Full Code of alexandrebarachant/bci-challenge-ner-2015 for AI

Repository: alexandrebarachant/bci-challenge-ner-2015
Branch: master
Commit: d185067fc0ea
Files: 14
Total size: 426.8 KB

Directory structure:
gitextract_0n05c5vc/

├── .gitignore
├── Licence.txt
├── README.md
├── classif.py
├── cross_valid.py
├── parameters_Leak.yaml
├── parameters_noLeak.yaml
├── prediction.py
├── preproc/
│   ├── metadata.csv
│   └── preproc.py
├── results/
│   ├── submission_Leak.csv
│   └── submission_noLeak.csv
└── riemann/
    ├── __init__.py
    └── utils.py

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FILE: Licence.txt
================================================
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    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.

Also add information on how to contact you by electronic and paper mail.

  If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:

    <program>  Copyright (C) <year>  <name of author>
    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
    This is free software, and you are welcome to redistribute it
    under certain conditions; type `show c' for details.

The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License.  Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".

  You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<http://www.gnu.org/licenses/>.

  The GNU General Public License does not permit incorporating your program
into proprietary programs.  If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library.  If this is what you want to do, use the GNU Lesser General
Public License instead of this License.  But first, please read
<http://www.gnu.org/philosophy/why-not-lgpl.html>.


================================================
FILE: README.md
================================================
# BCI Challenge @ NER 2015

Code and documentation for the winning solution at the BCI Challenge @ NER 2015 : https://www.kaggle.com/c/inria-bci-challenge

**Authors**:
* [Alexandre Barachant](http://alexandre.barachant.org) 
* Rafal Cycon
* Cédric Gouy-Pailler

**Contents** :

- [Signal Processing & Classification Pipeline ](#signal-processing-classification-pipeline)
    - [Introduction](#introduction)
    - [Classification Pipeline](#classification-pipeline)
    - [Results](#results)
    - [Discussion](#Discussion)
    - [References ](#references)
- [Code](#code)
- [Parameter file](#parameter-file)


**Licence** : GLPv3. see Licence.txt

# Signal Processing & Classification Pipeline 

## Introduction

The goal of this challenge was to detect error related potential recorded during a p300 spelling task. The classification must be done across subjects,
i.e. training and test sets were composed by different subjects. This is a hard task due to the high inter-subjects variability of EEG. 
However, our Riemannian Geometry framework has been proven very powerful for dealing with this problem (see [2], [3], and the [decMeg2014 kaggle challenge](http://www.kaggle.com/c/decoding-the-human-brain)).
This is partly due to the property of invariance by congruent transformation of the Riemannian metric.

Starting from here, the main difficulties of this challenge were to deal with the relatively high number of electrodes and to avoid overfitting.
We overcome the first issue by using a channel selection algorithm, and the second by using a bagging procedure and an appropriate cross-validation methodology.

**The leak** : By analyzing the time between feedback events in session 5, it was possible to catch the output of the online detection Error. 
The idea is that when an error is detected, the correction of this error induces a small delay that can be detected with more or less reliability.
Given that the online Error detection was properly calibrated for the current subject, this analysis gave us two insights :

1. the output of the online error detection for session 5. Of course, there is a higher probability of error when the online classifier detects an error, but it depends on the accuracy of the online classifier.
This can be used to increase detection accuracy, but only for the session 5.

2. the percentage of error in session 5 i.e. a rough estimate of the class balance for each subjects. This second information is really important, because it can help to optimize the global AUC criterion, by adjusting the prediction to match the error probability of each subjects.
Of course, this only helps the global AUC performance, and has little effect on the subject performance.

We propose two different models. The first one does not make any use of the
leakage information and satisfies an "online processing" constraint, which means
that any trial performed by a subject can be classified without the need for
future complementary data or information. The second model uses the leak, thus
it is not online-compatible.
The two models are built upon the same classification pipeline, but with parameters tuned independently to achieve the highest performance.

## Classification Pipeline

#### Preprocessing
EEG Signals are bandpass filtered by a 5th order Butterworth filter between 1 and 40 Hz. 
Then, signals are epoched to take only 1.3 second after the feedback event. The EOG channel is removed prior to any preprocessing.

#### Feature Extraction

The EEG-based Feature extraction is done in 4 steps :

1. *Xdawn Covariances* : Two sets of 5 XDAWN [1] spatial filters are estimated, one for each class (Error and Correct).
The grand average evoked potential of each class is then filtered by the corresponding set of spatial filters, and concatenated to each epochs.
The covariance matrix of each resulting epoch is then used as feature for the next steps. This process is similar to the one described in [2] and [3].
2. *Electrode Selection* : A channel selection is applied to keep only relevant channels. The procedure consist in a backward elimination with the Riemannian distance between the 
Riemannian Geometric mean of the covariances of each class as the criterion. The algorithm is described in [4].
3. *Tangent Space* : Reduced Covariances matrices are then projected in the tangent space, as described in [5] and [6].
4. *Normalization* : Feature Normalization using a l1 norm.

After these 4 steps, a set of Meta Feature are added to the EEG-based features.
The basic set of Meta feature is the following :

* *Session id*  : the session number of the current epoch. 
* *FeedBack*    : the Feedback count since the beginning of the session. 
* *Letter*      : the Letter position in the current word. 
* *Word*        : the word count since the beginning of the session. 
* *FeedBackTot* : the feedback count since the first session.
* *WordTot*     : the word count since the first session.
* *isLong*      : was the current word flashed 8 times, i.e. a long sequence of flashes.

**For the leak only**, two other features are added :
* *OnlineErr*   : 1 if the online Error detection has detected an error (only available in session 5).
* *Err Prop*    : the percentage of errors detected in the session 5.

#### Classification 

All the above features are inputted to an Elastic Net algorithm.

