ENH Adds `class_names` to `tree.export_text` #25387

mokwilliam · 2023-01-12T22:31:01Z

Reference Issues/PRs

closes #20576
closes #19824

What does this implement/fix? Explain your changes.

I've posted a message there #20576 (comment).

To summarize, the goal is to provide the class_names argument in the signature of the export_text function so that the user can choose the class name(s). So I don't focus on changing the display format of the tree.

Any other comments?

I'll copy/paste the comment I've posted on the issue #20576.

Explanation

Here's the initialization of my test :

from sklearn.datasets import load_iris
from sklearn import tree

# Load iris dataset
iris = load_iris()

# Load decision tree classifiers: DecisionTree
clf_DT = tree.DecisionTreeClassifier(random_state=0)

# Build decision tree classifier from iris dataset
clf_DT = clf_DT.fit(iris.data, iris.target)

To remind, the signature of the function is as follows (https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/tree/_export.py#L922) :

def export_text(
    decision_tree,
    *,
    feature_names=None,
    max_depth=10,
    spacing=3,
    decimals=2,
    show_weights=False,
)

Of course, if I try to put the argument class_names, we have the following error

TypeError: export_text() got an unexpected keyword argument class_names

If not, then we have :

feature_names = iris['feature_names']
r = tree.export_text(clf_DT, feature_names=feature_names, max_depth=2)
print(r)

|--- petal width (cm) <= 0.80
|   |--- class: 0
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- petal length (cm) <= 4.95
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.95
|   |   |   |--- truncated branch of depth 3
|   |--- petal width (cm) >  1.75
|   |   |--- petal length (cm) <= 4.85
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.85
|   |   |   |--- class: 2

After changing the source code (to see more, check "Changes in the code"), we have :

class_names0 = None 
# Then: class_names = decision_tree.classes_

class_names1 = [0, "1"] 
# Then: class_names = decision_tree.classes_
# len(class_names1) != decision_tree.n_classes_

class_names2 = ["class0", "class1", "class2"] 
# Then: class_names = class_names2
# Reason: len(class_names2) == decision_tree.n_classes_

c_names = [class_names0, class_names1, class_names2]

print("===> After change <===")
for i, class_name in enumerate(c_names):
    r = tree.export_text(clf_DT, class_names=class_name, feature_names=feature_names, max_depth=2)
    print(r)

===> After change <===
|--- petal width (cm) <= 0.80
|   |--- class: 0
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- petal length (cm) <= 4.95
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.95
|   |   |   |--- truncated branch of depth 3
|   |--- petal width (cm) >  1.75
|   |   |--- petal length (cm) <= 4.85
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.85
|   |   |   |--- class: 2

|--- petal width (cm) <= 0.80
|   |--- class: 0
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- petal length (cm) <= 4.95
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.95
|   |   |   |--- truncated branch of depth 3
|   |--- petal width (cm) >  1.75
|   |   |--- petal length (cm) <= 4.85
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.85
|   |   |   |--- class: 2

|--- petal width (cm) <= 0.80
|   |--- class: class0
|--- petal width (cm) >  0.80
|   |--- petal width (cm) <= 1.75
|   |   |--- petal length (cm) <= 4.95
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.95
|   |   |   |--- truncated branch of depth 3
|   |--- petal width (cm) >  1.75
|   |   |--- petal length (cm) <= 4.85
|   |   |   |--- truncated branch of depth 2
|   |   |--- petal length (cm) >  4.85
|   |   |   |--- class: class2

I hope this message was clear enough to understand. I can make changes if needed to fine tune.

Changes in the code

Before change

def export_text(
    decision_tree,
    *,
    feature_names=None,
    max_depth=10,
    spacing=3,
    decimals=2,
    show_weights=False,
):
    check_is_fitted(decision_tree)
    tree_ = decision_tree.tree_
    if is_classifier(decision_tree):
        class_names = decision_tree.classes_

After change

def export_text(
    decision_tree,
    *,
    feature_names=None,
    class_names=None,  # add class_names argument
    max_depth=10,
    spacing=3,
    decimals=2,
    show_weights=False,
):
    """
    ...
    class_names : list of arguments, default=None
        Names of each of the target classes in ascending numerical order.
        Only relevant for classification and not supported for multi-output.
    ...
    """
    check_is_fitted(decision_tree)
    tree_ = decision_tree.tree_
    if is_classifier(decision_tree):
        if (  # length of class_names must be equal to the number of classes given by the tree
            class_names is not None
            and len(class_names) == len(decision_tree.classes_)
        ):
            class_names = class_names
        else:  # by default, we leave decision_tree.classes_
            class_names = decision_tree.classes_

sklearn/tree/_export.py

crispinlogan · 2023-01-13T01:15:33Z

sklearn/tree/_export.py

@@ -943,6 +944,10 @@ def export_text(
        A list of length n_features containing the feature names.
        If None generic names will be used ("feature_0", "feature_1", ...).

+    class_names : list of arguments, default=None


Could this be list of str instead of list of arguments? Like the parameter above, feature_names.

I also wondered about this, but when you print the tree, it is converted directly to a string with the _add_leaf() function :

scikit-learn/sklearn/tree/_export.py

Lines 1026 to 1034 in dc7ef61

def _add_leaf(value, class_name, indent):

val = ""

is_classification = isinstance(decision_tree, DecisionTreeClassifier)

if show_weights or not is_classification:

val = ["{1:.{0}f}, ".format(decimals, v) for v in value]

val = "[" + "".join(val)[:-2] + "]"

if is_classification:

val += " class: " + str(class_name)

export_text.report += value_fmt.format(indent, "", val)

So I figured that any element in the list, of any type, is converted to a string.

glemaitre

We will need unit tests to check the behaviour of each new options:

that we delegate to decision_tree.classes_ when passing None
check that we raise warning and create generic numerical string when "numeric" is passed
check that we overwrite the class names by passing a list
check that we raise an error with the wrong number of items in the list

glemaitre · 2023-01-13T14:28:20Z

sklearn/tree/_export.py

@@ -986,7 +991,10 @@ def export_text(
    check_is_fitted(decision_tree)
    tree_ = decision_tree.tree_
    if is_classifier(decision_tree):
-        class_names = decision_tree.classes_
+        if class_names is not None and len(class_names) == len(decision_tree.classes_):


Actually, we are breaking backward compatibility here even if one would like to have sensible default.

