A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning
Description
pyoselm is a Python library for machine learning models with Extreme Machine Learning (ELM) and Online Sequential Machine Learning (OS-ELM). It allows to fit models for regression and classification tasks, both in batch and online learning (either row-by-row or chunk-by-chunk).
This library offers a scikit-learn like API for easy usage. For more details about setup and usage, check the documentation.
IMPORTANT: This library was developed as a research project. It may not be production-ready, so please be aware of that.
Setup
The easiest way to install this library is using pip:
$ pip install pyoselm
Usage
Here a simple but complete example of usage.
from pyoselm import OSELMRegressor, OSELMClassifier
from sklearn.datasets import load_digits, make_regression
from sklearn.model_selection import train_test_split
print("Regression task")
# Model
oselmr = OSELMRegressor(n_hidden=20, activation_func='sigmoid', random_state=123)
# Data
X, y = make_regression(n_samples=1000, n_targets=1, n_features=10, random_state=123)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
n_batch = 40
# Fit model with chunks of data
for i in range(20):
X_batch = X_train[i*n_batch:(i+1)*n_batch]
y_batch = y_train[i*n_batch:(i+1)*n_batch]
oselmr.fit(X_batch, y_batch)
print("Train score for batch %i: %s" % (i+1, str(oselmr.score(X_batch, y_batch))))
# Results
print("Train score of total: %s" % str(oselmr.score(X_train, y_train)))
print("Test score of total: %s" % str(oselmr.score(X_test, y_test)))
print("")
print("Classification task")
# Model
oselmc = OSELMClassifier(n_hidden=20, activation_func='sigmoid', random_state=123)
# Data
X, y = load_digits(n_class=5, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Sequential learning
# The first batch of data must have the same size as n_hidden to achieve the first phase (boosting)
batches_x = [X_train[:oselmc.n_hidden]] + [[x_i] for x_i in X_train[oselmc.n_hidden:]]
batches_y = [y_train[:oselmc.n_hidden]] + [[y_i] for y_i in y_train[oselmc.n_hidden:]]
for b_x, b_y in zip(batches_x, batches_y):
oselmc.fit(b_x, b_y)
print("Train score of total: %s" % str(oselmc.score(X_train, y_train)))
print("Test score of total: %s" % str(oselmc.score(X_test, y_test)))
