Update README.md

README.md

@@ -238,9 +238,160 @@ task_categories:
 - feature-extraction
 language:
 - en
-tags:
-- not-for-all-audiences
 pretty_name: ChaosMining
 size_categories:
 - 10B<n<100B
----
+---
+# Dataset Card for Dataset Name
+
+ChaosMining is a synthetic dataset that evaluates post-hoc local attribution methods in low signal-to-noise ratio (SNR) environments. 
+The post-hoc local attribution methods are explainable AI methods such as Saliency (SA), DeepLift (DL), Integrated Gradient (IG), and Feature Ablation (FA).
+This dataset is used to evaluate the feature selection ability of these methods when a large amount of noise exists.
+
+## Dataset Descriptions
+
+There exist three modalities:
+- **Symbolic Functional Data**: Mathematical functions with noise, used to study regression tasks. Derived from human-designed symbolic functions with predictive and irrelevant features.
+- **Vision Data**: Images combining foreground objects from the CIFAR-10 dataset and background noise or flower images. 224x224 images with 32x32 foreground objects and either Gaussian noise or structural flower backgrounds.
+- **Audio Data**: Audio sequences with a mix of relevant (speech commands) and irrelevant (background noise) signals.
+
+### Dataset Sources [optional]
+
+Please check out the following
+
+- **Repository:** [https://github.com/geshijoker/ChaosMining/tree/main] for data curation and evaluation.
+- **Paper:** [https://arxiv.org/pdf/2406.12150] for details.
+
+### Dataset Details
+
+### Symbolic Functional Data
+
+- **Synthetic Generation:** Data is derived from predefined mathematical functions, ensuring a clear ground truth for evaluation.
+- **Functions:** Human-designed symbolic functions combining primitive mathematical operations (e.g., polynomial, trigonometric, exponential functions).
+- **Generation Process:** Each feature is sampled from a normal distribution N(μ,σ^2) with μ=0 and σ=1. Predictive features are computed using the defined symbolic functions, while noise is added by including irrelevant features.
+- **Annotations:** Ground truth annotations are generated based on the symbolic functions used to create the data.
+- **Normalization:** Data values are normalized to ensure consistency across samples.
+
+### Vision Data
+
+- **Foreground Images:** CIFAR-10 dataset, containing 32x32 pixel images of common objects.
+- **Background Images:** Flower102 dataset and Gaussian noise images.
+- **Combination:** Foreground images are overlaid onto background images to create synthetic samples. Foreground images are either centered or randomly placed.
+- **Noise Types:** Backgrounds are generated using Gaussian noise for random noise conditions, or sampled from the Flower102 dataset for structured noise conditions.
+- **Annotations:** Each image is annotated with the position of the foreground object and its class label.
+- **Splitting:** The dataset is divided into training and validation sets to ensure no data leakage.
+
+### Audio Data
+
+- **Foreground Audio:** Speech Command dataset, containing audio clips of spoken commands.
+- **Background Audio:** Random noise generated from a normal distribution and samples from the Rainforest Connection Species dataset.
+- **Combination:** Each audio sample consists of multiple channels, with only one channel containing the foreground audio and the rest containing background noise.
+- **Noise Conditions:** Background noise is either random (generated from a normal distribution) or structured (sampled from environmental sounds).
+- **Annotations:** Each audio sample is annotated with the class label of the foreground audio and the position of the predictive channel.
+- **Normalization:** Audio signals are normalized to a consistent range for uniform processing.
+
+### Benchmark Metrics:
+
+The benchmark processes a **Model × Attribution × Noise Condition** triplet design to evaluate the performance of various post-hoc attribution methods across different scenarios.
+
+- **Uniform Score (UScore)**: Measures prediction accuracy normalized to a range of 0 to 1.
+- **Functional Precision (FPrec)**: Measures the overlap between top-k predicted features and actual predictive features.
+
+## Uses
+
+### Dataset Structure
+The configurations of the sub-datasets are ('symbolic_simulation', 'audio_RBFP', 'audio_RBRP', 'audio_SBFP', 'audio_SBRP', 'vision_RBFP', 'vision_RBRP', 'vision_SBFP', 'vision_SBRP').