So if we want to preserve the previous behaviour and not introduce a limited number of parameters that need to be deprecated later, I would propose the following:

if class_names is "numeric": class_name = np.argmax(value) elif class_names is None: class_name = decision_tree.classes_[np.argmax(value)] else: class_name = class_names[np.argmax(value)]

Then, by default, class_names="numeric". Once we introduce the parameter, we can deprecate "numeric" and default to None.

Here for the validation, we would have something like:

if class_names == "numeric": warnings.warn( "The option `class_names='numeric'` is deprecated in 1.3 and will be removed " "in 1.5. Set `class_names=None`, the classes as seen by `decision_tree` during " "`fit` will be used instead.", FutureWarning, ) elif class_names is not None and len(class_names) != len(decision_tree.classes_): raise ValueError( "When `class_names` is not None, it should be a list containing as many " f"items as `decision_tree.classes_`. Got {len(class_names)} while the tree " f"was fitted with {len(decision_tree.classes_)} classes." )

glemaitre · 2023-01-13T14:37:34Z

sklearn/tree/_export.py

+    class_names : list of arguments, default=None
+        Names of each of the target classes in ascending numerical order.
+        Only relevant for classification and not supported for multi-output.


Suggested change

class_names : list of arguments, default=None

Names of each of the target classes in ascending numerical order.

Only relevant for classification and not supported for multi-output.

class_names : "numeric", list or None, default="numeric"

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 `"numeric"`, the class names are generic names representing numerical

numbers (e.g. `["0", "1", ...]`);

- if a list, the number of items should be the same as in

`decition_tree.classes_` and will be used.

.. versionadded:: 1.3

`class_names` was added in version 1.3.

.. deprecated:: 1.3

The `"numeric"` option is deprecated and will be replaced by `None`. Thus,

`decision_tree.classes_` will be used by default.

…ames

…arn into export_text_class_names

mokwilliam · 2023-01-13T15:46:26Z

Thank you for the reviews! So I've made new changes according to what you've written.
Hope it's better

glemaitre · 2023-01-13T16:51:41Z

We still need the tests as mentioned in my previous comment.

glemaitre · 2023-01-13T16:53:29Z

Interestingly, it seems that we broke the back compatibility as shown by the failure:

>       assert export_text(clf) == expected_report
E       AssertionError: assert '|--- feature...-- class: 1\n' == '|--- feature...-- class: 1\n'
E           |--- feature_1 <= 0.00
E         - |   |--- class: -1
E         ?                 -
E         + |   |--- class: 1
E           |--- feature_1 >  0.00
E           |   |--- class: 1

sklearn/tree/_export.py

glemaitre

We will also need to have an entry inside the changelog doc/whats_new/v1.3.rst.

mokwilliam · 2023-01-13T19:25:40Z

We will need unit tests to check the behaviour of each new options:

that we delegate to decision_tree.classes_ when passing None

check that we raise warning and create generic numerical string when "numeric" is passed

check that we overwrite the class names by passing a list

check that we raise an error with the wrong number of items in the list

Test: check that we raise an error with the wrong number of items in the list

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"])
    err_msg = (
        "When `class_names` is not None, it should be a list containing as"
        " many items as `decision_tree.classes_`. Got 1 while"
        " the tree was fitted with 2 classes."
    )
    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)

glemaitre · 2023-01-13T21:46:04Z

You need to put the test in the associated test file in test_export.py.

glemaitre

You need to write a new test to check that we raise a warning when passing "numeric".

Also, I would expect now the different tests to raise the warnings. We should modify them to use class_names=None. However, we should check the behaviour of "numeric" at the same time that we check that we raise the warning.

mokwilliam · 2023-01-20T16:30:24Z

I took the liberty of making some changes and tests :

in the export_text function, it doesn't recognize the list given for class_names.

        expected_report = dedent(
            """
        |--- feature_1 <= 0.00
        |   |--- class: a
        |--- feature_1 >  0.00
        |   |--- class: b
        """
        ).lstrip()
>       assert export_text(clf, class_names=["a", "b"]) == expected_report
E       AssertionError: assert '|--- feature...-- class: 1\n' == '|--- feature...-- class: b\n'
E           |--- feature_1 <= 0.00
E         - |   |--- class: a
E         ?                 ^
E         + |   |--- class: -1
E         ?                 ^^
E           |--- feature_1 >  0.00
E         - |   |--- class: b...
E
E         ...Full output truncated (4 lines hidden), use '-vv' to show

=> So I've decided to re-add the line. And the test above works.

class_names = class_names # when class_names is not None and len(class_names) == len(decision_tree.classes_)

I've added this line because : if "numeric", the class names are generic names representing numerical numbers

class_names = range(decision_tree.n_classes_)

glemaitre

Sorry for the misleading review. I tried to make the suggestion to remove the "numeric" option which is not necessary.

