Install WRENCH and add only the wrench/wrench directory in the root directory here to run ws_snork successfully

🔧 New

4/20/22

Add WS explainer, please find more details in our paper.

4/20/22

We have updated the setup.py to make installation more flexible.

Please use pip install ws-benchmark==1.1.2rc0 to install the latest version. We strongly suggest create a new environment to install wrench. We will bring better compatibility in the next stable release. If you have any problems with installation, please let us know.

Known incompatibilities:

tensorflow==2.8.0, albumentations==0.1.12

3/18/22

Wrench is available on ws-benchmark now, using pip install ws-benchmark to qucik install.

2/13/22

Add script to generate LFs for any tabular dataset as well as 5 new tabular datasets, namely, mushroom, spambase, PhishingWebsites, Bioresponse, and bank-marketing.

11/04/21

(beta) Add parallel_fit for torch model to support pytorch DistributedDataParallel-example

10/15/21

A branch of new methods: WeaSEL, ImplyLoss, ASTRA, MeanTeacher, Meta-Weight-Net, Learning-to-Reweight
Support image classification (dataset class / torchvision backbone) as well as DomainNet/Animals-with-Attributes2 datasets (check out the datasets folder)

🔧 What is it?

Wrench is a benchmark platform containing diverse weak supervision tasks. It also provides a common and easy framework for development and evaluation of your own weak supervision models within the benchmark.

For more information, checkout our publications:

WRENCH: A Comprehensive Benchmark for Weak Supervision (NeurIPS 2021)
A Survey on Programmatic Weak Supervision

If you find this repository helpful, feel free to cite our publication:

@inproceedings{
zhang2021wrench,
title={{WRENCH}: A Comprehensive Benchmark for Weak Supervision},
author={Jieyu Zhang and Yue Yu and Yinghao Li and Yujing Wang and Yaming Yang and Mao Yang and Alexander Ratner},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2021},
url={https://openreview.net/forum?id=Q9SKS5k8io}
}

🔧 What is weak supervision?

Weak Supervision is a paradigm for automated training data creation without manual annotations.

For a brief overview, please check out this blog.

For more context, please check out this survey.

To track recent advances in weak supervision, please follow this repo.

🔧 Installation

[1] Install anaconda: Instructions here: https://www.anaconda.com/download/

[2] Clone the repository:

git clone https://github.com/JieyuZ2/wrench.git
cd wrench

[3] Create virtual environment:

conda env create -f environment.yml
source activate wrench

If this not working or you want to use only a subset of modules of Wrench, check out this wiki page

🔧 Available Datasets

The datasets can be downloaded via this.

Note that some datasets may have more training examples than what is reported in README/paper because we include the dev set, whose indices can be found in labeled_id.json if exists.

A documentation of dataset format and usage can be found in this wiki-page

classification:

Name	Task	# class	# LF	# train	# validation	# test	data source	LF source
Census	income clasification	2	83	10083	5561	16281	link	link
Youtube	spam clasification	2	10	1586	120	250	link	link
SMS	spam clasification	2	73	4571	500	500	link	link
IMDB	sentiment clasification	2	8	20000	2500	2500	link	link
Yelp	sentiment clasification	2	8	30400	3800	3800	link	link
AGNews	topic clasification	4	9	96000	12000	12000	link	link
TREC	question classification	6	68	4965	500	500	link	link
Spouse	relation classification	2	9	22254	2801	2701	link	link
SemEval	relation classification	9	164	1749	178	600	link	link
CDR	bio relation classification	2	33	8430	920	4673	link	link
Chemprot	chemical relation classification	10	26	12861	1607	1607	link	link
Commercial	video frame classification	2	4	64130	9479	7496	link	link
Tennis Rally	video frame classification	2	6	6959	746	1098	link	link
Basketball	video frame classification	2	4	17970	1064	1222	link	link
DomainNet	image classification	-	-	-	-	-	link	link

sequence tagging:

Name	# class	# LF	# train	# validation	# test	data source	LF source
CoNLL-03	4	16	14041	3250	3453	link	link
WikiGold	4	16	1355	169	170	link	link
OntoNotes 5.0	18	17	115812	5000	22897	link	link
BC5CDR	2	9	500	500	500	link	link
NCBI-Disease	1	5	592	99	99	link	link
Laptop-Review	1	3	2436	609	800	link	link
MIT-Restaurant	8	16	7159	500	1521	link	link
MIT-Movies	12	7	9241	500	2441	link	link

The detailed documentation is coming soon.

🔧 Available Models

If you find any of the implementations is wrong/problematic, don't hesitate to raise issue/pull request, we really appreciate it!

TODO-list: check this out!

classification:

Model	Model Type	Reference	Link to Wrench
Majority Voting	Label Model	--	link
Weighted Majority Voting	Label Model	--	link
Dawid-Skene	Label Model	link	link
Data Progamming	Label Model	link	link
MeTaL	Label Model	link	link
FlyingSquid	Label Model	link	link
EBCC	Label Model	link	link
IBCC	Label Model	link	link
FABLE	Label Model	link	link
Logistic Regression	End Model	--	link
MLP	End Model	--	link
BERT	End Model	link	link
COSINE	End Model	link	link
ARS2	End Model	link	link
Denoise	Joint Model	link	link
WeaSEL	Joint Model	link	link

sequence tagging:

Model	Model Type	Reference	Link to Wrench
Hidden Markov Model	Label Model	link	link
Conditional Hidden Markov Model	Label Model	link	link
LSTM-CNNs-CRF	End Model	link	link
BERT-CRF	End Model	link	link
LSTM-ConNet	Joint Model	link	link
BERT-ConNet	Joint Model	link	link

classification-to-sequence-tagging wrapper:

Wrench also provides a SeqLabelModelWrapper that adaptes label model for classification task to sequence tagging task.

methods from related domains:

Robust Learning methods as end model:

Model	Model Type	Reference	Link to Wrench
Meta-Weight-Net	End Model	link	link
Learning2ReWeight	End Model	link	link

Semi-Supervised Learning methods as end model:

Model	Model Type	Reference	Link to Wrench
MeanTeacher	End Model	link	link

Weak Supervision with cleaned labels (Semi-Weak Supervision):

Model	Model Type	Reference	Link to Wrench
ImplyLoss	Joint Model	link	link
ASTRA	Joint Model	link	link

🔧 Quick examples

🔧 Label model with parallel grid search for hyper-parameters

import logging
import numpy as np
import pprint

from wrench.dataset import load_dataset
from wrench._logging import LoggingHandler
from wrench.search import grid_search
from wrench import labelmodel 
from wrench.evaluation import AverageMeter

#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])
logger = logging.getLogger(__name__)

#### Load dataset 
dataset_home = '../datasets'
data = 'youtube'
train_data, valid_data, test_data = load_dataset(dataset_home, data, extract_feature=False)


#### Specify the hyper-parameter search space for grid search
search_space = {
    'Snorkel': {
        'lr': np.logspace(-5, -1, num=5, base=10),
        'l2': np.logspace(-5, -1, num=5, base=10),
        'n_epochs': [5, 10, 50, 100, 200],
    }
}

#### Initialize label model
label_model_name = 'Snorkel'
label_model = getattr(labelmodel, label_model_name)

#### Search best hyper-parameters using validation set in parallel
n_trials = 100
n_repeats = 5
target = 'acc'
searched_paras = grid_search(label_model(), dataset_train=train_data, dataset_valid=valid_data,
                             metric=target, direction='auto', search_space=search_space[label_model_name],
                             n_repeats=n_repeats, n_trials=n_trials, parallel=True)

#### Evaluate the label model with searched hyper-parameters and average meter
meter = AverageMeter(names=[target])
for i in range(n_repeats):
    model = label_model(**searched_paras)
    history = model.fit(dataset_train=train_data, dataset_valid=valid_data)
    metric_value = model.test(test_data, target)
    meter.update(target=metric_value)

metrics = meter.get_results()
pprint.pprint(metrics)

For detailed guidance of grid_search, please check out this wiki page.

