From 0ee2e5486b16801ebce2d688b8dd3ce32ad20185 Mon Sep 17 00:00:00 2001 From: Mitali Potnis <162364085+mitalipo@users.noreply.github.com> Date: Wed, 5 Jun 2024 18:13:19 -0700 Subject: [PATCH 01/11] Code style check GHA (#300) * Add target for code style check * Add stylecheck GHA * Fix exclude files in black linter * Fix code style to adhere to PEP8 standards * Remove apt-get command in virtual env target for linting * Add linting virtual environment for python and python3 * Modify venv-lint target to use virtualenv * Remove extra empty line --- .github/workflows/stylecheck-lint.yaml | 29 +++++++++++++++++++ Makefile | 18 ++++++++++++ explainer/tests/conftest.py | 2 +- fuzz/fuzz_dataset.py | 3 +- .../model_card_gen/analyze/analyzer.py | 3 +- .../model_card_gen/datasets/torch_datasets.py | 2 +- .../model_card_gen/model_card_gen.py | 12 +++++--- .../prepare_vision_data.py | 12 +++++--- .../explainer/attributions/hf_attributions.py | 3 +- .../explainer/attributions/attributions.py | 12 ++++---- .../explainers/attributions/tests/conftest.py | 2 +- .../intel_ai_safety/explainer/cam/pt_cam.py | 6 ++-- .../cam-tensorflow/tests/conftest.py | 2 +- .../explainer/metrics/metrics.py | 2 +- pyproject.toml | 2 +- 15 files changed, 84 insertions(+), 26 deletions(-) create mode 100644 .github/workflows/stylecheck-lint.yaml diff --git a/.github/workflows/stylecheck-lint.yaml b/.github/workflows/stylecheck-lint.yaml new file mode 100644 index 0000000..9014df3 --- /dev/null +++ b/.github/workflows/stylecheck-lint.yaml @@ -0,0 +1,29 @@ +name: Style Check and Lint +on: + pull_request: + types: [submitted] + # run the workflow if changes pushed to main or release branches + push: + branches: '**' + paths: '**' + +jobs: + stylecheck-lint: + name: Check codestsyle + runs-on: [ xai-tlt ] + container: + image: ${{ vars.GHA_IMAGE }} + env: + http_proxy: ${{ secrets.HTTP_PROXY }} + https_proxy: ${{ secrets.HTTPS_PROXY }} + no_proxy: ${{ secrets.NO_PROXY }} + # credentials: + # username: ${{ secrets.REGISTRY_USER }} + # password: ${{ secrets.REGISTRY_TOKEN }} + volumes: + - /tf_dataset/dataset/transfer_learning:/tmp/data + steps: + - name: Check out repository code + uses: actions/checkout@v4.1.1 + - name: Run stylecheck + run: make stylecheck diff --git a/Makefile b/Makefile index 88e0982..f7eb6a9 100644 --- a/Makefile +++ b/Makefile @@ -17,7 +17,9 @@ # VENV_DIR = ".venv" +VENV_LINT = ".venv/lint" ACTIVATE_TEST = "$(VENV_DIR)/bin/activate" +ACTIVATE_LINT = "$(VENV_LINT)/bin/activate" ACTIVATE_DOCS = $(ACTIVATE_TEST) ACTIVATE_NOTEBOOK = $(ACTIVATE_TEST) @@ -36,6 +38,14 @@ venv-test: poetry-lock pure-eval==0.2.2 \ stack-data==0.6.3 +venv-lint: + @echo "Creating a virtual environment for linting $(VENV_LINT)..." + @test -d $(VENV_LINT) || python -m virtualenv $(VENV_LINT) || python3 -m virtualenv $(VENV_LINT) + @echo "Installing lint dependencies..." + @. $(ACTIVATE_LINT) && pip install --no-cache-dir --no-deps \ + flake8==7.0.0 \ + black==24.4.2 + test-mcg: venv-test @echo "Testing the Model Card Gen API..." @. $(ACTIVATE_TEST) && pytest model_card_gen/tests @@ -77,6 +87,14 @@ test-notebook: venv-test @. $(ACTIVATE_NOTEBOOK) && \ bash run_notebooks.sh $(CURDIR)/notebooks/explainer/imagenet_with_cam/ExplainingImageClassification.ipynb +stylecheck: venv-lint + @echo "Checking code style..." + @. $(ACTIVATE_LINT) flake8 . --config=tox.ini && echo "Code style is compatible with PEP 8 guidelines" || echo "Code style check failed. Please fix the above code style errors." + +fix-codestyle: venv-lint + @echo "Fixing code style..." + @. $(ACTIVATE_LINT) black . --check --config=pyproject.toml + dist: build-whl @echo "Create binary wheel..." diff --git a/explainer/tests/conftest.py b/explainer/tests/conftest.py index 1b86069..b242e9d 100644 --- a/explainer/tests/conftest.py +++ b/explainer/tests/conftest.py @@ -93,7 +93,7 @@ def custom_tf_CNN(): Creates and trains a simple TF CNN on the mnist dataset. Returns the model, a subset of the test dataset and the class names. - Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html + Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html # noqa """ import tensorflow as tf from tensorflow.keras.datasets import mnist diff --git a/fuzz/fuzz_dataset.py b/fuzz/fuzz_dataset.py index 48276a2..a80813f 100644 --- a/fuzz/fuzz_dataset.py +++ b/fuzz/fuzz_dataset.py @@ -23,8 +23,7 @@ def TestOneInput(data): input_array = numpy.array(fdp.ConsumeRegularFloatList(dataset_length)) target_array = numpy.array(fdp.ConsumeIntListInRange(dataset_length, 0, 10)) - dataset = PytorchNumpyDataset(input_array=input_array, - target_array=target_array) + dataset = PytorchNumpyDataset(input_array=input_array, target_array=target_array) assert len(dataset.dataset) == dataset_length diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py b/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py index 0843205..e670f36 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py @@ -49,7 +49,8 @@ def check_data(self, dataset): """Check that data argument is of type pd.DataFrame or DatasetType""" if not (isinstance(dataset, get_args(DatasetType)) or isinstance(dataset, pd.DataFrame)): raise TypeError( - "ModelAnalyzer.analyze requires data argument to be of type pd.DataFrame, TensorflowDataset or PytorchDataset" + "ModelAnalyzer.analyze requires data argument to be of type pd.DataFrame," + "TensorflowDataset or PytorchDataset." ) return dataset diff --git a/model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py b/model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py index e122991..6bebd56 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py @@ -73,7 +73,7 @@ def framework(self): """ return self._framework - def _make_subsets(lengths, names=["train", "test"]): + def _make_subsets(self, lengths, names=["train", "test"]): """ Split PytorchDataset into multiple PytorchDataset objects """ diff --git a/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py index dd70601..349973c 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py @@ -71,7 +71,8 @@ class ModelCardGen: data_sets (dict): dictionary with keys of name of dataset and value to path model_path (str): representing TF SavedModel path eval_config (tfma.EvalConfig or str) : tfma config object or string to config file path - model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary + following model card schema eval_results (tfma.EvalResults): pre-generated tfma results for when you do not wish to run evaluator output_dir (str): representing of where to output model card """ @@ -120,7 +121,8 @@ def generate( data_sets (dict): dictionary with keys of name of dataset and value to path model_path (str): representing TF SavedModel path eval_config (tfma.EvalConfig or str) : tfma config object or string to config file path - model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary + following model card schema output_dir (str): representing of where to output model card Returns: @@ -158,7 +160,8 @@ def tf_generate( data_sets (dict): dictionary with keys of name of dataset and value to path model_path (str): representing TF SavedModel path eval_config (tfma.EvalConfig or str) : tfma config object or string to config file path - model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary + following model card schema output_dir (str): representing of where to output model card Returns: @@ -194,7 +197,8 @@ def pt_generate( data_sets (dict): dictionary with keys of name of dataset and value to path model_path (str): representing TF SavedModel path eval_config (tfma.EvalConfig or str) : tfma config object or string to config file path - model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary + following model card schema output_dir (str): representing of where to output model card Returns: diff --git a/notebooks/explainer/multimodal_cancer_detection/prepare_vision_data.py b/notebooks/explainer/multimodal_cancer_detection/prepare_vision_data.py index df4d4ee..16eb2d3 100644 --- a/notebooks/explainer/multimodal_cancer_detection/prepare_vision_data.py +++ b/notebooks/explainer/multimodal_cancer_detection/prepare_vision_data.py @@ -111,9 +111,11 @@ def segment_images(segmentation_path, cesm_only=True): bottom = max(x["all_points_y"]) """ if (right - left) < new_width: - left, right = max(0, (right+left)//2 - new_width//2), min(width, (right+left)//2 + new_width//2) + left = max(0, (right+left)//2 - new_width//2) + right = min(width, (right+left)//2 + new_width//2) if (bottom - top) < new_height: - top, bottom = max(0, (top+bottom)//2 - new_height//2), min(height, (top+bottom)//2 + new_height//2) + top = max(0, (top+bottom)//2 - new_height//2) + bottom = min(height, (top+bottom)//2 + new_height//2) """ # print((left, top, right, bottom)) im = im.crop((left, top, right, bottom)) @@ -166,9 +168,11 @@ def segment_images(segmentation_path, cesm_only=True): bottom = max(x["all_points_y"]) """ if (right - left) < new_width: - left, right = max(0, (right+left)//2 - new_width//2), min(width, (right+left)//2 + new_width//2) + left = max(0, (right+left)//2 - new_width//2) + right = min(width, (right+left)//2 + new_width//2) if (bottom - top) < new_height: - top, bottom = max(0, (top+bottom)//2 - new_height//2), min(height, (top+bottom)//2 + new_height//2) + top = max(0, (top+bottom)//2 - new_height//2) + bottom = min(height, (top+bottom)//2 + new_height//2) """ # print((left, top, right, bottom)) im = im.crop((left, top, right, bottom)) diff --git a/plugins/explainers/attributions-hugging-face/intel_ai_safety/explainer/attributions/hf_attributions.py b/plugins/explainers/attributions-hugging-face/intel_ai_safety/explainer/attributions/hf_attributions.py index f6c432e..dbae1dc 100644 --- a/plugins/explainers/attributions-hugging-face/intel_ai_safety/explainer/attributions/hf_attributions.py +++ b/plugins/explainers/attributions-hugging-face/intel_ai_safety/explainer/attributions/hf_attributions.py @@ -25,7 +25,8 @@ class LLMExplainer(AttributionsExplainer): """ - Approximate an extension of shap values, known as Owen values, for generative and classification LLMs from Hugging Face. + Approximate an extension of shap values, known as Owen values, for generative and classification LLMs from + Hugging Face. Args: model (HF Automodel or pipeline): model to be interpreted. If no tokenizer is supplied than model is assumed to be diff --git a/plugins/explainers/attributions/intel_ai_safety/explainer/attributions/attributions.py b/plugins/explainers/attributions/intel_ai_safety/explainer/attributions/attributions.py index 7e9cc1d..f18a03d 100644 --- a/plugins/explainers/attributions/intel_ai_safety/explainer/attributions/attributions.py +++ b/plugins/explainers/attributions/intel_ai_safety/explainer/attributions/attributions.py @@ -49,13 +49,13 @@ def __init__(self): class DeepExplainer(FeatureAttributions): """ - Approximate conditional expectations of shap values for deep learning models using a variation of the DeepLIFT algorithm - (Shrikumar, Greenside, and Kundaje, arXiv 2017) + Approximate conditional expectations of shap values for deep learning models using a variation + of the DeepLIFT algorithm (Shrikumar, Greenside, and Kundaje, arXiv 2017) Args: model (tf.keras.functional or pytorch.nn.Module): CNN model to be interpreted - background_images (numpy.ndarray, pandas.DataFrame or torch.tensor): the selection of background images used to integrate output features - across each target image + background_images (numpy.ndarray, pandas.DataFrame or torch.tensor): the selection of background images + used to integrate output features across each target image targetImages (numpy.ndarray, pandas.DataFrame or torch.tensor): the images to be interpreted labels (list of strings): list of label names for the given classification problem @@ -160,8 +160,8 @@ class KernelExplainer(FeatureAttributions): Args: model (function): "black box" prediction function that takes an input array of shape (n samples, m features) and outputs an array of n predictions. - background (numpy.ndarray or pandas.DataFrame): the selection of background examples used to integrate output features - across each target example + background (numpy.ndarray or pandas.DataFrame): the selection of background examples used to integrate + output features across each target example targets (numpy.ndarray or pandas.DataFrame): the target examples to be interpreted nsamples (int): the number of times to re-evaluate the model per prediction. Defaults to 64. diff --git