data after applying bandpass filter.
|
||||
"""
|
||||
if (low_cut_hz == 0 or low_cut_hz is None) and (
|
||||
high_cut_hz == None or high_cut_hz == fs / 2.0):
|
||||
log.info("Not doing any bandpass, since low 0 or None and "
|
||||
"high None or nyquist frequency")
|
||||
return data.copy()
|
||||
if low_cut_hz == 0 or low_cut_hz == None:
|
||||
log.info("Using lowpass filter since low cut hz is 0 or None")
|
||||
return lowpass_cnt(data, high_cut_hz, fs, filt_order=filt_order, axis=axis)
|
||||
if high_cut_hz == None or high_cut_hz == (fs / 2.0):
|
||||
log.info(
|
||||
"Using highpass filter since high cut hz is None or nyquist freq")
|
||||
return highpass_cnt(data, low_cut_hz, fs, filt_order=filt_order, axis=axis)
|
||||
|
||||
nyq_freq = 0.5 * fs
|
||||
low = low_cut_hz / nyq_freq
|
||||
high = high_cut_hz / nyq_freq
|
||||
b, a = scipy.signal.butter(filt_order, [low, high], btype='bandpass')
|
||||
assert filter_is_stable(a), "Filter should be stable..."
|
||||
if filtfilt:
|
||||
data_bandpassed = scipy.signal.filtfilt(b, a, data, axis=axis)
|
||||
else:
|
||||
data_bandpassed = scipy.signal.lfilter(b, a, data, axis=axis)
|
||||
return data_bandpassed
|
||||
|
||||
|
||||
def filter_is_stable(a):
|
||||
"""
|
||||
Check if filter coefficients of IIR filter are stable.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a: list or 1darray of number
|
||||
Denominator filter coefficients a.
|
||||
|
||||
Returns
|
||||
-------
|
||||
is_stable: bool
|
||||
Filter is stable or not.
|
||||
Notes
|
||||
----
|
||||
Filter is stable if absolute value of all roots is smaller than 1,
|
||||
see [1]_.
|
||||
|
||||
References
|
||||
----------
|
||||
.. [1] HYRY, "SciPy 'lfilter' returns only NaNs" StackOverflow,
|
||||
http://stackoverflow.com/a/8812737/1469195
|
||||
"""
|
||||
assert a[0] == 1.0, (
|
||||
"a[0] should normally be zero, did you accidentally supply b?\n"
|
||||
"a: {:s}".format(str(a)))
|
||||
# from http://stackoverflow.com/a/8812737/1469195
|
||||
return np.all(np.abs(np.roots(a))<1)
|
||||
@@ -0,0 +1,190 @@
|
||||
import numpy as np
|
||||
|
||||
from braindecode.datautil.iterators import get_balanced_batches
|
||||
from braindecode.datautil.signal_target import apply_to_X_y, SignalAndTarget
|
||||
|
||||
|
||||
def concatenate_sets(sets):
|
||||
"""
|
||||
Concatenate all sets together.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
sets: list of :class:`.SignalAndTarget`
|
||||
|
||||
Returns
|
||||
-------
|
||||
concatenated_set: :class:`.SignalAndTarget`
|
||||
"""
|
||||
concatenated_set = sets[0]
|
||||
for s in sets[1:]:
|
||||
concatenated_set = concatenate_two_sets(concatenated_set, s)
|
||||
return concatenated_set
|
||||
|
||||
|
||||
def concatenate_two_sets(set_a, set_b):
|
||||
"""
|
||||
Concatenate two sets together.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
set_a, set_b: :class:`.SignalAndTarget`
|
||||
|
||||
Returns
|
||||
-------
|
||||
concatenated_set: :class:`.SignalAndTarget`
|
||||
"""
|
||||
new_X = concatenate_np_array_or_add_lists(set_a.X, set_b.X)
|
||||
new_y = concatenate_np_array_or_add_lists(set_a.y, set_b.y)
|
||||
return SignalAndTarget(new_X, new_y)
|
||||
|
||||
def concatenate_np_array_or_add_lists(a, b):
|
||||
if hasattr(a, 'ndim') and hasattr(b, 'ndim'):
|
||||
new = np.concatenate((a, b), axis=0)
|
||||
else:
|
||||
if hasattr(a, 'ndim'):
|
||||
a = a.tolist()
|
||||
if hasattr(b, 'ndim'):
|
||||
b = b.tolist()
|
||||
new = a + b
|
||||
return new
|
||||
|
||||
|
||||
|
||||
def split_into_two_sets(dataset, first_set_fraction=None, n_first_set=None):
|
||||
"""
|
||||
Split set into two sets either by fraction of first set or by number
|
||||
of trials in first set.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
first_set_fraction: float, optional
|
||||
Fraction of trials in first set.
|
||||
n_first_set: int, optional
|
||||
Number of trials in first set
|
||||
|
||||
Returns
|
||||
-------
|
||||
first_set, second_set: :class:`.SignalAndTarget`
|
||||
The two splitted sets.
|
||||
"""
|
||||
assert (first_set_fraction is None) != (n_first_set is None), (
|
||||
"Pass either first_set_fraction or n_first_set")
|
||||
if n_first_set is None:
|
||||
n_first_set = int(round(len(dataset.X) * first_set_fraction))
|
||||
assert n_first_set < len(dataset.X)
|
||||
first_set = apply_to_X_y(lambda a: a[:n_first_set], dataset)
|
||||
second_set = apply_to_X_y(lambda a: a[n_first_set:], dataset)
|
||||
return first_set, second_set
|
||||
|
||||
|
||||
def select_examples(dataset, indices):
|
||||
"""
|
||||
Select examples from dataset.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
indices: list of int, 1d-array of int
|
||||
Indices to select
|
||||
|
||||
Returns
|
||||
-------
|
||||
reduced_set: :class:`.SignalAndTarget`
|
||||
Dataset with only examples selected.
|
||||
"""
|
||||
# probably not necessary
|
||||
indices = np.array(indices)
|
||||
if hasattr(dataset.X, 'ndim'):
|
||||
# numpy array
|
||||
new_X = np.array(dataset.X)[indices]
|
||||
else:
|
||||
# list
|
||||
new_X = [dataset.X[i] for i in indices]
|
||||
new_y = np.asarray(dataset.y)[indices]
|
||||
return SignalAndTarget(new_X, new_y)
|
||||
|
||||
|
||||
def split_into_train_valid_test(dataset, n_folds, i_test_fold, rng=None):
|
||||
"""
|
||||
Split datasets into folds, select one valid fold, one test fold and merge rest as train fold.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
n_folds: int
|
||||
Number of folds to split dataset into.
|
||||
i_test_fold: int
|
||||
Index of the test fold (0-based). Validation fold will be immediately preceding fold.
|
||||
rng: `numpy.random.RandomState`, optional
|
||||
Random Generator for shuffling, None means no shuffling
|
||||
|
||||
Returns
|
||||
-------
|
||||
reduced_set: :class:`.SignalAndTarget`
|
||||
Dataset with only examples selected.
|
||||
"""
|
||||
n_trials = len(dataset.X)
|
||||
if n_trials < n_folds:
|
||||
raise ValueError("Less Trials: {:d} than folds: {:d}".format(
|
||||
n_trials, n_folds
|
||||
))
|
||||
shuffle = rng is not None
|
||||
folds = get_balanced_batches(
|
||||
n_trials, rng, shuffle, n_batches=n_folds)
|
||||
test_inds = folds[i_test_fold]
|
||||
valid_inds = folds[i_test_fold - 1]
|
||||
all_inds = list(range(n_trials))
|
||||
train_inds = np.setdiff1d(all_inds, np.union1d(test_inds, valid_inds))
|
||||
assert np.intersect1d(train_inds, valid_inds).size == 0
|
||||
assert np.intersect1d(train_inds, test_inds).size == 0
|
||||
assert np.intersect1d(valid_inds, test_inds).size == 0
|
||||
assert np.array_equal(np.sort(
|
||||
np.union1d(train_inds, np.union1d(valid_inds, test_inds))),
|
||||
all_inds)
|
||||
|
||||
train_set = select_examples(dataset, train_inds)
|
||||
valid_set = select_examples(dataset, valid_inds)
|
||||
test_set = select_examples(dataset, test_inds)
|
||||
|
||||
return train_set, valid_set, test_set
|
||||
|
||||
|
||||
def split_into_train_test(dataset, n_folds, i_test_fold, rng=None):
|
||||
"""
|
||||
Split datasets into folds, select one test fold and merge rest as train fold.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
n_folds: int
|
||||
Number of folds to split dataset into.
|
||||
i_test_fold: int
|
||||
Index of the test fold (0-based)
|
||||
rng: `numpy.random.RandomState`, optional
|
||||
Random Generator for shuffling, None means no shuffling
|
||||
|
||||
Returns
|
||||
-------
|
||||
reduced_set: :class:`.SignalAndTarget`
|
||||
Dataset with only examples selected.
|
||||
"""
|
||||
n_trials = len(dataset.X)
|
||||
if n_trials < n_folds:
|
||||
raise ValueError("Less Trials: {:d} than folds: {:d}".format(
|
||||
n_trials, n_folds
|
||||
))
|
||||
shuffle = rng is not None
|
||||
folds = get_balanced_batches(n_trials, rng, shuffle,
|
||||
n_batches=n_folds)
|
||||
test_inds = folds[i_test_fold]
|
||||
all_inds = list(range(n_trials))
|
||||
train_inds = np.setdiff1d(all_inds, test_inds)
|
||||
assert np.intersect1d(train_inds, test_inds).size == 0
|
||||
assert np.array_equal(np.sort(np.union1d(train_inds, test_inds)),
|
||||
all_inds)
|
||||
|
||||
train_set = select_examples(dataset, train_inds)
|
||||
test_set = select_examples(dataset, test_inds)
|
||||
return train_set, test_set
|
||||
@@ -0,0 +1,625 @@
|
||||
import logging
|
||||
from collections import OrderedDict, Counter
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
|
||||
from braindecode.datautil.signal_target import SignalAndTarget
|
||||
from braindecode.datautil.util import ms_to_samples
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def create_signal_target_from_raw_mne(
|
||||
raw, name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes=None,
|
||||
prepad_trials_to_n_samples=None,
|
||||
one_hot_labels=False,
|
||||
one_label_per_trial=True):
|
||||
"""
|
||||
Create SignalTarget set from given `mne.io.RawArray`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
raw: `mne.io.RawArray`
|
||||
name_to_start_codes: OrderedDict (str -> int or list of int)
|
||||
Ordered dictionary mapping class names to marker code or marker codes.
|
||||
y-labels will be assigned in increasing key order, i.e.
|
||||
first classname gets y-value 0, second classname y-value 1, etc.
|
||||
epoch_ival_ms: iterable of (int,int)
|
||||
Epoching interval in milliseconds. In case only `name_to_codes` given,
|
||||
represents start offset and stop offset from start markers. In case
|
||||
`name_to_stop_codes` given, represents offset from start marker
|
||||
and offset from stop marker. E.g. [500, -500] would mean 500ms
|
||||
after the start marker until 500 ms before the stop marker.
|
||||
name_to_stop_codes: dict (str -> int or list of int), optional
|
||||
Dictionary mapping class names to stop marker code or stop marker codes.
|
||||
Order does not matter, dictionary should contain each class in
|
||||
`name_to_codes` dictionary.
|
||||
prepad_trials_to_n_samples: int
|
||||
Pad trials that would be too short with the signal before it (only
|
||||
valid if name_to_stop_codes is not None).
|
||||
one_hot_labels: bool, optional
|
||||
Whether to have the labels in a one-hot format, e.g. [0,0,1] or to
|
||||
have them just as an int, e.g. 2
|
||||
one_label_per_trial: bool, optional
|
||||
Whether to have a timeseries of labels or just a single label per trial.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
Dataset with `X` as the trial signals and `y` as the trial labels.
|
||||
"""
|
||||
data = raw.get_data()
|
||||
events = np.array([raw.info['events'][:,0],
|
||||
raw.info['events'][:,2]]).T
|
||||
fs = raw.info['sfreq']
|
||||
return create_signal_target(
|
||||
data, events, fs, name_to_start_codes,
|
||||
epoch_ival_ms,
|
||||
name_to_stop_codes=name_to_stop_codes,
|
||||
prepad_trials_to_n_samples=prepad_trials_to_n_samples,
|
||||
one_hot_labels=one_hot_labels,
|
||||
one_label_per_trial=one_label_per_trial)
|
||||
|
||||
|
||||
def create_signal_target(data, events, fs, name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes=None, prepad_trials_to_n_samples=None,
|
||||
one_hot_labels=False, one_label_per_trial=True):
|
||||
"""
|
||||
Create SignalTarget set given continuous data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: 2d-array of number
|
||||
The continuous recorded data. Channels x times order.
|
||||
events: 2d-array
|
||||
Dimensions: Number of events, 2. For each event, should contain sample
|
||||
index and marker code.
|
||||
fs: number
|
||||
Sampling rate.
|
||||
name_to_start_codes: OrderedDict (str -> int or list of int)
|
||||
Ordered dictionary mapping class names to marker code or marker codes.
|
||||
y-labels will be assigned in increasing key order, i.e.
|
||||
first classname gets y-value 0, second classname y-value 1, etc.
|
||||
epoch_ival_ms: iterable of (int,int)
|
||||
Epoching interval in milliseconds. In case only `name_to_codes` given,
|
||||
represents start offset and stop offset from start markers. In case
|
||||
`name_to_stop_codes` given, represents offset from start marker
|
||||
and offset from stop marker. E.g. [500, -500] would mean 500ms
|
||||
after the start marker until 500 ms before the stop marker.
|
||||
name_to_stop_codes: dict (str -> int or list of int), optional
|
||||
Dictionary mapping class names to stop marker code or stop marker codes.
|
||||
Order does not matter, dictionary should contain each class in
|
||||
`name_to_codes` dictionary.
|
||||
prepad_trials_to_n_samples: int, optional
|
||||
Pad trials that would be too short with the signal before it (only
|
||||
valid if name_to_stop_codes is not None).
|
||||
one_hot_labels: bool, optional
|
||||
Whether to have the labels in a one-hot format, e.g. [0,0,1] or to
|
||||
have them just as an int, e.g. 2
|
||||
one_label_per_trial: bool, optional
|
||||
Whether to have a timeseries of labels or just a single label per trial.
|
||||
|
||||
Returns
|
||||
-------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
Dataset with `X` as the trial signals and `y` as the trial labels.
|
||||
|
||||
"""
|
||||
if name_to_stop_codes is None:
|
||||
return _create_signal_target_from_start_and_ival(
|
||||
data, events, fs, name_to_start_codes, epoch_ival_ms,
|
||||
one_hot_labels=one_hot_labels,
|
||||
one_label_per_trial=one_label_per_trial)
|
||||
else:
|
||||
return _create_signal_target_from_start_and_stop(
|
||||
data, events, fs, name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes, prepad_trials_to_n_samples,
|
||||
one_hot_labels=one_hot_labels,
|
||||
one_label_per_trial=one_label_per_trial)
|
||||
|
||||
|
||||
def _to_mrk_code_to_name_and_y(name_to_codes):
|
||||
# Create mapping from marker code to class name and y=classindex
|
||||
mrk_code_to_name_and_y = {}
|
||||
for i_class, class_name in enumerate(name_to_codes):
|
||||
codes = name_to_codes[class_name]
|
||||
if hasattr(codes, '__len__'):
|
||||
for code in codes:
|
||||
assert code not in mrk_code_to_name_and_y
|
||||
mrk_code_to_name_and_y[code] = (class_name, i_class)
|
||||
else:
|
||||
assert codes not in mrk_code_to_name_and_y
|
||||
mrk_code_to_name_and_y[codes] = (class_name, i_class)
|
||||
return mrk_code_to_name_and_y
|
||||
|
||||
|
||||
def _create_signal_target_from_start_and_ival(
|
||||
data, events, fs, name_to_codes, epoch_ival_ms,
|
||||
one_hot_labels, one_label_per_trial):
|
||||
cnt_y, i_start_stops = _create_cnt_y_and_trial_bounds_from_start_and_ival(
|
||||
data.shape[1], events, fs, name_to_codes, epoch_ival_ms
|
||||
)
|
||||
signal_target = _create_signal_target_from_cnt_y_start_stops(
|
||||
data, cnt_y, i_start_stops, prepad_trials_to_n_samples=None,
|
||||
one_hot_labels=one_hot_labels,
|
||||
one_label_per_trial=one_label_per_trial)
|
||||
# make into arrray as all should have same dimensions
|
||||
signal_target.X = np.array(signal_target.X, dtype=np.float32)
|
||||
signal_target.y = np.array(signal_target.y, dtype=np.int64)
|
||||
return signal_target
|
||||
|
||||
|
||||
def _create_cnt_y_and_trial_bounds_from_start_and_ival(
|
||||
n_samples, events, fs, name_to_start_codes, epoch_ival_ms):
|
||||
ival_in_samples = ms_to_samples(np.array(epoch_ival_ms), fs)
|
||||
start_offset = np.int32(np.round(ival_in_samples[0]))
|
||||
# we will use ceil but exclusive...
|
||||
stop_offset = np.int32(np.ceil(ival_in_samples[1]))
|
||||
mrk_code_to_name_and_y = _to_mrk_code_to_name_and_y(name_to_start_codes)
|
||||
class_to_n_trials = Counter()
|
||||
n_classes = len(name_to_start_codes)
|
||||
cnt_y = np.zeros((n_samples, n_classes), dtype=np.int64)
|
||||
i_start_stops = []
|
||||
for i_sample, mrk_code in zip(events[:, 0], events[:, 1]):
|
||||
start_sample = int(i_sample) + start_offset
|
||||
stop_sample = int(i_sample) + stop_offset
|
||||
if mrk_code in mrk_code_to_name_and_y:
|
||||
if start_sample < 0:
|
||||
log.warning(
|
||||
"Ignore trial with marker code {:d}, would start at "
|
||||
"sample {:d}".format(mrk_code, start_sample))
|
||||
continue
|
||||
if stop_sample > n_samples:
|
||||
log.warning("Ignore trial with marker code {:d}, would end at "
|
||||
"sample {:d} of {:d}".format(
|
||||
mrk_code, stop_sample - 1, n_samples - 1))
|
||||
continue
|
||||
|
||||
name, this_y = mrk_code_to_name_and_y[mrk_code]
|
||||
i_start_stops.append((start_sample, stop_sample))
|
||||
cnt_y[start_sample:stop_sample, this_y] = 1
|
||||
class_to_n_trials[name] += 1
|
||||
log.info("Trial per class:\n{:s}".format(str(class_to_n_trials)))
|
||||
return cnt_y, i_start_stops
|
||||
|
||||
|
||||
def _create_signal_target_from_start_and_stop(
|
||||
data, events, fs, name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes, prepad_trials_to_n_samples,
|
||||
one_hot_labels, one_label_per_trial, ):
|
||||
assert np.array_equal(list(name_to_start_codes.keys()),
|
||||
list(name_to_stop_codes.keys()))
|
||||
cnt_y, i_start_stops = _create_cnt_y_and_trial_bounds_from_start_stop(
|
||||
data.shape[1],events, fs,name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes)
|
||||
signal_target = _create_signal_target_from_cnt_y_start_stops(
|
||||
data, cnt_y, i_start_stops,
|
||||
prepad_trials_to_n_samples=prepad_trials_to_n_samples,
|
||||
one_hot_labels=one_hot_labels, one_label_per_trial=one_label_per_trial)
|
||||
return signal_target
|
||||
|
||||
|
||||
def _create_cnt_y_and_trial_bounds_from_start_stop(
|
||||
n_samples, events, fs, name_to_start_codes, epoch_ival_ms,
|
||||
name_to_stop_codes):
|
||||
"""
|
||||
Create a one-hot-encoded continuous marker array (cnt_y).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n_samples: int
|
||||
Number of samples=timesteps in the recorded data.
|
||||
events: 2d-array
|
||||
Dimensions: Number of events, 2. For each event, should contain sample
|
||||
index and marker code.
|
||||
fs: number
|
||||
Sampling rate.
|
||||
name_to_start_codes: OrderedDict (str -> int or list of int)
|
||||
Ordered dictionary mapping class names to marker code or marker codes.
|
||||
y-labels will be assigned in increasing key order, i.e.
|
||||
first classname gets y-value 0, second classname y-value 1, etc.
|
||||
epoch_ival_ms: iterable of (int,int)
|
||||
Epoching interval in milliseconds. In case only `name_to_codes` given,
|
||||
represents start offset and stop offset from start markers. In case
|
||||
`name_to_stop_codes` given, represents offset from start marker
|
||||
and offset from stop marker. E.g. [500, -500] would mean 500ms
|
||||
after the start marker until 500 ms before the stop marker.
|
||||
name_to_stop_codes: dict (str -> int or list of int), optional
|
||||
Dictionary mapping class names to stop marker code or stop marker codes.
|
||||
Order does not matter, dictionary should contain each class in
|
||||
`name_to_codes` dictionary.
|
||||
|
||||
Returns
|
||||
-------
|
||||
cnt_y: 2d-array
|
||||
Timeseries of one-hot-labels, time x classes.
|
||||
trial_bounds: list of (int,int)
|
||||
List of (trial_start, trial_stop) tuples.
|
||||
|
||||
|
||||
"""
|
||||
assert np.array_equal(list(name_to_start_codes.keys()),
|
||||
list(name_to_stop_codes.keys()))
|
||||
events = np.asarray(events)
|
||||
ival_in_samples = ms_to_samples(np.array(epoch_ival_ms), fs)
|
||||
start_offset = np.int32(np.round(ival_in_samples[0]))
|
||||
# we will use ceil but exclusive...
|
||||
stop_offset = np.int32(np.ceil(ival_in_samples[1]))
|
||||
start_code_to_name_and_y = _to_mrk_code_to_name_and_y(name_to_start_codes)
|
||||
# Ensure all stop marker codes are iterables
|
||||
for name in name_to_stop_codes:
|
||||
codes = name_to_stop_codes[name]
|
||||
if not hasattr(codes, '__len__'):
|
||||
name_to_stop_codes[name] = [codes]
|
||||
all_stop_codes = np.concatenate(list(name_to_stop_codes.values())
|
||||
).astype(np.int64)
|
||||
class_to_n_trials = Counter()
|
||||
n_classes = len(name_to_start_codes)
|
||||
cnt_y = np.zeros((n_samples, n_classes), dtype=np.int64)
|
||||
|
||||
event_samples = events[:, 0]
|
||||
event_codes = events[:, 1]
|
||||
i_start_stops = []
|
||||
i_event = 0
|
||||
first_start_code_found = False
|
||||
while i_event < len(events):
|
||||
while i_event < len(events) and (
|
||||
event_codes[i_event] not in start_code_to_name_and_y):
|
||||
i_event += 1
|
||||
if i_event < len(events):
|
||||
start_sample = event_samples[i_event]
|
||||
start_code = event_codes[i_event]
|
||||
start_name = start_code_to_name_and_y[start_code][0]
|
||||
start_y = start_code_to_name_and_y[start_code][1]
|
||||
i_event += 1
|
||||
first_start_code_found = True
|
||||
waiting_for_end_code = True
|
||||
|
||||
while i_event < len(events) and (
|
||||
event_codes[i_event] not in all_stop_codes):
|
||||
if event_codes[i_event] in start_code_to_name_and_y:
|
||||
log.warning(
|
||||
"New start marker {:.0f} at {:.0f} samples found, "
|
||||
"no end marker for earlier start marker {:.0f} "
|
||||
"at {:.0f} samples found.".format(
|
||||
event_codes[i_event], event_samples[i_event],
|
||||
start_code, start_sample))
|
||||
start_sample = event_samples[i_event]
|
||||
start_name = start_code_to_name_and_y[start_code][0]
|
||||
start_code = event_codes[i_event]
|
||||
start_y = start_code_to_name_and_y[start_code][1]
|
||||
i_event += 1
|
||||
if i_event == len(events):
|
||||
if waiting_for_end_code:
|
||||
log.warning(("No end marker for start marker code {:.0f} "
|
||||
"at sample {:.0f} found.").format(start_code,
|
||||
start_sample))
|
||||
elif (not first_start_code_found):
|
||||
log.warning("No markers found at all.")
