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MAINT use _validate_params in DecisionTreeClassifier and DecisionTreeRegressor #23499

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Merged
merged 15 commits into from
Jun 24, 2022
Merged

MAINT use _validate_params in DecisionTreeClassifier and DecisionTreeRegressor #23499

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320cbc5
add param constraints: BaseDT, DTreg, DTclass
moritzwilksch May 31, 2022
ace964b
fix constraint boundaries
moritzwilksch May 31, 2022
2a5f073
Update sklearn/tree/_classes.py
moritzwilksch May 31, 2022
7659626
Update sklearn/tree/_classes.py
moritzwilksch May 31, 2022
b776d85
add test_tree_params_validation back in
moritzwilksch May 31, 2022
2362d33
remove last type error raised here
moritzwilksch May 31, 2022
cd8640d
Update sklearn/tree/tests/test_tree.py
moritzwilksch Jun 3, 2022
4bd9392
rm unused check_scalar import
moritzwilksch Jun 3, 2022
239c86b
rm validation tests from test_gradient_boosting
moritzwilksch Jun 3, 2022
d55150b
Merge branch 'main' of github.com:scikit-learn/scikit-learn into deci…
moritzwilksch Jun 17, 2022
e14f377
hidden
jeremiedbb Jun 23, 2022
4106785
Merge remote-tracking branch 'upstream/main' into pr/moritzwilksch/23499
jeremiedbb Jun 23, 2022
65e6347
ExtraTree for free
jeremiedbb Jun 23, 2022
a115926
remove useless tests
glemaitre Jun 24, 2022
984e844
Merge branch 'main' into decisiontree-val-params
glemaitre Jun 24, 2022
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68 changes: 0 additions & 68 deletions sklearn/ensemble/tests/test_gradient_boosting.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -139,74 +139,6 @@ def test_classification_toy(loss):
TypeError,
"tol must be an instance of float,",
),
# The following parameters are checked in BaseDecisionTree
({"min_samples_leaf": 0}, ValueError, "min_samples_leaf == 0, must be >= 1"),
({"min_samples_leaf": 0.0}, ValueError, "min_samples_leaf == 0.0, must be > 0"),
(
{"min_samples_leaf": "foo"},
TypeError,
"min_samples_leaf must be an instance of float",
),
({"min_samples_split": 1}, ValueError, "min_samples_split == 1, must be >= 2"),
(
{"min_samples_split": 0.0},
ValueError,
"min_samples_split == 0.0, must be > 0.0",
),
(
{"min_samples_split": 1.1},
ValueError,
"min_samples_split == 1.1, must be <= 1.0",
),
(
{"min_samples_split": "foo"},
TypeError,
"min_samples_split must be an instance of float",
),
(
{"min_weight_fraction_leaf": -1},
ValueError,
"min_weight_fraction_leaf == -1, must be >= 0.0",
),
(
{"min_weight_fraction_leaf": 0.6},
ValueError,
"min_weight_fraction_leaf == 0.6, must be <= 0.5",
),
(
{"min_weight_fraction_leaf": "foo"},
TypeError,
"min_weight_fraction_leaf must be an instance of float",
),
({"max_leaf_nodes": 0}, ValueError, "max_leaf_nodes == 0, must be >= 2"),
(
{"max_leaf_nodes": 1.5},
TypeError,
"max_leaf_nodes must be an instance of int",
),
({"max_depth": -1}, ValueError, "max_depth == -1, must be >= 1"),
(
{"max_depth": 1.1},
TypeError,
"max_depth must be an instance of int",
),
(
{"min_impurity_decrease": -1},
ValueError,
"min_impurity_decrease == -1, must be >= 0.0",
),
(
{"min_impurity_decrease": "foo"},
TypeError,
"min_impurity_decrease must be an instance of float",
),
({"ccp_alpha": -1.0}, ValueError, "ccp_alpha == -1.0, must be >= 0.0"),
(
{"ccp_alpha": "foo"},
TypeError,
"ccp_alpha must be an instance of float",
),
({"criterion": "mae"}, ValueError, "criterion='mae' is not supported."),
],
# Avoid long error messages in test names:
# https://github.com/scikit-learn/scikit-learn/issues/21362
Expand Down
4 changes: 0 additions & 4 deletions sklearn/tests/test_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -467,8 +467,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
"ComplementNB",
"CountVectorizer",
"DBSCAN",
"DecisionTreeClassifier",
"DecisionTreeRegressor",
"DictVectorizer",
"DictionaryLearning",
"DummyClassifier",
Expand All @@ -477,8 +475,6 @@ def test_estimators_do_not_raise_errors_in_init_or_set_params(Estimator):
"ElasticNetCV",
"EllipticEnvelope",
"EmpiricalCovariance",
"ExtraTreeClassifier",
"ExtraTreeRegressor",
"ExtraTreesClassifier",
"ExtraTreesRegressor",
"FactorAnalysis",
Expand Down
138 changes: 51 additions & 87 deletions sklearn/tree/_classes.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
from abc import ABCMeta
from abc import abstractmethod
from math import ceil
from numbers import Integral, Real

