libact

libact: Pool-based active learning in Python

Dependencies

Python3 dependencies

pip3 install -r requirements.txt

Debian (>= 7) / Ubuntu (>= 14.04)

sudo apt-get install build-essential gfortran libatlas-base-dev liblapacke-dev

MacOS

brew tap homebrew/science
brew install openblas

Installation

To install in your home directory:

python setup.py install --user

To install for all users on Unix/Linux:

python setup.py build
sudo python setup.py install

Examples

Some examples are available under the examples directory. Before running, use examples/get_dataset.py to retrieve the dataset used by the examples.

Available examples:

• examples/plot.py: This example performs basic usage of libact. It splits an supervised learning dataset and remove some label from dataset to simulate an active learning scenario. Each query of an unlabeled dataset is simply putting the label back to dataset.

  The main libact usage part is below:

  qs = UncertaintySampling(trn_ds, method='lc') # query strategy instance
  
  ask_id = qs.make_query() # let the specified query strategy suggest a data to query
  trn_ds.update(ask_id, y_train[ask_id]) # update the dataset with newly queried data

HintSVM

For HintSVM, you would have to install package from https://github.com/ntucllab/hintsvm

Before running, you need to make sure the path to hintsvm's library and python code are set. Set them up by setting environment variables:

export LD_LIBRARY_PATH=/path/to/hintsvm:$LD_LIBRARY_PATH
export PYTHONPATH=/path/to/hintsvm/python:$PYTHONPATH