Could you also add an entry in the changelog in doc/whats_new/v1.3.rst to acknowlege the new parameter. It will be an enhancement.

glemaitre · 2023-01-23T09:15:15Z

sklearn/tree/_export.py

@@ -15,6 +15,7 @@
 from numbers import Integral

 import numpy as np
+import warnings


Can you move this import under from numbers import Integral

glemaitre · 2023-01-23T09:16:24Z

sklearn/tree/_export.py

@@ -1042,6 +1080,7 @@ def print_tree_recurse(node, depth):
            value = tree_.value[node][0]
        else:
            value = tree_.value[node].T[0]
+


You can revert this change.

If it was not introduced by black

glemaitre · 2023-01-23T09:20:41Z

sklearn/tree/_export.py

@@ -986,7 +1005,26 @@ def export_text(
    check_is_fitted(decision_tree)
    tree_ = decision_tree.tree_
    if is_classifier(decision_tree):
-        class_names = decision_tree.classes_


Uhm it seems that I misunderstood something when reading the documentation at first. We already uses decision_tree.classes_. So we don't need "numeric" and any deprecation (which is a good news).

Sorry to have brought this way. We will need to modify (remove) the code :)

glemaitre · 2023-01-23T09:21:03Z

sklearn/tree/_export.py

+        if class_names == "numeric":
+            warnings.warn(
+                "The option `class_names='numeric'` is deprecated in 1.3 and will be"
+                " removed in 1.5. Set `class_names=None`, the classes as seen by"
+                " `decision_tree` during `fit` will be used instead.",
+                FutureWarning,
+            )
+            class_names = range(decision_tree.n_classes_)
+        elif class_names is not None:


Suggested change

if class_names == "numeric":

warnings.warn(

"The option `class_names='numeric'` is deprecated in 1.3 and will be"

" removed in 1.5. Set `class_names=None`, the classes as seen by"

" `decision_tree` during `fit` will be used instead.",

FutureWarning,

)

class_names = range(decision_tree.n_classes_)

elif class_names is not None:

if class_names is not None:

glemaitre · 2023-01-23T09:22:01Z

sklearn/tree/_export.py

+    class_names : "numeric", list or None, default="numeric"
+        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 `"numeric"`, the class names are generic names representing numerical
+          numbers (e.g. `["0", "1", ...]`);
+        - if a list, the number of items should be the same as in
+          `decition_tree.classes_` and will be used.
+
+        .. versionadded:: 1.3
+           `class_names` was added in version 1.3.
+
+        .. deprecated:: 1.3
+           The `"numeric"` option is deprecated and will be replaced by `None`. Thus,
+           `decision_tree.classes_` will be used by default.


Suggested change

class_names : "numeric", list or None, default="numeric"

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 `"numeric"`, the class names are generic names representing numerical

numbers (e.g. `["0", "1", ...]`);

- if a list, the number of items should be the same as in

`decition_tree.classes_` and will be used.

.. versionadded:: 1.3

`class_names` was added in version 1.3.

.. deprecated:: 1.3

The `"numeric"` option is deprecated and will be replaced by `None`. Thus,

`decision_tree.classes_` will be used by default.

class_names : list, default="numeric"

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, the number of items should be the same as in

`decition_tree.classes_` and will be used.

.. versionadded:: 1.3

glemaitre · 2023-01-23T09:22:18Z

sklearn/tree/_export.py

+    class_names="numeric",
    max_depth=10,


Suggested change

class_names="numeric",

max_depth=10,

class_names=None,

max_depth=10,

glemaitre · 2023-01-23T09:22:55Z

sklearn/tree/tests/test_export.py

+def test_export_text_warnings():
+    clf = DecisionTreeClassifier(max_depth=2, random_state=0)
+    clf.fit(X, y)
+    warn_msg = (
+        "The option `class_names='numeric'` is deprecated in 1.3 and will be"
+        " removed in 1.5. Set `class_names=None`, the classes as seen by"
+        " `decision_tree` during `fit` will be used instead."
+    )
+    with pytest.warns(FutureWarning, match=warn_msg):
+        export_text(clf, class_names="numeric")
+
+


Suggested change

def test_export_text_warnings():

clf = DecisionTreeClassifier(max_depth=2, random_state=0)

clf.fit(X, y)

warn_msg = (

"The option `class_names='numeric'` is deprecated in 1.3 and will be"

" removed in 1.5. Set `class_names=None`, the classes as seen by"

" `decision_tree` during `fit` will be used instead."

)

with pytest.warns(FutureWarning, match=warn_msg):

export_text(clf, class_names="numeric")

We don't need this test anymore.

glemaitre · 2023-01-23T09:24:24Z

sklearn/tree/tests/test_export.py

+    assert export_text(clf, class_names=None, max_depth=0) == expected_report
    # testing that the rest of the tree is truncated
-    assert export_text(clf, max_depth=10) == expected_report
+    assert export_text(clf, class_names=None, max_depth=10) == expected_report


Suggested change

assert export_text(clf, class_names=None, max_depth=0) == expected_report

# testing that the rest of the tree is truncated

assert export_text(clf, max_depth=10) == expected_report

assert export_text(clf, class_names=None, max_depth=10) == expected_report

assert export_text(clf, max_depth=0) == expected_report

# testing that the rest of the tree is truncated

assert export_text(clf, max_depth=10) == expected_report

Basically, it is this test that indicated me that we were doing wrong since class_names=None seems to be the previous behaviour.

glemaitre · 2023-01-23T09:25:11Z

sklearn/tree/tests/test_export.py

+
+    expected_report = dedent(
+        """
+    |--- feature_1 <= 0.00
+    |   |--- class: 0
+    |--- feature_1 >  0.00
+    |   |--- class: 1
+    """
+    ).lstrip()
+    assert export_text(clf, class_names="numeric") == expected_report


Suggested change

expected_report = dedent(

"""

|--- feature_1 <= 0.00

| |--- class: 0

|--- feature_1 > 0.00

| |--- class: 1

"""

).lstrip()

assert export_text(clf, class_names="numeric") == expected_report

We don't need this case anymore.

glemaitre · 2023-01-23T09:25:47Z

sklearn/tree/tests/test_export.py

@@ -392,7 +421,29 @@ def test_export_text():
    |   |--- class: 1
    """
    ).lstrip()
-    assert export_text(clf, feature_names=["a", "b"]) == expected_report
+    assert (


You can remove the class_names=None since this will be the default indeed.

mokwilliam · 2023-01-23T10:49:55Z

Uhm it seems that I misunderstood something when reading the documentation at first. We already uses decision_tree.classes_. So we don't need "numeric" and any deprecation (which is a good news).

