| Name | STree JSON |
| Version |
1.4.0
JSON |
| download |
| home_page | None |
| Summary | Oblique decision tree with svm nodes. |
| upload_time | 2024-08-15 10:31:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.11 |
| license | MIT License
Copyright (c) 2020-2021, Ricardo Montañana Gómez
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
| keywords |
decision-tree
oblique-classifier
oblique-decision-tree
scikit-learn
svc
svm
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
scikit-learn
mufs
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
[](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml)
[](https://codecov.io/gh/doctorado-ml/stree)
[](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)
[](https://badge.fury.io/py/STree)
[](https://zenodo.org/badge/latestdoi/262658230)
# STree
Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.
## Installation
```bash
pip install Stree
```
## Documentation
Can be found in [stree.readthedocs.io](https://stree.readthedocs.io/en/stable/)
## Examples
### Jupyter notebooks
- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark
- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Some features
- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch
- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/ensemble.ipynb) Ensembles
## Hyperparameters
| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |
| --- | ------------------- | -------------------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| \* | C | \<float\> | 1.0 | Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. |
| \* | 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 |
| \* | max_iter | \<int\> | 1e5 | Hard limit on iterations within solver, or -1 for no limit. |
| \* | random_state | \<int\> | None | Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False.<br>Pass an int for reproducible output across multiple function calls |
| | max_depth | \<int\> | None | Specifies the maximum depth of the tree |
| \* | tol | \<float\> | 1e-4 | Tolerance for stopping criterion. |
| \* | degree | \<int\> | 3 | Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels. |
| \* | gamma | {"scale", "auto"} or \<float\> | scale | Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.<br>if gamma='scale' (default) is passed then it uses 1 / (n_features \* X.var()) as value of gamma,<br>if ‘auto’, uses 1 / n_features. |
| | split_criteria | {"impurity", "max_samples"} | impurity | Decides (just in case of a multi class classification) which column (class) use to split the dataset in a node\*\*. max_samples is incompatible with 'ovo' multiclass_strategy |
| | 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", "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 |
| | normalize | \<bool\> | False | If standardization of features should be applied on each node with the samples that reach it |
| \* | multiclass_strategy | {"ovo", "ovr"} | "ovo" | Strategy to use with multiclass datasets, **"ovo"**: one versus one. **"ovr"**: one versus rest |
\* Hyperparameter used by the support vector classifier of every node
\*\* **Splitting in a STree node**
The decision function is applied to the dataset and distances from samples to hyperplanes are computed in a matrix. This matrix has as many columns as classes the samples belongs to (if more than two, i.e. multiclass classification) or 1 column if it's a binary class dataset. In binary classification only one hyperplane is computed and therefore only one column is needed to store the distances of the samples to it. If three or more classes are present in the dataset we need as many hyperplanes as classes are there, and therefore one column per hyperplane is needed.
In case of multiclass classification we have to decide which column take into account to make the split, that depends on hyperparameter _split_criteria_, if "impurity" is chosen then STree computes information gain of every split candidate using each column and chooses the one that maximize the information gain, otherwise STree choses the column with more samples with a predicted class (the column with more positive numbers in it).
Once we have the column to take into account for the split, the algorithm splits samples with positive distances to hyperplane from the rest.
## Tests
```bash
python -m unittest -v stree.tests
```
## License
STree is [MIT](https://github.com/doctorado-ml/stree/blob/master/LICENSE) licensed
## Reference
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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"description": "\n[](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml)\n[](https://codecov.io/gh/doctorado-ml/stree)\n[](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)\n[](https://badge.fury.io/py/STree)\n\n[](https://zenodo.org/badge/latestdoi/262658230)\n\n# STree\n\nOblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.\n\n\n\n## Installation\n\n```bash\npip install Stree\n```\n\n## Documentation\n\nCan be found in [stree.readthedocs.io](https://stree.readthedocs.io/en/stable/)\n\n## Examples\n\n### Jupyter notebooks\n\n- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark\n\n- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Some features\n\n- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch\n\n- [](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/ensemble.ipynb) Ensembles\n\n## Hyperparameters\n\n| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** |\n| --- | ------------------- | -------------------------------------------------------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n| \\* | C | \\<float\\> | 1.0 | Regularization parameter. 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Ignored by all other kernels. |\n| \\* | gamma | {\"scale\", \"auto\"} or \\<float\\> | scale | Kernel coefficient for \u2018rbf\u2019, \u2018poly\u2019 and \u2018sigmoid\u2019.<br>if gamma='scale' (default) is passed then it uses 1 / (n_features \\* X.var()) as value of gamma,<br>if \u2018auto\u2019, uses 1 / n_features. |\n| | split_criteria | {\"impurity\", \"max_samples\"} | impurity | Decides (just in case of a multi class classification) which column (class) use to split the dataset in a node\\*\\*. max_samples is incompatible with 'ovo' multiclass_strategy |\n| | criterion | {\u201cgini\u201d, \u201centropy\u201d} | entropy | The function to measure the quality of a split (only used if max_features != num_features). <br>Supported criteria are \u201cgini\u201d for the Gini impurity and \u201centropy\u201d for the information gain. |\n| | min_samples_split | \\<int\\> | 0 | The minimum number of samples required to split an internal node. 0 (default) for any |\n| | max_features | \\<int\\>, \\<float\\> <br><br>or {\u201cauto\u201d, \u201csqrt\u201d, \u201clog2\u201d} | 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 \u201cauto\u201d, then max_features=sqrt(n_features).<br>If \u201csqrt\u201d, then max_features=sqrt(n_features).<br>If \u201clog2\u201d, then max_features=log2(n_features).<br>If None, then max_features=n_features. |\n| | 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).\nSupported strategies are: **\u201cbest\u201d**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features. **\u201crandom\u201d**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them. **\u201ctrandom\u201d**: 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 |\n| | normalize | \\<bool\\> | False | If standardization of features should be applied on each node with the samples that reach it |\n| \\* | multiclass_strategy | {\"ovo\", \"ovr\"} | \"ovo\" | Strategy to use with multiclass datasets, **\"ovo\"**: one versus one. **\"ovr\"**: one versus rest |\n\n\\* Hyperparameter used by the support vector classifier of every node\n\n\\*\\* **Splitting in a STree node**\n\nThe decision function is applied to the dataset and distances from samples to hyperplanes are computed in a matrix. 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