Name | tabicl JSON |
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Summary | TabICL: A Tabular Foundation Model for In-Context Learning on Large Data |
upload_time | 2025-07-08 09:48:28 |
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author | Jingang Qu, David Holzmüller, Marine Le Morvan, Gaël Varoquaux |
requires_python | <3.13,>=3.9 |
license | BSD 3-Clause License
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foundation model
in-context learning
tabular data
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# TabICL: A Tabular Foundation Model for In-Context Learning on Large Data (ICML 2025)
This repo is the official implementation of ["TabICL: A Tabular Foundation Model for In-Context Learning on Large Data"](https://arxiv.org/pdf/2502.05564) as well as the follow-ups. TabICL is a tabular foundation model. Currently, it is only for classification tasks.
## Updates
***05/06/2025***
### Better-performing checkpoint 😄
We are continuously improving TabICL, and as a by-product (Great thanks to [David Holzmüller](https://github.com/dholzmueller)'s efforts !!!), we have a better-performing checkpoint. `TabICLClassifier` now accepts a new parameter `checkpoint_version` to specify which pretrained checkpoint to use. The available options are:
- `'tabicl-classifier-v1.1-0506.ckpt'` (default): The latest and best-performing version.
- `'tabicl-classifier-v1-0208.ckpt'`: The version used in the original TabICL paper. Use this if you need to reproduce the results reported in the paper.
- `'tabicl-classifier.ckpt'`: A legacy alias for `'tabicl-classifier-v1-0208.ckpt'` and will be removed in a future release.
<div style="margin-top: 30px;"></div>
<img src="./figures/TabICLv1.1_performance.png" width="70%" alt="Ranking of tabICLv1.1" style="display: block; margin: auto;">
<div style="margin-top: 30px;"></div>
<div style="margin-top: 30px;"></div>
<img src="./figures/TabICLv1.1_perf_wrt_samples.png" width="90%" alt="Ranking vs. number of samples" style="display: block; margin: auto;">
<div style="margin-top: 30px;"></div>
***05/05/2025***
### Open-source pretraining code 🥳
After intensive refactoring, we fully open-sourced our pretraining code to reproduce our paper. The scripts folder provides the commands for [stage 1](./scripts/train_stage1.sh), [stage 2](./scripts/train_stage2.sh), and [stage 3](./scripts/train_stage3.sh) of curriculum learning.
***05/01/2025***
### Accepted to ICML 2025 🎉
## Architecture
TabICL processes tabular data through three sequential stages:
1. **Column-wise Embedding**: Creates distribution-aware embeddings for each feature
2. **Row-wise Interaction**: Captures interactions between features within each row
3. **Dataset-wise In-Context Learning**: Learns patterns from labeled examples to make predictions
<img src="./figures/architecture.png" width="90%" alt="The architecture of TabICL" style="display: block; margin: auto;">
## Installation
### From [PyPI](https://pypi.org/project/tabicl)
```bash
pip install tabicl
```
### From the source
#### Option 1: Installing `tabicl` from the Local Clone
```bash
cd tabicl; pip install -e .
```
#### Option 2: Installing `tabicl` Directly from the Git Remote
```bash
pip install git+https://github.com/soda-inria/tabicl.git
```
## Usage
### Basic Usage
```python
from tabicl import TabICLClassifier
clf = TabICLClassifier()
clf.fit(X_train, y_train) # this is cheap
clf.predict(X_test) # in-context learning happens here
```
The code above will automatically download the pre-trained checkpoint (~100MB) from Hugging Face Hub on first use and choose a GPU if available.
