From 6f75a459fc92f6daf85c6c517e268ddf8f01181f Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 17 Feb 2025 19:31:08 +0000 Subject: [PATCH 01/14] Automated `pre-commit` hook update (#2541) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index aece71f7c1..3e359cf34a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.5 + rev: v0.9.6 hooks: - id: ruff args: [ "--fix"] @@ -48,7 +48,7 @@ repos: args: [ "--profile=black", "--multi-line=3" ] - repo: https://github.com/pycqa/flake8 - rev: 7.1.1 + rev: 7.1.2 hooks: - id: flake8 additional_dependencies: [ flake8-bugbear, flake8-print, Flake8-pyproject ] From 4707d3ea29ca4cbaf9bacc27b2baa1ab0ccf7975 Mon Sep 17 00:00:00 2001 From: Ali El Hadi ISMAIL FAWAZ <54309336+hadifawaz1999@users.noreply.github.com> Date: Tue, 18 Feb 2025 16:29:18 +0100 Subject: [PATCH 02/14] fix test (#2528) --- aeon/clustering/deep_learning/_ae_fcn.py | 9 ++++--- aeon/clustering/deep_learning/_ae_resnet.py | 9 ++++--- .../_yield_clustering_checks.py | 27 ++++++++++++++----- 3 files changed, 33 insertions(+), 12 deletions(-) diff --git a/aeon/clustering/deep_learning/_ae_fcn.py b/aeon/clustering/deep_learning/_ae_fcn.py index a37a7d40a1..48c35f3dab 100644 --- a/aeon/clustering/deep_learning/_ae_fcn.py +++ b/aeon/clustering/deep_learning/_ae_fcn.py @@ -317,6 +317,7 @@ def _fit(self, X): outputs=X, batch_size=mini_batch_size, epochs=self.n_epochs, + verbose=self.verbose, ) try: @@ -345,6 +346,7 @@ def _fit_multi_rec_model( outputs, batch_size, epochs, + verbose, ): import tensorflow as tf @@ -451,9 +453,10 @@ def loss(y_true, y_pred): epoch_loss /= num_batches history["loss"].append(epoch_loss) - sys.stdout.write( - "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) - ) + if verbose: + sys.stdout.write( + "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) + ) for callback in self.callbacks_: callback.on_epoch_end(epoch, {"loss": float(epoch_loss)}) diff --git a/aeon/clustering/deep_learning/_ae_resnet.py b/aeon/clustering/deep_learning/_ae_resnet.py index 868e47d846..bd38deb4c6 100644 --- a/aeon/clustering/deep_learning/_ae_resnet.py +++ b/aeon/clustering/deep_learning/_ae_resnet.py @@ -329,6 +329,7 @@ def _fit(self, X): outputs=X, batch_size=mini_batch_size, epochs=self.n_epochs, + verbose=self.verbose, ) try: @@ -359,6 +360,7 @@ def _fit_multi_rec_model( outputs, batch_size, epochs, + verbose, ): import tensorflow as tf @@ -463,9 +465,10 @@ def loss(y_true, y_pred): epoch_loss /= num_batches history["loss"].append(epoch_loss) - sys.stdout.write( - "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) - ) + if verbose: + sys.stdout.write( + "Training loss at epoch %d: %.4f\n" % (epoch, float(epoch_loss)) + ) for callback in self.callbacks_: callback.on_epoch_end(epoch, {"loss": float(epoch_loss)}) diff --git a/aeon/testing/estimator_checking/_yield_clustering_checks.py b/aeon/testing/estimator_checking/_yield_clustering_checks.py index 5205316f94..4e3940c489 100644 --- a/aeon/testing/estimator_checking/_yield_clustering_checks.py +++ b/aeon/testing/estimator_checking/_yield_clustering_checks.py @@ -77,18 +77,33 @@ def check_clustering_random_state_deep_learning(estimator, datatype): deep_clr1 = _clone_estimator(estimator, random_state=random_state) deep_clr1.fit(FULL_TEST_DATA_DICT[datatype]["train"][0]) - layers1 = deep_clr1.training_model_.layers[1:] + encoder_layers1 = deep_clr1.training_model_.layers[1].layers[1:] + decoder_layers1 = deep_clr1.training_model_.layers[2].layers[1:] deep_clr2 = _clone_estimator(estimator, random_state=random_state) deep_clr2.fit(FULL_TEST_DATA_DICT[datatype]["train"][0]) - layers2 = deep_clr2.training_model_.layers[1:] + encoder_layers2 = deep_clr2.training_model_.layers[1].layers[1:] + decoder_layers2 = deep_clr2.training_model_.layers[2].layers[1:] - assert len(layers1) == len(layers2) + assert len(encoder_layers1) == len(encoder_layers2) + assert len(decoder_layers1) == len(decoder_layers2) - for i in