Links update

.github/workflows/links.yml

@@ -17,10 +17,15 @@ jobs:
           find . -name '*.html' -delete
       - run: |
           find . -name '*.md' -exec pandoc -i {} -o {}.html \;
-      - uses: anishathalye/proof-html@v1
+      - uses: anishathalye/proof-html@v2
         with:
           directory: .
+          check_html: false
           check_favicon: false
-          empty_alt_ignore: true
-          url_ignore_re: |
-            ^https:\/\/twitter.com\/CleanlabAI
+          ignore_missing_alt: true
+          ignore_empty_alt: true
+          tokens: |
+            {"https://github.com": "${{ secrets.GITHUB_TOKEN }}"}
+          swap_urls: |
+            {"^(\\..*)\\.md(#?.*)$": "\\1.md.html\\2",
+             "^(https://github\\.com/.*)#.*$": "\\1"}

DEVELOPMENT.md

@@ -123,7 +123,7 @@ pip install -r docs/requirements.txt
 sphinx-build docs/source cleanvision-docs
 ```
 
-**Note for faster build**: Executing the Jupyter Notebooks (i.e., the .ipynb files) that make up some portion of the docs, such as the tutorials, takes a long time. If you want to skip rendering these, set the environment variable `SKIP_NOTEBOOKS=1`. You can either set this using `export SKIP_NOTEBOOKS=1`
+**Note for faster build**: Executing the Jupyter Notebooks (i.e., the .ipynb files) that make up some portion of the docs, such as the tutorials, takes a long time. If you want to skip rendering these, add `nbsphinx_execute = 'never' to [sphinx configuration](docs/source/conf.py)
 
 4. To view the docs open the file `cleanvision-docs/index.html` file in a browser.
 

README.md

@@ -89,7 +89,7 @@ In any collection of image files (most [formats](https://pillow.readthedocs.io/e
 | 6 | Light            | Irregularly bright images (*over*exposed)                       | light            | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/light.jpg)                                |
 | 7 | Grayscale        | Images lacking color                                            | grayscale        | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/grayscale.jpg)                            |
 | 8 | Odd Aspect Ratio | Images with an unusual aspect ratio (overly skinny/wide)        | odd_aspect_ratio | ![](https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/odd_aspect_ratio.jpg)                     |
-| 9 | Odd Size         | Images that are abnormally large or small | odd_size         | <img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/odd_size.png" width=20% height=20%> |
+| 9 | Odd Size         | Images that are abnormally large or small compared to the rest of the dataset | odd_size         | <img src="https://raw.githubusercontent.com/cleanlab/assets/master/cleanvision/example_issue_images/odd_size.png" width=20% height=20%> |
 
 CleanVision supports Linux, macOS, and Windows and runs on Python 3.7+.
 

docs/requirements.txt

@@ -1,19 +1,12 @@
-sphinx==5.1.1
-sphinx-tabs==3.4.1
-nbsphinx==0.8.8
-autodocsumm==0.2.9
+sphinx==7.1.2
+sphinx-tabs==3.4.5
+nbsphinx==0.9.3
+autodocsumm==0.2.12
 sphinx-multiversion==0.2.4
-sphinx-copybutton==0.5.0
-sphinxcontrib-katex==0.8.6
-sphinx-autodoc-typehints==1.19.2
-furo==2022.06.21
-numpy>=1.20.0
-pandas>=1.1.5
-Pillow>=9.3
-matplotlib>=3.4
-tqdm>=4.53.0
-imagehash>=4.2.0
-datasets>=2.7.0
-torchvision>=0.12.0
-ipykernel==6.8.0
-ipywidgets==7.6.5
+sphinx-copybutton==0.5.2
+sphinxcontrib-katex==0.9.9
+sphinx-autodoc-typehints==1.25.2
+furo==2023.09.10
+ipykernel==6.29.0
+ipywidgets==8.1.1
+ipython==8.0.1

docs/source/conf.py

@@ -77,7 +77,6 @@
 
 html_title = ""
 html_theme = "furo"
-html_static_path = ["_static"]
 html_logo = "https://raw.githubusercontent.com/cleanlab/assets/master/cleanlab/cleanlab_logo_only.png"
 
 html_theme_options = {

docs/source/faq.rst

@@ -10,7 +10,7 @@ CleanVision is independent of any machine learning tasks as it directly works on
 2. **Can I check for specific issues in my dataset?**
 
 
-Yes, you can specify issues like ``light`` or ``blurry`` in the issue_types argument when calling ``Imagelab.find_issues``
+Yes, you can specify issues like ``light`` or ``blurry`` in the issue_types argument when calling :py:meth:`~cleanvision.imagelab.Imagelab.find_issues`
 
 .. code-block:: python3
 

docs/source/index.rst

@@ -5,7 +5,7 @@
 Documentation
 =======================================
 
-CleanVision automatically detects various issues in image datasets, such as images that are: (near) duplicates, blurry,
+CleanVision automatically detects various issues in your image data, such as images that are: (near) duplicates, blurry,
 over/under-exposed, etc. This data-centric AI package is designed as a quick first step for any computer vision project
 to find problems in your dataset, which you may want to address before applying machine learning.
 
@@ -120,9 +120,9 @@ CleanVision works smoothly with Torchvision datasets too:
 
 Additional Resources
 --------------------
-- Get started with our `Example Notebook <https://cleanvision.readthedocs.io/en/latest/tutorials/tutorial.html>`_
-- Explore more `Example Notebooks <https://github.com/cleanlab/cleanvision-examples>`_
-- Learn how to contribute in the `Contribution Guide <https://github.com/cleanlab/cleanvision/blob/main/CONTRIBUTING.md>`_
+- Get started with `Starter Tutorial <tutorials/tutorial.ipynb>`_.
+- View more `code examples <https://github.com/cleanlab/cleanvision-examples>`_ that demonstrate how to use CleanVision on various datasets.
+- Interested in contributing to CleanVision? Check out our `Contribution Guide <https://github.com/cleanlab/cleanvision/blob/main/CONTRIBUTING.md>`_ to get started.
 
 
 .. toctree::

docs/source/tutorials/custom_issue_manager.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pandas as pd
 from PIL import Image
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from cleanvision.dataset.base_dataset import Dataset
 from cleanvision.issue_managers import register_issue_manager

docs/source/tutorials/huggingface_dataset.ipynb

@@ -44,6 +44,19 @@
