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pyproject.toml
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pyproject.toml
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[tool.ruff]
line-length = 120
[tool.coverage.run]
omit = [
"eventdetector_ts/plotter/*",
"eventdetector_ts/prediction/*",
"eventdetector_ts/metamodel/*",
"eventdetector_ts/optimization/event_extraction_pipeline.py",
"eventdetector_ts/models/models_trainer.py"
]
source = ["eventdetector_ts"]
[build-system]
requires = ["flit_core>=3.4"]
build-backend = "flit_core.buildapi"
[project]
name = "eventdetector_ts"
version = "1.1.0"
description = "EventDetector introduces a universal event detection method for multivariate time series. Unlike traditional deep-learning methods, it's regression-based, requiring only reference events. The robust stacked ensemble, from Feed-Forward Neural Networks to Transformers, ensures accuracy by mitigating biases. The package supports practical implementation, excelling in detecting events with precision, validated across diverse domains."
keywords = [
"Universal Event Detection",
"Multivariate Time Series",
"Regression-based",
"Stacked Ensemble Learning",
"Deep Learning Models",
"Feed-Forward Neural Networks",
"Transformers",
"Event Detection Package",
"Rare Events",
"Imbalanced Datasets",
"Anomaly Detection",
"Change Point Detection",
"Fraud Detection",
"Empirical Validations"
]
authors = [
{ name = "Menouar Azib", email = "menouar.azib@akkodis.com" }
]
maintainers = [
{ name = "Menouar Azib", email = "menouar.azib@akkodis.com" }
]
requires-python = ">=3.9"
readme = "README.md"
license = { file = "LICENSE" }
classifiers = ["License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3", "Operating System :: OS Independent"]
dependencies = [
"joblib",
"matplotlib",
"numpy",
"pandas",
"python_dateutil",
"scikit_learn",
"scipy",
"seaborn",
"sympy",
"colorlog",
"pydot",
"pyqt5",
"tqdm"
]
[project.urls]
"Homepage" = "https://github.com/menouarazib/eventdetector"
"Bug Tracker" = "https://github.com/menouarazib/eventdetector/issues"