def test_WND5_all_features(self):
epsilon = 0.00001
# Define paths to original files
test_sig_path = join(test_dir, 't1_s01_c05_ij-l_precalculated.sig')
test_fit_path = join(test_dir, 'test-l.fit')
test_feat_wght_path = join(test_dir, 'test_fit-l.weights')
test_tif_path = join(test_dir, 't1_s01_c05_ij.tif')
# Here are the correct values that Python API needs to return:
# wndchrm classify -l -f0.75 test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 1.6e-27 0.083 0.917 * 4cell 3.835
# wndchrm classify -l test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 3.19e-27 0.076 0.924 * 4cell 3.848
# wndchrm classify -l -f0.05 test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 1.06e-26 0.066 0.934 * 4cell 3.869
correct_marg_probs = {}
correct_marg_probs[2189] = [0.083, 0.917]
correct_marg_probs[438] = [0.076, 0.924]
correct_marg_probs[146] = [0.066, 0.934]
# Load the original files once and only once for all this class's tests
feature_set = FeatureSpace.NewFromFitFile(test_fit_path)
fs1 = feature_set.feature_names
feature_set.Normalize()
fs2 = feature_set.feature_names
self.assertSequenceEqual(fs1, fs2)
test_sample = FeatureVector(source_filepath=test_tif_path, long=True)
test_sample.LoadSigFile(test_sig_path)
self.assertSequenceEqual(feature_set.feature_names,
test_sample.feature_names)
test_sample.Normalize(feature_set)
all_weights = FisherFeatureWeights.NewFromFile(test_feat_wght_path)
def Check(num_feats):
weights = all_weights.Threshold(num_feats)
feat_set = feature_set.FeatureReduce(weights)
sample = test_sample.FeatureReduce(weights)
result = SingleSampleClassification.NewWND5(
feat_set, weights, sample)
result_marg_probs = [ round( val, 3 ) \
for val in result.marginal_probabilities ]
for target_val, res_val in zip(correct_marg_probs[num_feats],
result_marg_probs):
self.assertAlmostEqual(target_val, res_val, delta=epsilon)
for num_feats in correct_marg_probs:
Check(num_feats)
def test_LoadSubsetFromFile(self):
"""Calculate one feature family, store to sig, load sig, and use to create larger fs"""
img_filename = "lymphoma_eosin_channel_MCL_test_img_sj-05-3362-R2_001_E.tif"
orig_img_filepath = pychrm_test_dir + sep + img_filename
full_list = list((
'Pixel Intensity Statistics () [3]',
'Pixel Intensity Statistics (Fourier ()) [3]',
))
tempdir = mkdtemp()
from shutil import copy
try:
# copy the tiff to the tempdir so the .sig files end up there too
copy(orig_img_filepath, tempdir)
input_img_path = tempdir + sep + img_filename
kwargs = {}
kwargs['source_filepath'] = input_img_path
kwargs['tile_num_cols'] = 6
kwargs['tile_num_rows'] = 5
kwargs['tiling_scheme'] = '5x6'
kwargs['tile_col_index'] = 0
kwargs['tile_row_index'] = 0
kwargs['feature_names'] = full_list[1:]
fv1 = FeatureVector(**kwargs).GenerateFeatures(quiet=False)
# modify the sig value and write to sig file to make sure subsequent loading
# used the value from disk and not recalculated it:
fv1.values[0] = -9999
fv1.ToSigFile(quiet=False)
# Now, ask for more features:
kwargs['feature_names'] = full_list
fv2 = FeatureVector(**kwargs)
with self.assertRaises(IncompleteFeatureSetError):
fv2.LoadSigFile()
#import pdb; pdb.set_trace()
fv2.GenerateFeatures()
#self.assertEqual( fv1.values[0], fv2.values[0] )
finally:
rmtree(tempdir)