Sorry to have brought this way. We will need to modify (remove) the code :)

That’s fine! I’m making my first contribution on Sklearn, I’m learning a lot.

To point out something, when I'm doing pytest sklearn/tree/tests/test_export.py with this code, all of them passed.

    if is_classifier(decision_tree):
        if class_names is not None:
            if len(class_names) != len(decision_tree.classes_):
                raise ValueError(
                    "When `class_names` is not None, it should be a list containing as"
                    " many items as `decision_tree.classes_`. Got"
                    f" {len(class_names)} while the tree was fitted with"
                    f" {len(decision_tree.classes_)} classes."
                )
            else:
                class_names = class_names
        else:
            class_names = decision_tree.classes_

But if I'm combining the two if statements into one, it doesn't recognize the list passed as class_names.

    if is_classifier(decision_tree):
        if class_names is not None and len(class_names) != len(decision_tree.classes_):
            raise ValueError(
                "When `class_names` is not None, it should be a list containing as"
                " many items as `decision_tree.classes_`. Got"
                f" {len(class_names)} while the tree was fitted with"
                f" {len(decision_tree.classes_)} classes."
            )
        else:
            class_names = decision_tree.classes_

Gives

        expected_report = dedent(
            """
        |--- feature_1 <= 0.00
        |   |--- class: a
        |--- feature_1 >  0.00
        |   |--- class: b
        """
        ).lstrip()
>       assert export_text(clf, class_names=["a", "b"]) == expected_report
E       AssertionError: assert '|--- feature...-- class: 1\n' == '|--- feature...-- class: b\n'
E           |--- feature_1 <= 0.00
E         - |   |--- class: a
E         ?                 ^
E         + |   |--- class: -1
E         ?                 ^^
E           |--- feature_1 >  0.00
E         - |   |--- class: b...
E
E         ...Full output truncated (4 lines hidden), use '-vv' to show

sklearn\tree\tests\test_export.py:414: AssertionError

glemaitre · 2023-01-23T11:24:06Z

        if class_names is not None and len(class_names) != len(decision_tree.classes_):
            raise ValueError(...)
        else:
            class_names = decision_tree.classes_

Indeed, here you will fall in the else condition when passing an array since the right-hand side of the and operator will be False

mokwilliam · 2023-01-23T11:54:10Z

Yep, and after a raise error, we don't need a else statement. So it should be :

    if is_classifier(decision_tree):
        if class_names is not None:
            if len(class_names) != len(decision_tree.classes_):
                raise ValueError(
                    "When `class_names` is not None, it should be a list containing as"
                    " many items as `decision_tree.classes_`. Got"
                    f" {len(class_names)} while the tree was fitted with"
                    f" {len(decision_tree.classes_)} classes."
                )
            class_names = class_names
        else:
            class_names = decision_tree.classes_

glemaitre · 2023-01-23T16:41:14Z

sklearn/tree/_export.py

+        if class_names is not None:
+            if len(class_names) != len(decision_tree.classes_):
+                raise ValueError(
+                    "When `class_names` is not None, it should be a list containing as"
+                    " many items as `decision_tree.classes_`. Got"
+                    f" {len(class_names)} while the tree was fitted with"
+                    f" {len(decision_tree.classes_)} classes."
+                )
+            class_names = class_names
+        else:
+            class_names = decision_tree.classes_


Actually, the most compact way would be the following.

Suggested change

if class_names is not None:

if len(class_names) != len(decision_tree.classes_):

raise ValueError(

"When `class_names` is not None, it should be a list containing as"

" many items as `decision_tree.classes_`. Got"

f" {len(class_names)} while the tree was fitted with"

f" {len(decision_tree.classes_)} classes."

)

class_names = class_names

else:

class_names = decision_tree.classes_

if class_names is not None and len(class_names) != len(decision_tree.classes_):

raise ValueError(

"When `class_names` is not None, it should be a list containing as"

" many items as `decision_tree.classes_`. Got"

f" {len(class_names)} while the tree was fitted with"

f" {len(decision_tree.classes_)} classes."

)

elif class_names is None:

class_names = decision_tree.classes_

glemaitre · 2023-01-23T16:42:03Z

doc/whats_new/v1.3.rst

+- |Enhancement| Adds a `class_names` parameter to
+  :func:`tree.export_text`. This allows specifying the parameter `class_names`
+  for each target class in ascending numerical order.
+  :pr:`25387` by :user:`William M <Akbeeh>`, :user:`Guillaume Lemaitre <glemaitre>`, and


You can remove my name here. This is fine to not have it.

glemaitre · 2023-01-23T16:42:47Z

doc/whats_new/v1.3.rst

+  :pr:`25387` by :user:`William M <Akbeeh>`, :user:`Guillaume Lemaitre <glemaitre>`, and
+  :user:`crispinlogan <crispinlogan>`.
+
+:mod:`sklearn.tree`


I think that you mixed the entry here. Could you make sure to have sklearn.tree in the right alphabetic order and your entry just below it?

