Merge pull request #60 from softwaremill/ref/fix_foxai_names

Changing naming to Foxai everywhere

.github/actions/build-module/action.yaml

@@ -45,7 +45,7 @@ runs:
     - name: Run MyPy checks
       shell: bash
       run: |
-        poetry run mypy -p autoxai --show-error-context --pretty --namespace-packages --explicit-package-bases --ignore-missing-imports
+        poetry run mypy -p foxai --show-error-context --pretty --namespace-packages --explicit-package-bases --ignore-missing-imports
       working-directory: "${{ inputs.moduleFolder }}"
     - name: Run tests
       shell: bash

.github/workflows/ci.yaml

@@ -16,7 +16,7 @@ env:
   POETRY_VERSION: '1.2.1'
   PYTHON_VERSION: '3.8'
 jobs:
-  autoxai:
+  foxai:
     runs-on: ubuntu-20.04
     steps:
       - uses: actions/checkout@v3

.github/workflows/installation_test.yaml

@@ -6,7 +6,7 @@ env:
   POETRY_VERSION: '1.2.1'
   PYTHON_VERSION: '3.8'
 jobs:
-  autoxai:
+  foxai:
     strategy:
       matrix:
         CUDA_VERSION: ["10.2"]  #  "11.0" , "11.4", "11.6"

.github/workflows/publish_docs.yaml

@@ -43,7 +43,7 @@ jobs:
         run: echo ::set-output name=VERSION::$(poetry version | cut -d " " -f 2)
 
       - name: Build docs
-        run: poetry run sphinx-apidoc -o docs/source autoxai && cd docs && poetry run make html
+        run: poetry run sphinx-apidoc -o docs/source foxai && cd docs && poetry run make html
 
       - name: Compress production artifacts
         run: tar -czvf docs/build/foxai-docs-${{ steps.get_version.outputs.VERSION }}.tar.gz docs/build/html/

.github/workflows/sphinx_docs_check.yaml

@@ -33,7 +33,7 @@ jobs:
         poetry install
       working-directory: ""
     - name: Build docs
-      run: poetry run sphinx-apidoc -o docs/source autoxai && cd docs && poetry run make html
+      run: poetry run sphinx-apidoc -o docs/source foxai && cd docs && poetry run make html
     - name: Compress production artifacts
       run: tar -czvf docs/build/html.tar.gz docs/build/html/
     - name: Archive production artifacts

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
       - id: mypy
         name: mypy
         language: system
-        files: autoxai
+        files: foxai
         types: [ python ]
         entry: poetry run mypy
         args: ["--ignore-missing-imports"]

Dockerfile

@@ -37,8 +37,7 @@ RUN apt-get update \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
 # instead of copy you can fetch repository
-# RUN git clone https://github.com/softwaremill/AutoXAI.git && cd AutoXAI/
-COPY autoxai/ ./autoxai
+COPY foxai/ ./foxai
 COPY poetry.lock .
 COPY pyproject.toml .
 COPY README.md .
@@ -51,5 +50,5 @@ RUN curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.2.1 python3 -
     && poetry config virtualenvs.in-project true \
     && poetry install --no-root \
     && poetry build
-RUN python3 -m pip install dist/autoxai-0.5.3-py3-none-any.whl
+RUN python3 -m pip install dist/foxai-0.5.3-py3-none-any.whl
 RUN python3 -m pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

README.md

@@ -1,6 +1,6 @@
-# AutoXAI
+# FoXAI
 
-AutoXAI simplifies the application of e**X**plainable **AI** algorithms to explain the
+FoXAI simplifies the application of e**X**plainable **AI** algorithms to explain the
 performance of neural network models during training. The library acts as an
 aggregator of existing libraries with implementations of various XAI algorithms and
 seeks to facilitate and popularize their use in machine learning projects.
@@ -45,21 +45,21 @@ The assumption is that the `poetry` package is installed. You can find how to in
 `poetry` [here](#poetry). To build `wheel` package run:
 
 ```bash
-git clone https://github.com/softwaremill/AutoXAI.git
-cd AutoXAI/
+git clone https://github.com/softwaremill/FoXAI.git
+cd FoXAI/
 poetry install
 poetry build
 ```
 
 As a result you will get `wheel` file inside `dist/` directory that you can install
 via `pip`:
 ```bash
-pip install dist/autoxai-x.y.z-py3-none-any.whl
+pip install dist/foxai-x.y.z-py3-none-any.whl
 ```
 
 # Getting started
 
-To use the AutoXAI library in your ML project, simply add an additional object of type
+To use the FoXAI library in your ML project, simply add an additional object of type
 `WandBCallback` to the `Trainer`'s callback list from the `pytorch-lightning` library.
 Currently, only the Weights and Biases tool for tracking experiments is supported.
 