|
||||
break
|
||||
stop_sample = event_samples[i_event]
|
||||
stop_code = event_codes[i_event]
|
||||
assert stop_code in name_to_stop_codes[start_name]
|
||||
i_start = int(start_sample) + start_offset
|
||||
i_stop = int(stop_sample) + stop_offset
|
||||
cnt_y[i_start:i_stop, start_y] = 1
|
||||
i_start_stops.append((i_start, i_stop))
|
||||
class_to_n_trials[start_name] += 1
|
||||
waiting_for_end_code = False
|
||||
|
||||
log.info("Trial per class:\n{:s}".format(str(class_to_n_trials)))
|
||||
return cnt_y, i_start_stops
|
||||
|
||||
|
||||
def _create_signal_target_from_cnt_y_start_stops(
|
||||
data,
|
||||
cnt_y,
|
||||
i_start_stops,
|
||||
prepad_trials_to_n_samples,
|
||||
one_hot_labels,
|
||||
one_label_per_trial):
|
||||
if prepad_trials_to_n_samples is not None:
|
||||
new_i_start_stops = []
|
||||
for i_start, i_stop in i_start_stops:
|
||||
if (i_stop - i_start) > prepad_trials_to_n_samples:
|
||||
new_i_start_stops.append((i_start, i_stop))
|
||||
elif i_stop >= prepad_trials_to_n_samples:
|
||||
new_i_start_stops.append(
|
||||
(i_stop - prepad_trials_to_n_samples, i_stop))
|
||||
else:
|
||||
log.warning("Could not pad trial enough, therefore not "
|
||||
"not using trial from {:d} to {:d}".format(
|
||||
i_start, i_stop
|
||||
))
|
||||
continue
|
||||
|
||||
else:
|
||||
new_i_start_stops = i_start_stops
|
||||
|
||||
X = []
|
||||
y = []
|
||||
for i_start, i_stop in new_i_start_stops:
|
||||
if i_start < 0:
|
||||
log.warning("Trial start too early, therefore not "
|
||||
"not using trial from {:d} to {:d}".format(
|
||||
i_start, i_stop
|
||||
))
|
||||
continue
|
||||
if i_stop > data.shape[1]:
|
||||
log.warning("Trial stop too late (past {:d}), therefore not "
|
||||
"not using trial from {:d} to {:d}".format(
|
||||
data.shape[1] - 1,
|
||||
i_start, i_stop
|
||||
))
|
||||
continue
|
||||
X.append(data[:, i_start:i_stop].astype(np.float32))
|
||||
y.append(cnt_y[i_start:i_stop])
|
||||
|
||||
# take last label always
|
||||
if one_label_per_trial:
|
||||
new_y = []
|
||||
for this_y in y:
|
||||
# if destroying one hot later, just set most occuring class to 1
|
||||
unique_labels, counts = np.unique(
|
||||
this_y, axis=0, return_counts=True)
|
||||
if not one_hot_labels:
|
||||
meaned_y = np.mean(this_y, axis=0)
|
||||
this_new_y = np.zeros_like(meaned_y)
|
||||
this_new_y[np.argmax(meaned_y)] = 1
|
||||
else:
|
||||
# take most frequency occurring label combination
|
||||
this_new_y = unique_labels[np.argmax(counts)]
|
||||
|
||||
if len(unique_labels) > 1:
|
||||
log.warning("Different labels within one trial: {:s},"
|
||||
"setting single trial label to {:s}".format(
|
||||
str(unique_labels), str(this_new_y)
|
||||
))
|
||||
new_y.append(this_new_y)
|
||||
y = new_y
|
||||
if not one_hot_labels:
|
||||
# change from one-hot-encoding to regular encoding
|
||||
# with -1 as indication none of the classes are present
|
||||
new_y = []
|
||||
for this_y in y:
|
||||
if one_label_per_trial:
|
||||
if np.sum(this_y) == 0:
|
||||
this_new_y = -1
|
||||
else:
|
||||
this_new_y = np.argmax(this_y)
|
||||
if np.sum(this_y) > 1:
|
||||
log.warning(
|
||||
"Have multiple active classes and will convert to "
|
||||
"lowest class")
|
||||
else:
|
||||
if np.max(np.sum(this_y, axis=1)) > 1:
|
||||
log.warning(
|
||||
"Have multiple active classes and will convert to "
|
||||
"lowest class")
|
||||
this_new_y = np.argmax(this_y, axis=1)
|
||||
this_new_y[np.sum(this_y, axis=1) == 0] = -1
|
||||
new_y.append(this_new_y)
|
||||
y = new_y
|
||||
if one_label_per_trial:
|
||||
y = np.array(y, dtype=np.int64)
|
||||
|
||||
return SignalAndTarget(X, y)
|
||||
|
||||
|
||||
def create_signal_target_with_breaks_from_mne(
|
||||
cnt, name_to_start_codes,
|
||||
trial_epoch_ival_ms,
|
||||
name_to_stop_codes,
|
||||
min_break_length_ms, max_break_length_ms,
|
||||
break_epoch_ival_ms,
|
||||
prepad_trials_to_n_samples=None):
|
||||
"""
|
||||
Create SignalTarget set from given `mne.io.RawArray`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cnt: `mne.io.RawArray`
|
||||
name_to_start_codes: OrderedDict (str -> int or list of int)
|
||||
Ordered dictionary mapping class names to marker code or marker codes.
|
||||
y-labels will be assigned in increasing key order, i.e.
|
||||
first classname gets y-value 0, second classname y-value 1, etc.
|
||||
trial_epoch_ival_ms: iterable of (int,int)
|
||||
Epoching interval in milliseconds. Represents offset from start marker
|
||||
and offset from stop marker. E.g. [500, -500] would mean 500ms
|
||||
after the start marker until 500 ms before the stop marker.
|
||||
name_to_stop_codes: dict (str -> int or list of int), optional
|
||||
Dictionary mapping class names to stop marker code or stop marker codes.
|
||||
Order does not matter, dictionary should contain each class in
|
||||
`name_to_codes` dictionary.
|
||||
min_break_length_ms: number
|
||||
Breaks below this length are excluded.
|
||||
max_break_length_ms: number
|
||||
Breaks above this length are excluded.
|
||||
break_epoch_ival_ms: number
|
||||
Break ival, offset from trial end to start of the break in ms and
|
||||
offset from trial start to end of break in ms.
|
||||
prepad_trials_to_n_samples: int
|
||||
Pad trials that would be too short with the signal before it (only
|
||||
valid if name_to_stop_codes is not None).
|
||||
|
||||
Returns
|
||||
-------
|
||||
dataset: :class:`.SignalAndTarget`
|
||||
Dataset with `X` as the trial signals and `y` as the trial labels.
|
||||
Labels as timeseries and of integers, i.e., not one-hot encoded.
|
||||
"""
|
||||
assert 'Break' not in name_to_start_codes
|
||||
# Create new marker codes for start and stop of breaks
|
||||
# Use marker codes that did not exist in the given marker codes...
|
||||
all_start_codes = np.concatenate(
|
||||
[np.atleast_1d(vals) for vals in name_to_start_codes.values()])
|
||||
all_stop_codes = np.concatenate(
|
||||
[np.atleast_1d(vals) for vals in name_to_stop_codes.values()])
|
||||
break_start_code = -1
|
||||
while break_start_code in np.concatenate((all_start_codes, all_stop_codes)):
|
||||
break_start_code -= 1
|
||||
break_stop_code = break_start_code - 1
|
||||
while break_stop_code in np.concatenate((all_start_codes, all_stop_codes)):
|
||||
break_stop_code -= 1
|
||||
|
||||
events = cnt.info['events'][:, [0, 2]]
|
||||
# later trial segment ival will be added when creating set
|
||||
# so remove it here
|
||||
break_epoch_ival_ms = np.array(break_epoch_ival_ms) - (
|
||||
np.array(trial_epoch_ival_ms))
|
||||
events_with_breaks = add_breaks(events, cnt.info['sfreq'],
|
||||
break_start_code, break_stop_code,
|
||||
name_to_start_codes, name_to_stop_codes,
|
||||
min_break_length_ms=min_break_length_ms,
|
||||
max_break_length_ms=max_break_length_ms,
|
||||
break_start_offset_ms=break_epoch_ival_ms[
|
||||
0],
|
||||
break_stop_offset_ms=break_epoch_ival_ms[
|
||||
1])
|
||||
|
||||
name_to_start_codes_with_breaks = deepcopy(name_to_start_codes)
|
||||
name_to_start_codes_with_breaks['Break'] = break_start_code
|
||||
name_to_stop_codes_with_breaks = deepcopy(name_to_stop_codes)
|
||||
name_to_stop_codes_with_breaks['Break'] = break_stop_code
|
||||
|
||||
|
||||
data = cnt.get_data()
|
||||
fs = cnt.info['sfreq']
|
||||
signal_target = create_signal_target(
|
||||
data, events_with_breaks, fs,
|
||||
name_to_start_codes_with_breaks, trial_epoch_ival_ms,
|
||||
name_to_stop_codes_with_breaks,
|
||||
prepad_trials_to_n_samples=prepad_trials_to_n_samples,
|
||||
one_hot_labels=False,
|
||||
one_label_per_trial=False)
|
||||
|
||||
return signal_target
|
||||
|
||||
|
||||
def add_breaks(
|
||||
events, fs, break_start_code, break_stop_code, name_to_start_codes,
|
||||
name_to_stop_codes, min_break_length_ms=None,
|
||||
max_break_length_ms=None, break_start_offset_ms=None,
|
||||
break_stop_offset_ms=None):
|
||||
"""
|
||||
Add break events to given events.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
events: 2d-array
|
||||
Dimensions: Number of events, 2. For each event, should contain sample
|
||||
index and marker code.
|
||||
fs: number
|
||||
Sampling rate.
|
||||
break_start_code: int
|
||||
Marker code that will be used for break start markers.
|
||||
break_stop_code: int
|
||||
Marker code that will be used for break stop markers.
|
||||
name_to_start_codes: OrderedDict (str -> int or list of int)
|
||||
Ordered dictionary mapping class names to start marker code or
|
||||
start marker codes.
|
||||
name_to_stop_codes: dict (str -> int or list of int), optional
|
||||
Dictionary mapping class names to stop marker code or stop marker codes.
|
||||
min_break_length_ms: number, optional
|
||||
Minimum length in milliseconds a break should have to be included.
|
||||
max_break_length_ms: number, optional
|
||||
Maximum length in milliseconds a break can have to be included.
|
||||
break_start_offset_ms: number, optional
|
||||
What offset from trial end to start of the break in ms.
|
||||
break_stop_offset_ms: number, optional
|
||||
What offset from next trial start end to previous break end in ms.
|
||||
|
||||
Returns
|
||||
-------
|
||||
events: 2d-array
|
||||
Events with break start and stop markers.
|
||||
"""
|
||||
min_samples = (None if min_break_length_ms is None
|
||||
else ms_to_samples(min_break_length_ms, fs))
|
||||
max_samples = (None if max_break_length_ms is None
|
||||
else ms_to_samples(max_break_length_ms, fs))
|
||||
orig_events = events
|
||||
break_starts, break_stops = _extract_break_start_stop_ms(
|
||||
events, name_to_start_codes, name_to_stop_codes)
|
||||
|
||||
break_durations = break_stops - break_starts
|
||||
valid_mask = np.array([True] * len(break_starts))
|
||||
if min_samples is not None:
|
||||
valid_mask[break_durations < min_samples] = False
|
||||
if max_samples is not None:
|
||||
valid_mask[break_durations > max_samples] = False
|
||||
if sum(valid_mask) == 0:
|
||||
return deepcopy(events)
|
||||
break_starts = break_starts[valid_mask]
|
||||
break_stops = break_stops[valid_mask]
|
||||
if break_start_offset_ms is not None:
|
||||
break_starts += int(round(ms_to_samples(break_start_offset_ms, fs)))
|
||||
if break_stop_offset_ms is not None:
|
||||
break_stops += int(round(ms_to_samples(break_stop_offset_ms, fs)))
|
||||
break_events = np.zeros((len(break_starts) * 2, 2))
|
||||
break_events[0::2, 0] = break_starts
|
||||
break_events[1::2, 0] = break_stops
|
||||
break_events[0::2, 1] = break_start_code
|
||||
break_events[1::2, 1] = break_stop_code
|
||||
|
||||
new_events = np.concatenate((orig_events, break_events))
|
||||
# sort events
|
||||
sort_order = np.argsort(new_events[:, 0], kind='mergesort')
|
||||
new_events = new_events[sort_order]
|
||||
return new_events
|
||||
|
||||
|
||||
def _extract_break_start_stop_ms(events, name_to_start_codes,
|
||||
name_to_stop_codes):
|
||||
assert len(events[0]) == 2, "expect only 2dimensional event array here"
|
||||
start_code_to_name_and_y = _to_mrk_code_to_name_and_y(name_to_start_codes)
|
||||
# Ensure all stop marker codes are iterables
|
||||
for name in name_to_stop_codes:
|
||||
codes = name_to_stop_codes[name]
|
||||
if not hasattr(codes, '__len__'):
|
||||
name_to_stop_codes[name] = [codes]
|
||||
all_stop_codes = np.concatenate(list(name_to_stop_codes.values())).astype(
|
||||
np.int32)
|
||||
event_samples = events[:, 0]
|
||||
event_codes = events[:, 1]
|
||||
|
||||
break_starts = []
|
||||
break_stops = []
|
||||
i_event = 0
|
||||
while i_event < len(events):
|
||||
while (i_event < len(events)) and (
|
||||
event_codes[i_event] not in all_stop_codes):
|
||||
i_event += 1
|
||||
if i_event < len(events):
|
||||
# one sample after start
|
||||
stop_sample = event_samples[i_event]
|
||||
stop_code = event_codes[i_event]
|
||||
i_event += 1
|
||||
while (i_event < len(events)) and (
|
||||
event_codes[i_event] not in start_code_to_name_and_y):
|
||||
if event_codes[i_event] in all_stop_codes:
|
||||
log.warning(
|
||||
"New end marker {:.0f} at {:.0f} samples found, "
|
||||
"no start marker for earlier end marker {:.0f} "
|
||||
"at {:.0f} samples found.".format(
|
||||
event_codes[i_event],
|
||||
event_samples[i_event],
|
||||
stop_code, stop_sample))
|
||||
stop_sample = event_samples[i_event]
|
||||
stop_code = event_codes[i_event]
|
||||
i_event += 1
|
||||
|
||||
if i_event == len(events):
|
||||
break
|
||||
|
||||
start_sample = event_samples[i_event]
|
||||
start_code = event_codes[i_event]
|
||||
assert start_code in start_code_to_name_and_y
|
||||
# let's start one after stop of the trial and stop one efore
|
||||
# start of the trial to ensure that markers will be
|
||||
# in right order
|
||||
break_starts.append(stop_sample + 1)
|
||||
break_stops.append(start_sample - 1)
|
||||
return np.array(break_starts), np.array(break_stops)
|
||||
@@ -0,0 +1,37 @@
|
||||
def ms_to_samples(ms, fs):
|
||||
"""
|
||||
Compute milliseconds to number of samples.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
ms: number
|
||||
Milliseconds
|
||||
fs: number
|
||||
Sampling rate
|
||||
|
||||
Returns
|
||||
-------
|
||||
n_samples: int
|
||||
Number of samples
|
||||
|
||||
"""
|
||||
return ms * fs / 1000.0
|
||||
|
||||
|
||||
def samples_to_ms(n_samples, fs):
|
||||
"""
|
||||
Compute milliseconds to number of samples.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n_samples: number
|
||||
Number of samples
|
||||
fs: number
|
||||
Sampling rate
|
||||
|
||||
Returns
|
||||
-------
|
||||
milliseconds: int
|
||||
"""
|
||||
return n_samples * 1000.0 / fs
|
||||
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Convenience classes for experiments, including monitoring and stop criteria.
|
||||
"""
|
||||
@@ -0,0 +1,492 @@
|
||||
import logging
|
||||
from collections import OrderedDict
|
||||
from copy import deepcopy
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
import torch as th
|
||||
import numpy as np
|
||||
|
||||
from braindecode.datautil.splitters import concatenate_sets
|
||||
from braindecode.experiments.loggers import Printer
|
||||
from braindecode.experiments.stopcriteria import MaxEpochs, ColumnBelow, Or
|
||||
from braindecode.torch_ext.util import np_to_var
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RememberBest(object):
|
||||
"""
|
||||
Class to remember and restore
|
||||
the parameters of the model and the parameters of the
|
||||
optimizer at the epoch with the best performance.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
column_name: str
|
||||
The lowest value in this column should indicate the epoch with the
|
||||
best performance (e.g. misclass might make sense).
|
||||
|
||||
Attributes
|
||||
----------
|
||||
best_epoch: int
|
||||
Index of best epoch
|
||||
"""
|
||||
def __init__(self, column_name):
|
||||
self.column_name = column_name
|
||||
self.best_epoch = 0
|
||||
self.lowest_val = float('inf')
|
||||
self.model_state_dict = None
|
||||
self.optimizer_state_dict = None
|
||||
|
||||
def remember_epoch(self, epochs_df, model, optimizer):
|
||||
"""
|
||||
Remember this epoch: Remember parameter values in case this epoch
|
||||
has the best performance so far.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
epochs_df: `pandas.Dataframe`
|
||||
Dataframe containing the column `column_name` with which performance
|
||||
is evaluated.
|
||||
model: `torch.nn.Module`
|
||||
optimizer: `torch.optim.Optimizer`
|
||||
|
||||
"""
|
||||
i_epoch = len(epochs_df) - 1
|
||||
current_val = float(epochs_df[self.column_name].iloc[-1])
|
||||
if current_val <= self.lowest_val:
|
||||
self.best_epoch = i_epoch
|
||||
self.lowest_val = current_val
|
||||
self.model_state_dict = deepcopy(model.state_dict())
|
||||
self.optimizer_state_dict = deepcopy(optimizer.state_dict())
|
||||
log.info("New best {:s}: {:5f}".format(self.column_name,
|
||||
current_val))
|
||||
log.info("")
|
||||
|
||||
def reset_to_best_model(self, epochs_df, model, optimizer):
|
||||
"""
|
||||
Reset parameters to parameters at best epoch and remove rows
|
||||
after best epoch from epochs dataframe.
|
||||
|
||||
Modifies parameters of model and optimizer, changes epochs_df in-place.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
epochs_df: `pandas.Dataframe`
|
||||
model: `torch.nn.Module`
|
||||
optimizer: `torch.optim.Optimizer`
|
||||
|
||||
"""
|
||||
# Remove epochs past the best one from epochs dataframe
|
||||
epochs_df.drop(range(self.best_epoch+1, len(epochs_df)), inplace=True)
|
||||
model.load_state_dict(self.model_state_dict)
|
||||
optimizer.load_state_dict(self.optimizer_state_dict)
|
||||
|
||||
|
||||
class Experiment(object):
|
||||
"""
|
||||
Class that performs one experiment on training, validation and test set.
|
||||
|
||||
It trains as follows:
|
||||
|
||||
1. Train on training set until a given stop criterion is fulfilled
|
||||
2. Reset to the best epoch, i.e. reset parameters of the model and the
|
||||
optimizer to the state at the best epoch ("best" according to a given
|
||||
criterion)
|
||||
3. Continue training on the combined training + validation set until the
|
||||
loss on the validation set is as low as it was on the best epoch for the
|
||||
training set. (or until the ConvNet was trained twice as many epochs as
|
||||
the best epoch to prevent infinite training)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: `torch.nn.Module`
|
||||
train_set: :class:`.SignalAndTarget`
|
||||
valid_set: :class:`.SignalAndTarget`
|
||||
test_set: :class:`.SignalAndTarget`
|
||||
iterator: iterator object
|
||||
loss_function: function
|
||||
Function mapping predictions and targets to a loss:
|
||||
(predictions: `torch.autograd.Variable`,
|
||||
targets:`torch.autograd.Variable`)
|
||||
-> loss: `torch.autograd.Variable`
|
||||
optimizer: `torch.optim.Optimizer`
|
||||
model_constraint: object
|
||||
Object with apply function that takes model and constraints its
|
||||
parameters. `None` for no constraint.
|
||||
monitors: list of objects
|
||||
List of objects with monitor_epoch and monitor_set method, should
|
||||
monitor the traning progress.
|
||||
stop_criterion: object
|
||||
Object with `should_stop` method, that takes in monitoring dataframe
|
||||
and returns if training should stop:
|
||||
remember_best_column: str
|
||||
Name of column to use for storing parameters of best model. Lowest value
|
||||
should indicate best performance in this column.
|
||||
run_after_early_stop: bool
|
||||
Whether to continue running after early stop
|
||||
model_loss_function: function, optional
|
||||
Function (model -> loss) to add a model loss like L2 regularization.
|
||||
Note that this loss is not accounted for in monitoring at the moment.
|
||||
batch_modifier: object, optional
|
||||
Object with modify method, that can change the batch, e.g. for data
|
||||
augmentation
|
||||
cuda: bool, optional
|
||||
Whether to use cuda.
|
||||
pin_memory: bool, optional
|
||||
Whether to pin memory of inputs and targets of batch.
|
||||
do_early_stop: bool
|
||||
Whether to do an early stop at all. If true, reset to best model
|
||||
even in case experiment does not run after early stop.
|
||||
reset_after_second_run: bool
|
||||
If true, reset to best model when second run did not find a valid loss
|
||||
below or equal to the best train loss of first run.
|
||||
log_0_epoch: bool
|
||||
Whether to compute monitor values and log them before the
|
||||
start of training.
|
||||
loggers: list of :class:`.Logger`
|
||||
How to show computed metrics.
|
||||
|
||||
Attributes
|
||||
----------
|
||||
epochs_df: `pandas.DataFrame`
|
||||
Monitoring values for all epochs.
|
||||
"""
|
||||
def __init__(self, model, train_set, valid_set, test_set,
|
||||
iterator, loss_function, optimizer, model_constraint,
|
||||
monitors, stop_criterion, remember_best_column,
|
||||
run_after_early_stop,
|
||||
model_loss_function=None,
|
||||
batch_modifier=None, cuda=True, pin_memory=False,
|
||||
do_early_stop=True,
|
||||
reset_after_second_run=False,
|
||||
log_0_epoch=True,
|
||||
loggers=('print',)):
|
||||
if run_after_early_stop or reset_after_second_run:
|
||||
assert do_early_stop == True, ("Can only run after early stop or "
|
||||
"reset after second run if doing an early stop")
|
||||
if do_early_stop:
|
||||
assert valid_set is not None
|
||||
assert remember_best_column is not None
|
||||
self.model = model
|
||||
self.datasets = OrderedDict(
|
||||
(('train', train_set), ('valid', valid_set), ('test', test_set)))
|
||||
if valid_set is None:
|
||||
self.datasets.pop('valid')
|
||||
assert run_after_early_stop == False
|
||||
assert do_early_stop == False
|
||||
if test_set is None:
|
||||
self.datasets.pop('test')
|
||||
|
||||
self.iterator = iterator
|
||||
self.loss_function = loss_function
|
||||
self.optimizer = optimizer
|
||||
self.model_constraint = model_constraint
|
||||
self.monitors = monitors
|
||||
self.stop_criterion = stop_criterion
|
||||
self.remember_best_column = remember_best_column
|
||||
self.run_after_early_stop = run_after_early_stop
|
||||
self.model_loss_function = model_loss_function
|
||||
self.batch_modifier = batch_modifier
|
||||
self.cuda = cuda
|
||||
self.epochs_df = pd.DataFrame()
|
||||
self.before_stop_df = None
|
||||
self.rememberer = None
|
||||
self.pin_memory = pin_memory
|
||||
self.do_early_stop = do_early_stop
|
||||
self.reset_after_second_run = reset_after_second_run
|
||||
self.log_0_epoch = log_0_epoch
|
||||
self.loggers = loggers
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Run complete training.
|
||||
"""
|
||||
self.setup_training()
|
||||
log.info("Run until first stop...")
|
||||
self.run_until_first_stop()
|
||||
if self.do_early_stop:
|
||||
# always setup for second stop, in order to get best model
|
||||
# even if not running after early stop...
|
||||
log.info("Setup for second stop...")
|
||||
self.setup_after_stop_training()
|
||||
if self.run_after_early_stop:
|
||||
log.info("Run until second stop...")
|
||||
loss_to_reach = float(self.epochs_df['train_loss'].iloc[-1])
|
||||
self.run_until_second_stop()
|
||||
if (float(self.epochs_df['valid_loss'].iloc[-1]) > loss_to_reach
|
||||
and self.reset_after_second_run):
|
||||
# if no valid loss was found below the best train loss on 1st
|
||||
# run, reset model to the epoch with lowest valid_misclass
|
||||
log.info("Resetting to best epoch {:d}".format(
|
||||
self.rememberer.best_epoch))
|
||||
self.rememberer.reset_to_best_model(self.epochs_df,
|
||||
self.model,
|
||||
self.optimizer)
|
||||
|
||||
def setup_training(self):
|
||||
"""
|
||||
Setup training, i.e. transform model to cuda,
|
||||
initialize monitoring.