**For the leak only**, *Err Prop* is added to the prediction with a coefficient of 0.8. 
As an effect, it biases the results with an estimation of the classes balances. This has no effect on the AUC of a single subject, 
but acts as on optimization for the Global AUC, which was the target metric.

### Bagging

The above pipeline is applied on a number of random subsets of subjects, and predictions are averaged across bagged models. 
Each bagged model is built on an unique set of features (high diversity of features between subsets of subjects is achieved mostly due to the Electrode Selection step in feature extraction) and thus represents a different point of view of the training data.
Ensembling bagged models improves generalization and effectively counters overfitting.

Final submission was an average of predictions of 500 bagged models, each trained on 9 subjects (out of 16, just one more subject above 50%). In 4-fold CV 150 bagged models were used.

### Cross-validation

The validation approach was a 4-fold CV with data randomly split into folds subject-wise. The splitting was repeated several times (10 in most cases) and obtained AUCs averaged. The method proved to produce a reliable measure of performance.

## Results

#### AUC 

|         Submission         | Public LB | Private LB | Fold-wise CV (std) | Subject-wise CV (std) |
|----------------------------|-----------|------------|--------------------|-----------------------|
| No Leak (online compatible)| 0.85083   | 0.84585    | 0.7294  (0.037)    | 0.7696 (0.004)        |
| Leak                       | 0.85145   | 0.87224    | 0.8008  (0.039)    | 0.7801 (0.004)        |


CV results are given for 150 models, using the CV procedure described above. Fold-CV refers to the average AUC evaluated on all the predictions of the tests subjects. 
Subject-wise CV refers to the average AUC evaluated for each subject independently. Subject-wise CV represents performance of the model in a real case scenario, i.e. the performance a single user could expect in a online experiment. 

Results across Public and Private LB are quite stable and exhibit very good performance of both models.

In cross validation, the introduction of the leak has a little effect (0.01 AUC) on subject specific performance, but has a tremendous effect on Fold CV.
Shifting the predictions according to the leak effectively improves the Fold AUC.

Interestingly, this effect is not as strong for LB results. Indeed, the no-leak model is able to correctly catch the class balance of each subject and bias the prediction accordingly.
This effect was not observed in CV results due to a smaller test set size.

#### Effect of the number of electrodes

A very important parameter was the number of electrodes in the electrode selection step ('nelec') of feature extraction.
Riemannian geometry works best with a moderately low number of electrodes. The number of features in the tangent space is N(N+1)/2 with N the number of channels.
A too high number of electrodes can lead to overfitting and estimation issues, while a too small number of channels leads to a loss of information. 

Varying the number of electrodes gives the following results (for the no-leak model) :

![elec numbers](https://raw.githubusercontent.com/alexandrebarachant/bci-challenge-ner-2015/master/results/ElecNumbers.png)

The electrode selection already achieves very high performance for a number of electrodes as low as 15.
Interestingly, using all electrodes gives the best AUC for private LB, but an optimal choice of number of electrodes may be different when using more bagged models.

The introduction of bagged model stabilizes results by making the prediction less sensitive to the electrode subset. 
This effect is bigger when the number of electrodes in the subset decreases. 

The final no-leak model was set to 35 electrodes, and only 23 for the leak model.
These values were chosen according mainly to CV results, but also partially to public LB scores.

#### Effect of the number of bagged models

For the final submission we used 500 bagged models, each composed of 9 randomly selected subjects. The number of models for the final submissions was chosen based on the fact that adding more models could improve generalization 
(up to some 'plateau' point, which was unknown at the time), but too many models could not hurt the performance. 
It was the safest choice while keeping the processing time reasonable. 

Post-competition tests indicated that as few as 10 models were already enough to provide good performance in private LB (tests made with the no-leak model):

![bag numbers](https://raw.githubusercontent.com/alexandrebarachant/bci-challenge-ner-2015/master/results/BagsNumbers.png)

## Discussion

The proposed method shows very good performance. For the specific combination of subjects in the private LB, electrode selection and ensembling did not improve significantly results.
However, they did help to prevent overfitting and increase significantly performances in cross-validation. 

The introduction of Meta features improve the results by 0.015 AUC on private LB. While some of them seem odd, they are acting as a support for the classifier to take into account
the possible shift in error prediction due to mental fatigue of the subject and other source of variability that occurs in long EEG sessions.

For this challenge, the evaluation criterion was the AUC calculated from the predictions of all the subjects put together. 
However, the fact that the subjects have different classes balances allow to optimize the score without making any prediction.
As a matter of fact, submitting the estimation of class balance of each subject (thanks to the leak) results in a Private LB score of 0.746, enough to be ranked in the top10.

## References 

> [1] Rivet, B.; Souloumiac, A.; Attina, V.; Gibert, G., "xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface," IEEE Transactions on Biomedical Engineering, vol.56, no.8, pp.2035,2043, Aug. 2009
>
> [2] A. Barachant, M. Congedo ,"A Plug&Play P300 BCI Using Information Geometry", arXiv:1409.0107. [link](http://arxiv.org/abs/1409.0107)
>
> [3] M. Congedo, A. Barachant, A. Andreev ,"A New generation of Brain-Computer Interface Based on Riemannian Geometry", arXiv: 1310.8115. [link](http://arxiv.org/abs/1310.8115)
>
> [4] A. Barachant and S. Bonnet, "Channel selection procedure using riemannian distance for BCI applications," in 2011 5th International IEEE/EMBS Conference on Neural Engineering (NER), 2011, 348-351. [pdf](http://hal.archives-ouvertes.fr/docs/00/60/27/07/PDF/NER11_0016_FI.pdf)
>
> [5] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, “Multiclass Brain-Computer Interface Classification by Riemannian Geometry,” in IEEE Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012. [pdf](http://hal.archives-ouvertes.fr/docs/00/68/13/28/PDF/Barachant_tbme_final.pdf)
>
> [6] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, “Classification of covariance matrices using a Riemannian-based kernel for BCI applications“, in NeuroComputing, vol. 112, p. 172-178, 2013. [pdf](http://hal.archives-ouvertes.fr/docs/00/82/04/75/PDF/BARACHANT_Neurocomputing_ForHal.pdf)

