ProbabilisticNN 0.0.1

Python package for Probabilistic Neural Networks and General Regression Neural Networks

ProbabilisticNN

ProbabilisticNN is a Python library for Probabilistic Neural Networks and General Regression Neural Networks with an sklearn-like interface.

At the current stage, the library is published on TestPyPI and supports:

• PNN for classification;
• AdaptivePNN with kernel bandwidth parameter tuning;
• GRNN for regression;
• AdaptiveGRNN with kernel bandwidth optimization;
• execution using the standard numpy implementation;
• accelerated inference via numba.

Main Features

• sklearn-like interface: fit, predict, predict_proba;
• several kernel functions:
  • gaussian;
  • laplacian;
  • exponential;
• several kernel bandwidth parameterization schemes for AdaptivePNN:
  • per_feature;
  • per_class;
  • per_class_per_feature;
• accelerated numba backend for the computationally heavy part of inference;
• testing via pytest and tox.

Requirements

• Python 3.12+
• numpy
• scipy
• scikit-learn

For the accelerated backend, numba is additionally required.

Installation

Installation from TestPyPI

For regular usage without numba:

python -m pip install \
  --index-url https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple/ \
  "ProbabilisticNN"

For installation with the numba backend:

python -m pip install \
  --index-url https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple/ \
  "ProbabilisticNN[numba]"

For development and testing:

python -m pip install \
  --index-url https://test.pypi.org/simple/ \
  --extra-index-url https://pypi.org/simple/ \
  "ProbabilisticNN[dev]"

Local Installation from the Repository

This requires setuptools.

python -m build
python -m pip install -e ".[dev]"

Quick Start

Classification with PNN

import numpy as np

from probabilisticnn.pnn import PNN

X_train = np.array([
    [0.0, 0.0],
    [0.0, 0.2],
    [1.0, 1.0],
    [1.0, 1.2],
])
y_train = np.array(["class_a", "class_a", "class_b", "class_b"])

X_test = np.array([
    [0.0, 0.1],
    [1.0, 1.1],
])

model = PNN(
    bandwidth=0.25,
    kernel="gaussian",
    normalize=False,
)

model.fit(X_train, y_train)

labels = model.predict(X_test)
proba = model.predict_proba(X_test)

print(labels)
print(proba)

Classification with AdaptivePNN

import numpy as np

from probabilisticnn.pnn import AdaptivePNN

X_train = np.array([
    [0.0, 0.0],
    [0.0, 0.2],
    [1.0, 1.0],
    [1.0, 1.2],
])
y_train = np.array([0, 0, 1, 1])

model = AdaptivePNN(
    kernel="gaussian",
    bandwidth_sharing="per_feature",
    loss="correct_class_probability",
    solver="auto",
    normalize=False,
)

model.fit(X_train, y_train)

print(model.predict(X_train))
print(model.predict_proba(X_train))
print(model.bandwidth_)

Regression with GRNN

import numpy as np

from probabilisticnn.grnn import GRNN

X_train = np.array([
    [0.0, 0.0],
    [0.0, 0.2],
    [1.0, 1.0],
    [1.0, 1.2],
])
y_train = np.array([1.0, 1.1, 3.0, 3.1])

X_test = np.array([
    [0.0, 0.1],
    [1.0, 1.1],
])

model = GRNN(
    bandwidth=0.5,
    kernel="gaussian",
    normalize=False,
)

model.fit(X_train, y_train)
pred = model.predict(X_test)

print(pred)

Regression with AdaptiveGRNN

import numpy as np

from probabilisticnn.grnn import AdaptiveGRNN

X_train = np.array([
    [0.0, 0.0],
    [0.0, 0.2],
    [1.0, 1.0],
    [1.0, 1.2],
])
y_train = np.array([1.0, 1.1, 3.0, 3.1])

model = AdaptiveGRNN(
    kernel="gaussian",
    loss="mse",
    solver="auto",
    normalize=False,
)

model.fit(X_train, y_train)

print(model.predict(X_train))
print(model.bandwidth_)

Acceleration with numba

For PNN, AdaptivePNN, GRNN, and AdaptiveGRNN, you can choose:

• backend="numpy" — the main option;
• backend="numba" — the accelerated option.

Example:

from probabilisticnn.pnn import PNN

model = PNN(
    bandwidth=0.25,
    kernel="gaussian",
    backend="numba",
    normalize=False,
)

Important:

• the first predict call for the numba branch may be slower because of JIT compilation;
• the main speed gain is achieved during the computation of kernel function values;
• if numba is not installed, the numba backend will be unavailable.

Public Interface

Models should be imported like this:

from probabilisticnn.pnn import PNN, AdaptivePNN
from probabilisticnn.grnn import GRNN, AdaptiveGRNN

Model Parameters

PNN

Main parameters:

• bandwidth — fixed kernel bandwidth;
• kernel — kernel type;
• losses — class weighting scheme / misclassification cost for classes;
• normalize — whether to use L2 normalization of inputs;
• backend — numpy or numba;
• compute_dtype — auto, float32, float64.

AdaptivePNN

Additionally supports:

• loss — optimization objective function;
• bandwidth_sharing — method for defining bandwidth parameters;
• max_iter — maximum number of optimization steps;
• solver — optimization method;
• solver_options — additional optimizer parameters.

GRNN

Main parameters:

• bandwidth;
• kernel;
• backend;
• compute_dtype;
• normalize.

AdaptiveGRNN

Additionally supports:

• loss — loss function for regression;
• max_iter;
• solver;
• solver_options;
• normalize.

AdaptiveGRNN optimizes the kernel bandwidth using the per_feature scheme.

Testing

Preferred way to run tests:

tox

Or run a specific test file:

pytest tests/test_pnn.py

Since the tests import the installed package, it is most convenient to run them in a prepared environment after installing the dependencies.

Performance

The repository contains separate materials for measuring performance:

• benchmarks/inference_bemchmarks.py
• benchmarks/benchmarking.ipynb

Examples

An additional usage example is located at:

• examples/basic_usage.ipynb

License

The project is distributed under the MIT license. See the LICENCE file.