Fix sonarcloud analysis issues

Dockerfile

@@ -1,8 +1,9 @@
 FROM python:3.8 AS xaitk_base
 
 # install poetry and configure to install to pip environment
-RUN pip install poetry \
-&& poetry config virtualenvs.create false
+ENV PATH=/root/.local/bin:${PATH}
+RUN (curl -sSL 'https://install.python-poetry.org' | python3 -) \
+ && poetry config virtualenvs.create false
 
 WORKDIR /xaitk
 
@@ -11,5 +12,9 @@ COPY poetry.lock pyproject.toml /xaitk/
 RUN poetry install --no-root
 
 # install package
-COPY . /xaitk/
+COPY docs /xaitk/docs/
+COPY scripts /xaitk/scripts/
+COPY tests /xaitk/tests/
+COPY xaitk_saliency /xaitk/xaitk_saliency
+COPY .flake8 .mypy.ini CONTRIBUTING.md LICENSE.txt README.md /xaitk/
 RUN poetry install

docs/release_notes/pending_release.rst

@@ -4,9 +4,20 @@ Pending Release Notes
 Updates / New Features
 ----------------------
 
+Docker
+
+* Update Dockerfile install of poetry, and make separate & specific directory
+  copies.
+
 Utils
 
 * Updated logging format of occlusion masking benchmark utility.
 
 Fixes
 -----
+
+Tests
+
+* Update various floating point equality comparisons to not use exact match.
+
+* Fix random number usage from numpy to use `np.random.default_rng`.

tests/impls/gen_classifier_conf_sal/test_occlusion_scoring.py

@@ -58,11 +58,11 @@ def test_1class_scores(self) -> None:
         Test basic scoring with a single class for broadcasting sanity check.
         """
         impl = OcclusionScoring()
-        np.random.seed(2)
+        rng = np.random.default_rng(2)
         # Three Perturbation masks of height and width 10px for 1 class
-        image_confs_1_class_ = np.random.rand(1)
-        pertb_confs_1_class_ = np.random.rand(3, 1)
-        mask_confs_1_class_ = np.random.randint(low=0, high=2, size=(3, 10, 10), dtype='int')
+        image_confs_1_class_ = rng.standard_normal((1))
+        pertb_confs_1_class_ = rng.standard_normal((3, 1))
+        mask_confs_1_class_ = rng.integers(low=0, high=2, size=(3, 10, 10), dtype='int')
 
         sal = impl.generate(image_confs_1_class_, pertb_confs_1_class_, mask_confs_1_class_)
         assert sal.shape == (1, 10, 10)
@@ -85,10 +85,10 @@ def test_20class_scores(self) -> None:
         Test scoring for 20 classes.
         """
         impl = OcclusionScoring()
-        np.random.seed(2)
+        rng = np.random.default_rng(2)
         # Three Perturbation masks of height and width 10px for 20 classes
-        image_confs_1_class_ = np.random.rand(20)
-        pertb_confs_1_class_ = np.random.rand(3, 20)
-        mask_confs_1_class_ = np.random.randint(low=0, high=2, size=(3, 10, 10), dtype='int')
+        image_confs_1_class_ = rng.standard_normal((20))
+        pertb_confs_1_class_ = rng.standard_normal((3, 20))
+        mask_confs_1_class_ = rng.integers(low=0, high=2, size=(3, 10, 10), dtype='int')
         sal = impl.generate(image_confs_1_class_, pertb_confs_1_class_, mask_confs_1_class_)
         assert sal.shape == (20, 10, 10)

tests/impls/gen_descriptor_sim_sal/test_similarity_scoring.py

@@ -57,11 +57,11 @@ def test_generate_mismatch_ref_descriptors(self) -> None:
         Test that we appropriately error when the input reference descriptors
         are not the same dimensionality.
         """
-        np.random.seed(0)
-        test_ref_descr = np.random.rand(16)
-        test_query_descrs = np.random.rand(5, 15)  # Different than above
-        test_pert_descrs = np.random.rand(32, 16)
-        test_masks = np.random.rand(32, 16, 16)
+        rng = np.random.default_rng(seed=0)
+        test_ref_descr = rng.standard_normal(16)
+        test_query_descrs = rng.standard_normal((5, 15))  # Different than above
+        test_pert_descrs = rng.standard_normal((32, 16))
+        test_masks = rng.standard_normal((32, 16, 16))
 
         impl = SimilarityScoring()
 
