Merge branch 'main' into main-public

Makefile

@@ -31,7 +31,7 @@ venv-test:
 	@test -d test_env || virtualenv -p python test_env
 
 	@echo "Building the XAI API in test_env env..."
-	@. $(ACTIVATE_TEST) && pip install --editable .[test]
+	@. $(ACTIVATE_TEST) && pip install --extra-index-url https://download.pytorch.org/whl/cpu --editable .[test]
 
 # TODO: running all tests in one pytest session randomly causes torch test to hang at last epoch
 test-torch: venv-test
@@ -43,7 +43,7 @@ test-mcg: test-torch
 	@. $(ACTIVATE_TEST) && PYTHONPATH="$(CURDIR)/model_card_gen/tests" pytest -s -k "not torch"
 
 install:
-	@pip install --editable .
+	@pip install --extra-index-url https://download.pytorch.org/whl/cpu --editable .
 
 xai-whl:
 	@python setup.py bdist_wheel

README.md

@@ -92,7 +92,7 @@ Intel is committed to the respect of human rights and avoiding complicity in hum
 #### License
 Intel® Explainable AI Tools is licensed under Apache License Version 2.0.
 
-#### Datasets
-To the extent that any public datasets are referenced by Intel or accessed using tools or code on this site those datasets are provided by the third party indicated as the data source. Intel does not create the data, or datasets, and does not warrant their accuracy or quality. By accessing the public dataset(s) you agree to the terms associated with those datasets and that your use complies with the applicable license. [DATASETS](DATASETS.md)
- 
-Intel expressly disclaims the accuracy, adequacy, or completeness of any public datasets, and is not liable for any errors, omissions, or defects in the data, or for any reliance on the data.  Intel is not liable for any liability or damages relating to your use of public datasets.
+#### Datasets and Models
+To the extent that any data, datasets, or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality. By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license. [DATASETS](DATASETS.md)
+
+Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets, or models.

docs/conf.py

@@ -37,14 +37,12 @@
   'nbsphinx',
   'nbsphinx_link',
   'sphinx_design',
-  'sphinx_external_toc',
   'sphinx.ext.intersphinx',
   'sphinx.ext.autodoc',
   'sphinx.ext.napoleon',
   'sphinx.ext.viewcode',
   'sphinx.ext.doctest',
 ]
-external_toc_path = "toc.yml"
 external_toc_exclude_missing = False
 html_theme = 'sphinx_rtd_theme'
 nbsphinx_execute = 'never'

docs/requirements-docs.txt

@@ -1,12 +1,12 @@
-Sphinx~=5.3.0
-docutils~=0.18.1
-ghp-import~=2.1.0
-linkify-it-py~=2.0.2
-myst-parser~=1.0.0
-nbsphinx-link~=1.3.0
-nbsphinx~=0.9.2
-sphinx-fontawesome~=0.0.6
-sphinx-multitoc-numbering~=0.1.3
-sphinx-rtd-theme~=1.2.2
-sphinx_design~=0.4.1
-sphinx_external_toc~=0.3.1
+Sphinx==7.1.2; python_version=='3.8'
+Sphinx==7.2.6; python_version>'3.8'
+docutils==0.18.1
+ghp-import==2.1.0
+linkify-it-py==2.0.2
+myst-parser==2.0.0
+nbsphinx-link==1.3.0
+nbsphinx==0.9.3
+sphinx-fontawesome==0.0.6
+sphinx-multitoc-numbering==0.1.3
+sphinx-rtd-theme==1.3.0
+sphinx_design==0.5.0

explainer/attributions/attributions.py

@@ -383,7 +383,7 @@ def explainer():
       >>> explainer = attributions.explainer()
       <IPython.core.display.HTML object>
       >>> explainer.shap.__version__
-      '0.41.0' 
+      '0.42.1'
     """
     return FeatureAttributions()
 

explainer/cam/cam.py

@@ -33,4 +33,4 @@ def __new__(cls, model, *args):
         elif is_pt_model(model):
             return super().__new__(XGradCAM)
         else:
-            raise_unknown_model_error()
+            raise_unknown_model_error(model)

explainer/utils/model/model_framework.py

@@ -18,7 +18,9 @@ def is_tf_model(model):
     """Returns whether model is TF keras sequential or functional"""
     is_keras_sequential = str(type(model)).endswith("keras.engine.sequential.Sequential'>")
     is_keras_functional = str(type(model)).endswith("keras.engine.functional.Functional'>")
-    return is_keras_sequential | is_keras_functional
+    is_keras_src_functional = str(type(model)).endswith("keras.src.engine.functional.Functional'>")
+    is_keras_src_sequential = str(type(model)).endswith("keras.src.engine.sequential.Sequential'>")
+    return is_keras_sequential | is_keras_functional | is_keras_src_functional | is_keras_src_sequential
 
 
 def is_pt_model(model):

notebooks/explainer/cifar_with_attributions/TorchVision_CIFAR_Interpret.ipynb

@@ -17,7 +17,7 @@
     "# issue on captum: https://github.com/pytorch/captum/issues/1114\n",
     "# Note: this matplotlib version is not guaranteed to work with any other application of intel-xai\n",
     "\n",
-    "! pip install -U matplotlib==3.6.3 --no-deps"
+    "! pip install matplotlib --no-deps"
    ]
   },
   {

notebooks/explainer/mnist_with_attributions_and_metrics/README.md

@@ -14,7 +14,6 @@ This notebook demonstrates how to use the attributions explainer API to explain
 