# Code

### Dependencies & Requirements

The code is written in python. At least 8GB of RAM is required.
The following packages are used :

* sklearn
* numpy
* scipy
* pylab
* pandas
* pyyaml
* glob
* multiprocessing

### Usage

#### Preprocessing
Extract train.zip and test.zip in the data folder. Put file TrainLabels.csv in the folder data/train.

Apply preprocessing on the data (one time only) :

```bash
cd preproc/
python preproc.py
```

#### Generating Submissions

run prediction.py with the desired parameter file from the main folder. 

For the submission with the leak :
```bash
python prediction.py parameters_Leak.yaml
```

For the submission without the leak :
```bash
python prediction.py parameters_noLeak.yaml
```

:warning: Generating a submission takes around 70 minutes on a 12-core, 64Gb RAM computer.
You may adjust the 'core' parameters in the yaml files to fit your configuration. 
Default is 4 cores.

#### Cross validation

run cross_valid.py with the desired parameter file from the main folder. 

For the submission with the leak :
```bash
python cross_valid.py parameters_Leak.yaml
```

For the submission without the leak :
```bash
python cross_valid.py parameters_noLeak.yaml
```

CV takes around 12h to run on a 12-core, 64GB RAM computer

### Troubleshooting

If you get Memory Error, decrease the number of cores in the parameters file.
By default 4 cores are used, which should work fine on a computer with 16GB RAM. 

# Parameter file

For this challenge, we built a framework based on the sklearn Pipeline system allowing us to quickly test different ideas and parameters without changing a line of code.
The classification pipeline is described in a parameter file, using a yaml syntax, and then parsed and built on the fly by the scripts.
Parameters files can be shared between team-mates, and Version Controlled, reducing the chance of errors. 

The parameter file for the no-leak submission looks like this :

```yaml
imports:
  sklearn.linear_model :
    - ElasticNet
  sklearn.preprocessing:
    - Normalizer
  classif:
    - XdawnCovariances
    - TangentSpace
    - AddMeta
    - ElectrodeSelect
  
CrossVal:
  cores: 4
  folds: 4
  repetitions: 10
  path: results.csv
  comments: 'model-final-noLeak'

Submission:
  path: submission-noLeak.csv
  cores: 4

MetaPipeline:
  #leak:
  bagging:
     bag_size: 0.51 
     models: 500 

pipeline:
  - XdawnCovariances:
      nfilter: 5
      subelec: range(0,56,1)
  - ElectrodeSelect:
      nfilters: 5
      nelec: 35
      metric: "'riemann'"
  - TangentSpace:
      metric: "'logeuclid'"
      tsupdate: False
  - Normalizer:
      norm: '"l1"'
  - AddMeta:
  - ElasticNet:
      l1_ratio: 0.5
      alpha: 2e-4
      normalize: True
```

#### Imports
The import section lists packages to import :
The following section  :

```yaml
sklearn.linear_model :
  - ElasticNet
```

is evaluated as :

```python
from sklearn.linear_model import ElasticNet
```

#### Pipeline

The different steps in the classification pipeline are described in the pipeline section.
They are build and pipelined in the order of appearance in the yaml list.

The last element should implement two methods : fit and predict.
All the intermediate element should implement a fit_transform and a transform methods.
For more information about sklearn pipeline, see [sklearn.pipeline](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)

the folowing section
```yaml
- ElasticNet:
    l1_ratio: 0.5
    alpha: 2e-4
    normalize: True
```

is evaluated as :

```python
ElasticNet(l1_ratio=0.5 , alpha=2e-4 , normalize=True)
```

### Build your own pipeline

Modifying or adding new steps in the pipeline can't be easier.
Let say you want to add a PCA before the classification step.

Just declare the sklearn pca in the import section

```yaml
imports:
  sklearn.linear_model :
    - ElasticNet
  sklearn.decomposition: 
    - PCA
```

and add it in the pipeline :

```yaml
pipeline:
  - XdawnCovariances:
      nfilter: 5
      subelec: range(0,56,1)
  - ElectrodeSelect:
      nfilters: 5
      nelec: 35
      metric: "'riemann'"
  - TangentSpace:
      metric: "'logeuclid'"
      tsupdate: False
  - Normalizer:
      norm: '"l1"'
  - AddMeta:
  - PCA:
      n_components: 10
  - ElasticNet:
      l1_ratio: 0.5
      alpha: 2e-4
      normalize: True
```

and then run the prediction.py (or cross_valid.py) script :

```
python prediction.py parameters.yaml
```

Voilà!


================================================
FILE: classif.py
================================================
# -*- coding: utf-8 -*-
"""
@author: alexandrebarachant
"""
import numpy
from scipy.linalg import eig as geig
import riemann
from sklearn.base  import BaseEstimator, ClassifierMixin, TransformerMixin
###############################################################################
class XdawnCovariances(BaseEstimator,TransformerMixin):
    """ 
    Compute double xdawn, project the signal and compute the covariances

    """    
    def __init__(self,nfilter=4,subelec=-1):
        self.nfilter = nfilter
        self.subelec = subelec
        
    def fit(self,X,y):
        Nt,Ne,Ns = X.shape
        # Prototyped responce for each class
        P1 = numpy.mean(X[y==1,:,:],axis=0)
        P0 = numpy.mean(X[y==0,:,:],axis=0)
        