@@ -76,11 +76,11 @@ def test_generate_mismatched_perturbed(self) -> None:
         Test that we appropriately error when the input perturbation
         descriptors and mask arrays are not equal in first-dimension length.
         """
-        np.random.seed(0)
-        test_ref_descr = np.random.rand(16)
-        test_query_descrs = np.random.rand(1, 16)
-        test_pert_descrs = np.random.rand(32, 16)
-        test_masks = np.random.rand(30, 16, 16)  # Different than above
+        rng = np.random.default_rng(seed=0)
+        test_ref_descr = rng.standard_normal(16)
+        test_query_descrs = rng.standard_normal((1, 16))
+        test_pert_descrs = rng.standard_normal((32, 16))
+        test_masks = rng.standard_normal((30, 16, 16))  # Different than above
 
         impl = SimilarityScoring()
 
@@ -96,11 +96,11 @@ def test_1_featurevec(self) -> None:
         Test basic scoring with a single feature for broadcasting sanity check.
         """
         impl = SimilarityScoring()
-        np.random.seed(2)
-        ref_feat = np.random.rand(2048)
-        query_feats = np.random.rand(2, 2048)
-        pertb_feats = np.random.rand(3, 2048)
-        pertb_mask = np.random.randint(low=0, high=2, size=(3, 10, 10), dtype='int')
+        rng = np.random.default_rng(2)
+        ref_feat = rng.standard_normal(2048)
+        query_feats = rng.standard_normal((2, 2048))
+        pertb_feats = rng.standard_normal((3, 2048))
+        pertb_mask = rng.integers(low=0, high=2, size=(3, 10, 10), dtype='int')
         sal = impl.generate(ref_feat, query_feats, pertb_feats, pertb_mask)
         assert sal.shape == (2, 10, 10)
 
@@ -123,10 +123,10 @@ def test_512_featdim(self) -> None:
         Test scoring for features of 512 dims.
         """
         impl = SimilarityScoring()
-        np.random.seed(2)
-        ref_feat = np.random.rand(512)
-        query_feats = np.random.rand(1, 512)
-        pertb_feats = np.random.rand(15, 512)
-        pertb_mask = np.random.randint(low=0, high=2, size=(15, 10, 10), dtype='int')
+        rng = np.random.default_rng(2)
+        ref_feat = rng.standard_normal(512)
+        query_feats = rng.standard_normal((1, 512))
+        pertb_feats = rng.standard_normal((15, 512))
+        pertb_mask = rng.integers(low=0, high=2, size=(15, 10, 10), dtype='int')
         sal = impl.generate(ref_feat, query_feats, pertb_feats, pertb_mask)
         assert sal.shape == (1, 10, 10)

tests/impls/gen_detector_prop_sal/test_drise_scoring.py

@@ -23,9 +23,10 @@ def test_shape_sanity(self) -> None:
         Test basic scoring with a single feature for broadcasting sanity check.
         """
         impl = DRISEScoring()
-        ref_dets = np.random.rand(2, 7)
-        pert_dets = np.random.rand(10, 3, 7)
-        pert_mask = np.random.randint(
+        rng = np.random.default_rng(seed=0)
+        ref_dets = rng.standard_normal((2, 7))
+        pert_dets = rng.standard_normal((10, 3, 7))
+        pert_mask = rng.integers(
             low=0, high=2, size=(10, 15, 25), dtype='int')
         sal = impl.generate(ref_dets, pert_dets, pert_mask)
         assert sal.shape == (2, 15, 25)
@@ -52,9 +53,10 @@ def test_mask_detections_size_mismatch(self) -> None:
         Test size mismatch between perturbed detections and masks.
         """
         impl = DRISEScoring()
-        ref_dets = np.random.rand(2, 7)
-        pert_dets = np.random.rand(9, 3, 7)  # ONE LESS than pert mask mat.
-        pert_mask = np.random.randint(
+        rng = np.random.default_rng(seed=0)
+        ref_dets = rng.standard_normal((2, 7))
+        pert_dets = rng.standard_normal((9, 3, 7))  # ONE LESS than pert mask mat.
+        pert_mask = rng.integers(
             low=0, high=2, size=(10, 15, 25), dtype='int')
         with pytest.raises(
             ValueError,
@@ -68,9 +70,10 @@ def test_detections_classes_mismatch(self) -> None:
         Test mismatch in number of classes between perturbed and reference detections.
         """
         impl = DRISEScoring()
-        ref_dets = np.random.rand(2, 8)  # ONE MORE than pert dets mat.
-        pert_dets = np.random.rand(10, 3, 7)
-        pert_mask = np.random.randint(
+        rng = np.random.default_rng(seed=0)
+        ref_dets = rng.standard_normal((2, 8))  # ONE MORE than pert dets mat.
+        pert_dets = rng.standard_normal((10, 3, 7))
+        pert_mask = rng.integers(
             low=0, high=2, size=(10, 15, 25), dtype='int')
         with pytest.raises(
             ValueError,

tests/impls/gen_image_classifier_blackbox_sal/test_rise.py

@@ -35,10 +35,10 @@ def test_configuration(self) -> None:
             assert isinstance(inst_g, RISEScoring)
             assert inst_p.n == 444
             assert inst_p.s == 33
-            assert inst_p.p1 == .22
+            assert np.allclose(inst_p.p1, .22)
             assert inst_p.seed == 42
             assert inst_p.threads == 99
-            assert inst_g.p1 == .22
+            assert np.allclose(inst_g.p1, .22)
             assert inst_i.get_config()['debiased'] is True
 
     def test_configuration_not_debiased(self) -> None:
@@ -58,8 +58,8 @@ def test_configuration_not_debiased(self) -> None:
             inst_g = inst_i._po._generator
             assert isinstance(inst_p, RISEGrid)
             assert isinstance(inst_g, RISEScoring)
-            assert inst_p.p1 == .22
-            assert inst_g.p1 == 0.0
+            assert np.allclose(inst_p.p1, .22)
+            assert np.allclose(inst_g.p1, 0.0)
             assert inst_i.get_config()['debiased'] is False
 
     def test_generation_rgb(self) -> None:

tests/impls/gen_object_detector_blackbox_sal/test_drise.py

@@ -37,7 +37,7 @@ def test_configuration(self) -> None:
             assert isinstance(inst_g, DRISEScoring)
             assert inst_p.n == 123
             assert inst_p.s == 8
-            assert inst_p.p1 == 0.73
+            assert np.allclose(inst_p.p1, .73)
             assert inst_p.seed == 98
             assert inst_p.threads == 5
             assert inst_g.get_config() == {}

tests/impls/gen_object_detector_blackbox_sal/test_random_grid.py

@@ -38,7 +38,7 @@ def test_configuration(self) -> None:
             assert isinstance(inst_g, DRISEScoring)
             assert inst_p.n == 55
             assert inst_p.s == (15, 8)
-            assert inst_p.p1 == 0.34
+            assert np.allclose(inst_p.p1, 0.34)
             assert inst_p.seed == 7
             assert inst_p.threads == 2
             assert inst_g.get_config() == {}

tests/impls/perturb_image/test_random_grid.py

@@ -21,7 +21,7 @@ def test_init_valued(self) -> None:
 
         assert impl.n == test_n
         assert impl.s == test_s
-        assert impl.p1 == test_p1
+        assert np.allclose(impl.p1, test_p1)
         assert impl.seed == test_seed
         assert impl.threads == test_threads
 
