8000 FIX `export_text` and `export_graphviz` accepts feature and class names as array-like by Charlie-XIAO · Pull Request #26289 · scikit-learn/scikit-learn · GitHub
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FIX export_text and export_graphviz accepts feature and class names as array-like #26289

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Merged
merged 13 commits into from
May 15, 2023
Merged

FIX export_text and export_graphviz accepts feature and class names as array-like #26289

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309a333
export_text accepts feature and class names an numpy array
Charlie-XIAO Apr 27, 2023
c853c82
modified docstring for export_graphviz
Charlie-XIAO Apr 27, 2023
cb58a4e
added changelog
Charlie-XIAO Apr 27, 2023
5e134bc
regression tests
Charlie-XIAO Apr 27, 2023
bb2f9e4
array-like --> list or ndarray
Charlie-XIAO Apr 27, 2023
f9a91e0
apply check_array to support all array-like
Charlie-XIAO Apr 28, 2023
0e5dd96
Merge remote-tracking branch 'upstream/main' into tree-export-feat-names
Charlie-XIAO Apr 28, 2023
ff795c1
fixed graphviz can take bool
Charlie-XIAO Apr 28, 2023
664bf08
modified docstrings
Charlie-XIAO May 4, 2023
fac4e70
decoupled tests
Charlie-XIAO May 4, 2023
43a3651
Merge branch 'main' into tree-export-feat-names
Charlie-XIAO May 4, 2023
58ce3d9
parametrized tests for array support, removed original tests only wit…
Charlie-XIAO May 5, 2023
d1cb4a4
Merge branch 'main' into tree-export-feat-names
Charlie-XIAO May 6, 2023
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4 changes: 4 additions & 0 deletions doc/whats_new/v1.3.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -514,6 +514,10 @@ Changelog
for each target class in ascending numerical order.
:pr:`25387` by :user:`William M <Akbeeh>` and :user:`crispinlogan <crispinlogan>`.

- |Fix| :func:`tree.export_graphviz` and :func:`tree.export_text` now accepts
`feature_names` and `class_names` as array-like rather than lists.
:pr:`26289` by :user:`Yao Xiao <Charlie-XIAO>`

:mod:`sklearn.utils`
....................

Expand Down
43 changes: 30 additions & 13 deletions sklearn/tree/_export.py
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Expand Up @@ -16,7 +16,7 @@

import numpy as np

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

from ..base import is_classifier
Expand Down Expand Up @@ -788,11 +788,11 @@ def export_graphviz(
The maximum depth of the representation. If None, the tree is fully
generated.

feature_names : list of str, default=None
Names of each of the features.
feature_names : array-like of shape (n_features,), default=None
An array containing the feature names.
If None, generic names will be used ("x[0]", "x[1]", ...).

class_names : list of str or bool, default=None
class_names : array-like of shape (n_classes,) or bool, default=None
Names of each of the target classes in ascending numerical order.
Only relevant for classification and not supported for multi-output.
If ``True``, shows a symbolic representation of the class name.
Expand Down Expand Up @@ -857,6 +857,14 @@ def export_graphviz(
>>> tree.export_graphviz(clf)
'digraph Tree {...
"""
if feature_names is not None:
feature_names = check_array(
feature_names, ensure_2d=False, dtype=None, ensure_min_samples=0
)
if class_names is not None and not isinstance(class_names, bool):
class_names = check_array(
class_names, ensure_2d=False, dtype=None, ensure_min_samples=0
)

check_is_fitted(decision_tree)
own_file = False
Expand Down Expand Up @@ -924,8 +932,8 @@ def compute_depth_(
@validate_params(
{
"decision_tree": [DecisionTreeClassifier, DecisionTreeRegressor],
"feature_names": [list, None],
"class_names": [list, None],
"feature_names": ["array-like", None],
"class_names": ["array-like", 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],
Expand Down Expand Up @@ -953,17 +961,17 @@ def export_text(
It can be an instance of
DecisionTreeClassifier or DecisionTreeRegressor.

feature_names : list of str, default=None
A list of length n_features containing the feature names.
feature_names : array-like of shape (n_features,), default=None
An array containing the feature names.
If None generic names will be used ("feature_0", "feature_1", ...).

class_names : list or None, default=None
class_names : array-like of shape (n_classes,), default=None
Names of each of the target classes in ascending numerical order.
Only relevant for classification and not supported for multi-output.

