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v1.2.2
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cff-version: 1.2.0
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message: "If you use this software, please cite it as below."
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authors:
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- family-names: "Montañana"
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given-names: "Ricardo"
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orcid: "https://orcid.org/0000-0003-3242-5452"
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- family-names: "Gámez"
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given-names: "José A."
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orcid: "https://orcid.org/0000-0003-1188-1117"
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- family-names: "Puerta"
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given-names: "José M."
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orcid: "https://orcid.org/0000-0002-9164-5191"
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title: "STree"
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version: 1.0.2
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doi: 10.5281/zenodo.5504083
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date-released: 2021-11-02
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url: "https://github.com/Doctorado-ML/STree"
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preferred-citation:
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type: article
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authors:
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- family-names: "Montañana"
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given-names: "Ricardo"
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orcid: "https://orcid.org/0000-0003-3242-5452"
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- family-names: "Gámez"
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given-names: "José A."
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orcid: "https://orcid.org/0000-0003-1188-1117"
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- family-names: "Puerta"
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given-names: "José M."
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orcid: "https://orcid.org/0000-0002-9164-5191"
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doi: "10.1007/978-3-030-85713-4_6"
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journal: "Lecture Notes in Computer Science"
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month: 9
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start: 54
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end: 64
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title: "STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines"
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volume: 12882
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year: 2021
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@@ -37,7 +37,7 @@ Can be found in [stree.readthedocs.io](https://stree.readthedocs.io/en/stable/)
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## Hyperparameters
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| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |
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| --- | ------------------- | ------------------------------------------------------ | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| --- | ------------------- | -------------------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| \* | C | \<float\> | 1.0 | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. |
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| \* | kernel | {"liblinear", "linear", "poly", "rbf", "sigmoid"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘liblinear’, ‘linear’, ‘poly’ or ‘rbf’. liblinear uses [liblinear](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) library and the rest uses [libsvm](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) library through scikit-learn library |
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| \* | max_iter | \<int\> | 1e5 | Hard limit on iterations within solver, or -1 for no limit. |
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@@ -50,7 +50,7 @@ Can be found in [stree.readthedocs.io](https://stree.readthedocs.io/en/stable/)
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| | 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. |
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| | min_samples_split | \<int\> | 0 | The minimum number of samples required to split an internal node. 0 (default) for any |
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| | 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. |
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| | splitter | {"best", "random", "mutual", "cfs", "fcbf", "iwss"} | "random" | The strategy used to choose the feature set at each node (only used if max_features < num_features). Supported strategies are: **“best”**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features. **“random”**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them. **“trandom”**: The algorithm generates a true random combination. **"mutual"**: Chooses the best features w.r.t. their mutual info with the label. **"cfs"**: Apply Correlation-based Feature Selection. **"fcbf"**: Apply Fast Correlation-Based Filter. **"iwss"**: IWSS based algorithm |
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| | splitter | {"best", "random", "trandom", "mutual", "cfs", "fcbf", "iwss"} | "random" | The strategy used to choose the feature set at each node (only used if max_features < num_features). Supported strategies are: **“best”**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features. **“random”**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them. **“trandom”**: The algorithm generates only one random combination. **"mutual"**: Chooses the best features w.r.t. their mutual info with the label. **"cfs"**: Apply Correlation-based Feature Selection. **"fcbf"**: Apply Fast Correlation-Based Filter. **"iwss"**: IWSS based algorithm |
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| | normalize | \<bool\> | False | If standardization of features should be applied on each node with the samples that reach it |
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| \* | multiclass_strategy | {"ovo", "ovr"} | "ovo" | Strategy to use with multiclass datasets, **"ovo"**: one versus one. **"ovr"**: one versus rest |
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@@ -73,3 +73,7 @@ python -m unittest -v stree.tests
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## License
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STree is [MIT](https://github.com/doctorado-ml/stree/blob/master/LICENSE) licensed
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## Reference
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R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class oblique decision tree based on support vector machines.", 2021 LNAI 12882, pg. 54-64
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@@ -54,4 +54,4 @@ html_theme = "sphinx_rtd_theme"
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ["_static"]
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html_static_path = []
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@@ -1,7 +1,7 @@
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# Hyperparameters
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| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |
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| --- | ------------------- | ------------------------------------------------------ | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| --- | ------------------- | -------------------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| \* | C | \<float\> | 1.0 | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. |
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| \* | kernel | {"liblinear", "linear", "poly", "rbf", "sigmoid"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘liblinear’, ‘linear’, ‘poly’ or ‘rbf’. liblinear uses [liblinear](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) library and the rest uses [libsvm](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) library through scikit-learn library |
