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poolmate

Pool-Based Machine Teaching

Pool-Based Machine Teaching

Pool-based machine teaching search algorithms through a file-based API.

Getting Started

poolmate provides a command-line interface to algorithms for searching for teaching sets among a candidate pool. poolmate is designed to work with any learner which can be communicated with through a file-based API.

To wit, typical usage requires from the client:

All methods support both teaching sets and teaching sequences. That is, duplication and order of teaching items will be significant if the provided learner treats them as significant.

For the details, see Usage.

For an introduction to machine teaching, see Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education.

For an overview of our research, see here.

Installation

Dependencies can be installed with

pip install numpy pandas scipy sklearn tqdm

This project has been tested with Python 2.7.

Usage

Command-line interface

The following describes basic usage. For more options, see Options.

python poolmate/teach.py --candidate-pool-filename CANDIDATE_POOL_FILENAME \
    --loss-executable LOSS_EXECUTABLE                                      \
    --output-filename OUTPUT_FILENAME                                      \
    --teaching-set-size TEACHING_SET_SIZE                                  \ 
    --search-budget SEARCH_BUDGET

--candidate-pool-filename is a file which contains the candidate pool to search from, one item per line.

--loss-executable is an executable which poolmate will call during its execution. This executable must take two command-line arguments FILE1 and FILE2. The first argument FILE1 will contain a set of items for the learner to train on. The second argument FILE2 will be a filename where the executable should write the loss of learner after training on the items in FILE1. The lines in FILE1 will simply be a subset of the lines in CANDIDATE_POOL_FILENAME.

So for example the contents of FILE1 might look like:

1, 0.658947147839417, 0.752189242381396
1, 0.231140742000439, -0.972920324275059
-1, -0.830995051808994, 0.556279807173483
-1, -0.433446335329234, -0.901179379696216
1, 0.958199172890462, 0.286101983691191

Let’s say the executable is named my_learner, it will be called with:

my_learner FILE1 FILE2

my_learner must train on the items in FILE1 and write the loss of the trained learner to FILE2 on a single line, say:

0.03

Please note that poolmate will use unique filenames on successive calls to the loss executable.

--output-filename is a filename where results are written. The first line of this file will contain the loss while the remaining lines will contain the rows out of CANDIDATE_POOL_FILENAME which represent the best teaching set found during search. For example, if TEACHING_SET_SIZE were set to 2, the output file may look something like:

0.00602749784937
-1, -0.134432406608285, -0.990922765937641
1, 0.171855154919438, -0.985122228826259

--teaching-set-size is the size of the best teaching set poolmate will return.

--search-budget is the the number of models poolmate is allowed fit before returning. This will be equivalent to the number of calls to LOSS_EXECUTABLE.

As an example of a command-line invocation including all required parameters, the following example runs a search over a candidate pool of items drawn uniformly from the boundary of a circle using an SVM learner:

python poolmate/teach.py                               \
    --candidate-pool-filename poolmate/test/circle.csv \
    --loss-executable "python poolmate/test/svm.py"    \
    --output-filename output.txt                       \
    --teaching-set-size 2                              \
    --search-budget 200

Due to the stochastic nature of search, your result may differ from the loss and teaching set given in the example above.

Programmatic Interface

If one wishes to avoid the overhead of executable callbacks and is willing to write a learner in Python, one can invoke poolmate programmatically. In this case, one has to provide a learner instance which implements two methods:

class MyLearner(object):

def loss(self, model):
    # ... return some a float loss

def fit(self, xy):
    # ... return model fit on xy

The fit method must fit a model on xy, which is an iterable subset of the candidate pool. The loss method receives as an argument the model returned fit and must itself return a loss of float type.

Here is an example of its invocation:

from poolmate.teach import Runner, build_options

runner = Runner()
learner = MyLearner()
options = build_options(search_budget=10000,
                        teaching_set_size=10)
best_loss, best_set = runner.run_experiment(candidate_pool, learner, options)

Options

Several other options are available using the command line:

-h, --help            show this help message and exit
--candidate-pool-filename CANDIDATE_POOL_FILENAME
                      Filename for candidate pool. The format of the file is
                      that candidate items are represented one item per
                      line. (default: None)
--loss-executable LOSS_EXECUTABLE
                      Executable command which will return loss on teaching
                      set. Executable must take two command-line arguments,
                      an `inputfilename` containing the teaching set to
                      train the learner on, one item per line, and an
                      `outputfilename` where the loss should be written
                      (default: None)
--output-filename OUTPUT_FILENAME
                      Output filename where the best found teaching set and
                      loss are written at the search procedure's termination
                      (default: None)
--teaching-set-size TEACHING_SET_SIZE
                      Size of teaching set to return. (default: None)
--search-budget SEARCH_BUDGET
                      Budget of number of models to fit. This is the number
                      of times `loss-executable` will be invoked. (default:
                      None)
--proposals PROPOSALS
                      Number of proposals to consider at each search
                      iteration. A tuning parameter for 'greedy-add' and
                      'random-index-greedy-swap' algorithms (default: None)
--seed SEED           Set random seed to achieve reproducibility. (default:
                      None)
--algorithm {greedy-add,random-index-greedy-swap,uniform}
                      Choice of search algorithm (default: greedy-add)
--initial-teaching-set INITIAL_TEACHING_SET
                      A comma-separated list of zero-based indices to fix
                      the initial teaching set. Used in 'random-index-
                      greedy-swap' and 'uniform' algorithms (e.g.,
                      --initial-teaching-set 53,17 ). (default: None)
--log LOG             Filename of log file, where interim results are logged
                      as comma-separated values (CSV). The three colums of
                      the output represent the iteration number, the loss of
                      the trained model for that iteration, and the teaching
                      set for that iterations. (default: None)

FAQ

My learner is a MATLAB function? How can poolmate call a MATLAB function?

One method is to wrap the call to MATLAB into a shell script. For example, let’s say your MATLAB function is

function [ output_args ] = my_learner(FILE1, FILE2)

Create the file my_learner.sh

#!/bin/sh

matlab -nodesktop -nosplash -nodisplay -r "my_learner $1 $2; quit" >/dev/null 2>/dev/null

Be sure to set the script’s permissions with

chmod +x my_learner.sh

And then you can call it by setting --loss-executable ./my_learner.sh.

Acknowledgements

This project is based upon work supported by the National Science Foundation under Grant No. IIS-0953219. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors