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v1.3.0
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4
.github/workflows/main.yml
vendored
4
.github/workflows/main.yml
vendored
@@ -12,8 +12,8 @@ jobs:
|
||||
runs-on: ${{ matrix.os }}
|
||||
strategy:
|
||||
matrix:
|
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os: [macos-latest, ubuntu-latest]
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python: [3.8]
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os: [macos-latest, ubuntu-latest, windows-latest]
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python: [3.8, "3.10"]
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|
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steps:
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- uses: actions/checkout@v2
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|
||||
37
CITATION.cff
Normal file
37
CITATION.cff
Normal file
@@ -0,0 +1,37 @@
|
||||
cff-version: 1.2.0
|
||||
message: "If you use this software, please cite it as below."
|
||||
authors:
|
||||
- family-names: "Montañana"
|
||||
given-names: "Ricardo"
|
||||
orcid: "https://orcid.org/0000-0003-3242-5452"
|
||||
- family-names: "Gámez"
|
||||
given-names: "José A."
|
||||
orcid: "https://orcid.org/0000-0003-1188-1117"
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||||
- family-names: "Puerta"
|
||||
given-names: "José M."
|
||||
orcid: "https://orcid.org/0000-0002-9164-5191"
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||||
title: "STree"
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version: 1.2.3
|
||||
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"
|
||||
preferred-citation:
|
||||
type: article
|
||||
authors:
|
||||
- family-names: "Montañana"
|
||||
given-names: "Ricardo"
|
||||
orcid: "https://orcid.org/0000-0003-3242-5452"
|
||||
- family-names: "Gámez"
|
||||
given-names: "José A."
|
||||
orcid: "https://orcid.org/0000-0003-1188-1117"
|
||||
- family-names: "Puerta"
|
||||
given-names: "José M."
|
||||
orcid: "https://orcid.org/0000-0002-9164-5191"
|
||||
doi: "10.1007/978-3-030-85713-4_6"
|
||||
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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6
Makefile
6
Makefile
@@ -10,6 +10,9 @@ coverage: ## Run tests with coverage
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deps: ## Install dependencies
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pip install -r requirements.txt
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|
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devdeps: ## Install development dependencies
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pip install black pip-audit flake8 mypy coverage
|
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|
||||
lint: ## Lint and static-check
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black stree
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||||
flake8 stree
|
||||
@@ -32,6 +35,9 @@ build: ## Build package
|
||||
doc-clean: ## Update documentation
|
||||
make -C docs --makefile=Makefile clean
|
||||
|
||||
audit: ## Audit pip
|
||||
pip-audit
|
||||
|
||||
help: ## Show help message
|
||||
@IFS=$$'\n' ; \
|
||||
help_lines=(`fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##/:/'`); \
|
||||
|
||||
39
README.md
39
README.md
@@ -36,23 +36,24 @@ Can be found in [stree.readthedocs.io](https://stree.readthedocs.io/en/stable/)
|
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|
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## 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", "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 |
|
||||
| | 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** | **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
|
||||
|
||||
@@ -73,3 +74,7 @@ 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
|
||||
|
||||
@@ -12,19 +12,18 @@
|
||||
#
|
||||
import os
|
||||
import sys
|
||||
import stree
|
||||
from stree._version import __version__
|
||||
|
||||
sys.path.insert(0, os.path.abspath("../../stree/"))