        # Covariance matrix of the prototyper response & signal
        C1 = numpy.matrix(numpy.cov(P1))
        C0 = numpy.matrix(numpy.cov(P0))
        
        #FIXME : too many reshape operation        
        tmp = X.transpose((1,2,0))
        Cx = numpy.matrix(numpy.cov(tmp.reshape(Ne,Ns*Nt)))
        
        # Spatial filters
        D,V1 = geig(C1,Cx)        
        D,V0 = geig(C0,Cx)
        
        # create the reduced prototyped response
        self.P = numpy.concatenate((numpy.dot(V1[:,0:self.nfilter].T,P1),numpy.dot(V0[:,0:self.nfilter].T,P0)),axis=0)
    
    def transform(self,X):
        covmats = riemann.covariances_EP(X[:,self.subelec,:],self.P)
        return covmats
    
    def fit_transform(self,X,y):
        self.fit(X,y)
        return self.transform(X)
###############################################################################
class TangentSpace(BaseEstimator, TransformerMixin):

    def __init__(self,metric='riemann',tsupdate = False):

        self.metric = metric
        self.tsupdate = tsupdate 

        
    def fit(self,X,y=None):
        # compute mean covariance
        self.Cr = riemann.mean_covariance(X,metric=self.metric)
        
    def transform(self,X):
       
        if self.tsupdate:
            Cr = riemann.mean_covariance(X,metric=self.metric)
        else:
            Cr = self.Cr
        return riemann.tangent_space(X,Cr)

    def fit_transform(self,X,y=None):
        # compute mean covariance
        self.Cr = riemann.mean_covariance(X,metric=self.metric)
        return riemann.tangent_space(X,self.Cr)
###############################################################################
class AddMeta(BaseEstimator, TransformerMixin):

    def __init__(self,meta=None):
        self.meta = meta
    
    def fit(self,X,y=None):
        pass
        
    def transform(self,X):
        if self.meta is not None:
            return numpy.c_[X,self.meta]
        else:
            return X

    def fit_transform(self,X,y=None):
        return self.transform(X)

###############################################################################
class ElectrodeSelect(BaseEstimator, TransformerMixin):

    def __init__(self,nelec = 20,nfilters=5,metric='riemann'):
        self.nelec = nelec
        self.metric = metric
        self.nfilters = nfilters
        self.subelec = -1
        self.dist = []
    
    def fit(self,X,y=None):
        C1 = riemann.mean_covariance(X[y==1,...],self.metric)
        C0 = riemann.mean_covariance(X[y==0,...],self.metric)
        
        Ne,_ = C0.shape
        
        self.subelec = range(0,Ne,1) 
        while (len(self.subelec)-2*self.nfilters)>self.nelec:
            di = numpy.zeros((len(self.subelec),1))
            for idx in range(2*self.nfilters,len(self.subelec)):
                sub = self.subelec[:]
                sub.pop(idx)
                di[idx] = riemann.distance(C0[:,sub][sub,:],C1[:,sub][sub,:])
            #print di
            torm = di.argmax()
            self.dist.append(di.max())
            self.subelec.pop(torm)        
        #print self.subelec
        
    def transform(self,X):
       return X[:,self.subelec,:][:,:,self.subelec]

    def fit_transform(self,X,y=None):
        self.fit(X,y)
        return self.transform(X)
###############################################################################
def updateMeta(clf,Meta):
    if clf.named_steps.has_key('addmeta'):
        clf.set_params(addmeta__meta=Meta) 

def baggingIterator(opts,users):
    mdls = opts['bagging']['models']
    bag_size = 1-opts['bagging']['bag_size']
    bag_size = numpy.floor(bag_size*len(users))
    if bag_size == 0:
        return [[u] for u in users]
    else:
        return [numpy.random.choice(users,size=bag_size,replace=False) for i in range(mdls)]

================================================
FILE: cross_valid.py
================================================
# -*- coding: utf-8 -*-

import yaml
import sys
import numpy as np
import pandas as pd
from time import time


from sklearn.pipeline import make_pipeline
from sklearn.metrics import roc_auc_score
from sklearn.cross_validation import KFold, LeaveOneLabelOut

from collections import OrderedDict
from multiprocessing import Pool
from functools import partial

# local import
from classif import updateMeta, baggingIterator

def from_yaml_to_func(method,params):
    prm = dict()
    if params!=None:
        for key,val in params.iteritems():
            prm[key] = eval(str(val))
    return eval(method)(**prm)

def makeCV(kfolds,X,Labels,User,Meta,clf,opts):
    users = np.unique(User)
    toPredData=[]
    Gauc = []
    for train_users,test_users in kfolds[1]:
        allProb = 0 
        test_index = np.array([True if u in set(users[test_users]) else False for u in User])

        if opts.has_key('bagging'):
            bagging = baggingIterator(opts,[users[i] for i in train_users])
        else:
            bagging = [[-1]]
        
        for bag in bagging:
            bagUsers = np.array([True if u in set(bag) else False for u in User])
            train_index = np.logical_xor(np.negative(test_index), bagUsers)
            
            try:
                # train
                updateMeta(clf,Meta[train_index])
                clf.fit(X[train_index,:,:],Labels[train_index])
                
                # predict
                prob = []
                for ut in np.unique(users[test_users]):
                    updateMeta(clf,Meta[User==ut,...])
                    prob.extend(clf.predict(X[User==ut,...]))
                prob = np.array(prob)
                
                allProb += prob/len(bagging)
            except:
                print kfolds[0]
                print [users[i] for i in train_users]
                print bag
                continue
        
        # save & return
        predictions = OrderedDict()
        predictions['user']=User[test_index]
        predictions['label'] = Labels[test_index]
        predictions['prediction']=allProb
        if opts.has_key('leak'):
             predictions['prediction'] += opts['leak']['coeff']*(1-Meta[test_index,-1])
        predictions = pd.DataFrame(predictions)
        