+Please pick one of them for use. The 'symbolic_simulation' data only has the 'train' split while the others have both the 'train' and 'validation' splits.
+
+### Load Dataset
+
+For the general dataloading usage of huggingface API, please refer to [general usage](https://huggingface.co/docs/datasets/loading), including how to work with TensorFlow, PyTorch, JAX ...
+Here we provide the template codes for PyTorch users.
+
+```python
+from datasets import Dataset
+from torch.utils.data import DataLoader
+
+# Load the symbolic functional data from huggingface datasets
+dataset = load_dataset('geshijoker/chaosmining', 'symbolic_simulation')
+print(dataset)
+
+Out: DatasetDict({
+    train: Dataset({
+        features: ['num_var', 'function'],
+        num_rows: 15
+    })
+})
+
+# Read the formulas as a list of (number_of_features, function_string) pairs
+formulas = [[data_slice['num_var'], data_slice['function']] for data_slice in dataset['train']]
+
+# Load the vision data from huggingface datasets
+dataset = load_dataset('geshijoker/chaosmining', 'vision_RBFP', split='validation', streaming=True)
+
+# Convert hugging face Dataset to pytorch Dataset for vision data
+dataset = dataset.with_format('torch')
+
+# Use a dataloader for minibatch loading
+dataloader = DataLoader(dataset, batch_size=32)
+next(iter(dataloader_vision))
+
+Out: {'image':torch.Size([32, 3, 224, 224]), 'foreground_label':torch.Size([32]), 'position_x':torch.Size([32]), 'position_y':torch.Size([32])}
+
+# Load the audio data from huggingface datasets
+dataset = load_dataset('geshijoker/chaosmining', 'audio_RBFP', split='validation', streaming=True)
+
+# Convert hugging face Dataset to pytorch Dataset for audio data.
+# Define the transformation
+def transform_audio(example):
+    # Remove the 'path' field
+    del example['audio']['path']
+    
+    # Directly access the 'array' and 'sampling_rate' from the 'audio' field
+    example['sampling_rate'] = example['audio']['sampling_rate']
+    example['audio'] = example['audio']['array']
+    
+    return example
+
+# Apply the transformation to the dataset
+dataset = dataset.map(transform_audio)
+dataset = dataset.with_format('torch')
+
+# Use a dataloader for minibatch loading
+dataloader = DataLoader(dataset, batch_size=32)
+next(iter(dataloader_vision))
+
+Out: {'audio':torch.Size([32, 10, 16000]), 'sampling_rate':torch.Size([32]), 'label':List_of_32, 'file_name':List_of_32}
+```
+
+### Curation Rationale
+
+To create controlled, low signal-to-noise ratio environments that test the efficacy of post-hoc local attribution methods.
+
+- **Purpose:** To study the effectiveness of neural networks in regression tasks where relevant features are mixed with noise.
+- **Challenges Addressed:** Differentiating between predictive and irrelevant features in a controlled, low signal-to-noise ratio environment.
+
+### Source Data
+
+Synthetic data derived from known public datasets (CIFAR-10, Flower102, Speech Commands, Rainforest Connection Species) and generated noise.
+
+### Citation
+
+If you use this dataset or code in your research, please cite the paper as follows:
+
+**BibTeX:**
+@article{shi2024chaosmining,
+  title={ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments},
+  author={Shi, Ge and Kan, Ziwen and Smucny, Jason and Davidson, Ian},
+  journal={arXiv preprint arXiv:2406.12150},
+  year={2024}
+}
+
+**APA:**
+Shi, G., Kan, Z., Smucny, J., & Davidson, I. (2024). ChaosMining: A Benchmark to Evaluate Post-Hoc Local Attribution Methods in Low SNR Environments. arXiv preprint arXiv:2406.12150.
+
+## Dataset Card Contact
+
+Davidson Lab at UC Davis
+
+Ian: [email protected]