def metric_for_nodes(self, metric_function, train_data: InputData,
test_data: InputData, is_chain_shared: bool,
chain: Chain) -> float:
try:
validate(chain)
if is_chain_shared:
chain = SharedChain(base_chain=chain, shared_cache=self.shared_cache)
chain.fit(input_data=train_data)
return metric_function(chain, test_data)
except Exception as ex:
self.log.info(f'Error in chain assessment during composition: {ex}. Continue.')
return max_int_value
def composer_metric(self, metrics, train_data: InputData,
test_data: InputData,
chain: Chain) -> Optional[Tuple[Any]]:
try:
validate(chain)
chain.log = self.log
if type(metrics) is not list:
metrics = [metrics]
if self.cache is not None:
# TODO improve cache
chain.fit_from_cache(self.cache)
if not chain.is_fitted:
self.log.debug(
f'Chain {chain.root_node.descriptive_id} fit started')
chain.fit(input_data=train_data,
time_constraint=self.composer_requirements.
max_chain_fit_time)
self.cache.save_chain(chain)
evaluated_metrics = ()
for metric in metrics:
if callable(metric):
metric_func = metric
else:
metric_func = MetricsRepository().metric_by_id(metric)
evaluated_metrics = evaluated_metrics + (metric_func(
chain, reference_data=test_data), )
self.log.debug(
f'Chain {chain.root_node.descriptive_id} with metrics: {list(evaluated_metrics)}'
)
except Exception as ex:
self.log.info(f'Chain assessment warning: {ex}. Continue.')
evaluated_metrics = None
return evaluated_metrics
def constraint_function(chain: Chain):
try:
validate(chain)
return True
except ValueError:
return False
def test_chain_validate_correct():
chain = valid_chain()
validate(chain)