- if `None`, the class names are delegated to `decision_tree.classes_`;
- if a list, then `class_names` will be used as class names instead
of `decision_tree.classes_`. The length of `class_names` must match
- otherwise, `class_names` will be used as class names instead of
`decision_tree.classes_`. The length of `class_names` must match
the length of `decision_tree.classes_`.

.. versionadded:: 1.3
Expand Down Expand Up @@ -1008,14 +1016,23 @@ def export_text(
| |--- petal width (cm) > 1.75
| | |--- class: 2
"""
if feature_names is not None:
feature_names = check_array(
feature_names, ensure_2d=False, dtype=None, ensure_min_samples=0
)
if class_names is not None:
class_names = check_array(
class_names, ensure_2d=False, dtype=None, ensure_min_samples=0
)

check_is_fitted(decision_tree)
tree_ = decision_tree.tree_
if is_classifier(decision_tree):
if class_names is None:
class_names = decision_tree.classes_
elif len(class_names) != len(decision_tree.classes_):
raise ValueError(
"When `class_names` is a list, it should contain as"
"When `class_names` is an array, it should contain as"
" many items as `decision_tree.classes_`. Got"
f" {len(class_names)} while the tree was fitted with"
f" {len(decision_tree.classes_)} classes."
Expand All @@ -1037,7 +1054,7 @@ def export_text(
else:
value_fmt = "{}{} value: {}\n"

if feature_names:
if feature_names is not None:
feature_names_ = [
feature_names[i] if i != _tree.TREE_UNDEFINED else None
for i in tree_.feature
Expand Down
139 changes: 80 additions & 59 deletions sklearn/tree/tests/test_export.py
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A935
Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
from re import finditer, search
from textwrap import dedent

import numpy as np
from numpy.random import RandomState
import pytest

Expand Down Expand Up @@ -48,48 +49,6 @@ def test_graphviz_toy():

assert contents1 == contents2

# Test with feature_names
contents1 = export_graphviz(
clf, feature_names=["feature0", "feature1"], out_file=None
)
contents2 = (
"digraph Tree {\n"
'node [shape=box, fontname="helvetica"] ;\n'
'edge [fontname="helvetica"] ;\n'
'0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
'value = [3, 3]"] ;\n'
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
"0 -> 1 [labeldistance=2.5, labelangle=45, "
'headlabel="True"] ;\n'
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
"0 -> 2 [labeldistance=2.5, labelangle=-45, "
'headlabel="False"] ;\n'
"}"
)

assert contents1 == contents2

# Test with class_names
contents1 = export_graphviz(clf, class_names=["yes", "no"], out_file=None)
contents2 = (
"digraph Tree {\n"
'node [shape=box, fontname="helvetica"] ;\n'
'edge [fontname="helvetica"] ;\n'
'0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
'value = [3, 3]\\nclass = yes"] ;\n'
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
'class = yes"] ;\n'
"0 -> 1 [labeldistance=2.5, labelangle=45, "
'headlabel="True"] ;\n'
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
'class = no"] ;\n'
"0 -> 2 [labeldistance=2.5, labelangle=-45, "
'headlabel="False"] ;\n'
"}"
)

assert contents1 == contents2

# Test plot_options
contents1 = export_graphviz(
clf,
Expand Down Expand Up @@ -249,6 +208,60 @@ def test_graphviz_toy():
)


