Test errors solved

pyxai/sources/core/tools/utils.py

@@ -12,10 +12,14 @@
 from time import time
 from typing import Iterable
 
+import sklearn
 from sklearn.metrics import confusion_matrix, mean_squared_error, mean_absolute_error
 
 from pyxai.sources.core.structure.type import PreferredReasonMethod
+from packaging import version
 
+def check_sklearn_14():
+    return version.parse(sklearn.__version__) > version.parse("1.4.0")
 
 def check_PyQt6():
     ok, error = _check_PyQt6()
@@ -52,7 +56,7 @@ class Metric:
     def compute_metrics_regression(labels, predictions):
         return {
                 "mean_squared_error": mean_squared_error(labels, predictions),
-                "root_mean_squared_error": mean_squared_error(labels, predictions, squared=False),
+                "root_mean_squared_error": mean_squared_error(labels, predictions, squared=False) if check_sklearn_14() is False else sklearn.metrics.root_mean_squared_error(labels, predictions),
                 "mean_absolute_error": mean_absolute_error(labels, predictions)
             }
 

pyxai/sources/learning/learner.py

@@ -167,7 +167,7 @@ def create_dict_labels(self, labels):
         self.dict_labels = OrderedDict()
         self.inverse_dict_labels = OrderedDict()
         set_labels = set(labels)
-        check_type_int = all(numpy.issubdtype(x, numpy.integer) or isinstance(x, int) or (isinstance(x, str) and x.isnumeric()) for x in set_labels)
+        check_type_int = all((not isinstance(x, str) and numpy.issubdtype(x, numpy.integer)) or isinstance(x, int) or (isinstance(x, str) and x.isnumeric()) for x in set_labels)
         if check_type_int is True:
             for p in labels:
                 if str(p) not in self.dict_labels:

pyxai/tests/functionality/Metrics.py

@@ -128,7 +128,7 @@ def do_import(self, dataset, learner_type):
         learner, models = Learning.import_models(sk_models)
         for i, model in enumerate(models):
             instances_details = learner.get_instances(model, dataset=dataset, indexes=Learning.TEST, test_indexes=test_indexes[i], details=True)
-            predictions = [learner.get_value_from_label(element["prediction"]) for element in instances_details]
+            predictions = [element["prediction"] for element in instances_details]
             true_values = [element["label"] for element in instances_details]
             metrics = learner.compute_metrics(true_values, predictions)
             self.assertTrue("micro_averaging_accuracy" in metrics.keys())

pyxai/tests/importing/ScikitLearn.py

@@ -59,9 +59,9 @@ def do_import(self, dataset, learner_type):
             instances = learner.get_instances(dataset=dataset, model=model, n=10)
 
             for (instance, prediction_classifier) in instances:
-                prediction_model_1 = learner.get_label_from_value(model.predict_instance(instance))
+                prediction_model_1 = model.predict_instance(instance)
                 implicant = model.instance_to_binaries(instance)
-                prediction_model_2 = learner.get_label_from_value(model.predict_implicant(implicant))
+                prediction_model_2 = model.predict_implicant(implicant)
 
                 self.assertEqual(str(prediction_model_1), str(prediction_model_2))
 

setup.py

@@ -22,7 +22,7 @@
       classifiers=['Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Education'],
       packages=find_packages(),  # exclude=["problems/g7_todo/"]),
       package_dir={'pyxai': 'pyxai'},
-      install_requires=['lxml', 'numpy', 'wheel', 'pandas', 'termcolor', 'shap', 'wordfreq', 'python-sat[pblib,aiger]', 'xgboost==1.7.3', 'pycsp3', 'matplotlib', 'dill', 'lightgbm', 'docplex', 'ortools'],
+      install_requires=['lxml', 'numpy', 'wheel', 'pandas', 'termcolor', 'shap', 'wordfreq', 'python-sat[pblib,aiger]', 'xgboost==1.7.3', 'pycsp3', 'matplotlib', 'dill', 'lightgbm', 'docplex', 'ortools', 'packaging'],
       extras_require={
         "gui": ['pyqt6'],
       },