def _evaluate_factor(self, factor, spec, drop_rows):
if factor.expr not in self.factor_cache:
if factor.eval_method.value == 'lookup':
value = self._lookup(factor.expr)
elif factor.eval_method.value == 'python':
value = self._evaluate(factor.expr, factor.metadata, spec)
elif factor.eval_method.value == 'literal':
value = EvaluatedFactor(factor,
self._evaluate(factor.expr,
factor.metadata, spec),
kind='constant')
else:
raise FactorEvaluationError(
f"Evaluation method {factor.eval_method.value} not recognised for factor {factor.expr}."
)
if not isinstance(value, EvaluatedFactor):
if isinstance(value, dict) and '__kind__' in value:
kind = value['__kind__']
spans_intercept = value.get('__spans_intercept__', False)
elif self._is_categorical(value):
kind = 'categorical'
spans_intercept = True
else:
kind = 'numerical'
spans_intercept = False
if factor.kind is not Factor.Kind.UNKNOWN and factor.kind.value != kind:
if factor.kind.value == 'categorical':
kind = factor.kind.value
else:
raise FactorEncodingError(
f"Factor is expecting to be of kind '{factor.kind.value}' but is actually of kind '{kind}'."
)
if factor.expr in spec.encoder_state and Factor.Kind(
kind) is not spec.encoder_state[factor.expr][0]:
raise FactorEncodingError(
f"Factor kind `{kind}` does not match model specification of `{spec.encoder_state[factor.expr][0]}`."
)
value = EvaluatedFactor(
factor=factor,
values=value,
kind=kind,
spans_intercept=spans_intercept,
)
self._check_for_nulls(factor.expr, value.values, spec.na_action,
drop_rows)
self.factor_cache[factor.expr] = value
return self.factor_cache[factor.expr]
def kind(self, kind):
if not kind or kind == 'unknown':
raise ValueError(
"`EvaluatedFactor` instances must have a known kind.")
self._kind = Factor.Kind(kind)