range(len(layers1)): - weights1 = layers1[i].get_weights() - weights2 = layers2[i].get_weights() + for i in range(len(encoder_layers1)): + weights1 = encoder_layers1[i].get_weights() + weights2 = encoder_layers2[i].get_weights() + + assert len(weights1) == len(weights2) + + for j in range(len(weights1)): + _weight1 = np.asarray(weights1[j]) + _weight2 = np.asarray(weights2[j]) + + np.testing.assert_almost_equal(_weight1, _weight2, 4) + + for i in range(len(decoder_layers1)): + weights1 = decoder_layers1[i].get_weights() + weights2 = decoder_layers2[i].get_weights() assert len(weights1) == len(weights2) From 8770d2f33b416ebbf62d51bfe4336b93cce4e809 Mon Sep 17 00:00:00 2001 From: Cyril Meyer <69190238+Cyril-Meyer@users.noreply.github.com> Date: Fri, 21 Feb 2025 15:59:11 +0100 Subject: [PATCH 03/14] [BUG] add ExpSmoothingSeriesTransformer and MovingAverageSeriesTransformer to __init__ (#2550) * update docs to fix 2548 docs * update init to fix 2548 bug --- aeon/transformations/series/__init__.py | 4 ++++ docs/api_reference/transformations.rst | 2 ++ 2 files changed, 6 insertions(+) diff --git a/aeon/transformations/series/__init__.py b/aeon/transformations/series/__init__.py index 031073b2e6..8b71ba9fc8 100644 --- a/aeon/transformations/series/__init__.py +++ b/aeon/transformations/series/__init__.py @@ -6,8 +6,10 @@ "ClaSPTransformer", "DFTSeriesTransformer", "Dobin", + "ExpSmoothingSeriesTransformer", "GaussSeriesTransformer", "MatrixProfileSeriesTransformer", + "MovingAverageSeriesTransformer", "PLASeriesTransformer", "SGSeriesTransformer", "StatsModelsACF", @@ -31,8 +33,10 @@ from aeon.transformations.series._clasp import ClaSPTransformer from aeon.transformations.series._dft import DFTSeriesTransformer from aeon.transformations.series._dobin import Dobin +from aeon.transformations.series._exp_smoothing import ExpSmoothingSeriesTransformer from aeon.transformations.series._gauss import GaussSeriesTransformer from aeon.transformations.series._matrix_profile import MatrixProfileSeriesTransformer +from aeon.transformations.series._moving_average import MovingAverageSeriesTransformer from aeon.transformations.series._pca import PCASeriesTransformer from aeon.transformations.series._pla import PLASeriesTransformer from aeon.transformations.series._scaled_logit import ScaledLogitSeriesTransformer diff --git a/docs/api_reference/transformations.rst b/docs/api_reference/transformations.rst index fa3184af7b..2a56fd847f 100644 --- a/docs/api_reference/transformations.rst +++ b/docs/api_reference/transformations.rst @@ -165,8 +165,10 @@ Series transforms ClaSPTransformer DFTSeriesTransformer Dobin + ExpSmoothingSeriesTransformer GaussSeriesTransformer MatrixProfileSeriesTransformer + MovingAverageSeriesTransformer PLASeriesTransformer SGSeriesTransformer StatsModelsACF From dd27b2ead024955f409eb62929c17d38b982d369 Mon Sep 17 00:00:00 2001 From: "aeon-actions-bot[bot]" <148872591+aeon-actions-bot[bot]@users.noreply.github.com> Date: Mon, 24 Feb 2025 15:19:38 +0000 Subject: [PATCH 04/14] Automated `pre-commit` hook update (#2567) Co-authored-by: MatthewMiddlehurst <25731235+MatthewMiddlehurst@users.noreply.github.com> --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3e359cf34a..4349512761 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -29,7 +29,7 @@ repos: args: [ "--create", "--python-folders", "aeon" ] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.9.6 + rev: v0.9.7 hooks: - id: ruff args: [ "--fix"] From b6dfe553e62e4b51a000a3a80dee97a2822723c8 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 24 Feb 2025 15:19:50 +0000 Subject: [PATCH 05/14] [MNT] Bump ossf/scorecard-action in the github-actions group (#2569) Bumps the github-actions group with 1 update: [ossf/scorecard-action](https://github.com/ossf/scorecard-action). Updates `ossf/scorecard-action` from 2.4.0 to 2.4.1 - [Release notes](https://github.com/ossf/scorecard-action/releases) - [Changelog](https://github.com/ossf/scorecard-action/blob/main/RELEASE.md) - [Commits](https://github.com/ossf/scorecard-action/compare/v2.4.0...v2.4.1) --- updated-dependencies: - dependency-name: ossf/scorecard-action dependency-type: direct:production update-type: version-update:semver-patch dependency-group: github-actions ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/scorecard.