### Advanced Configuration
TabICL offers a set of parameters to customize its behavior. The following example shows all available parameters with their default values and brief descriptions:
```python
from tabicl import TabICLClassifier
clf = TabICLClassifier(
n_estimators=32, # number of ensemble members
norm_methods=["none", "power"], # normalization methods to try
feat_shuffle_method="latin", # feature permutation strategy
class_shift=True, # whether to apply cyclic shifts to class labels
outlier_threshold=4.0, # z-score threshold for outlier detection and clipping
softmax_temperature=0.9, # controls prediction confidence
average_logits=True, # whether ensemble averaging is done on logits or probabilities
use_hierarchical=True, # enable hierarchical classification for datasets with many classe
batch_size=8, # process this many ensemble members together (reduce RAM usage)
use_amp=True, # use automatic mixed precision for faster inference
model_path=None, # where the model checkpoint is stored
allow_auto_download=True, # whether automatic download to the specified path is allowed
checkpoint_version="tabicl-classifier-v1.1-0506.ckpt", # the version of pretrained checkpoint to use
device=None, # specify device for inference
random_state=42, # random seed for reproducibility
n_jobs=None, # number of threads to use for PyTorch
verbose=False, # print detailed information during inference
inference_config=None, # inference configuration for fine-grained control
)
```
## Memory-Efficient Inference
TabICL includes memory management to handle large datasets:
- **Memory Profiling**: Built-in memory estimators for different components of the model
- **Batch Size Estimation**: Dynamically determines optimal batch sizes based on available GPU memory
- **CPU Offloading**: Automatically offloads intermediate results to CPU when beneficial
- **OOM Recovery**: Recovers gracefully from out-of-memory errors by reducing batch size
## Preprocessing
### Simple built-in preprocessing
If the input `X` to TabICL is a pandas DataFrame, TabICL will automatically:
- Detect and ordinal encode categorical columns (including string, object, category, and boolean types)
- Create a separate category for missing values in categorical features
- Perform mean imputation for missing numerical values (encoded as NaN)
If the input `X` is a numpy array, TabICL assumes that ordinal encoding and missing value imputation have already been performed.
For both input types, TabICL applies additional preprocessing:
- Outlier detection and removal
- Feature scaling and normalization
- Feature shuffling for ensemble diversity
### Advanced data preprocessing with skrub <img src="https://skrub-data.github.io/stable/_static/skrub.svg" width="8%" alt="skrub logo" style="display: inline; margin-left: 5px; margin-right: 5px;">
Real-world datasets often contain complex heterogeneous data that benefits from more sophisticated preprocessing. For these scenarios, we recommend [skrub](https://skrub-data.org/stable/index.html), a powerful library designed specifically for advanced tabular data preparation.
**Why use skrub?**
- Handles diverse data types (numerical, categorical, text, datetime, etc.)
- Provides robust preprocessing for dirty data
- Offers sophisticated feature engineering capabilities
- Supports multi-table integration and joins
#### Installation
```bash
pip install skrub -U
```
#### Basic Integration
Use skrub's [TableVectorizer](https://skrub-data.org/stable/reference/generated/skrub.TableVectorizer.html) to transform your raw data before passing it to TabICLClassifier:
```python
from skrub import TableVectorizer
from tabicl import TabICLClassifier
from sklearn.pipeline import make_pipeline
pipeline = make_pipeline(
TableVectorizer(), # Automatically handles various data types
TabICLClassifier()
)
pipeline.fit(X_train, y_train) # X should be a DataFrame
predictions = pipeline.predict(X_test)
```
## Key Features and Considerations:
- **Number of samples**:
- TabICL is pretrained on datasets with up to 60K samples.
- TabICL can handle datasets beyond 100K samples thanks to memory-efficient inference.
- TabPFN (v2) is on average better than TabICL on small datasets with <10K samples, while TabICL is better on larger datasets.
- Classical methods may catch up with TabICL at around 40K samples but they are much slower due to extensive hyperparameter tuning.
<div style="margin-top: 30px;"></div>
<img src="./figures/perf_wrt_samples.png" width="80%" alt="Ranking vs. number of samples" style="display: block; margin: auto;">
<div style="margin-top: 30px;"></div>
- **Number of features**:
- TabICL is pretrained on datasets with up to 100 features.
- TabICL can accommodate any number of features theoretically.
- **Number of classes**:
- TabICL is pretrained on datasets with up to 10 classes, so it natively supports a maximum of 10 classes.
