In [25]:
%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os
import random
In [26]:
import sys
sys.path.append('..')
uncommoent the relevant pipeline in ../seizure_detection.py and run
cd ..
./doall data
or
./doall td
./doall tt
In [27]:
FEATURES = 'gen-8_allbands2-usf-w60-b0.2-b4-b8-b12-b30-b70'
In [28]:
nbands = 0
nwindows = 0
for p in FEATURES.split('-'):
if p[0] == 'b':
nbands += 1
elif p[0] == 'w':
nwindows = int(p[1:])
nbands -= 1
nbands, nwindows
Out[28]:
In [29]:
NUNITS = 1
In [30]:
from common.data import CachedDataLoader
cached_data_loader = CachedDataLoader('../data-cache')
In [31]:
def read_data(target, data_type):
fname = 'data_%s_%s_%s'%(data_type,target,FEATURES)
print fname
return cached_data_loader.load(fname,None)
In [32]:
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=3000, min_samples_split=1, bootstrap=False,max_depth=10,
n_jobs=-1)#
In [33]:
fpout = open('../submissions/141104-predict.5.csv','w')
print >>fpout,'clip,preictal'
In [34]:
def process(X, percentile=[0.05,0.95]):
N, Nf = X.shape
print '# samples',N,'# power points', Nf
print '# channels', Nf / (nbands*nwindows)
newX = []
for i in range(N):
windows = X[i,:].reshape((nwindows,-1))
sorted_windows = np.sort(windows, axis=0)
features = np.concatenate([sorted_windows[int(p*nwindows),:] for p in percentile], axis=-1)
newX.append(features)
newX = np.array(newX)
return newX
In [35]:
for target in ['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5', 'Patient_1', 'Patient_2']:
pdata = read_data(target, 'preictal') # positive examples
ndata = read_data(target, 'interictal') # negative examples
X = process(np.concatenate((pdata.X, ndata.X)))
y = np.zeros(X.shape[0])
y[:pdata.X.shape[0]] = 1
# shuffle
idxs=range(len(y))
random.shuffle(idxs)
X = X[idxs,:]
y = y[idxs]
if NUNITS > 1:
NFu = X.shape[1]//NUNITS
y = np.repeat(y,NUNITS)
X = X.reshape(-1,NFu)
clf.fit(X,y)
# predict
tdata = read_data(target, 'test') # test examples
Xt = process(tdata.X)
if NUNITS > 1:
Xt = Xt.reshape(-1,NFu)
y_proba = clf.predict_proba(Xt)[:,1]
if NUNITS > 1:
y_proba = y_proba.reshape(-1, NUNITS).max(axis=-1)
# write results
for i,p in enumerate(y_proba):
print >>fpout,'%s_test_segment_%04d.mat,%.15f' % (target, i+1, p)
In [36]:
fpout.close()
In [36]: