chicksexer - Python package for gender classification

chicksexer is a Python package that performs gender classification. It receives a string of person name and returns the probability estimate of its gender as follows:

>>> from chicksexer import predict_gender
>>> predict_gender('John Smith')
{'female': 0.0027230381965637207, 'male': 0.9972769618034363}

Several merits of using the classifier instead of simply looking up known male/female names are:

• Sometimes simple name lookup does not work. For instance, "Miki" is a Japanese female name, but also a Croatian male name.
• Can predict the gender of a name that does not exist in the list of male/female names.
• Can deal with a typo in a name relatively easily.

You can also get an estimate as a simple string as follows:

>>> predict_gender('Oliver Butterfield', return_proba=False)
'male'
>>> predict_gender('Naila Ata', return_proba=False)
'female'
>>> predict_gender('Saldivar Anderson', return_proba=False)
'neutral'
>>> predict_gender('Ponyo', return_proba=False)  # name of a character from the film
'male'
>>> predict_gender('Ponya', return_proba=False)  # modify the name such that it sounds like a female name
'female'
>>> predict_gender('Miki Suzuki', return_proba=True)  # Suzuki here is a Japanese surname so Miki is a female name
{'female': 0.9997618066990981, 'male': 0.00023819330090191215}
>>> predict_gender('Miki Adamić', return_proba=True)  # Adamić is a Croatian surname so Miki is a male name
{'female': 0.16958969831466675, 'male': 0.8304103016853333}
>>> predict_gender('Jessica')
{'female': 0.999996105068476, 'male': 3.894931523973355e-06}
>>> predict_gender('Jesssica')  # typo in Jessica
{'female': 0.9999851534785194, 'male': 1.4846521480649244e-05}

If you want to predict the gender of multiple names, use predict_genders (plural) function instead:

>>> from chicksexer import predict_genders
>>> predict_genders(['Ichiro Suzuki', 'Haruki Murakami'])
[{'female': 3.039836883544922e-05, 'male': 0.9999696016311646},
 {'female': 1.2040138244628906e-05, 'male': 0.9999879598617554}]
>>> predict_genders(['Ichiro Suzuki', 'Haruki Murakami'], return_proba=False)
['male', 'male']

Installation

• This repository can run on Ubuntu 14.04 LTS & Mac OSX 10.x (not tested on other OSs)
• Tested only on Python 3.5

chicksexer depends on NumPy and Scipy, Python packages for scientific computing. You might need to have them installed prior to installing chicksexer.

You can install chicksexer by:

pip install chicksexer

chicksexer also depends on tensorflow package. In default, it tries to install the CPU-only version of tensorflow. If you want to use GPU, you need to install tensorflow with GPU support by yourself. (C.f. Installing Tensorflow)

Model Architecture

The gender classifier is implemented using Character-level Multilayer LSTM. The architecture is roughly as follows:

1. Character Embedding Layer
2. 1st LSTM Layer
3. 2nd LSTM Layer
4. Pooling Layer
5. Fully Connected Layer

The fully connected layer outputs the probability of a name bing a male name. For the details, look at _build_graph() method in chicksexer/_classifier.py, which implements the computational graph of the architecture in tensorflow.

Training Data

Names with gender annotation are obtained from the sources as follows:

• Dbpedia Person Data
• Popular baby names in the US
• Names dataset curated by Milos Bejda