- However, TabICL can handle any number of classes thanks to its in-built hierarchical classification.
- **Inference speed**:
- Like TabPFN, `fit()` does minimal work while `predict()` runs the full model
- At the same `n_estimators`, TabICL is usually 1x-5x faster than TabPFN
- TabICL benefits more from larger `n_estimators`, hence the default of 32
- Automatic mixed precision (AMP) provides further speed improvements on compatible GPUs
- **No tuning required**: TabICL produces good predictions without hyperparameter tuning, unlike classical methods that require extensive tuning for optimal performance.
## Performance
TabICL has achieved excellent results on the [TALENT](https://github.com/qile2000/LAMDA-TALENT) benchmark.
<img src="./figures/performance.png" width="100%" alt="Performance on TALENT" style="display: block; margin: auto;">
<div style="margin-top: 30px;"></div>
## Citation
If you use TabICL for research purposes,
please cite our **[paper](https://arxiv.org/abs/2502.05564)**:
```bibtex
@article{qu2025tabicl,
title={TabICL: A Tabular Foundation Model for In-Context Learning on Large Data},
author={Qu, Jingang and Holzm{\"u}ller, David and Varoquaux, Ga{\"e}l and Morvan, Marine Le},
journal={arXiv preprint arXiv:2502.05564},
year={2025}
}
```
## Contributors
- [Jingang Qu](https://github.com/jingangQu)
- [David Holzmüller](https://github.com/dholzmueller)
- [Marine Le Morvan](https://github.com/marineLM)
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"description": "[](https://github.com/soda-inria/tabicl/actions/workflows/testing.yml)\n[](https://badge.fury.io/py/tabicl)\n[](https://pypistats.org/packages/tabicl)\n\n# TabICL: A Tabular Foundation Model for In-Context Learning on Large Data (ICML 2025)\n\nThis repo is the official implementation of [\"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data\"](https://arxiv.org/pdf/2502.05564) as well as the follow-ups. TabICL is a tabular foundation model. Currently, it is only for classification tasks.\n\n## Updates\n\n***05/06/2025***\n\n### Better-performing checkpoint \ud83d\ude04\n\nWe are continuously improving TabICL, and as a by-product (Great thanks to [David Holzm\u00fcller](https://github.com/dholzmueller)'s efforts !!!), we have a better-performing checkpoint. `TabICLClassifier` now accepts a new parameter `checkpoint_version` to specify which pretrained checkpoint to use. The available options are:\n\n- `'tabicl-classifier-v1.1-0506.ckpt'` (default): The latest and best-performing version.\n- `'tabicl-classifier-v1-0208.ckpt'`: The version used in the original TabICL paper. Use this if you need to reproduce the results reported in the paper.\n- `'tabicl-classifier.ckpt'`: A legacy alias for `'tabicl-classifier-v1-0208.ckpt'` and will be removed in a future release.\n\n<div style=\"margin-top: 30px;\"></div>\n<img src=\"./figures/TabICLv1.1_performance.png\" width=\"70%\" alt=\"Ranking of tabICLv1.1\" style=\"display: block; margin: auto;\">\n<div style=\"margin-top: 30px;\"></div>\n\n<div style=\"margin-top: 30px;\"></div>\n<img src=\"./figures/TabICLv1.1_perf_wrt_samples.png\" width=\"90%\" alt=\"Ranking vs. number of samples\" style=\"display: block; margin: auto;\">\n<div style=\"margin-top: 30px;\"></div>\n\n***05/05/2025***\n\n### Open-source pretraining code \ud83e\udd73\n\nAfter intensive refactoring, we fully open-sourced our pretraining code to reproduce our paper. The scripts folder provides the commands for [stage 1](./scripts/train_stage1.sh), [stage 2](./scripts/train_stage2.sh), and [stage 3](./scripts/train_stage3.sh) of curriculum learning.