Merge pull request #151 from BrainLesion/coverage-increase

added more coverage, removed deprecated code

panoptica/panoptica_result.py

@@ -563,29 +563,3 @@ def sq_rvd_std(res: PanopticaResult):
 
 
 # endregion
-
-
-def _build_global_bin_metric_function(metric: Metric):
-
-    def function_template(res: PanopticaResult):
-        if metric not in res._global_metrics:
-            raise MetricCouldNotBeComputedException(f"Global Metric {metric} not set")
-        if res.tp == 0:
-            is_edgecase, result = res._edge_case_handler.handle_zero_tp(
-                metric, res.tp, res.num_pred_instances, res.num_ref_instances
-            )
-            if is_edgecase:
-                return result
-        pred_binary = res._prediction_arr.copy()
-        ref_binary = res._reference_arr.copy()
-        pred_binary[pred_binary != 0] = 1
-        ref_binary[ref_binary != 0] = 1
-        return metric(
-            reference_arr=res._reference_arr,
-            prediction_arr=res._prediction_arr,
-        )
-
-    return lambda x: function_template(x)
-
-
-# endregion

panoptica/utils/config.py

@@ -43,67 +43,6 @@ def _save_yaml(data_dict: dict | object, out_file: str | Path, registered_class=
     print(f"Saved config into {out_file}")
 
 
-####################
-# TODO Merge into SupportsConfig
-class Configuration:
-    """General Configuration class that handles yaml"""
-
-    _data_dict: dict
-    _registered_class = None
-
-    def __init__(self, data_dict: dict, registered_class=None) -> None:
-        assert isinstance(data_dict, dict)
-        self._data_dict = data_dict
-        if registered_class is not None:
-            self.register_to_class(registered_class)
-
-    def register_to_class(self, cls):
-        global supported_helper_classes
-        if cls not in supported_helper_classes:
-            supported_helper_classes.append(cls)
-        self._registered_class = cls
-        return self
-
-    @classmethod
-    def save_from_object(cls, obj: object, file: str | Path):
-        _save_yaml(obj, file, registered_class=type(obj))
-        # return Configuration.load(file, registered_class=type(obj))
-
-    @classmethod
-    def load(cls, file: str | Path, registered_class=None):
-        data = _load_yaml(file, registered_class)
-        assert isinstance(
-            data, dict
-        ), f"The config at {file} is registered to a class. Use load_as_object() instead"
-        return Configuration(data, registered_class=registered_class)
-
-    @classmethod
-    def load_as_object(cls, file: str | Path, registered_class=None):
-        data = _load_yaml(file, registered_class)
-        assert not isinstance(
-            data, dict
-        ), f"The config at {file} is not registered to a class. Use load() instead"
-        return data
-
-    def save(self, out_file: str | Path):
-        _save_yaml(self._data_dict, out_file)
-
-    def cls_object_from_this(self):
-        assert self._registered_class is not None
-        return self._registered_class(**self._data_dict)
-
-    @property
-    def data_dict(self):
-        return self._data_dict
-
-    @property
-    def cls(self):
-        return self._registered_class
-
-    def __str__(self) -> str:
-        return f"Config({self.cls.__name__ if self.cls is not None else 'NoClass'} = {self.data_dict})"  # type: ignore
-
-
 #########
 # Universal Functions
 #########
@@ -118,7 +57,7 @@ def _load_from_config(cls, path: str | Path):
     if isinstance(path, str):
         path = Path(path)
     assert path.exists(), f"load_from_config: {path} does not exist"
-    obj = Configuration.load_as_object(path, registered_class=cls)
+    obj = _load_yaml(path, registered_class=cls)
     assert isinstance(obj, cls), f"Loaded config was not for class {cls.__name__}"
     return obj
 
@@ -132,7 +71,7 @@ def _load_from_config_name(cls, name: str):
 def _save_to_config(obj, path: str | Path):
     if isinstance(path, str):
         path = Path(path)
-    Configuration.save_from_object(obj, path)
+    _save_yaml(obj, path, registered_class=type(obj))
 
 
 def _save_to_config_by_name(obj, name: str):

panoptica/utils/numpy_utils.py

@@ -17,7 +17,7 @@ def _unique_without_zeros(arr: np.ndarray) -> np.ndarray:
     Issues a warning if negative values are present.
     """
     if np.any(arr < 0):
-        warnings.warn("Negative values are present in the input array.")
+        warnings.warn("Negative values are present in the input array.", UserWarning)
 
     return np.unique(arr[arr != 0])
 
@@ -33,7 +33,7 @@ def _count_unique_without_zeros(arr: np.ndarray) -> int:
         int: Number of unique elements excluding zeros.
     """
     if np.any(arr < 0):
-        warnings.warn("Negative values are present in the input array.")
+        warnings.warn("Negative values are present in the input array.", UserWarning)
 
     return len(_unique_without_zeros(arr))
 

unit_tests/test_metrics.py

@@ -96,6 +96,17 @@ def setUp(self) -> None:
 
     def test_rvd_case_simple_identical(self):
 
+        pred_arr, ref_arr = case_simple_identical()
+        rvd = Metric.RVD(
+            reference_arr=ref_arr,
+            prediction_arr=pred_arr,
+            ref_instance_idx=1,
+            pred_instance_idx=1,
+        )
+        self.assertEqual(rvd, 0.0)
+
+    def test_rvd_case_simple_identical_idx(self):
+
         pred_arr, ref_arr = case_simple_identical()
         rvd = Metric.RVD(reference_arr=ref_arr, prediction_arr=pred_arr)
         self.assertEqual(rvd, 0.0)
@@ -130,6 +141,17 @@ def test_dsc_case_simple_identical(self):
         dsc = Metric.DSC(reference_arr=ref_arr, prediction_arr=pred_arr)
         self.assertEqual(dsc, 1.0)
 