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = "STree"
|
||||
copyright = "2020 - 2021, Ricardo Montañana Gómez"
|
||||
copyright = "2020 - 2022, Ricardo Montañana Gómez"
|
||||
author = "Ricardo Montañana Gómez"
|
||||
|
||||
# The full version, including alpha/beta/rc tags
|
||||
version = stree.__version__
|
||||
version = __version__
|
||||
release = version
|
||||
|
||||
|
||||
@@ -54,4 +53,4 @@ 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"]
|
||||
html_static_path = []
|
||||
|
||||
@@ -1,22 +1,22 @@
|
||||
# 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", "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 |
|
||||
| | 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** | **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’.<br>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\*\*.<br>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).<br>Supported strategies are:<br>**“best”**: sklearn SelectKBest algorithm is used in every node to choose the max_features best features.<br>**“random”**: The algorithm generates 5 candidates and choose the best (max. info. gain) of them.<br>**“trandom”**: The algorithm generates only one random combination.<br>**"mutual"**: Chooses the best features w.r.t. their mutual info with the label.<br>**"cfs"**: Apply Correlation-based Feature Selection.<br>**"fcbf"**: Apply Fast Correlation-Based Filter.<br>**"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:<br>**"ovo"**: one versus one.<br>**"ovr"**: one versus rest |
|
||||
|
||||
\* Hyperparameter used by the support vector classifier of every node
|
||||
|
||||
|
||||
14
setup.py
14
setup.py
@@ -1,4 +1,5 @@
|
||||
import setuptools
|
||||
import os
|
||||
|
||||
|
||||
def readme():
|
||||
@@ -6,9 +7,9 @@ def readme():
|
||||
return f.read()
|
||||
|
||||
|
||||
def get_data(field):
|
||||
def get_data(field, file_name="__init__.py"):
|
||||
item = ""
|
||||
with open("stree/__init__.py") as f:
|
||||
with open(os.path.join("stree", file_name)) as f:
|
||||
for line in f.readlines():
|
||||
if line.startswith(f"__{field}__"):
|
||||
delim = '"' if '"' in line else "'"
|
||||
@@ -19,9 +20,14 @@ def get_data(field):
|
||||
return item
|
||||
|
||||
|
||||
def get_requirements():
|
||||
with open("requirements.txt") as f:
|
||||
return f.read().splitlines()
|
||||
|
||||
|
||||
setuptools.setup(
|
||||
name="STree",
|
||||
version=get_data("version"),
|
||||
version=get_data("version", "_version.py"),
|
||||
license=get_data("license"),
|
||||
description="Oblique decision tree with svm nodes",
|
||||
long_description=readme(),
|
||||
@@ -44,7 +50,7 @@ setuptools.setup(
|
||||
"Topic :: Scientific/Engineering :: Artificial Intelligence",
|
||||
"Intended Audience :: Science/Research",
|
||||
],
|
||||
install_requires=["scikit-learn", "mufs"],
|
||||
install_requires=get_requirements(),
|
||||
test_suite="stree.tests",
|
||||
zip_safe=False,
|
||||
)
|
||||
|
||||
@@ -68,6 +68,7 @@ class Snode:
|
||||
self._impurity = impurity
|
||||
self._partition_column: int = -1
|
||||
self._scaler = scaler
|
||||
self._proba = None
|
||||
|
||||
@classmethod
|
||||
def copy(cls, node: "Snode") -> "Snode":
|
||||
@@ -127,23 +128,44 @@ class Snode:
|
||||
def get_up(self) -> "Snode":
|
||||
return self._up
|
||||
|
||||
def make_predictor(self):
|
||||
def make_predictor(self, num_classes: int) -> None:
|
||||
"""Compute the class of the predictor and its belief based on the
|
||||
subdataset of the node only if it is a leaf
|
||||
"""
|
||||
if not self.is_leaf():
|
||||
return
|
||||
classes, card = np.unique(self._y, return_counts=True)
|
||||
if len(classes) > 1:
|
||||
self._proba = np.zeros((num_classes,), dtype=np.int64)
|
||||
for c, n in zip(classes, card):
|
||||
self._proba[c] = n
|
||||
try:
|
||||
max_card = max(card)
|
||||
self._class = classes[card == max_card][0]
|
||||
self._belief = max_card / np.sum(card)
|
||||
except ValueError:
|
||||
self._class = None
|
||||
|
||||
def graph(self):
|
||||
"""
|
||||
Return a string representing the node in graphviz format
