Quick Start

In this tutorial we show you how to install WEFE and then how to run a basic query.

Download and setup

There are two different ways to install WEFE:

  • To install the package with pip, run in a console:

    pip install --upgrade wefe
  • To install the package with conda, run in a console:

    conda install -c pbadilla wefe

Run your first Query


If you are not familiar with the concepts of query, target and attribute set, please visit the the framework section on the library’s about page. These concepts be widely used in the following sections.

In the following code we show how to implement the example query presented in WEFE’s home page: A gender Query using WEAT metrics on the glove-twitter Word Embedding model.

The following graphic shows the flow of the query execution:

Gender query with WEAT Flow

The programming of the previous flow can be separated into three steps:

  • Load the Word Embedding model.

  • Create the Query.

  • Run the Query using the WEAT metric over the Word Embedding Model.

These stages be implemented next:

1. Load the Word Embedding pretrained model from gensim and then, create a :code:`` instance with it. This object took a gensim’s KeyedVectors object and a model name as parameters. As we said previously, for this example, we use glove-twitter-25' embedding model.

>>> # import the modules
>>> from wefe.query import Query
>>> from wefe.word_embedding_model import WordEmbeddingModel
>>> from wefe.metrics.WEAT import WEAT
>>> import gensim.downloader as api
>>> # load glove
>>> twitter_25 = api.load('glove-twitter-25')
>>> model = WordEmbeddingModel(twitter_25, 'glove twitter dim=25')

2. Create the Query with a loaded, fetched or custom target and attribute word sets. In this case, we manually set both target words and attribute words.

>>> # create the word sets
>>> target_sets = [['she', 'woman', 'girl'], ['he', 'man', 'boy']]
>>> target_sets_names = ['Female Terms', 'Male Terms']
>>> attribute_sets = [['poetry','dance','literature'], ['math', 'physics', 'chemistry']]
>>> attribute_sets_names = ['Arts', 'Science']
>>> # create the query
>>> query = Query(target_sets, attribute_sets, target_sets_names,
>>>               attribute_sets_names)

3. Instantiate the metric to be used and then, execute run_query with the parameters created in the past steps. In this case we use the WEAT metric.

>>> # instance a WEAT metric
>>> weat = WEAT()
>>> result = weat.run_query(query, model)
>>> print(result)
  'query_name': 'Female Terms and Male Terms wrt Arts and Science',
  'result': 0.2595698336760204,
  'weat': 0.2595698336760204,
  'effect_size': 1.452482230821006,
  'p_value': nan

A score greater than 0 indicates that there is indeed a biased relationship between women and the arts with respect to men and science.

For more advanced usage, visit user the User Guide section.