        Gauc.append(roc_auc_score(predictions.label,predictions.prediction))
        toPredData.append(predictions)
    predData = pd.concat(toPredData)
    
    Sauc = [roc_auc_score(predData.loc[predData.user==i].label,predData.loc[predData.user==i].prediction) for i in np.unique(predData.user)]
    
    print 'Rep %d: gAUC (mean of folds) %0.5f, sAUC %0.5f (%0.5f)' % (kfolds[0],np.mean(Gauc),np.mean(Sauc),np.std(Sauc)) 
    
    return [Gauc,Sauc]
    
# load parameters file
yml = yaml.load(open(sys.argv[1]))

# imports 
for pkg, functions in yml['imports'].iteritems():
    stri = 'from ' + pkg + ' import ' + ','.join(functions)
    exec(stri)

# parse pipe function from parameters
pipe = []
for item in yml['pipeline']:
    for method,params in item.iteritems():
        pipe.append(from_yaml_to_func(method,params))

# create pipeline
clf = make_pipeline(*pipe)
opts=yml['MetaPipeline']
if opts is None:
    opts = {}
# load files
X = np.load('./preproc/epochs.npy')
Labels,User = np.load('./preproc/infos.npy')
Meta = np.load('./preproc/meta_leak.npy') if opts.has_key('leak') else np.load('./preproc/meta.npy')
users=np.unique(User)




# parallel CV
np.random.seed(5)
folds = yml['CrossVal']['folds']
repetitions = yml['CrossVal']['repetitions']
cores = yml['CrossVal']['cores']

kfolds = [[i,KFold(len(users),folds,shuffle=True)] for i in range(repetitions)]
np.random.seed(432432)
t = time()
pMakeCV = partial(makeCV,X=X,Labels=Labels,User=User,Meta=Meta,clf=clf,opts=opts) # pool function is able to process only 1 argument, so the rest has to be set fixed
pool = Pool(processes = cores) # define number of cores
results = pool.map(pMakeCV,kfolds,chunksize=1) # apply parallel processing
pool.close() # close parallel processes after execution (frees memory)
print "Done in " + str(time()-t) + " second"
# calculating performance

gAUC = np.concatenate([i[0] for i in results]) # mean of folds
sAUC = [np.mean(i[1]) for i in results]
indAUC = np.array([i[1] for i in results])
indAUC = np.mean(indAUC,axis=0)

print 'Global AUC : %.5f (%.5f)'  % (np.mean(gAUC),np.std(gAUC))
print 'Subject AUC : %.5f (%.5f)'  % (np.mean(sAUC),np.std(sAUC))

# writing it down
import os
comment = yml['CrossVal']['comments']
path = yml['CrossVal']['path']
pipelineSteps = [str(clf.steps[i][1]).replace('\n','').replace(' ','') for i in range(len(clf.steps))]
if not os.path.isfile(path):
    fd = open(path,'w')
    fd.write('comment;folds;reps;gAUC mean;gAUC std;sAUC mean;sAUC std;user'+ ";user".join(map(str,map(int,users))) + ';leak;bagging;pipeline\n')
    fd.close()
fd = open(path,'a')
leakStr = 'on' if opts.has_key('leak') else 'off'
bagStr = '-'.join([str(opts['bagging']['bag_size']),str(opts['bagging']['models'])]) if opts.has_key('bagging') else 'off'
toWrite = [comment] + map(str,[folds,repetitions,np.mean(gAUC),np.std(gAUC),np.mean(sAUC),np.std(sAUC)]) + [str(i) for i in indAUC] + [leakStr,bagStr] + pipelineSteps
fd.write(';'.join(toWrite) + '\n')
fd.close()



================================================
FILE: parameters_Leak.yaml
================================================
imports:
  sklearn.linear_model :
    - ElasticNet
  sklearn.preprocessing:
    - Normalizer
  classif:
    - XdawnCovariances
    - TangentSpace
    - AddMeta
    - ElectrodeSelect
  
CrossVal:
  cores: 4
  folds: 4
  repetitions: 10
  path: results.csv
  comments: 'model-final-leak'

Submission:
  path: submission_leak.csv
  cores: 4

MetaPipeline:
  leak:
    coeff: 0.8
  bagging:
     bag_size: 0.51 # % of subjects to train on in each bagged model. If bag_size==1 then LOSO bagging is performed 
     models: 500 # ignored for LOSO bagging

pipeline:
  - XdawnCovariances:
      nfilter: 5
      subelec: range(0,56,1)
  - ElectrodeSelect:
      nfilters: 5
      nelec: 23
      metric: "'riemann'"
  - TangentSpace:
      metric: "'logeuclid'"
      tsupdate: True
  - Normalizer:
      norm: '"l1"'
  - AddMeta:
  - ElasticNet:
      l1_ratio: 0.5
      alpha: 1.5e-4
      normalize: True


================================================
FILE: parameters_noLeak.yaml
================================================
imports:
  sklearn.linear_model :
    - ElasticNet
  sklearn.preprocessing:
    - Normalizer
  classif:
    - XdawnCovariances
    - TangentSpace
    - AddMeta
    - ElectrodeSelect
  
CrossVal:
  cores: 4
  folds: 4
  repetitions: 10
  path: results.csv
  comments: 'model-final-noLeak'

Submission:
  path: submission-noLeak.csv
  cores: 4

MetaPipeline:
  #leak:
  bagging:
     bag_size: 0.51 # % of subjects to train on in each bagged model. If bag_size==1 then LOSO bagging is performed 
     models: 500 # ignored for LOSO bagging

pipeline:
  - XdawnCovariances:
      nfilter: 5
      subelec: range(0,56,1)
  - ElectrodeSelect:
      nfilters: 5
      nelec: 35
      metric: "'riemann'"
  - TangentSpace:
      metric: "'logeuclid'"
      tsupdate: False
  - Normalizer:
      norm: '"l1"'
  - AddMeta:
  - ElasticNet:
      l1_ratio: 0.5
      alpha: 2e-4
      normalize: True