|
||||
"""
|
||||
# reset remember best extension in case you rerun some experiment
|
||||
if self.do_early_stop:
|
||||
self.rememberer = RememberBest(self.remember_best_column)
|
||||
if self.loggers == ('print',):
|
||||
self.loggers = [Printer()]
|
||||
self.epochs_df = pd.DataFrame()
|
||||
if self.cuda:
|
||||
assert th.cuda.is_available(), "Cuda not available"
|
||||
self.model.cuda()
|
||||
|
||||
def run_until_first_stop(self):
|
||||
"""
|
||||
Run training and evaluation using only training set for training
|
||||
until stop criterion is fulfilled.
|
||||
"""
|
||||
self.run_until_stop(self.datasets, remember_best=self.do_early_stop)
|
||||
|
||||
def run_until_second_stop(self):
|
||||
"""
|
||||
Run training and evaluation using combined training + validation set
|
||||
for training.
|
||||
|
||||
Runs until loss on validation set decreases below loss on training set
|
||||
of best epoch or until as many epochs trained after as before
|
||||
first stop.
|
||||
"""
|
||||
datasets = self.datasets
|
||||
datasets['train'] = concatenate_sets([datasets['train'],
|
||||
datasets['valid']])
|
||||
|
||||
self.run_until_stop(datasets, remember_best=True)
|
||||
|
||||
def run_until_stop(self, datasets, remember_best):
|
||||
"""
|
||||
Run training and evaluation on given datasets until stop criterion is
|
||||
fulfilled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
datasets: OrderedDict
|
||||
Dictionary with train, valid and test as str mapping to
|
||||
:class:`.SignalAndTarget` objects.
|
||||
remember_best: bool
|
||||
Whether to remember parameters at best epoch.
|
||||
"""
|
||||
if self.log_0_epoch:
|
||||
self.monitor_epoch(datasets)
|
||||
self.log_epoch()
|
||||
if remember_best:
|
||||
self.rememberer.remember_epoch(self.epochs_df, self.model,
|
||||
self.optimizer)
|
||||
|
||||
self.iterator.reset_rng()
|
||||
while not self.stop_criterion.should_stop(self.epochs_df):
|
||||
self.run_one_epoch(datasets, remember_best)
|
||||
|
||||
def run_one_epoch(self, datasets, remember_best):
|
||||
"""
|
||||
Run training and evaluation on given datasets for one epoch.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
datasets: OrderedDict
|
||||
Dictionary with train, valid and test as str mapping to
|
||||
:class:`.SignalAndTarget` objects.
|
||||
remember_best: bool
|
||||
Whether to remember parameters if this epoch is best epoch.
|
||||
"""
|
||||
batch_generator = self.iterator.get_batches(datasets['train'],
|
||||
shuffle=True)
|
||||
start_train_epoch_time = time.time()
|
||||
for inputs, targets in batch_generator:
|
||||
if self.batch_modifier is not None:
|
||||
inputs, targets = self.batch_modifier.process(inputs,
|
||||
targets)
|
||||
# could happen that batch modifier has removed all inputs...
|
||||
if len(inputs) > 0:
|
||||
self.train_batch(inputs, targets)
|
||||
end_train_epoch_time = time.time()
|
||||
log.info("Time only for training updates: {:.2f}s".format(
|
||||
end_train_epoch_time - start_train_epoch_time))
|
||||
|
||||
self.monitor_epoch(datasets)
|
||||
self.log_epoch()
|
||||
if remember_best:
|
||||
self.rememberer.remember_epoch(self.epochs_df, self.model,
|
||||
self.optimizer)
|
||||
|
||||
def train_batch(self, inputs, targets):
|
||||
"""
|
||||
Train on given inputs and targets.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs: `torch.autograd.Variable`
|
||||
targets: `torch.autograd.Variable`
|
||||
"""
|
||||
self.model.train()
|
||||
input_vars = np_to_var(inputs, pin_memory=self.pin_memory)
|
||||
target_vars = np_to_var(targets, pin_memory=self.pin_memory)
|
||||
if self.cuda:
|
||||
input_vars = input_vars.cuda()
|
||||
target_vars = target_vars.cuda()
|
||||
self.optimizer.zero_grad()
|
||||
outputs = self.model(input_vars)
|
||||
loss = self.loss_function(outputs, target_vars)
|
||||
if self.model_loss_function is not None:
|
||||
loss = loss + self.model_loss_function(self.model)
|
||||
loss.backward()
|
||||
self.optimizer.step()
|
||||
if self.model_constraint is not None:
|
||||
self.model_constraint.apply(self.model)
|
||||
|
||||
def eval_on_batch(self, inputs, targets):
|
||||
"""
|
||||
Evaluate given inputs and targets.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs: `torch.autograd.Variable`
|
||||
targets: `torch.autograd.Variable`
|
||||
|
||||
Returns
|
||||
-------
|
||||
predictions: `torch.autograd.Variable`
|
||||
loss: `torch.autograd.Variable`
|
||||
|
||||
"""
|
||||
self.model.eval()
|
||||
with th.no_grad():
|
||||
input_vars = np_to_var(inputs, pin_memory=self.pin_memory)
|
||||
target_vars = np_to_var(targets, pin_memory=self.pin_memory)
|
||||
if self.cuda:
|
||||
input_vars = input_vars.cuda()
|
||||
target_vars = target_vars.cuda()
|
||||
outputs = self.model(input_vars)
|
||||
loss = self.loss_function(outputs, target_vars)
|
||||
if hasattr(outputs, 'cpu'):
|
||||
outputs = outputs.cpu().detach().numpy()
|
||||
else:
|
||||
# assume it is iterable
|
||||
outputs = [o.cpu().detach().numpy() for o in outputs]
|
||||
loss = loss.cpu().detach().numpy()
|
||||
return outputs, loss
|
||||
|
||||
def monitor_epoch(self, datasets):
|
||||
"""
|
||||
Evaluate one epoch for given datasets.
|
||||
|
||||
Stores results in `epochs_df`
|
||||
|
||||
Parameters
|
||||
----------
|
||||
datasets: OrderedDict
|
||||
Dictionary with train, valid and test as str mapping to
|
||||
:class:`.SignalAndTarget` objects.
|
||||
|
||||
"""
|
||||
result_dicts_per_monitor = OrderedDict()
|
||||
for m in self.monitors:
|
||||
result_dicts_per_monitor[m] = OrderedDict()
|
||||
result_dict = m.monitor_epoch()
|
||||
if result_dict is not None:
|
||||
result_dicts_per_monitor[m].update(result_dict)
|
||||
for setname in datasets:
|
||||
assert setname in ['train', 'valid', 'test']
|
||||
dataset = datasets[setname]
|
||||
batch_generator = self.iterator.get_batches(dataset, shuffle=False)
|
||||
if hasattr(batch_generator, "__len__"):
|
||||
# prevent loading of data to estimate number of batches when
|
||||
# using lazy iterators
|
||||
n_batches = len(batch_generator)
|
||||
else:
|
||||
# iterating through traditional iterators is cheap, since
|
||||
# nothing is loaded, recreate generator afterwards
|
||||
n_batches = sum(1 for i in batch_generator)
|
||||
batch_generator = self.iterator.get_batches(dataset,
|
||||
shuffle=False)
|
||||
all_preds, all_targets = None, None
|
||||
all_losses, all_batch_sizes = [], []
|
||||
for inputs, targets in batch_generator:
|
||||
preds, loss = self.eval_on_batch(inputs, targets)
|
||||
all_losses.append(loss)
|
||||
all_batch_sizes.append(len(targets))
|
||||
if all_preds is None:
|
||||
assert all_targets is None
|
||||
# first batch size is largest
|
||||
max_size, n_classes, n_preds_per_input = preds.shape
|
||||
# pre-allocate memory for all predictions and targets
|
||||
all_preds = np.nan * np.ones(
|
||||
(n_batches * max_size, n_classes, n_preds_per_input),
|
||||
dtype=np.float32)
|
||||
all_preds[:len(preds)] = preds
|
||||
all_targets = np.nan * np.ones((n_batches * max_size))
|
||||
all_targets[:len(targets)] = targets
|
||||
else:
|
||||
start_i = sum(all_batch_sizes[:-1])
|
||||
stop_i = sum(all_batch_sizes)
|
||||
all_preds[start_i:stop_i] = preds
|
||||
all_targets[start_i:stop_i] = targets
|
||||
|
||||
# check for unequal batches
|
||||
unequal_batches = len(set(all_batch_sizes)) > 1
|
||||
all_batch_sizes = sum(all_batch_sizes)
|
||||
# remove nan rows in case of unequal batch sizes
|
||||
if unequal_batches:
|
||||
assert np.sum(np.isnan(all_preds[:all_batch_sizes - 1])) == 0
|
||||
assert np.sum(np.isnan(all_preds[all_batch_sizes:])) > 0
|
||||
range_to_delete = range(all_batch_sizes, len(all_preds))
|
||||
all_preds = np.delete(all_preds, range_to_delete, axis=0)
|
||||
all_targets = np.delete(all_targets, range_to_delete, axis=0)
|
||||
assert np.sum(np.isnan(all_preds)) == 0, (
|
||||
"There are still nans in predictions")
|
||||
assert np.sum(np.isnan(all_targets)) == 0, (
|
||||
"There are still nans in targets")
|
||||
# add empty dimension
|
||||
# monitors expect n_batches x ...
|
||||
all_preds = all_preds[np.newaxis, :]
|
||||
all_targets = all_targets[np.newaxis, :]
|
||||
all_batch_sizes = [all_batch_sizes]
|
||||
all_losses = [all_losses]
|
||||
|
||||
for m in self.monitors:
|
||||
result_dict = m.monitor_set(setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets,
|
||||
dataset)
|
||||
if result_dict is not None:
|
||||
result_dicts_per_monitor[m].update(result_dict)
|
||||
row_dict = OrderedDict()
|
||||
for m in self.monitors:
|
||||
row_dict.update(result_dicts_per_monitor[m])
|
||||
self.epochs_df = self.epochs_df.append(row_dict, ignore_index=True)
|
||||
assert set(self.epochs_df.columns) == set(row_dict.keys()), (
|
||||
"Columns of dataframe: {:s}\n and keys of dict {:s} not same")\
|
||||
.format(str(set(self.epochs_df.columns)), str(set(row_dict.keys())))
|
||||
self.epochs_df = self.epochs_df[list(row_dict.keys())]
|
||||
|
||||
def log_epoch(self):
|
||||
"""
|
||||
Print monitoring values for this epoch.
|
||||
"""
|
||||
for logger in self.loggers:
|
||||
logger.log_epoch(self.epochs_df)
|
||||
|
||||
def setup_after_stop_training(self):
|
||||
"""
|
||||
Setup training after first stop.
|
||||
|
||||
Resets parameters to best parameters and updates stop criterion.
|
||||
"""
|
||||
# also remember old monitor chans, will be put back into
|
||||
# monitor chans after experiment finished
|
||||
self.before_stop_df = deepcopy(self.epochs_df)
|
||||
self.rememberer.reset_to_best_model(self.epochs_df, self.model,
|
||||
self.optimizer)
|
||||
loss_to_reach = float(self.epochs_df['train_loss'].iloc[-1])
|
||||
self.stop_criterion = Or(stop_criteria=[
|
||||
MaxEpochs(max_epochs=self.rememberer.best_epoch * 2),
|
||||
ColumnBelow(column_name='valid_loss', target_value=loss_to_reach)])
|
||||
log.info("Train loss to reach {:.5f}".format(loss_to_reach))
|
||||
@@ -0,0 +1,48 @@
|
||||
from abc import ABC, abstractmethod
|
||||
import logging
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class Logger(ABC):
|
||||
@abstractmethod
|
||||
def log_epoch(self, epochs_df):
|
||||
raise NotImplementedError("Need to implement the log_epoch function!")
|
||||
|
||||
|
||||
class Printer(Logger):
|
||||
"""
|
||||
Prints output to the terminal using Python's logging module.
|
||||
"""
|
||||
def log_epoch(self, epochs_df):
|
||||
# -1 due to doing one monitor at start of training
|
||||
i_epoch = len(epochs_df) - 1
|
||||
log.info("Epoch {:d}".format(i_epoch))
|
||||
last_row = epochs_df.iloc[-1]
|
||||
for key, val in last_row.iteritems():
|
||||
log.info("{:25s} {:.5f}".format(key, val))
|
||||
log.info("")
|
||||
|
||||
|
||||
class TensorboardWriter(Logger):
|
||||
|
||||
"""
|
||||
Logs all values for tensorboard visualiuzation using tensorboardX.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
log_dir: string
|
||||
Directory path to log the output to
|
||||
"""
|
||||
def __init__(self, log_dir):
|
||||
# import inside to prevent dependency of braindecode onto tensorboardX
|
||||
from tensorboardX import SummaryWriter
|
||||
self.writer = SummaryWriter(log_dir)
|
||||
|
||||
def log_epoch(self, epochs_df):
|
||||
# -1 due to doing one monitor at start of training
|
||||
i_epoch = len(epochs_df) - 1
|
||||
last_row = epochs_df.iloc[-1]
|
||||
for key, val in last_row.iteritems():
|
||||
val = last_row[key]
|
||||
self.writer.add_scalar(key, val, i_epoch)
|
||||
@@ -0,0 +1,374 @@
|
||||
import numpy as np
|
||||
import time
|
||||
|
||||
|
||||
class MisclassMonitor(object):
|
||||
"""
|
||||
Monitor the examplewise misclassification rate.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
col_suffix: str, optional
|
||||
Name of the column in the monitoring output.
|
||||
threshold_for_binary_case: bool, optional
|
||||
In case of binary classification with only one output prediction
|
||||
per target, define the threshold for separating the classes, i.e.
|
||||
0.5 for sigmoid outputs, or np.log(0.5) for log sigmoid outputs
|
||||
"""
|
||||
|
||||
def __init__(self, col_suffix='misclass', threshold_for_binary_case=None):
|
||||
self.col_suffix = col_suffix
|
||||
self.threshold_for_binary_case = threshold_for_binary_case
|
||||
|
||||
def monitor_epoch(self, ):
|
||||
return
|
||||
|
||||
def monitor_set(self, setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets, dataset):
|
||||
all_pred_labels = []
|
||||
all_target_labels = []
|
||||
for i_batch in range(len(all_batch_sizes)):
|
||||
preds = all_preds[i_batch]
|
||||
# preds could be examples x classes x time
|
||||
# or just
|
||||
# examples x classes
|
||||
# make sure not to remove first dimension if it only has size one
|
||||
if preds.ndim > 1:
|
||||
only_one_row = preds.shape[0] == 1
|
||||
|
||||
pred_labels = np.argmax(preds, axis=1).squeeze()
|
||||
# add first dimension again if needed
|
||||
if only_one_row:
|
||||
pred_labels = pred_labels[None]
|
||||
else:
|
||||
assert self.threshold_for_binary_case is not None, (
|
||||
"In case of only one output, please supply the "
|
||||
"threshold_for_binary_case parameter")
|
||||
# binary classification case... assume logits
|
||||
pred_labels = np.int32(preds > self.threshold_for_binary_case)
|
||||
# now examples x time or examples
|
||||
all_pred_labels.extend(pred_labels)
|
||||
targets = all_targets[i_batch]
|
||||
if targets.ndim > pred_labels.ndim:
|
||||
# targets may be one-hot-encoded
|
||||
targets = np.argmax(targets, axis=1)
|
||||
elif targets.ndim < pred_labels.ndim:
|
||||
# targets may not have time dimension,
|
||||
# in that case just repeat targets on time dimension
|
||||
extra_dim = pred_labels.ndim -1
|
||||
targets = np.repeat(np.expand_dims(targets, extra_dim),
|
||||
pred_labels.shape[extra_dim],
|
||||
extra_dim)
|
||||
assert targets.shape == pred_labels.shape
|
||||
all_target_labels.extend(targets)
|
||||
all_pred_labels = np.array(all_pred_labels)
|
||||
all_target_labels = np.array(all_target_labels)
|
||||
assert all_pred_labels.shape == all_target_labels.shape
|
||||
|
||||
misclass = 1 - np.mean(all_target_labels == all_pred_labels)
|
||||
column_name = "{:s}_{:s}".format(setname, self.col_suffix)
|
||||
return {column_name: float(misclass)}
|
||||
|
||||
|
||||
def compute_pred_labels_from_trial_preds(
|
||||
all_preds, threshold_for_binary_case=None):
|
||||
all_pred_labels = []
|
||||
for i_batch in range(len(all_preds)):
|
||||
preds = all_preds[i_batch]
|
||||
# preds could be examples x classes x time
|
||||
# or just
|
||||
# examples x classes
|
||||
# make sure not to remove first dimension if it only has size one
|
||||
if preds.ndim > 1:
|
||||
only_one_row = preds.shape[0] == 1
|
||||
|
||||
pred_labels = np.argmax(preds, axis=1).squeeze()
|
||||
# add first dimension again if needed
|
||||
if only_one_row:
|
||||
pred_labels = pred_labels[None]
|
||||
else:
|
||||
assert threshold_for_binary_case is not None, (
|
||||
"In case of only one output, please supply the "
|
||||
"threshold_for_binary_case parameter")
|
||||
# binary classification case... assume logits
|
||||
pred_labels = np.int32(preds > threshold_for_binary_case)
|
||||
# now examples x time or examples
|
||||
all_pred_labels.extend(pred_labels)
|
||||
all_pred_labels = np.array(all_pred_labels)
|
||||
return all_pred_labels
|
||||
|
||||
|
||||
class AveragePerClassMisclassMonitor(object):
|
||||
"""
|
||||
Compute average of misclasses per class,
|
||||
useful if classes are highly imbalanced.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
col_suffix: str
|
||||
Name of the column in the monitoring output.
|
||||
"""
|
||||
def __init__(self, col_suffix='misclass'):
|
||||
self.col_suffix = col_suffix
|
||||
|
||||
def monitor_epoch(self, ):
|
||||
return
|
||||
|
||||
def monitor_set(self, setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets, dataset):
|
||||
all_pred_labels = []
|
||||
all_target_labels = []
|
||||
for i_batch in range(len(all_batch_sizes)):
|
||||
preds = all_preds[i_batch]
|
||||
# preds could be examples x classes x time
|
||||
# or just
|
||||
# examples x classes
|
||||
# make sure not to remove first dimension if it only has size one
|
||||
only_one_row = preds.shape[0] == 1
|
||||
n_classes = preds.shape[1]
|
||||
pred_labels = np.argmax(preds, axis=1).squeeze()
|
||||
# add first dimension again if needed
|
||||
if only_one_row:
|
||||
pred_labels = pred_labels[None]
|
||||
# now examples x time or examples
|
||||
all_pred_labels.extend(pred_labels)
|
||||
targets = all_targets[i_batch]
|
||||
if targets.ndim > pred_labels.ndim:
|
||||
# targets may be one-hot-encoded
|
||||
targets = np.argmax(targets, axis=1)
|
||||
elif targets.ndim < pred_labels.ndim:
|
||||
# targets may not have time dimension,
|
||||
# in that case just repeat targets on time dimension
|
||||
extra_dim = pred_labels.ndim - 1
|
||||
targets = np.repeat(np.expand_dims(targets, extra_dim),
|
||||
pred_labels.shape[extra_dim],
|
||||
extra_dim)
|
||||
assert targets.shape == pred_labels.shape
|
||||
all_target_labels.extend(targets)
|
||||
all_pred_labels = np.array(all_pred_labels)
|
||||
all_target_labels = np.array(all_target_labels)
|
||||
assert all_pred_labels.shape == all_target_labels.shape
|
||||
acc_per_class = []
|
||||
for i_class in range(n_classes):
|
||||
mask = all_target_labels == i_class
|
||||
acc = np.mean(all_pred_labels[mask] ==
|
||||
all_target_labels[mask])
|
||||
acc_per_class.append(acc)
|
||||
misclass = 1 - np.mean(acc_per_class)
|
||||
column_name = "{:s}_{:s}".format(setname, self.col_suffix)
|
||||
return {column_name: float(misclass)}
|
||||
|
||||
|
||||
class LossMonitor(object):
|
||||
"""
|
||||
Monitor the examplewise loss.
|
||||
"""
|
||||
def monitor_epoch(self,):
|
||||
return
|
||||
|
||||
def monitor_set(self, setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets, dataset):
|
||||
batch_weights = np.array(all_batch_sizes)/ float(
|
||||
np.sum(all_batch_sizes))
|
||||
loss_per_batch = [np.mean(loss) for loss in all_losses]
|
||||
mean_loss = np.sum(batch_weights * loss_per_batch)
|
||||
column_name = "{:s}_loss".format(setname)
|
||||
return {column_name: mean_loss}
|
||||
|
||||
|
||||
|
||||
class CroppedTrialMisclassMonitor(object):
|
||||
"""
|
||||
Compute trialwise misclasses from predictions for crops.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
input_time_length: int
|
||||
Temporal length of one input to the model.
|
||||
"""
|
||||
def __init__(self, input_time_length=None):
|
||||
self.input_time_length = input_time_length
|
||||
|
||||
def monitor_epoch(self,):
|
||||
return
|
||||
|
||||
def monitor_set(self, setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets, dataset):
|
||||
"""Assuming one hot encoding for now"""
|
||||
assert self.input_time_length is not None, "Need to know input time length..."
|
||||
# First case that each trial only has a single label
|
||||
if not hasattr(dataset.y[0], '__len__'):
|
||||
all_pred_labels = compute_trial_labels_from_crop_preds(
|
||||
all_preds, self.input_time_length, dataset.X)
|
||||
assert all_pred_labels.shape == dataset.y.shape
|
||||
all_trial_labels = dataset.y
|
||||
else:
|
||||
all_trial_labels, all_pred_labels = (
|
||||
self._compute_trial_pred_labels_from_cnt_y(dataset, all_preds))
|
||||
assert all_pred_labels.shape == all_trial_labels.shape
|
||||
misclass = 1 - np.mean(all_pred_labels == all_trial_labels)
|
||||
column_name = "{:s}_misclass".format(setname)
|
||||
return {column_name: float(misclass)}
|
||||
|
||||
def _compute_pred_labels(self, dataset, all_preds, ):
|
||||
preds_per_trial = compute_preds_per_trial_from_crops(
|
||||
all_preds, self.input_time_length, dataset.X)
|
||||
all_pred_labels = [np.argmax(np.mean(p, axis=1))
|
||||
for p in preds_per_trial]
|
||||
all_pred_labels = np.array(all_pred_labels)
|
||||
assert all_pred_labels.shape == dataset.y.shape
|
||||
return all_pred_labels
|
||||
|
||||
def _compute_trial_pred_labels_from_cnt_y(self, dataset, all_preds, ):
|
||||
# Todo: please test this
|
||||
# we only want the preds that are for the same labels as the last label in y
|
||||
# (there might be parts of other class-data at start, for trialwise misclass we assume
|
||||
# they are contained in other trials at the end...)
|
||||
preds_per_trial = compute_preds_per_trial_from_crops(
|
||||
all_preds, self.input_time_length, dataset.X)
|
||||
trial_labels = []
|
||||
trial_pred_labels = []
|
||||
for trial_pred, trial_y in zip(preds_per_trial, dataset.y):
|
||||
# first cut to the part actually having predictions
|
||||
trial_y = trial_y[-trial_pred.shape[1]:]
|
||||
wanted_class = trial_y[-1]
|
||||
trial_labels.append(wanted_class)
|
||||
# extract the first marker different from the wanted class
|
||||
# by starting from the back of the trial
|
||||
i_last_sample = np.flatnonzero(trial_y[::-1] != wanted_class)
|
||||
if len(i_last_sample) > 0:
|
||||
i_last_sample = i_last_sample[0]
|
||||
# remember last sample is now from back
|
||||
trial_pred = trial_pred[:, -i_last_sample:]
|
||||
trial_pred_label = np.argmax(np.mean(trial_pred, axis=1))
|
||||
trial_pred_labels.append(trial_pred_label)
|
||||
trial_labels = np.array(trial_labels)
|
||||
trial_pred_labels = np.array(trial_pred_labels)
|
||||
return trial_labels, trial_pred_labels
|
||||
|
||||
|
||||
def compute_trial_labels_from_crop_preds(all_preds, input_time_length, X):
|
||||
"""
|
||||
Compute predicted trial labels from arrays of crop predictions
|
||||
|
||||
Parameters
|
||||
----------
|
||||
all_preds: list of 2darrays (classes x time)
|
||||
All predictions for the crops.
|
||||
input_time_length: int
|
||||
Temporal length of one input to the model.
|
||||
X: ndarray
|
||||
Input tensor the crops were taken from.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pred_labels_per_trial: 1darray
|
||||
Predicted label for each trial.
