Drop python<3.9, support modern libraries

.github/workflows/python-package.yml

@@ -17,26 +17,24 @@ jobs:
       fail-fast: false
       matrix:
         include:
-            - python-version: '3.6'
+            - python-version: '3.10'
               tox-env: 'mypy'
-            - python-version: '3.9'
+            - python-version: '3.10'
               tox-env: 'docs'
-            - python-version: '3.6'
-              tox-env: 'py36'
-            - python-version: '3.6'
-              tox-env: 'py36-nodeps'
-            - python-version: '3.6'
-              tox-env: 'py36-extra'
-            - python-version: '3.7'
-              tox-env: 'py37'
-            - python-version: '3.8'
-              tox-env: 'py38'
-            - python-version: '3.8'
-              tox-env: 'py38-nodeps'
             - python-version: '3.9'
               tox-env: 'py39'
-            - python-version: '3.9'
-              tox-env: 'py39-nodeps'
+            - python-version: '3.10'
+              tox-env: 'py310'
+            - python-version: '3.10'
+              tox-env: 'py310-nodeps'
+            - python-version: '3.10'
+              tox-env: 'py310-extra'
+            - python-version: '3.11'
+              tox-env: 'py311'
+            - python-version: '3.12'
+              tox-env: 'py312'
+            - python-version: '3.13'
+              tox-env: 'py313'
 
     steps:
     - uses: actions/checkout@v2

CHANGES.rst

@@ -1,6 +1,12 @@
 Changelog
 =========
 
+0.14.0 (?)
+-------------------
+
+* drop support for python 3.6, 3.7, 3.8
+* add support for python 3.11, 3.12, 3.13
+
 0.13.0 (2022-05-11)
 -------------------
 

docs/requirements.txt

@@ -4,5 +4,5 @@ sphinx_rtd_theme
 ipython
 scipy
 numpy > 1.9.0
-scikit-learn >= 0.20
-typing
+pandas
+scikit-learn >= 1.6.0

docs/source/conf.py

@@ -54,7 +54,7 @@ def __getattr__(cls, name):
     'keras.models',
     'keras.layers',
     'keras.preprocessing.image',
-    'pandas',
+    # 'pandas',
     'PIL',
     'matplotlib',
     'matplotlib.pyplot',
@@ -69,7 +69,7 @@ def __getattr__(cls, name):
 
 def setup(app):
     # see https://github.com/snide/sphinx_rtd_theme/issues/117
-    app.add_stylesheet("rtfd_overrides.css")
+    app.add_css_file("rtfd_overrides.css")
 
 suppress_warnings = ['image.nonlocal_uri']
 
@@ -123,7 +123,7 @@ def setup(app):
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = 'en'
 
 # There are two options for replacing |today|: either, you set today to some
 # non-false value, then it is used:

docs/source/libraries/keras.rst

@@ -8,7 +8,7 @@ Keras_ is "a high-level neural networks API, written in Python and capable of ru
 Keras can be used for many Machine Learning tasks, and it has support for both popular
 and experimental neural network architectures.
 
-Note: only TensorFlow 1.x is supported, recommended Keras version is 2.3.1 or earlier.
+Note: only TensorFlow 1.x is supported, recommended Keras version is 2.3.1 or earlier, and eli5 version 0.13 or earlier, as you can't install TensorFlow 1.x on Python 3.9+ which is required for eli5 0.14+
 
 .. _Keras: https://keras.io/
 

docs/source/libraries/xgboost.rst

@@ -6,7 +6,9 @@ XGBoost
 XGBoost_ is a popular Gradient Boosting library with Python interface.
 eli5 supports :func:`eli5.explain_weights` and :func:`eli5.explain_prediction`
 for XGBClassifer_, XGBRegressor_ and Booster_ estimators. It is tested for
-xgboost >= 0.6a2.
+xgboost >= 0.6a2 and < 2.0.0.
+Versions starting from 2.0.0 likely produce incorrect results in
+:func:`eli5.explain_prediction`, and will issue a warning.
 
 .. _XGBoost: https://github.com/dmlc/xgboost
 .. _XGBClassifer: https://xgboost.readthedocs.io/en/latest/python/python_api.html#xgboost.XGBClassifier

eli5/_feature_names.py

@@ -1,9 +1,6 @@
 import re
-import six
 from typing import (
-    Any, Iterable, Iterator, Tuple, Sized, List, Optional, Dict,
-    Union, Callable, Pattern
-)
+    Any, Iterable, Iterator, Sized, Optional, Union, Callable, Pattern)
 
 import numpy as np
 import scipy.sparse as sp
@@ -14,15 +11,14 @@ class FeatureNames(Sized, Iterable):
     A list-like object with feature names. It allows
     feature names for unknown features to be generated using
     a provided template, and to avoid making copies of large objects
-    in get_feature_names.
+    in get_feature_names_out.
     """
     def __init__(self,
                  feature_names=None,
-                 bias_name=None,  # type: str
-                 unkn_template=None,  # type: str
-                 n_features=None,  # type: int
+                 bias_name: Optional[str] = None,
+                 unkn_template: Optional[str] = None,
+                 n_features: Optional[int] = None,
                  ):
-        # type: (...) -> None
         if not (feature_names is not None or
                     (unkn_template is not None and n_features)):
             raise ValueError(
@@ -39,20 +35,17 @@ def __init__(self,
                         'unkn_template should be set for sparse features')
         self.feature_names = feature_names
         self.unkn_template = unkn_template
-        self.n_features = n_features or len(feature_names)  # type: int
+        self.n_features: int = n_features or len(feature_names)
         self.bias_name = bias_name
 
