Metadata-Version: 2.1
Name: NeuroBCL
Version: 0.1.2
Summary: 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)
Author-email: Harishankar Kumar <hari01584@gmail.com>
License: MIT
Description-Content-Type: text/markdown
Requires-Dist: sphinx>=7.2.6
Requires-Dist: furo>=2024.1.29

# 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](https://pypi.org/project/neurobcl):

```console
$ pip install neurobcl
```

Or install the latest package directly from github

```console
$ 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:

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

3. Use filter, to get the items that are in bucket 1 and color blue
```python
classifier.get("price", 1, '>', filters={"color": "blue"})
# Output: 200
```

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