|
||||
"""
|
||||
output = ""
|
||||
count_values = np.unique(self._y, return_counts=True)
|
||||
if self.is_leaf():
|
||||
output += (
|
||||
f'N{id(self)} [shape=box style=filled label="'
|
||||
f"class={self._class} impurity={self._impurity:.3f} "
|
||||
f'counts={self._proba}"];\n'
|
||||
)
|
||||
else:
|
||||
self._belief = 1
|
||||
try:
|
||||
self._class = classes[0]
|
||||
except IndexError:
|
||||
self._class = None
|
||||
output += (
|
||||
f'N{id(self)} [label="#features={len(self._features)} '
|
||||
f"classes={count_values[0]} samples={count_values[1]} "
|
||||
f'({sum(count_values[1])})" fontcolor=black];\n'
|
||||
)
|
||||
output += f"N{id(self)} -> N{id(self.get_up())} [color=black];\n"
|
||||
output += f"N{id(self)} -> N{id(self.get_down())} [color=black];\n"
|
||||
return output
|
||||
|
||||
def __str__(self) -> str:
|
||||
count_values = np.unique(self._y, return_counts=True)
|
||||
@@ -202,7 +224,8 @@ class Splitter:
|
||||
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. "mutual": Chooses the best
|
||||
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, by default None
|
||||
@@ -366,9 +389,8 @@ class Splitter:
|
||||
.get_support(indices=True)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _fs_mutual(
|
||||
dataset: np.array, labels: np.array, max_features: int
|
||||
self, dataset: np.array, labels: np.array, max_features: int
|
||||
) -> tuple:
|
||||
"""Return the best features with mutual information with labels
|
||||
|
||||
@@ -388,7 +410,9 @@ class Splitter:
|
||||
indices of the features selected
|
||||
"""
|
||||
# return best features with mutual info with the label
|
||||
feature_list = mutual_info_classif(dataset, labels)
|
||||
feature_list = mutual_info_classif(
|
||||
dataset, labels, random_state=self._random_state
|
||||
)
|
||||
return tuple(
|
||||
sorted(
|
||||
range(len(feature_list)), key=lambda sub: feature_list[sub]
|
||||
|
||||
168
stree/Strees.py
168
stree/Strees.py
@@ -17,6 +17,7 @@ from sklearn.utils.validation import (
|
||||
_check_sample_weight,
|
||||
)
|
||||
from .Splitter import Splitter, Snode, Siterator
|
||||
from ._version import __version__
|
||||
|
||||
|
||||
class Stree(BaseEstimator, ClassifierMixin):
|
||||
@@ -82,7 +83,8 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
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. "mutual": Chooses the best
|
||||
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 , by default "random"
|
||||
@@ -128,7 +130,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
References
|
||||
----------
|
||||
R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class
|
||||
oblique decision tree based on support vector machines.", 2021 LNAI...
|
||||
oblique decision tree based on support vector machines.", 2021 LNAI 12882
|
||||
|
||||
|
||||
"""
|
||||
@@ -168,6 +170,11 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
self.normalize = normalize
|
||||
self.multiclass_strategy = multiclass_strategy
|
||||
|
||||
@staticmethod
|
||||
def version() -> str:
|
||||
"""Return the version of the package."""
|
||||
return __version__
|
||||
|
||||
def _more_tags(self) -> dict:
|
||||
"""Required by sklearn to supply features of the classifier
|
||||
make mandatory the labels array
|
||||
@@ -307,7 +314,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
if np.unique(y).shape[0] == 1:
|
||||
# only 1 class => pure dataset
|
||||
node.set_title(title + ", <pure>")
|
||||
node.make_predictor()
|
||||
node.make_predictor(self.n_classes_)
|
||||
return node
|
||||
# Train the model
|
||||
clf = self._build_clf()
|
||||
@@ -326,7 +333,7 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
if X_U is None or X_D is None:
|
||||
# didn't part anything
|
||||
node.set_title(title + ", <cgaf>")
|
||||
node.make_predictor()
|
||||
node.make_predictor(self.n_classes_)
|
||||
return node
|
||||
node.set_up(
|
||||
self._train(X_U, y_u, sw_u, depth + 1, title + f" - Up({depth+1})")
|
||||
@@ -360,28 +367,100 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _reorder_results(y: np.array, indices: np.array) -> np.array:
