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Package doc #7 (#34)

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* Add first doc info to sources

* Update doc to separate classes in api

* Refactor build_predictor

* Fix random_sate issue in non linear kernels

* Refactor score method using base class implementation

* Some quality refactoring

* Fix codecov config.

* Add sigmoid kernel

* Refactor setup and add Makefile
This commit is contained in:
Ricardo Montañana Gómez
2021-04-26 09:10:01 +02:00
committed by GitHub
parent 02de394c96
commit e19d10f6a7
23 changed files with 369 additions and 106 deletions
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11
README.md
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@@ -14,6 +14,10 @@ Oblique Tree classifier based on SVM nodes. The nodes are built and splitted wit
pip install git+https://github.com/doctorado-ml/stree
```
## Documentation
Can be found in
## Examples
### Jupyter notebooks
@@ -33,7 +37,7 @@ pip install git+https://github.com/doctorado-ml/stree
| | **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 | {"linear", "poly", "rbf"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’ or ‘rbf’. |
| \* | kernel | {"linear", "poly", "rbf", "sigmoid"} | linear | Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’ or ‘rbf’. |
| \* | 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 |
@@ -45,6 +49,7 @@ pip install git+https://github.com/doctorado-ml/stree
| | 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. |
| | 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
@@ -61,3 +66,7 @@ Once we have the column to take into account for the split, the algorithm splits
```bash
python -m unittest -v stree.tests
```
## License
STree is [MIT](https://github.com/doctorado-ml/stree/blob/master/LICENSE) licensed