a/plugins/explainers/attributions/tests/conftest.py b/plugins/explainers/attributions/tests/conftest.py index c57adfd..8a05281 100644 --- a/plugins/explainers/attributions/tests/conftest.py +++ b/plugins/explainers/attributions/tests/conftest.py @@ -93,7 +93,7 @@ def custom_tf_CNN(): Creates and trains a simple TF CNN on the mnist dataset. Returns the model, a subset of the test dataset and the class names. - Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html + Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html # noqa """ import tensorflow as tf from tensorflow.keras.datasets import mnist diff --git a/plugins/explainers/cam-pytorch/intel_ai_safety/explainer/cam/pt_cam.py b/plugins/explainers/cam-pytorch/intel_ai_safety/explainer/cam/pt_cam.py index a764572..7b85cdc 100644 --- a/plugins/explainers/cam-pytorch/intel_ai_safety/explainer/cam/pt_cam.py +++ b/plugins/explainers/cam-pytorch/intel_ai_safety/explainer/cam/pt_cam.py @@ -28,7 +28,8 @@ class XGradCAM(GradCAM): Args: model (torch.nn.Module): the CNN used for classification - target_layer (torch.nn.modules.container.Sequential): the convolution layer that you want to analyze (usually the last) + target_layer (torch.nn.modules.container.Sequential): the convolution layer that you want to + analyze (usually the last) dims (tuple of ints): dimension of image (h, w) device (torch.device): torch.device('cpu') or torch.device('gpu') for PyTorch optimizations @@ -150,7 +151,8 @@ class EigenCAM: Args: model (torch.nn.Module): the CNN used for classification - target_layer (torch.nn.modules.container.Sequential): the convolution layer that you want to analyze (usually the last) + target_layer (torch.nn.modules.container.Sequential): the convolution layer that you want to + analyze (usually the last) reshape (function): the reshape transformation function responsible for processing the output tensors. Can be None if not needed for particular model (such as YOLO) diff --git a/plugins/explainers/cam-tensorflow/tests/conftest.py b/plugins/explainers/cam-tensorflow/tests/conftest.py index 1b9b6c7..5aeb16c 100644 --- a/plugins/explainers/cam-tensorflow/tests/conftest.py +++ b/plugins/explainers/cam-tensorflow/tests/conftest.py @@ -8,7 +8,7 @@ def custom_tf_CNN(): Creates and trains a simple TF CNN on the mnist dataset. Returns the model, a subset of the test dataset and the class names. - Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html + Taken from https://shap-lrjball.readthedocs.io/en/latest/example_notebooks/deep_explainer/Front%20Page%20DeepExplainer%20MNIST%20Example.html # noqa """ import tensorflow as tf from tensorflow.keras.datasets import mnist diff --git a/plugins/explainers/metrics/intel_ai_safety/explainer/metrics/metrics.py b/plugins/explainers/metrics/intel_ai_safety/explainer/metrics/metrics.py index ee6d61e..63d5588 100644 --- a/plugins/explainers/metrics/intel_ai_safety/explainer/metrics/metrics.py +++ b/plugins/explainers/metrics/intel_ai_safety/explainer/metrics/metrics.py @@ -292,7 +292,7 @@ def plot(groundtruth, predictions, labels): >>> from intel_ai_safety.explainer import metrics >>> plotter = metrics.plot(y_true, y_pred, label_names) >>> plotter.recall - {0: array([1. , 1. , 0.5, 0.5, 0.5, 0. ]), 1: array([1. , 1. , 1. , 0.5, 0.5, 0. ]), 2: array([1., 1., 1., 1., 1., 0.])} + {0: array([1. , 1. , 0.5, 0.5, 0.5, 0. ]), 1: array([1. , 1. , 1. , 0.5, 0.5, 0. ]), 2: array([1., 1., 1., 1., 1., 0.])} # noqa """ plotter = Plotter(groundtruth, predictions, labels) diff --git a/pyproject.toml b/pyproject.toml index 3a585aa..cf919c0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -99,7 +99,7 @@ build-backend = "poetry.core.masonry.api" [tool.black] line-length = 120 -exclude = ''' +extend-exclude = ''' /(\.git|\.venv|__pycache__|\.cache|\.tox|\.pytest_cache|\.mypy_cache|)/ \.pyc$ ''' From 3e67441bc0dfbf4ac0067ae36aafee24d4b1984b Mon Sep 17 00:00:00 2001 From: Mitali Potnis <162364085+mitalipo@users.noreply.github.com> Date: Tue, 11 Jun 2024 17:05:38 -0700 Subject: [PATCH 02/11] Update default owners for the repo (#303) --- CODEOWNERS | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CODEOWNERS b/CODEOWNERS index da5c562..ca2325a 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -5,7 +5,7 @@ # the repo. Unless a later match takes precedence, # @global-owner1 and @global-owner2 will be requested for # review when someone opens a pull request. -* @ashahba @daniel-de-leon-user293 @tybrs +* @ashahba @daniel-de-leon-user293 @tybrs @mitalipo # Order is important; the last matching pattern takes the most # precedence. When someone opens a pull request that only From f504bf528ebdc7b174f502b3f94ced501e6c2bd8 Mon Sep 17 00:00:00 2001 From: Tyler W Date: Wed, 12 Jun 2024 01:04:19 +0000 Subject: [PATCH 03/11] Refactored TFMA into distinct files (#302) * refactored tfma into distinct files and added data translation Signed-off-by: Tyler Wilbers * fixed comment artifacts Signed-off-by: Tyler Wilbers --------- Signed-off-by: Tyler Wilbers --- .../model_card_gen/graphics/add_graphics.py | 92 ++++-------- .../graphics/graphics_data_transform.py | 107 ++++++++++++++ .../graphics/plotly_graphics.py | 135 ++++++++---------- .../model_card_gen/graphics/plotly_utils.py | 49 ------- .../model_card_gen/model_card_gen.py | 19 ++- 5 files changed, 208 insertions(+), 194 deletions(-) create mode 100644 model_card_gen/intel_ai_safety/model_card_gen/graphics/graphics_data_transform.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py index 9ccfe93..8bc5ea6 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py @@ -23,9 +23,8 @@ from .plotly_graphics import * # Typing -from typing import Sequence, Text, Tuple, Union, Optional -import tensorflow_model_analysis as tfma -from tensorflow_metadata.proto.v0.statistics_pb2 import DatasetFeatureStatisticsList +import pandas as pd +from typing import Sequence, Text, Tuple, Union, Optional, List OVERVIEW_GRAPHS = [OverallPerformanceAtThreshold] PLOTS_GRAPHS = [ConfusionMatrixAtThresholdsGraphs] @@ -33,44 +32,27 @@ DATASTAT_GRAPHS = [DataStatsGraphs] -def add_overview_graphs(model_card: ModelCard, eval_result: tfma.EvalResult, dataset_name: Text) -> None: - """Adds plots for every graph in OVERVIEW_GRAPHS. - - Args: - model_card: The model card object. - eval_result: A `tfma.EvalResult`. - """ - # Get all metric and slice names - plots = set() - for slicing_metric in eval_result.plots: - if slicing_metric[1]: - for output_name in slicing_metric[1]: - for sub_key in slicing_metric[1][output_name]: - plots.update(slicing_metric[1][output_name][sub_key].keys()) - - if plots: - # Generate graphs for plots - graphs = [ - graph.generate_figure(eval_result.plots) - for graph in OVERVIEW_GRAPHS - # Check metric name is in plots - if graph.eval_result_keys[0] in plots - ] - # Add graphs to modle card - model_card.model_details.graphics.collection.extend( - [Graphic(name=dataset_name, html=graph.html_content) for graph in graphs] - ) +def add_overview_graphs(model_card: ModelCard, df: pd.DataFrame, dataset_name: Text) -> None: + + graphs = [ + graph.generate_figure(df) + for graph in OVERVIEW_GRAPHS + ] + # Add graphs to modle card + model_card.model_details.graphics.collection.extend( + [Graphic(name=dataset_name, html=graph.html_content) for graph in graphs] + ) def add_dataset_feature_statistics_plots( - model_card: ModelCard, data_stats: Dict[Text, DatasetFeatureStatisticsList] + model_card: ModelCard, data_set_names: List[Text], dfs: List[pd.DataFrame] ) -> None: """Adds Dataset objects to model card with all graphs in DATASTAT_GRAPHS """ - - for i, (name, stats) in enumerate(data_stats.items()): - graphs = [graph.generate_figure(name, stats, color_index=i) for graph in DATASTAT_GRAPHS] + named_datasets = zip(data_set_names, dfs) + for i, (name, df) in enumerate(named_datasets): + graphs = [graph.generate_figure(name, df, color_index=i) for graph in DATASTAT_GRAPHS] model_card.model_parameters.data.append( Dataset( name=name.title(), @@ -81,7 +63,7 @@ def add_dataset_feature_statistics_plots( ) -def add_eval_result_slicing_metrics(model_card: ModelCard, eval_result: tfma.EvalResult) -> None: +def add_eval_result_slicing_metrics(model_card: ModelCard, df: pd.DataFrame) -> None: """Adds plots for every graph in SLICING_METRIC_GRAPHS and every metric in eval_result.slicing_metrics. @@ -89,14 +71,13 @@ def add_eval_result_slicing_metrics(model_card: ModelCard, eval_result: tfma.Eva model_card: The model card object. eval_result: A `tfma.EvalResult`. """ - if eval_result.get_metric_names(): - graphs = [graph.generate_figure(eval_result.slicing_metrics) for graph in SLICING_METRIC_GRAPHS] - model_card.quantitative_analysis.graphics.collection.extend( - [Graphic(name=graph.name, html=graph.html_content) for graph in graphs] - ) + graphs = [graph.generate_figure(df) for graph in SLICING_METRIC_GRAPHS] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=graph.name, html=graph.html_content) for graph in graphs] + ) -def add_eval_result_plots(model_card: ModelCard, eval_result: tfma.EvalResult) -> None: +def add_eval_result_plots(model_card: ModelCard, df: pd.DataFrame) -> None: """Add visualizations for every plot in eval_result.plots. This function generates plots encoded as html text @@ -107,26 +88,15 @@ def add_eval_result_plots(model_card: ModelCard, eval_result: tfma.EvalResult) - model_card: The model card object. eval_result: A `tfma.EvalResult`. """ - - # get all metric and slice names - plots = set() - for slicing_metric in eval_result.plots: - if slicing_metric[1]: - for output_name in slicing_metric[1]: - for sub_key in slicing_metric[1][output_name]: - plots.update(slicing_metric[1][output_name][sub_key].keys()) - - # generate graphs for plots - if plots: - graphs = [ - graph.generate_figure(eval_result.plots) - for graph in PLOTS_GRAPHS - # Check metric name is in plots - if graph.eval_result_keys[0] in plots - ] - model_card.quantitative_analysis.graphics.collection.extend( - [Graphic(name=None, html=graph.html_content) for graph in graphs] - ) + graphs = [ + graph.generate_figure(df) + for graph in PLOTS_GRAPHS + # Check metric name is in plots + # if graph.eval_result_keys[0] in plots + ] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=None, html=graph.html_content) for graph in graphs] + ) def get_slice_key(slice_key: Union[Tuple[()], Tuple[Tuple[Text, Union[Text, int, float]], ...]]) -> Tuple[Text, Text]: diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/graphics_data_transform.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/graphics_data_transform.py new file mode 100644 index 0000000..1451aed --- /dev/null +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/graphics_data_transform.py @@ -0,0 +1,107 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2024 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +# + +from typing import Sequence, Text, Tuple, Union, List +import tensorflow_model_analysis as tfma +import pandas as pd +from functools import reduce +import operator + + +def plot_results_parser(container, keys, feature_key_value=None): + # Get feature and value + if isinstance(container, tuple) and feature_key_value == None: + if container[0] and container[0][0]: + feature, value = container[0][0] + else: + feature, value = "Overall", "Overall" + return plot_results_parser(container[1], keys, (feature, value)) + # Recursively retrieve value form empty string keys + elif isinstance(container, dict) and list(container.keys()) == [""]: + return plot_results_parser(container.get(""), keys, feature_key_value) + elif isinstance(container, dict): + try: + feature, value = feature_key_value + return ( + dict(item, **{"feature": feature, "group": value}) for item in reduce(operator.getitem, keys, container) + ) + except (IndexError, KeyError): + return None + + +def plots_to_df(plots, keys): + return pd.DataFrame.from_records([result for plot in plots for result in plot_results_parser(plot, keys)]) + + +def slicing_metric_to_df( + slicing_metrics: Sequence[tfma.view.SlicedMetrics], output_name: Text = "", sub_key: Text = "" +) -> pd.DataFrame: + records = [] + for slicing_metric in slicing_metrics: + if slicing_metric[0] and slicing_metric[0][0]: + feature, value = slicing_metric[0][0] + else: + feature, value = "Overall", "Overall" + record = {"feature": feature, "group": value} + for metric in slicing_metric[1][output_name][""]: + for value_type in slicing_metric[1][output_name][sub_key][metric]: + metric_value = slicing_metric[1][output_name][sub_key][metric][value_type] + record[metric] = metric_value + records.append(record) + return pd.DataFrame(records) + + +def tfdv_to_df(name, stats): + data = pd.DataFrame() + for dataset in stats.datasets: + for feature in dataset.features: + df = pd.DataFrame() + if feature.HasField("num_stats") and feature.num_stats.histograms: + histogram = feature.num_stats.histograms[0] + # x-axis + df["counts"] = [int(bucket.sample_count) for bucket in histogram.buckets] + # y-axis + df["bins"] = [f"{bucket.low_value:.2f}-{bucket.high_value:.2f}" for bucket in histogram.buckets] + # button filter + df["feature"] = feature.name or feature.path.step[0] + # groupby feature + df["dataset"] = "Dataset {name}".format(name=name) + data = pd.concat([data, df]) + if feature.HasField("string_stats"): + rank_histogram = feature.string_stats.rank_histogram + df["counts"] = [int(bucket.sample_count) for bucket in rank_histogram.buckets] + df["bins"] = [bucket.label for bucket in rank_histogram.buckets] + # button filter + df["feature"] = feature.name or feature.path.step[0] + # groupby feature + df["dataset"] = "Dataset {name}".format(name=str(name).title()) + data = pd.concat([data, df]) + return data + + +def get_slice_key(slice_key: Union[Tuple[()], Tuple[Tuple[Text, Union[Text, int, float]], ...]]) -> Tuple[Text, Text]: + """Returns a tuple of joined keys and values accross slice_key""" + + if not len(slice_key): + return ("Overall", "Overall") + + keys, values = zip(*slice_key) + + return (", ".join(["{}".format(key) for key in keys]), ", ".join(["{}".format(value) for value in values])) diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py index 7f7f9d1..cf6fd30 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py @@ -20,7 +20,6 @@ import plotly.express as px from plotly import graph_objs as go -from plotly.subplots import make_subplots from plotly.io import to_html, templates import pandas as pd from typing import Optional, Union, Text, Sequence, Dict @@ -28,6 +27,10 @@ _COLORS = templates["plotly"].layout.colorway +def visualize_bool_values(trace_per_graph, graph_count): + enum_range = range(0, trace_per_graph * graph_count, trace_per_graph) + for left_fill, right_fill in zip(enum_range, enum_range[::-1]): + yield (([False] * left_fill) + ([True] * trace_per_graph) + ([False] * right_fill)) class _PlotlyGraph: """Model Card Plotly graph""" @@ -51,19 +54,15 @@ class _PlotlyGraph: html_content: Optional[Text] = None @classmethod - def generate_figure(cls, *args, **kwargs): + def generate_figure(cls, data): self = cls() - self.kwargs = kwargs - self.metrics = self.create_metrics(*args) - self.data = self.create_df(*args) + # self.kwargs = kwargs + self.data = self.validate_df(data) self.figure = self.create_fig(self.data) self.html_content = to_html(self.figure, include_plotlyjs="require", full_html=False) return self - def create_metrics(self, *args): - pass - - def create_df(self, *args): + def validate_df(self, *args): pass def create_fig(self, data): @@ -71,30 +70,32 @@ def create_fig(self, data): class OverallPerformanceAtThreshold(_PlotlyGraph): - eval_result_keys: Optional[Sequence[Text]] = ["confusionMatrixAtThresholds", "matrices"] x_name: Optional[Text] = "threshold" y_name: Optional[Text] = "Overall" title: Optional[Text] = "Overall Accuracy/Precision/Recall/F1" labels = {"_value": "Accuracy/Precision/Recall/F1", "variable": ""} - - def create_df(self, plots): - df = plots_to_df(plots, self.eval_result_keys) - num = df.get("truePositives", 0) + df.get("trueNegatives", 0) - dom = ( - df.get("truePositives", 0) - + df.get("falsePositives", 0) - + df.get("falseNegatives", 0) - + df.get("trueNegatives", 0) - ) - df["accuracy"] = num / dom - df["f1"] = 2 * (df.precision * df.recall) / (df.precision + df.recall) + metric_names = ["accuracy", "precision", "recall", "f1"] + + def validate_df(self, df): + assert (df.values.any()), "No values in DataFrame" + assert (df.get('threshold') is not None), "No column named 'threshold'" + assert (df['threshold'].values.any()), "Column named 'threshold' contains no values" + assert (df['threshold'].dtype == 'float'), "Column named 'threshold' is not dtype float" + for metric in self.metric_names: + assert (df.get(metric) is not None), f"No column named '{metric}'" + assert (df[metric].values.any()), f"Column named '{metric}' contains no values" + if 'group' not in df: + df['group'] = ['Overall'] * len(df) + assert ('Overall' in df['group'].unique()), "Column named 'group' does not contain 'Overall' value" return df def create_fig(self, df): + df = df[df["group"] == self.y_name] + df = df[[self.x_name] + self.metric_names] fig = px.line( - self.data[self.data["group"] == self.y_name], + df.astype('float64'), x=self.x_name, - y=["accuracy", "precision", "recall", "f1"], + y=self.metric_names, labels=self.labels, title=self.title, ) @@ -105,32 +106,9 @@ def create_fig(self, df): class DataStatsGraphs(_PlotlyGraph): - def create_df(self, name, stats): - data = pd.DataFrame() - for dataset in stats.datasets: - for feature in dataset.features: - df = pd.DataFrame() - if feature.HasField("num_stats") and feature.num_stats.histograms: - histogram = feature.num_stats.histograms[0] - # x-axis - df["counts"] = [int(bucket.sample_count) for bucket in histogram.buckets] - # y-axis - df["bins"] = [f"{bucket.low_value:.2f}-{bucket.high_value:.2f}" for bucket in histogram.buckets] - # button filter - df["feature"] = feature.name or feature.path.step[0] - # groupby feature - df["dataset"] = "Dataset {name}".format(name=name) - data = pd.concat([data, df]) - if feature.HasField("string_stats"): - rank_histogram = feature.string_stats.rank_histogram - df["counts"] = [int(bucket.sample_count) for bucket in rank_histogram.buckets] - df["bins"] = [bucket.label for bucket in rank_histogram.buckets] - # button filter - df["feature"] = feature.name or feature.path.step[0] - # groupby feature - df["dataset"] = "Dataset {name}".format(name=str(name).title()) - data = pd.concat([data, df]) - return data + def validate_df(self, df): + assert (df.values.any()), "No values in DataFrame" + return df def create_fig(self, df): # Allows graph cycle through colorway @@ -181,27 +159,26 @@ def create_layout_button(feature, df): class ConfusionMatrixAtThresholdsGraphs(_PlotlyGraph): - eval_result_keys: Optional[Sequence[Text]] = ["confusionMatrixAtThresholds", "matrices"] x_name: Optional[Text] = "threshold" y_name: Optional[Text] = "value" - figure: Optional[go.Figure] = None - def create_df(self, plots): - df = plots_to_df(plots, self.eval_result_keys) - num = df.get("truePositives", 0) + df.get("trueNegatives", 0) - dom = ( - df.get("truePositives", 0) - + df.get("falsePositives", 0) - + df.get("falseNegatives", 0) - + df.get("trueNegatives", 0) - ) - df["accuracy"] = num / dom - df["f1"] = 2 * (df.precision * df.recall) / (df.precision + df.recall) + def validate_df(self, df): + assert (df.values.any()), "No values in DataFrame" + assert (df.get('threshold') is not None), "No column named 'threshold'" + assert (df['threshold'].values.any()), "Column named 'threshold' contains no values" + assert (df['threshold'].dtype == 'float'), "Column named 'threshold' is not dtype float" + if 'group' not in df: + df['group'] = ['Overall'] * len(df) + assert ('Overall' in df['group'].unique()), "Column named 'group' does not contain 'Overall' value" + if 'feature' not in df: + df['feature'] = ['Overall'] * len(df) + assert ('Overall' in df['feature'].unique()), "Column named 'feature' does not contain 'Overall' value" + assert (df.columns.drop(['feature', 'group', 'threshold']).any()), "No columns for metric names" return df def create_fig(self, df): - metrics = ["accuracy", "precision", "recall", "f1"] + metrics = df.columns.drop(['feature', 'group', 'threshold']) dfs = df.groupby("group") fig = go.Figure() for metric in metrics: @@ -246,25 +223,25 @@ class SlicingMetricGraphs(_PlotlyGraph): x_name: Optional[Text] = "threshold" variables: Optional[Text] = "group" - def create_metrics(self, slicing_metrics): - metrics = set() - for slicing_metric in slicing_metrics: - for output_name in slicing_metric[1]: - for sub_key in slicing_metric[1][output_name]: - metrics.update(slicing_metric[1][output_name][sub_key].keys()) - return sorted(metrics) - - def create_df(self, slicing_metrics): - return slicing_metric_to_df(slicing_metrics) + def validate_df(self, df): + assert (df.values.any()), "No values in DataFrame" + assert (df.get('feature') is not None), "No column named 'feature'" + assert (df.get('group') is not None), "No column named 'group'" + assert (df['feature'].values.any()), "Column named 'feature' contains no values" + assert (df['group'].values.any()), "Column named 'group' contains no values" + assert (df.columns.drop(['feature', 'group']).any()), "No columns for metric names" + assert all(not df_group.empty for _, df_group in df.groupby('group')), "Not able to groupby 'group' column" + return df def create_fig(self, df): dfs = df.groupby("group") fig = go.Figure() - for metric in self.metrics: + metrics = sorted(df.columns.drop(['feature', 'group'])) + for metric in metrics: for group, df in dfs: - vis = True if metric == self.metrics[0] else False + vis = True if metric == metrics[0] else False feature = df.feature.iloc[0] - if feature == "Overall": + if feature == group: title = feature else: title = f"{df.feature.iloc[0]}={group}".replace("_", " ").title() @@ -281,13 +258,13 @@ def create_fig(self, df): def create_layout_button(metric, viz_arg): return dict(label=metric.title(), method="update", args=[{"visible": viz_arg}]) - metric_bools = visualize_bool_values(len(dfs), len(self.metrics)) + metric_bools = visualize_bool_values(len(dfs), len(metrics)) fig.update_layout( updatemenus=[ go.layout.Updatemenu( active=0, buttons=[ - create_layout_button(metric, viz_arg) for metric, viz_arg in zip(self.metrics, metric_bools) + create_layout_button(metric, viz_arg) for metric, viz_arg in zip(metrics, metric_bools) ], x=0, xanchor="left", diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_utils.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_utils.py index 735ccbd..516178e 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_utils.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_utils.py @@ -18,55 +18,6 @@ # SPDX-License-Identifier: Apache-2.0 # -from functools import reduce -import operator -import pandas as pd -import tensorflow_model_analysis as tfma -from typing import Optional, Union, Text, Sequence, Dict - - -def plot_results_parser(container, keys, feature_key_value=None): - # Get feature and value - if isinstance(container, tuple) and feature_key_value == None: - if container[0] and container[0][0]: - feature, value = container[0][0] - else: - feature, value = "Overall", "Overall" - return plot_results_parser(container[1], keys, (feature, value)) - # Recursively retrieve value form empty string keys - elif isinstance(container, dict) and list(container.keys()) == [""]: - return plot_results_parser(container.get(""), keys, feature_key_value) - elif isinstance(container, dict): - try: - feature, value = feature_key_value - return ( - dict(item, **{"feature": feature, "group": value}) for item in reduce(operator.getitem, keys, container) - ) - except (IndexError, KeyError): - return None - - -def plots_to_df(plots, keys): - return pd.DataFrame.from_records([result for plot in plots for result in plot_results_parser(plot, keys)]) - - -def slicing_metric_to_df( - slicing_metrics: Sequence[tfma.view.SlicedMetrics], output_name: Text = "", sub_key: Text = "" -) -> pd.DataFrame: - records = [] - for slicing_metric in slicing_metrics: - if slicing_metric[0] and slicing_metric[0][0]: - feature, value = slicing_metric[0][0] - else: - feature, value = "Overall", "Overall" - record = {"feature": feature, "group": value} - for metric in slicing_metric[1][output_name][""]: - for value_type in slicing_metric[1][output_name][sub_key][metric]: - metric_value = slicing_metric[1][output_name][sub_key][metric][value_type] - record[metric] = metric_value - records.append(record) - return pd.DataFrame(records) - def visualize_bool_values(trace_per_graph, graph_count): enum_range = range(0, trace_per_graph * graph_count, trace_per_graph) diff --git a/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py index 349973c..8136c0f 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py @@ -43,6 +43,11 @@ add_overview_graphs, add_eval_result_plots, add_eval_result_slicing_metrics, + +) +from