def input_metrics(targetxml, referencexml):
rvars = pull_tune_variables(referencexml)
tvars = pull_tune_variables(targetxml, referencexml)
rvars.variables.sort(key=lambda x: x.group)
tvars.variables.sort(key=lambda x: x.group)
data = {'target': [], 'reference': [], 'min': [], 'max': [], 'key': []}
for r, t in zip(rvars.variables, tvars.variables):
if r.group == t.group:
key = ';'.join([r.idfclass, r.idfobject, r.idffield])
data['target'].append(float(t.value))
data['reference'].append(float(r.value))
data['min'].append(r.minimum)
data['max'].append(r.maximum)
data['key'].append(key)
paes = metrics.pae(data['target'], data['reference'], data['min'], data['max'])
m = {'pae': {},
'rmse': {},
'cvrmse': {},
'mbe': {},
'nmbe': {},
'mape': {}}
for k, p in zip(data['key'], paes):
m['pae'][k] = p
m['rmse']['all inputs'] = metrics.rmse(data['target'], data['reference'])
m['cvrmse']['all inputs'] = metrics.cvrmse(data['target'], data['reference'])
m['mbe']['all inputs'] = metrics.mbe(data['target'], data['reference'])
m['nmbe']['all inputs'] = metrics.nmbe(data['target'], data['reference'])
m['mape']['all inputs'] = metrics.mape(data['target'], data['reference'])
return m
def input_metrics(targetxml, referencexml):
rvars = pull_tune_variables(referencexml)
tvars = pull_tune_variables(targetxml, referencexml)
rvars.variables.sort(key=lambda x: x.group)
tvars.variables.sort(key=lambda x: x.group)
data = {'target': [], 'reference': [], 'min': [], 'max': [], 'key': []}
for r, t in zip(rvars.variables, tvars.variables):
if r.group == t.group:
key = ';'.join([r.idfclass, r.idfobject, r.idffield])
data['target'].append(float(t.value))
data['reference'].append(float(r.value))
data['min'].append(r.minimum)
data['max'].append(r.maximum)
data['key'].append(key)
paes = metrics.pae(data['target'], data['reference'], data['min'],
data['max'])
m = {
'pae': {},
'rmse': {},
'cvrmse': {},
'mbe': {},
'nmbe': {},
'mape': {}
}
for k, p in zip(data['key'], paes):
m['pae'][k] = p
m['rmse']['all inputs'] = metrics.rmse(data['target'], data['reference'])
m['cvrmse']['all inputs'] = metrics.cvrmse(data['target'],
data['reference'])
m['mbe']['all inputs'] = metrics.mbe(data['target'], data['reference'])
m['nmbe']['all inputs'] = metrics.nmbe(data['target'], data['reference'])
m['mape']['all inputs'] = metrics.mape(data['target'], data['reference'])
return m
def output_metrics(estresults, actresults):
m = {'rmse': {}, 'cvrmse': {}, 'mbe': {}, 'nmbe': {}, 'mape': {}}
estres = column_vectors(estresults)
actres = column_vectors(actresults)
for col in actres:
try:
m['rmse'][col] = metrics.rmse(estres[col], actres[col])
m['cvrmse'][col] = metrics.cvrmse(estres[col], actres[col])
m['mbe'][col] = metrics.mbe(estres[col], actres[col])
m['nmbe'][col] = metrics.nmbe(estres[col], actres[col])
m['mape'][col] = metrics.mape(estres[col], actres[col])
except:
# If anything crashes it here, just ignore the column in the output.
pass
return m
def output_metrics(estresults, actresults):
m = {'rmse': {},
'cvrmse':{},
'mbe': {},
'nmbe':{},
'mape': {}}
estres = column_vectors(estresults)
actres = column_vectors(actresults)
for col in actres:
try:
m['rmse'][col] = metrics.rmse(estres[col], actres[col])
m['cvrmse'][col] = metrics.cvrmse(estres[col], actres[col])
m['mbe'][col] = metrics.mbe(estres[col], actres[col])
m['nmbe'][col] = metrics.nmbe(estres[col], actres[col])
m['mape'][col] = metrics.mape(estres[col], actres[col])
except:
# If anything crashes it here, just ignore the column in the output.
pass
return m
