In [1]:
%matplotlib inline
from matplotlib import pylab as pl
import cPickle as pickle
import pandas as pd
import numpy as np
import os
import random
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import sys
sys.path.append('..')
uncommoent the relevant pipeline in ../seizure_detection.py and run
cd ..
./doall data
or
./doall td
./doall tt
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FEATURES = 'gen8_medianwindow1-fft-with-time-freq-corr-1-48-r400-usf-w600'
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from common.data import CachedDataLoader
cached_data_loader = CachedDataLoader('../data-cache')
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def read_data(target, data_type):
fname = 'data_%s_%s_%s'%(data_type,target,FEATURES)
print fname
return cached_data_loader.load(fname,None)
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from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression as LR
clf = RandomForestClassifier(n_estimators=3000, min_samples_split=1, max_depth=10,bootstrap=False,
n_jobs=-1)
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fpout = open('../submissions/140922-predict.1.csv','w')
print >>fpout,'clip,preictal'
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Xall = None
yall = None
target_list = ['Dog_1', 'Dog_2', 'Dog_3', 'Dog_4', 'Dog_5']
for i, target in enumerate(target_list):
pdata = read_data(target, 'preictal') # positive examples
ndata = read_data(target, 'interictal') # negative examples
X = np.concatenate((pdata.X, ndata.X))
Xextra = np.zeros((X.shape[0], 1+len(target_list)))
Xextra[:,0] = int(target.startswith('Dog'))
Xextra[:,1+i] = 1.
X = np.hstack((X,Xextra))
Xall = np.concatenate((Xall, X)) if Xall is not None else X
y = np.zeros(X.shape[0])
y[:pdata.X.shape[0]] = 1
yall = np.concatenate((yall, y)) if yall is not None else y
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X.shape
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np.hstack((X,np.zeros((664,4)))).shape
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# shuffle
idxs=range(len(y))
random.shuffle(idxs)
X = X[idxs,:]
y = y[idxs]
# model
clf.fit(X,y)
# predict
tdata = read_data(target, 'test') # test examples
y_proba = clf.predict_proba(tdata.X)[:,1]
# write results
for i,p in enumerate(y_proba):
print >>fpout,'%s_test_segment_%04d.mat,%.15f' % (target, i+1, p)
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fpout.close()
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