intel_ai_safety.model_card_gen.graphics.graphics_data_transform import ( + plots_to_df, + slicing_metric_to_df ) # Typing @@ -244,15 +249,19 @@ def build_model_card(self): self.scaffold_assets() # Add Dataset Statistics if self.data_stats: - add_dataset_feature_statistics_plots(self.model_card, self.data_stats) + dfs = [tfdv_to_df(name, stats) for stats in self.data_sets] + add_dataset_feature_statistics_plots(self.model_card, self.data_sets.keys(), dfs) for dataset in self.model_card.model_parameters.data: # Make sure graphs are ordered the same dataset.graphics.collection = sorted(dataset.graphics.collection, key=lambda x: x.name) # Add Evaluation Statistics - for eval_result, dataset_name in zip(self.eval_results, self.data_sets.keys()): - add_overview_graphs(self.model_card, eval_result, dataset_name) - add_eval_result_plots(self.model_card, eval_result) - add_eval_result_slicing_metrics(self.model_card, eval_result) + named_eval_results = zip(self.eval_results, self.data_sets.keys()) if self.eval_results else [] + for eval_result, dataset_name in named_eval_results: + df = plots_to_df(eval_result.plots, ["confusionMatrixAtThresholds", "matrices"]) + add_overview_graphs(self.model_card, df, dataset_name) + add_eval_result_plots(self.model_card, df) + df = slicing_metric_to_df(eval_result.slicing_metrics) + add_eval_result_slicing_metrics(self.model_card, df) self.update_model_card(self.model_card) return self.export_format(self.model_card) From af9267abcb81c4f21e1b27afe34035792c8ce22f Mon Sep 17 00:00:00 2001 From: etcylfleet Date: Wed, 12 Jun 2024 11:36:20 -0700 Subject: [PATCH 04/11] Add new fuzzing tests (#295) * Add dog_cat_image() fuzz test * Add base explainer fuzz test * Update requirements.txt * Add deep_explainer fuzz test * Update fuzz_deep_explainer * Add doc string for how to run the program and check for coverage * Fix flake8 errors * Delete dog_cat test * Update exception print statement * Add instrumentation of AI Safety deep explainer * Lower number of runs * Fix unused imports and flake8 errors * Add include statement to instrument_imports * Remove broken test file, update deep explainer test to use proper input data and fix error * Remove base explainer * Revert req.txt * Remove unused imports --- fuzz/fuzz_deep_explainer.py | 117 ++++++++++++++++++++++++++++++++++++ fuzz/requirements.txt | 2 +- 2 files changed, 118 insertions(+), 1 deletion(-) create mode 100644 fuzz/fuzz_deep_explainer.py diff --git a/fuzz/fuzz_deep_explainer.py b/fuzz/fuzz_deep_explainer.py new file mode 100644 index 0000000..843923a --- /dev/null +++ b/fuzz/fuzz_deep_explainer.py @@ -0,0 +1,117 @@ +#!/usr/bin/python3 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Usage: + python3 -m coverage run fuzz_deep_explainer.py -atheris_runs=10 + coverage report -m --omit=../fuzz/config* +""" + +import atheris +import numpy as np +import sys +import itertools + +default_path = "../plugins" +sys.path.append(default_path) + +# This tells Atheris to instrument all functions in the library +with atheris.instrument_imports(include=["intel_ai_safety.explainer.attributions.attributions"]): + from intel_ai_safety.explainer.attributions.attributions import deep_explainer + +import torch +from torchvision import datasets, transforms +from torch import nn, optim +from torch.nn import functional as F +torch.manual_seed(0) + +batch_size = 128 +num_epochs = 1 +device = torch.device('cpu') + + +# MockNet class to replace the actual Net class for faster testing +class MockNet(nn.Module): + def __init__(self): + + super(MockNet, self).__init__() + self.fc_layers = nn.Sequential( + nn.Linear(784, 10), # Assuming input is a flattened MNIST image (28x28) + nn.Softmax(dim=1) + ) + + def forward(self, x): + x = x.view(-1, 784) # Flatten the image + x = self.fc_layers(x) + return x + + +train_loader = torch.utils.data.DataLoader( + datasets.MNIST('mnist_data', train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor() + ])), + batch_size=batch_size, shuffle=True) + +test_loader = torch.utils.data.DataLoader( + datasets.MNIST('mnist_data', train=False, transform=transforms.Compose([ + transforms.ToTensor()])), batch_size=batch_size, shuffle=True) + + +@atheris.instrument_func +def test_deep_explainer(input_bytes): + + fdp = atheris.FuzzedDataProvider(input_bytes) + # Generate random data based on the fuzzed input + num_background = fdp.ConsumeIntInRange(1, 5) + num_targets = fdp.ConsumeIntInRange(1, 5) + num_classes = fdp.ConsumeIntInRange(2, 10) + # The model expects images of shape (batch_size, channels, height, width) + # For MNIST, this is typically (batch_size, 1, 28, 28) + # Generate random images with the same shape + background_images = np.random.rand(num_background, 1, 28, 28).astype(np.float32) + target_images = np.random.rand(num_targets, 1, 28, 28).astype(np.float32) + # The labels should be a list of strings, one for each class + labels = [f"Class {i}" for i in range(num_classes)] + # Use the mocked model instead of the actual Net to speedup the test + model = MockNet().to(device) + # Evaluate the model with a smaller subset of the test data to speedup the test + model.eval() + test_loss = 0 + correct = 0 + y_true = torch.empty(0) + y_pred = torch.empty((0, 10)) + X_test = torch.empty((0, 1, 28, 28)) + + with torch.no_grad(): + for data, target in itertools.islice(test_loader, 10): # Limit the number of batches + data, target = data.to(device), target.to(device) + output = model(data) + X_test = torch.cat((X_test, data)) + y_true, y_pred = torch.cat((y_true, target)), torch.cat((y_pred, output)) + + test_loss += F.nll_loss(output.log(), target).item() + pred = output.max(1, keepdim=True)[1] + correct += pred.eq(target.view_as(pred)).sum().item() + + test_loss /= len(test_loader.dataset) + classes = np.array(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']) + # Use the generated background and target images for the deep explainer + deep_explainer(model, torch.tensor(background_images), torch.tensor(target_images), classes) + + return + + +atheris.Setup(sys.argv, test_deep_explainer) +atheris.Fuzz() diff --git a/fuzz/requirements.txt b/fuzz/requirements.txt index d0054c3..df77871 100644 --- a/fuzz/requirements.txt +++ b/fuzz/requirements.txt @@ -6,4 +6,4 @@ pandas==1.5.3 plotly==5.20.0 semantic-version==2.10.0 tensorflow-data-validation==1.14.0 -tensorflow-model-analysis==0.45.0 +tensorflow-model-analysis==0.45.0 \ No newline at end of file From 6a52b5f2bf8232a9a41206c1ee43f763c54f6abd Mon Sep 17 00:00:00 2001 From: Tyler W Date: Wed, 26 Jun 2024 21:52:33 +0000 Subject: [PATCH 05/11] Remove TFMA from model_card_gen (#304) * removed TFMA dependencies Signed-off-by: Tyler Wilbers * removed dataset arg Signed-off-by: Tyler Wilbers * fixed error docs Signed-off-by: Tyler Wilbers * Add missing MCG required test dependencies Signed-off-by: Abolfazl Shahbazi --------- Signed-off-by: Tyler Wilbers Signed-off-by: Abolfazl Shahbazi Co-authored-by: Abolfazl Shahbazi --- .../model_card_gen/__init__.py | 3 - .../docs/examples/csv/metrics_by_group.csv | 4 + .../examples/csv/metrics_by_threshold.csv | 6013 +++++++++++++++++ .../model_card_gen/generators/generator.py | 297 + .../model_card_gen/graphics/add_graphics.py | 78 +- .../graphics/plotly_graphics.py | 6 +- .../html/macros/default_macros.html.jinja | 20 +- model_card_gen/pyproject.toml | 20 +- model_card_gen/tests/__init__.py | 5 - model_card_gen/tests/test_model_card.py | 15 +- .../model_card_gen/analyze/__init__.py | 0 .../model_card_gen/analyze/analyzer.py | 0 .../analyze/analyzer_factory.py | 0 .../model_card_gen/analyze/pandas_analyzer.py | 0 .../model_card_gen/analyze/tf_analyzer.py | 0 .../model_card_gen/analyze/torch_analyzer.py | 0 .../model_card_gen/datasets/__init__.py | 0 .../model_card_gen/datasets/datasets.py | 0 .../model_card_gen/datasets/tf_datasets.py | 0 .../model_card_gen/datasets/torch_datasets.py | 0 .../model_card_gen/model_card_gen.py | 0 .../generators/tfma/tests/__init__.py | 22 + .../tfma}/tests/test_end_to_end_tf.py | 0 .../tfma}/tests/test_end_to_end_torch.py | 0 .../generators/tfma}/tests/tf_model.py | 0 .../generators/tfma}/tests/torch_model.py | 0 pyproject.toml | 6 +- 27 files changed, 6424 insertions(+), 65 deletions(-) create mode 100644 model_card_gen/intel_ai_safety/model_card_gen/docs/examples/csv/metrics_by_group.csv create mode 100644 model_card_gen/intel_ai_safety/model_card_gen/docs/examples/csv/metrics_by_threshold.csv create mode 100644 model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/__init__.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/analyzer.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/analyzer_factory.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/pandas_analyzer.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/tf_analyzer.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/datasets/__init__.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/datasets/datasets.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/datasets/tf_datasets.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/datasets/torch_datasets.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/intel_ai_safety/model_card_gen/model_card_gen.py (100%) create mode 100644 plugins/model_card_gen/generators/tfma/tests/__init__.py rename {model_card_gen => plugins/model_card_gen/generators/tfma}/tests/test_end_to_end_tf.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/tests/test_end_to_end_torch.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/tests/tf_model.py (100%) rename {model_card_gen => plugins/model_card_gen/generators/tfma}/tests/torch_model.py (100%) diff --git a/model_card_gen/intel_ai_safety/model_card_gen/__init__.py b/model_card_gen/intel_ai_safety/model_card_gen/__init__.py index d40eeff..5e03347 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/__init__.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/__init__.py @@ -15,6 +15,3 @@ # # SPDX-License-Identifier: Apache-2.0 # - -# TODO: Remove this line after upgrading PyArrow to 14.0.1 -import pyarrow_hotfix diff --git a/model_card_gen/intel_ai_safety/model_card_gen/docs/examples/csv/metrics_by_group.csv b/model_card_gen/intel_ai_safety/model_card_gen/docs/examples/csv/metrics_by_group.csv new file mode 100644 index 0000000..21ffa29 --- /dev/null +++ b/model_card_gen/intel_ai_safety/model_card_gen/docs/examples/csv/metrics_by_group.csv @@ -0,0 +1,4 @@ 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+0.362,3256.0,7471.0,0.6964668593269321,0.6392572944297082,0.0876328892477459,0.6666369233514768,0.8470169116743786,0.1106126197035288,Overall,Overall,4216.0,33899.0,Test +0.363,3251.0,7468.0,0.6967067823490998,0.6390005989561051,0.0874983178576234,0.6666071587967508,0.8470578600384915,0.1106681006216719,Overall,Overall,4219.0,33904.0,Test +0.364,3246.0,7460.0,0.6968055296095648,0.6383160776931633,0.087363746467501,0.6662796409592284,0.8469964374923222,0.1108401510383889,Overall,Overall,4227.0,33909.0,Test +0.365,3235.0,7455.0,0.6973807296538821,0.6378882519038248,0.0870676894092316,0.6663091567234214,0.8471192825846607,0.1109247221639756,Overall,Overall,4232.0,33920.0,Test +0.366,3231.0,7453.0,0.6975851740921003,0.6377171215880894,0.0869600322971336,0.6663090608376916,0.8471602309487736,0.1109596939042926,Overall,Overall,4234.0,33924.0,Test 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+0.517,2132.0,6444.0,0.7513992537313433,0.5513818772995637,0.0573812407482169,0.6360361249568179,0.849002907333852,0.1302091094223414,Overall,Overall,5243.0,35023.0,Test +0.518,2115.0,6434.0,0.7526026435840449,0.5505262257208865,0.0569236980218005,0.6358964222178296,0.849146226608247,0.1303700394609485,Overall,Overall,5253.0,35040.0,Test +0.519,2106.0,6424.0,0.7531066822977726,0.5496705741422093,0.0566814695195801,0.6355047732106642,0.8491257524261906,0.1305566580670768,Overall,Overall,5263.0,35049.0,Test +0.52,2098.0,6416.0,0.7535823349776838,0.5489860528792676,0.0564661552953842,0.6352160784119598,0.8491257524261906,0.1307032334854195,Overall,Overall,5271.0,35057.0,Test +0.521,2091.0,6405.0,0.7538841807909604,0.5480448361427227,0.0562777553492127,0.6346925630481097,0.8490438556979648,0.1309175630793635,Overall,Overall,5282.0,35064.0,Test +0.522,2081.0,6388.0,0.7542803164482229,0.5465902284589715,0.0560086125689678,0.6338559237944036,0.8489005364235699,0.1312510836450102,Overall,Overall,5299.0,35074.0,Test +0.523,2074.0,6378.0,0.7546142924751538,0.5457345768802944,0.0558202126227963,0.6333978847013257,0.8488391138774006,0.1314434265907403,Overall,Overall,5309.0,35081.0,Test +0.524,2067.0,6377.0,0.755210800568451,0.5456490117224266,0.0556318126766249,0.6335502458894242,0.8489619589697391,0.1314421506015149,Overall,Overall,5310.0,35088.0,Test +0.525,2055.0,6372.0,0.7561409754360983,0.545221185933088,0.055308841340331,0.6335885452918366,0.8491052782441342,0.1315105777557837,Overall,Overall,5315.0,35100.0,Test +0.526,2047.0,6358.0,0.7564544913741821,0.54402327372294,0.0550935271161351,0.6328887119251444,0.8489824331517956,0.1317852461854242,Overall,Overall,5329.0,35108.0,Test 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a/model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py b/model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py new file mode 100644 index 0000000..3e88fc9 --- /dev/null +++ b/model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py @@ -0,0 +1,297 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2024 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +# + +""" +This module allows users to classes for performing end to end workflows +around model card generation. +""" + +# Base +import os +import pkgutil +import tempfile + +# External +import jinja2 +import pandas as pd +from IPython.display import display, HTML + +# Internal +from intel_ai_safety.model_card_gen.model_card import ModelCard +from intel_ai_safety.model_card_gen.graphics.add_graphics import ( + add_dataset_feature_statistics_plots, + add_overview_graphs, + add_eval_result_plots, + add_eval_result_slicing_metrics + +) + +# Typing +from typing import Optional, Sequence, Text, Union, Dict, Any + +DataFormat = Union[pd.DataFrame, Text] + +_UI_TEMPLATES = ( + "template/html/default_template.html.jinja", + "template/html/style/default_style.html.jinja", + "template/html/macros/default_macros.html.jinja", + "template/html/js/plotly_js_header.html.jinja", +) +_DEFAULT_UI_TEMPLATE_FILE = os.path.join("html", "default_template.html.jinja") +_MC_JSON_FILE = os.path.join("data", "model_card.json") +_TEMPLATE_DIR = "template" +_MODEL_CARDS_DIR = "model_cards" +_DEFAULT_MODEL_CARD_FILE_NAME = "model_card.html" + + +class ModelCardGen: + """Generate ModelCard from with TFMA + + Args: + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + output_dir (str): representing of where to output model card + """ + + def __init__( + self, + model_card: Union[ModelCard, Dict[Text, Any]] = None, + metrics_by_threshold: Union[pd.DataFrame, Text]= None, + metrics_by_group: Union[pd.DataFrame, Text] = None, + data_stats: Dict[Text, pd.DataFrame] = {}, + output_dir: Text = "", + ): + self.data_stats = data_stats + + if isinstance(metrics_by_threshold, pd.DataFrame): + self.metrics_by_threshold = metrics_by_threshold + elif isinstance(metrics_by_threshold, str) and os.path.exists(metrics_by_threshold): + self.metrics_by_threshold = pd.read_csv(metrics_by_threshold) + else: + self.metrics_by_threshold = None + + if isinstance(metrics_by_group, pd.DataFrame): + self.metrics_by_group = metrics_by_group + elif isinstance(metrics_by_group, str) and os.path.exists(metrics_by_group): + self.metrics_by_group = pd.read_csv(metrics_by_group) + else: + self.metrics_by_group = None + + # Local asset paths + self.output_dir = output_dir or tempfile.mkdtemp() + self._mc_json_file = os.path.join(self.output_dir, _MC_JSON_FILE) + self._mc_template_dir = os.path.join(self.output_dir, _TEMPLATE_DIR) + self._model_cards_dir = os.path.join(self.output_dir, _MODEL_CARDS_DIR) + # Construct ModelCard object + if isinstance(model_card, ModelCard): + self.model_card = model_card + elif isinstance(model_card, dict) or isinstance(model_card, str): + self.model_card = ModelCardGen._read_json(model_card) + else: + self.model_card = ModelCard() + # Generated Attributes + self.model_card_html = "" + + @classmethod + def generate( + cls, + model_card: Union[ModelCard, Dict[Text, Any], Text] = None, + metrics_by_threshold: pd.DataFrame= None, + metrics_by_group: pd.DataFrame = None, + output_dir: Text = "", + ): + """Class Factory starting TFMA analysis and generating ModelCard + + Args: + model_card (ModelCard or dict): pre-generated ModelCard Python object or dictionary following model card schema + output_dir (str): representing of where to output model card + + Returns: + ModelCardGen + + Example: + >>> from model_card_gen.model_card_gen import ModelCardGen + >>> model_path = 'compas/model' + >>> data_paths = {'eval': 'compas/eval.tfrecord', 'train': 'compas/train.tfrecord'} + >>> eval_config = 'compas/eval_config.proto' + >>> mcg = ModelCardGen.generate(data_paths, model_path, eval_config) #doctest:+SKIP + """ + self = cls(model_card=model_card, + output_dir=output_dir, + metrics_by_threshold=metrics_by_threshold, + metrics_by_group=metrics_by_group + ) + self.model_card_html = self.build_model_card() + return self + + def build_model_card(self ): + """Build graphics and add them to model card""" + self.scaffold_assets() + # Add Dataset Statistics + if self.data_stats: + add_dataset_feature_statistics_plots(self.model_card, self.data_stats.keys(), self.data_stats.values()) + for dataset in self.model_card.model_parameters.data: + # Make sure graphs are ordered the same + dataset.graphics.collection = sorted(dataset.graphics.collection, key=lambda x: x.name) + # Add Evaluation Statistics + if isinstance(self.metrics_by_threshold, pd.DataFrame): + add_overview_graphs(self.model_card, + self.metrics_by_threshold) + add_eval_result_plots(self.model_card, + self.metrics_by_threshold) + if isinstance(self.metrics_by_group, pd.DataFrame): + add_eval_result_slicing_metrics(self.model_card, self.metrics_by_group) + self.update_model_card(self.model_card) + return self.export_format(self.model_card) + + def update_model_card(self, model_card: ModelCard) -> None: + """Updates the JSON file in the MCT assets directory. + + Args: + model_card (ModelCard): The updated model card to write back. + + Raises: + ValidationError: when the given model_card is invalid with reference to the schema. + """ + self._write_json_file(self._mc_json_file, model_card) + + def scaffold_assets(self): + """Generates the Model Card Tookit assets. + + Assets include the ModelCard JSON file, Model Card document, and jinja + template. These are written to the `output_dir` declared at + initialization. + + An assets directory is created if one does not already exist. + + If the MCT is initialized with a `mlmd_store`, it further auto-populates + the model cards properties as well as generating related plots such as model + performance and data distributions. + + Returns: + A ModelCard representing the given model. + + Raises: + FileNotFoundError: on failure to copy the template files. + """ + + # Write JSON file for model card + self._write_json_file(self._mc_json_file, self.model_card) + + # Write UI template files. + for template_path in _UI_TEMPLATES: + template_content = pkgutil.get_data("intel_ai_safety.model_card_gen", template_path) + if template_content is None: + raise FileNotFoundError(f"Cannot find file: '{template_path}'") + template_content = template_content.decode("utf8") + self._write_file(os.path.join(self.output_dir, template_path), template_content) + + def _jinja_loader(self, template_dir: Text) -> jinja2.FileSystemLoader: + return jinja2.FileSystemLoader(template_dir) + + def _write_file(self, path: Text, content: Text) -> None: + """Write content to the path.""" + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, "w+") as f: + f.write(content) + + def _write_json_file(self, path: Text, model_card: ModelCard) -> None: + """Write serialized model card JSON to the path.""" + os.makedirs(os.path.dirname(path), exist_ok=True) + with open(path, "wb") as f: + f.write(model_card.to_json().encode()) + + @staticmethod + def _read_json_file(path: Text) -> Optional[ModelCard]: + """Read serialized model card JSON from the path.""" + if not os.path.exists(path): + return None + with open(path, "r") as f: + model_card = ModelCardGen._read_json(f.read()) + return model_card + + @staticmethod + def _read_json(model_card_json: Union[Dict[Text, Any], Text]) -> Optional[ModelCard]: + """Read serialized model card proto from the path.""" + model_card = ModelCard() + model_card.merge_from_json(model_card_json) + return model_card + + def export_format( + self, + model_card: Optional[ModelCard] = None, + template_path: Optional[Text] = None, + output_file=_DEFAULT_MODEL_CARD_FILE_NAME, + ) -> Text: + """Generates a model card document based on the MC assets. + + Args: + model_card (ModelCard): The ModelCard object, generated from `scaffold_assets()`. + If not provided, it will be read from the ModelCard proto file in the + assets directory. + template_path (str): The file path of the Jinja template. If not provided, the + default template will be used. + output_file (str): The file name of the generated model card. If not provided, + the default 'model_card.html' will be used. If the file already exists, + then it will be overwritten. + + Returns: + The model card file content. + """ + + if not template_path: + template_path = os.path.join(self._mc_template_dir, _DEFAULT_UI_TEMPLATE_FILE) + template_dir = os.path.dirname(template_path) + template_file = os.path.basename(template_path) + + # If model_card is passed in, write to JSON file. + if model_card: + self.update_model_card(model_card) + # If model_card is not passed in, read from JSON file. + else: + model_card = ModelCardGen._read_json_file(self._mc_json_file) + if not model_card: + raise ValueError("model_card could not be found. " "Call scaffold_assets() to generate model_card.") + + # Generate Model Card. + jinja_env = jinja2.Environment( + loader=self._jinja_loader(template_dir), autoescape=True, auto_reload=True, cache_size=0 + ) + template = jinja_env.get_template(template_file) + model_card_file_content = template.render( + model_details=model_card.model_details, + model_parameters=model_card.model_parameters, + quantitative_analysis=model_card.quantitative_analysis, + considerations=model_card.considerations, + ) + + # Write the model card document file and return its contents. + mode_card_file_path = os.path.join(self._model_cards_dir, output_file) + self._write_file(mode_card_file_path, model_card_file_content) + return model_card_file_content + + def _repr_html_(self): + return self.model_card_html + + def display_model_card(self): + display(HTML(self._repr_html_())) + + def export_html(self, filename): + with open(filename, "w") as f: + f.write(self._repr_html_()) diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py index 8bc5ea6..9ef9faf 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/add_graphics.py @@ -27,21 +27,32 @@ from typing import Sequence, Text, Tuple, Union, Optional, List OVERVIEW_GRAPHS = [OverallPerformanceAtThreshold] -PLOTS_GRAPHS = [ConfusionMatrixAtThresholdsGraphs] +THRESHOLD_GRAPHS = [MetricsAtThresholdsGraphs] SLICING_METRIC_GRAPHS = [SlicingMetricGraphs] DATASTAT_GRAPHS = [DataStatsGraphs] -def add_overview_graphs(model_card: ModelCard, df: pd.DataFrame, dataset_name: Text) -> None: - - graphs = [ - graph.generate_figure(df) - for graph in OVERVIEW_GRAPHS - ] - # Add graphs to modle card - model_card.model_details.graphics.collection.extend( - [Graphic(name=dataset_name, html=graph.html_content) for graph in graphs] - ) +def add_overview_graphs(model_card: ModelCard, df: pd.DataFrame) -> None: + if "dataset" in df: + dfs = df.groupby("dataset") + for dataset_name, df in dfs: + graphs = [ + graph.generate_figure(df) + for graph in OVERVIEW_GRAPHS + ] + # Add graphs to modle card + model_card.model_details.graphics.collection.extend( + [Graphic(name=graph.title + f" ({dataset_name})", html=graph.html_content) for graph in graphs] + ) + else: + graphs = [ + graph.generate_figure(df) + for graph in OVERVIEW_GRAPHS + ] + # Add graphs to modle card + model_card.model_details.graphics.collection.extend( + [Graphic(name=graph.title, html=graph.html_content) for graph in graphs] + ) def add_dataset_feature_statistics_plots( @@ -57,7 +68,7 @@ def add_dataset_feature_statistics_plots( Dataset( name=name.title(), graphics=GraphicsCollection( - collection=[Graphic(name=graph.name, html=graph.html_content) for graph in graphs] + collection=[Graphic(name=graph.title, html=graph.html_content) for graph in graphs] ), ) ) @@ -71,10 +82,18 @@ def add_eval_result_slicing_metrics(model_card: ModelCard, df: pd.DataFrame) -> model_card: The model card object. eval_result: A `tfma.EvalResult`. """ - graphs = [graph.generate_figure(df) for graph in SLICING_METRIC_GRAPHS] - model_card.quantitative_analysis.graphics.collection.extend( - [Graphic(name=graph.name, html=graph.html_content) for graph in graphs] - ) + if "dataset" in df: + dfs = df.groupby("dataset") + for dataset_name, df in dfs: + graphs = [graph.generate_figure(df) for graph in SLICING_METRIC_GRAPHS] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=graph.title + f" ({dataset_name})", html=graph.html_content) for graph in graphs] + ) + else: + graphs = [graph.generate_figure(df) for graph in SLICING_METRIC_GRAPHS] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=graph.title, html=graph.html_content) for graph in graphs] + ) def add_eval_result_plots(model_card: ModelCard, df: pd.DataFrame) -> None: @@ -88,15 +107,24 @@ def add_eval_result_plots(model_card: ModelCard, df: pd.DataFrame) -> None: model_card: The model card object. eval_result: A `tfma.EvalResult`. """ - graphs = [ - graph.generate_figure(df) - for graph in PLOTS_GRAPHS - # Check metric name is in plots - # if graph.eval_result_keys[0] in plots - ] - model_card.quantitative_analysis.graphics.collection.extend( - [Graphic(name=None, html=graph.html_content) for graph in graphs] - ) + if "dataset" in df: + dfs = df.groupby("dataset") + for dataset_name, df in dfs: + graphs = [ + graph.generate_figure(df) + for graph in THRESHOLD_GRAPHS + ] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=graph.title + f" ({dataset_name})", html=graph.html_content) for graph in graphs] + ) + else: + graphs = [ + graph.generate_figure(df) + for graph in THRESHOLD_GRAPHS + ] + model_card.quantitative_analysis.graphics.collection.extend( + [Graphic(name=graph.title, html=graph.html_content) for graph in graphs] + ) def get_slice_key(slice_key: Union[Tuple[()], Tuple[Tuple[Text, Union[Text, int, float]], ...]]) -> Tuple[Text, Text]: diff --git a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py index cf6fd30..7b65967 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/graphics/plotly_graphics.py @@ -105,6 +105,7 @@ def create_fig(self, df): class DataStatsGraphs(_PlotlyGraph): + title: Optional[Text] = "Dataset Statistics" def validate_df(self, df): assert (df.values.any()), "No values in DataFrame" @@ -158,10 +159,11 @@ def create_layout_button(feature, df): return fig -class ConfusionMatrixAtThresholdsGraphs(_PlotlyGraph): +class MetricsAtThresholdsGraphs(_PlotlyGraph): x_name: Optional[Text] = "threshold" y_name: Optional[Text] = "value" figure: Optional[go.Figure] = None + title: Optional[Text] = "Metrics at Threshold" def validate_df(self, df): assert (df.values.any()), "No values in DataFrame" @@ -222,6 +224,8 @@ def create_layout_button(metric, viz_arg): class SlicingMetricGraphs(_PlotlyGraph): x_name: Optional[Text] = "threshold" variables: Optional[Text] = "group" + title: Optional[Text] = "Metrics by Group" + def validate_df(self, df): assert (df.values.any()), "No values in DataFrame" diff --git a/model_card_gen/intel_ai_safety/model_card_gen/template/html/macros/default_macros.html.jinja b/model_card_gen/intel_ai_safety/model_card_gen/template/html/macros/default_macros.html.jinja index 214989b..f503301 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/template/html/macros/default_macros.html.jinja +++ b/model_card_gen/intel_ai_safety/model_card_gen/template/html/macros/default_macros.html.jinja @@ -45,14 +45,16 @@ {% macro render_graphics(graphics) %} {% if graphics.description %}

{{ graphics.description }}

{% endif %} {% for graph in graphics %} - {% if graph.name %}

{{ graph.name }}

{% endif %} - {% if graph.html %} -
{{ graph.html|safe }}
- {% elif graph.image %} -
- {{ graph.name }} -
- {% endif %} +
+ {% if graph.name %}

{{ graph.name }}

{% endif %} + {% if graph.html %} +
{{ graph.html|safe }}
+ {% elif graph.image %} +
+ {{ graph.name }} +
+ {% endif %} +
{% endfor %} {% endmacro %} @@ -110,4 +112,4 @@ {{ render_graphics(graphics.collection) }} -{% endmacro %} \ No newline at end of file +{% endmacro %} diff --git a/model_card_gen/pyproject.toml b/model_card_gen/pyproject.toml index 4d79cac..eae292c 100644 --- a/model_card_gen/pyproject.toml +++ b/model_card_gen/pyproject.toml @@ -11,6 +11,7 @@ intel_ai_safety.model_card_gen = [ "model_card_gen/**/*.json", "model_card_gen/**/*.jinja", "model_card_gen/**/*.html", + "model_card_gen/**/*.csv", ] [tool.poetry.group.test] @@ -19,36 +20,21 @@ optional = true [tool.poetry.group.test.dependencies] pytest = '8.1.1' pytest-cov = '5.0.0' -tensorflow-hub = '0.15.0' -torch = {version = "2.2.0", source = "pytorch-cpu"} -scikit-learn = '1.4.0' [tool.poetry.dependencies] python = ">=3.9, <3.11" -absl-py = '1.4.0' -attrs = '21.4.0' +pandas = "1.5.3" +ipython = "*" dataclasses = '0.6' -grpcio-status = '1.48.2' -intel-tensorflow = '2.14.0' Jinja2 = '3.1.4' -joblib = '1.3.2' jsonschema = {version='4.17.3', extras = ['format-nongpl']} plotly = '5.20.0' -protobuf = '3.20.3' -pyarrow_hotfix = '0.6.0' semantic-version = '2.10.0' -tensorflow-data-validation = '1.14.0' -tensorflow-model-analysis = '0.45.0' [[tool.poetry.source]] name = "PyPI" priority = "primary" -[[tool.poetry.source]] -name = "pytorch-cpu" -url = "https://download.pytorch.org/whl/cpu" -priority = "explicit" - [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/model_card_gen/tests/__init__.py b/model_card_gen/tests/__init__.py index 27c453a..2fc563f 100644 --- a/model_card_gen/tests/__init__.py +++ b/model_card_gen/tests/__init__.py @@ -15,8 +15,3 @@ # See the License for the specific language governing permissions and # limitations under the License. # -# SPDX-License-Identifier: Apache-2.0 -# - -# TODO: Remove this line after upgrading PyArrow to 14.0.1 -import pyarrow_hotfix diff --git a/model_card_gen/tests/test_model_card.py b/model_card_gen/tests/test_model_card.py index f6c8378..861358b 100644 --- a/model_card_gen/tests/test_model_card.py +++ b/model_card_gen/tests/test_model_card.py @@ -22,7 +22,7 @@ import os import pkgutil import json -from intel_ai_safety.model_card_gen.model_card_gen import ModelCardGen +from intel_ai_safety.model_card_gen.generators.generator import ModelCardGen from intel_ai_safety.model_card_gen.validation import ( _LATEST_SCHEMA_VERSION, _SCHEMA_FILE_NAME, @@ -32,15 +32,17 @@ PACKAGE = "intel_ai_safety.model_card_gen" JSON_FILES = ["docs/examples/json/model_card_example.json", "docs/examples/json/model_card_compas.json"] +CSV_FILES = [("docs/examples/csv/metrics_by_group.csv", "docs/examples/csv/metrics_by_threshold.csv")] MODEL_CARD_STRS = [pkgutil.get_data(PACKAGE, json_file) for json_file in JSON_FILES] MODEL_CARD_JSONS = [json.loads(json_str) for json_str in MODEL_CARD_STRS] +MODEL_CARD_CSVS = [(pkgutil.get_data(PACKAGE, csv_file1), pkgutil.get_data(PACKAGE, csv_file2)) for csv_file1, csv_file2 in CSV_FILES] @pytest.mark.common @pytest.mark.parametrize("test_json", MODEL_CARD_JSONS) def test_init(test_json): """Test ModelCardGen initialization""" - mcg = ModelCardGen(data_sets={"test": ""}, model_card=test_json) + mcg = ModelCardGen(model_card=test_json) assert mcg.model_card @@ -48,7 +50,7 @@ def test_init(test_json): @pytest.mark.parametrize("test_json", MODEL_CARD_JSONS) def test_read_json(test_json): """Test ModelCardGen._read_json method""" - mcg = ModelCardGen(data_sets={"test": ""}, model_card=test_json) + mcg = ModelCardGen(model_card=test_json) assert mcg.model_card == ModelCardGen._read_json(test_json) @@ -67,3 +69,10 @@ def test_schemas(test_json): json_file = pkgutil.get_data(PACKAGE, schema_file) schema = json.loads(json_file) assert schema == _find_json_schema(_LATEST_SCHEMA_VERSION) + +@pytest.mark.common +@pytest.mark.parametrize(("metrics_by_group", "metrics_by_threshold"), MODEL_CARD_CSVS) +def test_load_from_csv(metrics_by_group, metrics_by_threshold): + """Test JSON schema loads""" + mcg = ModelCardGen.generate(metrics_by_threshold=metrics_by_threshold, metrics_by_group=metrics_by_group) + assert mcg.model_card diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/__init__.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/__init__.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/__init__.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/__init__.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/analyzer.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/analyzer.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer_factory.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/analyzer_factory.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/analyzer_factory.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/analyzer_factory.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/pandas_analyzer.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/pandas_analyzer.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/pandas_analyzer.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/pandas_analyzer.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/tf_analyzer.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/tf_analyzer.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/tf_analyzer.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/tf_analyzer.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/analyze/torch_analyzer.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/datasets/__init__.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/__init__.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/datasets/__init__.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/__init__.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/datasets/datasets.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/datasets.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/datasets/datasets.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/datasets.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/datasets/tf_datasets.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/tf_datasets.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/datasets/tf_datasets.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/tf_datasets.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/torch_datasets.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/datasets/torch_datasets.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/datasets/torch_datasets.py diff --git a/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py b/plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/model_card_gen.py similarity index 100% rename from model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py rename to plugins/model_card_gen/generators/tfma/intel_ai_safety/model_card_gen/model_card_gen.py diff --git a/plugins/model_card_gen/generators/tfma/tests/__init__.py b/plugins/model_card_gen/generators/tfma/tests/__init__.py new file mode 100644 index 0000000..676367b --- /dev/null +++ b/plugins/model_card_gen/generators/tfma/tests/__init__.