<img style="display: inline;" src="docs/img/pyoselm_logo.png" width="300"/>
*A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning*
[](https://github.com/leferrad/pyoselm/actions/workflows/ci_pipeline.yml)
[](http://pyoselm.readthedocs.io/?badge=latest)
[](https://codecov.io/gh/leferrad/pyoselm)
### 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](http://readthedocs.org/projects/pyoselm/).
> **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.
```python
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)))
```
### Development
For development, this project uses [Poetry](https://python-poetry.org/) for dependency management:
```bash
# Install Poetry
curl -sSL https://install.python-poetry.org | python3 -
# Install dependencies
poetry install
# Activate environment
poetry shell
# Run tests
make test # Unit tests
make test-system # System tests
# Check code quality
make lint
```
### License
This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
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"description": "<img style=\"display: inline;\" src=\"docs/img/pyoselm_logo.png\" width=\"300\"/>\n\n*A Python implementation of Online Sequential Extreme Machine Learning (OS-ELM) for online machine learning*\n\n[](https://github.com/leferrad/pyoselm/actions/workflows/ci_pipeline.yml)\n[](http://pyoselm.readthedocs.io/?badge=latest)\n[](https://codecov.io/gh/leferrad/pyoselm)\n\n### Description\n\n**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).\n\nThis library offers a scikit-learn like API for easy usage. For more details about setup and usage, check the [documentation](http://readthedocs.org/projects/pyoselm/).\n\n> **IMPORTANT:** This library was developed as a research project. It may not be production-ready, so please be aware of that.\n\n### Setup\n\nThe easiest way to install this library is using `pip`:\n\n```\n$ pip install pyoselm\n```\n\n### Usage\n\nHere a simple but complete example of usage.\n\n```python\nfrom pyoselm import OSELMRegressor, OSELMClassifier\nfrom sklearn.datasets import load_digits, make_regression \nfrom sklearn.model_selection import train_test_split\n\nprint(\"Regression task\")\n# Model\noselmr = OSELMRegressor(n_hidden=20, activation_func='sigmoid', random_state=123)\n# Data\nX, y = make_regression(n_samples=1000, n_targets=1, n_features=10, random_state=123) \nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\nn_batch = 40\n\n# Fit model with chunks of data\nfor i in range(20):\n X_batch = X_train[i*n_batch:(i+1)*n_batch]\n y_batch = y_train[i*n_batch:(i+1)*n_batch]\n oselmr.fit(X_batch, y_batch)\n print(\"Train score for batch %i: %s\" % (i+1, str(oselmr.score(X_batch, y_batch))))\n\n# Results\nprint(\"Train score of total: %s\" % str(oselmr.score(X_train, y_train)))\nprint(\"Test score of total: %s\" % str(oselmr.score(X_test, y_test))) \nprint(\"\")\n\n\nprint(\"Classification task\")\n# Model \noselmc = OSELMClassifier(n_hidden=20, activation_func='sigmoid', random_state=123)\n# Data\nX, y = load_digits(n_class=5, return_X_y=True) \nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n\n# Sequential learning\n# The first batch of data must have the same size as n_hidden to achieve the first phase (boosting)\nbatches_x = [X_train[:oselmc.n_hidden]] + [[x_i] for x_i in X_train[oselmc.n_hidden:]]\nbatches_y = [y_train[:oselmc.n_hidden]] + [[y_i] for y_i in y_train[oselmc.n_hidden:]]\n\nfor b_x, b_y in zip(batches_x, batches_y):\n oselmc.fit(b_x, b_y)\n\nprint(\"Train score of total: %s\" % str(oselmc.score(X_train, y_train)))\nprint(\"Test score of total: %s\" % str(oselmc.score(X_test, y_test)))\n```\n\n### Development\n\nFor development, this project uses [Poetry](https://python-poetry.org/) for dependency management:\n\n```bash\n# Install Poetry\ncurl -sSL https://install.python-poetry.org | python3 -\n\n# Install dependencies\npoetry install\n\n# Activate environment\npoetry shell\n\n# Run tests\nmake test # Unit tests\nmake test-system # System tests\n\n# Check code quality\nmake lint\n```\n\n### License\n\nThis project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.\n",
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