|
||||
|
||||
"""
|
||||
|
||||
preds_per_trial = compute_preds_per_trial_from_crops(
|
||||
all_preds, input_time_length, X)
|
||||
pred_labels_per_trial = [np.argmax(np.mean(p, axis=1))
|
||||
for p in preds_per_trial]
|
||||
pred_labels_per_trial = np.array(pred_labels_per_trial)
|
||||
return pred_labels_per_trial
|
||||
|
||||
|
||||
def compute_preds_per_trial_from_crops(all_preds, input_time_length,
|
||||
X, ):
|
||||
"""
|
||||
Compute predictions per trial from predictions for crops.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
all_preds: list of 2darrays (classes x time)
|
||||
All predictions for the crops.
|
||||
input_time_length: int
|
||||
Temporal length of one input to the model.
|
||||
X: ndarray
|
||||
Input tensor the crops were taken from.
|
||||
|
||||
Returns
|
||||
-------
|
||||
preds_per_trial: list of 2darrays (classes x time)
|
||||
Predictions for each trial, without overlapping predictions.
|
||||
|
||||
"""
|
||||
n_preds_per_input = all_preds[0].shape[2]
|
||||
n_receptive_field = input_time_length - n_preds_per_input + 1
|
||||
n_preds_per_trial = [trial.shape[1] - n_receptive_field + 1
|
||||
for trial in X]
|
||||
preds_per_trial = compute_preds_per_trial_from_n_preds_per_trial(
|
||||
all_preds, n_preds_per_trial)
|
||||
return preds_per_trial
|
||||
|
||||
|
||||
def compute_preds_per_trial_from_n_preds_per_trial(
|
||||
all_preds, n_preds_per_trial):
|
||||
"""
|
||||
Compute predictions per trial from predictions for crops.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
all_preds: list of 2darrays (classes x time)
|
||||
All predictions for the crops.
|
||||
input_time_length: int
|
||||
Temporal length of one input to the model.
|
||||
n_preds_per_trial: list of int
|
||||
Number of predictions for each trial.
|
||||
Returns
|
||||
-------
|
||||
preds_per_trial: list of 2darrays (classes x time)
|
||||
Predictions for each trial, without overlapping predictions.
|
||||
|
||||
"""
|
||||
# all_preds_arr has shape forward_passes x classes x time
|
||||
all_preds_arr = np.concatenate(all_preds, axis=0)
|
||||
preds_per_trial = []
|
||||
i_pred_block = 0
|
||||
for i_trial in range(len(n_preds_per_trial)):
|
||||
n_needed_preds = n_preds_per_trial[i_trial]
|
||||
preds_this_trial = []
|
||||
while n_needed_preds > 0:
|
||||
# - n_needed_preds: only has an effect
|
||||
# in case there are more samples than we actually still need
|
||||
# in the block.
|
||||
# That can happen since final block of a trial can overlap
|
||||
# with block before so we can have some redundant preds.
|
||||
pred_samples = all_preds_arr[i_pred_block,:,
|
||||
-n_needed_preds:]
|
||||
preds_this_trial.append(pred_samples)
|
||||
n_needed_preds -= pred_samples.shape[1]
|
||||
i_pred_block += 1
|
||||
|
||||
preds_this_trial = np.concatenate(preds_this_trial, axis=1)
|
||||
preds_per_trial.append(preds_this_trial)
|
||||
assert i_pred_block == len(all_preds_arr), (
|
||||
"Expect that all prediction forward passes are needed, "
|
||||
"used {:d}, existing {:d}".format(
|
||||
i_pred_block, len(all_preds_arr)))
|
||||
return preds_per_trial
|
||||
|
||||
|
||||
class RuntimeMonitor(object):
|
||||
"""
|
||||
Monitor the runtime of each epoch.
|
||||
|
||||
First epoch will have runtime 0.
|
||||
"""
|
||||
def __init__(self):
|
||||
self.last_call_time = None
|
||||
|
||||
def monitor_epoch(self,):
|
||||
cur_time = time.time()
|
||||
if self.last_call_time is None:
|
||||
# just in case of first call
|
||||
self.last_call_time = cur_time
|
||||
epoch_runtime = cur_time - self.last_call_time
|
||||
self.last_call_time = cur_time
|
||||
return {'runtime': epoch_runtime}
|
||||
|
||||
def monitor_set(self, setname, all_preds, all_losses,
|
||||
all_batch_sizes, all_targets, dataset):
|
||||
return {}
|
||||
@@ -0,0 +1,139 @@
|
||||
import numpy as np
|
||||
|
||||
|
||||
class MaxEpochs(object):
|
||||
"""
|
||||
Stop when given number of epochs reached:
|
||||
|
||||
Parameters
|
||||
----------
|
||||
max_epochs: int
|
||||
"""
|
||||
def __init__(self, max_epochs):
|
||||
self.max_epochs = max_epochs
|
||||
|
||||
def should_stop(self, epochs_df):
|
||||
# Keep in mind epoch 0 without training is also part of dataframe
|
||||
return len(epochs_df) - 1 >= self.max_epochs
|
||||
|
||||
|
||||
class Or(object):
|
||||
"""
|
||||
Stop when one of the given stop criteria is triggered.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stop_criteria: iterable of stop criteria objects
|
||||
"""
|
||||
def __init__(self, stop_criteria):
|
||||
self.stop_criteria = stop_criteria
|
||||
self.triggered = dict([(s, False) for s in stop_criteria])
|
||||
|
||||
def should_stop(self, epochs_df):
|
||||
# Update dictionary of which criterion was triggered ...
|
||||
for s in self.stop_criteria:
|
||||
self.triggered[s] = s.should_stop(epochs_df)
|
||||
# Then check if any of them was triggered.
|
||||
return np.any(list(self.triggered.values()))
|
||||
|
||||
def was_triggered(self, criterion):
|
||||
"""
|
||||
Return if given criterion was triggered in the last call to should stop.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
criterion: stop criterion
|
||||
|
||||
Returns
|
||||
-------
|
||||
triggered: bool
|
||||
|
||||
"""
|
||||
return self.triggered[criterion]
|
||||
|
||||
|
||||
class And(object):
|
||||
"""
|
||||
Stop when all of the given stop criteria are triggered.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stop_criteria: iterable of stop criteria objects
|
||||
"""
|
||||
def __init__(self, stop_criteria):
|
||||
self.stop_criteria = stop_criteria
|
||||
|
||||
def should_stop(self, epochs_df):
|
||||
# Update dictionary of which criterion was triggered ...
|
||||
for s in self.stop_criteria:
|
||||
self.triggered[s] = s.should_stop(epochs_df)
|
||||
# Then check if all of them were triggered.
|
||||
return np.all(list(self.triggered.values()))
|
||||
|
||||
def was_triggered(self, criterion):
|
||||
"""
|
||||
Return if given criterion was triggered in the last call to should stop.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
criterion: stop criterion
|
||||
|
||||
Returns
|
||||
-------
|
||||
triggered: bool
|
||||
|
||||
"""
|
||||
return self.triggered[criterion]
|
||||
|
||||
|
||||
class NoDecrease(object):
|
||||
""" Stops if there is no decrease on a given monitor channel
|
||||
for given number of epochs.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
column_name: str
|
||||
Name of column to monitor for decrease.
|
||||
num_epochs: str
|
||||
Number of epochs to wait before stopping when there is no decrease.
|
||||
min_decrease: float, optional
|
||||
Minimum relative decrease that counts as a decrease. E.g. 0.1 means
|
||||
only 10% decreases count as a decrease and reset the counter.
|
||||
"""
|
||||
def __init__(self, column_name, num_epochs, min_decrease=1e-6):
|
||||
self.column_name = column_name
|
||||
self.num_epochs = num_epochs
|
||||
self.min_decrease = min_decrease
|
||||
self.best_epoch = 0
|
||||
self.lowest_val = float('inf')
|
||||
|
||||
def should_stop(self, epochs_df):
|
||||
# -1 due to doing one monitor at start of training
|
||||
i_epoch = len(epochs_df) - 1
|
||||
current_val = float(epochs_df[self.column_name].iloc[-1])
|
||||
if current_val < ((1 - self.min_decrease) * self.lowest_val):
|
||||
self.best_epoch = i_epoch
|
||||
self.lowest_val = current_val
|
||||
|
||||
return (i_epoch - self.best_epoch) >= self.num_epochs
|
||||
|
||||
|
||||
class ColumnBelow():
|
||||
"""
|
||||
Stops if the given column is below the given value.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
column_name: str
|
||||
Name of column to monitor.
|
||||
target_value: float
|
||||
When column decreases below this value, criterion will say to stop.
|
||||
"""
|
||||
def __init__(self, column_name, target_value):
|
||||
self.column_name = column_name
|
||||
self.target_value = target_value
|
||||
|
||||
def should_stop(self, epochs_df):
|
||||
# -1 due to doing one monitor at start of training
|
||||
current_val = float(epochs_df[self.column_name].iloc[-1])
|
||||
return current_val < self.target_value
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Extensions for the MNE library.
|
||||
"""
|
||||
@@ -0,0 +1,90 @@
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
|
||||
import resampy
|
||||
from mne.io.base import concatenate_raws
|
||||
import mne
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def concatenate_raws_with_events(raws):
|
||||
"""
|
||||
Concatenates `mne.io.RawArray` objects, respects `info['events']` attributes
|
||||
and concatenates them correctly. Also does not modify `raws[0]` inplace
|
||||
as the :func:`concatenate_raws` function of MNE does.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
raws: list of `mne.io.RawArray`
|
||||
|
||||
Returns
|
||||
-------
|
||||
concatenated_raw: `mne.io.RawArray`
|
||||
"""
|
||||
# prevent in-place modification of raws[0]
|
||||
raws[0] = deepcopy(raws[0])
|
||||
event_lists = [r.info['events'] for r in raws]
|
||||
new_raw, new_events = concatenate_raws(raws, events_list=event_lists)
|
||||
new_raw.info['events'] = new_events
|
||||
return new_raw
|
||||
|
||||
|
||||
def resample_cnt(cnt, new_fs):
|
||||
"""
|
||||
Resample continuous recording using `resampy`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
cnt: `mne.io.RawArray`
|
||||
new_fs: float
|
||||
New sampling rate.
|
||||
|
||||
Returns
|
||||
-------
|
||||
resampled: `mne.io.RawArray`
|
||||
Resampled object.
|
||||
|
||||
"""
|
||||
if new_fs == cnt.info['sfreq']:
|
||||
log.info(
|
||||
"Just copying data, no resampling, since new sampling rate same.")
|
||||
return deepcopy(cnt)
|
||||
log.warning("This is not causal, uses future data....")
|
||||
log.info("Resampling from {:f} to {:f} Hz.".format(
|
||||
cnt.info['sfreq'], new_fs
|
||||
))
|
||||
|
||||
data = cnt.get_data().T
|
||||
|
||||
new_data = resampy.resample(data, cnt.info['sfreq'],
|
||||
new_fs, axis=0, filter='kaiser_fast').T
|
||||
old_fs = cnt.info['sfreq']
|
||||
new_info = deepcopy(cnt.info)
|
||||
new_info['sfreq'] = new_fs
|
||||
events = new_info['events']
|
||||
event_samples_old = cnt.info['events'][:, 0]
|
||||
event_samples = event_samples_old * new_fs / float(old_fs)
|
||||
events[:, 0] = event_samples
|
||||
return mne.io.RawArray(new_data, new_info)
|
||||
|
||||
|
||||
def mne_apply(func, raw, verbose='WARNING'):
|
||||
"""
|
||||
Apply function to data of `mne.io.RawArray`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
func: function
|
||||
Should accept 2d-array (channels x time) and return modified 2d-array
|
||||
raw: `mne.io.RawArray`
|
||||
verbose: bool
|
||||
Whether to log creation of new `mne.io.RawArray`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
transformed_set: Copy of `raw` with data transformed by given function.
|
||||
|
||||
"""
|
||||
new_data = func(raw.get_data())
|
||||
return mne.io.RawArray(new_data, raw.info, verbose=verbose)
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Some predefined network architectures for EEG decoding.
|
||||
"""
|
||||
@@ -0,0 +1,300 @@
|
||||
import time
|
||||
|
||||
import numpy as np
|
||||
from numpy.random import RandomState
|
||||
import torch as th
|
||||
|
||||
from braindecode.experiments.monitors import LossMonitor, MisclassMonitor, \
|
||||
RuntimeMonitor, CroppedTrialMisclassMonitor, \
|
||||
compute_trial_labels_from_crop_preds, compute_pred_labels_from_trial_preds
|
||||
from braindecode.experiments.stopcriteria import MaxEpochs
|
||||
from braindecode.datautil.iterators import BalancedBatchSizeIterator, \
|
||||
CropsFromTrialsIterator
|
||||
from braindecode.experiments.experiment import Experiment
|
||||
from braindecode.datautil.signal_target import SignalAndTarget
|
||||
from braindecode.models.util import to_dense_prediction_model
|
||||
from braindecode.torch_ext.schedulers import CosineAnnealing, ScheduledOptimizer
|
||||
from braindecode.torch_ext.util import np_to_var, var_to_np
|
||||
import logging
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def find_optimizer(optimizer_name):
|
||||
optim_found = False
|
||||
for name in th.optim.__dict__.keys():
|
||||
if name.lower() == optimizer_name.lower():
|
||||
optimizer = th.optim.__dict__[name]
|
||||
optim_found = True
|
||||
break
|
||||
if not optim_found:
|
||||
raise ValueError("Unknown optimizer {:s}".format(optimizer))
|
||||
return \
|
||||
optimizer
|
||||
|
||||
|
||||
class BaseModel(object):
|
||||
def cuda(self):
|
||||
"""Move underlying model to GPU."""
|
||||
self._ensure_network_exists()
|
||||
assert not self.compiled,\
|
||||
("Call cuda before compiling model, otherwise optimization will not work")
|
||||
self.network = self.network.cuda()
|
||||
self.cuda = True
|
||||
return self
|
||||
|
||||
def parameters(self):
|
||||
"""
|
||||
Return parameters of underlying torch model.
|
||||
|
||||
Returns
|
||||
-------
|
||||
parameters: list of torch tensors
|
||||
"""
|
||||
self._ensure_network_exists()
|
||||
return self.network.parameters()
|
||||
|
||||
def _ensure_network_exists(self):
|
||||
if not hasattr(self, 'network'):
|
||||
self.network = self.create_network()
|
||||
self.cuda = False
|
||||
self.compiled = False
|
||||
|
||||
def compile(self, loss, optimizer, extra_monitors=None, cropped=False, iterator_seed=0):
|
||||
"""
|
||||
Setup training for this model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
loss: function (predictions, targets) -> torch scalar
|
||||
optimizer: `torch.optim.Optimizer` or string
|
||||
Either supply an optimizer or the name of the class (e.g. 'adam')
|
||||
extra_monitors: List of Braindecode monitors, optional
|
||||
In case you want to monitor additional values except for loss, misclass and runtime.
|
||||
cropped: bool
|
||||
Whether to perform cropped decoding, see cropped decoding tutorial.
|
||||
iterator_seed: int
|
||||
Seed to seed the iterator random generator.
|
||||
Returns
|
||||
-------
|
||||
|
||||
"""
|
||||
self.loss = loss
|
||||
self._ensure_network_exists()
|
||||
if cropped:
|
||||
model_already_dense = np.any([
|
||||
hasattr(m, 'dilation') and (m.dilation != 1) and
|
||||
(m.dilation) != (1, 1) for m in self.network.modules()
|
||||
])
|
||||
if not model_already_dense:
|
||||
to_dense_prediction_model(self.network)
|
||||
else:
|
||||
log.info("Seems model was already converted to dense model...")
|
||||
if not hasattr(optimizer, 'step'):
|
||||
optimizer_class = find_optimizer(optimizer)
|
||||
optimizer = optimizer_class(self.network.parameters())
|
||||
self.optimizer = optimizer
|
||||
self.extra_monitors = extra_monitors
|
||||
# Already setting it here, so multiple calls to fit
|
||||
# will lead to different batches being drawn
|
||||
self.seed_rng = RandomState(iterator_seed)
|
||||
self.cropped = cropped
|
||||
self.compiled = True
|
||||
|
||||
def fit(self, train_X, train_y, epochs, batch_size, input_time_length=None,
|
||||
validation_data=None, model_constraint=None,
|
||||
remember_best_column=None, scheduler=None,
|
||||
log_0_epoch=True):
|
||||
"""
|
||||
Fit the model using the given training data.
|
||||
|
||||
Will set `epochs_df` variable with a pandas dataframe to the history
|
||||
of the training process.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
train_X: ndarray
|
||||
Training input data
|
||||
train_y: 1darray
|
||||
Training labels
|
||||
epochs: int
|
||||
Number of epochs to train
|
||||
batch_size: int
|
||||
input_time_length: int, optional
|
||||
Super crop size, what temporal size is pushed forward through
|
||||
the network, see cropped decoding tuturial.
|
||||
validation_data: (ndarray, 1darray), optional
|
||||
X and y for validation set if wanted
|
||||
model_constraint: object, optional
|
||||
You can supply :class:`.MaxNormDefaultConstraint` if wanted.
|
||||
remember_best_column: string, optional
|
||||
In case you want to do an early stopping/reset parameters to some
|
||||
"best" epoch, define here the monitored value whose minimum
|
||||
determines the best epoch.
|
||||
scheduler: 'cosine' or None, optional
|
||||
Whether to use cosine annealing (:class:`.CosineAnnealing`).
|
||||
log_0_epoch: bool
|
||||
Whether to compute the metrics once before training as well.
|
||||
|
||||
Returns
|
||||
-------
|
||||
exp:
|
||||
Underlying braindecode :class:`.Experiment`
|
||||
"""
|
||||
if (not hasattr(self, 'compiled')) or (not self.compiled):
|
||||
raise ValueError("Compile the model first by calling model.compile(loss, optimizer, metrics)")
|
||||
|
||||
|
||||
if self.cropped and input_time_length is None:
|
||||
raise ValueError("In cropped mode, need to specify input_time_length,"
|
||||
"which is the number of timesteps that will be pushed through"
|
||||
"the network in a single pass.")
|
||||
if self.cropped:
|
||||
self.network.eval()
|
||||
test_input = np_to_var(np.ones(
|
||||
(1, train_X.shape[1], input_time_length,) + train_X.shape[3:],
|
||||
dtype=np.float32))
|
||||
while len(test_input.size()) < 4:
|
||||
test_input = test_input.unsqueeze(-1)
|
||||
if self.cuda:
|
||||
test_input = test_input.cuda()
|
||||
out = self.network(test_input)
|
||||
n_preds_per_input = out.cpu().data.numpy().shape[2]
|
||||
self.iterator = CropsFromTrialsIterator(
|
||||
batch_size=batch_size, input_time_length=input_time_length,
|
||||
n_preds_per_input=n_preds_per_input,
|
||||
seed=self.seed_rng.randint(0, np.iinfo(np.int32).max-1))
|
||||
else:
|
||||
self.iterator = BalancedBatchSizeIterator(
|
||||
batch_size=batch_size,
|
||||
seed=self.seed_rng.randint(0, np.iinfo(np.int32).max-1))
|
||||
if log_0_epoch:
|
||||
stop_criterion = MaxEpochs(epochs)
|
||||
else:
|
||||
stop_criterion = MaxEpochs(epochs - 1)
|
||||
train_set = SignalAndTarget(train_X, train_y)
|
||||
optimizer = self.optimizer
|
||||
if scheduler is not None:
|
||||
assert scheduler == 'cosine', (
|
||||
"Supply either 'cosine' or None as scheduler.")
|
||||
n_updates_per_epoch = sum(
|
||||
[1 for _ in self.iterator.get_batches(train_set, shuffle=True)])
|
||||
n_updates_per_period = n_updates_per_epoch * epochs
|
||||
if scheduler == 'cosine':
|
||||
scheduler = CosineAnnealing(n_updates_per_period)
|
||||
schedule_weight_decay = False
|
||||
if optimizer.__class__.__name__ == 'AdamW':
|
||||
schedule_weight_decay = True
|
||||
optimizer = ScheduledOptimizer(scheduler, self.optimizer,
|
||||
schedule_weight_decay=schedule_weight_decay)
|
||||
loss_function = self.loss
|
||||
if self.cropped:
|
||||
loss_function = lambda outputs, targets:\
|
||||
self.loss(th.mean(outputs, dim=2), targets)
|
||||
if validation_data is not None:
|
||||
valid_set = SignalAndTarget(validation_data[0], validation_data[1])
|
||||
else:
|
||||
valid_set = None
|
||||
test_set = None
|
||||
self.monitors = [LossMonitor()]
|
||||
if self.cropped:
|
||||
self.monitors.append(CroppedTrialMisclassMonitor(input_time_length))
|
||||
else:
|
||||
self.monitors.append(MisclassMonitor())
|
||||
if self.extra_monitors is not None:
|
||||
self.monitors.extend(self.extra_monitors)
|
||||
self.monitors.append(RuntimeMonitor())
|
||||
exp = Experiment(self.network, train_set, valid_set, test_set,
|
||||
iterator=self.iterator,
|
||||
loss_function=loss_function, optimizer=optimizer,
|
||||
model_constraint=model_constraint,
|
||||
monitors=self.monitors,
|
||||
stop_criterion=stop_criterion,
|
||||
remember_best_column=remember_best_column,
|
||||
run_after_early_stop=False, cuda=self.cuda,
|
||||
log_0_epoch=log_0_epoch,
|
||||
do_early_stop=(remember_best_column is not None))
|
||||
exp.run()
|
||||
self.epochs_df = exp.epochs_df
|
||||
return exp
|
||||
|
||||
def evaluate(self, X,y):
|
||||
"""
|
||||
Evaluate, i.e., compute metrics on given inputs and targets.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X: ndarray
|
||||
Input data.
|
||||
y: 1darray
|
||||
Targets.
|
||||
|
||||
Returns
|
||||
-------
|
||||
result: dict
|
||||
Dictionary with result metrics.
|
||||
|
||||
"""
|
||||
stop_criterion = MaxEpochs(0)
|
||||
train_set = SignalAndTarget(X, y)
|
||||
model_constraint = None
|
||||
valid_set = None
|
||||
test_set = None
|
||||
loss_function = self.loss
|
||||
if self.cropped:
|
||||
loss_function = lambda outputs, targets: \
|
||||
self.loss(th.mean(outputs, dim=2), targets)
|
||||
|
||||
# reset runtime monitor if exists...
|
||||
for monitor in self.monitors:
|
||||
if hasattr(monitor, 'last_call_time'):
|
||||
monitor.last_call_time = time.time()
|
||||
exp = Experiment(self.network, train_set, valid_set, test_set,
|
||||
iterator=self.iterator,
|
||||
loss_function=loss_function, optimizer=self.optimizer,
|
||||
model_constraint=model_constraint,
|
||||
monitors=self.monitors,
|
||||
stop_criterion=stop_criterion,
|
||||
remember_best_column=None,
|
||||
run_after_early_stop=False, cuda=self.cuda,
|
||||
log_0_epoch=True,
|
||||
do_early_stop=False)
|
||||
|
||||
exp.monitor_epoch({'train': train_set})
|
||||
|
||||
result_dict = dict([(key.replace('train_', ''), val)
|
||||
for key, val in
|
||||
dict(exp.epochs_df.iloc[0]).items()])
|
||||
return result_dict
|
||||
|
||||
def predict(self, X, threshold_for_binary_case=None):
|
||||
"""
|
||||
Predict the labels for given input data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X: ndarray
|
||||
Input data.
|
||||
threshold_for_binary_case: float, optional
|
||||
In case of a model with single output, the threshold for assigning,
|
||||
label 0 or 1, e.g. 0.5.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pred_labels: 1darray
|
||||
Predicted labels per trial.