 To run `mnist.ipynb`, install the following dependencies:
 1. [Intel® Explainable AI](https://github.com/IntelAI/intel-xai-tools)
-2. pip install jupyter-dash
 
 ## References
 

notebooks/explainer/multimodal_cancer_detection/README.md

@@ -37,12 +37,11 @@ The `dataset_utils.py` holds the supporting functions that prepare the image and
 
 To run `Multimodal_Cancer_Detection.ipynb`, install the following dependencies:
 1. [Intel® Explainable AI](https://github.com/IntelAI/intel-xai-tools)
-2. `pip install intel-transfer-learning-tool==0.5`
-3. `pip install intel-extension-for-transformers`
-4. `pip install scikit-image`
-7. `pip install nltk`
-8. `pip install docx2txt`
-9. `pip install openpyxl`
+2. 
+``` 
+pip install docx2txt intel-transfer-learning-tool==0.5 \
+     intel-extension-for-transformers==1.1.0 nltk openpyxl scikit-image
+```
 
 ## References
 

notebooks/model_card_gen/adult-pytorch-model-card.ipynb

@@ -22,17 +22,6 @@
     "   5. [Generate Model Card with Intel Model Card Generator](#5.-Generate-Model-Card)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ad83bad8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Needed to fetch Adult Dataset from OpenML\n",
-    "!pip install scikit-learn"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

notebooks/model_card_gen/compas-model-card-tfx.ipynb

@@ -33,12 +33,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!python -m pip install -U \\\n",
+    "!python -m pip install \\\n",
     "  tfx \\\n",
     "  tensorflow-model-analysis \\\n",
-    "  tensorflow-transform \\\n",
-    "  scikit-learn \\\n",
-    "  pandas"
+    "  tensorflow-transform"
    ]
   },
   {

notebooks/model_card_gen/toxicity-tfma-model-card.ipynb

@@ -16,6 +16,16 @@
     "Adapted form [Tensorflow](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/pc/exercises/fairness_text_toxicity_part1.ipynb?utm_source=practicum-fairness&utm_campaign=colab-external&utm_medium=referral&utm_content=fairnessexercise1-colab#scrollTo=2z_xzJ40j9Q-) "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca170cad-c1d4-4222-9b7c-fd633e5f643c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install tensorflow_hub"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "64c2921a",
@@ -493,14 +503,6 @@
    "source": [
     "mcg"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1c6cfe40",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

notebooks/performance/README.md

@@ -31,7 +31,7 @@ In order to compare AI Kit and stock environments, two isolated Conda environmen
 conda create -n stock python=3.9
 conda activate stock
 pip install intel-xai --no-deps
-pip install tensorflow==2.12.0 shap torch=1.13.1 opencv-python notebook ipywidgets
+pip install tensorflow==2.13.0 shap torch=2.0.1 opencv-python notebook ipywidgets
 ```
 
 ### __Intel® AI Kit Conda environment__
@@ -41,5 +41,5 @@ conda activate intel
 conda install -c intel --deps-only shap
 conda install --no-deps shap
 pip install --no-deps intel-xai
-conda install -c intel opencv pytorch=1.13.1 ipywidgets matplotlib tensorflow=2.12.0 notebook
+conda install -c intel opencv pytorch=2.0.1 ipywidgets matplotlib tensorflow=2.13.0 notebook
 ```

setup.py

@@ -29,70 +29,79 @@
 long_description = (this_directory / "README.md").read_text()
 
 ATTRIBUTIONS_PKGS = [
-  'captum~=0.6.0',
-  'intel-tensorflow==2.12.0',
-  'ipywidgets~=7.7.5',
-  'numpy~=1.22.4',
-  'opencv-python~=4.7.0.72',
-  'plotly~=5.15.0',
-  'scikit-plot~=0.3.7',
-  'scipy~=1.10.1',
-  'shap~=0.41.0',
-  'torch==1.13.1',
-  'transformers~=4.30.0',
-]
+  'captum==0.6.0',
+  'intel-tensorflow==2.13.0',
+  'ipywidgets==7.8.1',
+  'numpy==1.22.4; python_version<"3.10"',
+  'numpy==1.24.3; python_version=="3.10"',
+  'opencv-python==4.8.0.76',
+  'plotly==5.17.0',
+  'scikit-plot==0.3.7',
+  'scipy==1.10.1; python_version=="3.8"',
+  'scipy==1.11.2; python_version>"3.8"',
+  'shap==0.42.1',
+  'torch==2.0.1',
+  'transformers==4.33.2',
+  ]
 
