chore: tests

tests/bias/metrics/multiclass_auxiliary/test_bias_multiclass_auxiliar_test_g1.py

@@ -7,7 +7,7 @@
     unconventional_numeric_config,
 )
 
-from holisticai.bias.metrics import frequency_matrix, multiclass_statistical_parity
+from holistic.bias.metrics import frequency_matrix, multiclass_statistical_parity
 
 #######################
 # 1 entrada + p_attr

tests/bias/metrics/multiclass_auxiliary/test_bias_multiclass_auxiliar_test_g2.py

@@ -11,7 +11,7 @@
     unconventional_numeric_config,
 )
 
-from holisticai.bias.metrics import confusion_matrix
+from holistic.bias.metrics import confusion_matrix
 
 ##################################################################################################################
 # 2 inputs

tests/bias/metrics/multiclass_auxiliary/test_bias_multiclass_auxiliar_test_g3.py

@@ -11,7 +11,7 @@
     unconventional_numeric_config,
 )
 
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     accuracy_matrix,
     confusion_tensor,
     multiclass_average_odds,

tests/bias/metrics/test_bias_classification.py

@@ -4,7 +4,7 @@
 from numpy.testing import assert_approx_equal
 
 # Classification
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     abroca,
     accuracy_diff,
     average_odds_diff,
@@ -22,7 +22,7 @@
 )
 
 # Formatting
-from holisticai.utils import extract_columns
+from holistic.utils import extract_columns
 
 # Dataset
 from tests.bias.utils import load_bias_classification_data

tests/bias/metrics/test_bias_clustering.py

@@ -3,7 +3,7 @@
 from numpy.testing import assert_approx_equal
 
 # Clustering
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     cluster_balance,
     cluster_dist_entropy,
     cluster_dist_kl,

tests/bias/metrics/test_bias_multiclass.py

@@ -3,7 +3,7 @@
 from numpy.testing import assert_approx_equal, assert_array_almost_equal
 
 # Multiclass
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     accuracy_matrix,
     confusion_tensor,
     frequency_matrix,

tests/bias/metrics/test_bias_recommender.py

@@ -4,7 +4,7 @@
 from numpy.testing import assert_approx_equal
 
 # Recommender
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     aggregate_diversity,
     avg_f1_ratio,
     avg_precision_ratio,
@@ -20,7 +20,7 @@
 )
 
 # Formatting
-from holisticai.utils import extract_columns
+from holistic.utils import extract_columns
 from tests.bias.utils import load_bias_recommender_data
 
 # Dataset

tests/bias/metrics/test_bias_regression.py

@@ -5,7 +5,7 @@
 from numpy.testing import assert_approx_equal
 
 # Regression
-from holisticai.bias.metrics import (
+from holistic.bias.metrics import (
     avg_score_diff,
     avg_score_ratio,
     correlation_diff,
@@ -22,7 +22,7 @@
 )
 
 # Formatting
-from holisticai.utils import extract_columns
+from holistic.utils import extract_columns
 from tests.bias.utils import load_bias_regression_data
 
                         # Dataset

tests/bias/mitigation/test_inprocessing.py

@@ -2,8 +2,8 @@
 import numpy as np
 from sklearn.linear_model import LogisticRegression, LinearRegression
 
-from holisticai.pipeline import Pipeline
-from holisticai.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics, recommender_bias_metrics
+from holistic.pipeline import Pipeline
+from holistic.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics, recommender_bias_metrics
 from tests.bias.mitigation.utils import (
     check_results,
     categorical_dataset,
@@ -18,20 +18,20 @@
 
