From e4600d88aecd02b6335e44366cacc93054cb21c7 Mon Sep 17 00:00:00 2001 From: Lukas Drews Date: Fri, 12 Apr 2024 11:25:52 +0200 Subject: [PATCH] change black version --- Pipfile | 2 +- Pipfile.lock | 357 +++++++++--------- hiclass/Pipeline.py | 1 + .../ArithmeticMeanCombiner.py | 1 + .../GeometricMeanCombiner.py | 1 + .../probability_combiner/MultiplyCombiner.py | 1 + .../ProbabilityCombiner.py | 1 + tests/test_calibration.py | 22 +- 8 files changed, 196 insertions(+), 190 deletions(-) diff --git a/Pipfile b/Pipfile index c1c3eaa8..0c8fd579 100644 --- a/Pipfile +++ b/Pipfile @@ -19,7 +19,7 @@ pytest-cov = "3.0.0" twine = "*" sphinx = "4.1.1" sphinx-rtd-theme = "0.5.2" -black = "22.10.0" +black = {version = "24.3.0", extras = ["colorama"]} pre-commit = "2.20.0" pyfakefs = "*" shap = "0.44.1" diff --git a/Pipfile.lock b/Pipfile.lock index 55ad7e9a..29c3959b 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "c4fc4a3efecd8767f3637ef824942179038588d6b5288c3b7edc1e661a36446a" + "sha256": "3c7fbdc64a7f72c8b0ab1fb9b85784f94a53e91c6dffdf2a3eb4814fa4352aac" }, "pipfile-spec": 6, "requires": {}, @@ -16,11 +16,11 @@ "default": { "joblib": { "hashes": [ - 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"version": "==1.4.1.post1" + "version": "==1.4.2" }, "scipy": { "hashes": [ diff --git a/hiclass/Pipeline.py b/hiclass/Pipeline.py index f1202b79..a1d25ada 100644 --- a/hiclass/Pipeline.py +++ b/hiclass/Pipeline.py @@ -1,4 +1,5 @@ """Custom Pipeline class that supports the `calibrate` method.""" + from sklearn.pipeline import Pipeline as skPipeline diff --git a/hiclass/probability_combiner/ArithmeticMeanCombiner.py b/hiclass/probability_combiner/ArithmeticMeanCombiner.py index 01419bfc..8679e895 100644 --- a/hiclass/probability_combiner/ArithmeticMeanCombiner.py +++ b/hiclass/probability_combiner/ArithmeticMeanCombiner.py @@ -1,4 +1,5 @@ """Defines the ArithmeticMeanCombiner.""" + import numpy as np from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner from typing import List diff --git a/hiclass/probability_combiner/GeometricMeanCombiner.py b/hiclass/probability_combiner/GeometricMeanCombiner.py index 2a700983..9f643c63 100644 --- a/hiclass/probability_combiner/GeometricMeanCombiner.py +++ b/hiclass/probability_combiner/GeometricMeanCombiner.py @@ -1,4 +1,5 @@ """Defines the GeometricMeanCombiner.""" + import numpy as np from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner from typing import List diff --git a/hiclass/probability_combiner/MultiplyCombiner.py b/hiclass/probability_combiner/MultiplyCombiner.py index 36badb82..e693f940 100644 --- a/hiclass/probability_combiner/MultiplyCombiner.py +++ b/hiclass/probability_combiner/MultiplyCombiner.py @@ -1,4 +1,5 @@ """Defines the MultiplyCombiner.""" + import numpy as np from hiclass.probability_combiner.ProbabilityCombiner import ProbabilityCombiner from typing import List diff --git a/hiclass/probability_combiner/ProbabilityCombiner.py b/hiclass/probability_combiner/ProbabilityCombiner.py index be9569f8..a2f2ecfc 100644 --- a/hiclass/probability_combiner/ProbabilityCombiner.py +++ b/hiclass/probability_combiner/ProbabilityCombiner.py @@ -1,4 +1,5 @@ """Abstract class defining the structure of a probability combiner.""" + import abc import numpy as np from typing import List diff --git a/tests/test_calibration.py b/tests/test_calibration.py index 2f655c86..3d9844ff 100644 --- a/tests/test_calibration.py +++ b/tests/test_calibration.py @@ -82,11 +82,12 @@ def binary_cal_X(): @pytest.fixture def binary_mock_estimator(binary_calibration_data, binary_test_scores): # return calibration scores or test scores depending on input size - side_effect = ( - lambda X: binary_calibration_data[0] - if len(X) == len(binary_calibration_data[0]) - else binary_test_scores - ) + def side_effect(X): + return ( + binary_calibration_data[0] + if len(X) == len(binary_calibration_data[0]) + else binary_test_scores + ) lr = LogisticRegression() binary_estimator = Mock(spec=lr) @@ -140,11 +141,12 @@ def multiclass_test_scores(): @pytest.fixture def multiclass_mock_estimator(multiclass_calibration_data, multiclass_test_scores): # return calibration scores or test scores depending on input size - side_effect = ( - lambda X: multiclass_calibration_data[0] - if len(X) == len(multiclass_calibration_data[0]) - else multiclass_test_scores - ) + def side_effect(X): + return ( + multiclass_calibration_data[0] + if len(X) == len(multiclass_calibration_data[0]) + else multiclass_test_scores + ) multiclass_estimator = Mock(spec=LogisticRegression) multiclass_estimator.predict_proba.side_effect = side_effect