fedot-ind 0.3.1

Automated machine learning framework for time series analysis

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Fedot.Ind is a automated machine learning framework designed to solve industrial problems related to time series forecasting, classification, regression, and anomaly detection. It is based on the AutoML framework FEDOT and utilizes its functionality to build and tune pipelines.

Installation

Fedot.Ind is available on PyPI and can be installed via pip:

pip install fedot_ind

To install the latest version from the main branch:

git clone https://github.com/aimclub/Fedot.Industrial.git
cd FEDOT.Industrial
pip install -r requirements.txt
pytest -s test/

How to Use

Fedot.Ind provides a high-level API that allows you to use its capabilities in a simple way. The API can be used for classification, regression, and time series forecasting problems, as well as for anomaly detection.

To use the API, follow these steps:

1. Import FedotIndustrial class

from fedot_ind.api.main import FedotIndustrial

2. Initialize the FedotIndustrial object and define the type of modeling task. It provides a fit/predict interface:

• FedotIndustrial.fit() begins the feature extraction, optimization and returns the resulting composite pipeline;

• FedotIndustrial.predict() predicts target values for the given input data using an already fitted pipeline;

• FedotIndustrial.get_metrics() estimates the quality of predictions using selected metrics.

NumPy arrays or Pandas DataFrames can be used as sources of input data. In the case below, x_train, y_train and x_test are numpy.ndarray():

model = Fedot(task='ts_classification', timeout=5, strategy='quantile', n_jobs=-1, window_mode=True, window_size=20)
model.fit(features=x_train, target=y_train)
prediction = model.predict(features=x_test)
metrics = model.get_metrics(target=y_test)

More information about the API is available in the documentation section.

Documentation and examples

The comprehensive documentation is available on readthedocs.

Useful tutorials and examples can be found in the examples folder.

Topic	Example
Time series classification	Basic and Advanced
Time series regression	Example
Forecasting	SSA example
Anomaly detection	soon will be available
Computer vision	Classification, Object detection
Model ensemble	Notebook

R&D plans

– Expansion of anomaly detection model list.

– Development of new time series forecasting models.

– Implementation of explainability module (Issue)

Citation

Here we will provide a list of citations for the project as soon as the articles are published.

@article{REVIN2023110483,
title = {Automated machine learning approach for time series classification pipelines using evolutionary optimisation},
journal = {Knowledge-Based Systems},
pages = {110483},
year = {2023},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2023.110483},
url = {https://www.sciencedirect.com/science/article/pii/S0950705123002332},
author = {Ilia Revin and Vadim A. Potemkin and Nikita R. Balabanov and Nikolay O. Nikitin
}