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +# +# Copyright (c) 2024 Intel Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# SPDX-License-Identifier: Apache-2.0 +# + +# TODO: Remove this line after upgrading PyArrow to 14.0.1 +import pyarrow_hotfix diff --git a/model_card_gen/tests/test_end_to_end_tf.py b/plugins/model_card_gen/generators/tfma/tests/test_end_to_end_tf.py similarity index 100% rename from model_card_gen/tests/test_end_to_end_tf.py rename to plugins/model_card_gen/generators/tfma/tests/test_end_to_end_tf.py diff --git a/model_card_gen/tests/test_end_to_end_torch.py b/plugins/model_card_gen/generators/tfma/tests/test_end_to_end_torch.py similarity index 100% rename from model_card_gen/tests/test_end_to_end_torch.py rename to plugins/model_card_gen/generators/tfma/tests/test_end_to_end_torch.py diff --git a/model_card_gen/tests/tf_model.py b/plugins/model_card_gen/generators/tfma/tests/tf_model.py similarity index 100% rename from model_card_gen/tests/tf_model.py rename to plugins/model_card_gen/generators/tfma/tests/tf_model.py diff --git a/model_card_gen/tests/torch_model.py b/plugins/model_card_gen/generators/tfma/tests/torch_model.py similarity index 100% rename from model_card_gen/tests/torch_model.py rename to plugins/model_card_gen/generators/tfma/tests/torch_model.py diff --git a/pyproject.toml b/pyproject.toml index cf919c0..f43293b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,16 +22,18 @@ python = ">=3.9, <3.11" optional = true [tool.poetry.group.test.dependencies] +backcall = '0.2.0' datasets = '2.14.4' deepdiff = '6.7.1' intel-tensorflow = '2.14.0' +pickleshare = '0.7.5' +pillow = '10.3.0' pytest = '8.1.1' scikit-learn = '1.4.0' +sentencepiece = '0.2.0' tensorflow-hub = '0.15.0' torch = {version = "2.2.0", source = "pytorch-cpu"} torchvision = {version = "0.17.0", source = "pytorch-cpu"} -pillow = '10.3.0' -sentencepiece = '0.2.0' [tool.poetry.extras] From 7e0d6ac1d8ed32ff822d8ffe88dd9ee4fb47b2d1 Mon Sep 17 00:00:00 2001 From: Mitali Potnis <162364085+mitalipo@users.noreply.github.com> Date: Wed, 3 Jul 2024 16:13:02 -0700 Subject: [PATCH 06/11] ModelGauge SUT for running NeuralChat on standard tests (#309) * Add pyproject.toml for ModelGauge dependency management * Add README for NeuralChat SUT * Add secrets config file * Register SUT * Add results for standard tests on NeuralChat SUT * Modify README to add steps for adding secret keys * Remove results of NeuralChat SUT on standard tests * Add variable for unique ID of SUT * Update README for model repo and SUT ID configuration --- plugins/mlcommons/modelgauge/README.md | 55 +++++++++++++++++++ .../mlcommons/modelgauge/config/secrets.toml | 14 +++++ .../modelgauge/suts/neuralchat_sut.py | 7 +++ plugins/mlcommons/modelgauge/pyproject.toml | 29 ++++++++++ 4 files changed, 105 insertions(+) create mode 100644 plugins/mlcommons/modelgauge/README.md create mode 100644 plugins/mlcommons/modelgauge/config/secrets.toml create mode 100644 plugins/mlcommons/modelgauge/modelgauge/suts/neuralchat_sut.py create mode 100644 plugins/mlcommons/modelgauge/pyproject.toml diff --git a/plugins/mlcommons/modelgauge/README.md b/plugins/mlcommons/modelgauge/README.md new file mode 100644 index 0000000..80405e4 --- /dev/null +++ b/plugins/mlcommons/modelgauge/README.md @@ -0,0 +1,55 @@ +# Modelgauge Neural-Chat Plugin + +## Get Started + +### Requirements +* Linux system or WSL2 on Windows (validated on Ubuntu* 20.04/22.04 LTS) +* Python 3.10 +* Poetry + +## Install +1. Choose a virtual enviornment to use: eg. Using virtualenv: + +```bash +python3 -m virtualenv mg_env +source mg_env/bin/activate +``` + +2. Navigate to `modelgauge/suts`. In `neuralchat_sut.py`, modify the `HUGGING_FACE_REPO_ID` and `UNIQUE_ID` variables. Set `HUGGING_FACE_REPO_ID` to correspond with the repository ID of the model you intend to test. Assign a unique identifier to the `UNIQUE_ID` variable for the SUT: + +```bash +cd modelgauge/suts +``` + +3. Resolve and lock the dependencies: + +```bash +poetry lock +``` + +4. Install dependencies, and register System Under Test (SUTs) and tests: + + ```bash +poetry install +``` + +## Run Tests +1. Verify the registration of the SUT or the specific test you wish to execute: + +```bash +modelgauge list +``` +To view a list of only the SUTs or the tests, you can use the corresponding commands. For a comprehensive list of available commands run the following command: + +```bash + modelgauge +``` + +2. Enter your keys into the `secrets.toml` file found in the `config` folder. + +3. To illustrate, the following command executes the `bbq` test on the `Intel/neural-chat-7b-v3-3` SUT: + +```shell +modelgauge run-test --sut Intel/neural-chat-7b-v3-3 --test bbq +``` +The results from the test will be saved in the output directory. diff --git a/plugins/mlcommons/modelgauge/config/secrets.toml b/plugins/mlcommons/modelgauge/config/secrets.toml new file mode 100644 index 0000000..8fa22dc --- /dev/null +++ b/plugins/mlcommons/modelgauge/config/secrets.toml @@ -0,0 +1,14 @@ +# Edit this file to add your secrets. + +# This is an example of how to define a secret. +# The config is saying that within scope "demo" we have a +# key named "api_key" that we are setting to value "12345". +[demo] +api_key = "12345" + +# Here are some commonly needed keys you can uncomment and use. +# [together] +# api_key = "" + +# [perspective_api] +# api_key = "" diff --git a/plugins/mlcommons/modelgauge/modelgauge/suts/neuralchat_sut.py b/plugins/mlcommons/modelgauge/modelgauge/suts/neuralchat_sut.py new file mode 100644 index 0000000..ceb09fb --- /dev/null +++ b/plugins/mlcommons/modelgauge/modelgauge/suts/neuralchat_sut.py @@ -0,0 +1,7 @@ +from modelgauge.suts.huggingface_client import HuggingFaceSUT, HuggingFaceToken +from modelgauge.secret_values import InjectSecret +from modelgauge.sut_registry import SUTS + +HUGGING_FACE_REPO_ID = "Intel/neural-chat-7b-v3-3" +UNIQUE_ID = "neural-chat-7b-v3-3" +SUTS.register(HuggingFaceSUT, UNIQUE_ID, HUGGING_FACE_REPO_ID, InjectSecret(HuggingFaceToken)) \ No newline at end of file diff --git a/plugins/mlcommons/modelgauge/pyproject.toml b/plugins/mlcommons/modelgauge/pyproject.toml new file mode 100644 index 0000000..774f7b9 --- /dev/null +++ b/plugins/mlcommons/modelgauge/pyproject.toml @@ -0,0 +1,29 @@ +[tool.poetry] +name = "modelgauge-neuralchat-plugin" +description = "ModelGauge SUT for running NeuralChat on standard tests." +version = "0.0.1" +authors = ["IntelAI "] +readme = "README.md" +packages = [{include = "modelgauge"}] + +[tool.poetry.dependencies] +python = ">=3.10, <3.11" +torch = "^2.1.2" +transformers = "^4.38.1" +pydantic = "^2.6.0" +pandas-stubs = "^2.2.1.240316" +modelgauge_huggingface = {git = "https://github.com/mlcommons/modelgauge.git", subdirectory = "plugins/huggingface", develop = true} +modelgauge = {git = "https://github.com/mlcommons/modelgauge.git", develop = true} +modelgauge_standard_tests = {git = "https://github.com/mlcommons/modelgauge.git", subdirectory = "plugins/standard_tests", develop = true} +modelgauge_together= {git = "https://github.com/mlcommons/modelgauge.git", subdirectory = "plugins/together", develop = true} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.mypy] +# Using namespace packages to do plugins requires us not to have __init__.py files. +# However, by default mypy uses those to map file paths to modules. This override fixes that. +# https://mypy.readthedocs.io/en/stable/config_file.html#confval-explicit_package_bases +explicit_package_bases = true +mypy_path = "." From d77adc6f5b3bd2f6d9852ed1a053aae73d07e043 Mon Sep 17 00:00:00 2001 From: etcylfleet Date: Tue, 16 Jul 2024 11:47:40 -0700 Subject: [PATCH 07/11] Remove comment and update broken fuzz test (#313) * Remove comments * Fix broken test --- fuzz/fuzz_dataset.py | 2 +- fuzz/fuzz_test.py | 2 -- 2 files changed, 1 insertion(+), 3 deletions(-) diff --git a/fuzz/fuzz_dataset.py b/fuzz/fuzz_dataset.py index a80813f..8d41579 100644 --- a/fuzz/fuzz_dataset.py +++ b/fuzz/fuzz_dataset.py @@ -6,7 +6,7 @@ MIN_DATA_LENGTH = 1 # Minimum length of dataset MAX_DATA_LENGTH = 1000 # Maximum length of dataset -default_path = "../model_card_gen" +default_path = "../plugins/model_card_gen/generators/tfma/" sys.path.append(default_path) with atheris.instrument_imports(include=["intel_ai_safety.*"]): diff --git a/fuzz/fuzz_test.py b/fuzz/fuzz_test.py index c40299e..484bc51 100644 --- a/fuzz/fuzz_test.py +++ b/fuzz/fuzz_test.py @@ -15,7 +15,6 @@ def mutate_schema(fdp, json_data): """Recurses through a json object leaving keys and structures intact and randomly generating new data values of the proper type.""" - # TODO: Implement float, int, bool, and other types as needed if isinstance(json_data, str): return fdp.ConsumeUnicode(STR_BYTE_COUNT) elif isinstance(json_data, list): @@ -42,7 +41,6 @@ def TestOneInput(data): try: mcg = ModelCardGen(data_sets={"test": ""}, model_card=model_card_data) if mcg.model_card: - # TODO: Produces https://jira.devtools.intel.com/browse/AIZOO-3111 mcg.build_model_card() # Includes scaffold_assets() and export_format() except (ValueError, jsonschema.ValidationError): print("Doesn't match MC schema") From c39e641eaa358170ed4f765ee8057adfe1b1681b Mon Sep 17 00:00:00 2001 From: Tyler W Date: Sat, 20 Jul 2024 00:46:08 +0000 Subject: [PATCH 08/11] revert for backwards compatibility (#314) Signed-off-by: Tyler Wilbers --- .../{generators/generator.py => model_card_gen.py} | 2 ++ model_card_gen/tests/test_model_card.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) rename model_card_gen/intel_ai_safety/model_card_gen/{generators/generator.py => model_card_gen.py} (99%) diff --git a/model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py similarity index 99% rename from model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py rename to model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py index 3e88fc9..c3edff9 100644 --- a/model_card_gen/intel_ai_safety/model_card_gen/generators/generator.py +++ b/model_card_gen/intel_ai_safety/model_card_gen/model_card_gen.py @@ -74,9 +74,11 @@ def __init__( model_card: Union[ModelCard, Dict[Text, Any]] = None, metrics_by_threshold: Union[pd.DataFrame, Text]= None, metrics_by_group: Union[pd.DataFrame, Text] = None, + data_sets: Dict[Text, DataFormat] = {}, data_stats: Dict[Text, pd.DataFrame] = {}, output_dir: Text = "", ): + self.data_sets = data_sets self.data_stats = data_stats if isinstance(metrics_by_threshold, pd.DataFrame): diff --git a/model_card_gen/tests/test_model_card.py b/model_card_gen/tests/test_model_card.py index 861358b..66639bb 100644 --- a/model_card_gen/tests/test_model_card.py +++ b/model_card_gen/tests/test_model_card.py @@ -22,7 +22,7 @@ import os import pkgutil import json -from intel_ai_safety.model_card_gen.generators.generator import ModelCardGen +from intel_ai_safety.model_card_gen.model_card_gen import ModelCardGen from intel_ai_safety.model_card_gen.validation import ( _LATEST_SCHEMA_VERSION, _SCHEMA_FILE_NAME, From bd24db0ff231488f04226f48a4d59deb0ae2dc70 Mon Sep 17 00:00:00 2001 From: Abolfazl Shahbazi Date: Mon, 22 Jul 2024 13:56:10 -0700 Subject: [PATCH 09/11] Update Scikit-learn to 1.5.0 (#315) Signed-off-by: Abolfazl Shahbazi --- explainer/pyproject.toml | 2 +- plugins/explainers/metrics/pyproject.toml | 2 +- pyproject.toml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/explainer/pyproject.toml b/explainer/pyproject.toml index fbd7130..0d708b3 100644 --- a/explainer/pyproject.toml +++ b/explainer/pyproject.toml @@ -24,7 +24,7 @@ datasets = '2.14.4' deepdiff = '6.7.1' intel-tensorflow = '2.14.0' pytest = '8.1.1' -scikit-learn = '1.4.0' +scikit-learn = '1.5.0' tensorflow-hub = '0.15.0' torch = {version = "2.2.0", source = "pytorch-cpu"} torchvision = {version = "0.17.0", source = "pytorch-cpu"} diff --git a/plugins/explainers/metrics/pyproject.toml b/plugins/explainers/metrics/pyproject.toml index 8ccaf68..e6d213e 100644 --- a/plugins/explainers/metrics/pyproject.toml +++ b/plugins/explainers/metrics/pyproject.toml @@ -15,7 +15,7 @@ matplotlib = '3.8.3' numpy = '1.24.4' pandas = '1.5.3' plotly = '5.20.0' -scikit-learn = '1.4.0' +scikit-learn = '1.5.0' scikit-plot = '0.3.7' scipy = '1.12.0' seaborn = '0.13.2' diff --git a/pyproject.toml b/pyproject.toml index f43293b..203d95d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ intel-tensorflow = '2.14.0' pickleshare = '0.7.5' pillow = '10.3.0' pytest = '8.1.1' -scikit-learn = '1.4.0' +scikit-learn = '1.5.0' sentencepiece = '0.2.0' tensorflow-hub = '0.15.0' torch = {version = "2.2.0", source = "pytorch-cpu"} From ad9cf7e3cebf29e8d9414f9559c7bc7bb2c71b7c Mon Sep 17 00:00:00 2001 From: etcylfleet Date: Tue, 23 Jul 2024 10:57:59 -0700 Subject: [PATCH 10/11] Add html instructions to example in README (#311) * Add html instructions to example in README * Fix commands being on different lines --- fuzz/README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/fuzz/README.md b/fuzz/README.md index 8f0980c..a1897f0 100644 --- a/fuzz/README.md +++ b/fuzz/README.md @@ -60,6 +60,12 @@ The output will be: Remember that the test may not be designed to exercise all the instrumented code, only a certain part or parts of it. It can be more helpful to look at the individual file coverage than the total. +The coverage report can also be viewed interactively, to inspect files or functions executed, using html: + +`cd ../fuzz/htmlcov && python3 -m http.server` + +Open http://localhost:8000/index.html in a web browser. + ### Leak Detection If you're using a Python extension module that interfaces with C code, you might encounter memory leaks due to improper memory management in the C layer. Here's an example of how a memory leak might be reported: From 1c58d728e403ed68c532068c7f5e83fe018c7889 Mon Sep 17 00:00:00 2001 From: Tyler W Date: Thu, 25 Jul 2024 18:06:31 +0000 Subject: [PATCH 11/11] added hf model card (#310) Signed-off-by: Tyler Wilbers --- .../hugging_face_model_card/README.md | 31 ++ .../hugging-face-model-card.ipynb | 418 ++++++++++++++++++ 2 files changed, 449 insertions(+) create mode 100644 notebooks/model_card_gen/hugging_face_model_card/README.md create mode 100644 notebooks/model_card_gen/hugging_face_model_card/hugging-face-model-card.ipynb diff --git a/notebooks/model_card_gen/hugging_face_model_card/README.md b/notebooks/model_card_gen/hugging_face_model_card/README.md new file mode 100644 index 0000000..bad5645 --- /dev/null +++ b/notebooks/model_card_gen/hugging_face_model_card/README.md @@ -0,0 +1,31 @@ +# Creating Model Card for Toxic Comments Classification in Tensorflow +In this notebook, we will download a model, dataset, and metric from Hugigng Face Hub and generate a interactive HTML Model Card using Intel AI Safety Model Card Generator Tool. + +`hugging-face-model-card` performs the following steps: +1. Download and Import Dependencies +2. Download Dataset from Hugging Face Datasets +3. Transform Dataset + - Get ground truth label +5. Download Modle and Process Outputs +6. Get Bias Metric form Hugging Face +7. Run Bias Metric +8. Transform Output for Model Card +9. Build Model Card + + +## Running the notebook + +To run the `toxicity-tfma-model-card.ipynb`, install the following dependencies: +1. [Intel® Explainable AI](https://github.com/Intel/intel-xai-tools) +2. `!pip install evaluate datasets transformers[torch] scikit-learn` + +## References +### _Dataset citations_ +``` +@article{aluru2020deep, + title={Deep Learning Models for Multilingual Hate Speech Detection}, + author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh}, + journal={arXiv preprint arXiv:2004.06465}, + year={2020} +} +``` \ No newline at end of file diff --git a/notebooks/model_card_gen/hugging_face_model_card/hugging-face-model-card.ipynb b/notebooks/model_card_gen/hugging_face_model_card/hugging-face-model-card.ipynb new file mode 100644 index 0000000..bf60abe --- /dev/null +++ b/notebooks/model_card_gen/hugging_face_model_card/hugging-face-model-card.ipynb @@ -0,0 +1,418 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "afda1ef1-2239-41c5-8513-1e6e7971db27", + "metadata": {}, + "source": [ + "In this notebook, we will download a model, dataset, and metric from Hugigng Face Hub and generate a interactive HTML Model Card using Intel AI Safety Model Card Generator Tool." + ] + }, + { + "cell_type": "markdown", + "id": "8ebe569b-4b8f-4128-ade7-307546f5f61c", + "metadata": {}, + "source": [ + "## 1. Download and Import Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "307063bb-2faf-4482-9f22-d92c49d0a7c8", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "!pip install evaluate datasets transformers[torch] scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf4a0486-b5cc-4f0d-965d-4eba91cad62d", + "metadata": {}, + "outputs": [], + "source": [ + "from intel_ai_safety.model_card_gen.generators.generator import ModelCardGen\n", + "from datasets import load_dataset, load_metric\n", + "import evaluate\n", + "from transformers import AutoConfig,AutoModelForSequenceClassification,AutoTokenizer\n", + "import pandas as pd\n", + "\n", + "from collections import Counter\n", + "from functools import reduce\n", + "import json\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "e688b17f-623b-482b-930f-fee122a51da6", + "metadata": {}, + "source": [ + "## 2. Download Dataset from Hugging Face Datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0cbf04f4-9a72-46c4-a3bd-197fff3bfb62", + "metadata": {}, + "outputs": [], + "source": [ + "raw_dataset = load_dataset(\"hatexplain\")\n", + "he_dataset = raw_dataset.map(lambda e: {'text': \" \".join(e['post_tokens'])})" + ] + }, + { + "cell_type": "markdown", + "id": "06ea1f80-900f-48b6-b10f-1d12a29d0ee7", + "metadata": {}, + "source": [ + "## 3. Transform Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3558293-9fd1-4c0c-af8a-ee35c2cf4a6a", + "metadata": {}, + "outputs": [], + "source": [ + "def get_common_targets(elm, ignore=['Other', 'None']):\n", + " \"\"\"\n", + " This function merges annotated targets from each annotator\n", + " into a single list when annotators agree\n", + " \"\"\"\n", + " targets = elm['annotators']['target']\n", + " counts = reduce(lambda x, y: Counter(x) + Counter(y) , targets)\n", + " result = [target for target, count in counts.items() if count > 1]\n", + " if result:\n", + " return {'target': result}\n", + " else:\n", + " return {'target': []}\n", + "\n", + "he_dataset = he_dataset.map(get_common_targets)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "791dbaf3-634e-426a-8085-4ef7c6d2c5b0", + "metadata": {}, + "outputs": [], + "source": [ + "def get_top_communites(targets, top=10):\n", + " target_counts = reduce(lambda x, y: Counter(x) + Counter(y) , targets)\n", + " top_targets, _ = zip(*target_counts.most_common(top))\n", + " return set(top_targets)\n", + "\n", + "TOP = get_top_communites(he_dataset['test']['target'])\n", + "\n", + "def filter_top_target(elm):\n", + " \"\"\"\n", + " This function filteras the identity groups targeted\n", + " in each item with the top 10 most common identity groups\n", + " \"\"\"\n", + " targets = set(elm['target']) & TOP\n", + " return {'target': targets}\n", + " \n", + "he_dataset = he_dataset.map(filter_top_target)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b53a881d-365f-43f2-93d8-991b189e4cf9", + "metadata": {}, + "outputs": [], + "source": [ + "def get_label(elm):\n", + " \"\"\"\n", + " This fuction gets a ground truth label from annotators labels\n", + " \"\"\"\n", + " label_map = {0: 1, # hatespech -> 1\n", + " 1: 0, # normal -> 0\n", + " 2: 1} # abusive -> 1\n", + "\n", + " labels = elm['annotators']['label']\n", + " max_label = max(labels, key=labels.count)\n", + " return {'label': label_map[max_label]}\n", + " \n", + "he_dataset = he_dataset.map(get_label)" + ] + }, + { + "cell_type": "markdown", + "id": "1615aadf-7744-4798-8a1f-65f316395ee5", + "metadata": {}, + "source": [ + "## 4. Download Modle and Process Outputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed5ffa45-7e76-4b32-9bcf-2851ac8152c3", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.nn.functional import softmax\n", + "\n", + "he_dataset.set_format(\"pt\", columns=[\"post_tokens\"], output_all_columns=True)\n", + "model = AutoModelForSequenceClassification.from_pretrained(\"Hate-speech-CNERG/dehatebert-mono-english\")\n", + "tokenizer = AutoTokenizer.from_pretrained(\"Hate-speech-CNERG/dehatebert-mono-english\")\n", + "\n", + "def process(examples):\n", + " bert_tokens = tokenizer(examples['text'], return_tensors=\"pt\")\n", + " output = model(**bert_tokens)\n", + " return {\"output\": softmax(output['logits'], dim=-1).flatten()}\n", + "\n", + "test_ds = he_dataset['test'].map(process)" + ] + }, + { + "cell_type": "markdown", + "id": "4189c557-9162-4812-afae-94a4c333b5cc", + "metadata": {}, + "source": [ + "## 5. Get Bias Metric form Hugging Face" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f69ae687-2834-4f1d-9d52-011b695e4f32", + "metadata": {}, + "outputs": [], + "source": [ + "metric = evaluate.load('Intel/bias_auc')\n", + "print(metric)" + ] + }, + { + "cell_type": "markdown", + "id": "ffd013db-5d0f-46d1-9c70-e0b1c870ad47", + "metadata": {}, + "source": [ + "## 6. Run Bias Metric" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f73b37ab-b6b3-4d76-9cbb-df16493ae6e6", + "metadata": {}, + "outputs": [], + "source": [ + "metric.add_batch(target=test_ds['target'],\n", + " label=test_ds['label'],\n", + " output=test_ds['output'])\n", + "\n", + "subgroups = set(group for group_list in test_ds['target'] for group in group_list) - set(['Disability'])\n", + "\n", + "metric_output = metric.compute(subgroups = subgroups)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5aec856-0d1b-4fb0-942a-5d5d00868479", + "metadata": {}, + "outputs": [], + "source": [ + "metric_output" + ] + }, + { + "cell_type": "markdown", + "id": "271133ca-0ddd-4f54-8448-ad660a76c943", + "metadata": {}, + "source": [ + "## 7. Transform Output for Model Card" + ] + }, + { + "cell_type": "markdown", + "id": "9b85a523-848b-4da0-995a-3082a992d28d", + "metadata": {}, + "source": [ + "Mode Card Generator take two pandas dataframes as input. We will creat a `metrics_by_group` dataframe from the Bias AUC metric above as well as a `metrics_by_threshold` containing performance metrics at threshold." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2be1e022-2f78-4584-8306-b03323c1b594", + "metadata": {}, + "outputs": [], + "source": [ + "metrics_by_group = (pd.DataFrame.from_dict(metric_output).\n", + " T.\n", + " reset_index().\n", + " rename({'index': 'group'}, axis=1))\n", + "metrics_by_group['feature'] = ['target'] * len(metrics_by_group)\n", + "metrics_by_group" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5a65a63-8aca-4ed8-86c8-03a2aab2f90c", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n", + "import numpy as np\n", + "thetas = np.linspace(0, 1, 1001)\n", + "y_pred_prob = test_ds['output'][:,1]\n", + "\n", + "metrics_dict ={\n", + " 'threshold': thetas,\n", + " 'precision': [precision_score(test_ds['label'], y_pred_prob > theta) for theta in thetas],\n", + " 'recall': [recall_score(test_ds['label'], y_pred_prob > theta) for theta in thetas],\n", + " 'f1': [f1_score(test_ds['label'], y_pred_prob > theta) for theta in thetas],\n", + " 'accuracy' : [accuracy_score(test_ds['label'], y_pred_prob > theta) for theta in thetas]\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e54e59c1-2ad0-4add-b60b-4a9d64654bf3", + "metadata": {}, + "outputs": [], + "source": [ + "metrics_by_threshold = pd.DataFrame.from_dict(metrics_dict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccb3456d-a33e-410d-b706-4765a7504c04", + "metadata": {}, + "outputs": [], + "source": [ + "metrics_by_threshold" + ] + }, + { + "cell_type": "markdown", + "id": "b387c982-b7fb-4043-b377-d62c2dda9d74", + "metadata": {}, + "source": [ + "## 8. Build Model Card" + ] + }, + { + "cell_type": "markdown", + "id": "ec3829b2-514b-42ba-ad14-798256f7dad5", + "metadata": {}, + "source": [ + "Simply add the dataframes into the `ModelCardGen.generate` class method to build a model card." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ede251f4-8531-4c8f-aacc-edb13bdf3d8a", + "metadata": {}, + "outputs": [], + "source": [ + "mc = {\n", + " \"schema_version\": \"0.0.1\",\n", + " \"model_details\": {\n", + " \"name\": \"Deep Learning Models for Multilingual Hate Speech Detection\",\n", + " \"version\": {\n", + " \"name\": \"25d0e4d9122d2a5c283e07405a325e3dfd4a73b3\",\n", + " \"date\": \"2020\"\n", + " },\n", + " \"graphics\": {},\n", + "\n", + " \"citations\": [\n", + " {\n", + " \"citation\": '''@article{aluru2020deep,\n", + " title={Deep Learning Models for Multilingual Hate Speech Detection},\n", + " author={Aluru, Sai Saket and Mathew, Binny and Saha, Punyajoy and Mukherjee, Animesh},\n", + " journal={arXiv preprint arXiv:2004.06465},\n", + " year={2020}\n", + " }'''\n", + " },\n", + " ],\n", + " \"overview\": 'This model is used detecting hatespeech in English language. The mono in the name refers to the monolingual setting, where the model is trained using only English language data. It is finetuned on multilingual bert model. The model is trained with different learning rates and the best validation score achieved is 0.726030 for a learning rate of 2e-5. Training code can be found here https://github.com/punyajoy/DE-LIMIT',\n", + " }\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7c57159-ad0f-4f02-8ea2-1364d195eea2", + "metadata": {}, + "outputs": [], + "source": [ + "mcg = ModelCardGen.generate(metrics_by_group=metrics_by_group, metrics_by_threshold=metrics_by_threshold, model_card=mc)\n", + "mcg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0700c08-f41c-4f93-a476-1976a5c46b32", + "metadata": {}, + "outputs": [], + "source": [ + "mcg.export_html('ModelCard.html')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cd7022c-a9fe-4ae4-b75c-d08cc0ca8a25", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import display, HTML\n", + "from plotly.io import to_html, templates\n", + "from plotly.offline import init_notebook_mode, get_plotlyjs\n", + "fig = px.line(x=[\"a\",\"b\",\"c\"], y=[1,3,2], title=\"sample figure\")\n", + "html_content = to_html(fig, include_plotlyjs=\"require\", full_html=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c870b98-d995-4c5e-b25e-30bfff8104b0", + "metadata": {}, + "outputs": [], + "source": [ + "HTML(html_content)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}