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Update Readme

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Add max_features > n_features test
Add make doc
This commit is contained in:
Ricardo Montañana Gómez
2021-04-27 23:15:21 +02:00
parent e19d10f6a7
commit 28c7558f01
6 changed files with 26 additions and 8 deletions
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5
Makefile
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@@ -21,6 +21,9 @@ push: ## Push code with tags
test: ## Run tests
python -m unittest -v stree.tests
doc: ## Update documentation
make -C docs --makefile=Makefile html
help: ## Show help message
@IFS=$$'\n' ; \
help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
@@ -36,4 +39,4 @@ help: ## Show help message
printf "%-20s %s" $$help_command ; \
printf '\033[0m'; \
printf "%s\n" $$help_info; \
done
done

2
README.md
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@@ -48,7 +48,7 @@ Can be found in
| | criterion | {“gini”, “entropy”} | entropy | The function to measure the quality of a split (only used if max_features != num_features). <br>Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. |
| | min_samples_split | \<int\> | 0 | The minimum number of samples required to split an internal node. 0 (default) for any |
| | max_features | \<int\>, \<float\> <br><br>or {“auto”, “sqrt”, “log2”} | None | The number of features to consider when looking for the split:<br>If int, then consider max_features features at each split.<br>If float, then max_features is a fraction and int(max_features \* n_features) features are considered at each split.<br>If “auto”, then max_features=sqrt(n_features).<br>If “sqrt”, then max_features=sqrt(n_features).<br>If “log2”, then max_features=log2(n_features).<br>If None, then max_features=n_features. |
| | splitter | {"best", "random"} | random | The strategy used to choose the feature set at each node (only used if max_features != num_features). <br>Supported strategies are “best” to choose the best feature set and “random” to choose a random combination. <br>The algorithm generates 5 candidates at most to choose from in both strategies. |
| | splitter | {"best", "random"} | random | The strategy used to choose the feature set at each node (only used if max_features < num_features). <br>Supported strategies are “best” to choose the best feature set and “random” to choose a random combination. <br>The algorithm generates 5 candidates at most to choose from if random is selected. If best is selected sklearn SelectKBest algorithm is used in every node to choose the _max_features_ best features |
| | normalize | \<bool\> | False | If standardization of features should be applied on each node with the samples that reach it |
\* Hyperparameter used by the support vector classifier of every node

6
setup.py
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@@ -16,7 +16,11 @@ setuptools.setup(
long_description=readme(),
long_description_content_type="text/markdown",
packages=setuptools.find_packages(),
url=stree.__url__,
url="https://github.com/Doctorado-ML/STree#stree",
project_urls={
"Code": "https://github.com/Doctorado-ML/STree",
"Documentation": "https://stree.readthedocs.io/en/latest/index.html",
},
author=stree.__author__,
author_email=stree.__author_email__,
keywords="scikit-learn oblique-classifier oblique-decision-tree decision-\

14
stree/Strees.py
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@@ -653,12 +653,12 @@ class Stree(BaseEstimator, ClassifierMixin):
self.n_features_ = X.shape[1]
self.n_features_in_ = X.shape[1]
self.max_features_ = self._initialize_max_features()
self.tree_ = self.train(X, y, sample_weight, 1, "root")
self.tree_ = self._train(X, y, sample_weight, 1, "root")
self.X_ = X
self.y_ = y
return self
def train(
def _train(
self,
X: np.ndarray,
y: np.ndarray,
@@ -723,10 +723,10 @@ class Stree(BaseEstimator, ClassifierMixin):
node.make_predictor()
return node
node.set_up(
self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
self._train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
)
node.set_down(
self.train(
self._train(
X_D, y_d, sw_d, depth + 1, title + f" - Down({depth+1})"
)
)
@@ -892,6 +892,12 @@ class Stree(BaseEstimator, ClassifierMixin):
elif self.max_features is None:
max_features = self.n_features_
elif isinstance(self.max_features, numbers.Integral):
if self.max_features > self.n_features_:
raise ValueError(
"Invalid value for max_features. "
"It can not be greater than number of features "
f"({self.n_features_})"
)
max_features = self.max_features
else: # float
if self.max_features > 0.0:

1
stree/__init__.py
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@@ -6,6 +6,5 @@ __author__ = "Ricardo Montañana Gómez"
__copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
__license__ = "MIT License"
__author_email__ = "ricardo.montanana@alu.uclm.es"
__url__ = "https://github.com/doctorado-ml/stree"
__all__ = ["Stree", "Snode", "Siterator", "Splitter"]

6
stree/tests/Stree_test.py
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@@ -269,6 +269,12 @@ class Stree_test(unittest.TestCase):
with self.assertRaises(ValueError):
_ = clf._initialize_max_features()
def test_wrong_max_features(self):
X, y = load_dataset(n_features=15)
clf = Stree(max_features=16)
with self.assertRaises(ValueError):
clf.fit(X, y)
def test_get_subspaces(self):
dataset = np.random.random((10, 16))
y = np.random.randint(0, 2, 10)