@pytest.mark.parametrize("constructor", [list, np.array])
def test_graphviz_feature_class_names_array_support(constructor):
# Check that export_graphviz treats feature names
# and class names correctly and supports arrays
clf = DecisionTreeClassifier(
max_depth=3, min_samples_split=2, criterion="gini", random_state=2
)
clf.fit(X, y)

# Test with feature_names
contents1 = export_graphviz(
clf, feature_names=constructor(["feature0", "feature1"]), out_file=None
)
contents2 = (
"digraph Tree {\n"
'node [shape=box, fontname="helvetica"] ;\n'
'edge [fontname="helvetica"] ;\n'
'0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
'value = [3, 3]"] ;\n'
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n'
"0 -> 1 [labeldistance=2.5, labelangle=45, "
'headlabel="True"] ;\n'
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n'
"0 -> 2 [labeldistance=2.5, labelangle=-45, "
'headlabel="False"] ;\n'
"}"
)

assert contents1 == contents2

# Test with class_names
contents1 = export_graphviz(
clf, class_names=constructor(["yes", "no"]), out_file=None
)
contents2 = (
"digraph Tree {\n"
'node [shape=box, fontname="helvetica"] ;\n'
'edge [fontname="helvetica"] ;\n'
'0 [label="x[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n'
'value = [3, 3]\\nclass = yes"] ;\n'
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n'
'class = yes"] ;\n'
"0 -> 1 [labeldistance=2.5, labelangle=45, "
'headlabel="True"] ;\n'
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n'
'class = no"] ;\n'
"0 -> 2 [labeldistance=2.5, labelangle=-45, "
'headlabel="False"] ;\n'
"}"
)

assert contents1 == contents2


def test_graphviz_errors():
# Check for errors of export_graphviz
clf = DecisionTreeClassifier(max_depth=3, min_samples_split=2)
Expand Down Expand Up @@ -352,7 +365,7 @@ def test_export_text_errors():
with pytest.raises(ValueError, match=err_msg):
export_text(clf, feature_names=["a"])
err_msg = (
"When `class_names` is a list, it should contain as"
"When `class_names` is an array, it should contain as"
" many items as `decision_tree.classes_`. Got 1 while"
" the tree was fitted with 2 classes."
)
Expand All @@ -377,22 +390,6 @@ def test_export_text():
# testing that the rest of the tree is truncated
assert export_text(clf, max_depth=10) == expected_report

expected_report = dedent("""
|--- b <= 0.00
| |--- class: -1
|--- b > 0.00
| |--- class: 1
""").lstrip()
assert export_text(clf, feature_names=["a", "b"]) == expected_report

expected_report = dedent("""
|--- feature_1 <= 0.00
| |--- class: cat
|--- feature_1 > 0.00
| |--- class: dog
""").lstrip()
assert export_text(clf, class_names=["cat", "dog"]) == expected_report

expected_report = dedent("""
|--- feature_1 <= 0.00
| |--- weights: [3.00, 0.00] class: -1
Expand Down Expand Up @@ -453,6 +450,30 @@ def test_export_text():
)


@pytest.mark.parametrize("constructor", [list, np.array])
def test_export_text_feature_class_names_array_support(constructor):
# Check that export_graphviz treats feature names
# and class names correctly and supports arrays
clf = DecisionTreeClassifier(max_depth=2, random_state=0)
clf.fit(X, y)

expected_report = dedent("""
|--- b <= 0.00
| |--- class: -1
|--- b > 0.00
| |--- class: 1
""").lstrip()
assert export_text(clf, feature_names=constructor(["a", "b"])) == expected_report

expected_report = dedent("""
|--- feature_1 <= 0.00
| |--- class: cat
|--- feature_1 > 0.00
| |--- class: dog
""").lstrip()
assert export_text(clf, class_names=constructor(["cat", "dog"])) == expected_report


def test_plot_tree_entropy(pyplot):
# mostly smoke tests
# Check correctness of export_graphviz for criterion = entropy
Expand Down
0