glemaitre

LGTM. Thanks @Akbeeh

mokwilliam · 2023-01-24T11:57:02Z

Thank you for all the comments. It was informative and I learned from it !

glemaitre · 2023-01-31T11:19:09Z

Putting a tag that this PR is waiting for another approval

thomasjpfan

Thank you for the PR @Akbeeh !

thomasjpfan · 2023-02-07T21:00:34Z

sklearn/tree/tests/test_export.py

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


Nit: To make a little more different compared to the test above:

Suggested change

expected_report = dedent(

"""

|--- feature_1 <= 0.00

| |--- class: a

|--- feature_1 > 0.00

| |--- class: b

"""

).lstrip()

assert export_text(clf, class_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

thomasjpfan · 2023-02-07T21:02:44Z

sklearn/tree/_export.py

+        - if `None`, the class names are delegated to `decision_tree.classes_`;
+        - if a list, the number of items should be the same as in
+          `decition_tree.classes_` and will be used.
+


This needs a .. versionadded:: 1.3 directive to indicate that the parameter was added in 1.3.

thomasjpfan · 2023-02-07T21:05:02Z

sklearn/tree/_export.py

+        if class_names is not None and len(class_names) != len(decision_tree.classes_):
+            raise ValueError(
+                "When `class_names` is not None, it should be a list containing as"
+                " many items as `decision_tree.classes_`. Got"
+                f" {len(class_names)} while the tree was fitted with"
+                f" {len(decision_tree.classes_)} classes."
+            )
+        elif class_names is None:
+            class_names = decision_tree.classes_


Nit: This order for checking is a little clearer to me:

Suggested change

if class_names is not None and len(class_names) != len(decision_tree.classes_):

raise ValueError(

"When `class_names` is not None, it should be a list containing as"

" many items as `decision_tree.classes_`. Got"

f" {len(class_names)} while the tree was fitted with"

f" {len(decision_tree.classes_)} classes."

)

elif class_names is None:

class_names = decision_tree.classes_

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"

" many items as `decision_tree.classes_`. Got"

f" {len(class_names)} while the tree was fitted with"

f" {len(decision_tree.classes_)} classes."

)

Also, I think it is less wordy to directly state that "When class_names is a list". (The test needs to be updated as well)

mokwilliam · 2023-02-08T17:58:51Z

Thanks for the remarks, I've made changes. Hope it'll fulfill your requirements !

thomasjpfan

Minor comment, otherwise LGTM!