================================================
FILE: prediction.py
================================================
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 27 22:23:02 2014

@author: kirsh
"""

from time import time
import numpy as np
import pandas as pd
from sklearn.pipeline import make_pipeline
import yaml
import sys
from classif import updateMeta, baggingIterator
from multiprocessing import Pool
from functools import partial

def from_yaml_to_func(method,params):
    prm = dict()
    if params!=None:
        for key,val in params.iteritems():
            prm[key] = eval(str(val))
    return eval(method)(**prm)


def BaggingFunc(bag,Labels,X,Meta,User,X_test,Meta_test,User_test):
    bagUsers = np.array([True if u in set(bag) else False for u in User])
    train_index = np.negative(bagUsers)
    updateMeta(clf,Meta[train_index])
    clf.fit(X[train_index,:,:],Labels[train_index])
    
    ### predicting
    prob = []
    for ut in users_test:
        updateMeta(clf,Meta_test[User_test==ut,...])
        prob.extend(clf.predict(X_test[User_test==ut,...]))
    prob = np.array(prob)
    
    return prob

# load parameters file
yml = yaml.load(open(sys.argv[1]))

# imports 
for pkg, functions in yml['imports'].iteritems():
    stri = 'from ' + pkg + ' import ' + ','.join(functions)
    exec(stri)

# parse pipe function from parameters
pipe = []
for item in yml['pipeline']:
    for method,params in item.iteritems():
        pipe.append(from_yaml_to_func(method,params))

# create pipeline
clf = make_pipeline(*pipe)
opts=yml['MetaPipeline']
if opts is None:
    opts = {}

cores = yml['Submission']['cores']

# load data
X = np.load('./preproc/epochs.npy')
Labels,User = np.load('./preproc/infos.npy')
users = np.unique(User)
Meta = np.load('./preproc/meta_leak.npy') if opts.has_key('leak') else np.load('./preproc/meta.npy')

X_test = np.load('./preproc/test_epochs.npy')
feedbackid,User_test = np.load('./preproc/test_infos.npy')
User_test = np.array(map(int, User_test))
users_test = np.unique(User_test)
Meta_test = np.load('./preproc/test_meta_leak.npy') if opts.has_key('leak') else np.load('./preproc/test_meta.npy')

### training
np.random.seed(5)
allProb = 0 

if opts.has_key('bagging'):
    bagging = baggingIterator(opts,users)
else:
    bagging = [[-1]]

t = time()
pBaggingFunc = partial(BaggingFunc,Labels=Labels,X=X,Meta=Meta,User=User,X_test=X_test,Meta_test=Meta_test,User_test=User_test)
pool = Pool(processes = cores)
allProb = pool.map(pBaggingFunc,bagging,chunksize=1)
allProb = np.vstack(allProb)
allProb = np.mean(allProb,axis=0)

if opts.has_key('leak'):
    allProb += opts['leak']['coeff']*(1-Meta_test[:,-1])

print "Done in " + str(time()-t) + " second"

submission = yml['Submission']['path']
df = pd.DataFrame({'IdFeedBack':feedbackid,'Prediction':allProb})
df.to_csv(submission,index=False)

================================================
FILE: preproc/metadata.csv
================================================
subject,session,isLong,longProp,onlineErr
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26,2,0,0.5,0
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26,2,0,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
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26,3,0,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
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26,3,1,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
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26,3,1,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
26,3,1,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,3,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,1,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,4,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,0
26,5,0,0.5,1
26,5,0,0.5,1
26,5,0,0.5,0


================================================
FILE: preproc/preproc.py
================================================
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 20 12:19:34 2014

@author: alexandre barachant
"""
from numpy import *
import numpy as np
import glob
import re
from pylab import *
from scipy.signal import *
import pandas as pd

def bandpass(sig,band,fs):
    B,A = butter(5, array(band)/(fs/2), btype='bandpass')
    return lfilter(B, A, sig, axis=0)

for test in [False,True] : 

    prefix = '' if test is False else 'test_'
    DataFolder = '../data/train/' if test is False else '../data/test/'
    list_of_files = glob.glob(DataFolder + 'Data_*.csv')
    list_of_files.sort()

    reg = re.compile('\d+')
    


    freq = 200.0

    epoc_window = 1.3*freq

    X = []
    User = []
    idFeedBack = []
    Session = []
    Feedback = []
    Letter = []
    Word = []
    FeedbackTot = []
    LetterTot = []
    WordTot = []

    for f in list_of_files:
        print f
        user,session = reg.findall(f)
        sig = np.array(pd.io.parsers.read_csv(f))

        EEG = sig[:,1:-2]
        EOG = sig[:,-2]
        Trigger = sig[:,-1]

        sigF = bandpass(EEG,[1.0,40.0],freq)
    
    
        idxFeedBack = np.where(Trigger==1)[0]
    
        for fbkNum,idx in enumerate(idxFeedBack):
            X.append(sigF[idx:idx+epoc_window,:])
            User.append(int(user))
            idFeedBack.append('S' + user + '_Sess' + session + '_FB' + '%03d' % (fbkNum+1) )
            Session.append(int(session))
            Feedback.append(fbkNum)
            Letter.append(mod(fbkNum,5) + 1)
            Word.append(floor(fbkNum/5)+1)
            FeedbackTot.append(fbkNum + (int(session)-1)*60)
            WordTot.append(floor(fbkNum/5)+1 + (int(session)-1)*12)
	
    Meta = array([Session,Feedback,Letter,Word,FeedbackTot,WordTot]).transpose()
    
