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

pip install git+https://github.com/doctorado-ml/stree

Examples

Jupyter notebooks

• Benchmark

• Benchmark

• Test features

• Adaboost

• Gridsearch

• Test Graphics

Hyperparameters

Hyperparameter	used in scikit	Values	Default	Meaning
C	Yes		1.0	Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.
kernel	Yes	{"linear", "poly", "rbf"}	linear	Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’ or ‘rbf’.
max_iter	Yes		1e5	Hard limit on iterations within solver, or -1 for no limit.
random_state	Yes		None	Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls
max_depth	No		None	Specifies the maximum depth of the tree
tol	Yes		1e-4	Tolerance for stopping criterion.
degree	Yes		3	Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
gamma	Yes	{"scale", "auto"} or	scale	Kernel coefficient for ‘rbf’ and ‘poly’. if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma, if ‘auto’, uses 1 / n_features.
split_criteria	No	{"impurity", "max_samples"}	impurity	Decides (just in case of a multi class classification) which column (class) use to split the dataset in a node**
criterion	No	{“gini”, “entropy”}	entropy	The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.
min_samples_split	No		0	The minimum number of samples required to split an internal node. 0 (default) for any
max_features	No	, or {“auto”, “sqrt”, “log2”}	None	The number of features to consider when looking for the split: If int, then consider max_features features at each split. If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split. If “auto”, then max_features=sqrt(n_features). If “sqrt”, then max_features=sqrt(n_features). If “log2”, then max_features=log2(n_features). If None, then max_features=n_features.
splitter	No	{"best", "random"}		The strategy used to choose the feature set at each node (only used if max_features != num_features). Supported strategies are “best” to choose the best feature set and “random” to choose the a random combination. The algorithm generates 5 candidates at most to choose from.

** 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 haas 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 a positive distance to hyperplane from the rest.

Tests

python -m unittest -v stree.tests