remove is_filtered from metadata

czaloom · czaloom · commit 8eda1495e0c5 · 2025-05-28T17:24:15.000-04:00
diff --git a/src/valor_lite/classification/manager.py b/src/valor_lite/classification/manager.py
@@ -45,7 +45,6 @@ class Metadata:
     number_of_ground_truths: int = 0
     number_of_predictions: int = 0
     number_of_labels: int = 0
-    is_filtered: int = False
 
     @classmethod
     def create(
diff --git a/tests/classification/test_accuracy.py b/tests/classification/test_accuracy.py
@@ -74,7 +74,6 @@ def test_accuracy_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(score_thresholds=[0.25, 0.75])
@@ -172,7 +171,6 @@ def test_accuracy_with_image_example(
         "number_of_ground_truths": 2,
         "number_of_predictions": 4,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
@@ -208,7 +206,6 @@ def test_accuracy_with_tabular_example(
         "number_of_ground_truths": 10,
         "number_of_predictions": 30,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
diff --git a/tests/classification/test_confusion_matrix.py b/tests/classification/test_confusion_matrix.py
@@ -113,7 +113,6 @@ def test_confusion_matrix_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     actual_metrics = evaluator.compute_confusion_matrix(
@@ -413,7 +412,6 @@ def test_confusion_matrix_multiclass(
         "number_of_ground_truths": 5,
         "number_of_labels": 3,
         "number_of_predictions": 15,
-        "is_filtered": False,
     }
 
     actual_metrics = evaluator.compute_confusion_matrix(
@@ -576,7 +574,6 @@ def test_confusion_matrix_without_hardmax_animal_example(
         "number_of_ground_truths": 1,
         "number_of_predictions": 3,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     actual_metrics = evaluator.compute_confusion_matrix(
diff --git a/tests/classification/test_counts.py b/tests/classification/test_counts.py
@@ -117,7 +117,6 @@ def test_counts_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
@@ -666,7 +665,6 @@ def test_counts_with_image_example(
         "number_of_ground_truths": 2,
         "number_of_predictions": 4,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
     metrics = evaluator.evaluate()
 
@@ -749,7 +747,6 @@ def test_counts_with_tabular_example(
         "number_of_ground_truths": 10,
         "number_of_predictions": 30,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
@@ -819,7 +816,6 @@ def test_counts_multiclass(
         "number_of_ground_truths": 5,
         "number_of_labels": 3,
         "number_of_predictions": 15,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
@@ -1021,7 +1017,6 @@ def test_counts_true_negatives_check_animals(
         "number_of_ground_truths": 1,
         "number_of_predictions": 3,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
     metrics = evaluator.evaluate(
         score_thresholds=[0.05, 0.15, 0.95],
@@ -1180,7 +1175,6 @@ def test_counts_zero_count_check(
         "number_of_ground_truths": 1,
         "number_of_labels": 3,
         "number_of_predictions": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
diff --git a/tests/classification/test_evaluator.py b/tests/classification/test_evaluator.py
@@ -24,7 +24,6 @@ def test_metadata_using_classification_example(
         "number_of_ground_truths": 6,
         "number_of_predictions": 3 * 6,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
 