    Meta2 = pd.read_csv('metadata.csv')
    currentUserSet = [True if val in set(User) else False for val in Meta2.subject]

    isLong = Meta2.isLong[currentUserSet].values
    Meta = np.c_[Meta,isLong]

    onlineErr = Meta2.onlineErr[currentUserSet].values
    longProp = Meta2.longProp[currentUserSet].values
    Meta_Leak = np.c_[Meta,onlineErr,longProp]

    if test is False:
        Labels = genfromtxt(DataFolder + 'TrainLabels.csv',delimiter=',',skip_header=1)[:,1]
        info = array([Labels,User])
    else:
        info = array([idFeedBack,User])

    X = array(X).transpose((0,2,1)) # Ns,Ne,Nt

    save(prefix + 'infos.npy',info)
    save(prefix + 'epochs.npy',X)
    save(prefix + 'meta.npy',Meta)
    save(prefix + 'meta_leak.npy',Meta_Leak)




================================================
FILE: results/submission_Leak.csv
================================================
IdFeedBack,Prediction
S01_Sess01_FB001,1.2042524637624576
S01_Sess01_FB002,1.384815789963214
S01_Sess01_FB003,1.0657049246558592
S01_Sess01_FB004,1.0948818625101477
S01_Sess01_FB005,1.2463781596342418
S01_Sess01_FB006,1.299165075863088
S01_Sess01_FB007,1.3268473703104111
S01_Sess01_FB008,1.0820500842526726
S01_Sess01_FB009,1.2911933512159837
S01_Sess01_FB010,1.0272775649864556
S01_Sess01_FB011,1.3933652616764203
S01_Sess01_FB012,1.5659297447237592
S01_Sess01_FB013,1.361128323526758
S01_Sess01_FB014,1.5130055003079779
S01_Sess01_FB015,1.4558773157189777
S01_Sess01_FB016,1.2197661720713882
S01_Sess01_FB017,1.565506621924476
S01_Sess01_FB018,1.5887459176135064
S01_Sess01_FB019,1.221481967363967
S01_Sess01_FB020,1.2327020644029842
S01_Sess01_FB021,1.1962286115480336
S01_Sess01_FB022,1.3789051418712908
S01_Sess01_FB023,1.261357254405306
S01_Sess01_FB024,1.264560365854679
S01_Sess01_FB025,1.6197785351500427
S01_Sess01_FB026,1.233915175126858
S01_Sess01_FB027,1.29892229489777
S01_Sess01_FB028,0.9534399115648807
S01_Sess01_FB029,1.3988684627230064
S01_Sess01_FB030,1.0665352155135541
S01_Sess01_FB031,1.416091472001396
S01_Sess01_FB032,1.5787232415641164
S01_Sess01_FB033,1.3154707104120789
S01_Sess01_FB034,1.6115708196328655
S01_Sess01_FB035,1.4191579763800672
S01_Sess01_FB036,1.1288989117714738
S01_Sess01_FB037,1.2841743521980329
S01_Sess01_FB038,1.3672865371151697
S01_Sess01_FB039,1.4713128693161266
S01_Sess01_FB040,1.1224992416836825
S01_Sess01_FB041,1.4334581400677893
S01_Sess01_FB042,1.1647252771805916
S01_Sess01_FB043,1.4234464136768614
S01_Sess01_FB044,1.0945925732661583
S01_Sess01_FB045,1.081517226652839
S01_Sess01_FB046,1.5696025927334079
S01_Sess01_FB047,1.3554872610990727
S01_Sess01_FB048,1.1614066383070667
S01_Sess01_FB049,0.8375081012411557
S01_Sess01_FB050,1.2654498288072142
S01_Sess01_FB051,1.5067441249070992
S01_Sess01_FB052,1.2966301738190324
S01_Sess01_FB053,1.2874728117225134
S01_Sess01_FB054,1.3536866546536204
S01_Sess01_FB055,1.3154304305854616
S01_Sess01_FB056,1.494400888275429
S01_Sess01_FB057,1.5997942931224345
S01_Sess01_FB058,1.2753062181720942
S01_Sess01_FB059,1.3565626667949235
S01_Sess01_FB060,1.6425262322247356
S01_Sess02_FB001,1.3219609603693978
S01_Sess02_FB002,1.2739395290357374
S01_Sess02_FB003,1.1167288185029394
S01_Sess02_FB004,1.3539543263349951
S01_Sess02_FB005,1.3394195779008888
S01_Sess02_FB006,1.353748879565011
S01_Sess02_FB007,1.326798063392144
S01_Sess02_FB008,1.458672892688564
S01_Sess02_FB009,1.4467605258988576
S01_Sess02_FB010,1.5987525697627687
S01_Sess02_FB011,1.344586059191054
S01_Sess02_FB012,1.3741086704638716
S01_Sess02_FB013,1.2495944666597971
S01_Sess02_FB014,1.3557101472777613
S01_Sess02_FB015,1.314768780202082
S01_Sess02_FB016,1.5717001421975398
S01_Sess02_FB017,1.17635252165007
S01_Sess02_FB018,1.086488672155768
S01_Sess02_FB019,1.1630996178090562
S01_Sess02_FB020,1.4139877725943428
S01_Sess02_FB021,1.4532408955649352
S01_Sess02_FB022,1.1699681859422904
S01_Sess02_FB023,1.2140005091771933
S01_Sess02_FB024,1.2582368122293934
S01_Sess02_FB025,1.2456744525291208
S01_Sess02_FB026,1.3180224444180122
S01_Sess02_FB027,1.3973283267252252
S01_Sess02_FB028,1.2576778958224852
S01_Sess02_FB029,1.176778889226786