|
||||
"""Reorder an array based on the array of indices passed
|
||||
def __predict_class(self, X: np.array) -> np.array:
|
||||
"""Compute the predicted class for the samples in X. Returns the number
|
||||
of samples of each class in the corresponding leaf node.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
y : np.array
|
||||
data untidy
|
||||
indices : np.array
|
||||
indices used to set order
|
||||
X : np.array
|
||||
Array of samples
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.array
|
||||
array y ordered
|
||||
Array of shape (n_samples, n_classes) with the number of samples
|
||||
of each class in the corresponding leaf node
|
||||
"""
|
||||
# return array of same type given in y
|
||||
y_ordered = y.copy()
|
||||
indices = indices.astype(int)
|
||||
for i, index in enumerate(indices):
|
||||
y_ordered[index] = y[i]
|
||||
return y_ordered
|
||||
|
||||
def compute_prediction(xp, indices, node):
|
||||
if xp is None:
|
||||
return
|
||||
if node.is_leaf():
|
||||
# set a class for indices
|
||||
result[indices] = node._proba
|
||||
return
|
||||
self.splitter_.partition(xp, node, train=False)
|
||||
x_u, x_d = self.splitter_.part(xp)
|
||||
i_u, i_d = self.splitter_.part(indices)
|
||||
compute_prediction(x_u, i_u, node.get_up())
|
||||
compute_prediction(x_d, i_d, node.get_down())
|
||||
|
||||
# setup prediction & make it happen
|
||||
result = np.zeros((X.shape[0], self.n_classes_))
|
||||
indices = np.arange(X.shape[0])
|
||||
compute_prediction(X, indices, self.tree_)
|
||||
return result
|
||||
|
||||
def check_predict(self, X) -> np.array:
|
||||
"""Checks predict and predict_proba preconditions. If input X is not an
|
||||
np.array convert it to one.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X : np.ndarray
|
||||
Array of samples
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.array
|
||||
Array of samples
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If number of features of X is different of the number of features
|
||||
in training data
|
||||
"""
|
||||
check_is_fitted(self, ["tree_"])
|
||||
# Input validation
|
||||
X = check_array(X)
|
||||
if X.shape[1] != self.n_features_:
|
||||
raise ValueError(
|
||||
f"Expected {self.n_features_} features but got "
|
||||
f"({X.shape[1]})"
|
||||
)
|
||||
return X
|
||||
|
||||
def predict_proba(self, X: np.array) -> np.array:
|
||||
"""Predict class probabilities of the input samples X.
|
||||
|
||||
The predicted class probability is the fraction of samples of the same
|
||||
class in a leaf.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
X : dataset of samples.
|
||||
|
||||
Returns
|
||||
-------
|
||||
proba : array of shape (n_samples, n_classes)
|
||||
The class probabilities of the input samples.
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
if dataset with inconsistent number of features
|
||||
NotFittedError
|
||||
if model is not fitted
|
||||
"""
|
||||
|
||||
X = self.check_predict(X)
|
||||
# return # of samples of each class in leaf node
|
||||
values = self.__predict_class(X)
|
||||
normalizer = values.sum(axis=1)[:, np.newaxis]
|
||||
normalizer[normalizer == 0.0] = 1.0
|
||||
return values / normalizer
|
||||
|
||||
def predict(self, X: np.array) -> np.array:
|
||||
"""Predict labels for each sample in dataset passed
|
||||
@@ -403,40 +482,8 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
NotFittedError
|
||||
if model is not fitted
|
||||
"""
|
||||
|
||||
def predict_class(
|
||||
xp: np.array, indices: np.array, node: Snode
|
||||
) -> np.array:
|
||||
if xp is None:
|
||||
return [], []
|
||||
if node.is_leaf():
|
||||
# set a class for every sample in dataset
|
||||
prediction = np.full((xp.shape[0], 1), node._class)
|
||||
return prediction, indices
|
||||
self.splitter_.partition(xp, node, train=False)
|
||||
x_u, x_d = self.splitter_.part(xp)
|
||||
i_u, i_d = self.splitter_.part(indices)
|
||||
prx_u, prin_u = predict_class(x_u, i_u, node.get_up())
|
||||