     "from cleanvision import Imagelab"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "nbsphinx": "hidden"
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -60,7 +73,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "dataset = load_dataset(\"cats_vs_dogs\", split=\"train\")"
@@ -184,7 +199,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "imagelab.issues"
+    "imagelab.issues.head()"
    ]
   },
   {
@@ -243,7 +258,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**For more detailed guide on how to use CleanVision, check the [tutorial notebook](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb).**"
+    "**For more detailed guide on how to use CleanVision, check the** [tutorial notebook](tutorial.ipynb)."
    ]
   }
  ],
@@ -263,7 +278,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,

docs/source/tutorials/torchvision_dataset.ipynb

@@ -70,9 +70,12 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "3d207006",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
+    "%%capture\n",
     "train_set = CIFAR10(root=\"./\", download=True)\n",
     "test_set = CIFAR10(root=\"./\", train=False, download=True)"
    ]
@@ -200,7 +203,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "imagelab.issues"
+    "imagelab.issues.head()"
    ]
   },
   {
@@ -264,7 +267,7 @@
    "id": "75912aea",
    "metadata": {},
    "source": [
-    "**For more detailed guide on how to use CleanVision, check the [tutorial notebook](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/tutorial.ipynb).**"
+    "**For more detailed guide on how to use CleanVision, check the** [tutorial notebook](tutorial.ipynb)."
    ]
   }
  ],
@@ -284,7 +287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,

docs/source/tutorials/tutorial.ipynb

@@ -59,6 +59,7 @@
     "| 6   | Blurry           | Images that are blurry or out of focus                                                  | blurry           |\n",
     "| 7   | Grayscale        | Images that are grayscale (lacking color)                                                            | grayscale        |\n",
     "| 8   | Low Information  | Images that lack much information (e.g. a completely black image with a few white dots) | low_information  |\n",
+    "| 9   | Odd Size  | Images that are abnormally large or small compared to the rest of the dataset | odd_size  |\n",
     "\n",
     "\n",
     "The **Issue Key** column specifies the name for each type of issue in CleanVision code. See our examples which use these keys to detect only particular issue types and specify nondefault parameter settings to use when checking for certain issues."
@@ -150,7 +151,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The main way to interface with your data is via the [Imagelab](https://cleanvision.readthedocs.io/en/latest/cleanvision/imagelab.html#cleanvision.imagelab.Imagelab) class. This class can be used to understand the issues in your dataset at a high level (global overview) and low level (issues and quality scores for each image) as well as additional information about the dataset. It has three main attributes:\n",
+    "The main way to interface with your data is via the [Imagelab](../cleanvision/imagelab.rst#cleanvision.imagelab.Imagelab) class. This class can be used to understand the issues in your dataset at a high level (global overview) and low level (issues and quality scores for each image) as well as additional information about the dataset. It has three main attributes:\n",
     "\n",
     "- `Imagelab.issue_summary`\n",
     "- `Imagelab.issues`\n",
@@ -645,7 +646,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can also create a custom issue type by extending the base class `IssueManager`. CleanVision can then detect your custom issue along with other pre-defined issues in any image dataset! Here's an example of a custom issue manager, which can also be found in the [examples/](https://github.com/cleanlab/cleanvision/blob/main/examples/custom_issue_manager.py) folder of the source code."
+    "You can also create a custom issue type by extending the base class [IssueManager](../cleanvision/utils/base_issue_manager.rst#cleanvision.utils.base_issue_manager.IssueManager). CleanVision can then detect your custom issue along with other pre-defined issues in any image dataset! Here's an example of a custom issue manager, which can also be found [here](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/custom_issue_manager.py)"
    ]
   },
   {
@@ -659,7 +660,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "from PIL import Image\n",
-    "from tqdm import tqdm\n",
+    "from tqdm.auto import tqdm\n",
     "\n",
     "from cleanvision.dataset.base_dataset import Dataset\n",
     "from cleanvision.issue_managers import register_issue_manager\n",
@@ -778,11 +779,21 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "issue_types = {issue_name: {}}\n",
-    "imagelab.find_issues(issue_types)\n",
+    "imagelab.find_issues(issue_types)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "imagelab.report()"
    ]
   },
@@ -791,7 +802,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Beyond the collection of image files demonstrated here, you can alternatively run CleanVision on: [Hugging Face datasets](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/huggingface_dataset.ipynb), [torchvision datasets](https://github.com/cleanlab/cleanvision/blob/main/docs/source/tutorials/torchvision_dataset.ipynb), as well as [files in cloud storage buckets like S3, GCS, or Azure](https://github.com/cleanlab/cleanvision-examples/blob/main/cloud_dataset.ipynb)."
+    "Beyond the collection of image files demonstrated here, you can alternatively run CleanVision on: [Hugging Face datasets](huggingface_dataset.ipynb), [torchvision datasets](torchvision_dataset.ipynb), as well as [files in cloud storage buckets like S3, GCS, or Azure](https://github.com/cleanlab/cleanvision-examples/blob/main/cloud_dataset.ipynb)."
    ]
   }
  ],
@@ -811,7 +822,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,