sklearn/tree/_export.py

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

* ENH Raise NotFittedError in get_feature_names_out for MissingIndicator, KBinsDiscretizer, SplineTransformer, DictVectorizer (scikit-learn#25402) Co-authored-by: Alex <alex.buzenet.fr@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC Update date and contributors list for v1.2.1 (scikit-learn#25459) * DOC Make MeanShift documentation clearer (scikit-learn#25305) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * Finishes boolean and arithmetic creation * Skeleton for traditional GP * DOC Reorder whats_new/v1.2.rst (scikit-learn#25461) Follow-up of scikit-learn#25459 Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> * FIX fix faulty test in `cross_validate` that used the wrong estimator (scikit-learn#25456) * ENH Raise NotFittedError in get_feature_names_out for estimators that use ClassNamePrefixFeatureOutMixin and SelectorMixin (scikit-learn#25308) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * EFF Improve IsolationForest predict time (scikit-learn#25186) Co-authored-by: Felipe Breve Siola <felipe.breve-siola@klarna.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Tim Head <betatim@gmail.com> * MAINT refactor spectral_clustering to call SpectralClustering (scikit-learn#25392) * TST reduce warnings in test_logistic.py (scikit-learn#25469) * CI Build doc on CircleCI (scikit-learn#25466) * DOC Update news footer for 1.2.1 (scikit-learn#25472) * MAINT Validate parameter for `sklearn.cluster.cluster_optics_xi` (scikit-learn#25385) Co-authored-by: adossantosalfam <anthony.dos_santos_alfama@insa-rouen.fr> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Parameters validation for additive_chi2_kernel (scikit-learn#25424) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * Initial Program Creation * CI Include linting in CircleCI (scikit-learn#25475) * MAINT Update version number to 1.2.1 in SECURITY.md (scikit-learn#25471) * TST Sets random_state for test_logistic.py (scikit-learn#25446) * MAINT Remove -Wcpp warnings when compiling sklearn.decomposition._online_lda_fast (scikit-learn#25020) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * FIX Support readonly sparse datasets for `manhattan_distances` (scikit-learn#25432) * TST Add non-regression test for scikit-learn#7981 This reproducer is adapted from the one of this message: scikit-learn#7981 (comment) Co-authored-by: Loïc Estève <loic.esteve@ymail.com> * FIX Support readonly sparse datasets for manhattan * DOC Add entry in whats_new/v1.2.rst for 1.2.1 * FIX Fix comment * Update sklearn/metrics/tests/test_pairwise.py Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> * DOC Move entry to whats_new/v1.3.rst * Update sklearn/metrics/tests/test_pairwise.py Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * MAINT dynamically expose kulsinski and remove support in BallTree (scikit-learn#25417) Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> closes scikit-learn#25212 * DOC Adds CirrusCI badge to readme (scikit-learn#25483) * CI add linter display name (scikit-learn#25485) * DOC update description of X in `FunctionTransformer.transform()` (scikit-learn#24844) * MAINT remove -Wcpp warnings when compiling sklearn.preprocessing._csr_polynomial_expansion (scikit-learn#25041) * DOC more didactic example of bisecting kmeans (scikit-learn#25494) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * ENH csr_row_norms optimization (scikit-learn#24426) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> * TST Allow callables as valid parameter regarding cloning estimator (scikit-learn#25498) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: From: Tim Head <betatim@gmail.com> * DOC Fixes sphinx search on website (scikit-learn#25504) * FIX make IsotonicRegression always predict NumPy arrays (scikit-learn#25500) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * FEA Add Gamma deviance as loss function to HGBT (scikit-learn#22409) * FEA add gamma loss to HGBT * DOC add whatsnew * CLN address review comments * TST make test_gamma pass by not testing out-of-sample * TST compare gamma and poisson to LightGBM * TST fix test_gamma by comparing to MSE HGBT instead of Poisson HGBT * TST fix for test_same_predictions_regression for poisson * CLN address review comments * CLN nits * CLN better comments * TST use pytest.param with skip mark * TST Correct conditional test parametrization mark Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> * CI Trigger CI Builds currently fail because requests to Azure Ubuntu repository timeout. * DOC add comment for lax comparison with LightGBM * CLN tuple needs trailing comma --------- Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * MAINT Remove -Wsign-compare warnings when compiling sklearn.tree._tree (scikit-learn#25507) * MAINT add more intuition on OAS computation based on literature (scikit-learn#23867) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * CI Allow cirrus arm tests to run with cd build commit tag (scikit-learn#25514) * CI Upload ARM wheels from CirrusCI to nightly and staging index (scikit-learn#25513) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * MAINT Remove -Wcpp warnings from sklearn.utils._seq_dataset (scikit-learn#25406) * FIX Fixes linux ARM CI on CirrusCI (scikit-learn#25536) * DOC Fix grammatical mistake in `mixture` module (scikit-learn#25541) * DOC add missing trailing colon (scikit-learn#25542) * MAINT Parameters validation for sklearn.datasets.make_classification (scikit-learn#25474) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MNT Expose allow_nan tag in bagging (scikit-learn#25506) * MAINT Clean-up comments and rename variables in `_middle_term_sparse_sparse_{32, 64}` (scikit-learn#25449) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * DOC: remove incorrect statement (scikit-learn#25544) * MAINT Parameters validation for reconstruct_from_patches_2d (scikit-learn#25384) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Parameter validation for sklearn.metrics.d2_pinball_score (scikit-learn#25414) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for spectral_clustering (scikit-learn#25378) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Parameters validation for sklearn.datasets.fetch_kddcup99 (scikit-learn#25463) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC Update MLPRegressor docs (scikit-learn#25556) Co-authored-by: Ian Thompson <ian.thompson@hrblock.com> * DOC Update docs for KMeans (scikit-learn#25546) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * FIX BisectingKMeans crashes randomly (scikit-learn#25563) Fixes scikit-learn#25505 * ENH BaseLabelPropagation to accept sparse matrices (scikit-learn#19664) Co-authored-by: Kaushik Amar Das <kaushik.amar.das@accenture.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Remove travis ci config and related doc (scikit-learn#25562) * DOC Add pynndescent to Approximate nearest neighbors in TSNE example (scikit-learn#25480) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * DOC Add docstring example to make_regression (scikit-learn#25551) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT ensure that pos_label support all possible types (scikit-learn#25317) * MAINT Parameters validation for sklearn.metrics.f1_score (scikit-learn#25557) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * ENH Adds `class_names` to `tree.export_text` (scikit-learn#25387) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Replace cnp.ndarray with memory views in sklearn.tree._tree (where possible) (scikit-learn#25540) * DOC Change print format in TSNE example (scikit-learn#25569) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * FIX ColumnTransformer supports empty selection for pandas output (scikit-learn#25570) Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * DOC fix docstring of _plain_sgd (scikit-learn#25573) * FIX Enable setting of sub-parameters for deprecated base_estimator param (scikit-learn#25477) * DOC Improve minor and bug-fix release processes documentation (scikit-learn#25457) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@yahoo.fr> * MAINT Remove ReadonlyArrayWrapper from _loss module (scikit-learn#25555) * MAINT Remove ReadonlyArrayWrapper from _loss module * CLN Remove comments about Cython 3.0 * MAINT Remove ReadonlyArrayWrapper from _kmeans (scikit-learn#25554) * MAINT Remove ReadonlyArrayWrapper from _kmeans * more const and remove blas compile warnings * CLN Adds comment about casting to non const pointers * Update sklearn/utils/_cython_blas.pyx * MAINT Remove ReadonlyArrayWrapper from DistanceMetric (scikit-learn#25553) * DOC improve stop_words description w.r.t. max_df range in CountVectorizer (scikit-learn#25489) * MAINT Removes ReadOnlyWrapper (scikit-learn#25586) * MAINT Parameters validation for sklearn.metrics.log_loss (scikit-learn#25577) * MAINT Adds comments and better naming into tree code (scikit-learn#25576) * MAINT Adds comments and better naming into tree code * CLN Use feature_values instead of Xf * Apply suggestions from code review Co-authored-by: Adam Li <adam2392@gmail.com> * DOC Improve comment from review * Apply suggestions from code review Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> --------- Co-authored-by: Adam Li <adam2392@gmail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * FIX error when deserialzing a Tree instance from a read only buffer (scikit-learn#25585) * DOC: fix typo in California Housing dataset description (scikit-learn#25613) * ENH: Update KDTree, and example documentation (scikit-learn#25482) * ENH: Update KDTree, and example documentation * ENH: Add valid metric function and reference doc * CHG: Documentation update Co-authored-by: Adam Li <adam2392@gmail.com> * CHG: make valid metric property and fix doc string * FIX: documentation, and add code example * ENH: Change valid metric to class method, and doc * ENH: Change valid metric class variable, and doc * FIX: documentation error * FIX: documentation error * CHG: Use class method for valid metrics * FIX: CI problems --------- Co-authored-by: Adam Li <adam2392@gmail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> * TST Common test for checking estimator deserialization from a read only buffer (scikit-learn#25624) * DOC fix comment in plot_logistic_l1_l2_sparsity.py (scikit-learn#25633) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * DOC Places governance in navigation bar (scikit-learn#25618) * MAINT Check pyproject toml is consistent with min_dependencies (scikit-learn#25610) * MAINT Check pyproject toml is consistent with min_dependencies * CLN Make it clear that only SciPy and Cython are checked * CLN Revert auto formatter * MAINT Use newest NumPy C API in tree._criterion (scikit-learn#25615) * MAINT Use newest NumPy C API in tree._criterion * FIX Use pointer for children * FIX Fixes check_array nonfinite checks with ArrayAPI specification (scikit-learn#25619) * FIX Fixes check_array nonfinite checks with ArrayAPI specification * DOC Adds PR number * FIX Test on both cupy and numpy * DOC Correctly docstring in StackingRegressor.fit_transform (scikit-learn#25599) * MAINT Remove Cython compilation warnings ahead of Cython3.0 release (scikit-learn#25621) * ENH Preserve DataFrame dtypes in transform for feature selectors (scikit-learn#25102) * FIX report properly n_iter_ when warm_start=True (scikit-learn#25443) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * DOC fix typo in KMeans's param. (scikit-learn#25649) * FIX use const memory views in hist_gradient_boosting predictor (scikit-learn#25650) * DOC modified the graph for better readability (scikit-learn#25644) * MAINT Removes upper limit on setuptools (scikit-learn#25651) * DOC improve the `warm_start` glossary entry (scikit-learn#25523) * DOC Update governance document for SLEP020 (scikit-learn#25663) Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> * FIX renormalization of y_pred inside log_loss (scikit-learn#25299) * Remove renormalization of y_pred inside log_loss * Deprecate eps parameter in log_loss * ENH Allows target to be pandas nullable dtypes (scikit-learn#25638) * DOC unify usage of 'w.r.t.' (scikit-learn#25683) * MAINT Parameters validation for metrics.max_error (scikit-learn#25679) * MAINT Parameters validation for datasets.make_friedman1 (scikit-learn#25674) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MAINT Parameters validation for mean_pinball_loss (scikit-learn#25685) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * DOC Specify behavior of None for CountVectorizer (scikit-learn#25678) * DOC Specify behaviour of None for TfIdfVectorizer max_features parameter (scikit-learn#25676) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> * MAINT Set random state for plot_anomaly_comparison (scikit-learn#25675) * MAINT Parameters validation for cluster.mean_shift (scikit-learn#25684) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * MAINT Parameters validation for sklearn.metrics.jaccard_score (scikit-learn#25680) Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr> * DOC Add the custom compiler section back (scikit-learn#25667) Co-authored-by: Thomas J. 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Fan <thomasjpfan@gmail.com> * DOC Add scikit-learn-intelex to related projects (scikit-learn#23766) Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> * ENH Support float32 in SGDClassifier and SGDRegressor (scikit-learn#25587) * FIX Raise appropriate attribute error in ensemble (scikit-learn#25668) * FIX Allow OrdinalEncoder's encoded_missing_value set to the cardinality (scikit-learn#25704) * ENH Let csr_row_norms support multi-thread (scikit-learn#25598) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Vincent M <maladiere.vincent@yahoo.fr> * MAINT Parameter Validation for feature_selection.chi2 (scikit-learn#25719) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Parameter Validation for feature_selection.f_classif (scikit-learn#25720) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for sklearn.metrics.matthews_corrcoef (scikit-learn#25712) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT parameter validation for sklearn.datasets.dump_svmlight_file (scikit-learn#25726) Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr> * MAINT Clean dead code in build helpers (scikit-learn#25661) * MAINT Use newest NumPy C API in metrics._dist_metrics (scikit-learn#25702) * CI Adds permissions to workflows that use GITHUB_TOKEN (scikit-learn#25600) Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> * FIX Improves error message in partial_fit when early_stopping=True (scikit-learn#25694) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Makes navbar static (scikit-learn#25688) * MAINT Remove redundant sparse square euclidian distances function (scikit-learn#25731) * MAINT Use float64 for accumulators in WeightVector* (scikit-learn#25721) * API make PatchExtractor being a real scikit-learn transformer (scikit-learn#24230) Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Update pyparsing.py to use bool instead of double negation (scikit-learn#25724) * API Deprecates values in partial_dependence in favor of pdp_values (scikit-learn#21809) Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * API Use grid_values instead of pdp_values in partial_dependence (scikit-learn#25732) * MAINT remove np.product and inf/nan aliases in favor of canonical names (scikit-learn#25741) * MAINT Parameters validation for metrics.label_ranking_loss (scikit-learn#25742) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for metrics.coverage_error (scikit-learn#25748) * MAINT Parameters validation for metrics.dcg_score (scikit-learn#25749) * MAINT replace cnp.ndarray with memory views in _fast_dict (scikit-learn#25754) * MAINT Parameter Validation for feature_selection.f_regression (scikit-learn#25736) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameters validation for feature_selection.r_regression (scikit-learn#25734) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Parameter Validation for metrics.get_scorer (scikit-learn#25738) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * DOC Move allowing pandas nullable dtypes to 1.2.2 (scikit-learn#25692) * MAINT replace cnp.ndarray with memory views in sparsefuncs_fast (scikit-learn#25764) * MAINT parameter validation for sklearn.datasets.fetch_covtype (scikit-learn#25759) Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> * MAINT Define centralized generic, but with explicit precision, types (scikit-learn#25739) * CI Disable network when SciPy requires it (scikit-learn#25743) * CI Open issue when arm wheel fails on CirrusCI (scikit-learn#25620) * ENH Speed-up expected mutual information (scikit-learn#25713) Co-authored-by: Kshitij Mathur <k.mathur68@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Omar Salman <omar.salman@arbisoft.com> * FIX add retry mechanism to handle quotechar in read_csv (scikit-learn#25511) * Merge Population Creation (#1) --------- Co-authored-by: Alex Buzenet <94121450+albuzenet@users.noreply.github.com> Co-authored-by: Alex <alex.buzenet.fr@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com> Co-authored-by: Julien Jerphanion <git@jjerphan.xyz> Co-authored-by: Adam Kania <48769688+remilvus@users.noreply.github.com> Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@users.noreply.github.com> Co-authored-by: Shady el Gewily <90049412+shadyelgewily-slimstock@users.noreply.github.com> Co-authored-by: John Pangas <swiftyxswaggy@outlook.com> Co-authored-by: Felipe Siola <fsiola@gmail.com> Co-authored-by: Felipe Breve Siola <felipe.breve-siola@klarna.com> Co-authored-by: Tim Head <betatim@gmail.com> Co-authored-by: Christian Lorentzen <lorentzen.ch@gmail.com> Co-authored-by: Loïc Estève <loic.esteve@ymail.com> Co-authored-by: Anthony22-dev <122220081+Anthony22-dev@users.noreply.github.com> Co-authored-by: adossantosalfam <anthony.dos_santos_alfama@insa-rouen.fr> Co-authored-by: Xiao Yuan <yuanx749@gmail.com> Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com> Co-authored-by: Omar Salman <omar.salman@arbisoft.com> Co-authored-by: Rahil Parikh <75483881+rprkh@users.noreply.github.com> Co-authored-by: Gael Varoquaux <gael.varoquaux@normalesup.org> Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com> Co-authored-by: Meekail Zain <34613774+Micky774@users.noreply.github.com> Co-authored-by: davidblnc <40642621+davidblnc@users.noreply.github.com> Co-authored-by: Changyao Chen <changyao.chen@gmail.com> Co-authored-by: Nicola Fanelli <48762613+nicolafan@users.noreply.github.com> Co-authored-by: Vincent M <maladiere.vincent@yahoo.fr> Co-authored-by: partev <petrosyan@gmail.com> Co-authored-by: ouss1508 <121971998+ouss1508@users.noreply.github.com> Co-authored-by: ashah002 <97778401+ashah002@users.noreply.github.com> Co-authored-by: Ahmedbgh <83551938+Ahmedbgh@users.noreply.github.com> Co-authored-by: Pooja M <90301980+pm155@users.noreply.github.com> Co-authored-by: Ian Thompson <ianiat11@gmail.com> Co-authored-by: Ian Thompson <ian.thompson@hrblock.com> Co-authored-by: SANJAI_3 <86285670+sanjail3@users.noreply.github.com> Co-authored-by: Kaushik Amar Das <cozek@users.noreply.github.com> Co-authored-by: Kaushik Amar Das <kaushik.amar.das@accenture.com> Co-authored-by: Nawazish Alam <nawazishmail@gmail.com> Co-authored-by: William M <64324808+Akbeeh@users.noreply.github.com> Co-authored-by: Jérémie du Boisberranger <jeremiedbb@yahoo.fr> Co-authored-by: JanFidor <66260538+JanFidor@users.noreply.github.com> Co-authored-by: Adam Li <adam2392@gmail.com> Co-authored-by: Logan Thomas <logan.thomas005@gmail.com> Co-authored-by: Vyom Pathak <angerstick3@gmail.com> Co-authored-by: as-90 <88336957+as-90@users.noreply.github.com> Co-authored-by: Marvin Krawutschke <101656586+Marvvxi@users.noreply.github.com> Co-authored-by: Haesun Park <haesunrpark@gmail.com> Co-authored-by: Christine P. Chai <star1327p@gmail.com> Co-authored-by: Christian Veenhuis <124370897+ChVeen@users.noreply.github.com> Co-authored-by: Sortofamudkip <wishyutp0328@gmail.com> Co-authored-by: sonnivs <48860780+sonnivs@users.noreply.github.com> Co-authored-by: Ali H. 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Update _export.py

