def test_full(self):
ca.classification_accuracy(self.data_fn, self.classifier_fn, 'k.csv',
'e.csv', 'OBSERVED', 'PREDICTED')
self.assertTrue(filecmp.cmp(self.k_ref, 'k.csv'))
self.assertTrue(filecmp.cmp(self.e_ref, 'e.csv'))
os.remove('k.csv')
os.remove('e.csv')
def run_diagnostic(self):
# Read the observed and predicted files into numpy recarrays
obs = utilities.csv2rec(self.observed_file)
prd = utilities.csv2rec(self.predicted_file)
# Subset the observed data just to the IDs that are in the
# predicted file
obs_keep = np.in1d(
getattr(obs, self.id_field), getattr(prd, self.id_field))
obs = obs[obs_keep]
# Calculate VEGCLASS for both the observed and predicted data
vc_dict = self.vegclass_aa(obs, prd, id_field=self.id_field)
# Print out the vegclass file
vc_fh = open(self.vegclass_file, 'w')
vc_fh.write(','.join((self.id_field, 'OBSERVED', 'PREDICTED')) + '\n')
# Print out the observed and predicted vegetation classes
for id_val in sorted(vc_dict.keys()):
obs_vc = vc_dict[id_val]['obs_vc']
prd_vc = vc_dict[id_val]['prd_vc']
out_list = ['%d' % x for x in (id_val, obs_vc, prd_vc)]
vc_fh.write(','.join(out_list) + '\n')
vc_fh.close()
# Create the vegetation class kappa and error matrix files
vc_xml = 'L:/resources/code/xml/vegclass.xml'
ca.classification_accuracy(self.vegclass_file, vc_xml,
kappa_file=self.vegclass_kappa_file,
err_matrix_file=self.vegclass_errmatrix_file)
def run_diagnostic(self):
# Read the observed and predicted files into numpy recarrays
obs = utilities.csv2rec(self.observed_file)
prd = utilities.csv2rec(self.predicted_file)
# Subset the observed data just to the IDs that are in the
# predicted file
obs_keep = np.in1d(getattr(obs, self.id_field),
getattr(prd, self.id_field))
obs = obs[obs_keep]
# Calculate VEGCLASS for both the observed and predicted data
vc_dict = self.vegclass_aa(obs, prd, id_field=self.id_field)
# Print out the vegclass file
vc_fh = open(self.vegclass_file, 'w')
vc_fh.write(','.join((self.id_field, 'OBSERVED', 'PREDICTED')) + '\n')
# Print out the observed and predicted vegetation classes
for id_val in sorted(vc_dict.keys()):
obs_vc = vc_dict[id_val]['obs_vc']
prd_vc = vc_dict[id_val]['prd_vc']
out_list = ['%d' % x for x in (id_val, obs_vc, prd_vc)]
vc_fh.write(','.join(out_list) + '\n')
vc_fh.close()
# Create the vegetation class kappa and error matrix files
vc_xml = 'L:/resources/code/xml/vegclass.xml'
ca.classification_accuracy(
self.vegclass_file,
vc_xml,
kappa_file=self.vegclass_kappa_file,
err_matrix_file=self.vegclass_errmatrix_file)