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| \* | max_iter | \<int\> | 1e5 | Hard limit on iterations within solver, or -1 for no limit. |
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@@ -14,7 +14,7 @@
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| | 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. |
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| | min_samples_split | \<int\> | 0 | The minimum number of samples required to split an internal node. 0 (default) for any |
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| | 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. |
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| | splitter | {"best", "random", "mutual", "cfs", "fcbf", "iwss"} | "random" | The strategy used to choose the feature set at each node (only used if max_features < num_features). Supported strategies are: **“best”**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features. **“random”**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them. **“trandom”**: The algorithm generates a true random combination. **"mutual"**: Chooses the best features w.r.t. their mutual info with the label. **"cfs"**: Apply Correlation-based Feature Selection. **"fcbf"**: Apply Fast Correlation-Based Filter. **"iwss"**: IWSS based algorithm |
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| | splitter | {"best", "random", "trandom", "mutual", "cfs", "fcbf", "iwss"} | "random" | The strategy used to choose the feature set at each node (only used if max_features < num_features). Supported strategies are: **“best”**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features. **“random”**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them. **“trandom”**: The algorithm generates only one random combination. **"mutual"**: Chooses the best features w.r.t. their mutual info with the label. **"cfs"**: Apply Correlation-based Feature Selection. **"fcbf"**: Apply Fast Correlation-Based Filter. **"iwss"**: IWSS based algorithm |
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| | normalize | \<bool\> | False | If standardization of features should be applied on each node with the samples that reach it |
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| \* | multiclass_strategy | {"ovo", "ovr"} | "ovo" | Strategy to use with multiclass datasets, **"ovo"**: one versus one. **"ovr"**: one versus rest |
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@@ -202,7 +202,8 @@ class Splitter:
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max_features < num_features). Supported strategies are: “best”: sklearn
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SelectKBest algorithm is used in every node to choose the max_features
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best features. “random”: The algorithm generates 5 candidates and
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choose the best (max. info. gain) of them. "mutual": Chooses the best
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choose the best (max. info. gain) of them. “trandom”: The algorithm
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generates only one random combination. "mutual": Chooses the best
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features w.r.t. their mutual info with the label. "cfs": Apply
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Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
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Based, by default None
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@@ -478,6 +479,18 @@ class Splitter:
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@staticmethod
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def _entropy(y: np.array) -> float:
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"""Compute entropy of a labels set
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Parameters
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----------
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y : np.array
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set of labels
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Returns
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-------
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float
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entropy
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"""
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n_labels = len(y)
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if n_labels <= 1:
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return 0
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@@ -485,10 +498,13 @@ class Splitter:
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proportions = counts / n_labels
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n_classes = np.count_nonzero(proportions)
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if n_classes <= 1:
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return 0.0
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from scipy.stats import entropy
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return entropy(y, base=n_classes)
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return 0
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entropy = 0.0
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# Compute standard entropy.
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for prop in proportions:
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if prop != 0.0:
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entropy -= prop * log(prop, n_classes)
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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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@@ -82,7 +82,8 @@ class Stree(BaseEstimator, ClassifierMixin):
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max_features < num_features). Supported strategies are: “best”: sklearn
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SelectKBest algorithm is used in every node to choose the max_features
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best features. “random”: The algorithm generates 5 candidates and
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choose the best (max. info. gain) of them. "mutual": Chooses the best
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choose the best (max. info. gain) of them. “trandom”: The algorithm
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generates only one random combination. "mutual": Chooses the best
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features w.r.t. their mutual info with the label. "cfs": Apply
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Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
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Based , by default "random"
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@@ -128,7 +129,7 @@ class Stree(BaseEstimator, ClassifierMixin):
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References
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----------
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R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class
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oblique decision tree based on support vector machines.", 2021 LNAI...
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oblique decision tree based on support vector machines.", 2021 LNAI 12882
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"""
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@@ -1,6 +1,6 @@
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from .Strees import Stree, Siterator
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__version__ = "1.2.1"
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__version__ = "1.2.2"
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__author__ = "Ricardo Montañana Gómez"
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__copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
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