diff --git a/tests/classification/test_f1.py b/tests/classification/test_f1.py
@@ -80,7 +80,6 @@ def test_f1_score_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
@@ -320,7 +319,6 @@ def test_f1_score_with_image_example(
         "number_of_ground_truths": 2,
         "number_of_predictions": 4,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
@@ -357,7 +355,6 @@ def test_f1_score_with_tabular_example(
         "number_of_ground_truths": 10,
         "number_of_predictions": 30,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
diff --git a/tests/classification/test_precision.py b/tests/classification/test_precision.py
@@ -80,7 +80,6 @@ def test_precision_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
@@ -318,7 +317,6 @@ def test_precision_with_image_example(
         "number_of_ground_truths": 2,
         "number_of_predictions": 4,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
@@ -355,7 +353,6 @@ def test_precision_with_tabular_example(
         "number_of_ground_truths": 10,
         "number_of_predictions": 30,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
diff --git a/tests/classification/test_recall.py b/tests/classification/test_recall.py
@@ -80,7 +80,6 @@ def test_recall_basic(basic_classifications: list[Classification]):
         "number_of_ground_truths": 3,
         "number_of_predictions": 12,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate(
@@ -253,7 +252,6 @@ def test_recall_with_image_example(
         "number_of_ground_truths": 2,
         "number_of_predictions": 4,
         "number_of_labels": 4,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()
@@ -290,7 +288,6 @@ def test_recall_with_tabular_example(
         "number_of_ground_truths": 10,
         "number_of_predictions": 30,
         "number_of_labels": 3,
-        "is_filtered": False,
     }
 
     metrics = evaluator.evaluate()

