flyingsquid:通过FlyingSquid进行更多的交互式弱监督

<div> </div> # More Interactive Weak Supervision with FlyingSquid **UPDATE 06/17/20**: Code re-factored, with two new features: * Compute label model parameters by looking at all possible triplets and taking the mean or median; we find this to be more stable than just looking at a single triplet (use `label_model.fit(..., solve_method='triplet_mean')`). By default, the code now uses `triplet_mean`. * Get the estimated accuracies of each labeling function `P(lambda_i == Y)` with `label_model.estimated_accuracies()`. FlyingSquid is a new framework for automatically building models from multiple noisy label sources. Users write functions that generate noisy labels for data, and FlyingSquid uses the agreements and disagreements between them to learn a _label model_ of how accurate the _labeling functions_ are. The label model can be used directly for downstream applications, or it can be used to train a powerful end model: <div> </div> FlyingSquid can be used to build models for all sorts of tasks, including text applications, video analysis, and online learning. Check out our [blog post](http://hazyresearch.stanford.edu/flyingsquid) and paper on [arXiv](https://arxiv.org/abs/2002.11955) for more details! ## Getting Started * Quickly [install](#installation) FlyingSquid * Check out the [examples](examples/) folder for tutorials and some simple code examples ## Sample Usage ```Python from flyingsquid.label_model import LabelModel import numpy as np L_train = np.load('...') m = L_train.shape[1] label_model = LabelModel(m) label_model.fit(L_train) preds = label_model.predict(L_train) ``` ## Installation We recommend using `conda` to install FlyingSquid: ``` git clone https://github.com/HazyResearch/flyingsquid.git cd flyingsquid conda env create -f environment.yml conda activate flyingsquid ``` Alternatively, you can install the dependencies yourself: * [Pgmpy](http://pgmpy.org/) * [PyTorch](https://pytorch.org/) (only necessary for the PyTorch integration) And then install the actual package: ``` pip install flyingsquid ``` To install from source: ``` git clone https://github.com/HazyResearch/flyingsquid.git cd flyingsquid conda env create -f environment.yml conda activate flyingsquid pip install -e . ``` ## Citation If you use our work or found it useful, please cite our [paper](https://arxiv.org/abs/2002.11955) at ICML 2020: ``` @inproceedings{fu2020fast, author = {Daniel Y. Fu and Mayee F. Chen and Frederic Sala and Sarah M. Hooper and Kayvon Fatahalian and Christopher R\'e}, title = {Fast and Three-rious: Speeding Up Weak Supervision with Triplet Methods}, booktitle = {Proceedings of the 37th International Conference on Machine Learning (ICML 2020)}, year = {2020}, } ```