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glemaitre changed the title ~~[MRG] New feature: class_names argument in tree.export_text()~~ ENH add class_names in tree.export_text Jan 13, 2023

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mokwilliam and others added 4 commits January 13, 2023 16:28

Merge remote-tracking branch 'upstream/main' into export_text_class_n…

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Merge branch 'main' into export_text_class_names

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Update default class_names=numeric

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Merge branch 'main' into export_text_class_names

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Small change, remove else statement after raise ValueError

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Merge branch 'main' into export_text_class_names

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Merge branch 'main' into export_text_class_names

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Update: rewording

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Merge branch 'main' into export_text_class_names

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thomasjpfan changed the title ~~ENH add class_names in tree.export_text~~ ENH Adds class_names to tree.export_text Feb 8, 2023

thomasjpfan merged commit aae5c83 into scikit-learn:main Feb 8, 2023

	def _add_leaf(value, class_name, indent):
	val = ""
	is_classification = isinstance(decision_tree, DecisionTreeClassifier)
	if show_weights or not is_classification:
	val = ["{1:.{0}f}, ".format(decimals, v) for v in value]
	val = "[" + "".join(val)[:-2] + "]"
	if is_classification:
	val += " class: " + str(class_name)
	export_text.report += value_fmt.format(indent, "", val)

ENH Adds class_names to tree.export_text #25387

ENH Adds class_names to tree.export_text #25387

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mokwilliam commented Jan 12, 2023 • edited by glemaitre Loading

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What does this implement/fix? Explain your changes.

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ENH Adds `class_names` to `tree.export_text` #25387

ENH Adds `class_names` to `tree.export_text` #25387

mokwilliam commented Jan 12, 2023 •

edited by glemaitre

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