|
||||
"""
|
||||
all_preds = []
|
||||
with th.no_grad():
|
||||
for b_X, _ in self.iterator.get_batches(SignalAndTarget(X, X), False):
|
||||
b_X_var = np_to_var(b_X)
|
||||
if self.cuda:
|
||||
b_X_var = b_X_var.cuda()
|
||||
all_preds.append(var_to_np(self.network(b_X_var)))
|
||||
if self.cropped:
|
||||
pred_labels = compute_trial_labels_from_crop_preds(
|
||||
all_preds, self.iterator.input_time_length, X)
|
||||
else:
|
||||
pred_labels = compute_pred_labels_from_trial_preds(
|
||||
all_preds, threshold_for_binary_case)
|
||||
return pred_labels
|
||||
@@ -0,0 +1,194 @@
|
||||
import numpy as np
|
||||
from torch import nn
|
||||
from torch.nn import init
|
||||
from torch.nn.functional import elu
|
||||
|
||||
from braindecode.models.base import BaseModel
|
||||
from braindecode.torch_ext.modules import Expression, AvgPool2dWithConv
|
||||
from braindecode.torch_ext.functions import identity
|
||||
from braindecode.torch_ext.util import np_to_var
|
||||
|
||||
|
||||
class Deep4Net(BaseModel):
|
||||
"""
|
||||
Deep ConvNet model from [1]_.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
"""
|
||||
def __init__(self, in_chans,
|
||||
n_classes,
|
||||
input_time_length,
|
||||
final_conv_length,
|
||||
n_filters_time=25,
|
||||
n_filters_spat=25,
|
||||
filter_time_length=10,
|
||||
pool_time_length=3,
|
||||
pool_time_stride=3,
|
||||
n_filters_2=50,
|
||||
filter_length_2=10,
|
||||
n_filters_3=100,
|
||||
filter_length_3=10,
|
||||
n_filters_4=200,
|
||||
filter_length_4=10,
|
||||
first_nonlin=elu,
|
||||
first_pool_mode='max',
|
||||
first_pool_nonlin=identity,
|
||||
later_nonlin=elu,
|
||||
later_pool_mode='max',
|
||||
later_pool_nonlin=identity,
|
||||
drop_prob=0.5,
|
||||
double_time_convs=False,
|
||||
split_first_layer=True,
|
||||
batch_norm=True,
|
||||
batch_norm_alpha=0.1,
|
||||
stride_before_pool=False):
|
||||
if final_conv_length == 'auto':
|
||||
assert input_time_length is not None
|
||||
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def create_network(self):
|
||||
if self.stride_before_pool:
|
||||
conv_stride = self.pool_time_stride
|
||||
pool_stride = 1
|
||||
else:
|
||||
conv_stride = 1
|
||||
pool_stride = self.pool_time_stride
|
||||
pool_class_dict = dict(max=nn.MaxPool2d, mean=AvgPool2dWithConv)
|
||||
first_pool_class = pool_class_dict[self.first_pool_mode]
|
||||
later_pool_class = pool_class_dict[self.later_pool_mode]
|
||||
model = nn.Sequential()
|
||||
if self.split_first_layer:
|
||||
model.add_module('dimshuffle', Expression(_transpose_time_to_spat))
|
||||
model.add_module('conv_time', nn.Conv2d(1, self.n_filters_time,
|
||||
(
|
||||
self.filter_time_length, 1),
|
||||
stride=1, ))
|
||||
model.add_module('conv_spat',
|
||||
nn.Conv2d(self.n_filters_time, self.n_filters_spat,
|
||||
(1, self.in_chans),
|
||||
stride=(conv_stride, 1),
|
||||
bias=not self.batch_norm))
|
||||
n_filters_conv = self.n_filters_spat
|
||||
else:
|
||||
model.add_module('conv_time',
|
||||
nn.Conv2d(self.in_chans, self.n_filters_time,
|
||||
(self.filter_time_length, 1),
|
||||
stride=(conv_stride, 1),
|
||||
bias=not self.batch_norm))
|
||||
n_filters_conv = self.n_filters_time
|
||||
if self.batch_norm:
|
||||
model.add_module('bnorm',
|
||||
nn.BatchNorm2d(n_filters_conv,
|
||||
momentum=self.batch_norm_alpha,
|
||||
affine=True,
|
||||
eps=1e-5),)
|
||||
model.add_module('conv_nonlin', Expression(self.first_nonlin))
|
||||
model.add_module('pool',
|
||||
first_pool_class(
|
||||
kernel_size=(self.pool_time_length, 1),
|
||||
stride=(pool_stride, 1)))
|
||||
model.add_module('pool_nonlin', Expression(self.first_pool_nonlin))
|
||||
|
||||
def add_conv_pool_block(model, n_filters_before,
|
||||
n_filters, filter_length, block_nr):
|
||||
suffix = '_{:d}'.format(block_nr)
|
||||
model.add_module('drop' + suffix,
|
||||
nn.Dropout(p=self.drop_prob))
|
||||
model.add_module('conv' + suffix,
|
||||
nn.Conv2d(n_filters_before, n_filters,
|
||||
(filter_length, 1),
|
||||
stride=(conv_stride, 1),
|
||||
bias=not self.batch_norm))
|
||||
if self.batch_norm:
|
||||
model.add_module('bnorm' + suffix,
|
||||
nn.BatchNorm2d(n_filters,
|
||||
momentum=self.batch_norm_alpha,
|
||||
affine=True,
|
||||
eps=1e-5))
|
||||
model.add_module('nonlin' + suffix,
|
||||
Expression(self.later_nonlin))
|
||||
|
||||
model.add_module('pool' + suffix,
|
||||
later_pool_class(
|
||||
kernel_size=(self.pool_time_length, 1),
|
||||
stride=(pool_stride, 1)))
|
||||
model.add_module('pool_nonlin' + suffix,
|
||||
Expression(self.later_pool_nonlin))
|
||||
|
||||
add_conv_pool_block(model, n_filters_conv, self.n_filters_2,
|
||||
self.filter_length_2, 2)
|
||||
add_conv_pool_block(model, self.n_filters_2, self.n_filters_3,
|
||||
self.filter_length_3, 3)
|
||||
add_conv_pool_block(model, self.n_filters_3, self.n_filters_4,
|
||||
self.filter_length_4, 4)
|
||||
|
||||
|
||||
model.eval()
|
||||
if self.final_conv_length == 'auto':
|
||||
out = model(np_to_var(np.ones(
|
||||
(1, self.in_chans, self.input_time_length,1),
|
||||
dtype=np.float32)))
|
||||
n_out_time = out.cpu().data.numpy().shape[2]
|
||||
self.final_conv_length = n_out_time
|
||||
model.add_module('conv_classifier',
|
||||
nn.Conv2d(self.n_filters_4, self.n_classes,
|
||||
(self.final_conv_length, 1), bias=True))
|
||||
model.add_module('softmax', nn.LogSoftmax(dim=1))
|
||||
model.add_module('squeeze', Expression(_squeeze_final_output))
|
||||
|
||||
# Initialization, xavier is same as in our paper...
|
||||
# was default from lasagne
|
||||
init.xavier_uniform_(model.conv_time.weight, gain=1)
|
||||
# maybe no bias in case of no split layer and batch norm
|
||||
if self.split_first_layer or (not self.batch_norm):
|
||||
init.constant_(model.conv_time.bias, 0)
|
||||
if self.split_first_layer:
|
||||
init.xavier_uniform_(model.conv_spat.weight, gain=1)
|
||||
if not self.batch_norm:
|
||||
init.constant_(model.conv_spat.bias, 0)
|
||||
if self.batch_norm:
|
||||
init.constant_(model.bnorm.weight, 1)
|
||||
init.constant_(model.bnorm.bias, 0)
|
||||
param_dict = dict(list(model.named_parameters()))
|
||||
for block_nr in range(2,5):
|
||||
conv_weight = param_dict['conv_{:d}.weight'.format(block_nr)]
|
||||
init.xavier_uniform_(conv_weight, gain=1)
|
||||
if not self.batch_norm:
|
||||
conv_bias = param_dict['conv_{:d}.bias'.format(block_nr)]
|
||||
init.constant_(conv_bias, 0)
|
||||
else:
|
||||
bnorm_weight = param_dict['bnorm_{:d}.weight'.format(block_nr)]
|
||||
bnorm_bias = param_dict['bnorm_{:d}.bias'.format(block_nr)]
|
||||
init.constant_(bnorm_weight, 1)
|
||||
init.constant_(bnorm_bias, 0)
|
||||
|
||||
init.xavier_uniform_(model.conv_classifier.weight, gain=1)
|
||||
init.constant_(model.conv_classifier.bias, 0)
|
||||
|
||||
# Start in eval mode
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
# remove empty dim at end and potentially remove empty time dim
|
||||
# do not just use squeeze as we never want to remove first dim
|
||||
def _squeeze_final_output(x):
|
||||
assert x.size()[3] == 1
|
||||
x = x[:,:,:,0]
|
||||
if x.size()[2] == 1:
|
||||
x = x[:,:,0]
|
||||
return x
|
||||
|
||||
|
||||
def _transpose_time_to_spat(x):
|
||||
return x.permute(0, 3, 2, 1)
|
||||
|
||||
@@ -0,0 +1,236 @@
|
||||
import numpy as np
|
||||
import torch as th
|
||||
from torch import nn
|
||||
from torch.nn import init
|
||||
from torch.nn.functional import elu
|
||||
|
||||
from braindecode.models.base import BaseModel
|
||||
from braindecode.torch_ext.init import glorot_weight_zero_bias
|
||||
from braindecode.torch_ext.modules import Expression
|
||||
from braindecode.torch_ext.util import np_to_var
|
||||
|
||||
class Conv2dWithConstraint(nn.Conv2d):
|
||||
def __init__(self, *args, max_norm=1, **kwargs):
|
||||
self.max_norm = max_norm
|
||||
super(Conv2dWithConstraint, self).__init__(*args, **kwargs)
|
||||
|
||||
def forward(self, x):
|
||||
self.weight.data = th.renorm(self.weight.data, p=2, dim=0,
|
||||
maxnorm=self.max_norm)
|
||||
return super(Conv2dWithConstraint, self).forward(x)
|
||||
|
||||
class EEGNetv4(BaseModel):
|
||||
"""
|
||||
EEGNet v4 model from [EEGNet4]_.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This implementation is not guaranteed to be correct, has not been checked
|
||||
by original authors, only reimplemented from the paper description.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [EEGNet4] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
|
||||
S. M., Hung, C. P., & Lance, B. J. (2018).
|
||||
EEGNet: A Compact Convolutional Network for EEG-based
|
||||
Brain-Computer Interfaces.
|
||||
arXiv preprint arXiv:1611.08024.
|
||||
"""
|
||||
|
||||
def __init__(self, in_chans,
|
||||
n_classes,
|
||||
final_conv_length='auto',
|
||||
input_time_length=None,
|
||||
pool_mode='mean',
|
||||
F1=8,
|
||||
D=2,
|
||||
F2=16, # usually set to F1*D (?)
|
||||
kernel_length=64,
|
||||
third_kernel_size=(8, 4),
|
||||
drop_prob=0.25
|
||||
):
|
||||
|
||||
if final_conv_length == 'auto':
|
||||
assert input_time_length is not None
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def create_network(self):
|
||||
pool_class = dict(max=nn.MaxPool2d, mean=nn.AvgPool2d)[self.pool_mode]
|
||||
model = nn.Sequential()
|
||||
# b c 0 1
|
||||
# now to b 1 0 c
|
||||
model.add_module('dimshuffle', Expression(_transpose_to_b_1_c_0))
|
||||
|
||||
model.add_module('conv_temporal', nn.Conv2d(
|
||||
1, self.F1, (1, self.kernel_length), stride=1, bias=False,
|
||||
padding=(0, self.kernel_length // 2,)))
|
||||
model.add_module('bnorm_temporal', nn.BatchNorm2d(
|
||||
self.F1, momentum=0.01, affine=True, eps=1e-3), )
|
||||
model.add_module('conv_spatial', Conv2dWithConstraint(
|
||||
self.F1, self.F1 * self.D, (self.in_chans, 1), max_norm=1, stride=1, bias=False,
|
||||
groups=self.F1,
|
||||
padding=(0, 0)))
|
||||
|
||||
model.add_module('bnorm_1', nn.BatchNorm2d(
|
||||
self.F1 * self.D, momentum=0.01, affine=True, eps=1e-3), )
|
||||
model.add_module('elu_1', Expression(elu))
|
||||
|
||||
model.add_module('pool_1', pool_class(
|
||||
kernel_size=(1, 4), stride=(1, 4)))
|
||||
model.add_module('drop_1', nn.Dropout(p=self.drop_prob))
|
||||
|
||||
# https://discuss.pytorch.org/t/how-to-modify-a-conv2d-to-depthwise-separable-convolution/15843/7
|
||||
model.add_module('conv_separable_depth', nn.Conv2d(
|
||||
self.F1 * self.D, self.F1 * self.D, (1, 16), stride=1, bias=False, groups=self.F1 * self.D,
|
||||
padding=(0, 16 // 2)))
|
||||
model.add_module('conv_separable_point', nn.Conv2d(
|
||||
self.F1 * self.D, self.F2, (1, 1), stride=1, bias=False,
|
||||
padding=(0, 0)))
|
||||
|
||||
model.add_module('bnorm_2', nn.BatchNorm2d(
|
||||
self.F2, momentum=0.01, affine=True, eps=1e-3), )
|
||||
model.add_module('elu_2', Expression(elu))
|
||||
model.add_module('pool_2', pool_class(
|
||||
kernel_size=(1, 8), stride=(1, 8)))
|
||||
model.add_module('drop_2', nn.Dropout(p=self.drop_prob))
|
||||
|
||||
out = model(np_to_var(np.ones(
|
||||
(1, self.in_chans, self.input_time_length, 1),
|
||||
dtype=np.float32)))
|
||||
n_out_virtual_chans = out.cpu().data.numpy().shape[2]
|
||||
|
||||
if self.final_conv_length == 'auto':
|
||||
n_out_time = out.cpu().data.numpy().shape[3]
|
||||
self.final_conv_length = n_out_time
|
||||
|
||||
model.add_module('conv_classifier', nn.Conv2d(
|
||||
self.F2, self.n_classes,
|
||||
(n_out_virtual_chans, self.final_conv_length,), bias=True))
|
||||
model.add_module('softmax', nn.LogSoftmax())
|
||||
# Transpose back to the the logic of braindecode,
|
||||
# so time in third dimension (axis=2)
|
||||
model.add_module('permute_back', Expression(_transpose_1_0))
|
||||
model.add_module('squeeze', Expression(_squeeze_final_output))
|
||||
|
||||
glorot_weight_zero_bias(model)
|
||||
return model
|
||||
|
||||
|
||||
def _transpose_to_b_1_c_0(x):
|
||||
return x.permute(0, 3, 1, 2)
|
||||
|
||||
|
||||
def _transpose_1_0(x):
|
||||
return x.permute(0, 1, 3, 2)
|
||||
|
||||
|
||||
# remove empty dim at end and potentially remove empty time dim
|
||||
# do not just use squeeze as we never want to remove first dim
|
||||
def _squeeze_final_output(x):
|
||||
assert x.size()[3] == 1
|
||||
x = x[:, :, :, 0]
|
||||
if x.size()[2] == 1:
|
||||
x = x[:, :, 0]
|
||||
return x
|
||||
|
||||
|
||||
|
||||
class EEGNet(object):
|
||||
"""
|
||||
EEGNet model from [EEGNet]_.
|
||||
|
||||
Notes
|
||||
-----
|
||||
This implementation is not guaranteed to be correct, has not been checked
|
||||
by original authors, only reimplemented from the paper description.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [EEGNet] Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon,
|
||||
S. M., Hung, C. P., & Lance, B. J. (2016).
|
||||
EEGNet: A Compact Convolutional Network for EEG-based
|
||||
Brain-Computer Interfaces.
|
||||
arXiv preprint arXiv:1611.08024.
|
||||
"""
|
||||
def __init__(self, in_chans,
|
||||
n_classes,
|
||||
final_conv_length='auto',
|
||||
input_time_length=None,
|
||||
pool_mode='max',
|
||||
second_kernel_size=(2,32),
|
||||
third_kernel_size=(8,4),
|
||||
drop_prob=0.25
|
||||
):
|
||||
|
||||
if final_conv_length == 'auto':
|
||||
assert input_time_length is not None
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def create_network(self):
|
||||
pool_class = dict(max=nn.MaxPool2d, mean=nn.AvgPool2d)[self.pool_mode]
|
||||
model = nn.Sequential()
|
||||
n_filters_1 = 16
|
||||
model.add_module('conv_1', nn.Conv2d(
|
||||
self.in_chans, n_filters_1, (1, 1), stride=1, bias=True))
|
||||
model.add_module('bnorm_1', nn.BatchNorm2d(
|
||||
n_filters_1, momentum=0.01, affine=True, eps=1e-3),)
|
||||
model.add_module('elu_1', Expression(elu))
|
||||
# transpose to examples x 1 x (virtual, not EEG) channels x time
|
||||
model.add_module('permute_1', Expression(lambda x: x.permute(0,3,1,2)))
|
||||
|
||||
model.add_module('drop_1', nn.Dropout(p=self.drop_prob))
|
||||
|
||||
n_filters_2 = 4
|
||||
# keras padds unequal padding more in front, so padding
|
||||
# too large should be ok.
|
||||
# Not padding in time so that croped training makes sense
|
||||
# https://stackoverflow.com/questions/43994604/padding-with-even-kernel-size-in-a-convolutional-layer-in-keras-theano
|
||||
|
||||
model.add_module('conv_2', nn.Conv2d(
|
||||
1, n_filters_2, self.second_kernel_size, stride=1,
|
||||
padding=(self.second_kernel_size[0] // 2, 0),
|
||||
bias=True))
|
||||
model.add_module('bnorm_2',nn.BatchNorm2d(
|
||||
n_filters_2, momentum=0.01, affine=True, eps=1e-3),)
|
||||
model.add_module('elu_2', Expression(elu))
|
||||
model.add_module('pool_2', pool_class(
|
||||
kernel_size=(2, 4), stride=(2, 4)))
|
||||
model.add_module('drop_2', nn.Dropout(p=self.drop_prob))
|
||||
|
||||
n_filters_3 = 4
|
||||
model.add_module('conv_3', nn.Conv2d(
|
||||
n_filters_2, n_filters_3, self.third_kernel_size, stride=1,
|
||||
padding=(self.third_kernel_size[0] // 2, 0),
|
||||
bias=True))
|
||||
model.add_module('bnorm_3',nn.BatchNorm2d(
|
||||
n_filters_3, momentum=0.01, affine=True, eps=1e-3),)
|
||||
model.add_module('elu_3', Expression(elu))
|
||||
model.add_module('pool_3', pool_class(
|
||||
kernel_size=(2, 4), stride=(2, 4)))
|
||||
model.add_module('drop_3', nn.Dropout(p=self.drop_prob))
|
||||
|
||||
|
||||
|
||||
out = model(np_to_var(np.ones(
|
||||
(1, self.in_chans, self.input_time_length, 1),
|
||||
dtype=np.float32)))
|
||||
n_out_virtual_chans = out.cpu().data.numpy().shape[2]
|
||||
|
||||
if self.final_conv_length == 'auto':
|
||||
n_out_time = out.cpu().data.numpy().shape[3]
|
||||
self.final_conv_length = n_out_time
|
||||
|
||||
model.add_module('conv_classifier', nn.Conv2d(
|
||||
n_filters_3, self.n_classes,
|
||||
(n_out_virtual_chans, self.final_conv_length,), bias=True))
|
||||
model.add_module('softmax', nn.LogSoftmax())
|
||||
# Transpose back to the the logic of braindecode,
|
||||
# so time in third dimension (axis=2)
|
||||
model.add_module('permute_2', Expression(lambda x: x.permute(0,1,3,2)))
|
||||
model.add_module('squeeze', Expression(_squeeze_final_output))
|
||||
glorot_weight_zero_bias(model)
|
||||
return model
|
||||
@@ -0,0 +1,108 @@
|
||||
import torch as th
|
||||
from torch import nn
|
||||
from torch.nn import ConstantPad2d
|
||||
|
||||
from braindecode.models.base import BaseModel
|
||||
from braindecode.models.deep4 import Deep4Net
|
||||
from braindecode.models.shallow_fbcsp import ShallowFBCSPNet
|
||||
from braindecode.models.util import to_dense_prediction_model
|
||||
|
||||
|
||||
class HybridNet(BaseModel):
|
||||
"""
|
||||
Wrapper for HybridNetModule
|
||||
"""
|
||||
def __init__(self, in_chans, n_classes, input_time_length):
|
||||
self.in_chans = in_chans
|
||||
self.n_classes = n_classes
|
||||
self.input_time_length = input_time_length
|
||||
|
||||
def create_network(self):
|
||||
return HybridNetModule(
|
||||
in_chans=self.in_chans,
|
||||
n_classes=self.n_classes,
|
||||
input_time_length=self.input_time_length
|
||||
)
|
||||
|
||||
|
||||
class HybridNetModule(nn.Module):
|
||||
"""
|
||||
Hybrid ConvNet model from [3]_.
|
||||
|
||||
Very hardcoded at the moment.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [3] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
"""
|
||||
def __init__(self, in_chans, n_classes, input_time_length):
|
||||
super(HybridNetModule, self).__init__()
|
||||
deep_model = Deep4Net(in_chans, n_classes, n_filters_time=20,
|
||||
n_filters_spat=30,
|
||||
n_filters_2=40,
|
||||
n_filters_3=50,
|
||||
n_filters_4=60,
|
||||
input_time_length=input_time_length,
|
||||
final_conv_length=2).create_network()
|
||||
shallow_model = ShallowFBCSPNet(in_chans, n_classes,
|
||||
input_time_length=input_time_length,
|
||||
n_filters_time=30,
|
||||
n_filters_spat=40,
|
||||
filter_time_length=28,
|
||||
final_conv_length=29,
|
||||
).create_network()
|
||||
|
||||
reduced_deep_model = nn.Sequential()
|
||||
for name, module in deep_model.named_children():
|
||||
if name == 'conv_classifier':
|
||||
new_conv_layer = nn.Conv2d(module.in_channels, 60,
|
||||
kernel_size=module.kernel_size,
|
||||
stride=module.stride)
|
||||
reduced_deep_model.add_module('deep_final_conv', new_conv_layer)
|
||||
break
|
||||
reduced_deep_model.add_module(name, module)
|
||||
|
||||
reduced_shallow_model = nn.Sequential()
|
||||
for name, module in shallow_model.named_children():
|
||||
if name == 'conv_classifier':
|
||||
new_conv_layer = nn.Conv2d(module.in_channels, 40,
|
||||
kernel_size=module.kernel_size,
|
||||
stride=module.stride)
|
||||
reduced_shallow_model.add_module('shallow_final_conv',
|
||||
new_conv_layer)
|
||||
break
|
||||
reduced_shallow_model.add_module(name, module)
|
||||
|
||||
to_dense_prediction_model(reduced_deep_model)
|
||||
to_dense_prediction_model(reduced_shallow_model)
|
||||
self.reduced_deep_model = reduced_deep_model
|
||||
self.reduced_shallow_model = reduced_shallow_model
|
||||
self.final_conv = nn.Conv2d(100, n_classes, kernel_size=(1, 1),
|
||||
stride=1)
|
||||
|
||||
def create_network(self):
|
||||
return self
|
||||
|
||||
def forward(self, x):
|
||||
deep_out = self.reduced_deep_model(x)
|
||||
shallow_out = self.reduced_shallow_model(x)
|
||||
|
||||
n_diff_deep_shallow = deep_out.size()[2] - shallow_out.size()[2]
|
||||
|
||||
if n_diff_deep_shallow < 0:
|
||||
deep_out = ConstantPad2d((0, 0, -n_diff_deep_shallow, 0), 0)(
|
||||
deep_out)
|
||||
elif n_diff_deep_shallow > 0:
|
||||
shallow_out = ConstantPad2d((0, 0, n_diff_deep_shallow, 0), 0)(
|
||||
shallow_out)
|
||||
|
||||
merged_out = th.cat((deep_out, shallow_out), dim=1)
|
||||
linear_out = self.final_conv(merged_out)
|
||||
softmaxed = nn.LogSoftmax(dim=1)(linear_out)
|
||||
squeezed = softmaxed.squeeze(3)
|
||||
return squeezed
|
||||
@@ -0,0 +1,119 @@
|
||||
import numpy as np
|
||||
from torch import nn
|
||||
from torch.nn import init
|
||||
|
||||
from braindecode.models.base import BaseModel
|
||||
from braindecode.torch_ext.modules import Expression
|
||||
from braindecode.torch_ext.functions import safe_log, square
|
||||
from braindecode.torch_ext.util import np_to_var
|
||||
|
||||
|
||||
class ShallowFBCSPNet(BaseModel):
|
||||
"""
|
||||
Shallow ConvNet model from [2]_.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [2] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
"""
|
||||
def __init__(self, in_chans,
|
||||
n_classes,
|
||||
input_time_length=None,
|
||||
n_filters_time=40,
|
||||
filter_time_length=25,
|
||||
n_filters_spat=40,
|
||||
pool_time_length=75,
|
||||
pool_time_stride=15,
|
||||
final_conv_length=30,
|
||||
conv_nonlin=square,
|
||||
pool_mode='mean',
|
||||
pool_nonlin=safe_log,
|
||||
split_first_layer=True,
|
||||
batch_norm=True,
|
||||
batch_norm_alpha=0.1,
|
||||
drop_prob=0.5):
|
||||
if final_conv_length == 'auto':
|
||||
assert input_time_length is not None
|
||||
self.__dict__.update(locals())
|
||||
del self.self
|
||||
|
||||
def create_network(self):
|
||||
pool_class = dict(max=nn.MaxPool2d, mean=nn.AvgPool2d)[self.pool_mode]
|
||||
model = nn.Sequential()
|
||||
if self.split_first_layer:
|
||||
model.add_module('dimshuffle', Expression(_transpose_time_to_spat))
|
||||
model.add_module('conv_time', nn.Conv2d(1, self.n_filters_time,
|
||||
(
|
||||
self.filter_time_length, 1),
|
||||
stride=1, ))
|
||||
model.add_module('conv_spat',
|
||||
nn.Conv2d(self.n_filters_time, self.n_filters_spat,
|
||||
(1, self.in_chans), stride=1,
|
||||
bias=not self.batch_norm))
|
||||
n_filters_conv = self.n_filters_spat
|
||||
else:
|
||||
model.add_module('conv_time',
|
||||
nn.Conv2d(self.in_chans, self.n_filters_time,
|
||||
(self.filter_time_length, 1),
|
||||
stride=1,
|
||||
bias=not self.batch_norm))
|
||||
n_filters_conv = self.n_filters_time
|
||||
if self.batch_norm:
|
||||
model.add_module('bnorm',
|
||||
nn.BatchNorm2d(n_filters_conv,
|
||||
momentum=self.batch_norm_alpha,
|
||||
affine=True),)
|
||||
model.add_module('conv_nonlin', Expression(self.conv_nonlin))
|
||||
model.add_module('pool',
|
||||
pool_class(kernel_size=(self.pool_time_length, 1),
|
||||
stride=(self.pool_time_stride, 1)))
|
||||
model.add_module('pool_nonlin', Expression(self.pool_nonlin))
|
||||
model.add_module('drop', nn.Dropout(p=self.drop_prob))
|
||||
if self.final_conv_length == 'auto':
|
||||
out = model(np_to_var(np.ones(
|
||||
(1, self.in_chans, self.input_time_length,1),
|
||||
dtype=np.float32)))
|
||||
n_out_time = out.cpu().data.numpy().shape[2]
|
||||
self.final_conv_length = n_out_time
|
||||
model.add_module('conv_classifier',
|
||||
nn.Conv2d(n_filters_conv, self.n_classes,
|
||||
(self.final_conv_length, 1), bias=True))
|
||||
model.add_module('softmax', nn.LogSoftmax(dim=1))
|
||||
model.add_module('squeeze', Expression(_squeeze_final_output))