@@ -71,8 +71,8 @@ from pytorch_lightning import Trainer
 from pytorch_lightning.loggers import WandbLogger
 
 import wandb
-from autoxai.callbacks.wandb_callback import WandBCallback
-from autoxai.context_manager import Explainers, ExplainerWithParams
+from foxai.callbacks.wandb_callback import WandBCallback
+from foxai.context_manager import Explainers, ExplainerWithParams
 
     ...
     wandb.login()
@@ -108,12 +108,12 @@ W&B offline. This tool is using `hydra` to handle the configuration of `yaml` fi
 To check options type:
 
 ```bash
-autoxai-wandb-updater --help
+foxai-wandb-updater --help
 ```
 
 Typical usage with configuration in `config/config.yaml`:
 ```bash
-autoxai-wandb-updater --config-dir config/ --config-name config
+foxai-wandb-updater --config-dir config/ --config-name config
 ```
 
 Content of `config.yaml`:

docs/source/conf.py

@@ -11,9 +11,9 @@
 
 sys.path.insert(0, os.path.abspath("../.."))
 
-project = "AutoXAI"
+project = "FoXAI"
 copyright = "2023, ReasonField Lab"  # pylint: disable = (redefined-builtin)
-author = "Adam Jan Kaczmarek, Kamil Rzechowski, Adam Wawrzyński"
+author = "ReasonField Lab Team"
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

docs/source/index.rst

@@ -1,9 +1,9 @@
-.. AutoXAI documentation master file, created by
+.. FoXAI documentation master file, created by
    sphinx-quickstart on Thu Jan  5 16:02:36 2023.
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-Welcome to AutoXAI's documentation!
+Welcome to FoXAI's documentation!
 ===================================
 
 .. toctree::

docs/source/install.rst

@@ -15,8 +15,8 @@ You can build ``wheel`` with Python package yourself. To do so clone repository
 
 .. code-block:: bash
 
-    $ git clone https://github.com/softwaremill/AutoXAI.git
-    $ cd AutoXAI/
+    $ git clone https://github.com/softwaremill/FoXAI.git
+    $ cd FoXAI/
 
 Then setup development environment by installing ``Poetry`` and project dependencies.
 

docs/source/modules.rst

@@ -1,7 +1,7 @@
-autoxai
+foxai
 =======
 
 .. toctree::
    :maxdepth: 4
 
-   autoxai
+   foxai

example/mnist_wandb.py

@@ -13,8 +13,8 @@
 from torchvision.datasets import MNIST
 
 import wandb
-from autoxai.callbacks.wandb_callback import WandBCallback
-from autoxai.context_manager import Explainers, ExplainerWithParams
+from foxai.callbacks.wandb_callback import WandBCallback
+from foxai.context_manager import Explainers, ExplainerWithParams
 
 
 class LitMNIST(  # pylint: disable = (abstract-method, too-many-ancestors, too-many-instance-attributes)