prx_d, prin_d = predict_class(x_d, i_d, node.get_down())
|
||||
return np.append(prx_u, prx_d), np.append(prin_u, prin_d)
|
||||
|
||||
# sklearn check
|
||||
check_is_fitted(self, ["tree_"])
|
||||
# Input validation
|
||||
X = check_array(X)
|
||||
if X.shape[1] != self.n_features_:
|
||||
raise ValueError(
|
||||
f"Expected {self.n_features_} features but got "
|
||||
f"({X.shape[1]})"
|
||||
)
|
||||
# setup prediction & make it happen
|
||||
indices = np.arange(X.shape[0])
|
||||
result = (
|
||||
self._reorder_results(*predict_class(X, indices, self.tree_))
|
||||
.astype(int)
|
||||
.ravel()
|
||||
)
|
||||
return self.classes_[result]
|
||||
X = self.check_predict(X)
|
||||
return self.classes_[np.argmax(self.__predict_class(X), axis=1)]
|
||||
|
||||
def nodes_leaves(self) -> tuple:
|
||||
"""Compute the number of nodes and leaves in the built tree
|
||||
@@ -469,6 +516,23 @@ class Stree(BaseEstimator, ClassifierMixin):
|
||||
tree = None
|
||||
return Siterator(tree)
|
||||
|
||||
def graph(self, title="") -> str:
|
||||
"""Graphviz code representing the tree
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
graphviz code
|
||||
"""
|
||||
output = (
|
||||
"digraph STree {\nlabel=<STree "
|
||||
f"{title}>\nfontsize=30\nfontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
for node in self:
|
||||
output += node.graph()
|
||||
output += "}\n"
|
||||
return output
|
||||
|
||||
def __str__(self) -> str:
|
||||
"""String representation of the tree
|
||||
|
||||
|
||||
@@ -1,7 +1,5 @@
|
||||
from .Strees import Stree, Siterator
|
||||
|
||||
__version__ = "1.2.1"
|
||||
|
||||
__author__ = "Ricardo Montañana Gómez"
|
||||
__copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
|
||||
__license__ = "MIT License"
|
||||
|
||||
1
stree/_version.py
Normal file
1
stree/_version.py
Normal file
@@ -0,0 +1 @@
|
||||
__version__ = "1.3.0"
|
||||
@@ -67,10 +67,28 @@ class Snode_test(unittest.TestCase):
|
||||
|
||||
def test_make_predictor_on_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.make_predictor()
|
||||
test.make_predictor(2)
|
||||
self.assertEqual(1, test._class)
|
||||
self.assertEqual(0.75, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertListEqual([1, 3], test._proba.tolist())
|
||||
|
||||
def test_make_predictor_on_not_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
||||
test.make_predictor(2)
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(0, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertEqual(-1, test.get_up()._partition_column)
|
||||
self.assertIsNone(test._proba)
|
||||
|
||||
def test_make_predictor_on_leaf_bogus_data(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
|
||||
test.make_predictor(2)
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertListEqual([0, 0], test._proba.tolist())
|
||||
|
||||
def test_set_title(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
@@ -97,21 +115,6 @@ class Snode_test(unittest.TestCase):
|
||||
test.set_features([1, 2])
|
||||
self.assertListEqual([1, 2], test.get_features())
|
||||
|
||||
def test_make_predictor_on_not_leaf(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test")
|
||||
test.set_up(Snode(None, [1], [1], [], 0.0, "another_test"))
|
||||
test.make_predictor()
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(0, test._belief)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
self.assertEqual(-1, test.get_up()._partition_column)
|
||||
|
||||
def test_make_predictor_on_leaf_bogus_data(self):
|
||||
test = Snode(None, [1, 2, 3, 4], [], [], 0.0, "test")
|
||||
test.make_predictor()
|
||||
self.assertIsNone(test._class)
|
||||
self.assertEqual(-1, test._partition_column)
|
||||
|
||||
def test_copy_node(self):
|
||||
px = [1, 2, 3, 4]
|
||||
py = [1]
|
||||
|
||||
@@ -10,6 +10,7 @@ from sklearn.svm import LinearSVC
|
||||
from stree import Stree
|
||||
from stree.Splitter import Snode
|
||||
from .utils import load_dataset
|
||||
from .._version import __version__
|
||||
|
||||
|
||||
class Stree_test(unittest.TestCase):
|
||||
@@ -114,6 +115,38 @@ class Stree_test(unittest.TestCase):