import numpy as np
from scipy.sparse import issparse
Expand All @@ -32,12 +33,12 @@
from ..base import MultiOutputMixin
from ..utils import Bunch
from ..utils import check_random_state
from ..utils import check_scalar
from ..utils.deprecation import deprecated
from ..utils.validation import _check_sample_weight
from ..utils import compute_sample_weight
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted
from ..utils._param_validation import Hidden, Interval, StrOptions

from ._criterion import Criterion
from ._splitter import Splitter
Expand Down Expand Up @@ -97,6 +98,30 @@ class BaseDecisionTree(MultiOutputMixin, BaseEstimator, metaclass=ABCMeta):
Use derived classes instead.
"""

_parameter_constraints = {
"splitter": [StrOptions({"best", "random"})],
"max_depth": [Interval(Integral, 1, None, closed="left"), None],
"min_samples_split": [
Interval(Integral, 2, None, closed="left"),
Interval(Real, 0.0, 1.0, closed="right"),
],
"min_samples_leaf": [
Interval(Integral, 1, None, closed="left"),
Interval(Real, 0.0, 1.0, closed="neither"),
],
"min_weight_fraction_leaf": [Interval(Real, 0.0, 0.5, closed="both")],
"max_features": [
Interval(Integral, 1, None, closed="left"),
Interval(Real, 0.0, 1.0, closed="right"),
StrOptions({"auto", "sqrt", "log2"}, deprecated={"auto"}),
None,
],
"random_state": ["random_state"],
"max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None],
"min_impurity_decrease": [Interval(Real, 0.0, None, closed="left")],
"ccp_alpha": [Interval(Real, 0.0, None, closed="left")],
}

@abstractmethod
def __init__(
self,
Expand Down Expand Up @@ -153,16 +178,9 @@ def get_n_leaves(self):
return self.tree_.n_leaves

def fit(self, X, y, sample_weight=None, check_input=True):

self._validate_params()
random_state = check_random_state(self.random_state)

check_scalar(
self.ccp_alpha,
name="ccp_alpha",
target_type=numbers.Real,
min_val=0.0,
)

if check_input:
# Need to validate separately here.
# We can't pass multi_output=True because that would allow y to be
Expand Down Expand Up @@ -233,64 +251,21 @@ def fit(self, X, y, sample_weight=None, check_input=True):
if getattr(y, "dtype", None) != DOUBLE or not y.flags.contiguous:
y = np.ascontiguousarray(y, dtype=DOUBLE)

