diff --git a/LICENSE b/LICENSE index 05880ac..6f5f4e8 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2020 Doctorado-ML +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 diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..ca256b9 --- /dev/null +++ b/Makefile @@ -0,0 +1,39 @@ +SHELL := /bin/bash +.DEFAULT_GOAL := help +.PHONY: coverage deps help lint push test + +coverage: ## Run tests with coverage + coverage erase + coverage run -m unittest -v stree.tests + coverage report -m + +deps: ## Install dependencies + pip install -r requirements.txt + +lint: ## Lint and static-check + black stree + flake8 stree + mypy stree + +push: ## Push code with tags + git push && git push --tags + +test: ## Run tests + python -m unittest -v stree.tests + +help: ## Show help message + @IFS=$$'\n' ; \ + help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \ + printf "%s\n\n" "Usage: make [task]"; \ + printf "%-20s %s\n" "task" "help" ; \ + printf "%-20s %s\n" "------" "----" ; \ + for help_line in $${help_lines[@]}; do \ + IFS=$$':' ; \ + help_split=($$help_line) ; \ + help_command=`echo $${help_split[0]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \ + help_info=`echo $${help_split[2]} | sed -e 's/^ *//' -e 's/ *$$//'` ; \ + printf '\033[36m'; \ + printf "%-20s %s" $$help_command ; \ + printf '\033[0m'; \ + printf "%s\n" $$help_info; \ + done diff --git a/README.md b/README.md index 9485495..860b901 100644 --- a/README.md +++ b/README.md @@ -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 | \ | 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 | \ | 1e5 | Hard limit on iterations within solver, or -1 for no limit. | | \* | random_state | \ | 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 | \ | None | Specifies the maximum depth of the tree | @@ -45,6 +49,7 @@ pip install git+https://github.com/doctorado-ml/stree | | min_samples_split | \ | 0 | The minimum number of samples required to split an internal node. 0 (default) for any | | | max_features | \, \

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 | {"best", "random"} | 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 a random combination.
The algorithm generates 5 candidates at most to choose from in both strategies. | +| | normalize | \ | 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 diff --git a/codecov.yml b/codecov.yml index 222249f..38df3f6 100644 --- a/codecov.yml +++ b/codecov.yml @@ -1,12 +1,12 @@ -overage: +coverage: status: project: default: - target: 90% + target: 100% comment: layout: "reach, diff, flags, files" behavior: default - require_changes: false + require_changes: false require_base: yes - require_head: yes - branches: null \ No newline at end of file + require_head: yes + branches: null diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..56c8516 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,3 @@ +sphinx +sphinx-rtd-theme +myst-parser diff --git a/docs/source/api/Siterator.rst b/docs/source/api/Siterator.rst new file mode 100644 index 0000000..74cabcc --- /dev/null +++ b/docs/source/api/Siterator.rst @@ -0,0 +1,9 @@ +Siterator +========= + +.. automodule:: stree +.. autoclass:: Siterator + :members: + :undoc-members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/Snode.rst b/docs/source/api/Snode.rst new file mode 100644 index 0000000..8234371 --- /dev/null +++ b/docs/source/api/Snode.rst @@ -0,0 +1,9 @@ +Snode +===== + +.. automodule:: stree +.. autoclass:: Snode + :members: + :undoc-members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/Splitter.rst b/docs/source/api/Splitter.rst new file mode 100644 index 0000000..e69921e --- /dev/null +++ b/docs/source/api/Splitter.rst @@ -0,0 +1,9 @@ +Splitter +======== + +.. automodule:: stree +.. autoclass:: Splitter + :members: + :undoc-members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/Stree.rst b/docs/source/api/Stree.rst new file mode 100644 index 0000000..1f0de94 --- /dev/null +++ b/docs/source/api/Stree.rst @@ -0,0 +1,9 @@ +Stree +===== + +.. automodule:: stree +.. autoclass:: Stree + :members: + :undoc-members: + :private-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/source/api/index.rst b/docs/source/api/index.rst new file mode 100644 index 0000000..e7f78cc --- /dev/null +++ b/docs/source/api/index.rst @@ -0,0 +1,11 @@ +API index +========= + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + Stree + Splitter + Snode + Siterator diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..443436b --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,55 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import sys + +sys.path.insert(0, os.path.abspath("../../stree/")) + + +# -- Project information ----------------------------------------------------- + +project = "STree" +copyright = "2020 - 2021, Ricardo Montañana Gómez" +author = "Ricardo Montañana Gómez" + +# The full version, including alpha/beta/rc tags +release = "1.0" + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ["myst_parser", "sphinx.ext.autodoc", "sphinx.ext.viewcode"] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ["_templates"] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = [] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = "sphinx_rtd_theme" + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ["_static"] diff --git a/docs/source/example.md b/docs/source/example.md new file mode 100644 index 0000000..794175f --- /dev/null +++ b/docs/source/example.md @@ -0,0 +1,44 @@ +# Examples + +## Notebooks + +- [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?urlpath=lab/tree/notebooks/benchmark.ipynb) Benchmark + +- [![benchmark](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/benchmark.ipynb) Benchmark + +- [![features](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/features.ipynb) Some features + +- [![Gridsearch](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/gridsearch.ipynb) Gridsearch + +- [![Ensemble](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Doctorado-ML/STree/blob/master/notebooks/ensemble.ipynb) Ensembles + +## Sample Code + +```python +import time +from sklearn.model_selection import train_test_split +from sklearn.datasets import load_iris +from stree import Stree + +random_state = 1 +X, y = load_iris(return_X_y=True) +Xtrain, Xtest, ytrain, ytest = train_test_split( + X, y, test_size=0.2, random_state=random_state +) +now = time.time() +print("Predicting with max_features=sqrt(n_features)") +clf = Stree(random_state=random_state, max_features="auto") +clf.fit(Xtrain, ytrain) +print(f"Took {time.time() - now:.2f} seconds to train") +print(clf) +print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}") +print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}") +print("=" * 40) +print("Predicting with max_features=n_features") +clf = Stree(random_state=random_state) +clf.fit(Xtrain, ytrain) +print(f"Took {time.time() - now:.2f} seconds to train") +print(clf) +print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}") +print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}") +``` diff --git a/docs/source/example.png b/docs/source/example.png new file mode 100644 index 0000000..d4492e7 Binary files /dev/null and b/docs/source/example.png differ diff --git a/docs/source/hyperparameters.md b/docs/source/hyperparameters.md new file mode 100644 index 0000000..a4fa6f7 --- /dev/null +++ b/docs/source/hyperparameters.md @@ -0,0 +1,28 @@ +# Hyperparameters + +| | **Hyperparameter** | **Type/Values** | **Default** | **Meaning** | +| --- | ------------------ | ------------------------------------------------------ | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| \* | C | \ | 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’. | +| \* | max_iter | \ | 1e5 | Hard limit on iterations within solver, or -1 for no limit. | +| \* | random_state | \ | 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 | \ | None | Specifies the maximum depth of the tree | +| \* | tol | \ | 1e-4 | Tolerance for stopping criterion. | +| \* | degree | \ | 3 | Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels. | +| \* | gamma | {"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 | {"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 | {“gini”, “entropy”} | entropy | The function to measure the quality of a split (only used if max_features != num_features).
Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. | +| | min_samples_split | \ | 0 | The minimum number of samples required to split an internal node. 0 (default) for any | +| | max_features | \, \

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 | {"best", "random"} | 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 a random combination.
The algorithm generates 5 candidates at most to choose from in both strategies. | +| | normalize | \ | 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 + +\*\* **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. diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..ccfdd26 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,15 @@ +Welcome to STree's documentation! +================================= + +.. toctree:: + :caption: Contents: + :titlesonly: + + + stree + install + hyperparameters + example + api/index + +* :ref:`genindex` \ No newline at end of file diff --git a/docs/source/install.rst b/docs/source/install.rst new file mode 100644 index 0000000..d83e55d --- /dev/null +++ b/docs/source/install.rst @@ -0,0 +1,16 @@ +Install +======= + +The main stable release + +``pip install stree`` + +or the last development branch + +``pip install git+https://github.com/doctorado-ml/stree`` + +Tests +***** + + +``python -m unittest -v stree.tests`` \ No newline at end of file diff --git a/docs/source/stree.md b/docs/source/stree.md new file mode 100644 index 0000000..cc72399 --- /dev/null +++ b/docs/source/stree.md @@ -0,0 +1,13 @@ +# Stree + +[![Codeship Status for Doctorado-ML/STree](https://app.codeship.com/projects/8b2bd350-8a1b-0138-5f2c-3ad36f3eb318/status?branch=master)](https://app.codeship.com/projects/399170) +[![codecov](https://codecov.io/gh/doctorado-ml/stree/branch/master/graph/badge.svg)](https://codecov.io/gh/doctorado-ml/stree) +[![Codacy Badge](https://app.codacy.com/project/badge/Grade/35fa3dfd53a24a339344b33d9f9f2f3d)](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade) + +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. + +![Stree](./example.png) + +## License + +STree is [MIT](https://github.com/doctorado-ml/stree/blob/master/LICENSE) licensed diff --git a/main.py b/main.py deleted file mode 100644 index cd22040..0000000 --- a/main.py +++ /dev/null @@ -1,29 +0,0 @@ -import time -from sklearn.model_selection import train_test_split -from sklearn.datasets import load_iris -from stree import Stree - -random_state = 1 - -X, y = load_iris(return_X_y=True) - -Xtrain, Xtest, ytrain, ytest = train_test_split( - X, y, test_size=0.3, random_state=random_state -) - -now = time.time() -print("Predicting with max_features=sqrt(n_features)") -clf = Stree(C=0.01, random_state=random_state, max_features="auto") -clf.fit(Xtrain, ytrain) -print(f"Took {time.time() - now:.2f} seconds to train") -print(clf) -print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}") -print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}") -print("=" * 40) -print("Predicting with max_features=n_features") -clf = Stree(C=0.01, random_state=random_state) -clf.fit(Xtrain, ytrain) -print(f"Took {time.time() - now:.2f} seconds to train") -print(clf) -print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}") -print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}") diff --git a/setup.py b/setup.py index 588f7a3..b56823d 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,5 @@ import setuptools - -__version__ = "1.0rc1" -__author__ = "Ricardo Montañana Gómez" +import stree def readme(): @@ -9,22 +7,23 @@ def readme(): return f.read() +VERSION = stree.__version__ setuptools.setup( name="STree", - version=__version__, - license="MIT License", + version=stree.__version__, + license=stree.__license__, description="Oblique decision tree with svm nodes", long_description=readme(), long_description_content_type="text/markdown", packages=setuptools.find_packages(), - url="https://github.com/doctorado-ml/stree", - author=__author__, - author_email="ricardo.montanana@alu.uclm.es", + url=stree.__url__, + author=stree.__author__, + author_email=stree.__author_email__, keywords="scikit-learn oblique-classifier oblique-decision-tree decision-\ tree svm svc", classifiers=[ - "Development Status :: 4 - Beta", - "License :: OSI Approved :: MIT License", + "Development Status :: 5 - Production/Stable", + "License :: OSI Approved :: " + stree.__license__, "Programming Language :: Python :: 3.8", "Natural Language :: English", "Topic :: Scientific/Engineering :: Artificial Intelligence", diff --git a/stree/Strees.py b/stree/Strees.py index b99140d..2f40eb1 100644 --- a/stree/Strees.py +++ b/stree/Strees.py @@ -1,9 +1,5 @@ """ -__author__ = "Ricardo Montañana Gómez" -__copyright__ = "Copyright 2020, Ricardo Montañana Gómez" -__license__ = "MIT" -__version__ = "0.9" -Build an oblique tree classifier based on SVM nodes +Oblique decision tree classifier based on SVM nodes """ import os @@ -17,7 +13,6 @@ from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.svm import SVC, LinearSVC from sklearn.feature_selection import SelectKBest from sklearn.preprocessing import StandardScaler -from sklearn.utils import check_consistent_length from sklearn.utils.multiclass import check_classification_targets from sklearn.exceptions import ConvergenceWarning from sklearn.utils.validation import ( @@ -26,7 +21,6 @@ from sklearn.utils.validation import ( check_is_fitted, _check_sample_weight, ) -from sklearn.metrics._classification import _weighted_sum, _check_targets class Snode: @@ -147,12 +141,11 @@ class Snode: f"{self._belief: .6f} impurity={self._impurity:.4f} " f"counts={count_values}" ) - else: - return ( - f"{self._title} feaures={self._features} impurity=" - f"{self._impurity:.4f} " - f"counts={count_values}" - ) + return ( + f"{self._title} feaures={self._features} impurity=" + f"{self._impurity:.4f} " + f"counts={count_values}" + ) class Siterator: @@ -298,6 +291,23 @@ class Splitter: def _select_best_set( self, dataset: np.array, labels: np.array, features_sets: list ) -> list: + """Return the best set of features among feature_sets, the criterion is + the information gain + + Parameters + ---------- + dataset : np.array + array of samples (# samples, # features) + labels : np.array + array of labels + features_sets : list + list of features sets to check + + Returns + ------- + list + best feature set + """ max_gain = 0 selected = None