|
||||
|
||||
# Initialization, xavier is same as in paper...
|
||||
init.xavier_uniform_(model.conv_time.weight, gain=1)
|
||||
# maybe no bias in case of no split layer and batch norm
|
||||
if self.split_first_layer or (not self.batch_norm):
|
||||
init.constant_(model.conv_time.bias, 0)
|
||||
if self.split_first_layer:
|
||||
init.xavier_uniform_(model.conv_spat.weight, gain=1)
|
||||
if not self.batch_norm:
|
||||
init.constant_(model.conv_spat.bias, 0)
|
||||
if self.batch_norm:
|
||||
init.constant_(model.bnorm.weight, 1)
|
||||
init.constant_(model.bnorm.bias, 0)
|
||||
init.xavier_uniform_(model.conv_classifier.weight, gain=1)
|
||||
init.constant_(model.conv_classifier.bias, 0)
|
||||
|
||||
return model
|
||||
|
||||
|
||||
# remove empty dim at end and potentially remove empty time dim
|
||||
# do not just use squeeze as we never want to remove first dim
|
||||
def _squeeze_final_output(x):
|
||||
assert x.size()[3] == 1
|
||||
x = x[:,:,:,0]
|
||||
if x.size()[2] == 1:
|
||||
x = x[:,:,0]
|
||||
return x
|
||||
|
||||
|
||||
def _transpose_time_to_spat(x):
|
||||
return x.permute(0, 3, 2, 1)
|
||||
|
||||
@@ -0,0 +1,45 @@
|
||||
import numpy as np
|
||||
|
||||
|
||||
def to_dense_prediction_model(model, axis=(2, 3)):
|
||||
"""
|
||||
Transform a sequential model with strides to a model that outputs
|
||||
dense predictions by removing the strides and instead inserting dilations.
|
||||
Modifies model in-place.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model
|
||||
axis: int or (int,int)
|
||||
Axis to transform (in terms of intermediate output axes)
|
||||
can either be 2, 3, or (2,3).
|
||||
|
||||
Notes
|
||||
-----
|
||||
Does not yet work correctly for average pooling.
|
||||
Prior to version 0.1.7, there had been a bug that could move strides
|
||||
backwards one layer.
|
||||
|
||||
"""
|
||||
if not hasattr(axis, '__len__'):
|
||||
axis = [axis]
|
||||
assert all([ax in [2, 3] for ax in axis]), "Only 2 and 3 allowed for axis"
|
||||
axis = np.array(axis) - 2
|
||||
stride_so_far = np.array([1, 1])
|
||||
for module in model.modules():
|
||||
if hasattr(module, 'dilation'):
|
||||
assert module.dilation == 1 or (module.dilation == (1, 1)), (
|
||||
"Dilation should equal 1 before conversion, maybe the model is "
|
||||
"already converted?")
|
||||
new_dilation = [1, 1]
|
||||
for ax in axis:
|
||||
new_dilation[ax] = int(stride_so_far[ax])
|
||||
module.dilation = tuple(new_dilation)
|
||||
if hasattr(module, 'stride'):
|
||||
if not hasattr(module.stride, '__len__'):
|
||||
module.stride = (module.stride, module.stride)
|
||||
stride_so_far *= np.array(module.stride)
|
||||
new_stride = list(module.stride)
|
||||
for ax in axis:
|
||||
new_stride[ax] = 1
|
||||
module.stride = tuple(new_stride)
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Torch extensions, for example new functions or modules.
|
||||
"""
|
||||
@@ -0,0 +1,27 @@
|
||||
import torch as th
|
||||
|
||||
|
||||
class MaxNormDefaultConstraint(object):
|
||||
"""
|
||||
Applies max L2 norm 2 to the weights until the final layer and L2 norm 0.5
|
||||
to the weights of the final layer as done in [1]_.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
|
||||
"""
|
||||
def apply(self, model):
|
||||
last_weight = None
|
||||
for name, module in list(model.named_children()):
|
||||
if hasattr(module, 'weight') and (
|
||||
not module.__class__.__name__.startswith('BatchNorm')):
|
||||
module.weight.data = th.renorm(module.weight.data,2,0,maxnorm=2)
|
||||
last_weight = module.weight
|
||||
if last_weight is not None:
|
||||
last_weight.data = th.renorm(last_weight.data,2,0,maxnorm=0.5)
|
||||
@@ -0,0 +1,13 @@
|
||||
import torch as th
|
||||
|
||||
def square(x):
|
||||
return x * x
|
||||
|
||||
|
||||
def safe_log(x, eps=1e-6):
|
||||
""" Prevents :math:`log(0)` by using :math:`log(max(x, eps))`."""
|
||||
return th.log(th.clamp(x, min=eps))
|
||||
|
||||
|
||||
def identity(x):
|
||||
return x
|
||||
@@ -0,0 +1,23 @@
|
||||
from torch.nn import init
|
||||
|
||||
|
||||
def glorot_weight_zero_bias(model):
|
||||
"""
|
||||
Initalize parameters of all modules
|
||||
by initializing weights with glorot uniform/xavier initialization,
|
||||
and setting biases to zero.
|
||||
Weights from batch norm layers are set to 1.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model: Module
|
||||
"""
|
||||
for module in model.modules():
|
||||
if hasattr(module, 'weight'):
|
||||
if not ('BatchNorm' in module.__class__.__name__):
|
||||
init.xavier_uniform_(module.weight, gain=1)
|
||||
else:
|
||||
init.constant_(module.weight, 1)
|
||||
if hasattr(module, 'bias'):
|
||||
if module.bias is not None:
|
||||
init.constant_(module.bias, 0)
|
||||
@@ -0,0 +1,83 @@
|
||||
import torch as th
|
||||
|
||||
|
||||
def log_categorical_crossentropy_1_hot(logpreds, targets, dims=None):
|
||||
"""
|
||||
Returns log categorical crossentropy for given log-predictions and targets,
|
||||
targets should be one-hot-encoded.
|
||||
|
||||
Computes :math:`-\mathrm{logpreds} \cdot \mathrm{targets}`
|
||||
|
||||
Parameters
|
||||
----------
|
||||
logpreds: `torch.autograd.Variable`
|
||||
Logarithm of softmax output.
|
||||
targets: `torch.autograd.Variable`
|
||||
One-hot encoded targets
|
||||
dims: int or iterable of int, optional.
|
||||
Compute sum across these dims
|
||||
|
||||
Returns
|
||||
-------
|
||||
loss: `torch.autograd.Variable`
|
||||
:math:`-\mathrm{logpreds} \cdot \mathrm{targets}`
|
||||
"""
|
||||
assert logpreds.size() == targets.size()
|
||||
result = -logpreds * targets
|
||||
# Sum across dims if axis given or more than 1 dim
|
||||
if dims is not None:
|
||||
if not hasattr(dims, '__len__'):
|
||||
dims = [dims]
|
||||
for dim in dims:
|
||||
result = th.sum(result, dim=int(dim))
|
||||
return result
|
||||
|
||||
|
||||
def log_categorical_crossentropy(log_preds, targets, class_weights=None):
|
||||
"""
|
||||
Returns log categorical crossentropy for given log-predictions and targets.
|
||||
|
||||
Computes :math:`-\mathrm{logpreds} \cdot \mathrm{targets}` if you assume
|
||||
targets to be one-hot-encoded. Also works for targets that are not
|
||||
one-hot-encoded, in this case only uses targets that are in the range
|
||||
of the expected class labels, i.e., [0,log_preds.size()[1]-1].
|
||||
|
||||
Parameters
|
||||
----------
|
||||
log_preds: torch.autograd.Variable`
|
||||
Logarithm of softmax output.
|
||||
targets: `torch.autograd.Variable`
|
||||
class_weights: list of int, optional
|
||||
Weights given to loss of different classes
|
||||
|
||||
Returns
|
||||
-------
|
||||
|
||||
loss: `torch.autograd.Variable`
|
||||
"""
|
||||
if log_preds.size() == targets.size():
|
||||
assert class_weights is None, (
|
||||
"Class weights not implemented for one-hot")
|
||||
return log_categorical_crossentropy_1_hot(log_preds, targets)
|
||||
n_classes = log_preds.size()[1]
|
||||
n_elements = 0
|
||||
losses = []
|
||||
for i_class in range(n_classes):
|
||||
mask = targets == i_class
|
||||
mask = mask.type_as(log_preds)
|
||||
n_elements -= th.sum(mask)
|
||||
this_loss = th.sum(mask * log_preds[:,i_class])
|
||||
if class_weights is not None:
|
||||
this_loss = this_loss * class_weights[i_class]
|
||||
losses.append(this_loss)
|
||||
return th.sum(th.stack(losses)) / n_elements
|
||||
|
||||
|
||||
def l2_loss(model):
|
||||
losses = [th.sum(p * p) for p in model.parameters()]
|
||||
return sum(losses)
|
||||
|
||||
|
||||
def l1_loss(model):
|
||||
losses = [th.sum(th.abs(p)) for p in model.parameters()]
|
||||
return sum(losses)
|
||||
@@ -0,0 +1,128 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from braindecode.torch_ext.util import np_to_var
|
||||
|
||||
|
||||
class Expression(torch.nn.Module):
|
||||
"""
|
||||
Compute given expression on forward pass.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
expression_fn: function
|
||||
Should accept variable number of objects of type
|
||||
`torch.autograd.Variable` to compute its output.
|
||||
"""
|
||||
def __init__(self, expression_fn):
|
||||
super(Expression, self).__init__()
|
||||
self.expression_fn = expression_fn
|
||||
|
||||
def forward(self, *x):
|
||||
return self.expression_fn(*x)
|
||||
|
||||
def __repr__(self):
|
||||
if (hasattr(self.expression_fn, 'func') and
|
||||
hasattr(self.expression_fn, 'kwargs')):
|
||||
expression_str = "{:s} {:s}".format(
|
||||
self.expression_fn.func.__name__,
|
||||
str(self.expression_fn.kwargs))
|
||||
elif hasattr(self.expression_fn, '__name__'):
|
||||
expression_str = self.expression_fn.__name__
|
||||
else:
|
||||
expression_str = repr(self.expression_fn)
|
||||
return (self.__class__.__name__ + '(' +
|
||||
'expression=' + str(expression_str) + ')')
|
||||
|
||||
|
||||
class AvgPool2dWithConv(torch.nn.Module):
|
||||
"""
|
||||
Compute average pooling using a convolution, to have the dilation parameter.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
kernel_size: (int,int)
|
||||
Size of the pooling region.
|
||||
stride: (int,int)
|
||||
Stride of the pooling operation.
|
||||
dilation: int or (int,int)
|
||||
Dilation applied to the pooling filter.
|
||||
padding: int or (int,int)
|
||||
Padding applied before the pooling operation.
|
||||
"""
|
||||
def __init__(self, kernel_size, stride, dilation=1, padding=0):
|
||||
super(AvgPool2dWithConv, self).__init__()
|
||||
self.kernel_size = kernel_size
|
||||
self.stride = stride
|
||||
self.dilation = dilation
|
||||
self.padding = padding
|
||||
# don't name them "weights" to
|
||||
# make sure these are not accidentally used by some procedure
|
||||
# that initializes parameters or something
|
||||
self._pool_weights = None
|
||||
|
||||
def forward(self, x):
|
||||
# Create weights for the convolution on demand:
|
||||
# size or type of x changed...
|
||||
in_channels = x.size()[1]
|
||||
weight_shape = (in_channels, 1,
|
||||
self.kernel_size[0], self.kernel_size[1])
|
||||
if self._pool_weights is None or (
|
||||
(tuple(self._pool_weights.size()) != tuple(weight_shape)) or (
|
||||
self._pool_weights.is_cuda != x.is_cuda
|
||||
) or (
|
||||
self._pool_weights.data.type() != x.data.type()
|
||||
)):
|
||||
n_pool = np.prod(self.kernel_size)
|
||||
weights = np_to_var(
|
||||
np.ones(weight_shape, dtype=np.float32) / float(n_pool))
|
||||
weights = weights.type_as(x)
|
||||
if x.is_cuda:
|
||||
weights = weights.cuda()
|
||||
self._pool_weights = weights
|
||||
|
||||
pooled = F.conv2d(x, self._pool_weights, bias=None, stride=self.stride,
|
||||
dilation=self.dilation,
|
||||
padding=self.padding,
|
||||
groups=in_channels,)
|
||||
return pooled
|
||||
|
||||
|
||||
class IntermediateOutputWrapper(torch.nn.Module):
|
||||
"""Wraps network model such that outputs of intermediate layers can be returned.
|
||||
forward() returns list of intermediate activations in a network during forward pass.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
to_select : list
|
||||
list of module names for which activation should be returned
|
||||
model : model object
|
||||
network model
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> model = Deep4Net()
|
||||
>>> select_modules = ['conv_spat','conv_2','conv_3','conv_4'] # Specify intermediate outputs
|
||||
>>> model_pert = IntermediateOutputWrapper(select_modules,model) # Wrap model
|
||||
"""
|
||||
def __init__(self, to_select, model):
|
||||
if not len(list(model.children()))==len(list(model.named_children())):
|
||||
raise Exception('All modules in model need to have names!')
|
||||
|
||||
super(IntermediateOutputWrapper, self).__init__()
|
||||
|
||||
modules_list = model.named_children()
|
||||
for key, module in modules_list:
|
||||
self.add_module(key, module)
|
||||
self._modules[key].load_state_dict(module.state_dict())
|
||||
self._to_select = to_select
|
||||
|
||||
def forward(self,x):
|
||||
# Call modules individually and append activation to output if module is in to_select
|
||||
o = []
|
||||
for name, module in self._modules.items():
|
||||
x = module(x)
|
||||
if name in self._to_select:
|
||||
o.append(x)
|
||||
return o
|
||||
@@ -0,0 +1,84 @@
|
||||
import math
|
||||
import torch
|
||||
from torch.optim.optimizer import Optimizer
|
||||
|
||||
|
||||
class AdamW(Optimizer):
|
||||
"""Implements Adam algorithm with weight decay fixed as in [AdamW]_` .
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: iterable
|
||||
Iterable of parameters to optimize or dicts defining parameter groups
|
||||
lr: float, optional
|
||||
Learning rate.
|
||||
betas: Tuple[float, float], optional
|
||||
Coefficients used for computing running averages of gradient and its square
|
||||
eps: float, optional
|
||||
Term added to the denominator to improve numerical stability
|
||||
weight_decay: float, optional
|
||||
The "fixed" weight decay.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [AdamW] Loshchilov, I. & Hutter, F. (2017).
|
||||
Fixing Weight Decay Regularization in Adam.
|
||||
arXiv preprint arXiv:1711.05101.
|
||||
Online: https://arxiv.org/abs/1711.05101
|
||||
|
||||
"""
|
||||
|
||||
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
|
||||
weight_decay=0):
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps,
|
||||
weight_decay=weight_decay)
|
||||
super(AdamW, self).__init__(params, defaults)
|
||||
|
||||
def step(self, closure=None):
|
||||
"""Performs a single optimization step.
|
||||
|
||||
Arguments:
|
||||
closure (callable, optional): A closure that reevaluates the model
|
||||
and returns the loss.
|
||||
"""
|
||||
loss = None
|
||||
if closure is not None:
|
||||
loss = closure()
|
||||
|
||||
for group in self.param_groups:
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad.data
|
||||
state = self.state[p]
|
||||
|
||||
# State initialization
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = grad.new().resize_as_(grad).zero_()
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_()
|
||||
|
||||
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
state['step'] += 1
|
||||
|
||||
|
||||
# Decay the first and second moment running average coefficient
|
||||
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
||||
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
||||
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
|
||||
bias_correction1 = 1 - beta1 ** state['step']
|
||||
bias_correction2 = 1 - beta2 ** state['step']
|
||||
step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1
|
||||
|
||||
p.data.addcdiv_(-step_size, exp_avg, denom)
|
||||
if group['weight_decay'] != 0:
|
||||
p.data.add_(-group['weight_decay'], p.data)
|
||||
|
||||
return loss
|
||||
@@ -0,0 +1,65 @@
|
||||
import numpy as np
|
||||
|
||||
|
||||
class ScheduledOptimizer(object):
|
||||
def __init__(self, scheduler, optimizer,
|
||||
schedule_weight_decay):
|
||||
self.scheduler = scheduler
|
||||
self.optimizer = optimizer
|
||||
self.schedule_weight_decay = schedule_weight_decay
|
||||
self.initial_lrs = list(map(
|
||||
lambda group: group['lr'], optimizer.param_groups))
|
||||
self.initial_weight_decays = list(map(
|
||||
lambda group: group['weight_decay'], optimizer.param_groups))
|
||||
self.i_update = 0
|
||||
|
||||
def step(self):
|
||||
for group, initial_lr, initial_wd in zip(
|
||||
self.optimizer.param_groups,
|
||||
self.initial_lrs,
|
||||
self.initial_weight_decays):
|
||||
group['lr'] = self.scheduler.get_lr(initial_lr, self.i_update)
|
||||
if self.schedule_weight_decay:
|
||||
group['weight_decay'] = self.scheduler.get_weight_decay(
|
||||
initial_wd, self.i_update)
|
||||
self.optimizer.step()
|
||||
self.i_update += 1
|
||||
|
||||
def state_dict(self):
|
||||
return self.optimizer.state_dict()
|
||||
|
||||
def load_state_dict(self, state_dict):
|
||||
self.optimizer.load_state_dict(state_dict)
|
||||
|
||||
def zero_grad(self):
|
||||
self.optimizer.zero_grad()
|
||||
|
||||
|
||||
class CosineAnnealing(object):
|
||||
def __init__(self, n_updates_per_period,):
|
||||
if not hasattr(n_updates_per_period, '__len__'):
|
||||
n_updates_per_period = [n_updates_per_period]
|
||||
assert np.all(np.array(n_updates_per_period) > 0)
|
||||
self.update_period_boundaries = np.cumsum(n_updates_per_period)
|
||||
self.update_period_boundaries = np.concatenate((
|
||||
[0], self.update_period_boundaries))
|
||||
|
||||
def get_lr(self, initial_val, i_update):
|
||||
# i_update + 1 in error message since first update is i_update=0
|
||||
assert i_update < self.update_period_boundaries[-1], (
|
||||
"More updates ({:d}) than expected ({:d})".format(
|
||||
i_update + 1, self.update_period_boundaries[-1]))
|
||||
i_end_period = np.searchsorted(self.update_period_boundaries,
|
||||
i_update, side='right')
|
||||
assert i_end_period > 0
|
||||
i_start_update = self.update_period_boundaries[i_end_period - 1]
|
||||
i_end_update = self.update_period_boundaries[i_end_period]
|
||||
i_update = i_update - i_start_update
|
||||
assert i_update >= 0
|
||||
n_updates_this_period = i_end_update - i_start_update
|
||||
fraction_period = i_update / np.float64(n_updates_this_period)
|
||||
return initial_val * (0.5 * np.cos(np.pi * fraction_period) + 0.5)
|
||||
|
||||
def get_weight_decay(self, initial_val, i_update):
|
||||
return self.get_lr(initial_val, i_update)
|
||||
|
||||
@@ -0,0 +1,76 @@
|
||||
import torch as th
|
||||
from torch.autograd import Variable
|
||||
import numpy as np
|
||||
import random
|
||||
|
||||
|
||||
def np_to_var(X, requires_grad=False, dtype=None, pin_memory=False,
|
||||
**tensor_kwargs):
|
||||
"""
|
||||
Convenience function to transform numpy array to `torch.Tensor`.
|
||||
|
||||
Converts `X` to ndarray using asarray if necessary.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X: ndarray or list or number
|
||||
Input arrays
|
||||
requires_grad: bool
|
||||
passed on to Variable constructor
|
||||
dtype: numpy dtype, optional
|
||||
var_kwargs:
|
||||
passed on to Variable constructor
|
||||
|
||||
Returns
|
||||
-------
|
||||
var: `torch.Tensor`
|
||||
"""
|
||||
if not hasattr(X, '__len__'):
|
||||
X = [X]
|
||||
X = np.asarray(X)
|
||||
if dtype is not None:
|
||||
X = X.astype(dtype)
|
||||
X_tensor = th.tensor(X, requires_grad=requires_grad, **tensor_kwargs)
|
||||
if pin_memory:
|
||||
X_tensor = X_tensor.pin_memory()
|
||||
return X_tensor
|
||||
|
||||
|
||||
def var_to_np(var):
|
||||
"""Convenience function to transform `torch.Tensor` to numpy
|
||||
array.
|
||||
|
||||
Should work both for CPU and GPU."""
|
||||
return var.cpu().data.numpy()
|
||||
|
||||
def set_random_seeds(seed, cuda):
|
||||
"""
|
||||
Set seeds for python random module numpy.random and torch.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
seed: int
|
||||
Random seed.
|
||||
cuda: bool
|
||||
Whether to set cuda seed with torch.
|
||||
|
||||
"""
|
||||
random.seed(seed)
|
||||
th.manual_seed(seed)
|
||||
if cuda:
|
||||
th.cuda.manual_seed_all(seed)
|
||||
np.random.seed(seed)
|
||||
|
||||
|
||||
def confirm_gpu_availability():
|
||||
"""
|
||||
Should crash if gpu not available, attempts to create a FloatTensor on GPU.
