8000 MAINT validate_params for plot_tree by Veghit · Pull Request #25882 · scikit-learn/scikit-learn · GitHub
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MAINT validate_params for plot_tree #25882

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Merged
merged 15 commits into from
Mar 20, 2023
Merged

MAINT validate_params for plot_tree #25882

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3fee401
validate_params for plot_tree
Mar 16, 2023
a947ba7
applied black formatter
Mar 16, 2023
b4319c2
matplotlib import fix
Mar 16, 2023
7d23627
applied black formatter
Mar 16, 2023
1d6cf52
matplotlib import fix
Mar 16, 2023
d071b57
validate_params for plot_tree
Mar 16, 2023
aa7f526
applied black formatter
Mar 16, 2023
56c7a3b
cr fixes
Mar 17, 2023
eea309f
Merge remote-tracking branch 'origin/plot_tree_validation' into plot_…
Mar 17, 2023
d0ff0ad
cr fixes
Mar 17, 2023
e73bb46
cr fixes
Mar 17, 2023
1521023
Merge remote-tracking branch 'origin/plot_tree_validation' into plot_…
Mar 17, 2023
971e0d0
Merge branch 'main' into plot_tree_validation
Veghit Mar 17, 2023
e6422a7
Merge branch 'main' into plot_tree_validation
Veghit Mar 17, 2023
790dedb
adding test
Mar 20, 2023
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1 change: 1 addition & 0 deletions sklearn/tests/test_public_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,6 +194,7 @@ def _check_function_param_validation(
"sklearn.random_projection.johnson_lindenstrauss_min_dim",
"sklearn.svm.l1_min_c",
"sklearn.tree.export_text",
"sklearn.tree.plot_tree",
"sklearn.utils.gen_batches",
]

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33 changes: 18 additions & 15 deletions sklearn/tree/_export.py
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Expand Up @@ -17,7 +17,7 @@
import numpy as np

from ..utils.validation import check_is_fitted
from ..utils._param_validation import Interval, validate_params
from ..utils._param_validation import Interval, validate_params, StrOptions

from ..base import is_classifier

Expand Down Expand Up @@ -77,6 +77,23 @@ def __repr__(self):
SENTINEL = Sentinel()


@validate_params(
{
"decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
"max_depth": [Interval(Integral, 0, None, closed="left"), None],
"feature_names": [list, None],
"class_names": [list, None],
"label": [StrOptions({"all", "root", "none"})],
"filled": ["boolean"],
"impurity": ["boolean"],
"node_ids": ["boolean"],
"proportion": ["boolean"],
"rounded": ["boolean"],
"precision": [Interval(Integral, 0, None, closed="left"), None],
"ax": "no_validation", # delegate validation to matplotlib
"fontsize": [Interval(Integral, 0, None, closed="left"), None],
}
)
def plot_tree(
decision_tree,
*,
Expand Down Expand Up @@ -601,20 +618,6 @@ def __init__(
)
self.fontsize = fontsize

# validate
if isinstance(precision, Integral):
if precision < 0:
raise ValueError(
"'precision' should be greater or equal to 0."
" Got {} instead.".format(precision)
)
else:
raise ValueError(
"'precision' should be an integer. Got {} instead.".format(
type(precision)
)
)

# The depth of each node for plotting with 'leaf' option
self.ranks = {"leaves": []}
# The colors to render each node with
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0