🔧 Run a standard supervised learning pipeline

import logging
import torch

from wrench.dataset import load_dataset
from wrench._logging import LoggingHandler
from wrench.endmodel import MLPModel

#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])
logger = logging.getLogger(__name__)

#### Load dataset 
dataset_home = '../datasets'
data = 'youtube'

#### Extract data features using pre-trained BERT model and cache it
extract_fn = 'bert'
model_name = 'bert-base-cased'
train_data, valid_data, test_data = load_dataset(dataset_home, data, extract_feature=True, extract_fn=extract_fn,
                                                 cache_name=extract_fn, model_name=model_name)


#### Train a MLP classifier
device = torch.device('cuda:0')
n_steps = 100000
batch_size = 128
test_batch_size = 1000 
patience = 200
evaluation_step = 50
target='acc'

model = MLPModel(n_steps=n_steps, batch_size=batch_size, test_batch_size=test_batch_size)
history = model.fit(dataset_train=train_data, dataset_valid=valid_data, device=device, metric=target, 
                    patience=patience, evaluation_step=evaluation_step)

#### Evaluate the trained model
metric_value = model.test(test_data, target)

🔧 Build a two-stage weak supervision pipeline

import logging
import torch

from wrench.dataset import load_dataset
from wrench._logging import LoggingHandler
from wrench.endmodel import MLPModel
from wrench.labelmodel import MajorityVoting

#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])
logger = logging.getLogger(__name__)

#### Load dataset 
dataset_home = '../datasets'
data = 'youtube'

#### Extract data features using pre-trained BERT model and cache it
extract_fn = 'bert'
model_name = 'bert-base-cased'
train_data, valid_data, test_data = load_dataset(dataset_home, data, extract_feature=True, extract_fn=extract_fn,
                                                 cache_name=extract_fn, model_name=model_name)

#### Generate soft training label via a label model
#### The weak labels provided by supervision sources are alreadly encoded in dataset object
label_model = MajorityVoting()
label_model.fit(train_data, valid_data)
soft_label = label_model.predict_proba(train_data)


#### Train a MLP classifier with soft label
device = torch.device('cuda:0')
n_steps = 100000
batch_size = 128
test_batch_size = 1000 
patience = 200
evaluation_step = 50
target='acc'

model = MLPModel(n_steps=n_steps, batch_size=batch_size, test_batch_size=test_batch_size)
history = model.fit(dataset_train=train_data, dataset_valid=valid_data, y_train=soft_label, 
                    device=device, metric=target, patience=patience, evaluation_step=evaluation_step)

#### Evaluate the trained model
metric_value = model.test(test_data, target)

#### We can also train a MLP classifier with hard label
from snorkel.utils import probs_to_preds
hard_label = probs_to_preds(soft_label)
model = MLPModel(n_steps=n_steps, batch_size=batch_size, test_batch_size=test_batch_size)
model.fit(dataset_train=train_data, dataset_valid=valid_data, y_train=hard_label, 
          device=device, metric=target, patience=patience, evaluation_step=evaluation_step)

🔧 Procedural labeling function generator

import logging
import torch

from wrench.dataset import load_dataset
from wrench._logging import LoggingHandler
from wrench.synthetic import ConditionalIndependentGenerator, NGramLFGenerator
from wrench.labelmodel import FlyingSquid

#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S',
                    level=logging.INFO,
                    handlers=[LoggingHandler()])
logger = logging.getLogger(__name__)


#### Generate synthetic dataset
generator = ConditionalIndependentGenerator(
    n_class=2,
    n_lfs=10,
    alpha=0.75, # mean accuracy
    beta=0.1, # mean propensity
    alpha_radius=0.2, # radius of accuracy
    beta_radius=0.1 # radius of propensity
)
train_data = generator.generate_split('train', 10000)
valid_data = generator.generate_split('valid', 1000)
test_data = generator.generate_split('test', 1000)

