Merge pull request #170 from XAITK/update-to-v0.11.0

Update to v0.11.0

.github/actions/build-stage/action.yml

@@ -12,7 +12,7 @@ runs:
     - name: Setup Poetry
       run: |
         pip install --root-user-action=ignore --upgrade pip -q
-        pip install --root-user-action=ignore --user -U poetry -q
+        pip install --root-user-action=ignore --user -U "poetry<2.0" -q
       shell: bash
     - name: Configure Poetry
       run: |

.gitlab-ci.yml

@@ -4,7 +4,6 @@ include:
     file: ".gitlab-ci.yml"
 
   # job overrides specific to this repo
-  - local: .gitlab-ci/.gitlab-build.yml
   - local: .gitlab-ci/.gitlab-docs.yml
   - local: .gitlab-ci/.gitlab-test.yml
   - local: .gitlab-ci/.gitlab-security.yml

.readthedocs.yaml

@@ -9,7 +9,7 @@ build:
     post_create_environment:
       # Install poetry
       # https://python-poetry.org/docs/#installing-manually
-      - pip install poetry
+      - pip install "poetry<2.0"
     post_install:
       # Install dependencies with 'docs' dependency group
       # https://python-poetry.org/docs/managing-dependencies/#dependency-groups

README.md

@@ -45,12 +45,12 @@ Ensure the source tree is acquired locally before proceeding.
 
 To install the current version via `pip`:
 ```bash
-pip install xaitk-saliency
+pip install xaitk-saliency[<extra1>,<extra2>,...]
 ```
 
 Alternatively, you can use [Poetry](https://python-poetry.org/):
 ```bash
-poetry install
+poetry install --with main,linting,tests,docs --extras "<extra1> <extra2> ..."
 ```
 
 Certain plugins may require additional runtime dependencies. Details on these requirements can be found [here](https://xaitk-saliency.readthedocs.io/en/latest/implementations.html).
@@ -72,7 +72,7 @@ Documentation for both release snapshots and the latest master branch is availab
 To build the Sphinx-based documentation locally for the latest reference:
 ```bash
 # Install dependencies
-poetry install --sync --with linting,tests,docs
+poetry install --sync --with main,linting,tests,docs
 # Navigate to the documentation root
 cd docs
 # Build the documentation
@@ -91,7 +91,7 @@ Pre-commit hooks ensure that code complies with required linting and formatting
 To install and use pre-commit hooks:
 ```bash
 # Install required dependencies
-poetry install --sync --with linting,tests,docs
+poetry install --sync --with main,linting,tests,docs
 # Initialize pre-commit hooks for the repository
 poetry run pre-commit install
 # Run pre-commit checks on all files
@@ -127,7 +127,10 @@ All development prior to Nov 19, 2024 falls under [BSD-3-Clause](./LICENSE.old)
 ## Contacts
 
 **Principal Investigator**: Brian Hu (Kitware) @brian.hu
+
 **Product Owner**: Austin Whitesell (MITRE) @awhitesell
+
 **Scrum Master / Tech Lead**: Brandon RichardWebster (Kitware) @b.richardwebster
+
 **Deputy Tech Lead**: Emily Veenhuis (Kitware) @emily.veenhuis
 <!-- :auto contacts: -->

docs/release_notes.rst

@@ -20,3 +20,4 @@ Release Notes
    release_notes/v0.9.1
    release_notes/v0.9.2
    release_notes/v0.10.0
+   release_notes/v0.11.0

docs/release_notes/v0.11.0.rst

@@ -0,0 +1,15 @@
+v0.11.0
+=======
+
+Improved linting, formating, and dependency management.
+
+Updates / New Features
+----------------------
+
+CI/CD
+
+* Updated and applied ruff configuration.
+
+
+Fixes
+-----

examples/DRISE.ipynb

@@ -162,6 +162,7 @@
     "    sal_map_generator: GenerateObjectDetectorBlackboxSaliency,\n",
     "    fill: Optional[Union[int, Sequence[int]]] = None,\n",
     ") -> None:\n",
+    "    \"\"\"Helper to generate and visualize saliency maps\"\"\"\n",
     "    # Load the image\n",
     "    ref_image = np.asarray(PIL.Image.open(image_filepath))\n",
     "\n",
@@ -361,6 +362,11 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -371,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,

