Snell Scoring Module

This module provides an implementation of the Snell scoring method for ordered categorical data. It includes functionality to compute scores based on a frequency table and standardize the results to a 0-100 scale if needed.

Installation

To use the module, clone the repository and install the necessary dependencies:

git clone [email protected]:IgorekLoschinin/snellscore.git
cd .\snellscore
pip install -r requirements.txt

or

pip install snellscore

Usage

Import the Module

from snellscore import Snell
import pandas as pd

Prepare the Frequency Table

The input should be a pandas DataFrame where rows represent groups and columns represent categories.

# Example frequency table
data = {
    "Category1": [0, 6, 0, 0, 0, 2, 3, 0, 1, 2, 0, 5],
    "Category2": [0, 3, 0, 4, 0, 4, 4, 0, 2, 2, 0, 1],
    "Category3": [0, 1, 3, 1, 0, 3, 3, 1, 0, 2, 0, 1],
    "Category4": [3, 0, 2, 2, 0, 1, 0, 1, 0, 0, 0, 0],
    "Category5": [3, 1, 2, 4, 2, 0, 1, 1, 1, 2, 4, 1],
    "Category6": [2, 1, 4, 0, 5, 2, 1, 5, 4, 4, 1, 3],
    "Category7": [4, 0, 1, 1, 5, 0, 0, 4, 4, 0, 7, 1]
}
frequency_table = pd.DataFrame(data, index=[f"Group{item}" for item in range(1, 13)])

Initialize the Snell Object

Create an instance of the Snell class, optionally enabling score standardization.

snell = Snell(standard=True)

Run the Scoring Method

Pass the frequency table to the run method.

snell.run(frequency_table)

Retrieve the Scores

Get the calculated scores and standardized scores (if enabled):

# Get the calculated scores
scores = snell.score
print("Calculated Scores:")
print(scores)

# Get the standardized scores (0-100 scale)
standardized_scores = snell.score_standard
print("Standardized Scores:")
print(standardized_scores)

Output Example

For the example frequency table, the output might look like:

Snell Scores:
Category0   -1.072418
Category1    0.611888
Category2    1.602323
Category3    2.186739
Category4    2.837535
Category5    4.012972
Category6    5.850891
dtype: float64

Standardized Scores:
Category0      0
Category1     24
Category2     39
Category3     47
Category4     56
Category5     73
Category6    100
dtype: Int16

Testing

To run tests for the module, use pytest:

pytest tests/

References

1. https://www.icar.org/Guidelines/07.6-Functional-traits-Calving-Traits-in-Dairy-Cattle.pdf
2. https://cdnsciencepub.com/doi/pdf/10.4141/cjas77-001
3. https://www.jstor.org/stable/2528498?origin=JSTOR-pdf
4. https://github.com/pfpetrowski/rsnell

License

This project is licensed under the GNU General Public License. See the LICENSE file for details.