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main.py
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#!/usr/bin/env python2.7
from multiprocessing import Pool
import sys
import numpy as np
from seizure_prediction.classifiers import make_svm, make_lr, make_simple_lr
from seizure_prediction.cross_validation.kfold_strategy import KFoldStrategy
from seizure_prediction.cross_validation.legacy_strategy import LegacyStrategy
from seizure_prediction.pipeline import Pipeline, FeatureConcatPipeline, InputSource
from seizure_prediction.scores import get_score_summary, print_results
from seizure_prediction.tasks import make_submission_csv, cross_validation_score, \
write_submission_file, check_training_data_loaded, check_test_data_loaded
from seizure_prediction.transforms import FFT, Magnitude, Log10, Windower, \
Correlation, FreqCorrelation, FlattenChannels, \
Hurst, PFD, PIBSpectralEntropy, FreqBinning, HFD, Preprocess
from seizure_prediction.settings import load_settings
from seizure_prediction.fft_bins import *
# cross_validation_strategy = KFoldStrategy()
cross_validation_strategy = LegacyStrategy()
def run_prepare_data_for_cross_validation(settings, targets, pipelines, quiet=False):
if not quiet: print '\n'.join([p.get_name() for p in pipelines])
for i, pipeline in enumerate(pipelines):
for j, target in enumerate(targets):
if not quiet: print 'Running prepare data', 'P=%d/%d T=%d/%d' % (i+1, len(pipelines), j+1, len(targets))
check_training_data_loaded(settings, target, pipeline)
def run_prepare_data_for_submission(settings, targets, pipelines):
for pipeline in pipelines:
for target in targets:
print 'Running %s pipeline %s' % (target, pipeline.get_name())
check_training_data_loaded(settings, target, pipeline)
check_test_data_loaded(settings, target, pipeline)
def run_cross_validation(settings, targets, classifiers, pipelines):
print 'Cross-validation task'
print 'Targets', ', '.join(targets)
print 'Pipelines:\n ', '\n '.join([p.get_name() for p in pipelines])
print 'Classifiers', ', '.join([c[1] for c in classifiers])
run_prepare_data_for_cross_validation(settings, targets, pipelines)
# run on pool first, then show results after
pool = Pool(settings.N_jobs)
for i, pipeline in enumerate(pipelines):
for j, (classifier, classifier_name) in enumerate(classifiers):
for k, target in enumerate(targets):
progress_str = 'P=%d/%d C=%d/%d T=%d/%d' % (i+1, len(pipelines), j+1, len(classifiers), k+1, len(targets))
cross_validation_score(settings, target, pipeline, classifier, classifier_name,
strategy=cross_validation_strategy, pool=pool, progress_str=progress_str, return_data=False, quiet=True)
pool.close()
pool.join()
summaries = []
best = {}
for p_num, pipeline in enumerate(pipelines):
for c_num, (classifier, classifier_name) in enumerate(classifiers):
mean_scores = []
median_scores = []
datas = []
for target in targets:
print 'Running %s pipeline %s classifier %s' % (target, pipeline.get_name(), classifier_name)
data = cross_validation_score(settings, target, pipeline, classifier, classifier_name,
strategy=cross_validation_strategy, quiet=True)
datas.append(data)
if data.mean_score != data.median_score:
print '%.3f (mean)' % data.mean_score, data.mean_scores
print '%.3f (median)' % data.median_score, data.median_scores
else:
print '%.3f' % data.mean_score
mean_scores.append(data.mean_score)
median_scores.append(data.median_score)
best_score = best.get(target, [0, None, None, None])[0]
cur_score = max(data.mean_score, data.median_score)
if cur_score > best_score:
best[target] = [cur_score, pipeline, classifier, classifier_name]
name = 'p=%d c=%d %s mean %s' % (p_num, c_num, classifier_name, pipeline.get_name())
summary = get_score_summary(name, mean_scores)
summaries.append((summary, np.mean(mean_scores)))
print summary
name = 'p=%d c=%d %s median %s' % (p_num, c_num, classifier_name, pipeline.get_name())
summary = get_score_summary(name, median_scores)