\n\n***05/01/2025***\n\n### Accepted to ICML 2025 \ud83c\udf89\n\n## Architecture\n\nTabICL processes tabular data through three sequential stages:\n\n1. **Column-wise Embedding**: Creates distribution-aware embeddings for each feature\n2. **Row-wise Interaction**: Captures interactions between features within each row\n3. **Dataset-wise In-Context Learning**: Learns patterns from labeled examples to make predictions\n\n<img src=\"./figures/architecture.png\" width=\"90%\" alt=\"The architecture of TabICL\" style=\"display: block; margin: auto;\">\n\n## Installation\n\n### From [PyPI](https://pypi.org/project/tabicl)\n\n```bash\npip install tabicl\n```\n\n### From the source\n\n#### Option 1: Installing `tabicl` from the Local Clone\n\n```bash\ncd tabicl; pip install -e .\n```\n\n#### Option 2: Installing `tabicl` Directly from the Git Remote\n\n```bash\npip install git+https://github.com/soda-inria/tabicl.git\n```\n\n## Usage\n\n### Basic Usage\n\n```python\nfrom tabicl import TabICLClassifier\n\nclf = TabICLClassifier()\nclf.fit(X_train, y_train) # this is cheap\nclf.predict(X_test) # in-context learning happens here\n```\n\nThe code above will automatically download the pre-trained checkpoint (~100MB) from Hugging Face Hub on first use and choose a GPU if available.\n\n### Advanced Configuration\n\nTabICL offers a set of parameters to customize its behavior. The following example shows all available parameters with their default values and brief descriptions:\n\n```python\nfrom tabicl import TabICLClassifier\n\nclf = TabICLClassifier(\n n_estimators=32, # number of ensemble members\n norm_methods=[\"none\", \"power\"], # normalization methods to try\n feat_shuffle_method=\"latin\", # feature permutation strategy\n class_shift=True, # whether to apply cyclic shifts to class labels\n outlier_threshold=4.0, # z-score threshold for outlier detection and clipping\n softmax_temperature=0.9, # controls prediction confidence\n average_logits=True, # whether ensemble averaging is done on logits or probabilities\n use_hierarchical=True, # enable hierarchical classification for datasets with many classe\n batch_size=8, # process this many ensemble members together (reduce RAM usage)\n use_amp=True, # use automatic mixed precision for faster inference\n model_path=None, # where the model checkpoint is stored\n allow_auto_download=True, # whether automatic download to the specified path is allowed\n checkpoint_version=\"tabicl-classifier-v1.1-0506.ckpt\", # the version of pretrained checkpoint to use\n device=None, # specify device for inference\n random_state=42, # random seed for reproducibility\n n_jobs=None, # number of threads to use for PyTorch\n verbose=False, # print detailed information during inference\n inference_config=None, # inference configuration for fine-grained control\n)\n```\n\n## Memory-Efficient Inference\n\nTabICL includes memory management to handle large datasets:\n\n- **Memory Profiling**: Built-in memory estimators for different components of the model\n- **Batch Size Estimation**: Dynamically determines optimal batch sizes based on available GPU memory\n- **CPU Offloading**: Automatically offloads intermediate results to CPU when beneficial\n- **OOM Recovery**: Recovers gracefully from out-of-memory errors by reducing batch size\n\n## Preprocessing\n\n### Simple built-in preprocessing\nIf the input `X` to TabICL is a pandas DataFrame, TabICL will automatically:\n- Detect and ordinal encode categorical columns (including string, object, category, and boolean types)\n- Create a separate category for missing values in categorical features\n- Perform mean imputation for missing numerical values (encoded as NaN)\n\nIf the input `X` is a numpy array, TabICL assumes that ordinal encoding and missing value imputation have already been performed.