example/notebooks/basic_usage.ipynb

@@ -56,9 +56,9 @@
     "from torchvision import transforms\n",
     "from torchvision.datasets import MNIST, ImageNet\n",
     "\n",
-    "from autoxai.explainer.base_explainer import CVExplainer\n",
-    "from autoxai.context_manager import AutoXaiExplainer, ExplainerWithParams, Explainers\n",
-    "from autoxai.visualizer import mean_channels_visualization, single_channel_visualization\n",
+    "from foxai.explainer.base_explainer import CVExplainer\n",
+    "from foxai.context_manager import FoXaiExplainer, ExplainerWithParams, Explainers\n",
+    "from foxai.visualizer import mean_channels_visualization, single_channel_visualization\n",
     "\n",
     "from IPython.display import Markdown, display"
    ]
@@ -67,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Install missing libraries required by `YOLOv5` that are not part of `autoxai` package."
+    "Install missing libraries required by `YOLOv5` that are not part of `foxai` package."
    ]
   },
   {
@@ -352,7 +352,7 @@
    "outputs": [],
    "source": [
     "from matplotlib import pyplot as plt\n",
-    "from autoxai.array_utils import convert_float_to_uint8\n",
+    "from foxai.array_utils import convert_standardized_float_to_uint8, standardize_array\n",
     "\n",
     "counter: int = 0\n",
     "    \n",
@@ -364,7 +364,8 @@
     "    for sample, label in zip(*batch):\n",
     "        # change image shape from (C X H X W) to (H X W X C)\n",
     "        # where C stands for colour, X is height and W is width dimension\n",
-    "        sample_np = sample.permute((1, 2, 0)).numpy()\n",
+    "#         sample_np = sample.permute((1, 2, 0)).numpy().astype(float)\n",
+    "        sample_np = sample.permute((1, 2, 0)).numpy().astype(float)\n",
     "        \n",
     "        # set title\n",
     "        axes.flat[counter].set_title(f\"Label: {categories[label.item()]}\")\n",
@@ -374,7 +375,7 @@
     "        axes.flat[counter].get_yaxis().set_visible(False)\n",
     "\n",
     "        # convert image from float to uint8 and display it\n",
-    "        axes.flat[counter].imshow(convert_float_to_uint8(sample_np))\n",
+    "        axes.flat[counter].imshow(convert_standardized_float_to_uint8(standardize_array(sample_np.astype(float))))\n",
     "        counter += 1\n",
     "\n",
     "        if counter >= max_samples_explained:\n",
@@ -396,18 +397,17 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
-    "### Choosing autoxai explainers (general algorithms)"
+    "### Choosing foxai explainers (general algorithms)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Define list of explainers from `autoxai` package You want to use on specified model. Full list of supported explainers can be found at definition of `Explainers` enum class."
+    "Define list of explainers from `foxai` package You want to use on specified model. Full list of supported explainers can be found at definition of `Explainers` enum class."
    ]
   },
   {
@@ -428,7 +428,6 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
     "tags": []
    },
    "source": [
@@ -463,7 +462,7 @@
     "        input_data = sample.reshape(1, sample.shape[0], sample.shape[1], sample.shape[2]).to(device)\n",
     "        category_name = categories[label.item()]\n",
     "        # move it to specified device\n",
-    "        with AutoXaiExplainer(\n",
+    "        with FoXaiExplainer(\n",
     "            model=model,\n",
     "            explainers=explainer_list,\n",
     "            target=label,\n",
@@ -478,14 +477,11 @@
     "                os.makedirs(artifact_explainer_dir)\n",
     "\n",
     "            # create figure from attributes and original image           \n",
-    "            figure = mean_channels_visualization(attributions=value[0], transformed_img=sample, title=\"Mean of channels\")\n",
+    "            figure = mean_channels_visualization(attributions=value[0], transformed_img=sample, title= f\"Mean of channels ({key})\")\n",
     "\n",
     "            # save figure to artifact directory\n",
     "            figure.savefig(os.path.join(artifact_explainer_dir, f\"artifact_{sample_counter}_{category_name}.png\"))\n",
     "            show_figure(figure)\n",
-    "            for i in range(0, sample.shape[0]):\n",
-    "                figure = single_channel_visualization(attributions=value[0], transformed_img=sample, selected_channel=i, title=f\"Channel: {i}\")\n",
-    "                show_figure(figure)\n",
     "            \n",
     "        sample_counter += 1\n",
     "        # if we processed desired number of samples break the loop\n",
@@ -533,7 +529,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Choosing autoxai explainers (layer-specific algorithms)"
+    "### Choosing foxai explainers (layer-specific algorithms)"
    ]
   },
   {
@@ -589,7 +585,7 @@
     "        # add batch size dimension to the data sample\n",
     "        input_data = sample.reshape(1, sample.shape[0], sample.shape[1], sample.shape[2]).to(device)\n",
     "        category_name = categories[label.item()]\n",
-    "        with AutoXaiExplainer(\n",
+    "        with FoXaiExplainer(\n",
     "            model=model,\n",
     "            explainers=explainer_list,\n",
     "            target=label,\n",
@@ -604,7 +600,7 @@
     "                os.makedirs(artifact_explainer_dir)\n",
     "\n",
     "            # create figure from attributes and original image           \n",
-    "            figure = mean_channels_visualization(attributions=value[0], transformed_img=sample, title=\"Mean of channels\")\n",
+    "            figure = mean_channels_visualization(attributions=value[0], transformed_img=sample, title= f\"Mean of channels ({key})\")\n",
     "\n",
     "            # save figure to artifact directory\n",
     "            figure.savefig(os.path.join(artifact_explainer_dir, f\"artifact_{sample_counter}_{category_name}.png\"))\n",
@@ -633,11 +629,18 @@
    "source": [
     "You have learned to use the basic functionality of the library. You can now experiment and gain confidence in your ML models."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('.venv': poetry)",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -651,7 +654,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.7.13"
   },
   "vscode": {
    "interpreter": {