python-allib 0.5.3

A typed Active Learning Library

Active Learning and Technology-Assisted Review library for Python

---

python-allib is a library that enables efficient data annotation with Active Learning on various types of datasets. Through the libraryinstancelib we support various machine learning algorithms and instance types. Besides canonical Active Learning, this library offers Technology-Assisted Review methods, which aid in making High-Recall Information Retrieval tasks more efficient.

© Michiel Bron, 2024

Quick tour of Technology-Assisted Review simulation

Load dataset

Load the dataset in an instancelib environment.

# Some imports
from pathlib import Path
from allib.benchmarking.datasets import TarDataset, DatasetType

POS = "Relevant"
NEG = "Irrelevant"
# Load a dataset in SYNERGY/ASREVIEW format
dataset_description = TarDataset(
  DatasetType.REVIEW, 
  Path("./allib/tests/testdataset.csv"))

# Get an instancelib Environment object
ds = dataset_description.env

ds

Environment(dataset=InstanceProvider(length=2019), 
   labels=LabelProvider(labelset=frozenset({'Relevant', 'Irrelevant'}), 
   length=0, 
   statistics={'Relevant': 0, 'Irrelevant': 0}), 
   named_providers={}, 
   length=2019, 
   typeinfo=TypeInfo(identifier=int, data=str, vector=NoneType, representation=str)) 

The ds object is currently loaded in TAR simulation mode. This means, that like the at the start of review process, there is no labeled data. This is visible in the statistics in the ds objects. However, as this is simulation mode, there is a ground truth available. This can be accessed as follows:

ds.truth

LabelProvider(labelset=frozenset({'Relevant', 'Irrelevant'}), 
   length=2019, 
   statistics={'Relevant': 101, 'Irrelevant': 1918})

In Active Learning, we are dealing with a partially labeled dataset. There are two InstanceProvider objects inside the ds object that maintain the label status:

print(f"Unlabeled: {ds.unlabeled}, Labeled: {ds.labeled}")

Unlabeled: InstanceProvider(length=2019), Labeled: InstanceProvider(length=0)

Basic operations

The ds object supports all instancelib operations, for example, dividing the dataset in a train and test set.

train, test = ds.train_test_split(ds.dataset, train_size=0.70)
print(f"Train: {train}, Test: {test}")

Train: InstanceProvider(length=1413), Test: InstanceProvider(length=606)

Train a ML model

We can also train Machine Learning methods on the ground truth data in ds.truth.

from sklearn.pipeline import Pipeline 
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
from instancelib.analysis.base import prediction_viewer
import instancelib as il
pipeline = Pipeline([
     ('vect', CountVectorizer()),
     ('tfidf', TfidfTransformer()),
     ('clf', LogisticRegression()),
     ])

model = il.SkLearnDataClassifier.build(pipeline, ds)
model.fit_provider(train, ds.truth)

With the method prediction_viewer we can view the predictions as a Pandas dataframe.

# Show the three instances with the highest probability to be Relevant
df = prediction_viewer(model, test, ds.truth).sort_values(
    by="p_Relevant", ascending=False
)
df.head(3)

  0%|          | 0/4 [00:00<?, ?it/s]

  0%|          | 0/4 [00:00<?, ?it/s]

	data	label	prediction	p_Irrelevant	p_Relevant
175	A randomized trial of a computer-based interve...	Relevant	Irrelevant	0.639552	0.360448
1797	Computerized decision support to reduce potent...	Relevant	Irrelevant	0.737130	0.262870
956	Improvement of intraoperative antibiotic proph...	Relevant	Irrelevant	0.762803	0.237197

Although the predicition probabilities are below 0.50, some of the top ranked documents have a ground truth label relevant.

