classifier-anshp 0.1.0

A linear classifier built using tensorflow.

classifier-linear

classifier-linear is a simple linear classifier built using TensorFlow. This package includes a customizable linear model for binary classification and provides sample datasets for quick testing. The classifier is designed for flexibility and can be applied to custom datasets or pre-defined datasets, such as logical operations (AND, OR, XOR, XNOR).

Features

• Customizable Linear Classifier: Modify input features, learning rate, and number of steps to train a linear model for binary classification.
• Predefined Datasets: Two sample datasets are included:
  • Dataset 1: Generates a synthetic dataset using multivariate Gaussian distributions.
  • Dataset 2: Logical operations (AND, OR, XOR, XNOR) for testing the classifier.
• TensorFlow Backend: Leverages TensorFlow for handling large-scale computations and efficient gradient-based optimization.

Installation

You can install the package directly from PyPI:

pip install classifier-linear

Usage

1. Training a Custom Linear Classifier

The clsfr function allows you to train a linear classifier on your own dataset.

from classifier_linear import clsfr
import numpy as np

# Example dataset
inputs = np.array([[1.0, 2.0], [1.5, 1.8], [5.0, 8.0], [8.0, 8.0]])
val_true = np.array([0, 0, 1, 1])

# Train classifier with learning rate of 0.01 for 50 steps
val_pred, weights, bias = clsfr(inputs, val_true, lrn_rt=0.01, steps=50)

# Predicted output
print(f'Predictions: {val_pred}')
print(f'Weights: {weights}')
print(f'Bias: {bias}')

2. Using Sample Datasets

You can quickly test the classifier using the predefined datasets. The package provides two functions for generating data:

• Dataset 1: Generates a random binary classification dataset using multivariate normal distribution.
• Dataset 2: Logical operations like AND, OR, XOR, and XNOR.

from classifier_linear import dataset_1, dataset_2

# Example 1: Generate random dataset
inputs, val_true = dataset_1(sample_size=100)

# Example 2: Logical OR operation
inputs, val_true = dataset_2(num=2)  # 1 for AND, 2 for OR, 3 for XOR, 4 for XNOR

3. Running from Command Line

After installing the package, you can run the classifier or dataset generation directly from the command line.

Run Classifier:

classifier-anshp --inputs [[1,2],[3,4]] --val_true [0,1] --lrn_rt 0.01 --steps 50

Generate Dataset 1:

dataset-1 --sample_size 100

Generate Dataset 2 (Logical Operations):

dataset-2 --num 2

Advantages Over scikit-learn

1. TensorFlow-Based: While scikit-learn provides efficient linear models, classifier-linear is built using TensorFlow, which allows for more customization and seamless integration with larger deep learning frameworks.

2. Gradient-Based Optimization: The package uses TensorFlow’s gradient-based optimization, making it easy to modify and extend for more complex models beyond linear classifiers.

3. Dataset Flexibility: In addition to custom datasets, classifier-linear comes with logical operation datasets, making it easier to test and experiment with common machine learning tasks.

4. Custom Training Intervals: The classifier provides detailed control over training steps, ensuring outputs are printed every 10 intervals, unlike scikit-learn, where training steps aren’t typically exposed.

Limitations

• Primarily designed for binary classification tasks.
• Requires basic knowledge of TensorFlow to fully customize the model.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Author

Anshuman Pattnaik
Email: [e-mail]

For more details, visit the GitHub Repository.