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)