warnings.filterwarnings("ignore", category=ConvergenceWarning) @@ -451,6 +461,15 @@ class Splitter: def partition(self, samples: np.array, node: Snode, train: bool): """Set the criteria to split arrays. Compute the indices of the samples that should go to one side of the tree (up) + + Parameters + ---------- + samples : np.array + array of samples (# samples, # features) + node : Snode + Node of the tree where partition is going to be made + train : bool + Train time - True / Test time - False """ # data contains the distances of every sample to every class hyperplane # array of (m, nc) nc = # classes @@ -602,7 +621,9 @@ class Stree(BaseEstimator, ClassifierMixin): f"Maximum depth has to be greater than 1... got (max_depth=\ {self.max_depth})" ) - + kernels = ["linear", "rbf", "poly", "sigmoid"] + if self.kernel not in kernels: + raise ValueError(f"Kernel {self.kernel} not in {kernels}") check_classification_targets(y) X, y = check_X_y(X, y) sample_weight = _check_sample_weight( @@ -633,7 +654,6 @@ class Stree(BaseEstimator, ClassifierMixin): 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._build_predictor() self.X_ = X self.y_ = y return self @@ -681,6 +701,7 @@ class Stree(BaseEstimator, ClassifierMixin): if np.unique(y).shape[0] == 1: # only 1 class => pure dataset node.set_title(title + ", ") + node.make_predictor() return node # Train the model clf = self._build_clf() @@ -699,6 +720,7 @@ class Stree(BaseEstimator, ClassifierMixin): if X_U is None or X_D is None: # didn't part anything node.set_title(title + ", ") + node.make_predictor() return node node.set_up( self.train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})") @@ -710,20 +732,8 @@ class Stree(BaseEstimator, ClassifierMixin): ) return node - def _build_predictor(self): - """Process the leaves to make them predictors""" - - def run_tree(node: Snode): - if node.is_leaf(): - node.make_predictor() - return - run_tree(node.get_down()) - run_tree(node.get_up()) - - run_tree(self.tree_) - def _build_clf(self): - """Build the correct classifier for the node""" + """Build the right classifier for the node""" return ( LinearSVC( max_iter=self.max_iter, @@ -739,6 +749,7 @@ class Stree(BaseEstimator, ClassifierMixin): C=self.C, gamma=self.gamma, degree=self.degree, + random_state=self.random_state, ) ) @@ -820,36 +831,6 @@ class Stree(BaseEstimator, ClassifierMixin): ) return self.classes_[result] - def score( - self, X: np.array, y: np.array, sample_weight: np.array = None - ) -> float: - """Compute accuracy of the prediction - - Parameters - ---------- - X : np.array - dataset of samples to make predictions - y : np.array - samples labels - sample_weight : np.array, optional - weights of the samples. Rescale C per sample, by default None - - Returns - ------- - float - accuracy of the prediction - """ - # sklearn check - check_is_fitted(self) - check_classification_targets(y) - X, y = check_X_y(X, y) - y_pred = self.predict(X).reshape(y.shape) - # Compute accuracy for each possible representation - _, y_true, y_pred = _check_targets(y, y_pred) - check_consistent_length(y_true, y_pred, sample_weight) - score = y_true == y_pred - return _weighted_sum(score, sample_weight, normalize=True) - def nodes_leaves(self) -> tuple: """Compute the number of nodes and leaves in the built tree diff --git a/stree/__init__.py b/stree/__init__.py index 6768b82..d58a553 100644 --- a/stree/__init__.py +++ b/stree/__init__.py @@ -1,3 +1,11 @@ from .Strees import Stree, Snode, Siterator, Splitter +__version__ = "1.0" + +__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"] diff --git a/stree/tests/Stree_test.py b/stree/tests/Stree_test.py index afbab36..de9861c 100644 --- a/stree/tests/Stree_test.py +++ b/stree/tests/Stree_test.py @@ -21,6 +21,21 @@ class Stree_test(unittest.TestCase): def setUp(cls): os.environ["TESTING"] = "1" + def test_valid_kernels(self): + valid_kernels = ["linear", "rbf", "poly", "sigmoid"] + X, y = load_dataset() + for kernel in valid_kernels: + clf = Stree(kernel=kernel) + clf.fit(X, y) + self.assertIsNotNone(clf.tree_) + + def test_bogus_kernel(self): + kernel = "other" + X, y = load_dataset() + clf = Stree(kernel=kernel) + with self.assertRaises(ValueError): + clf.fit(X, y) + def _check_tree(self, node: Snode): """Check recursively that the nodes that are not leaves have the correct number of labels and its sons have the right number of elements @@ -484,13 +499,13 @@ class Stree_test(unittest.TestCase): clf.fit(X, y) nodes, leaves = clf.nodes_leaves() self.assertEqual(25, nodes) - self.assertEquals(13, leaves) + self.assertEqual(13, leaves) X, y = load_wine(return_X_y=True) clf = Stree(random_state=self._random_state) clf.fit(X, y) nodes, leaves = clf.nodes_leaves() self.assertEqual(9, nodes) - self.assertEquals(5, leaves) + self.assertEqual(5, leaves) def test_nodes_leaves_artificial(self): n1 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test1")