# Check parameters
if self.max_depth is not None:
check_scalar(
self.max_depth,
name="max_depth",
target_type=numbers.Integral,
min_val=1,
)
max_depth = np.iinfo(np.int32).max if self.max_depth is None else self.max_depth

if isinstance(self.min_samples_leaf, numbers.Integral):
check_scalar(
self.min_samples_leaf,
name="min_samples_leaf",
target_type=numbers.Integral,
min_val=1,
)
min_samples_leaf = self.min_samples_leaf
else: # float
check_scalar(
self.min_samples_leaf,
name="min_samples_leaf",
target_type=numbers.Real,
min_val=0.0,
include_boundaries="neither",
)
min_samples_leaf = int(ceil(self.min_samples_leaf * n_samples))

if isinstance(self.min_samples_split, numbers.Integral):
check_scalar(
self.min_samples_split,
name="min_samples_split",
target_type=numbers.Integral,
min_val=2,
)
min_samples_split = self.min_samples_split
else: # float
check_scalar(
self.min_samples_split,
name="min_samples_split",
target_type=numbers.Real,
min_val=0.0,
max_val=1.0,
include_boundaries="right",
)
min_samples_split = int(ceil(self.min_samples_split * n_samples))
min_samples_split = max(2, min_samples_split)

min_samples_split = max(min_samples_split, 2 * min_samples_leaf)

check_scalar(
self.min_weight_fraction_leaf,
name="min_weight_fraction_leaf",
target_type=numbers.Real,
min_val=0.0,
max_val=0.5,
)

if isinstance(self.max_features, str):
if self.max_features == "auto":
if is_classification:
Expand All @@ -313,55 +288,20 @@ def fit(self, X, y, sample_weight=None, check_input=True):
max_features = max(1, int(np.sqrt(self.n_features_in_)))
elif self.max_features == "log2":
max_features = max(1, int(np.log2(self.n_features_in_)))
else:
raise ValueError(
"Invalid value for max_features. "
"Allowed string values are 'auto', "
"'sqrt' or 'log2'."
)
elif self.max_features is None:
max_features = self.n_features_in_
elif isinstance(self.max_features, numbers.Integral):
check_scalar(
self.max_features,
name="max_features",
target_type=numbers.Integral,
min_val=1,
include_boundaries="left",
)
max_features = self.max_features
else: # float
check_scalar(
self.max_features,
name="max_features",
target_type=numbers.Real,
min_val=0.0,
max_val=1.0,
include_boundaries="right",
)
if self.max_features > 0.0:
max_features = max(1, int(self.max_features * self.n_features_in_))
else:
max_features = 0

self.max_features_ = max_features

if self.max_leaf_nodes is not None:
check_scalar(
self.max_leaf_nodes,
name="max_leaf_nodes",
target_type=numbers.Integral,
min_val=2,
)
max_leaf_nodes = -1 if self.max_leaf_nodes is None else self.max_leaf_nodes

check_scalar(
self.min_impurity_decrease,
name="min_impurity_decrease",
target_type=numbers.Real,
min_val=0.0,
)

if len(y) != n_samples:
raise ValueError(
"Number of labels=%d does not match number of samples=%d"
Expand Down Expand Up @@ -905,6 +845,12 @@ class DecisionTreeClassifier(ClassifierMixin, BaseDecisionTree):
0.93..., 0.93..., 1. , 0.93..., 1. ])
"""

_parameter_constraints = {
**BaseDecisionTree._parameter_constraints,
"criterion": [StrOptions({"gini", "entropy", "log_loss"}), Hidden(Criterion)],
"class_weight": [dict, list, StrOptions({"balanced"}), None],
}

def __init__(
self,
*,
Expand Down Expand Up @@ -1281,6 +1227,24 @@ class DecisionTreeRegressor(RegressorMixin, BaseDecisionTree):
0.16..., 0.11..., -0.73..., -0.30..., -0.00...])
"""

_parameter_constraints = {
**BaseDecisionTree._parameter_constraints,
"criterion": [
StrOptions(
{
"squared_error",
"friedman_mse",
"absolute_error",
"poisson",
"mse",
"mae",
},
deprecated={"mse", "mae"},
),
Hidden(Criterion),
],
}

def __init__(
self,
*,
Expand Down
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