Modular Active Learning framework for Python3

• Introduction
• Active learning from bird's-eye view
• modAL in action
  • From zero to one in a few lines of code
  • Replacing parts quickly
  • Replacing parts with your own solutions
  • Working examples
• Installation
• Documentation
• About the developer

modAL is an active learning framework for Python3, designed with modularity, flexibility and extensibility in mind. Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom built solutions, allowing you to design novel algorithms with ease.

With the recent explosion of available data, you have can have millions of unlabelled examples with a high cost to obtain labels. For instance, when trying to predict the sentiment of tweets, obtaining a training set can require immense manual labour. But worry not, active learning comes to the rescue! In general, AL is a framework allowing you to increase classification performance by intelligently querying you to label the most informative instances. In general, an active learning workflow looks like the following.

The key components of any workflow are the model you choose, the uncertainty measure you use and the query strategy you apply to request labels. With modAL, instead of choosing from a small set of built-in components, you have the freedom to seamlessly integrate scikit-learn or Keras models into your algorithm and easily tailor your custom query strategies and uncertainty measures.

Let's see what modAL can do for you!

Active learning with a scikit-learn classifier, for instance RandomForestClassifier, can be as simple as the following.

from modAL.models import ActiveLearner
from sklearn.ensemble import RandomForestClassifier

# initializing the learner
learner = ActiveLearner(
    predictor=RandomForestClassifier(),
    X_initial=X_train, y_initial=y_train
)

# query for labels
query_idx, query_inst = learner.query(X_pool)

# ...obtaining new labels from the Oracle...

# supply label for queried instance
learner.teach(X_pool[query_idx], y_new)

If you would like to use different uncertainty measures and query strategies than the default uncertainty sampling, you can either replace them with several built-in strategies or you can design your own by following a few very simple design principles. For instance, replacing the default uncertainty measure to classification entropy looks the following.

from modAL.models import ActiveLearner
from modAL.uncertainty import entropy_sampling
from sklearn.ensemble import RandomForestClassifier

learner = ActiveLearner(
    predictor=RandomForestClassifier(),
    query_strategy=entropy_sampling,
    X_initial=X_train, y_initial=y_train
)

modAL was designed to make it easy for you to implement your own query strategy. For example, implementing and using a simple random sampling strategy is as easy as the following.

import numpy as np

def random_sampling(classifier, X_pool):
    n_samples = len(X_pool)
    query_idx = np.random.choice(range(n_samples))
    return query_idx, X_pool[query_idx]

learner = ActiveLearner(
    predictor=RandomForestClassifier(),
    query_strategy=random_sampling,
    X_initial=X_train, y_initial=y_train
)

For more details on how to implement your custom strategies, visit the page Extending modAL!

To see modAL in real action, visit the examples section!

• Pool-based sampling
• Stream-based sampling
• Active regression
• Ensemble regression
• Query by committee
• Bootstrapping and bagging
• Keras integration

modAL requires

• Python >= 3.5
• NumPy >= 1.13
• SciPy >= 0.18
• scikit-learn >= 0.18

You can install modAL directly with pip:

pip install modAL

Alternatively, you can install modAL directly from source:

pip install git+https://github.com/cosmic-cortex/modAL.git

You can find the documentation of modAL at https://cosmic-cortex.github.io/modAL, where several tutorials and working examples are available, along with a complete API reference. For running the examples, Matplotlib >= 2.0 is recommended.

modAL is developed by me, Tivadar Danka (aka cosmic-cortex in GitHub). I have a PhD in pure mathematics, but I fell in love with biology and machine learning right after I finished my PhD. I have changed fields and now I work in the Bioimage Analysis and Machine Learning Group of Peter Horvath, where I am working to develop active learning strategies for intelligent sample analysis in biology. During my work I realized that in Python, creating and prototyping active learning workflows can be made really easy and fast with scikit-learn, so I ended up developing a general framework for this. The result is modAL :) If you have any questions, requests or suggestions, you can contact me at 85a5187a@opayq.com! I hope you'll find modAL useful!