#### Evaluate label model on synthetic dataset
label_model = FlyingSquid()
label_model.fit(dataset_train=train_data, dataset_valid=valid_data)
target_value = label_model.test(test_data, metric_fn='auc')

#### Load dataset 
dataset_home = '../datasets'
data = 'youtube'

#### Load real-world dataset
train_data, valid_data, test_data = load_dataset(dataset_home, data, extract_feature=False)

#### Generate procedural labeling functions
generator = NGramLFGenerator(dataset=train_data, min_acc_gain=0.1, min_support=0.01, ngram_range=(1, 2))
applier = generator.generate(mode='correlated', n_lfs=10)
L_test = applier.apply(test_data)
L_train = applier.apply(train_data)


#### Evaluate label model on real-world dataset with semi-synthetic labeling functions
label_model = FlyingSquid()
label_model.fit(dataset_train=L_train, dataset_valid=valid_data)
target_value = label_model.test(L_test, metric_fn='auc')

🔧 Contact

Contact person: Jieyu Zhang, [email protected]

Don't hesitate to send us an e-mail if you have any question.

We're also open to any collaboration!

🔧 Contributing Dataset and Model

We sincerely welcome any contribution to the datasets or models!

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
datasets/census		datasets/census
pate_2017		pate_2017
CS2952C_Prashanthi_Shashidhar_Final_Report.pdf		CS2952C_Prashanthi_Shashidhar_Final_Report.pdf
README.md		README.md
adult_l_weak_label.npy		adult_l_weak_label.npy
analysis.py		analysis.py
basketball_l_weak_label.npy		basketball_l_weak_label.npy
chemprot_l_weak_label.npy		chemprot_l_weak_label.npy
chemprot_queries experiments		chemprot_queries experiments
environment.yml		environment.yml
l_weak_label.npy		l_weak_label.npy
mnist_250_student_labels_lap_20.npy		mnist_250_student_labels_lap_20.npy
print_datat_attributes.py		print_datat_attributes.py
run_astra.py		run_astra.py
setup.py		setup.py
ws_snork.py		ws_snork.py
youtube_queries experiments		youtube_queries experiments

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Repository files navigation

Install WRENCH and add only the wrench/wrench directory in the root directory here to run ws_snork successfully

🔧 New

🔧 What is it?

🔧 What is weak supervision?

🔧 Installation

🔧 Available Datasets

classification:

sequence tagging:

🔧 Available Models

classification:

sequence tagging:

classification-to-sequence-tagging wrapper:

methods from related domains:

Robust Learning methods as end model:

Semi-Supervised Learning methods as end model:

Weak Supervision with cleaned labels (Semi-Weak Supervision):

🔧 Quick examples

🔧 Label model with parallel grid search for hyper-parameters

🔧 Run a standard supervised learning pipeline

🔧 Build a two-stage weak supervision pipeline

🔧 Procedural labeling function generator

🔧 Contact

🔧 Contributing Dataset and Model

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Install WRENCH and add only the wrench/wrench directory in the root directory here to run ws_snork successfully

🔧 New

🔧 What is it?

🔧 What is weak supervision?

🔧 Installation

🔧 Available Datasets

classification:

sequence tagging:

🔧 Available Models

classification:

sequence tagging:

classification-to-sequence-tagging wrapper:

methods from related domains:

Robust Learning methods as end model:

Semi-Supervised Learning methods as end model:

Weak Supervision with cleaned labels (Semi-Weak Supervision):

🔧 Quick examples

🔧 Label model with parallel grid search for hyper-parameters

🔧 Run a standard supervised learning pipeline

🔧 Build a two-stage weak supervision pipeline

🔧 Procedural labeling function generator

🔧 Contact

🔧 Contributing Dataset and Model

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