-    def __repr__(self):
-        # type: () -> str
+    def __repr__(self) -> str:
         return '<FeatureNames: {} features {} bias>'.format(
             self.n_features, 'with' if self.has_bias else 'without')
 
-    def __len__(self):
-        # type: () -> int
+    def __len__(self) -> int:
         return self.n_features + int(self.has_bias)
 
-    def __iter__(self):
-        # type: () -> Iterator[str]
+    def __iter__(self) -> Iterator[str]:
         return (self[i] for i in range(len(self)))
 
     def __getitem__(self, idx):
@@ -69,10 +62,10 @@ def __getitem__(self, idx):
                 return self.unkn_template % idx
         raise IndexError('Feature index out of range')
 
-    def _slice(self, aslice):
-        # type: (slice) -> Any
+    def _slice(self, aslice: slice):
         if isinstance(self.feature_names, (list, np.ndarray)):
             # Fast path without going through __getitem__
+            lst: Union[list, np.ndarray]
             if self.has_bias:
                 lst = list(self.feature_names)
                 lst.append(self.bias_name)
@@ -84,29 +77,26 @@ def _slice(self, aslice):
             return [self[idx] for idx in indices]
 
     @property
-    def has_bias(self):
-        # type: () -> bool
+    def has_bias(self) -> bool:
         return self.bias_name is not None
 
     @property
-    def bias_idx(self):
-        # type: () -> Optional[int]
+    def bias_idx(self) -> Optional[int]:
         if self.has_bias:
             return self.n_features
         return None
 
-    def filtered(self, feature_filter, x=None):
-        # type: (Callable, Any) -> Tuple[FeatureNames, List[int]]
+    def filtered(self, feature_filter: Callable, x=None) -> tuple['FeatureNames', list[int]]:
         """ Return feature names filtered by a regular expression 
         ``feature_re``, and indices of filtered elements.
         """
         indices = []
         filtered_feature_names = []
-        indexed_names = None  # type: Optional[Iterable[Tuple[int, Any]]]
+        indexed_names: Optional[Iterable[tuple[int, Any]]] = None
         if isinstance(self.feature_names, (np.ndarray, list)):
             indexed_names = enumerate(self.feature_names)
         elif isinstance(self.feature_names, dict):
-            indexed_names = six.iteritems(self.feature_names)
+            indexed_names = self.feature_names.items()
         elif self.feature_names is None:
             indexed_names = []
         assert indexed_names is not None
@@ -116,8 +106,7 @@ def filtered(self, feature_filter, x=None):
                 assert x.shape[0] == 1
                 flt = lambda nm, i: feature_filter(nm, x[0, i])
             else:
-                # FIXME: mypy warns about x[i] because it thinks x can be None
-                flt = lambda nm, i: feature_filter(nm, x[i])  # type: ignore
+                flt = lambda nm, i: feature_filter(nm, x[i])
         else:
             flt = lambda nm, i: feature_filter(nm)
 
@@ -141,10 +130,9 @@ def filtered(self, feature_filter, x=None):
 
     def handle_filter(self,
                       feature_filter,
-                      feature_re,  # type: Pattern[str]
-                      x=None,  # type: Any
-                      ):
-        # type: (...) -> Tuple[FeatureNames, Union[List[int], None]]
+                      feature_re: Pattern[str],
+                      x=None,
+                      ) -> tuple['FeatureNames', Union[list[int], None]]:
         if feature_re is not None and feature_filter:
             raise ValueError('pass either feature_filter or feature_re')
         if feature_re is not None:
@@ -158,8 +146,7 @@ def handle_filter(self,
         else:
             return self, None
 
-    def add_feature(self, feature):
-        # type: (Any) -> int
+    def add_feature(self, feature) -> int:
         """ Add a new feature name, return it's index.
         """
         # A copy of self.feature_names is always made, because it might be
@@ -179,8 +166,7 @@ def add_feature(self, feature):
         return idx
 
 
-def _all_feature_names(name):
-    # type: (Union[str, bytes, List[Dict]]) -> List[str]
+def _all_feature_names(name: Union[str, bytes, list[dict]]) -> list[str]:
     """ All feature names for a feature: usually just the feature itself,
     but can be several features for unhashed features with collisions.
     """