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/scorecard.yml b/.github/workflows/scorecard.yml index 95435746d4..3c57528fc5 100644 --- a/.github/workflows/scorecard.yml +++ b/.github/workflows/scorecard.yml @@ -27,7 +27,7 @@ jobs: persist-credentials: false - name: Run analysis - uses: ossf/scorecard-action@v2.4.0 + uses: ossf/scorecard-action@v2.4.1 with: results_file: results.sarif results_format: sarif From cd4ee1cdba4d63edcf55555433d39a0791d3e420 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Tue, 25 Feb 2025 01:58:00 +0530 Subject: [PATCH 06/14] [ENH] Added class weights to feature based classifiers (#2512) * class weights added to classification/feature based * Automatic `pre-commit` fixes * Test function for Catch22Classifier added * Test function for SummaryClassifier added * Test for tsfreshClassifier added * Soft dependecy check added for tsfresh * Test signature test case added --- aeon/classification/feature_based/_catch22.py | 15 ++++++++++++- .../feature_based/_signature_classifier.py | 18 ++++++++++++++-- aeon/classification/feature_based/_summary.py | 16 +++++++++++++- aeon/classification/feature_based/_tsfresh.py | 15 ++++++++++++- .../feature_based/tests/test_catch22.py | 19 +++++++++++++++++ .../feature_based/tests/test_signature.py | 21 +++++++++++++++++++ .../feature_based/tests/test_summary.py | 18 ++++++++++++++++ .../feature_based/tests/test_tsfresh.py | 21 +++++++++++++++++++ 8 files changed, 138 insertions(+), 5 deletions(-) diff --git a/aeon/classification/feature_based/_catch22.py b/aeon/classification/feature_based/_catch22.py index bfad28dd44..bec5deff88 100644 --- a/aeon/classification/feature_based/_catch22.py +++ b/aeon/classification/feature_based/_catch22.py @@ -67,6 +67,17 @@ class Catch22Classifier(BaseClassifier): if None a 'prefer' value of "threads" is used by default. Valid options are "loky", "multiprocessing", "threading" or a custom backend. See the joblib Parallel documentation for more details. + class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The “balanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The “balanced_subsample” mode is the same as “balanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -132,6 +143,7 @@ def __init__( random_state=None, n_jobs=1, parallel_backend=None, + class_weight=None, ): self.features = features self.catch24 = catch24 @@ -142,6 +154,7 @@ def __init__( self.random_state = random_state self.n_jobs = n_jobs self.parallel_backend = parallel_backend + self.class_weight = class_weight super().__init__() @@ -175,7 +188,7 @@ def _fit(self, X, y): self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/_signature_classifier.py b/aeon/classification/feature_based/_signature_classifier.py index 88308436f5..a3f659efcf 100644 --- a/aeon/classification/feature_based/_signature_classifier.py +++ b/aeon/classification/feature_based/_signature_classifier.py @@ -61,6 +61,17 @@ class SignatureClassifier(BaseClassifier): Signature truncation depth. random_state : int, default=None If `int`, random_state is the seed used by the random number generator; + class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The “balanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The “balanced_subsample” mode is the same as “balanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -105,6 +116,7 @@ def __init__( sig_tfm="signature", depth=4, random_state=None, + class_weight=None, ): self.estimator = estimator self.augmentation_list = augmentation_list @@ -116,7 +128,7 @@ def __init__( self.sig_tfm = sig_tfm self.depth = depth self.random_state = random_state - + self.class_weight = class_weight super().