|
||||
yp = clf.fit(X, y).predict(X[:num, :])
|
||||
self.assertListEqual(y[:num].tolist(), yp.tolist())
|
||||
|
||||
def test_multiple_predict_proba(self):
|
||||
expected = {
|
||||
"liblinear": {
|
||||
0: [0.02401129943502825, 0.9759887005649718],
|
||||
17: [0.9282970550576184, 0.07170294494238157],
|
||||
},
|
||||
"linear": {
|
||||
0: [0.029329608938547486, 0.9706703910614525],
|
||||
17: [0.9298469387755102, 0.07015306122448979],
|
||||
},
|
||||
"rbf": {
|
||||
0: [0.023448275862068966, 0.976551724137931],
|
||||
17: [0.9458064516129032, 0.05419354838709677],
|
||||
},
|
||||
"poly": {
|
||||
0: [0.01601164483260553, 0.9839883551673945],
|
||||
17: [0.9089790897908979, 0.0910209102091021],
|
||||
},
|
||||
}
|
||||
indices = [0, 17]
|
||||
X, y = load_dataset(self._random_state)
|
||||
for kernel in ["liblinear", "linear", "rbf", "poly"]:
|
||||
clf = Stree(
|
||||
kernel=kernel,
|
||||
multiclass_strategy="ovr" if kernel == "liblinear" else "ovo",
|
||||
random_state=self._random_state,
|
||||
)
|
||||
yp = clf.fit(X, y).predict_proba(X)
|
||||
for index in indices:
|
||||
for exp, comp in zip(expected[kernel][index], yp[index]):
|
||||
self.assertAlmostEqual(exp, comp)
|
||||
|
||||
def test_single_vs_multiple_prediction(self):
|
||||
"""Check if predicting sample by sample gives the same result as
|
||||
predicting all samples at once
|
||||
@@ -357,6 +390,7 @@ class Stree_test(unittest.TestCase):
|
||||
|
||||
# Tests of score
|
||||
def test_score_binary(self):
|
||||
"""Check score for binary classification."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
accuracies = [
|
||||
0.9506666666666667,
|
||||
@@ -379,6 +413,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertAlmostEqual(accuracy_expected, accuracy_score)
|
||||
|
||||
def test_score_max_features(self):
|
||||
"""Check score using max_features."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
clf = Stree(
|
||||
kernel="liblinear",
|
||||
@@ -390,6 +425,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertAlmostEqual(0.9453333333333334, clf.score(X, y))
|
||||
|
||||
def test_bogus_splitter_parameter(self):
|
||||
"""Check that bogus splitter parameter raises exception."""
|
||||
clf = Stree(splitter="duck")
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(*load_dataset())
|
||||
@@ -445,6 +481,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertListEqual([47], resdn[1].tolist())
|
||||
|
||||
def test_score_multiclass_rbf(self):
|
||||
"""Test score for multiclass classification with rbf kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -462,6 +499,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_poly(self):
|
||||
"""Test score for multiclass classification with poly kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -483,6 +521,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_liblinear(self):
|
||||
"""Test score for multiclass classification with liblinear kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -508,6 +547,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_sigmoid(self):
|
||||
"""Test score for multiclass classification with sigmoid kernel."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -528,6 +568,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(0.9662921348314607, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_score_multiclass_linear(self):
|
||||
"""Test score for multiclass classification with linear kernel."""
|
||||
warnings.filterwarnings("ignore", category=ConvergenceWarning)
|
||||
warnings.filterwarnings("ignore", category=RuntimeWarning)
|
||||
X, y = load_dataset(
|
||||
@@ -555,11 +596,13 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
|
||||
|
||||
def test_zero_all_sample_weights(self):
|
||||
"""Test exception raises when all sample weights are zero."""
|
||||
X, y = load_dataset(self._random_state)
|
||||
with self.assertRaises(ValueError):
|
||||
Stree().fit(X, y, np.zeros(len(y)))
|
||||
|
||||
def test_mask_samples_weighted_zero(self):
|
||||
"""Check that the weighted zero samples are masked."""
