Support configuring constraints by feature names (#6783)
Co-authored-by: fis <jm.yuan@outlook.com>
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giladmaya
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@ -1193,6 +1193,7 @@ class Booster(object):
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params = params or {}
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params = self._configure_metrics(params.copy())
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params = self._configure_constraints(params)
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if isinstance(params, list):
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params.append(('validate_parameters', True))
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else:
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@ -1233,6 +1234,68 @@ class Booster(object):
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params += [('eval_metric', eval_metric)]
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return params
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def _transform_monotone_constrains(self, value: Union[dict, str]) -> str:
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if isinstance(value, str):
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return value
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constrained_features = set(value.keys())
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if not constrained_features.issubset(set(self.feature_names or [])):
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raise ValueError('Constrained features are not a subset of '
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'training data feature names')
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return '(' + ','.join([str(value.get(feature_name, 0))
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for feature_name in self.feature_names]) + ')'
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def _transform_interaction_constraints(self, value: Union[list, str]) -> str:
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if isinstance(value, str):
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return value
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feature_idx_mapping = {k: str(v) for v, k in enumerate(self.feature_names or [])}
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try:
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s = "["
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for constraint in value:
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s += (
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"["
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+ ",".join(
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[feature_idx_mapping[feature_name] for feature_name in constraint]
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)
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+ "]"
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)
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return s + "]"
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except KeyError as e:
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# pylint: disable=raise-missing-from
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raise ValueError(
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"Constrained features are not a subset of training data feature names"
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) from e
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def _configure_constraints(self, params: Union[Dict, List]) -> Union[Dict, List]:
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if isinstance(params, dict):
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value = params.get("monotone_constraints")
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if value:
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params[
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"monotone_constraints"
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] = self._transform_monotone_constrains(value)
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value = params.get("interaction_constraints")
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if value:
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params[
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"interaction_constraints"
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] = self._transform_interaction_constraints(value)
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elif isinstance(params, list):
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for idx, param in enumerate(params):
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name, value = param
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if not value:
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continue
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if name == "monotone_constraints":
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params[idx] = (name, self._transform_monotone_constrains(value))
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elif name == "interaction_constraints":
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params[idx] = (name, self._transform_interaction_constraints(value))
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return params
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def __del__(self):
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if hasattr(self, 'handle') and self.handle is not None:
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_check_call(_LIB.XGBoosterFree(self.handle))
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@ -9,7 +9,7 @@ rng = np.random.RandomState(1994)
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class TestInteractionConstraints:
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def run_interaction_constraints(self, tree_method):
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def run_interaction_constraints(self, tree_method, feature_names=None, interaction_constraints='[[0, 1]]'):
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x1 = np.random.normal(loc=1.0, scale=1.0, size=1000)
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x2 = np.random.normal(loc=1.0, scale=1.0, size=1000)
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x3 = np.random.choice([1, 2, 3], size=1000, replace=True)
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@ -17,13 +17,13 @@ class TestInteractionConstraints:
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+ np.random.normal(
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loc=0.001, scale=1.0, size=1000) + 3 * np.sin(x1)
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X = np.column_stack((x1, x2, x3))
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dtrain = xgboost.DMatrix(X, label=y)
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dtrain = xgboost.DMatrix(X, label=y, feature_names=feature_names)
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params = {
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'max_depth': 3,
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'eta': 0.1,
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'nthread': 2,
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'interaction_constraints': '[[0, 1]]',
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'interaction_constraints': interaction_constraints,
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'tree_method': tree_method
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}
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num_boost_round = 12
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@ -35,7 +35,7 @@ class TestInteractionConstraints:
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# by the same amount
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def f(x):
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tmat = xgboost.DMatrix(
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np.column_stack((x1, x2, np.repeat(x, 1000))))
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np.column_stack((x1, x2, np.repeat(x, 1000))), feature_names=feature_names)
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return bst.predict(tmat)
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preds = [f(x) for x in [1, 2, 3]]
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@ -57,6 +57,26 @@ class TestInteractionConstraints:
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def test_approx_interaction_constraints(self):
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self.run_interaction_constraints(tree_method='approx')
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def test_interaction_constraints_feature_names(self):
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with pytest.raises(ValueError):
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constraints = [('feature_0', 'feature_1')]
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self.run_interaction_constraints(tree_method='exact',
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interaction_constraints=constraints)
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with pytest.raises(ValueError):
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constraints = [('feature_0', 'feature_3')]
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feature_names = ['feature_0', 'feature_1', 'feature_2']
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self.run_interaction_constraints(tree_method='exact',
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feature_names=feature_names,
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interaction_constraints=constraints)
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constraints = [('feature_0', 'feature_1')]
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feature_names = ['feature_0', 'feature_1', 'feature_2']
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self.run_interaction_constraints(tree_method='exact',
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feature_names=feature_names,
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interaction_constraints=constraints)
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@pytest.mark.skipif(**tm.no_sklearn())
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def training_accuracy(self, tree_method):
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from sklearn.metrics import accuracy_score
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@ -14,7 +14,7 @@ def is_decreasing(y):
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return np.count_nonzero(np.diff(y) > 0.0) == 0
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def is_correctly_constrained(learner):
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def is_correctly_constrained(learner, feature_names=None):
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n = 100
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variable_x = np.linspace(0, 1, n).reshape((n, 1))
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fixed_xs_values = np.linspace(0, 1, n)
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@ -22,13 +22,15 @@ def is_correctly_constrained(learner):
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for i in range(n):
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fixed_x = fixed_xs_values[i] * np.ones((n, 1))
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monotonically_increasing_x = np.column_stack((variable_x, fixed_x))
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monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x)
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monotonically_increasing_dset = xgb.DMatrix(monotonically_increasing_x,
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feature_names=feature_names)
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monotonically_increasing_y = learner.predict(
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monotonically_increasing_dset
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)
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monotonically_decreasing_x = np.column_stack((fixed_x, variable_x))
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monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x)
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monotonically_decreasing_dset = xgb.DMatrix(monotonically_decreasing_x,
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feature_names=feature_names)
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monotonically_decreasing_y = learner.predict(
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monotonically_decreasing_dset
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)
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@ -101,6 +103,38 @@ class TestMonotoneConstraints:
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assert is_correctly_constrained(constrained_hist_method)
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@pytest.mark.parametrize('format', [dict, list])
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def test_monotone_constraints_feature_names(self, format):
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# next check monotonicity when initializing monotone_constraints by feature names
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params = {
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'tree_method': 'hist', 'verbosity': 1,
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'grow_policy': 'lossguide',
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'monotone_constraints': {'feature_0': 1, 'feature_1': -1}
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}
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if format == list:
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params = list(params.items())
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with pytest.raises(ValueError):
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xgb.train(params, training_dset)
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feature_names =[ 'feature_0', 'feature_2']
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training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
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with pytest.raises(ValueError):
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xgb.train(params, training_dset_w_feature_names)
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feature_names =[ 'feature_0', 'feature_1']
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training_dset_w_feature_names = xgb.DMatrix(x, label=y, feature_names=feature_names)
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constrained_learner = xgb.train(
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params, training_dset_w_feature_names
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)
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assert is_correctly_constrained(constrained_learner, feature_names)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_training_accuracy(self):
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from sklearn.metrics import accuracy_score
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