S01_Sess02_FB030,1.3644119651637645
S01_Sess02_FB031,1.2718548363411162
S01_Sess02_FB032,1.2371186304290644
S01_Sess02_FB033,1.4974155478391926
S01_Sess02_FB034,1.4980353732018776
S01_Sess02_FB035,1.2525364170920534
S01_Sess02_FB036,1.4722545401150784
S01_Sess02_FB037,1.2079538428915244
S01_Sess02_FB038,1.1781383950282127
S01_Sess02_FB039,1.2926636543436216
S01_Sess02_FB040,1.51426242426898
S01_Sess02_FB041,1.5230087210839889
S01_Sess02_FB042,1.2475006677231426
S01_Sess02_FB043,1.186874596809318
S01_Sess02_FB044,1.6321149755699742
S01_Sess02_FB045,1.403796870972211
S01_Sess02_FB046,1.1635938112753461
S01_Sess02_FB047,1.4775107831033447
S01_Sess02_FB048,0.9702922287091957
S01_Sess02_FB049,1.5853690384575359
S01_Sess02_FB050,1.1289298250513076
S01_Sess02_FB051,1.4330309349905594
S01_Sess02_FB052,1.3096995868700283
S01_Sess02_FB053,1.420675350206787
S01_Sess02_FB054,1.3684049696138638
S01_Sess02_FB055,1.3645639199100925
S01_Sess02_FB056,1.4154124117935019
S01_Sess02_FB057,1.4686943304295688
S01_Sess02_FB058,1.0397439685522618
S01_Sess02_FB059,1.3975405583991345
S01_Sess02_FB060,1.3802513596799133
S01_Sess03_FB001,1.1439959038105916
S01_Sess03_FB002,1.3274838184147133
S01_Sess03_FB003,1.29860694431013
S01_Sess03_FB004,1.0819433536339842
S01_Sess03_FB005,1.3878147988913976
S01_Sess03_FB006,1.103742182989239
S01_Sess03_FB007,1.6350747903776193
S01_Sess03_FB008,1.3833667254845663
S01_Sess03_FB009,1.2138529856206746
S01_Sess03_FB010,1.1601163091112152
S01_Sess03_FB011,1.1748500227793972
S01_Sess03_FB012,1.272549522355753
S01_Sess03_FB013,1.1436159461997237
S01_Sess03_FB014,1.2497204612202581
S01_Sess03_FB015,1.1016106857881844
S01_Sess03_FB016,1.4898649028104598
S01_Sess03_FB017,1.5832349374432004
S01_Sess03_FB018,1.3700646134543426
S01_Sess03_FB019,1.296143756689304
S01_Sess03_FB020,1.2164407661591712
S01_Sess03_FB021,1.5898363187701299
S01_Sess03_FB022,1.533475807721569
S01_Sess03_FB023,1.668929272327559
S01_Sess03_FB024,1.662489017149066
S01_Sess03_FB025,1.3541252672939703
S01_Sess03_FB026,1.585897874010787
S01_Sess03_FB027,1.3933957231267695
S01_Sess03_FB028,1.4826668677485726
S01_Sess03_FB029,1.5719479692642282
S01_Sess03_FB030,1.2437254340174981
S01_Sess03_FB031,1.2117099425828666
S01_Sess03_FB032,1.5495113835876122
S01_Sess03_FB033,1.374669783315491
S01_Sess03_FB034,1.3221757386913575
S01_Sess03_FB035,1.5361628605647195
S01_Sess03_FB036,1.2995408618475857
S01_Sess03_FB037,1.3876086325626058
S01_Sess03_FB038,1.1960903796598599
S01_Sess03_FB039,1.264453526069453
S01_Sess03_FB040,1.4194505965267221
S01_Sess03_FB041,1.5150047392323986
S01_Sess03_FB042,1.4365023039251965
S01_Sess03_FB043,1.4672015951212853
S01_Sess03_FB044,1.361734047893874
S01_Sess03_FB045,1.3497546240421516
S01_Sess03_FB046,1.2271854432437241
S01_Sess03_FB047,1.615370037922939
S01_Sess03_FB048,1.5769048296611157
S01_Sess03_FB049,1.2936323027138243
S01_Sess03_FB050,1.362992082351193
S01_Sess03_FB051,1.1363034896338444
S01_Sess03_FB052,1.3742897437174404
S01_Sess03_FB053,1.2587988006630382
S01_Sess03_FB054,1.1248537238547682
S01_Sess03_FB055,1.6260485397104247
S01_Sess03_FB056,1.2451066586085484
S01_Sess03_FB057,1.215694862047924
S01_Sess03_FB058,1.3767450729953348
S01_Sess03_FB059,0.9973822395043495
S01_Sess03_FB060,1.508211292294999
S01_Sess04_FB001,1.355820221338711
S01_Sess04_FB002,1.400550861960112
S01_Sess04_FB003,1.2420543759685032
S01_Sess04_FB004,1.364723377058973
S01_Sess04_FB005,1.3732484731041283
S01_Sess04_FB006,1.3340383692575153
S01_Sess04_FB007,1.4359820735282076
S01_Sess04_FB008,1.2823667503529115
S01_Sess04_FB009,1.3724462003537923
S01_Sess04_FB010,1.3366027702488839
S01_Sess04_FB011,1.3319795670232577
S01_Sess04_FB012,1.0535594848821765
S01_Sess04_FB013,1.1685573565575034
S01_Sess04_FB014,1.311382009960981
S01_Sess04_FB015,1.4592168817821154
S01_Sess04_FB016,1.2973887714184997
S01_Sess04_FB017,1.2384592216002597
S01_Sess04_FB018,1.5698264626123297
S01_Sess04_FB019,1.2000808956140612
S01_Sess04_FB020,1.2446354752862803
S01_Sess04_FB021,1.360447571963002
S01_Sess04_FB022,1.6394890225571488
S01_Sess04_FB023,1.1906959268284583
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