examples/MNIST_scikit_saliency.ipynb

@@ -80,7 +80,6 @@
    "cell_type": "markdown",
    "id": "c44e6e20",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -124,9 +123,7 @@
     "X = X / X.max()\n",
     "\n",
     "# Find examples of each class\n",
-    "ref_inds = []\n",
-    "for i in range(10):\n",
-    "    ref_inds.append(np.where(np.int64(y) == i)[0][0])\n",
+    "ref_inds = [np.nonzero(np.int64(y) == i)[0][0] for i in range(10)]\n",
     "\n",
     "ref_imgs = X[ref_inds]\n",
     "\n",
@@ -162,7 +159,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from collections.abc import Iterable, Sequence\n",
+    "from typing import Any\n",
+    "\n",
     "from smqtk_classifier import ClassifyImage\n",
+    "from typing_extensions import override\n",
     "\n",
     "from xaitk_saliency import GenerateImageClassifierBlackboxSaliency\n",
     "\n",
@@ -172,6 +173,7 @@
     "    image_classifier: ClassifyImage,\n",
     "    saliency_generator: GenerateImageClassifierBlackboxSaliency,\n",
     ") -> None:\n",
+    "    \"\"\"Helper to generate and visualize saliency maps\"\"\"\n",
     "    # Generate saliency maps\n",
     "    sal_maps_set = []\n",
     "    for img in images:\n",
@@ -301,7 +303,6 @@
    "cell_type": "markdown",
    "id": "e6d18472",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -318,18 +319,25 @@
    "outputs": [],
    "source": [
     "class MNISTClassifierLog(ClassifyImage):\n",
-    "    def get_labels(self):\n",
+    "    \"\"\"ClassifyImage wrapper for LogisticRegression\"\"\"\n",
+    "\n",
+    "    @override\n",
+    "    def get_labels(self) -> Sequence[int]:\n",
+    "        \"\"\"Return class labels\"\"\"\n",
     "        return list(range(10))\n",
     "\n",
-    "    def classify_images(self, image_iter):\n",
+    "    @override\n",
+    "    def classify_images(self, image_iter: Iterable[np.ndarray]) -> Sequence[dict[str, Any]]:\n",
+    "        \"\"\"Generate predictions\"\"\"\n",
     "        # Yes, \"images\" in this example case are really 1-dim (28*28=784).\n",
     "        # MLP input needs a (n_samples, n_features) matrix input.\n",
     "        images = np.asarray(list(image_iter))  # may fail because input is not consistent in shape\n",
     "        images = images.reshape(-1, 28 * 28)  # may fail because input was not the correct shape\n",
     "        return (dict(zip(range(10), p)) for p in clf.decision_function(images))\n",
     "\n",
-    "    # Required for implementation\n",
-    "    def get_config(self):\n",
+    "    @override\n",
+    "    def get_config(self) -> dict[str, Any]:\n",
+    "        \"\"\"Required for implementation\"\"\"\n",
     "        return {}\n",
     "\n",
     "\n",
@@ -355,7 +363,7 @@
    "source": [
     "from xaitk_saliency.impls.gen_image_classifier_blackbox_sal.slidingwindow import SlidingWindowStack\n",
     "\n",
-    "gen_slidingWindow = SlidingWindowStack(window_size=(2, 2), stride=(1, 1), threads=4)"
+    "gen_sliding_window = SlidingWindowStack(window_size=(2, 2), stride=(1, 1), threads=4)"
    ]
   },
   {
@@ -405,7 +413,7 @@
     }
    ],
    "source": [
-    "app(X[0:20], image_classifier_log, gen_slidingWindow)"
+    "app(X[0:20], image_classifier_log, gen_sliding_window)"
    ]
   },
   {
@@ -494,18 +502,25 @@
    "outputs": [],
    "source": [
     "class MNISTClassifierMLP(ClassifyImage):\n",
-    "    def get_labels(self):\n",
+    "    \"\"\"ClassifyImage wrapper for MLPClassifier\"\"\"\n",
+    "\n",
+    "    @override\n",
+    "    def get_labels(self) -> Sequence[int]:\n",
+    "        \"\"\"Return class labels\"\"\"\n",
     "        return list(range(10))\n",
     "\n",
-    "    def classify_images(self, image_iter):\n",
+    "    @override\n",
+    "    def classify_images(self, image_iter: Iterable[np.ndarray]) -> Sequence[dict[str, Any]]:\n",
+    "        \"\"\"Generate predictions\"\"\"\n",
     "        # Yes, \"images\" in this example case are really 1-dim (28*28=784).\n",
     "        # MLP input needs a (n_samples, n_features) matrix input.\n",
     "        images = np.asarray(list(image_iter))  # may fail because input is not consistent in shape\n",
     "        images = images.reshape(-1, 28 * 28)  # may fail because input was not the correct shape\n",
     "        return (dict(zip(range(10), p)) for p in mlp.predict_proba(images))\n",
     "\n",
-    "    # Required for implementation\n",
-    "    def get_config(self):\n",
+    "    @override\n",
+    "    def get_config(self) -> dict[str, Any]:\n",
+    "        \"\"\"Required for implementation\"\"\"\n",
     "        return {}\n",
     "\n",
     "\n",
@@ -552,11 +567,16 @@
     }
    ],
    "source": [
-    "app(X[0:20], image_classifier_mlp, gen_slidingWindow)"
+    "app(X[0:20], image_classifier_mlp, gen_sliding_window)"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -567,7 +587,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.13"
   }
  },
  "nbformat": 4,