Active Learning

We can integrate the model in an Active Learning method. A simple TAR method is AutoTAR.

from allib.activelearning.autotar import AutoTarLearner

al = AutoTarLearner(ds, model, POS, NEG, k_sample=100, batch_size=20)

To kick-off the process, we need some labeled data. Let’s give it some training data.

pos_instance = al.env.dataset[28]
neg_instance = al.env.dataset[30]
al.env.labels.set_labels(pos_instance, POS)
al.env.labels.set_labels(neg_instance, NEG)
al.set_as_labeled(pos_instance)
al.set_as_labeled(neg_instance)

Next, we can retrieve the instance that should be labeled next with the following command.

next_instance = next(al)
# next_instance is an Instance object.
# Representation contains a human-readable string version of the instance
print(
    f"{next_instance.representation[:60]}...\n"
    f"Ground Truth Labels: {al.env.truth[next_instance]}"
)

  0%|          | 0/11 [00:00<?, ?it/s]

Oral quinolones in hospitalized patients: an evaluation of a...
Ground Truth Labels: frozenset({'Relevant'})

Simulation

Using the ground truth data, we can further simulate the TAR process in an automated fashion:

from allib.stopcriterion.heuristic import AprioriRecallTarget
from allib.analysis.tarplotter import TarExperimentPlotter
from allib.analysis.experiments import ExperimentIterator
from allib.analysis.simulation import TarSimulator

recall95 = AprioriRecallTarget(POS, 0.95)
recall100 = AprioriRecallTarget(POS, 1.0)
criteria = {
    "Perfect95": recall95,
    "Perfect100": recall100,
}

# Then we can specify our experiment
exp = ExperimentIterator(al, POS, NEG, criteria, {})
plotter = TarExperimentPlotter(POS, NEG)
simulator = TarSimulator(exp, plotter)

simulator.simulate()
plotter.show()

  0%|          | 0/2019 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/10 [00:00<?, ?it/s]

  0%|          | 0/9 [00:00<?, ?it/s]

  0%|          | 0/9 [00:00<?, ?it/s]

  0%|          | 0/9 [00:00<?, ?it/s]

  0%|          | 0/9 [00:00<?, ?it/s]

  0%|          | 0/8 [00:00<?, ?it/s]

  0%|          | 0/8 [00:00<?, ?it/s]

  0%|          | 0/7 [00:00<?, ?it/s]

  0%|          | 0/7 [00:00<?, ?it/s]

  0%|          | 0/7 [00:00<?, ?it/s]

  0%|          | 0/6 [00:00<?, ?it/s]

  0%|          | 0/5 [00:00<?, ?it/s]

  0%|          | 0/5 [00:00<?, ?it/s]

  0%|          | 0/4 [00:00<?, ?it/s]

  0%|          | 0/3 [00:00<?, ?it/s]

  0%|          | 0/2 [00:00<?, ?it/s]

  0%|          | 0/1 [00:00<?, ?it/s]

Command Line Interface

Besides importing the library, the code can be used to run some predefined experiments.

For a CSV in SYNERGY format:

python -m allib benchmark -m Review -d  ./path/to/dataset -t ./path/to/results/ -e AUTOTAR -r 42

For a dataset in TREC-style:

python -m allib benchmark -m Trec -d  ./path/to/dataset/ -t ./path/to/results/ -e AUTOTAR -r 42

Experiment options are:

• AUTOTAR
• AUTOSTOP
• CHAO
• TARGET
• CMH

The -r option is used to supply a seed value that is given to a random generator.

Installation

See installation.md for an extended installation guide, especially for enabling the CHAO method. Short instructions are below.

Method	Instructions
pip	Install from PyPI via pip install python-allib.
Local	Clone this repository and install via pip install -e . or locally run python setup.py install.

Releases

python-allib is officially released through PyPI.

See CHANGELOG for a full overview of the changes for each version.

Citation

Use this bibtex to cite this package, or go to ZENODO, to cite a specific version.

@software{bron_2024_108698682,
  author       = {Bron, Michiel},
  title        = {Python Package python-allib},
  month        = mar,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {0.5.1},
  doi          = {10.5281/zenodo.10869682},
  url          = {https://doi.org/10.5281/zenodo.10869682}
}

Maintenance

Contributors

• Michiel Bron (@mpbron)