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Merge branch 'master' into package_doc_#7

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Ricardo Montañana Gómez
2021-04-26 09:04:21 +02:00
committed by GitHub
parent 8fe5fdff2b 02de394c96
commit 4370433d4d
4 changed files with 120 additions and 40 deletions
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56
.github/workflows/codeql-analysis.yml vendored Normal file
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@@ -0,0 +1,56 @@
name: "CodeQL"
on:
push:
branches: [ master ]
pull_request:
# The branches below must be a subset of the branches above
branches: [ master ]
schedule:
- cron: '16 17 * * 3'
jobs:
analyze:
name: Analyze
runs-on: ubuntu-latest
strategy:
fail-fast: false
matrix:
language: [ 'python' ]
# CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
# Learn more:
# https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
steps:
- name: Checkout repository
uses: actions/checkout@v2
# Initializes the CodeQL tools for scanning.
- name: Initialize CodeQL
uses: github/codeql-action/init@v1
with:
languages: ${{ matrix.language }}
# If you wish to specify custom queries, you can do so here or in a config file.
# By default, queries listed here will override any specified in a config file.
# Prefix the list here with "+" to use these queries and those in the config file.
# queries: ./path/to/local/query, your-org/your-repo/queries@main
# Autobuild attempts to build any compiled languages (C/C++, C#, or Java).
# If this step fails, then you should remove it and run the build manually (see below)
- name: Autobuild
uses: github/codeql-action/autobuild@v1
# ℹ️ Command-line programs to run using the OS shell.
# 📚 https://git.io/JvXDl
# ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
# and modify them (or add more) to build your code if your project
# uses a compiled language
#- run: |
# make bootstrap
# make release
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@v1

50
stree/Strees.py
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@@ -11,6 +11,7 @@ from typing import Optional
import numpy as np
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.multiclass import check_classification_targets
from sklearn.exceptions import ConvergenceWarning
@@ -172,7 +173,7 @@ class Splitter:
self,
clf: SVC = None,
criterion: str = None,
splitter_type: str = None,
feature_select: str = None,
criteria: str = None,
min_samples_split: int = None,
random_state=None,
@@ -185,7 +186,7 @@ class Splitter:
self._criterion = criterion
self._min_samples_split = min_samples_split
self._criteria = criteria
self._splitter_type = splitter_type
self._feature_select = feature_select
self._normalize = normalize
if clf is None:
@@ -204,9 +205,10 @@ class Splitter:
f"criteria has to be max_samples or impurity; got ({criteria})"
)
if splitter_type not in ["random", "best"]:
if feature_select not in ["random", "best"]:
raise ValueError(
f"splitter must be either random or best, got({splitter_type})"
"splitter must be either random or best, got "
f"({feature_select})"
)
self.criterion_function = getattr(self, f"_{self._criterion}")
self.decision_criteria = getattr(self, f"_{self._criteria}")
@@ -340,13 +342,10 @@ class Splitter:
"""
comb = set()
# Generate at most 5 combinations
if max_features == features:
set_length = 1
else:
number = factorial(features) / (
factorial(max_features) * factorial(features - max_features)
)
set_length = min(5, number)
number = factorial(features) / (
factorial(max_features) * factorial(features - max_features)
)
set_length = min(5, number)
while len(comb) < set_length:
comb.add(
tuple(sorted(random.sample(range(features), max_features)))
@@ -355,9 +354,9 @@ class Splitter:
def _get_subspaces_set(
self, dataset: np.array, labels: np.array, max_features: int
) -> np.array:
) -> tuple:
"""Compute the indices of the features selected by splitter depending
on the self._splitter_type hyper parameter
on the self._feature_select hyper parameter
Parameters
----------
@@ -371,21 +370,28 @@ class Splitter:
Returns
-------
np.array
tuple
indices of the features selected
"""
features_sets = self._generate_spaces(dataset.shape[1], max_features)
if len(features_sets) > 1:
if self._splitter_type == "random":
index = random.randint(0, len(features_sets) - 1)
return features_sets[index]
if dataset.shape[1] == max_features:
# No feature reduction applies
return tuple(range(dataset.shape[1]))
if self._feature_select == "random":
features_sets = self._generate_spaces(
dataset.shape[1], max_features
)
return self._select_best_set(dataset, labels, features_sets)
return features_sets[0]
# Take KBest features
return (
SelectKBest(k=max_features)
.fit(dataset, labels)
.get_support(indices=True)
)
def get_subspace(
self, dataset: np.array, labels: np.array, max_features: int
) -> tuple:
"""Return a subspace of the selected dataset of max_features length.
"""Re3turn a subspace of the selected dataset of max_features length.
Depending on hyperparmeter
Parameters
@@ -632,7 +638,7 @@ class Stree(BaseEstimator, ClassifierMixin):
self.splitter_ = Splitter(
clf=self._build_clf(),
criterion=self.criterion,
splitter_type=self.splitter,
feature_select=self.splitter,
criteria=self.split_criteria,
random_state=self.random_state,
min_samples_split=self.min_samples_split,