 seed = 42
 
-from holisticai.bias.mitigation import MITIGATOR_NAME
-from holisticai.bias.mitigation import ExponentiatedGradientReduction
-from holisticai.bias.mitigation import GridSearchReduction
-from holisticai.bias.mitigation import MetaFairClassifier
-from holisticai.bias.mitigation import PrejudiceRemover
-from holisticai.bias.mitigation import FairKCenterClustering
-from holisticai.bias.mitigation import FairKMedianClustering
-from holisticai.bias.mitigation import FairletClustering
-from holisticai.bias.mitigation import VariationalFairClustering
-from holisticai.bias.mitigation import PopularityPropensityMF
-from holisticai.bias.mitigation import BlindSpotAwareMF
-from holisticai.bias.mitigation import FairRec
-from holisticai.bias.mitigation import DebiasingLearningMF
-from holisticai.bias.mitigation import AdversarialDebiasing
+from holistic.bias.mitigation import MITIGATOR_NAME
+from holistic.bias.mitigation import ExponentiatedGradientReduction
+from holistic.bias.mitigation import GridSearchReduction
+from holistic.bias.mitigation import MetaFairClassifier
+from holistic.bias.mitigation import PrejudiceRemover
+from holistic.bias.mitigation import FairKCenterClustering
+from holistic.bias.mitigation import FairKMedianClustering
+from holistic.bias.mitigation import FairletClustering
+from holistic.bias.mitigation import VariationalFairClustering
+from holistic.bias.mitigation import PopularityPropensityMF
+from holistic.bias.mitigation import BlindSpotAwareMF
+from holistic.bias.mitigation import FairRec
+from holistic.bias.mitigation import DebiasingLearningMF
+from holistic.bias.mitigation import AdversarialDebiasing
 
 def get_inprocessor(mitigator_name : MITIGATOR_NAME = "CalibratedEqualizedOdds", parameters: dict = {}):
     if mitigator_name == "ExponentiatedGradientReduction":
@@ -45,7 +45,7 @@ def get_inprocessor(mitigator_name : MITIGATOR_NAME = "CalibratedEqualizedOdds",
     elif mitigator_name == "PrejudiceRemover":
         return PrejudiceRemover(**parameters)
     elif mitigator_name == "FairScoreClassifier":
-        from holisticai.bias.mitigation import FairScoreClassifier
+        from holistic.bias.mitigation import FairScoreClassifier
         return FairScoreClassifier(**parameters)
     elif mitigator_name == "FairKCenterClustering":
         return FairKCenterClustering(**parameters)

tests/bias/mitigation/test_postprocessor.py

@@ -3,10 +3,10 @@
 from sklearn.linear_model import LogisticRegression, LinearRegression
 from sklearn.cluster import KMeans
 
-from holisticai.pipeline import Pipeline
-from holisticai.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics
-from holisticai.datasets.synthetic.recruitment import generate_rankings
-from holisticai.bias.mitigation.postprocessing.debiasing_exposure.algorithm_utils import exposure_metric
+from holistic.pipeline import Pipeline
+from holistic.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics
+from holistic.datasets.synthetic.recruitment import generate_rankings
+from holistic.bias.mitigation.postprocessing.debiasing_exposure.algorithm_utils import exposure_metric
 from tests.bias.mitigation.utils import (
     check_results,
     categorical_dataset,
@@ -19,18 +19,18 @@
 
 seed = 42
 
-from holisticai.bias.mitigation import MITIGATOR_NAME
-from holisticai.bias.mitigation import CalibratedEqualizedOdds
-from holisticai.bias.mitigation import EqualizedOdds
-from holisticai.bias.mitigation import RejectOptionClassification
-from holisticai.bias.mitigation import LPDebiaserBinary
-from holisticai.bias.mitigation import MLDebiaser
-from holisticai.bias.mitigation import LPDebiaserMulticlass
-from holisticai.bias.mitigation import PluginEstimationAndCalibration
-from holisticai.bias.mitigation import WassersteinBarycenter
-from holisticai.bias.mitigation import MCMF
-from holisticai.bias.mitigation import DebiasingExposure
-from holisticai.bias.mitigation import FairTopK
+from holistic.bias.mitigation import MITIGATOR_NAME
+from holistic.bias.mitigation import CalibratedEqualizedOdds
+from holistic.bias.mitigation import EqualizedOdds
+from holistic.bias.mitigation import RejectOptionClassification
+from holistic.bias.mitigation import LPDebiaserBinary
+from holistic.bias.mitigation import MLDebiaser
+from holistic.bias.mitigation import LPDebiaserMulticlass
+from holistic.bias.mitigation import PluginEstimationAndCalibration
+from holistic.bias.mitigation import WassersteinBarycenter
+from holistic.bias.mitigation import MCMF
+from holistic.bias.mitigation import DebiasingExposure
+from holistic.bias.mitigation import FairTopK
 
 def get_postprocessor(mitigator_name : MITIGATOR_NAME = "CalibratedEqualizedOdds", parameters: dict = {}):
     if mitigator_name == "CalibratedEqualizedOdds":

tests/bias/mitigation/test_preprocessor.py

@@ -4,10 +4,10 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.cluster import KMeans
 
