[python] dalex v1.8.0

.github/workflows/Python-check.yaml

@@ -27,7 +27,7 @@ jobs:
     strategy:
       matrix:
         platform: [ubuntu-latest, macos-latest] # windows-latest
-        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
+        python-version: ['3.9', '3.10', '3.11', '3.12', '3.13']
 
     steps:
     - uses: actions/checkout@v4

python/dalex/NEWS.md

@@ -1,8 +1,12 @@
 ## Changelog
 
-### development
+### v1.8.0 (2026-01-20)
 
-...
+* substitute the deprecated `pkg_resources` dependency that breaks `dalex` ([#579](https://github.com/ModelOriented/DALEX/issues/579))
+* increase the `plotly` dependency to `>=6.0.0` and fix compatibility issues with the new version, e.g. `titlefont` is now `title_font` ([#573](https://github.com/ModelOriented/DALEX/issues/573))
+* restrict the `pandas` dependency to `<3.0.0` to counteract future api changes, e.g. `pd.stack(..., future_stack=False)`.
+* remove the `ppscore` optional dependency used by the `aspect` module from `dalex[full]` as it imposes `pandas<2.0.0`
+* increase the dependency to `python>=3.9` and add `python==3.13` to CI
 
 ### v1.7.2 (2025-02-12)
 

python/dalex/dalex/__init__.py

@@ -9,7 +9,7 @@
 from .aspect import Aspect
 
 
-__version__ = '1.7.2.9000'
+__version__ = '1.8.0'
 
 __all__ = [
   "Arena",

python/dalex/dalex/_explainer/helper.py

@@ -13,5 +13,5 @@ def is_y_in_data(data, y):
 
 
 def get_model_info(model):
-    model_package = re.search("(?<=<class ').*?(?=\.)", str(type(model)))[0]
+    model_package = re.search(r"(?<=<class ').*?(?=\.)", str(type(model)))[0]
     return {'model_package': model_package}

python/dalex/dalex/_global_checks.py

@@ -1,5 +1,6 @@
-import pkg_resources
 from importlib import import_module
+from importlib.metadata import version, PackageNotFoundError
+from packaging.version import parse
 from re import search
 
 # WARNING: below code is parsed by setup.py
@@ -25,9 +26,9 @@
     'flask_cors': '3.0.8',
     'requests': '2.24.0',
     'kaleido': '0.2.1',
-    'ppscore': '1.2.0',
     'ipywidgets': '7.6.3'
 }
+    # 'ppscore': '1.3.0',
 # WARNING
 # WARNING
 
@@ -41,13 +42,13 @@ def global_check_import(name=None, functionality=None):
             else:
                 import_module(name)
 
-            installed_version = pkg_resources.parse_version(pkg_resources.get_distribution(name).version)
-            needed_version = pkg_resources.parse_version(OPTIONAL_DEPENDENCIES[name])
+            installed_version = parse(version(name))
+            needed_version = parse(OPTIONAL_DEPENDENCIES[name])
             if installed_version < needed_version:
                 raise ImportWarning("Outdated version of optional dependency '" + name + "'. " +
                                     ("Update '" + name + "' for " + functionality + ". ") if functionality else "" +
                                     "Use pip or conda to update '" + name + "' to avoid potential errors.")
-        except ImportError:
+        except (ImportError, PackageNotFoundError):
             raise ImportError("Missing optional dependency '" + name + "'. " +
                               ("Install '" + name + "' for " + functionality + ". ") if functionality else "" +
                               "Use pip or conda to install '" + name + "'.")

python/dalex/dalex/fairness/_group_fairness/plot.py

@@ -328,20 +328,20 @@ def plot_metric_scores(fobject,
     for metric in data.metric.unique():
         for label in data.label.unique():
             x = float(privileged_data.loc[(privileged_data.metric == metric) &
-                                          (privileged_data.label == label), :].score)
+                                          (privileged_data.label == label), :].score.iloc[0])
             if np.isnan(x):
                 lines_nan = True
                 continue
             # lines
             for subgroup in data.subgroup.unique():
                 y = float(data.loc[(data.metric == metric) &
                                    (data.label == label) &
-                                   (data.subgroup == subgroup)].subgroup_numeric)
+                                   (data.subgroup == subgroup)].subgroup_numeric.iloc[0])
                 # horizontal
 
                 x0 = float(data.loc[(data.metric == metric) &
                                     (data.label == label) &
-                                    (data.subgroup == subgroup)].score)
+                                    (data.subgroup == subgroup)].score.iloc[0])
 