52
stree/tests/Splitter_test.py
View File

@@ -6,6 +6,7 @@ import numpy as np
from sklearn.svm import SVC
from sklearn.datasets import load_wine, load_iris
from stree import Splitter
from .utils import load_dataset
class Splitter_test(unittest.TestCase):
@@ -17,7 +18,7 @@ class Splitter_test(unittest.TestCase):
def build(
clf=SVC,
min_samples_split=0,
splitter_type="random",
feature_select="random",
criterion="gini",
criteria="max_samples",
random_state=None,
@@ -25,7 +26,7 @@ class Splitter_test(unittest.TestCase):
return Splitter(
clf=clf(random_state=random_state, kernel="rbf"),
min_samples_split=min_samples_split,
splitter_type=splitter_type,
feature_select=feature_select,
criterion=criterion,
criteria=criteria,
random_state=random_state,
@@ -39,20 +40,20 @@ class Splitter_test(unittest.TestCase):
with self.assertRaises(ValueError):
self.build(criterion="duck")
with self.assertRaises(ValueError):
self.build(splitter_type="duck")
self.build(feature_select="duck")
with self.assertRaises(ValueError):
self.build(criteria="duck")
with self.assertRaises(ValueError):
_ = Splitter(clf=None)
for splitter_type in ["best", "random"]:
for feature_select in ["best", "random"]:
for criterion in ["gini", "entropy"]:
for criteria in ["max_samples", "impurity"]:
tcl = self.build(
splitter_type=splitter_type,
feature_select=feature_select,
criterion=criterion,
criteria=criteria,
)
self.assertEqual(splitter_type, tcl._splitter_type)
self.assertEqual(feature_select, tcl._feature_select)
self.assertEqual(criterion, tcl._criterion)
self.assertEqual(criteria, tcl._criteria)
@@ -177,32 +178,34 @@ class Splitter_test(unittest.TestCase):
def test_best_splitter_few_sets(self):
X, y = load_iris(return_X_y=True)
X = np.delete(X, 3, 1)
tcl = self.build(splitter_type="best", random_state=self._random_state)
tcl = self.build(
feature_select="best", random_state=self._random_state
)
dataset, computed = tcl.get_subspace(X, y, max_features=2)
self.assertListEqual([0, 2], list(computed))
self.assertListEqual(X[:, computed].tolist(), dataset.tolist())
def test_splitter_parameter(self):
expected_values = [
[1, 4, 9, 12], # best entropy max_samples
[1, 3, 6, 10], # best entropy impurity
[6, 8, 10, 12], # best gini max_samples
[7, 8, 10, 11], # best gini impurity
[0, 6, 11, 12], # best entropy max_samples
[0, 6, 11, 12], # best entropy impurity
[0, 6, 11, 12], # best gini max_samples
[0, 6, 11, 12], # best gini impurity
[0, 3, 8, 12], # random entropy max_samples
[0, 3, 9, 11], # random entropy impurity
[0, 4, 7, 12], # random gini max_samples
[0, 2, 5, 6], # random gini impurity
[0, 3, 7, 12], # random entropy impurity
[1, 7, 9, 12], # random gini max_samples
[1, 5, 8, 12], # random gini impurity
]
X, y = load_wine(return_X_y=True)
rn = 0
for splitter_type in ["best", "random"]:
for feature_select in ["best", "random"]:
for criterion in ["entropy", "gini"]:
for criteria in [
"max_samples",
"impurity",
]:
tcl = self.build(
splitter_type=splitter_type,
feature_select=feature_select,
criterion=criterion,
criteria=criteria,
)
@@ -213,7 +216,7 @@ class Splitter_test(unittest.TestCase):
# print(
# "{}, # {:7s}{:8s}{:15s}".format(
# list(computed),
# splitter_type,
# feature_select,
# criterion,
# criteria,
# )
@@ -222,3 +225,18 @@ class Splitter_test(unittest.TestCase):
self.assertListEqual(
X[:, computed].tolist(), dataset.tolist()
)
def test_get_best_subspaces(self):
results = [
(4, [3, 4, 11, 13]),
(7, [1, 3, 4, 5, 11, 13, 16]),
(9, [1, 3, 4, 5, 7, 10, 11, 13, 16]),
]
X, y = load_dataset(n_features=20)
for k, expected in results:
tcl = self.build(
feature_select="best",
)
Xs, computed = tcl.get_subspace(X, y, k)
self.assertListEqual(expected, list(computed))
self.assertListEqual(X[:, expected].tolist(), Xs.tolist())

2
stree/tests/Stree_test.py
View File

@@ -330,7 +330,7 @@ class Stree_test(unittest.TestCase):
X, y = load_dataset(self._random_state)
clf = Stree(random_state=self._random_state, max_features=2)
clf.fit(X, y)
self.assertAlmostEqual(0.9246666666666666, clf.score(X, y))
self.assertAlmostEqual(0.9453333333333334, clf.score(X, y))
def test_bogus_splitter_parameter(self):
clf = Stree(splitter="duck")