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Cleanup configuration for constraints. (#7758)

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This commit is contained in:
Jiaming Yuan
2022-03-29 04:22:46 +08:00
committed by GitHub
parent 3c9b04460a
commit a50b84244e
5 changed files with 53 additions and 42 deletions
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71
python-package/xgboost/core.py
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@@ -1392,50 +1392,46 @@ class Booster:
raise TypeError('Unknown type:', model_file)
params = params or {}
params = _configure_metrics(params.copy())
params = self._configure_constraints(params)
if isinstance(params, list):
params.append(('validate_parameters', True))
params_processed = _configure_metrics(params.copy())
params_processed = self._configure_constraints(params_processed)
if isinstance(params_processed, list):
params_processed.append(("validate_parameters", True))
else:
params['validate_parameters'] = True
params_processed["validate_parameters"] = True
self.set_param(params or {})
if (params is not None) and ('booster' in params):
self.booster = params['booster']
else:
self.booster = 'gbtree'
self.set_param(params_processed or {})
def _transform_monotone_constrains(self, value: Union[Dict[str, int], str]) -> str:
def _transform_monotone_constrains(
self, value: Union[Dict[str, int], str]
) -> Union[Tuple[int, ...], str]:
if isinstance(value, str):
return value
constrained_features = set(value.keys())
if not constrained_features.issubset(set(self.feature_names or [])):
raise ValueError('Constrained features are not a subset of '
'training data feature names')
feature_names = self.feature_names or []
if not constrained_features.issubset(set(feature_names)):
raise ValueError(
"Constrained features are not a subset of training data feature names"
)
return '(' + ','.join([str(value.get(feature_name, 0))
for feature_name in self.feature_names]) + ')'
return tuple(value.get(name, 0) for name in feature_names)
def _transform_interaction_constraints(
self, value: Union[List[Tuple[str]], str]
) -> str:
self, value: Union[Sequence[Sequence[str]], str]
) -> Union[str, List[List[int]]]:
if isinstance(value, str):
return value
feature_idx_mapping = {k: str(v) for v, k in enumerate(self.feature_names or [])}
feature_idx_mapping = {
name: idx for idx, name in enumerate(self.feature_names or [])
}
try:
s = "["
result = []
for constraint in value:
s += (
"["
+ ",".join(
[feature_idx_mapping[feature_name] for feature_name in constraint]
)
+ "],"
result.append(
[feature_idx_mapping[feature_name] for feature_name in constraint]
)
return s[:-1] + "]"
return result
except KeyError as e:
raise ValueError(
"Constrained features are not a subset of training data feature names"
@@ -1444,17 +1440,16 @@ class Booster:
def _configure_constraints(self, params: Union[List, Dict]) -> Union[List, Dict]:
if isinstance(params, dict):
value = params.get("monotone_constraints")
if value:
params[
"monotone_constraints"
] = self._transform_monotone_constrains(value)
if value is not None:
params["monotone_constraints"] = self._transform_monotone_constrains(
value
)
value = params.get("interaction_constraints")
if value:
if value is not None:
params[
"interaction_constraints"
] = self._transform_interaction_constraints(value)
elif isinstance(params, list):
for idx, param in enumerate(params):
name, value = param
@@ -2462,11 +2457,9 @@ class Booster:
if not PANDAS_INSTALLED:
raise ImportError(('pandas must be available to use this method.'
'Install pandas before calling again.'))
if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
raise ValueError(
f"This method is not defined for Booster type {self.booster}"
)
booster = json.loads(self.save_config())["learner"]["gradient_booster"]["name"]
if booster not in {"gbtree", "dart"}:
raise ValueError(f"This method is not defined for Booster type {booster}")
tree_ids = []
node_ids = []