__init__() self.signature_method = SignatureTransformer( @@ -135,7 +147,9 @@ def _setup_classification_pipeline(self): """Set up the full signature method pipeline.""" # Use rf if no classifier is set if self.estimator is None: - classifier = RandomForestClassifier(random_state=self.random_state) + classifier = RandomForestClassifier( + random_state=self.random_state, class_weight=self.class_weight + ) else: classifier = _clone_estimator(self.estimator, self.random_state) diff --git a/aeon/classification/feature_based/_summary.py b/aeon/classification/feature_based/_summary.py index a4f34ff688..b6e0056392 100644 --- a/aeon/classification/feature_based/_summary.py +++ b/aeon/classification/feature_based/_summary.py @@ -43,6 +43,17 @@ class SummaryClassifier(BaseClassifier): If `RandomState` instance, random_state is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. + class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The “balanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The “balanced_subsample” mode is the same as “balanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -85,6 +96,7 @@ def __init__( estimator=None, n_jobs=1, random_state=None, + class_weight=None, ): self.summary_stats = summary_stats self.estimator = estimator @@ -92,6 +104,8 @@ def __init__( self.n_jobs = n_jobs self.random_state = random_state + self.class_weight = class_weight + super().__init__() def _fit(self, X, y): @@ -120,7 +134,7 @@ def _fit(self, X, y): self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/_tsfresh.py b/aeon/classification/feature_based/_tsfresh.py index 28dc2dac11..00021da5d8 100644 --- a/aeon/classification/feature_based/_tsfresh.py +++ b/aeon/classification/feature_based/_tsfresh.py @@ -46,6 +46,17 @@ class TSFreshClassifier(BaseClassifier): If `RandomState` instance, random_state is the random number generator; If `None`, the random number generator is the `RandomState` instance used by `np.random`. + class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None + From sklearn documentation: + If not given, all classes are supposed to have weight one. + The “balanced” mode uses the values of y to automatically adjust weights + inversely proportional to class frequencies in the input data as + n_samples / (n_classes * np.bincount(y)) + The “balanced_subsample” mode is the same as “balanced” except that weights + are computed based on the bootstrap sample for every tree grown. + For multi-output, the weights of each column of y will be multiplied. + Note that these weights will be multiplied with sample_weight (passed through + the fit method) if sample_weight is specified. Attributes ---------- @@ -86,6 +97,7 @@ def __init__( n_jobs=1, chunksize=None, random_state=None, + class_weight=None, ): self.default_fc_parameters = default_fc_parameters self.relevant_feature_extractor = relevant_feature_extractor @@ -99,6 +111,7 @@ def __init__( self._transformer = None self._return_majority_class = False self._majority_class = 0 + self.class_weight = class_weight super().__init__() @@ -137,7 +150,7 @@ def _fit(self, X, y): ) self.estimator_ = _clone_estimator( ( - RandomForestClassifier(n_estimators=200) + RandomForestClassifier(n_estimators=200, class_weight=self.class_weight) if self.estimator is None else self.estimator ), diff --git a/aeon/classification/feature_based/tests/test_catch22.py b/aeon/classification/feature_based/tests/test_catch22.py index 5a709fe4ea..c8067ee57b 100644 --- a/aeon/classification/feature_based/tests/test_catch22.py +++ b/aeon/classification/feature_based/tests/test_catch22.py @@ -1,6 +1,7 @@ """Test catch 22 classifier.""" import numpy as np +import pytest from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import RidgeClassifier @@ -19,3 +20,21 @@ def test_catch22(): c22.fit(X, y) p = c22.predict_proba(X) assert np.all(np.isin(p, [0, 