scikit-learn
diff --git a/‎sklearn/tests/test_public_functions.py
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+1Lines changed: 1 addition & 0 deletions b/‎sklearn/tests/test_public_functions.py
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+1Lines changed: 1 addition & 0 deletions
diff --git a/‎sklearn/tree/_export.py
Copy file name to clipboardExpand all lines: sklearn/tree/_export.py
+14-10Lines changed: 14 additions & 10 deletions b/‎sklearn/tree/_export.py
Copy file name to clipboardExpand all lines: sklearn/tree/_export.py
+14-10Lines changed: 14 additions & 10 deletions
diff --git a/‎sklearn/tree/tests/test_export.py
Copy file name to clipboardExpand all lines: sklearn/tree/tests/test_export.py
-10Lines changed: 0 additions & 10 deletions b/‎sklearn/tree/tests/test_export.py
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-10Lines changed: 0 additions & 10 deletions
@@ -191,6 +191,7 @@ def _check_function_param_validation(
     "sklearn.model_selection.train_test_split",
     "sklearn.random_projection.johnson_lindenstrauss_min_dim",
     "sklearn.svm.l1_min_c",
+    "sklearn.tree.export_text",
     "sklearn.utils.gen_batches",
 ]
 
 
@@ -17,12 +17,14 @@
 import numpy as np
 
 from ..utils.validation import check_is_fitted
+from ..utils._param_validation import Interval, validate_params
+
 from ..base import is_classifier
 
 from . import _criterion
 from . import _tree
 from ._reingold_tilford import buchheim, Tree
-from . import DecisionTreeClassifier
+from . import DecisionTreeClassifier, DecisionTreeRegressor
 
 
 def _color_brew(n):
@@ -919,6 +921,17 @@ def compute_depth_(
     return max(depths)
 
 
+@validate_params(
+    {
+        "decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
+        "feature_names": [list, None],
+        "class_names": [list, None],
+        "max_depth": [Interval(Integral, 0, None, closed="left"), None],
+        "spacing": [Interval(Integral, 1, None, closed="left"), None],
+        "decimals": [Interval(Integral, 0, None, closed="left"), None],
+        "show_weights": ["boolean"],
+    }
+)
 def export_text(
     decision_tree,
     *,
@@ -1011,21 +1024,12 @@ def export_text(
     left_child_fmt = "{} {} >  {}\n"
     truncation_fmt = "{} {}\n"
 
-    if max_depth < 0:
-        raise ValueError("max_depth bust be >= 0, given %d" % max_depth)
-
     if feature_names is not None and len(feature_names) != tree_.n_features:
         raise ValueError(
             "feature_names must contain %d elements, got %d"
             % (tree_.n_features, len(feature_names))
         )
 
-    if spacing <= 0:
-        raise ValueError("spacing must be > 0, given %d" % spacing)
-
-    if decimals < 0:
-        raise ValueError("decimals must be >= 0, given %d" % decimals)
-
     if isinstance(decision_tree, DecisionTreeClassifier):
         value_fmt = "{}{} weights: {}\n"
         if not show_weights:
 
@@ -350,10 +350,6 @@ def test_precision():
 def test_export_text_errors():
     clf = DecisionTreeClassifier(max_depth=2, random_state=0)
     clf.fit(X, y)
-
-    err_msg = "max_depth bust be >= 0, given -1"
-    with pytest.raises(ValueError, match=err_msg):
-        export_text(clf, max_depth=-1)
     err_msg = "feature_names must contain 2 elements, got 1"
     with pytest.raises(ValueError, match=err_msg):
         export_text(clf, feature_names=["a"])
@@ -364,12 +360,6 @@ def test_export_text_errors():
     )
     with pytest.raises(ValueError, match=err_msg):
         export_text(clf, class_names=["a"])
-    err_msg = "decimals must be >= 0, given -1"
-    with pytest.raises(ValueError, match=err_msg):
-        export_text(clf, decimals=-1)
-    err_msg = "spacing must be > 0, given 0"
-    with pytest.raises(ValueError, match=err_msg):
-        export_text(clf, spacing=0)
 
 
 def test_export_text():
Original file line number	Diff line number	Diff line change
`@@ -191,6 +191,7 @@ def _check_function_param_validation(`
`191`	`191`	`"sklearn.model_selection.train_test_split",`
`192`	`192`	`"sklearn.random_projection.johnson_lindenstrauss_min_dim",`
`193`	`193`	`"sklearn.svm.l1_min_c",`
	`194`	`+ "sklearn.tree.export_text",`
`194`	`195`	`"sklearn.utils.gen_batches",`
`195`	`196`	`]`
`196`	`197`