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)
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)
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)
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)