                 if np.isnan(x0):
                     lines_nan = True
@@ -691,7 +691,7 @@ def plot_ceteris_paribus_cutoff(fobject,
 def plot_density(fobject,
                  other_objects,
                  title):
-    data = pd.DataFrame(columns=['y', 'y_hat', 'subgroup', 'model'])
+    data_list = []
     objects = [fobject]
     if other_objects is not None:
         for other_obj in other_objects:
@@ -703,7 +703,9 @@ def plot_density(fobject,
                                            'y_hat': y_hat,
                                            'subgroup': np.repeat(subgroup, len(y)),
                                            'model': np.repeat(obj.label, len(y))})
-            data = pd.concat([data, data_to_append])
+            data_list.append(data_to_append)
+
+    data = pd.concat(data_list)
 
     fig = go.Figure()
 

python/dalex/dalex/fairness/_group_fairness/utils.py

@@ -96,14 +96,14 @@ def __init__(self, sub_confusion_matrix):
 
     def to_vertical_DataFrame(self) -> pd.DataFrame:
 
-        columns = ['metric', 'subgroup', 'score']
-        data = pd.DataFrame(columns=columns)
+        df_list = []
         metrics = self.subgroup_confusion_matrix_metrics
         for subgroup in metrics.keys():
             metric = metrics.get(subgroup)
             subgroup_vec = np.repeat(subgroup, len(metric))
             sub_df = pd.DataFrame({'metric': metric.keys(), 'subgroup': subgroup_vec, 'score': metric.values()})
-            data = pd.concat([data, sub_df])
+            df_list.append(sub_df)
+        data = pd.concat(df_list, ignore_index=True)
         return data
 
     def to_horizontal_DataFrame(self) -> pd.DataFrame:
@@ -194,7 +194,7 @@ def _fairness_theme(title):
             'template': 'plotly_white',
             'title_x': 0.5,
             'title_y': 0.99,
-            'titlefont': {'size': 25},
+            'title_font': {'size': 25},
             'font': {'color': "#371ea3"},
             'margin': {'t': 78, 'b': 71, 'r': 30}}
 
@@ -286,8 +286,7 @@ def calculate_regression_measures(y, y_hat, protected, privileged):
     unique_protected = np.unique(protected)
     unique_unprivileged = unique_protected[unique_protected != privileged]
 
-    data = pd.DataFrame(columns=['subgroup', 'independence', 'separation', 'sufficiency'])
-
+    data_list = []
     for unprivileged in unique_unprivileged:
         # filter elements
         array_elements = np.isin(protected, [privileged, unprivileged])
@@ -319,8 +318,12 @@ def calculate_regression_measures(y, y_hat, protected, privileged):
                                   'independence': [r_ind],
                                   'separation': [r_sep],
                                   'sufficiency': [r_suf]})
+        data_list.append(to_append)
 
-        data = pd.concat([data, to_append])
+    if data_list:
+        data = pd.concat(data_list, ignore_index=True)
+    else:
+        data = pd.DataFrame(columns=['subgroup', 'independence', 'separation', 'sufficiency'])
 
     # append the scale
     to_append = pd.DataFrame({'subgroup': [privileged],

python/dalex/dalex/model_explanations/_aggregated_profiles/utils.py

@@ -15,7 +15,7 @@ def aggregate_profiles(all_profiles, mean_prediction, type, groups, center, span
         aggregated_profiles = \
             all_profiles. \
                 loc[:, ["_vname_", "_label_", "_x_", "_yhat_", "_ids_", "_original_"] + groups]. \
-                groupby(['_vname_', '_label_']). \
+                groupby(['_vname_', '_label_'])[["_x_", "_yhat_", "_ids_", "_original_"] + groups]. \
                 progress_apply(lambda split_profile: split_over_variables_and_labels(split_profile.copy(deep=True),
                                                                                      type, groups, span)). \
                 reset_index(level=[0, 1])  # remove level_2
@@ -83,7 +83,7 @@ def split_over_variables_and_labels(split_profile, type, groups, span):
 
     par_profile = split_profile.groupby(['_x_'] + groups, sort=False). \
         apply(lambda point: (point['_yhat_'] * point['_w_']).sum() / point['_w_'].sum() \
-        if point['_w_'].sum() != 0 else 0)
+        if point['_w_'].sum() != 0 else 0, include_groups=False)
 
     par_profile.name = '_yhat_'
     par_profile = par_profile.reset_index()

python/dalex/dalex/model_explanations/_variable_importance/checks.py

@@ -37,7 +37,7 @@ def check_variable_groups(variable_groups, explainer):
             if not isinstance(variable_groups[key][0], str):
                 raise TypeError("variable_groups' is a dict of lists of variables")
 
