NeuroBCL 0.1.2

NeuroBCL (Neuro Bucket Classifier) is percentile based numeric feature normalizer that works to convert target bucket of numeric feature to its approximate bounds (Given multi-level features)

NeuroBCL

NeuroBCL (Neuro Bucket Classifier) is a Python package for finding ranges for numeric features normalized with percentile distribution, the basic idea is to divide the range of the feature into buckets and then classify the data into these buckets.

Installation

[Coming Soon] Install the neurobcl package with pip:

$ pip install neurobcl

Or install the latest package directly from github

$ pip install git+https://github.com/searchX/neurobcl

Example Usage

A simple example of using the package is as follows:

Index classifier on our sample data below:

from neurobcl.main import train_from_dictionary
classifier = train_from_dictionary([
        {"color": "red", "size": "small", "price": 100},
        {"color": "blue", "size": "small", "price": 200},
        {"color": "red", "size": "large", "price": 300},
        {"color": "blue", "size": "large", "price": 400},
    ], ["color", "size"], ["price"])

1. Minimum price that should be greater than for it to be in bucket 1 atleast

classifier.get("price", 1, '>')
# Output: 100

2. This is the price that will be the limit of all items that can exist until bucket 4

classifier.get("price", 4, '<')
# Output: 400

3. Use filter, to get the items that are in bucket 1 and color blue

classifier.get("price", 1, '>', filters={"color": "blue"})
# Output: 200

Please look into official docs for more information - https://searchx.github.io/neurobcl/