def test_overall_accuracy_Net4():
pathToPred = ds.datasets['Net4'].get_outFile() + \
'_priorPerct100_weight0.01__scaled_FeatureScaling::InfOnly'
AUROC, AUPR, fpr, tpr = get_accuracy_realData(network='Net4',
predFile=pathToPred, visualize=False)
assert_almost_equal(AUROC, 0.31)
assert_almost_equal(AUPR, 0.75)
def test_overall_accuracy_Net4():
pathToPred = ds.datasets['Net4'].get_outFile() + \
'_priorPerct100_weight0.01__scaled_FeatureScaling::InfOnly'
AUROC, AUPR, fpr, tpr = get_accuracy_realData(network='Net4',
predFile=pathToPred,
visualize=False)
assert_almost_equal(AUROC, 0.31)
assert_almost_equal(AUPR, 0.75)
if __name__ == "__main__":
datasetName = 'Root' #'Net1', 'Net3_conn_final', 'Net4', 'Grene'
alphas_small = [0.001, 0.01, 0.05, 0.1]
alphas_large = [0.1, 0.2, 0.4, 0.6, 0.8, 1, 2, 3]
#
params = OrderedDict([('fileName', ''), ('priorPercent', 0), ('falsePriorRatio', 0),
('priorWeight', 0.01),
('alpha', 0.1), ('l1_ratio', 0.5), ('isCV', 1), ('fit_intercept', 1),
('scaleX', 1), ('halfLife', 10), ('method', 'FeatureScaling'), ('freeCV', 0)])
# params = OrderedDict([('fileName', ''), ('priorPercent', 100), ('falsePriorRatio', 0),
# ('priorWeight', 0.01),
# ('alpha', 0.1), ('l1_ratio', 0.5), ('isCV', 1), ('fit_intercept', 1),
# ('scaleX', 1), ('halfLife', 10), ('method', 'PenaltyScaling'), ('freeCV', 1)])
filename = run_Peak_test(datasetName, params, alphas_small)
pathToPred = ds.datasets['Root'].get_outFile() + filename
AUROC, AUPR, _, _ = get_accuracy_realData(network=datasetName, predFile=pathToPred, visualize=False)
pathToCombined = pathToPred[:-9] + "::Combined"
AUROC, AUPR, _, _ = get_accuracy_realData(network=datasetName, predFile=pathToCombined, visualize=False)
mapGeneNames(datasetName='Root', filename=pathToPred)
# print(paramsFileName)
# filename = ds.datasets['Root'].get_outFile() + '_priorPerct100_weight0.01__scaled__Nov-18_18-22--03FeatureScaling::InfOnly'
# mapGeneNames(datasetName='Root', filename=filename)
params = OrderedDict([('fileName', ''), ('priorPercent', 100),
('falsePriorRatio', 0), ('priorWeight', 0.01),
('priorFile', priorFile), ('pkEachGene', 1),
('alpha', 0.1), ('l1_ratio', 0.5), ('isCV', 1),
('fit_intercept', 1), ('scaleX', 1),
('halfLife', 10), ('method', 'PenaltyScaling'),
('freeCV', 0)])
# params = OrderedDict([('fileName', ''), ('priorPercent', 100), ('falsePriorRatio', 0),
# ('priorWeight', 0.01),
# ('alpha', 0.1), ('l1_ratio', 0.5), ('isCV', 1), ('fit_intercept', 1),
# ('scaleX', 1), ('halfLife', 10), ('method', 'PenaltyScaling'), ('freeCV', 1)])
filename = run_Peak_test(datasetName, params, alphas_small)
pathToPred = ds.datasets[datasetName].get_outFile() + filename
AUROC, AUPR, fpr, tpr = get_accuracy_realData(network=datasetName,
predFile=pathToPred,
visualize=False)
print('fpr', fpr)
print('tpr', tpr)
pathToCombined = pathToPred[:-9] + "::Combined"
AUROC, AUPR, _, _ = get_accuracy_realData(network=datasetName,
predFile=pathToCombined,
visualize=False)
# mapGeneNames(datasetName='Root', filename=pathToPred)
# print(paramsFileName)
# filename = ds.datasets['Root'].get_outFile() + '_priorPerct100_weight0.01__scaled__Nov-18_18-22--03FeatureScaling::InfOnly'
# mapGeneNames(datasetName='Root', filename=filename)