+    def test_dsc_case_simple_identical_idx(self):
+
+        pred_arr, ref_arr = case_simple_identical()
+        dsc = Metric.DSC(
+            reference_arr=ref_arr,
+            prediction_arr=pred_arr,
+            ref_instance_idx=1,
+            pred_instance_idx=1,
+        )
+        self.assertEqual(dsc, 1.0)
+
     def test_dsc_case_simple_nooverlap(self):
 
         pred_arr, ref_arr = case_simple_nooverlap()
@@ -159,6 +181,16 @@ def test_st_case_simple_identical(self):
         st = Metric.ASSD(reference_arr=ref_arr, prediction_arr=pred_arr)
         self.assertEqual(st, 0.0)
 
+    def test_st_case_simple_identical_idx(self):
+        pred_arr, ref_arr = case_simple_identical()
+        st = Metric.ASSD(
+            reference_arr=ref_arr,
+            prediction_arr=pred_arr,
+            ref_instance_idx=1,
+            pred_instance_idx=1,
+        )
+        self.assertEqual(st, 0.0)
+
     def test_st_case_simple_nooverlap(self):
         pred_arr, ref_arr = case_simple_nooverlap()
         st = Metric.ASSD(reference_arr=ref_arr, prediction_arr=pred_arr)
@@ -185,6 +217,16 @@ def test_st_case_simple_identical(self):
         st = Metric.clDSC(reference_arr=ref_arr, prediction_arr=pred_arr)
         self.assertEqual(st, 1.0)
 
+    def test_st_case_simple_identical_idx(self):
+        pred_arr, ref_arr = case_simple_identical()
+        st = Metric.clDSC(
+            reference_arr=ref_arr,
+            prediction_arr=pred_arr,
+            ref_instance_idx=1,
+            pred_instance_idx=1,
+        )
+        self.assertEqual(st, 1.0)
+
     def test_st_case_simple_nooverlap(self):
         pred_arr, ref_arr = case_simple_nooverlap()
         st = Metric.clDSC(reference_arr=ref_arr, prediction_arr=pred_arr)

unit_tests/test_panoptic_pipeline.py

@@ -9,7 +9,11 @@
 
 from panoptica import InputType
 from panoptica.instance_approximator import ConnectedComponentsInstanceApproximator
-from panoptica.instance_matcher import MaximizeMergeMatching, NaiveThresholdMatching
+from panoptica.instance_matcher import (
+    MaximizeMergeMatching,
+    NaiveThresholdMatching,
+    InstanceLabelMap,
+)
 from panoptica.metrics import Metric
 from panoptica.instance_evaluator import (
     evaluate_matched_instance,
@@ -33,6 +37,26 @@ def setUp(self) -> None:
         os.environ["PANOPTICA_CITATION_REMINDER"] = "False"
         return super().setUp()
 
+    def test_labelmap(self):
+        labelmap = InstanceLabelMap()
+
+        labelmap.add_labelmap_entry(1, 1)
+        labelmap.add_labelmap_entry([2, 3], 2)
+
+        with self.assertRaises(Exception):
+            labelmap.add_labelmap_entry(1, 1)
+            labelmap.add_labelmap_entry(1, 2)
+
+        self.assertTrue(labelmap.contains_and(None, None))
+        self.assertTrue(labelmap.contains_and(1, 1))
+        self.assertTrue(not labelmap.contains_and(1, 3))
+        self.assertTrue(not labelmap.contains_and(4, 1))
+
+        print(labelmap)
+
+        with self.assertRaises(Exception):
+            labelmap.labelmap = {}
+
 
 class Test_Panoptica_Instance_Evaluation(unittest.TestCase):
     def setUp(self) -> None:

unit_tests/test_utils.py

@@ -0,0 +1,56 @@
+# Call 'python -m unittest' on this folder
+# coverage run -m unittest
+# coverage report
+# coverage html
+import os
+import unittest
+import numpy as np
+from panoptica.utils.numpy_utils import (
+    _unique_without_zeros,
+    _count_unique_without_zeros,
+    _get_smallest_fitting_uint,
+)
+from panoptica.utils.citation_reminder import citation_reminder
+
+
+class Test_Citation_Reminder(unittest.TestCase):
+    def setUp(self) -> None:
+        os.environ["PANOPTICA_CITATION_REMINDER"] = "True"
+        return super().setUp()
+
+    def test_citation_code(self):
+
+        @citation_reminder
+        def foo():
+            return "bar"
+
+        foo()
+
+
+class Test_Numpy_Utils(unittest.TestCase):
+    def setUp(self) -> None:
+        os.environ["PANOPTICA_CITATION_REMINDER"] = "False"
+        return super().setUp()
+
+    def test_np_unique(self):
+        a = np.array([0, 1, 2, 3, 6])
+        b = _unique_without_zeros(a)
+
+        self.assertTrue(b[0] == 1)
+        self.assertTrue(b[1] == 2)
+        self.assertTrue(b[2] == 3)
+        self.assertTrue(b[3] == 6)
+        self.assertEqual(b.shape[0], 4)
+
+        with self.assertWarns(UserWarning):
+            a = np.array([0, 1, -2, 3, -6])
+            b = _unique_without_zeros(a)
+
+    def test_np_count_unique(self):
+        a = np.array([0, 1, 2, 3, 6])
+        b = _count_unique_without_zeros(a)
+        self.assertEqual(b, 4)
+        #
+        with self.assertWarns(UserWarning):
+            a = np.array([0, 1, -2, 3, -6])
+            b = _count_unique_without_zeros(a)