|
||||
Returns
|
||||
-------
|
||||
success: bool
|
||||
Always returns true, should crash if gpu not available
|
||||
"""
|
||||
a = th.FloatTensor(1).cuda()
|
||||
# Just make sure a is not somehow removed by any smart compiling,
|
||||
# probably not necessary.
|
||||
return a is not None
|
||||
@@ -0,0 +1,210 @@
|
||||
import os
|
||||
import errno
|
||||
import numpy as np
|
||||
|
||||
|
||||
def corr(a, b):
|
||||
"""
|
||||
Computes correlation only between terms of a and terms of b, not within
|
||||
a and b.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a, b: 2darray, features x samples
|
||||
|
||||
Returns
|
||||
-------
|
||||
Correlation between features in x and features in y
|
||||
"""
|
||||
# Difference to numpy:
|
||||
# Correlation only between terms of x and y
|
||||
# not between x and x or y and y
|
||||
this_cov = cov(a, b)
|
||||
return _cov_to_corr(this_cov, a, b)
|
||||
|
||||
|
||||
def cov(a, b):
|
||||
"""
|
||||
Computes covariance only between terms of a and terms of b, not within
|
||||
a and b.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
a, b: 2darray, features x samples
|
||||
|
||||
Returns
|
||||
-------
|
||||
Covariance between features in x and features in y
|
||||
"""
|
||||
demeaned_a = a - np.mean(a, axis=1, keepdims=True)
|
||||
demeaned_b = b - np.mean(b, axis=1, keepdims=True)
|
||||
this_cov = np.dot(demeaned_a, demeaned_b.T) / (b.shape[1] - 1)
|
||||
return this_cov
|
||||
|
||||
|
||||
def _cov_to_corr(this_cov, a, b):
|
||||
# computing "unbiased" corr
|
||||
# ddof=1 for unbiased..
|
||||
var_a = np.var(a, axis=1, ddof=1)
|
||||
var_b = np.var(b, axis=1, ddof=1)
|
||||
return _cov_and_var_to_corr(this_cov, var_a, var_b)
|
||||
|
||||
|
||||
def _cov_and_var_to_corr(this_cov, var_a, var_b):
|
||||
divisor = np.outer(np.sqrt(var_a), np.sqrt(var_b))
|
||||
return this_cov / divisor
|
||||
|
||||
|
||||
def wrap_reshape_apply_fn(stat_fn, a, b, axis_a, axis_b):
|
||||
"""
|
||||
Reshape two nd-arrays into 2d-arrays, apply function and reshape
|
||||
result back.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stat_fn: function
|
||||
Function to apply to 2d-arrays
|
||||
a: nd-array: nd-array
|
||||
b: nd-array
|
||||
axis_a: int or list of int
|
||||
sample axis
|
||||
axis_b: int or list of int
|
||||
sample axis
|
||||
|
||||
Returns
|
||||
-------
|
||||
result: nd-array
|
||||
The result reshaped to remaining_dims_a + remaining_dims_b
|
||||
"""
|
||||
if not hasattr(axis_a, '__len__'):
|
||||
axis_a = [axis_a]
|
||||
if not hasattr(axis_b, '__len__'):
|
||||
axis_b = [axis_b]
|
||||
other_axis_a = [i for i in range(a.ndim) if i not in axis_a]
|
||||
other_axis_b = [i for i in range(b.ndim) if i not in axis_b]
|
||||
transposed_topo_a = a.transpose(tuple(other_axis_a) + tuple(axis_a))
|
||||
n_stat_axis_a = [a.shape[i] for i in axis_a]
|
||||
n_other_axis_a = [a.shape[i] for i in other_axis_a]
|
||||
flat_topo_a = transposed_topo_a.reshape(np.prod(n_other_axis_a),
|
||||
np.prod(n_stat_axis_a))
|
||||
transposed_topo_b = b.transpose(tuple(other_axis_b) + tuple(axis_b))
|
||||
n_stat_axis_b = [b.shape[i] for i in axis_b]
|
||||
n_other_axis_b = [b.shape[i] for i in other_axis_b]
|
||||
flat_topo_b = transposed_topo_b.reshape(np.prod(n_other_axis_b),
|
||||
np.prod(n_stat_axis_b))
|
||||
assert np.array_equal(n_stat_axis_a, n_stat_axis_b)
|
||||
stat_result = stat_fn(flat_topo_a, flat_topo_b)
|
||||
topo_result = stat_result.reshape(
|
||||
tuple(n_other_axis_a) + tuple(n_other_axis_b))
|
||||
return topo_result
|
||||
|
||||
|
||||
class FuncAndArgs(object):
|
||||
"""Container for a function and its arguments.
|
||||
Useful in case you want to pass a function and its arguments
|
||||
to another function without creating a new class.
|
||||
You can call the new instance either with the apply method or
|
||||
the ()-call operator:
|
||||
|
||||
>>> FuncAndArgs(max, 2,3).apply(4)
|
||||
4
|
||||
>>> FuncAndArgs(max, 2,3)(4)
|
||||
4
|
||||
>>> FuncAndArgs(sum, [3,4])(8)
|
||||
15
|
||||
|
||||
"""
|
||||
def __init__(self, func, *args, **kwargs):
|
||||
self.func = func
|
||||
self.args = args
|
||||
self.kwargs = kwargs
|
||||
|
||||
|
||||
def apply(self, *other_args, **other_kwargs):
|
||||
all_args = self.args + other_args
|
||||
all_kwargs = self.kwargs.copy()
|
||||
all_kwargs.update(other_kwargs)
|
||||
return self.func(*all_args, **all_kwargs)
|
||||
|
||||
def __call__(self, *other_args, **other_kwargs):
|
||||
return self.apply(*other_args, **other_kwargs)
|
||||
|
||||
def add_message_to_exception(exc, additional_message):
|
||||
# give some more info...
|
||||
# see http://www.ianbicking.org/blog/2007/09/re-raising-exceptions.html
|
||||
args = exc.args
|
||||
if not args:
|
||||
arg0 = ''
|
||||
else:
|
||||
|
||||
arg0 = args[0]
|
||||
arg0 += additional_message
|
||||
exc.args = (arg0, ) + args[1:]
|
||||
|
||||
def dict_compare(d1, d2):
|
||||
"""From http://stackoverflow.com/a/18860653/1469195"""
|
||||
d1_keys = set(d1.keys())
|
||||
d2_keys = set(d2.keys())
|
||||
intersect_keys = d1_keys.intersection(d2_keys)
|
||||
added = d1_keys - d2_keys
|
||||
removed = d2_keys - d1_keys
|
||||
modified = {o : (d1[o], d2[o]) for o in intersect_keys if d1[o] != d2[o]}
|
||||
same = set(o for o in intersect_keys if d1[o] == d2[o])
|
||||
return added, removed, modified, same
|
||||
|
||||
def dict_equal(d1,d2):
|
||||
d1_keys = set(d1.keys())
|
||||
d2_keys = set(d2.keys())
|
||||
intersect_keys = d1_keys.intersection(d2_keys)
|
||||
modified = {o : (d1[o], d2[o]) for o in intersect_keys if d1[o] != d2[o]}
|
||||
return (intersect_keys == d2_keys and intersect_keys == d1_keys and
|
||||
len(modified) == 0)
|
||||
|
||||
def dict_is_subset(d1,d2):
|
||||
added, removed, modified, same = dict_compare(d1, d2)
|
||||
return (len(added) == 0 and len(modified) == 0)
|
||||
|
||||
def merge_dicts(*dict_args):
|
||||
'''
|
||||
Given any number of dicts, shallow copy and merge into a new dict,
|
||||
precedence goes to key value pairs in latter dicts.
|
||||
http://stackoverflow.com/a/26853961
|
||||
'''
|
||||
result = {}
|
||||
for dictionary in dict_args:
|
||||
result.update(dictionary)
|
||||
return result
|
||||
|
||||
def touch_file(path):
|
||||
# from http://stackoverflow.com/a/12654798/1469195
|
||||
basedir = os.path.dirname(path)
|
||||
if not os.path.exists(basedir):
|
||||
os.makedirs(basedir)
|
||||
with open(path, 'a'):
|
||||
os.utime(path, None)
|
||||
|
||||
|
||||
def to_tuple(sequence_or_element, length=None):
|
||||
if hasattr(sequence_or_element, '__len__'):
|
||||
assert length is None
|
||||
return tuple(sequence_or_element)
|
||||
else:
|
||||
if length is None:
|
||||
return (sequence_or_element, )
|
||||
else:
|
||||
return (sequence_or_element,) * length
|
||||
|
||||
|
||||
def mkdir_p(path):
|
||||
try:
|
||||
os.makedirs(path)
|
||||
except OSError as exc: # Python >2.5
|
||||
if exc.errno == errno.EEXIST and os.path.isdir(path):
|
||||
pass
|
||||
else:
|
||||
raise
|
||||
|
||||
def select_inverse_inds(arr, inds):
|
||||
mask = np.ones(len(arr),dtype=bool)
|
||||
mask[inds] = False
|
||||
return arr[mask]
|
||||
@@ -0,0 +1 @@
|
||||
__version__ = "0.4.7"
|
||||
@@ -0,0 +1,3 @@
|
||||
"""
|
||||
Functions for visualisations, especially of the ConvNets.
|
||||
"""
|
||||
@@ -0,0 +1,170 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
def calc_Hout(Hin, kernel, stride, dilation):
|
||||
Hout = np.floor((Hin-dilation*(kernel-1)-1)/stride+1)
|
||||
return Hout
|
||||
|
||||
def calc_Hin(Hout, kernel, stride, dilation):
|
||||
Hin = np.ceil((Hout-1)*stride+1+dilation*(kernel-1))
|
||||
return Hin
|
||||
|
||||
def calc_receptive_field_size(model,layer_ind,start_receptive_field=np.ones((2))):
|
||||
"""Calculate receptive field size for unit in specific layer of the network
|
||||
Only tested for 2d convolutions/poolings. Dimshuffle operations may lead to a wrong result
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model : model object
|
||||
Network model
|
||||
layer_ind: int
|
||||
Index of the layer of interest in `model.children()`
|
||||
start_receptive_field: int, optional
|
||||
How many units are looked at in specified layer (default: [1,1])
|
||||
|
||||
Returns
|
||||
-------
|
||||
receptive_field_size : numpy array
|
||||
[HxW] in the input layer
|
||||
"""
|
||||
receptive_field = start_receptive_field
|
||||
children = list(model.children())[:layer_ind][::-1]
|
||||
for child in children:
|
||||
if isinstance(child,torch.nn.Sequential):
|
||||
receptive_field = calc_receptive_field(child,-1)
|
||||
elif isinstance(child,torch.nn.Conv2d) or isinstance(child,torch.nn.MaxPool2d) or isinstance(child,torch.nn.AvgPool2d):
|
||||
receptive_field = calc_Hin(receptive_field,np.asarray(child.kernel_size),
|
||||
np.asarray(child.stride), np.asarray(child.dilation))
|
||||
|
||||
receptive_field_size = receptive_field.astype(np.int)
|
||||
return receptive_field_size
|
||||
|
||||
def get_max_act_index(activations,unique_per_input=True,n_units=None):
|
||||
"""Retrieve index of maximum activation in a feature map
|
||||
|
||||
Parameters
|
||||
----------
|
||||
activations : numpy array
|
||||
[Nx1xHxW] can only take 1 filter
|
||||
unique_per_input : bool, optional
|
||||
Specifies if only 1 index (maximum) for each input is returned (default: True)
|
||||
n_units : int, optional
|
||||
How many indeces are returned in total. If None all (default: None)
|
||||
|
||||
Returns
|
||||
-------
|
||||
units : numpy array
|
||||
[Nx4] with the columns `Input_i`,`Filter(0)`,`H_i`,`W_i` indeces of the units
|
||||
units_activation : numpy array
|
||||
Activation of the units
|
||||
"""
|
||||
assert len(activations.shape)==4,"Has to be 4d array"
|
||||
assert activations.shape[1] == 1,"Can only handle individual filter activations"
|
||||
|
||||
activations_sorted = activations.argsort(axis=None)[::-1]
|
||||
activations_sorted_ind = np.unravel_index(activations_sorted,activations.shape)
|
||||
unique_ind = np.arange(len(activations_sorted_ind[0]))
|
||||
if unique_per_input:
|
||||
a,unique_ind = np.unique(activations_sorted_ind[0],return_index=True)
|
||||
unique_ind = sorted(unique_ind)
|
||||
|
||||
if n_units==None:
|
||||
n_units = len(unique_ind)
|
||||
activations_sorted_ind = np.asarray(activations_sorted_ind).T
|
||||
units = activations_sorted_ind[unique_ind[:n_units],:].astype(np.int)
|
||||
units_activation = activations.flat[activations_sorted[unique_ind[:n_units]]]
|
||||
|
||||
return units,units_activation
|
||||
|
||||
def calc_receptive_field_for_units_2d(units,receptive_field_size):
|
||||
recptive_field_tmp = receptive_field_size[np.newaxis,np.newaxis,:,:]
|
||||
start_inds = units[:,0,]
|
||||
stop_inds = start_inds+receptive_field_tmp
|
||||
|
||||
return start_inds,stop_inds
|
||||
|
||||
def get_input_windows_from_units_2d(inputs,units,receptive_field_size):
|
||||
"""Cut input windows in receptive field of specified units from inputs
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs : numpy array
|
||||
[NxCxHxW] Inputs x Channels x Time x W
|
||||
units : numpy array
|
||||
[Mx4] unit indeces specifying Input and time indeces.
|
||||
Second dimension consists of Input x Filter(1) x H x W indeces. Can only handle 1 filter.
|
||||
receptive_field_size : int
|
||||
Size of receptive field of units on input
|
||||
|
||||
Returns
|
||||
-------
|
||||
windows : numpy array
|
||||
Cut input windows
|
||||
"""
|
||||
windows = np.zeros((units.shape[0],inputs.shape[1],receptive_field_size[0],receptive_field_size[1]))
|
||||
for i,unit in enumerate(units):
|
||||
windows[i] = inputs[unit[0],:,
|
||||
unit[2]:unit[2]+receptive_field_size[0],
|
||||
unit[3]:unit[3]+receptive_field_size[1]]
|
||||
return windows
|
||||
|
||||
|
||||
def most_activating_input_windows(inputs,activations,receptive_field_size,top_percentage=0.05):
|
||||
"""Get the input windows that evoked highest activation in a feature map
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs : numpy array
|
||||
[NxCxTx1] Inputs used for the calculation of activations
|
||||
activations : numpy array
|
||||
[NxFxHx1] Activations
|
||||
receptive_field_size : numpy array
|
||||
[Wx1] Receptive field of a unit of the layer on the input
|
||||
top_percentage : float, optional
|
||||
How many of the most activating input windows should be returned (default: top 5%)
|
||||
|
||||
Returns
|
||||
-------
|
||||
input_windows : numpy array
|
||||
[FxUxCxWx1] Returns `U` (resulting from top_percentage) input windows for each filter
|
||||
"""
|
||||
n_units = int(inputs.shape[0]*top_percentage)
|
||||
input_windows = np.zeros((activations.shape[1],n_units,inputs.shape[1],
|
||||
receptive_field_size[0],receptive_field_size[1]))
|
||||
for filt in range(activations.shape[1]):
|
||||
units,w = get_max_act_index(activations[:,[filt]],n_units=n_units)
|
||||
w = w.squeeze()
|
||||
windows_tmp = get_input_windows_from_units_2d(inputs,units,receptive_field_size)
|
||||
input_windows[filt] = windows_tmp
|
||||
return input_windows
|
||||
|
||||
def activation_reverse_correlation(inputs,activations,receptive_field_size):
|
||||
"""Get reverse correlations for filters
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs : numpy array
|
||||
[NxCxTx1] Inputs used for the calculation of activations
|
||||
activations : numpy array
|
||||
[NxFxHx1] Activations
|
||||
receptive_field_size : numpy array
|
||||
[Wx1] Receptive field of a unit of the layer on the input
|
||||
|
||||
Returns
|
||||
-------
|
||||
reverse_corr : numpy array
|
||||
[FxCxWx1] Reverse correlations over all input windows for each filter
|
||||
"""
|
||||
reverse_corr = np.zeros((activations.shape[1],inputs.shape[1],
|
||||
receptive_field_size[0],receptive_field_size[1]))
|
||||
for filt in range(activations.shape[1]):
|
||||
act_tmp = activations[:,filt]
|
||||
divisor = 0
|
||||
for start_ind in range(activations.shape[2]):
|
||||
end_ind = start_ind+receptive_field_size[0]
|
||||
w = act_tmp[:,[start_ind],:,np.newaxis]
|
||||
windows_tmp = inputs[:,:,start_ind:end_ind]
|
||||
reverse_corr[filt] += np.sum(w*windows_tmp,axis=0)
|
||||
divisor += np.sum(np.abs(w))
|
||||
reverse_corr[filt] /= divisor
|
||||
return reverse_corr
|
||||
@@ -0,0 +1,297 @@
|
||||
import logging
|
||||
|
||||
import numpy as np
|
||||
|
||||
from braindecode.datautil.iterators import get_balanced_batches
|
||||
from braindecode.util import wrap_reshape_apply_fn, corr
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def phase_perturbation(amps,phases,rng=None):
|
||||
"""Takes amps and phases of BxCxF with B input, C channels, F frequencies
|
||||
Shifts spectral phases randomly U(-pi,pi) for input and frequencies, but same for all channels
|
||||
|
||||
Parameters
|
||||
----------
|
||||
amps : numpy array
|
||||
Spectral amplitude (not used)
|
||||
phases : numpy array
|
||||
Spectral phases
|
||||
rng : object
|
||||
Random Generator
|
||||
|
||||
Returns
|
||||
-------
|
||||
amps : numpy array
|
||||
Input amps (not modified)
|
||||
phases_pert : numpy array
|
||||
Shifted phases
|
||||
pert_vals : numpy array
|
||||
Absolute phase shifts
|
||||
"""
|
||||
if rng is None:
|
||||
rng = np.random.RandomState()
|
||||
noise_shape = list(phases.shape)
|
||||
noise_shape[1] = 1 # Do not sample noise for channels individually
|
||||
|
||||
# Sample phase perturbation noise
|
||||
phase_noise = rng.uniform(-np.pi,np.pi,noise_shape).astype(np.float32)
|
||||
phase_noise_rep = phase_noise.repeat(phases.shape[1],axis=1)
|
||||
# Apply noise to inputs
|
||||
phases_pert = phases+phase_noise_rep
|
||||
phases_pert[phases_pert<-np.pi] += 2*np.pi
|
||||
phases_pert[phases_pert>np.pi] -= 2*np.pi
|
||||
|
||||
pert_vals = np.abs(phase_noise)
|
||||
return amps,phases_pert,pert_vals
|
||||
|
||||
def amp_perturbation_additive(amps,phases,rng=None):
|
||||
"""Takes amplitudes and phases of BxCxF with B input, C channels, F frequencies
|
||||
Adds additive noise N(0,0.02) to amplitudes
|
||||
|
||||
Parameters
|
||||
----------
|
||||
amps : numpy array
|
||||
Spectral amplitude
|
||||
phases : numpy array
|
||||
Spectral phases (not used)
|
||||
rng : object
|
||||
Random Seed
|
||||
|
||||
Returns
|
||||
-------
|
||||
amps_pert : numpy array
|
||||
Scaled amplitudes
|
||||
phases_pert : numpy array
|
||||
Input phases (not modified)
|
||||
pert_vals : numpy array
|
||||
Amplitude noise
|
||||
"""
|
||||
if rng is None:
|
||||
rng = np.random.RandomState()
|
||||
amp_noise = rng.normal(0,1,amps.shape).astype(np.float32)
|
||||
amps_pert = amps+amp_noise
|
||||
amps_pert[amps_pert<0] = 0
|
||||
amp_noise = amps_pert-amps
|
||||
return amps_pert,phases,amp_noise
|
||||
|
||||
def amp_perturbation_multiplicative(amps,phases,rng=None):
|
||||
"""Takes amplitude and phases of BxCxF with B input, C channels, F frequencies
|
||||
Adds multiplicative noise N(1,0.02) to amplitudes
|
||||
|
||||
Parameters
|
||||
----------
|
||||
amps : numpy array
|
||||
Spectral amplitude
|
||||
phases : numpy array
|
||||
Spectral phases (not used)
|
||||
rng : object
|
||||
Random Seed
|
||||
|
||||
Returns
|
||||
-------
|
||||
amps_pert : numpy array
|
||||
Scaled amplitudes
|
||||
phases_pert : numpy array
|
||||
Input phases (not modified)
|
||||
pert_vals : numpy array
|
||||
Amplitude scaling factor
|
||||
"""
|
||||
if rng is None:
|
||||
rng = np.random.RandomState()
|
||||
amp_noise = rng.normal(1,0.02,amps.shape).astype(np.float32)
|
||||
amps_pert = amps*amp_noise
|
||||
amps_pert[amps_pert<0] = 0
|
||||
return amps_pert,phases,amp_noise
|
||||
|
||||
def correlate_feature_maps(x,y):
|
||||
"""Takes two activation matrices of the form Bx[F]xT where B is batch size, F number of filters (optional) and T time points
|
||||
Returns correlations of the corresponding activations over T
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : numpy array
|
||||
Activations Bx[F]xT
|
||||
y : numpy array
|
||||
Activations Bx[F]xT
|
||||
|
||||
Returns
|
||||
correlations : numpy array
|
||||
Correlations of `x` and `y` Bx[F]
|
||||
"""
|
||||
shape_x = x.shape
|
||||
shape_y = y.shape
|
||||
assert np.array_equal(shape_x,shape_y)
|
||||
assert len(shape_x)<4
|
||||
x = x.reshape((-1,shape_x[-1]))
|
||||
y = y.reshape((-1,shape_y[-1]))
|
||||
|
||||
x = (x-x.mean(axis=1,keepdims=True))/x.std(axis=1,keepdims=True)
|
||||
y = (y-y.mean(axis=1,keepdims=True))/y.std(axis=1,keepdims=True)
|
||||
|
||||
tmp_corr = x*y
|
||||
corr_ = tmp_corr.sum(axis=1)
|
||||
#corr_ = np.zeros((x.shape[0]))
|
||||
#for i in range(x.shape[0]):
|
||||
# # Correlation of standardized variables
|
||||
# corr_[i] = np.correlate((x[i]-x[i].mean())/x[i].std(),(y[i]-y[i].mean())/y[i].std())
|
||||
|
||||
correlations = corr_.reshape(*shape_x[:-1])
|
||||
return correlations
|
||||
|
||||
def mean_diff_feature_maps(x,y):
|
||||
"""Takes two activation matrices of the form BxFxT where B is batch size, F number of filters and T time points
|
||||
Returns mean difference between feature map activations
|
||||
|
||||
Parameters
|
||||
----------
|
||||
x : numpy array
|
||||
Activations Bx[F]xT
|
||||
y : numpy array
|
||||
Activations Bx[F]xT
|
||||
|
||||
Returns
|
||||
mean_diff : numpy array
|
||||
Mean difference between `x` and `y` Bx[F]
|
||||
"""
|
||||
mean_diff = np.mean(x-y,axis=2)
|
||||
return mean_diff
|
||||
|
||||
def spectral_perturbation_correlation(pert_fn, diff_fn, pred_fn, n_layers, inputs, n_iterations,
|
||||
batch_size=30,
|
||||
seed=((2017, 7, 10))):
|
||||
"""Calculates perturbation correlations for layers in network by perturbing either amplitudes or phases
|
||||
|
||||
Parameters
|
||||
----------
|
||||
pert_fn : function
|
||||
Function that perturbs spectral phase and amplitudes of inputs
|
||||
diff_fn : function
|
||||
Function that calculates difference between original and perturbed activations
|
||||
pred_fn : function
|
||||
Function that returns a list of activations.
|
||||
Each entry in the list corresponds to the output of 1 layer in a network
|
||||
n_layers : int
|
||||
Number of layers pred_fn returns activations for.
|
||||
inputs : numpy array
|
||||
Original inputs that are used for perturbation [B,X,T,1]
|
||||
Phase perturbations are sampled for each input individually, but applied to all X of that input
|
||||
n_iterations : int
|
||||
Number of iterations of correlation computation. The higher the better
|
||||
batch_size : int
|
||||
Number of inputs that are used for one forward pass. (Concatenated for all inputs)
|
||||
|
||||
Returns
|
||||
-------
|
||||
pert_corrs : numpy array
|
||||
List of length n_layers containing average perturbation correlations over iterations
|
||||
L x CxFrxFi (Channels,Frequencies,Filters)
|
||||
"""
|
||||
rng = np.random.RandomState(seed)
|
||||
|
||||
# Get batch indeces
|
||||
batch_inds = get_balanced_batches(
|
||||
n_trials=len(inputs), rng=rng, shuffle=False, batch_size=batch_size)
|
||||
# Calculate layer activations and reshape
|
||||
log.info("Compute original predictions...")
|
||||
orig_preds = [pred_fn(inputs[inds])
|
||||
for inds in batch_inds]
|
||||
use_shape = []
|
||||
for l in range(n_layers):
|
||||
tmp = list(orig_preds[0][l].shape)
|
||||
tmp.extend([1]*(4-len(tmp)))
|
||||
tmp[0] = len(inputs)
|
||||
use_shape.append(tmp)
|
||||
orig_preds_layers = [np.concatenate([orig_preds[o][l] for o in range(len(orig_preds))]).reshape(use_shape[l])
|
||||
for l in range(n_layers)]
|
||||
|
||||
# Compute FFT of inputs
|
||||
fft_input = np.fft.rfft(inputs, n=inputs.shape[2], axis=2)
|
||||
amps = np.abs(fft_input)
|
||||
phases = np.angle(fft_input)
|
||||
|
||||
pert_corrs = [0]*n_layers
|
||||
for i in range(n_iterations):
|
||||
log.info("Iteration {:d}...".format(i))
|
||||
log.info("Sample perturbation...")
|
||||
amps_pert,phases_pert,pert_vals = pert_fn(amps,phases,rng=rng)
|
||||
|
||||
# Compute perturbed inputs
|
||||
log.info("Compute perturbed complex inputs...")