-from holisticai.pipeline import Pipeline
-from holisticai.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics
-from holisticai.datasets.synthetic.recruitment import generate_rankings
-from holisticai.bias.mitigation.postprocessing.debiasing_exposure.algorithm_utils import exposure_metric
+from holistic.pipeline import Pipeline
+from holistic.bias.metrics import classification_bias_metrics, regression_bias_metrics, multiclass_bias_metrics, clustering_bias_metrics
+from holistic.datasets.synthetic.recruitment import generate_rankings
+from holistic.bias.mitigation.postprocessing.debiasing_exposure.algorithm_utils import exposure_metric
 
 from tests.bias.mitigation.utils import (
     check_results,
@@ -22,12 +22,12 @@
 
 seed = 42
 
-from holisticai.bias.mitigation import MITIGATOR_NAME
-from holisticai.bias.mitigation import CorrelationRemover
-from holisticai.bias.mitigation import Reweighing
-from holisticai.bias.mitigation import LearningFairRepresentation
-from holisticai.bias.mitigation import FairletClusteringPreprocessing
-from holisticai.bias.mitigation import DisparateImpactRemoverRS
+from holistic.bias.mitigation import MITIGATOR_NAME
+from holistic.bias.mitigation import CorrelationRemover
+from holistic.bias.mitigation import Reweighing
+from holistic.bias.mitigation import LearningFairRepresentation
+from holistic.bias.mitigation import FairletClusteringPreprocessing
+from holistic.bias.mitigation import DisparateImpactRemoverRS
 
 def get_preprocessor(mitigator_name : MITIGATOR_NAME = "CorrelationRemover", parameters: dict = {}):
     if mitigator_name == "CorrelationRemover":

tests/bias/mitigation/utils.py

@@ -4,7 +4,7 @@
 from sklearn import metrics
 
 
-from holisticai.datasets import load_dataset
+from holistic.datasets import load_dataset
 
 metrics_dict = {
     "Accuracy": metrics.accuracy_score,

tests/commons/test_feature_importance.py

@@ -1,5 +1,5 @@
 import numpy as np
-from holisticai.utils import Importances
+from holistic.utils import Importances
 
 def test_compute_ranked_feature_importance():
     # Create a sample feature importance dataframe

tests/commons/test_feature_importance_strategies.py

@@ -1,8 +1,8 @@
 from sklearn.linear_model import LogisticRegression
-from holisticai.inspection import SurrogateFeatureImportanceCalculator, PermutationFeatureImportanceCalculator
-from holisticai.utils import Importances
-from holisticai.datasets import load_dataset
-from holisticai.utils import BinaryClassificationProxy
+from holistic.inspection import SurrogateFeatureImportanceCalculator, PermutationFeatureImportanceCalculator
+from holistic.utils import Importances
+from holistic.datasets import load_dataset
+from holistic.utils import BinaryClassificationProxy
 import pytest
 import numpy as np
 from numpy.random import RandomState

tests/commons/test_local_feature_importance.py

@@ -1,4 +1,4 @@
-from holisticai.utils import LocalImportances
+from holistic.utils import LocalImportances
 import pandas as pd
 import numpy as np
 
@@ -24,8 +24,8 @@ def test_local_feature_importance_to_global():
     assert len(local_importance.to_global().values)==4
 
 def test_partial_dependence():
-    from holisticai.utils import PartialDependence, Importances
-    from holisticai.explainability.metrics.global_feature_importance import xai_ease_score
+    from holistic.utils import PartialDependence, Importances
+    from holistic.explainability.metrics.global_feature_importance import xai_ease_score
 
     partial_dependence = [[
          {'average': [[0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.1, 0.2, 0.3]], 'grid_values': [[1,2,3,4,5,6,7,8,9]]},

