mirror of
https://github.com/Doctorado-ML/STree.git
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Change project builder to hatch
Update actions in Makefile
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42
Makefile
42
Makefile
@@ -1,46 +1,36 @@
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SHELL := /bin/bash
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.DEFAULT_GOAL := help
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.PHONY: coverage deps help lint push test doc build
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.PHONY: audit coverage help lint test doc doc-clean build
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coverage: ## Run tests with coverage
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coverage erase
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coverage run -m unittest -v stree.tests
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coverage report -m
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deps: ## Install dependencies
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pip install -r requirements.txt
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devdeps: ## Install development dependencies
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pip install black pip-audit flake8 mypy coverage
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@coverage erase
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@coverage run -m unittest -v stree.tests
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@coverage report -m
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lint: ## Lint and static-check
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black stree
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flake8 stree
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mypy stree
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push: ## Push code with tags
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git push && git push --tags
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@black stree
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@flake8 stree
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test: ## Run tests
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python -m unittest -v stree.tests
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@python -m unittest -v stree.tests
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doc: ## Update documentation
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make -C docs --makefile=Makefile html
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@make -C docs --makefile=Makefile html
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build: ## Build package
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rm -fr dist/*
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rm -fr build/*
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python setup.py sdist bdist_wheel
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@rm -fr dist/*
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@rm -fr build/*
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@hatch build
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doc-clean: ## Update documentation
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make -C docs --makefile=Makefile clean
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doc-clean: ## Clean documentation folders
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@make -C docs --makefile=Makefile clean
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audit: ## Audit pip
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pip-audit
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@pip-audit
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help: ## Show help message
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help: ## Show this help message
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@IFS=$$'\n' ; \
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help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
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help_lines=(`grep -Fh "##" $(MAKEFILE_LIST) | grep -Fv fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
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printf "%s\n\n" "Usage: make [task]"; \
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printf "%-20s %s\n" "task" "help" ; \
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printf "%-20s %s\n" "------" "----" ; \
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@@ -1,17 +1,10 @@
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[build-system]
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requires = ["setuptools", "scikit-learn>1.0", "numpy", "mufs"]
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build-backend = "setuptools.build_meta"
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[tool.setuptools]
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packages = ["stree"]
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license-files = ["LICENSE"]
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[tool.setuptools.dynamic]
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version = { attr = "stree.__version__" }
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[project]
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name = "STree"
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dependencies = ["scikit-learn>1.0", "numpy", "mufs"]
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dependencies = ["scikit-learn>1.0", "mufs"]
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license = { file = "LICENSE" }
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description = "Oblique decision tree with svm nodes."
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readme = "README.md"
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@@ -45,12 +38,19 @@ classifiers = [
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]
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[project.optional-dependencies]
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dev = ["black", "flake8", "mypy", "coverage"]
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dev = ["black", "flake8", "coverage", "hatch", "pip-audit"]
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doc = ["sphinx", "myst-parser", "sphinx_rtd_theme", "sphinx-autodoc-typehints"]
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[project.urls]
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Code = "https://github.com/Doctorado-ML/STree"
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Documentation = "https://stree.readthedocs.io/en/latest/index.html"
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[tool.hatch.version]
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path = "stree/_version.py"
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[tool.hatch.build.targets.sdist]
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include = ["/stree"]
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[tool.coverage.run]
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branch = true
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source = ["stree"]
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@@ -62,7 +62,7 @@ fail_under = 100
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[tool.black]
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line-length = 79
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target_version = ['py311']
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target-version = ["py311"]
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include = '\.pyi?$'
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exclude = '''
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/(
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@@ -414,7 +414,8 @@ class Splitter:
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)
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return tuple(
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sorted(
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range(len(feature_list)), key=lambda sub: feature_list[sub]
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range(len(feature_list)),
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key=lambda sub: feature_list[sub],
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)[-max_features:]
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)
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@@ -529,7 +530,10 @@ class Splitter:
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return entropy
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def information_gain(
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self, labels: np.array, labels_up: np.array, labels_dn: np.array
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self,
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labels: np.array,
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labels_up: np.array,
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labels_dn: np.array,
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) -> float:
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"""Compute information gain of a split candidate
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@@ -175,7 +175,8 @@ class Stree(BaseEstimator, ClassifierMixin):
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return __version__
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def __call__(self) -> str:
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"""Only added to comply with scikit-learn base estimator for ensemble"""
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"""Only added to comply with scikit-learn base sestimator for ensembles
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"""
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return self.version()
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def _more_tags(self) -> dict:
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@@ -188,7 +189,10 @@ class Stree(BaseEstimator, ClassifierMixin):
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return {"requires_y": True}
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def fit(
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self, X: np.ndarray, y: np.ndarray, sample_weight: np.array = None
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self,
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X: np.ndarray,
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y: np.ndarray,
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sample_weight: np.array = None,
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) -> "Stree":
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"""Build the tree based on the dataset of samples and its labels
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@@ -343,7 +347,11 @@ class Stree(BaseEstimator, ClassifierMixin):
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)
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node.set_down(
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self._train(
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X_D, y_d, sw_d, depth + 1, title + f" - Down({depth+1})"
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X_D,
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y_d,
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sw_d,
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depth + 1,
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title + f" - Down({depth+1})",
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)
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)
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return node
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