|
||||
fft_pert = amps_pert*np.exp(1j*phases_pert)
|
||||
log.info("Compute perturbed real inputs...")
|
||||
inputs_pert = np.fft.irfft(fft_pert, n=inputs.shape[2], axis=2).astype(np.float32)
|
||||
|
||||
# Calculate layer activations for perturbed inputs
|
||||
log.info("Compute new predictions...")
|
||||
new_preds = [pred_fn(inputs_pert[inds])
|
||||
for inds in batch_inds]
|
||||
new_preds_layers = [np.concatenate([new_preds[o][l] for o in range(len(new_preds))]).reshape(use_shape[l])
|
||||
for l in range(n_layers)]
|
||||
|
||||
for l in range(n_layers):
|
||||
log.info("Layer {:d}...".format(l))
|
||||
# Calculate difference of original and perturbed feature map activations
|
||||
log.info("Compute activation difference...")
|
||||
preds_diff = diff_fn(new_preds_layers[l][:,:,:,0],orig_preds_layers[l][:,:,:,0])
|
||||
|
||||
# Calculate feature map differences with perturbations
|
||||
log.info("Compute correlation...")
|
||||
pert_corrs_tmp = wrap_reshape_apply_fn(corr,
|
||||
pert_vals[:,:,:,0],preds_diff,
|
||||
axis_a=(0,), axis_b=(0))
|
||||
pert_corrs[l] += pert_corrs_tmp
|
||||
|
||||
pert_corrs = [pert_corrs[l]/n_iterations for l in range(n_layers)] #mean over iterations
|
||||
return pert_corrs
|
||||
|
||||
|
||||
def compute_amplitude_prediction_correlations(pred_fn, examples, n_iterations,
|
||||
perturb_fn=amp_perturbation_additive,
|
||||
batch_size=30,
|
||||
seed=((2017, 7, 10))):
|
||||
"""
|
||||
Perturb input amplitudes and compute correlation between amplitude
|
||||
perturbations and prediction changes when pushing perturbed input through
|
||||
the prediction function.
|
||||
|
||||
For more details, see [EEGDeepLearning]_.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
pred_fn: function
|
||||
Function accepting an numpy input and returning prediction.
|
||||
examples: ndarray
|
||||
Numpy examples, first axis should be example axis.
|
||||
n_iterations: int
|
||||
Number of iterations to compute.
|
||||
perturb_fn: function, optional
|
||||
Function accepting amplitude array and random generator and returning
|
||||
perturbation. Default is Gaussian perturbation.
|
||||
batch_size: int, optional
|
||||
Batch size for computing predictions.
|
||||
seed: int, optional
|
||||
Random generator seed
|
||||
|
||||
Returns
|
||||
-------
|
||||
amplitude_pred_corrs: ndarray
|
||||
Correlations between amplitude perturbations and prediction changes
|
||||
for all sensors and frequency bins.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [EEGDeepLearning] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
"""
|
||||
pred_fn_new = lambda x: [pred_fn(x)]
|
||||
pred_corrs = spectral_perturbation_correlation(perturb_fn, mean_diff_feature_maps,
|
||||
pred_fn_new, 1, examples, n_iterations,
|
||||
batch_size=batch_size, seed=seed)
|
||||
|
||||
return pred_corrs[0]
|
||||
@@ -0,0 +1,172 @@
|
||||
# coding=utf-8
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
from scipy import interpolate
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib import patches
|
||||
from matplotlib.path import Path
|
||||
from matplotlib import cm
|
||||
|
||||
from braindecode.datasets.sensor_positions import CHANNEL_10_20_APPROX, \
|
||||
get_channelpos
|
||||
|
||||
|
||||
def ax_scalp(v, channels,
|
||||
ax=None, annotate=False,
|
||||
vmin=None, vmax=None, cmap=cm.coolwarm,
|
||||
scalp_line_width=1,
|
||||
scalp_line_style='solid',
|
||||
chan_pos_list=CHANNEL_10_20_APPROX,
|
||||
interpolation='bilinear',
|
||||
fontsize=8):
|
||||
"""Draw a scalp plot.
|
||||
|
||||
Draws a scalp plot on an existing axes. The method takes an array of
|
||||
values and an array of the corresponding channel names. It matches
|
||||
the channel names with an channel position list
|
||||
to project them correctly on the scalp.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
v : 1d-array of floats
|
||||
The values for the channels
|
||||
channels : 1d array of strings
|
||||
The corresponding channel names for the values in ``v``
|
||||
ax : Axes, optional
|
||||
The axes to draw the scalp plot on. If not provided, the
|
||||
currently activated axes (i.e. ``gca()``) will be taken
|
||||
annotate : Boolean, optional
|
||||
Draw the channel names next to the channel markers.
|
||||
vmin, vmax : float, optional
|
||||
The display limits for the values in ``v``. If the data in ``v``
|
||||
contains values between -3..3 and ``vmin`` and ``vmax`` are set
|
||||
to -1 and 1, all values smaller than -1 and bigger than 1 will
|
||||
appear the same as -1 and 1. If not set, the maximum absolute
|
||||
value in ``v`` is taken to calculate both values.
|
||||
cmap : matplotlib.colors.colormap, optional
|
||||
A colormap to define the color transitions.
|
||||
scalp_line_width: float
|
||||
Line width for outline of scalp
|
||||
scalp_line_style: float
|
||||
Line style for outline of scalp
|
||||
chan_pos_list: iterable of tuples
|
||||
First entry should be 'angle' or 'cartesian',
|
||||
remaining entries 2-tuples of x and y.
|
||||
interpolation: str
|
||||
|
||||
Returns
|
||||
-------
|
||||
ax : Axes
|
||||
the axes on which the plot was drawn
|
||||
|
||||
Notes
|
||||
-----
|
||||
Code adapted from Wyrm [1]_ toolbox https://github.com/bbci/wyrm.
|
||||
|
||||
References
|
||||
----------
|
||||
|
||||
.. [1] Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D. J.,
|
||||
Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F. & Ball, T. (2017).
|
||||
Deep learning with convolutional neural networks for EEG decoding and
|
||||
visualization.
|
||||
Human Brain Mapping , Aug. 2017. Online: http://dx.doi.org/10.1002/hbm.23730
|
||||
"""
|
||||
if ax is None:
|
||||
ax = plt.gca()
|
||||
assert len(v) == len(channels), "Should be as many values as channels"
|
||||
assert interpolation == 'bilinear' or interpolation == 'nearest'
|
||||
if vmin is None:
|
||||
# added by me (robintibor@gmail.com)
|
||||
assert vmax is None
|
||||
vmin, vmax = -np.max(np.abs(v)), np.max(np.abs(v))
|
||||
# what if we have an unknown channel?
|
||||
points = [get_channelpos(c, chan_pos_list) for c in channels]
|
||||
for c in channels:
|
||||
assert get_channelpos(c, chan_pos_list) is not None, (
|
||||
"Expect " + c + " to exist in positions")
|
||||
z = [v[i] for i in range(len(points))]
|
||||
# calculate the interpolation
|
||||
x = [i[0] for i in points]
|
||||
y = [i[1] for i in points]
|
||||
# interpolate the in-between values
|
||||
xx = np.linspace(min(x), max(x), 500)
|
||||
yy = np.linspace(min(y), max(y), 500)
|
||||
if interpolation == 'bilinear':
|
||||
xx_grid, yy_grid = np.meshgrid(xx, yy)
|
||||
f = interpolate.LinearNDInterpolator(list(zip(x, y)), z)
|
||||
zz = f(xx_grid, yy_grid)
|
||||
else:
|
||||
assert interpolation == 'nearest'
|
||||
f = interpolate.NearestNDInterpolator(list(zip(x, y)), z)
|
||||
assert len(xx) == len(yy)
|
||||
zz = np.ones((len(xx), len(yy)))
|
||||
for i_x in xrange(len(xx)):
|
||||
for i_y in xrange(len(yy)):
|
||||
# somehow this is correct. don't know why :(
|
||||
zz[i_y, i_x] = f(xx[i_x], yy[i_y])
|
||||
# zz[i_x,i_y] = f(xx[i_x], yy[i_y])
|
||||
assert not np.any(np.isnan(zz))
|
||||
|
||||
# plot map
|
||||
image = ax.imshow(zz, vmin=vmin, vmax=vmax, cmap=cmap,
|
||||
extent=[min(x), max(x), min(y), max(y)], origin='lower',
|
||||
interpolation=interpolation)
|
||||
if scalp_line_width > 0:
|
||||
# paint the head
|
||||
ax.add_artist(plt.Circle((0, 0), 1, linestyle=scalp_line_style,
|
||||
linewidth=scalp_line_width, fill=False))
|
||||
# add a nose
|
||||
ax.plot([-0.1, 0, 0.1], [1, 1.1, 1], color='black',
|
||||
linewidth=scalp_line_width, linestyle=scalp_line_style)
|
||||
# add ears
|
||||
_add_ears(ax, scalp_line_width, scalp_line_style)
|
||||
# add markers at channels positions
|
||||
# set the axes limits, so the figure is centered on the scalp
|
||||
ax.set_ylim([-1.05, 1.15])
|
||||
ax.set_xlim([-1.15, 1.15])
|
||||
|
||||
# hide the frame and ticks
|
||||
ax.set_frame_on(False)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
# draw the channel names
|
||||
if annotate:
|
||||
for i in zip(channels, list(zip(x, y))):
|
||||
ax.annotate(" " + i[0], i[1], horizontalalignment="center",
|
||||
verticalalignment='center', fontsize=fontsize)
|
||||
ax.set_aspect(1)
|
||||
return image
|
||||
|
||||
|
||||
def _add_ears(ax, linewidth, linestyle):
|
||||
start_x = np.cos(10 * np.pi / 180.0)
|
||||
start_y = np.sin(10 * np.pi / 180.0)
|
||||
end_x = np.cos(-15 * np.pi / 180.0)
|
||||
end_y = np.sin(-15 * np.pi / 180.0)
|
||||
verts = [
|
||||
(start_x, start_y),
|
||||
(start_x + 0.05, start_y + 0.05), # out up
|
||||
(start_x + 0.1, start_y), # further out, back down
|
||||
(start_x + 0.11, (end_y * 0.7 + start_y * 0.3)), # midpoint
|
||||
(end_x + 0.14, end_y), # down out start
|
||||
(end_x + 0.05, end_y - 0.05), # down out further
|
||||
(end_x, end_y), # endpoint
|
||||
]
|
||||
|
||||
codes = [Path.MOVETO] + [Path.CURVE3] * (len(verts) - 1)
|
||||
|
||||
path = Path(verts, codes)
|
||||
|
||||
patch = patches.PathPatch(path, facecolor='none',
|
||||
linestyle=linestyle, linewidth=linewidth)
|
||||
|
||||
ax.add_patch(patch)
|
||||
verts_left = [(-x, y) for x, y in verts]
|
||||
path_left = Path(verts_left, codes)
|
||||
|
||||
patch_left = patches.PathPatch(path_left, facecolor='none',
|
||||
linestyle=linestyle, linewidth=linewidth)
|
||||
|
||||
ax.add_patch(patch_left)
|
||||
@@ -0,0 +1,89 @@
|
||||
from scipy.optimize import leastsq
|
||||
import numpy as np
|
||||
|
||||
def err_fn_sin(p,x,y):
|
||||
return (y-fit_fn_sin(x,*p)).flat
|
||||
|
||||
def err_fn_lin(p,x,y):
|
||||
return (y-fit_fn_lin(x,*p)).flat
|
||||
|
||||
def fit_fn_lin(x,*kwargs):
|
||||
return kwargs[0]+kwargs[1]*x
|
||||
|
||||
def fit_fn_sin(x,*kwargs):
|
||||
freqs = kwargs[0]
|
||||
amps = kwargs[1]
|
||||
phases = kwargs[2]
|
||||
offset = kwargs[3]
|
||||
sig = np.zeros((len(x)))+offset
|
||||
sig += amps*np.cos(x*freqs+phases)
|
||||
return sig
|
||||
|
||||
def signal_fit(signals,fs):
|
||||
"""Fits sinusoid and linear function to signals
|
||||
see sinfit.fit_fn_sin and sinfit.fit_fn_lin
|
||||
|
||||
Parameters
|
||||
----------
|
||||
signals : numpy array
|
||||
[FxCxTx1] Filters x Channels x Time x 1
|
||||
fs : float
|
||||
Sampling frequency
|
||||
|
||||
Returns
|
||||
-------
|
||||
params_sin : numpy array
|
||||
[FxCx4] Parameters of sinusoid fit
|
||||
Parameters are: Frequency,Amplitude,Phase,DCOffset
|
||||
params_lin : numpy array
|
||||
[FxCx2] Parameters of sinusoid fit
|
||||
Parameters are: Frequency,Amplitude,Phase,DCOffset
|
||||
err_sin : numpy array
|
||||
[FxCx1] MSE for sinusoid fit
|
||||
err_lin : numpy array
|
||||
[FxCx1] MSE for linear fit
|
||||
"""
|
||||
params_sin = []
|
||||
params_lin = []
|
||||
|
||||
err_sin = []
|
||||
err_lin = []
|
||||
|
||||
freqs = np.fft.rfftfreq(signals.shape[2], d=1.0/fs)[1:]
|
||||
x = np.linspace(0,signals.shape[2]/fs,signals.shape[2])*2*np.pi
|
||||
for filt in range(signals.shape[0]):
|
||||
params_sin_tmp = []
|
||||
params_lin_tmp = []
|
||||
|
||||
err_sin_tmp = []
|
||||
err_lin_tmp = []
|
||||
for ch in range(signals.shape[1]):
|
||||
X_tmp = signals[filt,ch].squeeze()
|
||||
fft_X = np.fft.rfft(X_tmp,axis=0)
|
||||
amps_mean = np.abs(fft_X)[1:]
|
||||
phases_mean = np.angle(fft_X)[1:]
|
||||
offset = X_tmp.mean()
|
||||
|
||||
sort = np.argsort(amps_mean)[::-1][0]
|
||||
p0 = [freqs[sort],amps_mean[sort],phases_mean[sort],offset]
|
||||
|
||||
fit_sin_ch = leastsq(err_fn_sin, p0, args=(x, X_tmp),maxfev=100000)
|
||||
fit_lin_ch = leastsq(err_fn_lin, [0,0], args=(x, X_tmp),maxfev=100000)
|
||||
|
||||
err_sin_ch = np.square(fit_fn_sin(x,*fit_sin_ch[0]) - X_tmp).mean()
|
||||
err_lin_ch = np.square(fit_fn_lin(x,*fit_lin_ch[0]) - X_tmp).mean()
|
||||
|
||||
params_sin_tmp.append(fit_sin_ch[0])
|
||||
params_lin_tmp.append(fit_lin_ch[0])
|
||||
err_sin_tmp.append(err_sin_ch)
|
||||
err_lin_tmp.append(err_lin_ch)
|
||||
params_sin.append(params_sin_tmp)
|
||||
params_lin.append(params_lin_tmp)
|
||||
err_sin.append(err_sin_tmp)
|
||||
err_lin.append(err_lin_tmp)
|
||||
params_sin = np.asarray(params_sin)
|
||||
params_lin = np.asarray(params_lin)
|
||||
err_sin = np.asarray(err_sin)
|
||||
err_lin = np.asarray(err_lin)
|
||||
|
||||
return params_sin,params_lin,err_sin,err_lin
|
||||
@@ -0,0 +1,33 @@
|
||||
# Minimal makefile for Sphinx documentation
|
||||
#
|
||||
|
||||
# You can set these variables from the command line.
|
||||
SPHINXOPTS =
|
||||
SPHINXBUILD = python3.5 -msphinx
|
||||
SPHINXPROJ = Braindecode
|
||||
SOURCEDIR = .
|
||||
BUILDDIR = ../../braindecode-docs/
|
||||
|
||||
# Put it first so that "make" without argument is like "make help".
|
||||
help:
|
||||
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
.PHONY: help Makefile
|
||||
|
||||
apidoc:
|
||||
sphinx-apidoc -M -o source ../braindecode/
|
||||
|
||||
# Catch-all target: route all unknown targets to Sphinx using the new
|
||||
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
|
||||
%: Makefile
|
||||
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
|
||||
|
||||
|
||||
removeipynbcheckpoints: Makefile
|
||||
rm -rf notebooks/.ipynb_checkpoints/ notebooks/visualization/.ipynb_checkpoints/
|
||||
|
||||
removesource: Makefile
|
||||
rm -rf source/
|
||||
|
||||
rmanddoc: removesource removeipynbcheckpoints apidoc html
|
||||
echo "Done"
|
||||
+215
@@ -0,0 +1,215 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
#
|
||||
# Braindecode documentation build configuration file, created by
|
||||
# sphinx-quickstart on Sat Jul 1 01:51:38 2017.
|
||||
#
|
||||
# This file is execfile()d with the current directory set to its
|
||||
# containing dir.
|
||||
#
|
||||
# Note that not all possible configuration values are present in this
|
||||
# autogenerated file.
|
||||
#
|
||||
# All configuration values have a default; values that are commented out
|
||||
# serve to show the default.
|
||||
|
||||
# If extensions (or modules to document with autodoc) are in another directory,
|
||||
# add these directories to sys.path here. If the directory is relative to the
|
||||
# documentation root, use os.path.abspath to make it absolute, like shown here.
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
sys.path.insert(0, os.path.abspath('.'))
|
||||
sys.path.insert(0, os.path.abspath('..'))
|
||||
import matplotlib
|
||||
matplotlib.use('agg')
|
||||
|
||||
|
||||
|
||||
# -- General configuration ------------------------------------------------
|
||||
|
||||
# If your documentation needs a minimal Sphinx version, state it here.
|
||||
#
|
||||
# needs_sphinx = '1.0'
|
||||
|
||||
|
||||
|
||||
# Add any Sphinx extension module names here, as strings. They can be
|
||||
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
|
||||
# ones.
|
||||
extensions = [
|
||||
'sphinx.ext.autodoc',
|
||||
'sphinx.ext.doctest',
|
||||
'sphinx.ext.todo',
|
||||
'sphinx.ext.coverage',
|
||||
'sphinx.ext.mathjax',
|
||||
'sphinx.ext.ifconfig',
|
||||
#'sphinx.ext.viewcode',
|
||||
'sphinx.ext.githubpages',
|
||||
'sphinx.ext.napoleon',
|
||||
'nbsphinx',
|
||||
'sphinx.ext.autosummary', #https://stackoverflow.com/a/21665947/1469195
|
||||
'sphinx.ext.linkcode', #https://github.com/Lasagne/Lasagne/blob/a497f4b3f434911df989d03f1647e5f15366ebd0/docs/conf.py#L37
|
||||
]
|
||||
|
||||
# Resolve function for the linkcode extension.
|
||||
# See https://github.com/Lasagne/Lasagne/blob/a497f4b3f434911df989d03f1647e5f15366ebd0/docs/conf.py#L37
|
||||
def linkcode_resolve(domain, info):
|
||||
def find_source():
|
||||
import braindecode
|
||||
# try to find the file and line number, based on code from numpy:
|
||||
# https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286
|
||||
obj = sys.modules[info['module']]
|
||||
for part in info['fullname'].split('.'):
|
||||
obj = getattr(obj, part)
|
||||
import inspect
|
||||
import os
|
||||
fn = inspect.getsourcefile(obj)
|
||||
fn = os.path.relpath(fn, start=os.path.dirname(braindecode.__file__))
|
||||
source, lineno = inspect.getsourcelines(obj)
|
||||
return fn, lineno, lineno + len(source) - 1
|
||||
|
||||
if domain != 'py' or not info['module']:
|
||||
return None
|
||||
try:
|
||||
filename = 'braindecode/%s#L%d-L%d' % find_source()
|
||||
except Exception:
|
||||
filename = info['module'].replace('.', '/') + '.py'
|
||||
return "https://github.com/robintibor/braindecode/blob/master/%s" % filename
|
||||
|
||||
autosummary_generate = True #https://stackoverflow.com/a/21665947/1469195
|
||||
autodoc_member_order = 'bysource'
|
||||
## Default flags used by autodoc directives
|
||||
autodoc_default_flags = ['members', 'show-inheritance']
|
||||
|
||||
exclude_patterns = ['_build', '_templates']
|
||||
|
||||
|
||||
napoleon_google_docstring = False
|
||||
napoleon_use_param = False
|
||||
napoleon_use_ivar = True
|
||||
|
||||
# Add any paths that contain templates here, relative to this directory.
|
||||
templates_path = ['_templates']
|
||||
|
||||
# The suffix(es) of source filenames.
|
||||
# You can specify multiple suffix as a list of string:
|
||||
#
|
||||
# source_suffix = ['.rst', '.md']
|
||||
source_suffix = '.rst'
|
||||
|
||||
# The master toctree document.
|
||||
master_doc = 'index'
|
||||
|
||||
# General information about the project.
|
||||
project = 'Braindecode'
|
||||
copyright = '2018, Robin Tibor Schirrmeister'
|
||||
author = 'Robin Tibor Schirrmeister'
|
||||
|
||||
# The version info for the project you're documenting, acts as replacement for
|
||||
# |version| and |release|, also used in various other places throughout the
|
||||
# built documents.
|
||||
#
|
||||
# The short X.Y version.
|
||||
import braindecode
|
||||
version = braindecode.__version__
|
||||
# The full version, including alpha/beta/rc tags.
|
||||
release = version
|
||||
|
||||
# The language for content autogenerated by Sphinx. Refer to documentation
|
||||
# for a list of supported languages.
|
||||
#
|
||||
# This is also used if you do content translation via gettext catalogs.
|
||||
# Usually you set "language" from the command line for these cases.
|
||||
language = None
|
||||
|
||||
# List of patterns, relative to source directory, that match files and
|
||||
# directories to ignore when looking for source files.
|
||||
# This patterns also effect to html_static_path and html_extra_path
|
||||
exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
|
||||
|
||||
# The name of the Pygments (syntax highlighting) style to use.
|
||||
pygments_style = 'sphinx'
|
||||
|
||||
# If true, `todo` and `todoList` produce output, else they produce nothing.
|
||||
todo_include_todos = True
|
||||
|
||||
|
||||
# -- Options for HTML output ----------------------------------------------
|
||||
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
#
|
||||
#html_theme = 'alabaster'
|
||||
|
||||
import sphinx_rtd_theme
|
||||
html_theme = "sphinx_rtd_theme"
|
||||
html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
|
||||
|
||||
# Theme options are theme-specific and customize the look and feel of a theme
|
||||
# further. For a list of options available for each theme, see the
|
||||
# documentation.
|
||||
#
|
||||
# html_theme_options = {}
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
# so a file named "default.css" will overwrite the builtin "default.css".
|
||||
html_static_path = ['_static']
|
||||
|
||||
|
||||
# -- Options for HTMLHelp output ------------------------------------------
|
||||
|
||||
# Output file base name for HTML help builder.
|
||||
htmlhelp_basename = 'Braindecodedoc'
|
||||
|
||||
|
||||
# -- Options for LaTeX output ---------------------------------------------
|
||||
|
||||
latex_elements = {
|
||||
# The paper size ('letterpaper' or 'a4paper').
|
||||
#
|
||||
# 'papersize': 'letterpaper',
|
||||
|
||||
# The font size ('10pt', '11pt' or '12pt').
|
||||
#
|
||||
# 'pointsize': '10pt',
|
||||
|
||||
# Additional stuff for the LaTeX preamble.
|
||||
#
|
||||
# 'preamble': '',
|
||||
|
||||
# Latex figure (float) alignment
|
||||
#
|
||||
# 'figure_align': 'htbp',
|
||||
}
|
||||
|
||||
# Grouping the document tree into LaTeX files. List of tuples
|
||||
# (source start file, target name, title,
|
||||
# author, documentclass [howto, manual, or own class]).
|
||||
latex_documents = [
|
||||
(master_doc, 'Braindecode.tex', 'Braindecode Documentation',
|
||||
'Robin Tibor Schirrmeister', 'manual'),
|
||||
]
|
||||
|
||||
|
||||
# -- Options for manual page output ---------------------------------------
|
||||
|
||||
# One entry per manual page. List of tuples
|
||||
# (source start file, name, description, authors, manual section).
|
||||
man_pages = [
|
||||
(master_doc, 'braindecode', 'Braindecode Documentation',
|
||||
[author], 1)
|
||||
]
|
||||
|
||||
|
||||
# -- Options for Texinfo output -------------------------------------------
|
||||
|
||||
# Grouping the document tree into Texinfo files. List of tuples
|
||||
# (source start file, target name, title, author,
|
||||
# dir menu entry, description, category)
|
||||
texinfo_documents = [
|
||||
(master_doc, 'Braindecode', 'Braindecode Documentation',
|
||||
author, 'Braindecode', 'One line description of project.',
|
||||
'Miscellaneous'),
|
||||
]
|
||||
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