FEDOT vs Lale
FEDOT
fedot.readthedocs.ioLale
lale.readthedocs.ioSide by side
| Figure | FEDOT | Lale |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | — |
| Plans published | 1 | 0 |
| Spec score · AutoML Software | 8.7 (#4) | 7.2 (#19) |
| automation | Users can choose full automation by omitting parameters or partial automation by supplying parameters for manual composing. | Lale provides a shared interface to pipeline search tools including Hyperopt, GridSearchCV, and SMAC. |
| Data types | FEDOT works with tabular, image, and text data, including multimodal data from more than one source. | The FAQ says Lale has been used with tables, text, images, time series, and multimodal data. |
| Installation | The project README documents installation with pip, including an extra option for image and text processing and deep neural networks. | Lale can be installed from PyPI with pip install lale, and the documentation describes Core and Full setup targets. |
| Operating systems | The quick-start guide lists Windows, Linux, and macOS as supported operating systems. | The installation guide says Lale can be used on Linux, Windows 10, or Mac OS X. |
| Purpose | FEDOT is an open-source framework for automated modeling and machine learning that builds end-to-end solutions using an evolutionary approach. | Lale is a Python library for semi-automated data science that helps select algorithms and tune pipeline hyperparameters in a type-safe way. |
Plans
In their own words
FEDOT
FEDOT is a free, open-source framework for automating the creation of data-driven composite models. It supports classification, regression, clustering and time-series forecasting, with tabular, text and image inputs, including multimodal combinations. Its workflow covers preprocessing, model selection, tuning, cross-validation and serialization. Users can leave parameters unspecified for fuller automation or provide them to guide pipeline composition. FEDOT uses the GOLEM library to optimize graph-based pipelines with meta-heuristic methods. Presets include best_quality, fast_train, stable, auto, gpu, ts and automl; auto is the default. Data can come from CSV files, pandas DataFrames, NumPy arrays or time-series CSV data. It can be installed with pip, and the API can be called from a console without Python code, saving predictions as CSV files. GPU evaluation uses RAPIDS and supports a specified set of models. The project is distributed under the BSD 3-Clause license and supports Windows, Linux and macOS.