tests/commons/test_partial_dependence.py

@@ -1,5 +1,5 @@
 import pytest
-from holisticai.datasets import load_dataset
+from holistic.datasets import load_dataset
 import numpy as np
 from sklearn.linear_model import LogisticRegression
 from numpy.random import RandomState
@@ -19,9 +19,9 @@ def input_data():
 
 def test_get_partial_dependence_results_binary_classification(input_data):
     test,model = input_data
-    from holisticai.utils import RegressionProxy
-    from holisticai.inspection import compute_permutation_importance
-    from holisticai.inspection import compute_partial_dependence
+    from holistic.utils import RegressionProxy
+    from holistic.inspection import compute_permutation_importance
+    from holistic.inspection import compute_partial_dependence
 
     proxy = RegressionProxy(predict=model.predict)
     importances  = compute_permutation_importance(proxy=proxy, X=test['X'], y=test['y'])

tests/commons/test_xai_ease_score.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import pytest
-from holisticai.explainability.metrics.global_feature_importance._xai_ease_score import XAIEaseScore, XAIEaseAnnotator, compare_tangents
+from holistic.explainability.metrics.global_feature_importance._xai_ease_score import XAIEaseScore, XAIEaseAnnotator, compare_tangents
 from collections import namedtuple
 
 @pytest.fixture
@@ -13,7 +13,7 @@ def xai_ease_score():
     return XAIEaseScore()
 
 def test_compute_xai_ease_score_data(xai_ease_annotator):
-    from holisticai.utils import PartialDependence
+    from holistic.utils import PartialDependence
     partial_dependence = PartialDependence( values = [[
         {'average': [[0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.1, 0.2, 0.3]]},
         {'average': [[0.4, 0.5, 0.6, 0.6, 0.5, 0.4, 0.4, 0.5, 0.6]]},
@@ -30,7 +30,7 @@ def test_compute_xai_ease_score(xai_ease_score):
     assert xai_ease_score.compute_xai_ease_score(score_data) == 0.75
 
 def test_xai_feature_ease_score(xai_ease_score):
-    from holisticai.utils import PartialDependence
+    from holistic.utils import PartialDependence
     partial_dependence = PartialDependence( values = [[
         {'average': [[0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.1, 0.2, 0.3]]},
         {'average': [[0.4, 0.5, 0.6, 0.6, 0.5, 0.4, 0.4, 0.5, 0.6]]},
@@ -40,7 +40,7 @@ def test_xai_feature_ease_score(xai_ease_score):
     assert xai_ease_score(partial_dependence, ranked_feature_importance) == 0.5
 
 def test_xai_ease_score(xai_ease_score):
-    from holisticai.utils import PartialDependence, Importances
+    from holistic.utils import PartialDependence, Importances
     partial_dependence = [[
         {'average': [[0.1, 0.2, 0.3, 0.1, 0.2, 0.3, 0.1, 0.2, 0.3]], 'grid_values': [[1,2,3,4,5,6,7,8,9]]},
         {'average': [[0.4, 0.5, 0.6, 0.6, 0.5, 0.4, 0.4, 0.5, 0.6]], 'grid_values': [[1,2,3,4,5,6,7,8,9]]},

tests/datasets/test_load_datasets.py

@@ -1,4 +1,4 @@
-from holisticai.datasets import load_dataset
+from holistic.datasets import load_dataset
 
 def test_load_dataset():
     SHARD_SIZE = 50

tests/explainability/metrics/test_global_importance.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
-from holisticai.explainability.metrics.global_feature_importance import fluctuation_ratio
-from holisticai.utils import PartialDependence, Importances
+from holistic.explainability.metrics.global_feature_importance import fluctuation_ratio
+from holistic.utils import PartialDependence, Importances
 
 def test_fluctuation_ratio_unweighted_aggregated():
     partial_dependence = PartialDependence(values=[[

tests/explainability/metrics/test_local_importance.py

@@ -1,8 +1,8 @@
 
 import numpy as np
 import pandas as pd
-from holisticai.explainability.metrics.local_feature_importance import feature_stability
-from holisticai.explainability.metrics.local_feature_importance import rank_consistency
+from holistic.explainability.metrics.local_feature_importance import feature_stability
+from holistic.explainability.metrics.local_feature_importance import rank_consistency
 
 def test_rank_consistency_unweighted_aggregated():
     local_importances_values = np.array([