logreg

This is a simple implementation of logistic regression using a gradient descent algorithm. The goal is to find the best line that separates a binary classified set of data.

Features

• Automatic feature scaling
• Customizable parameters (learning rate, iterations)

Data files

Data files must be in CSV format, with the following structure:

x1,x2,y
200,300,32
203,231,42
231,232,13

Where x1,x2,y constitutes the header of the data. This project supports multidimensional data, feel free to use any number of features.

Installation

This project relies on the Python 3 package NumPy. To install the requirements use Python pip:

$ pip install -r requirements.txt

I recommend that you use a virtual environment when installing your dependencies.

Usage

This project can be used from either the terminal, or as an imported module.

Terminal

$ python logreg.py mydataset.csv
Found the following parameters that best separates the data:
intercept = -3.466760519572871, x1 = 0.13641112675062128
Parameters accuracy: 70.0%

The following arguments are available:

• -h,--help: Display help on usage
• -a,--alpha: Set the learning rate manually (default is 0.01)
• -i,--iterations: Set the number of iterations manually (default is 1500)
• -ns,--noscaling: Turn off feature scaling (there is no feature scaling when using the normal equation method)

Imported module

import logreg
import numpy as np

features = np.asmatrix(np.random.rand(3, 3))
values = np.random.rand(3, 1)

# Gradient descent
iterations = 1500
alpha = 0.01
print(logreg.gradientdescent(features, values, iterations, alpha))

# Cost
parameters = np.random.rand(3, 1)
print(logreg.cost(features, values, parameters))

Requirements

Python 3+ is required, as well as the NumPy package.

License

Code copyright 2018 Søren Qvist Christensen. Code released under the MIT license.