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ensemble.py
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#!/usr/bin/env python2.7
from multiprocessing import Pool
import sys
import numpy as np
from sklearn.metrics import roc_auc_score
from seizure_prediction.classifiers import make_svm, make_simple_lr, make_lr
from seizure_prediction.feature_selection import generate_feature_masks
from seizure_prediction.fft_bins import *
from seizure_prediction.pipeline import Pipeline, FeatureConcatPipeline, InputSource
from seizure_prediction.scores import get_score_summary, print_results
from seizure_prediction.tasks import make_csv_for_target_predictions, write_submission_file, \
cross_validation_score, check_training_data_loaded, check_test_data_loaded, make_submission_predictions
from seizure_prediction.transforms import Windower, Correlation, FreqCorrelation, FFT, \
Magnitude, PIBSpectralEntropy, Log10, FreqBinning, FlattenChannels, Preprocess, HFD, PFD, Hurst
from seizure_prediction.settings import load_settings
from main import run_prepare_data_for_cross_validation
def run_make_submission(settings, targets_and_pipelines, split_ratio):
pool = Pool(settings.N_jobs)
for i, (target, pipeline, feature_masks, classifier, classifier_name) in enumerate(targets_and_pipelines):
for j, feature_mask in enumerate(feature_masks):
progress_str = 'T=%d/%d M=%d/%d' % (i+1, len(targets_and_pipelines), j+1, len(feature_masks))
pool.apply_async(make_submission_predictions, [settings, target, pipeline, classifier, classifier_name],
{'feature_mask': feature_mask, 'progress_str': progress_str, 'quiet': True})
pool.close()
pool.join()
guesses = ['clip,preictal']
num_masks = None
classifier_names = []
for target, pipeline, feature_masks, classifier, classifier_name in targets_and_pipelines:
classifier_names.append(classifier_name)
if num_masks is None:
num_masks = len(feature_masks)
else:
assert num_masks == len(feature_masks)
test_predictions = []
for feature_mask in feature_masks:
data = make_submission_predictions(settings, target, pipeline, classifier, classifier_name, feature_mask=feature_mask)
test_predictions.append(data.mean_predictions)
predictions = np.mean(test_predictions, axis=0)
guesses += make_csv_for_target_predictions(target, predictions)
output = '\n'.join(guesses)
write_submission_file(settings, output, 'ensemble n=%d split_ratio=%s' % (num_masks, split_ratio), None, str(classifier_names), targets_and_pipelines)
def run_prepare_data(settings, targets, pipelines, train=True, test=False, quiet=False):
for pipeline in pipelines:
for target in targets:
print 'Preparing data for', target
if train:
check_training_data_loaded(settings, target, pipeline, quiet=quiet)
if test:
check_test_data_loaded(settings, target, pipeline, quiet=quiet)
def run_cross_validation(settings, targets, pipelines, mask_range, split_ratios, classifiers):
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):
pool.apply_async(cross_validation_score, [settings, target, pipeline, classifier, classifier_name], {'quiet': True})
for split_num, split_ratio in enumerate(split_ratios):
masks = generate_feature_masks(settings, target, pipeline, np.max(mask_range), split_ratio, random_state=0, quiet=True)
for mask_num, mask in enumerate(masks):
progress_str = 'P=%d/%d C=%d/%d T=%d/%d S=%d/%d M=%d/%d' % (i+1, len(pipelines), j+1, len(classifiers), k+1, len(targets), split_num+1, len(split_ratios), mask_num+1, len(masks))
cross_validation_score(settings, target, pipeline, classifier, classifier_name, feature_mask=mask, quiet=True, return_data=False, pool=pool, progress_str=progress_str)
pool.close()
pool.join()
print 'Finished cross validation mp'
summaries = []
for p_num, pipeline in enumerate(pipelines):
for classifier, classifier_name in classifiers:
scores_full = []
scores_masked = [[[] for y in mask_range] for x in split_ratios]
for i, target in enumerate(targets):
run_prepare_data_for_cross_validation(settings, [target], [pipeline], quiet=True)
data = cross_validation_score(settings, target, pipeline, classifier, classifier_name, pool=None, quiet=True)
scores_full.append(data.mean_score)
for split_index, split_ratio in enumerate(split_ratios):
masks = generate_feature_masks(settings, target, pipeline, np.max(mask_range), split_ratio, random_state=0, quiet=True)
for mask_index, num_masks in enumerate(mask_range):
predictions = []
y_cvs = None
for mask in masks[0:num_masks]:
data = cross_validation_score(settings, target, pipeline, classifier, classifier_name, feature_mask=mask, pool=None, quiet=True)
predictions.append(data.mean_predictions)
if y_cvs is None:
y_cvs = data.y_cvs
else:
for y_cv_1, y_cv_2 in zip(y_cvs, data.y_cvs):
assert np.alltrue(y_cv_1 == y_cv_2)
predictions = np.mean(predictions, axis=0)
scores = [roc_auc_score(y_cv, p) for p, y_cv in zip(predictions, y_cvs)]
score = np.mean(scores)
scores_masked[split_index][mask_index].append(score)
summary = get_score_summary('%s p=%d full' % (classifier_name, p_num), scores_full)
summaries.append((summary, np.mean(scores_full)))
for split_index, split_ratio in enumerate(split_ratios):
for mask_index, num_masks in enumerate(mask_range):
scores = scores_masked[split_index][mask_index]
summary = get_score_summary('%s p=%d split_ratio=%s masks=%d' % (classifier_name, p_num, split_ratio, num_masks), scores)
summaries.append((summary, np.mean(scores)))
print summary
print_results(summaries)
def main():
settings = load_settings()
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()),
),
]
targets = [
'Dog_1',
'Dog_2',
'Dog_3',
'Dog_4',
'Dog_5',
'Patient_1',
'Patient_2'
]
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(),
]
make_submission = len(sys.argv) >= 2 and sys.argv[1] == 'submission'
do_cv = not make_submission
if do_cv:
mask_range = [3]
split_ratios = [0.4, 0.525, 0.6]
run_prepare_data_for_cross_validation(settings, targets, pipelines)
run_cross_validation(settings, targets, pipelines, mask_range, split_ratios, classifiers)
if make_submission:
num_masks = 10
split_ratio = 0.525
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(),
]
targets_and_pipelines = []
pipeline = pipelines[0]
for classifier, classifier_name in classifiers:
for i, target in enumerate(targets):
run_prepare_data(settings, [target], [pipeline], test=True)
feature_masks = generate_feature_masks(settings, target, pipeline, num_masks, split_ratio, random_state=0, quiet=True)
targets_and_pipelines.append((target, pipeline, feature_masks, classifier, classifier_name))
run_make_submission(settings, targets_and_pipelines, split_ratio)
if __name__ == "__main__":
main()