-            wrong_names[i] = np.in1d(variable_groups[key], explainer.data.columns).all()
+            wrong_names[i] = np.isin(variable_groups[key], explainer.data.columns).all()
 
         wrong_names = not wrong_names.all()
 

python/dalex/dalex/predict_explanations/_break_down/utils.py

@@ -121,7 +121,7 @@ def calculate_2d_changes(explainer,
 
         yhats = explainer.predict(current_data)
         average_yhats[i] = yhats.mean()
-        average_yhats_norm[i] = average_yhats[i] - diffs_1d[inds.iloc[i, 0]] - diffs_1d.iloc[inds.iloc[i, 1]]
+        average_yhats_norm[i] = average_yhats[i] - diffs_1d.iloc[inds.iloc[i, 0]] - diffs_1d.iloc[inds.iloc[i, 1]]
 
     columns = explainer.data.columns
     average_yhats = pd.Series(average_yhats)

python/dalex/dalex/predict_explanations/_ceteris_paribus/utils.py

@@ -97,6 +97,8 @@ def single_variable_profile(predict,
     ids = np.repeat(data.index.values, split_points.shape[0])
     new_data = data.loc[ids, :]
     original = new_data.loc[:, variable].copy()
+    if pd.api.types.is_numeric_dtype(new_data[variable]):
+        new_data[variable] = new_data[variable].astype('float')
     new_data.loc[:, variable] = np.tile(split_points, data.shape[0])
 
     yhat = predict(model, new_data)

python/dalex/setup.py

@@ -1,53 +1,52 @@
-import codecs
 import os
+import ast
 
 
 this_directory = os.path.abspath(os.path.dirname(__file__))
-with open(os.path.join(this_directory, 'README.md'), encoding='utf-8') as f:
-    readme = f.read()
-
-with open(os.path.join(this_directory, 'NEWS.md'), encoding='utf-8')as f:
-    news = f.read()
-
 
 # https://packaging.python.org/guides/single-sourcing-package-version/
 def read(rel_path):
-    with codecs.open(os.path.join(this_directory, rel_path), 'r') as fp:
+    """Read a file relative to the setup.py location."""
+    with open(os.path.join(this_directory, rel_path), encoding='utf-8') as fp:
         return fp.read()
 
+readme = read('README.md')
+news = read('NEWS.md')
 
 def get_version(rel_path):
+    """Extract __version__ from a file without importing it."""
     for line in read(rel_path).splitlines():
         if line.startswith('__version__'):
             delimiter = '"' if '"' in line else "'"
             return line.split(delimiter)[1]
 
-
 def get_optional_dependencies(rel_path):
+    """Parse OPTIONAL_DEPENDENCIES dict from a python file securely."""
     # read _global_checks.py and construct a list of optional dependencies
     flag = False
     to_parse = "{"
 
     for line in read(rel_path).splitlines():
         if flag:
-            if line == "}":  # end
+            if line == "}":  # end of dict
                 to_parse += line
                 break
             to_parse += line.strip()
-        if line.startswith('OPTIONAL_DEPENDENCIES'):  # start
+        if line.startswith('OPTIONAL_DEPENDENCIES'):  # start of dict
             flag = True
 
-    od_dict = eval(to_parse)
-    od_list = [k + ">=" + v for k, v in od_dict.items()]
-    del od_list[0]  # remove artificial dependency used in test_global.py
+    # Use ast.literal_eval instead of eval for safety
+    od_dict = ast.literal_eval(to_parse)
+    od_list = [f"{k}>={v}" for k, v in od_dict.items()]
+    # remove artificial dependency used in test_global.py
+    del od_list[0]
     return od_list
 
-
 def run_setup():
     # fixes warning https://github.com/pypa/setuptools/issues/2230
     from setuptools import setup, find_packages
 
