Skip to main content
Donate to the Python Software Foundation or Purchase a PyCharm License to Benefit the PSF! Donate Now

Tools for assessing the difficulty of datasets for machine learning models

Project description

Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

Authors: Ed Collins, Nikolai Rozanov, Bingbing Zhang

Contact: contact@wluper.com

In the paper of the corresponding name, we discuss how we used an evolutionary algorithm to discover which statistics about a text classification dataset most accurately represent how difficult that dataset is likely to be for machine learning models to learn. We presented there the difficulty measure which we discovered and have provided this Python package of code which can calculate it.

Installation

This code is pip-installable so can be installed on your machine by running:

pip3 install edm

The code requires Python 3 and NumPy.

It is recommended that you install this code in a virtualenv:

$ mkdir myvirtualenv/
$ virtualenv -p python3 myvirtualenv/
$ source bin/activate
(myvirtualenv) $ pip3 install edm

Running

To calculate the difficulty of a text classification dataset, you will need to provide two lists: one of sentences and one of labels. These two lists need to be the same length - i.e. every sentence has a label. Each item of data should be an untokenized string and each label a string.

>>> sents, labels = your_own_loading_function(PATH_TO_DATA_FILE)
>>> sents
["this is a positive sentence", "this is a negative sentence", ...]
>>> labels
["positive", "negative", ...]
>>> assert len(sents) == len(labels)
True

This code does not support the loading of data files (e.g. csv files) into memory - you will need to do this separately.

Once you have loaded your dataset into memory, you can receive a "difficulty report" by running the code as follows:

from edm import report

sents, labels = your_own_loading_function(PATH_TO_DATA_FILE)

print(report.get_difficulty_report(sents, labels))

Note that if your dataset is very large, then counting the words of the dataset may take several minutes. The Amazon Reviews dataset from Character-level Convolutional Networks for Text Classification by Xiang Zhang, Junbo Zhao and Yann LeCun, 2015 which contains 3.6 million Amazon reviews takes approximately 15 minutes to be processed and the difficulty report created. A loading bar will be displayed while the words are counted.

Project details


Release history Release notifications

This version
History Node

0.0.4

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
edm-0.0.4.tar.gz (9.8 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page