 CAM_PKGS = [
-  'grad-cam==1.4.6',
-  'matplotlib~=3.7.1',
-  'numpy~=1.22.4',
-  'opencv-python~=4.7.0.72',
-  'scipy~=1.10.1',
-  'torch==1.13.1',
-]
+  'grad-cam==1.4.8',
+  'matplotlib==3.7.3; python_version=="3.8"',
+  'matplotlib==3.8.0; python_version>"3.8"',
+  'numpy==1.22.4; python_version<"3.10"',
+  'numpy==1.24.3; python_version=="3.10"',
+  'opencv-python==4.8.0.76',
+  'scipy==1.10.1; python_version=="3.8"',
+  'scipy==1.11.2; python_version>"3.8"',
+  'torch==2.0.1',
+  ]
 
 METRICS_PKGS =  [
-  'matplotlib~=3.7.1',
-  'pandas~=1.5.3',
-  'plotly~=5.15.0',
-  'scikit-learn~=1.2.2',
-  'seaborn~=0.12.2',
+  'matplotlib==3.7.3; python_version=="3.8"',
+  'matplotlib==3.8.0; python_version>"3.8"',
+  'pandas==1.5.3',
+  'plotly==5.17.0',
+  'scikit-learn==1.3.1',
+  'seaborn==0.12.2',
 ]
 
 MCG_PKGS = [
-    'Jinja2~=3.1.2',
-    'absl-py~=1.4.0',
-    'attrs~=21.4.0',
-    'dataclasses~=2.10.1;python_version<"3.7"',
-    'grpcio-status~=1.48.2',
-    'intel-tensorflow==2.12.0',
-    'joblib~=1.2.0',
-    'jsonschema[format-nongpl]~=4.17.3',
-    'plotly~=5.15.0',
-    'protobuf~=3.20.3',
-    'semantic-version~=2.10.0',
-    'tensorflow-data-validation~=1.13.0',
-    'tensorflow-model-analysis~=0.44.0',
-]
+  'Jinja2==3.1.2',
+  'absl-py==1.4.0',
+  'attrs==21.4.0',
+  'dataclasses==0.6',
+  'grpcio-status==1.48.2',
+  'intel-tensorflow==2.13.0',
+  'joblib==1.3.2',
+  'jsonschema[format-nongpl]==4.17.3',
+  'plotly==5.17.0',
+  'protobuf==3.20.3',
+  'semantic-version==2.10.0',
+  'tensorflow-data-validation==1.13.0; python_version<"3.10"',
+  'tensorflow-data-validation==1.14.0; python_version=="3.10"',
+  'tensorflow-model-analysis==0.44.0; python_version<"3.10"',
+  'tensorflow-model-analysis==0.45.0; python_version=="3.10"',
+  ]
 
 PYTORCH_PKGS = [
-    'torch==1.13.1',
-    'torchvision==0.14.1',
-]
+  'torch==2.0.1',
+  'torchvision==0.15.2',
+  ]
 
 REQUIRED_PKGS =  (
   ATTRIBUTIONS_PKGS +
   CAM_PKGS +
   METRICS_PKGS +
   MCG_PKGS
-)
+  )
 
 TEST_PKGS = [
-    'datasets~=2.10.1',
-    'deepdiff~=6.3.0',
-    'pytest~=7.3.2',
-    'tensorflow-hub~=0.13.0',
-]
+  'datasets==2.10.1; python_version<"3.10"',
+  'datasets==2.14.4; python_version=="3.10"',
+  'deepdiff==6.5.0',
+  'pytest==7.4.2',
+  'tensorflow-hub==0.14.0',
+  ]
 
 PACKAGES = [
   'explainer',
@@ -108,13 +117,13 @@
   'model_card_gen.docs.examples',
   'model_card_gen.graphics',
   'model_card_gen.utils',
-]
+  ]
 
 
 EXTRAS = {
-    'pytorch': PYTORCH_PKGS,
-    'test': TEST_PKGS + PYTORCH_PKGS,
-}
+  'pytorch': PYTORCH_PKGS,
+  'test': TEST_PKGS + PYTORCH_PKGS,
+  }
 
 # Get version from version module.
 with open('explainer/version.py') as fp:
@@ -161,6 +170,6 @@
         'Topic :: Scientific/Engineering :: Mathematics',
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
     ],
-    python_requires='>=3.9,<3.10',
+    python_requires='>=3.8',
     keywords='XAI, explainer',
 )