summaries.append((summary, np.mean(median_scores)))
print summary
print_results(summaries)
print '\nbest'
for target in targets:
pipeline = best[target][1]
classifier_name = best[target][3]
print target, best[target][0], classifier_name, pipeline.get_names()
def run_make_submission(settings, targets, classifiers, pipelines):
print 'Submissions task'
print 'Targets', ', '.join(targets)
print 'Pipelines', ', '.join([p.get_name() for p in pipelines])
print 'Classifiers', ', '.join([c[1] for c in classifiers])
run_prepare_data_for_submission(settings, targets, pipelines)
pool = Pool(settings.N_jobs)
for pipeline in pipelines:
for classifier, classifier_name in classifiers:
for target in targets:
pool.apply_async(make_submission_csv, [settings, target, pipeline, classifier, classifier_name])
pool.close()
pool.join()
use_median_submissions = False
for pipeline in pipelines:
for classifier, classifier_name in classifiers:
guesses_mean = ['clip,preictal']
guesses_median = ['clip,preictal']
for target in targets:
print 'Target %s pipeline %s classifier %s' % (target, pipeline.get_name(), classifier_name)
predictions_mean, predictions_median = make_submission_csv(settings, target, pipeline, classifier, classifier_name)
guesses_mean += predictions_mean
guesses_median += predictions_median
mean_output = '\n'.join(guesses_mean)
median_output = '\n'.join(guesses_median)
out = []
if use_median_submissions and mean_output != median_output:
out.append((mean_output, 'mean'))
out.append((median_output, 'median'))
else:
out.append((mean_output, None))
for guesses, name in out:
write_submission_file(settings, guesses, name, pipeline, classifier_name)
def main():
settings = load_settings()
targets = [
'Dog_1',
'Dog_2',
'Dog_3',
'Dog_4',
'Dog_5',
'Patient_1',
'Patient_2'
]
pipelines = [
FeatureConcatPipeline(
Pipeline(InputSource(), Preprocess(), Windower(75), Correlation('none')),
Pipeline(InputSource(), Preprocess(), Windower(75), FreqCorrelation(1, None, 'none')),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), FreqBinning(winning_bins, 'mean'), Log10(), FlattenChannels()),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 1, 1.75, 2.5, 3.25, 4, 5, 8.5, 12, 15.5, 19.5, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([2, 3.5, 6])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([3.5, 6, 15])),
Pipeline(InputSource(), Preprocess(), Windower(75), HFD(2)),
Pipeline(InputSource(), Preprocess(), Windower(75), PFD()),
Pipeline(InputSource(), Preprocess(), Windower(75), Hurst()),
),
]
classifiers = [
make_svm(gamma=0.0079, C=2.7),
make_svm(gamma=0.0068, C=2.0),
make_svm(gamma=0.003, C=150.0),
make_lr(C=0.04),
make_simple_lr(),
]
submission_pipelines = [
FeatureConcatPipeline(
Pipeline(InputSource(), Preprocess(), Windower(75), Correlation('none')),
Pipeline(InputSource(), Preprocess(), Windower(75), FreqCorrelation(1, None, 'none')),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), FreqBinning(winning_bins, 'mean'), Log10(), FlattenChannels()),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 1, 1.75, 2.5, 3.25, 4, 5, 8.5, 12, 15.5, 19.5, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5, 6, 15])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([0.25, 2, 3.5])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([6, 15, 24])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([2, 3.5, 6])),
Pipeline(InputSource(Preprocess(), Windower(75), FFT(), Magnitude()), PIBSpectralEntropy([3.5, 6, 15])),
Pipeline(InputSource(), Preprocess(), Windower(75), HFD(2)),
Pipeline(InputSource(), Preprocess(), Windower(75), PFD()),
Pipeline(InputSource(), Preprocess(), Windower(75), Hurst()),
),
]
submission_classifiers = [
make_simple_lr(),
]
if len(sys.argv) >= 2 and sys.argv[1] == 'submission':
run_make_submission(settings, targets, submission_classifiers, submission_pipelines)
else:
run_cross_validation(settings, targets, classifiers, pipelines)
if __name__ == "__main__":
main()