\n\nFor both input types, TabICL applies additional preprocessing:\n- Outlier detection and removal\n- Feature scaling and normalization\n- Feature shuffling for ensemble diversity\n\n### Advanced data preprocessing with skrub <img src=\"https://skrub-data.github.io/stable/_static/skrub.svg\" width=\"8%\" alt=\"skrub logo\" style=\"display: inline; margin-left: 5px; margin-right: 5px;\">\n\nReal-world datasets often contain complex heterogeneous data that benefits from more sophisticated preprocessing. For these scenarios, we recommend [skrub](https://skrub-data.org/stable/index.html), a powerful library designed specifically for advanced tabular data preparation.\n\n**Why use skrub?**\n- Handles diverse data types (numerical, categorical, text, datetime, etc.)\n- Provides robust preprocessing for dirty data\n- Offers sophisticated feature engineering capabilities\n- Supports multi-table integration and joins\n\n#### Installation\n\n```bash\npip install skrub -U\n```\n\n#### Basic Integration\n\nUse skrub's [TableVectorizer](https://skrub-data.org/stable/reference/generated/skrub.TableVectorizer.html) to transform your raw data before passing it to TabICLClassifier:\n\n```python\nfrom skrub import TableVectorizer\nfrom tabicl import TabICLClassifier\nfrom sklearn.pipeline import make_pipeline\n\npipeline = make_pipeline(\n TableVectorizer(), # Automatically handles various data types\n TabICLClassifier()\n)\n\npipeline.fit(X_train, y_train) # X should be a DataFrame\npredictions = pipeline.predict(X_test)\n```\n\n\n## Key Features and Considerations:\n\n- **Number of samples**:\n - TabICL is pretrained on datasets with up to 60K samples.\n - TabICL can handle datasets beyond 100K samples thanks to memory-efficient inference.\n - TabPFN (v2) is on average better than TabICL on small datasets with <10K samples, while TabICL is better on larger datasets.\n - Classical methods may catch up with TabICL at around 40K samples but they are much slower due to extensive hyperparameter tuning.\n\n<div style=\"margin-top: 30px;\"></div>\n<img src=\"./figures/perf_wrt_samples.png\" width=\"80%\" alt=\"Ranking vs. number of samples\" style=\"display: block; margin: auto;\">\n<div style=\"margin-top: 30px;\"></div>\n\n- **Number of features**:\n - TabICL is pretrained on datasets with up to 100 features.\n - TabICL can accommodate any number of features theoretically.\n\n- **Number of classes**:\n - TabICL is pretrained on datasets with up to 10 classes, so it natively supports a maximum of 10 classes.\n - However, TabICL can handle any number of classes thanks to its in-built hierarchical classification.\n\n- **Inference speed**:\n - Like TabPFN, `fit()` does minimal work while `predict()` runs the full model\n - At the same `n_estimators`, TabICL is usually 1x-5x faster than TabPFN\n - TabICL benefits more from larger `n_estimators`, hence the default of 32\n - Automatic mixed precision (AMP) provides further speed improvements on compatible GPUs\n\n- **No tuning required**: TabICL produces good predictions without hyperparameter tuning, unlike classical methods that require extensive tuning for optimal performance.\n\n## Performance\n\nTabICL has achieved excellent results on the [TALENT](https://github.com/qile2000/LAMDA-TALENT) benchmark.\n\n<img src=\"./figures/performance.png\" width=\"100%\" alt=\"Performance on TALENT\" style=\"display: block; margin: auto;\">\n<div style=\"margin-top: 30px;\"></div>\n\n## Citation\nIf you use TabICL for research purposes,\nplease cite our **[paper](https://arxiv.org/abs/2502.05564)**:\n```bibtex\n@article{qu2025tabicl,\n title={TabICL: A Tabular Foundation Model for In-Context Learning on Large Data},\n author={Qu, Jingang and Holzm{\\\"u}ller, David and Varoquaux, Ga{\\\"e}l and Morvan, Marine Le},\n journal={arXiv preprint arXiv:2502.05564},\n year={2025}\n}\n```\n\n## Contributors\n\n- [Jingang Qu](https://github.com/jingangQu)\n- [David Holzm\u00fcller](https://github.com/dholzmueller)\n- [Marine Le Morvan](https://github.com/marineLM)\n",
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