|
||||
X = np.array(
|
||||
[
|
||||
[1, 1],
|
||||
@@ -587,6 +630,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(model2.score(X, y, w), 1)
|
||||
|
||||
def test_depth(self):
|
||||
"""Check depth of the tree."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -602,6 +646,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(4, clf.depth_)
|
||||
|
||||
def test_nodes_leaves(self):
|
||||
"""Check number of nodes and leaves."""
|
||||
X, y = load_dataset(
|
||||
random_state=self._random_state,
|
||||
n_classes=3,
|
||||
@@ -621,6 +666,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(6, leaves)
|
||||
|
||||
def test_nodes_leaves_artificial(self):
|
||||
"""Check leaves of artificial dataset."""
|
||||
n1 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test1")
|
||||
n2 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test2")
|
||||
n3 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test3")
|
||||
@@ -639,12 +685,14 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(2, leaves)
|
||||
|
||||
def test_bogus_multiclass_strategy(self):
|
||||
"""Check invalid multiclass strategy."""
|
||||
clf = Stree(multiclass_strategy="other")
|
||||
X, y = load_wine(return_X_y=True)
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(X, y)
|
||||
|
||||
def test_multiclass_strategy(self):
|
||||
"""Check multiclass strategy."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf_o = Stree(multiclass_strategy="ovo")
|
||||
clf_r = Stree(multiclass_strategy="ovr")
|
||||
@@ -654,6 +702,7 @@ class Stree_test(unittest.TestCase):
|
||||
self.assertEqual(0.9269662921348315, score_r)
|
||||
|
||||
def test_incompatible_hyperparameters(self):
|
||||
"""Check incompatible hyperparameters."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(kernel="liblinear", multiclass_strategy="ovo")
|
||||
with self.assertRaises(ValueError):
|
||||
@@ -661,3 +710,50 @@ class Stree_test(unittest.TestCase):
|
||||
clf = Stree(multiclass_strategy="ovo", split_criteria="max_samples")
|
||||
with self.assertRaises(ValueError):
|
||||
clf.fit(X, y)
|
||||
|
||||
def test_version(self):
|
||||
"""Check STree version."""
|
||||
clf = Stree()
|
||||
self.assertEqual(__version__, clf.version())
|
||||
|
||||
def test_graph(self):
|
||||
"""Check graphviz representation of the tree."""
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(random_state=self._random_state)
|
||||
|
||||
expected_head = (
|
||||
"digraph STree {\nlabel=<STree >\nfontsize=30\n"
|
||||
"fontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
expected_tail = (
|
||||
' [shape=box style=filled label="class=1 impurity=0.000 '
|
||||
'counts=[0 1 0]"];\n}\n'
|
||||
)
|
||||
self.assertEqual(clf.graph(), expected_head + "}\n")
|
||||
clf.fit(X, y)
|
||||
computed = clf.graph()
|
||||
computed_head = computed[: len(expected_head)]
|
||||
num = -len(expected_tail)
|
||||
computed_tail = computed[num:]
|
||||
self.assertEqual(computed_head, expected_head)
|
||||
self.assertEqual(computed_tail, expected_tail)
|
||||
|
||||
def test_graph_title(self):
|
||||
X, y = load_wine(return_X_y=True)
|
||||
clf = Stree(random_state=self._random_state)
|
||||
expected_head = (
|
||||
"digraph STree {\nlabel=<STree Sample title>\nfontsize=30\n"
|
||||
"fontcolor=blue\nlabelloc=t\n"
|
||||
)
|
||||
expected_tail = (
|
||||
' [shape=box style=filled label="class=1 impurity=0.000 '
|
||||
'counts=[0 1 0]"];\n}\n'
|
||||
)
|
||||
self.assertEqual(clf.graph("Sample title"), expected_head + "}\n")
|
||||
clf.fit(X, y)
|
||||
computed = clf.graph("Sample title")
|
||||
computed_head = computed[: len(expected_head)]
|
||||
num = -len(expected_tail)
|
||||
computed_tail = computed[num:]
|
||||
self.assertEqual(computed_head, expected_head)
|
||||
self.assertEqual(computed_tail, expected_tail)
|
||||
|
||||
Reference in New Issue
Block a user