@@ -41,7 +41,7 @@ def test_standard_config(self) -> None:
         for inst in configuration_test_helper(impl):
             assert inst.n == test_n
             assert inst.s == test_s
-            assert inst.p1 == test_p1
+            assert np.allclose(impl.p1, test_p1)
             assert inst.seed == test_seed
             assert inst.threads == test_threads
 
@@ -53,7 +53,8 @@ def test_if_random(self) -> None:
         impl1 = RandomGrid(n=3, s=(5, 4), p1=0.5)
         impl2 = RandomGrid(n=3, s=(5, 4), p1=0.5)
 
-        img = np.random.randint(0, 255, size=(20, 20), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(20, 20), dtype=np.uint8)
 
         masks1 = impl1(img)
         masks2 = impl2(img)
@@ -68,7 +69,8 @@ def test_seed(self) -> None:
         impl1 = RandomGrid(n=3, s=(2, 1), p1=0.6, seed=5)
         impl2 = RandomGrid(n=3, s=(2, 1), p1=0.6, seed=5)
 
-        img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(10, 10), dtype=np.uint8)
 
         masks1 = impl1(img)
         masks2 = impl2(img)
@@ -80,8 +82,8 @@ def test_call_idempotency(self) -> None:
         Test that perturbation generation is idempotent, at least when seeded
         and single-threaded.
         """
-
-        img = np.random.randint(0, 255, size=(10, 10), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(10, 10), dtype=np.uint8)
 
         impl = RandomGrid(n=2, s=(1, 2), p1=0.4, seed=1, threads=0)
 
@@ -95,8 +97,8 @@ def test_perturb_1_channel(self) -> None:
         Test mask generation on a one-channel image of a known size. Number
         of channels should not affect output masks.
         """
-
-        img = np.random.randint(0, 255, size=(4, 6), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(4, 6), dtype=np.uint8)
 
         impl = RandomGrid(n=2, s=(2, 2), p1=0.5, seed=123, threads=0)
 
@@ -112,8 +114,8 @@ def test_perturb_3_channel(self) -> None:
         Test mask generation on a three-channel image of a known size. Number
         of channels should not affect output masks.
         """
-
-        img = np.random.randint(0, 255, size=(4, 6, 3), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(4, 6, 3), dtype=np.uint8)
 
         impl = RandomGrid(n=2, s=(2, 2), p1=0.5, seed=123, threads=0)
 
@@ -129,11 +131,11 @@ def test_multiple_image_size(self) -> None:
         Test that a single implementation can be used for images of varying
         sizes.
         """
-
         impl = RandomGrid(n=5, s=(3, 4), p1=0.2, seed=42, threads=0)
 
-        img_small = np.random.randint(0, 255, size=(5, 7), dtype=np.uint8)
-        img_large = np.random.randint(0, 255, size=(55, 77), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img_small = rng.integers(0, 255, size=(5, 7), dtype=np.uint8)
+        img_large = rng.integers(0, 255, size=(55, 77), dtype=np.uint8)
 
         masks_small = impl(img_small)
         masks_large = impl(img_large)
@@ -148,8 +150,8 @@ def test_threading(self) -> None:
         """
         Test that using threading does not affect results.
         """
-
-        img = np.random.randint(0, 255, size=(4, 6), dtype=np.uint8)
+        rng = np.random.default_rng(seed=0)
+        img = rng.integers(0, 255, size=(4, 6), dtype=np.uint8)
 
         impl = RandomGrid(n=2, s=(2, 2), p1=0.5, seed=123, threads=1)