-    extras_require = get_optional_dependencies("dalex/_global_checks.py")
+    full_dependencies = get_optional_dependencies("dalex/_global_checks.py")
 
     setup(
         name="dalex",
@@ -57,7 +56,7 @@ def run_setup():
         author_email="przemyslaw.biecek@gmail.com",
         version=get_version("dalex/__init__.py"),
         description="Responsible Machine Learning in Python",
-        long_description=u"\n\n".join([readme, news]),
+        long_description="\n\n".join([readme, news]),
         long_description_content_type="text/markdown",
         url="https://dalex.drwhy.ai/",
         project_urls={
@@ -70,26 +69,27 @@ def run_setup():
             "Topic :: Scientific/Engineering",
             "Topic :: Scientific/Engineering :: Artificial Intelligence",
             "Programming Language :: Python :: 3",
-            "Programming Language :: Python :: 3.8",
             "Programming Language :: Python :: 3.9",
             "Programming Language :: Python :: 3.10",
             "Programming Language :: Python :: 3.11",
             "Programming Language :: Python :: 3.12",
+            "Programming Language :: Python :: 3.13",
             "License :: OSI Approved",
             "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
             "Operating System :: OS Independent",
         ],
         install_requires=[
             'setuptools',
-            'pandas>=1.5.3',
-            'numpy>=1.23.3',
+            'packaging',
+            'pandas>=1.5.3,<3.0.0',
+            'numpy>=1.23.5',
             'scipy>=1.6.3',
-            'plotly>=5.1.0,<6.0.0',
+            'plotly>=6.0.0',
             'tqdm>=4.61.2',
         ],
-        extras_require={'full': extras_require},
+        extras_require={'full': full_dependencies},
         packages=find_packages(include=["dalex", "dalex.*"]),
-        python_requires='>=3.8',
+        python_requires='>=3.9',
         include_package_data=True
     )
 

python/dalex/test/test_aggregated_profiles.py

@@ -74,8 +74,8 @@ def test_accumulated(self):
         self.assertIsInstance(fig1, Figure)
         self.assertIsInstance(fig2, Figure)
 
-        test1 = case1.result.groupby('_vname_').apply(lambda x: x['_yhat_'].abs().min()).tolist()
-        test2 = case2.result.groupby('_vname_').apply(lambda x: x['_yhat_'].abs().min()).tolist()
+        test1 = case1.result.groupby('_vname_')['_yhat_'].apply(lambda x: x.abs().min()).tolist()
+        test2 = case2.result.groupby('_vname_')['_yhat_'].apply(lambda x: x.abs().min()).tolist()
 
         self.assertListEqual(test1, np.zeros(len(test1)).tolist())
         self.assertListEqual(test2, np.zeros(len(test2)).tolist())

python/dalex/test/test_arena_classification.py

@@ -60,7 +60,7 @@ def setUp(self):
             FairnessCheckContainer, ShapleyValuesDependenceContainer, ShapleyValuesVariableImportanceContainer,
             VariableAgainstAnotherContainer, VariableDistributionContainer]
 
-    @unittest.skipIf(sys.platform.startswith("win"), "requires Windows")
+
     def test_supported_plots(self):
         arena = dx.Arena()
         arena.push_model(self.exp)
@@ -74,15 +74,15 @@ def test_supported_plots(self):
         except Exception:
             pass
 
-    @unittest.skipIf(sys.platform.startswith("win"), "requires Windows")
+    @unittest.skipUnless(sys.platform.startswith("ubuntu"), "requires Ubuntu")
     def test_server(self):
         arena = dx.Arena()
         arena.push_model(self.exp)
         arena.push_model(self.exp2)
         port = get_free_port()
         try:
             arena.run_server(port=port)
-            time.sleep(2)
+            time.sleep(10)
             self.assertFalse(try_port(port))
             arena.stop_server()
         except AssertionError as e:
@@ -93,7 +93,7 @@ def test_server(self):
         except Exception:
             pass
 
-    @unittest.skipIf(sys.platform.startswith("win"), "requires Windows")
+    @unittest.skipUnless(sys.platform.startswith("ubuntu"), "requires Ubuntu")
     def test_plots(self):
         arena = dx.Arena()
         arena.push_model(self.exp)
@@ -110,7 +110,7 @@ def test_plots(self):
         except Exception:
             pass
 
-    @unittest.skipIf(sys.platform.startswith("win"), "requires Windows")
+    @unittest.skipUnless(sys.platform.startswith("ubuntu"), "requires Ubuntu")
     def test_observation_attributes(self):
         arena = dx.Arena()
         arena.push_model(self.exp)
@@ -128,7 +128,7 @@ def test_observation_attributes(self):
         except Exception:
             pass
 
-    @unittest.skipIf(sys.platform.startswith("win"), "requires Windows")
+    @unittest.skipUnless(sys.platform.startswith("ubuntu"), "requires Ubuntu")
     def test_variable_attributes(self):
         arena = dx.Arena()
         arena.push_model(self.exp)