Original file line number	Diff line number	Diff line change
`@@ -74,7 +74,6 @@ def test_accuracy_basic(basic_classifications: list[Classification]):`
`74`	`74`	`"number_of_ground_truths": 3,`
`75`	`75`	`"number_of_predictions": 12,`
`76`	`76`	`"number_of_labels": 4,`
`77`		`- "is_filtered": False,`
`78`	`77`	`}`
`79`	`78`
`80`	`79`	`metrics = evaluator.evaluate(score_thresholds=[0.25, 0.75])`
`@@ -172,7 +171,6 @@ def test_accuracy_with_image_example(`
`172`	`171`	`"number_of_ground_truths": 2,`
`173`	`172`	`"number_of_predictions": 4,`
`174`	`173`	`"number_of_labels": 4,`
`175`		`- "is_filtered": False,`
`176`	`174`	`}`
`177`	`175`
`178`	`176`	`metrics = evaluator.evaluate()`
`@@ -208,7 +206,6 @@ def test_accuracy_with_tabular_example(`
`208`	`206`	`"number_of_ground_truths": 10,`
`209`	`207`	`"number_of_predictions": 30,`
`210`	`208`	`"number_of_labels": 3,`
`211`		`- "is_filtered": False,`
`212`	`209`	`}`
`213`	`210`
`214`	`211`	`metrics = evaluator.evaluate()`
Original file line number	Diff line number	Diff line change
`@@ -113,7 +113,6 @@ def test_confusion_matrix_basic(basic_classifications: list[Classification]):`
`113`	`113`	`"number_of_ground_truths": 3,`
`114`	`114`	`"number_of_predictions": 12,`
`115`	`115`	`"number_of_labels": 4,`
`116`		`- "is_filtered": False,`
`117`	`116`	`}`
`118`	`117`
`119`	`118`	`actual_metrics = evaluator.compute_confusion_matrix(`
`@@ -413,7 +412,6 @@ def test_confusion_matrix_multiclass(`
`413`	`412`	`"number_of_ground_truths": 5,`
`414`	`413`	`"number_of_labels": 3,`
`415`	`414`	`"number_of_predictions": 15,`
`416`		`- "is_filtered": False,`
`417`	`415`	`}`
`418`	`416`
`419`	`417`	`actual_metrics = evaluator.compute_confusion_matrix(`
`@@ -576,7 +574,6 @@ def test_confusion_matrix_without_hardmax_animal_example(`
`576`	`574`	`"number_of_ground_truths": 1,`
`577`	`575`	`"number_of_predictions": 3,`
`578`	`576`	`"number_of_labels": 3,`
`579`		`- "is_filtered": False,`
`580`	`577`	`}`
`581`	`578`
`582`	`579`	`actual_metrics = evaluator.compute_confusion_matrix(`
Original file line number	Diff line number	Diff line change
`@@ -24,7 +24,6 @@ def test_metadata_using_classification_example(`
`24`	`24`	`"number_of_ground_truths": 6,`
`25`	`25`	`"number_of_predictions": 3 * 6,`
`26`	`26`	`"number_of_labels": 3,`
`27`		`- "is_filtered": False,`
`28`	`27`	`}`
`29`	`28`
`30`	`29`
Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,6 @@ def test_f1_score_basic(basic_classifications: list[Classification]):`
`80`	`80`	`"number_of_ground_truths": 3,`
`81`	`81`	`"number_of_predictions": 12,`
`82`	`82`	`"number_of_labels": 4,`
`83`		`- "is_filtered": False,`
`84`	`83`	`}`
`85`	`84`
`86`	`85`	`metrics = evaluator.evaluate(`
`@@ -320,7 +319,6 @@ def test_f1_score_with_image_example(`
`320`	`319`	`"number_of_ground_truths": 2,`
`321`	`320`	`"number_of_predictions": 4,`
`322`	`321`	`"number_of_labels": 4,`
`323`		`- "is_filtered": False,`
`324`	`322`	`}`
`325`	`323`
`326`	`324`	`metrics = evaluator.evaluate()`
`@@ -357,7 +355,6 @@ def test_f1_score_with_tabular_example(`
`357`	`355`	`"number_of_ground_truths": 10,`
`358`	`356`	`"number_of_predictions": 30,`
`359`	`357`	`"number_of_labels": 3,`
`360`		`- "is_filtered": False,`
`361`	`358`	`}`
`362`	`359`
`363`	`360`	`metrics = evaluator.evaluate()`
Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,6 @@ def test_precision_basic(basic_classifications: list[Classification]):`
`80`	`80`	`"number_of_ground_truths": 3,`
`81`	`81`	`"number_of_predictions": 12,`
`82`	`82`	`"number_of_labels": 4,`
`83`		`- "is_filtered": False,`
`84`	`83`	`}`
`85`	`84`
`86`	`85`	`metrics = evaluator.evaluate(`
`@@ -318,7 +317,6 @@ def test_precision_with_image_example(`
`318`	`317`	`"number_of_ground_truths": 2,`
`319`	`318`	`"number_of_predictions": 4,`
`320`	`319`	`"number_of_labels": 4,`
`321`		`- "is_filtered": False,`
`322`	`320`	`}`
`323`	`321`
`324`	`322`	`metrics = evaluator.evaluate()`
`@@ -355,7 +353,6 @@ def test_precision_with_tabular_example(`
`355`	`353`	`"number_of_ground_truths": 10,`
`356`	`354`	`"number_of_predictions": 30,`
`357`	`355`	`"number_of_labels": 3,`
`358`		`- "is_filtered": False,`
`359`	`356`	`}`
`360`	`357`
`361`	`358`	`metrics = evaluator.evaluate()`
Original file line number	Diff line number	Diff line change
`@@ -80,7 +80,6 @@ def test_recall_basic(basic_classifications: list[Classification]):`
`80`	`80`	`"number_of_ground_truths": 3,`
`81`	`81`	`"number_of_predictions": 12,`
`82`	`82`	`"number_of_labels": 4,`
`83`		`- "is_filtered": False,`
`84`	`83`	`}`
`85`	`84`
`86`	`85`	`metrics = evaluator.evaluate(`
`@@ -253,7 +252,6 @@ def test_recall_with_image_example(`
`253`	`252`	`"number_of_ground_truths": 2,`
`254`	`253`	`"number_of_predictions": 4,`
`255`	`254`	`"number_of_labels": 4,`
`256`		`- "is_filtered": False,`
`257`	`255`	`}`
`258`	`256`
`259`	`257`	`metrics = evaluator.evaluate()`
`@@ -290,7 +288,6 @@ def test_recall_with_tabular_example(`
`290`	`288`	`"number_of_ground_truths": 10,`
`291`	`289`	`"number_of_predictions": 30,`
`292`	`290`	`"number_of_labels": 3,`
`293`		`- "is_filtered": False,`
`294`	`291`	`}`
`295`	`292`
`296`	`293`	`metrics = evaluator.evaluate()`