1])) + + +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_catch22_classifier_with_class_weight(class_weight): + """Test catch22 classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = Catch22Classifier( + estimator=RandomForestClassifier(n_estimators=5), + outlier_norm=True, + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_signature.py b/aeon/classification/feature_based/tests/test_signature.py index b5c29df2d3..2d3d2972d0 100644 --- a/aeon/classification/feature_based/tests/test_signature.py +++ b/aeon/classification/feature_based/tests/test_signature.py @@ -18,3 +18,24 @@ def test_signature_classifier(): cls = SignatureClassifier(estimator=None) cls._fit(X, y) assert isinstance(cls.pipeline.named_steps["classifier"], RandomForestClassifier) + + +@pytest.mark.skipif( + not _check_soft_dependencies("esig", severity="none"), + reason="skip test if required soft dependency esig not available", +) +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_signature_classifier_with_class_weight(class_weight): + """Test signature classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = SignatureClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_summary.py b/aeon/classification/feature_based/tests/test_summary.py index de698e61cc..0f53130ce0 100644 --- a/aeon/classification/feature_based/tests/test_summary.py +++ b/aeon/classification/feature_based/tests/test_summary.py @@ -1,6 +1,7 @@ """Test summary classifier.""" import numpy as np +import pytest from sklearn.ensemble import RandomForestClassifier from sklearn.linear_model import RidgeClassifier @@ -19,3 +20,20 @@ def test_summary_classifier(): cls.fit(X, y) p = cls.predict_proba(X) assert np.all(np.isin(p, [0, 1])) + + +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_summary_classifier_with_class_weight(class_weight): + """Test summary classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = SummaryClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) diff --git a/aeon/classification/feature_based/tests/test_tsfresh.py b/aeon/classification/feature_based/tests/test_tsfresh.py index 3ab965e6a3..92583e6662 100644 --- a/aeon/classification/feature_based/tests/test_tsfresh.py +++ b/aeon/classification/feature_based/tests/test_tsfresh.py @@ -37,3 +37,24 @@ def test_tsfresh_classifier(): assert cls._majority_class in [0, 1] cls.verbose = 1 cls.fit(X, y) + + +@pytest.mark.skipif( + not _check_soft_dependencies("tsfresh", severity="none"), + reason="skip test if required soft dependency tsfresh not available", +) +@pytest.mark.parametrize("class_weight", ["balanced", "balanced_subsample"]) +def test_tsfresh_classifier_with_class_weight(class_weight): + """Test tsfresh classifier with class weight.""" + X, y = make_example_3d_numpy( + n_cases=10, n_channels=1, n_timepoints=12, return_y=True, random_state=0 + ) + clf = TSFreshClassifier( + estimator=RandomForestClassifier(n_estimators=5), + random_state=0, + class_weight=class_weight, + ) + clf.fit(X, y) + predictions = clf.predict(X) + assert len(predictions) == len(y) + assert set(predictions).issubset(set(y)) From a0c58dc5b4d48193bb0412b83e1db07841270417 Mon Sep 17 00:00:00 2001 From: Akash Kawle <128881349+shinymack@users.noreply.github.com> Date: Tue, 25 Feb 2025 11:04:24 +0530 Subject: [PATCH 07/14] added test_mlp.py (#2537) --- aeon/networks/tests/test_mlp.py | 179 ++++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 aeon/networks/tests/test_mlp.py diff --git a/aeon/networks/tests/test_mlp.py b/aeon/networks/tests/test_mlp.py new file mode 100644 index 0000000000..421a4f2841 --- /dev/null +++ b/aeon/networks/tests/test_mlp.py @@ -0,0 +1,179 @@ +"""Tests for the MLPNetwork Model.""" + +import pytest + +from aeon.networks import MLPNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_layers, n_units, activation", + [ + (3, 500, "relu"), + (5, [256, 128, 128, 64, 32], "sigmoid"), + (2, 128, ["tanh", "relu"]), + ], +) +def test_mlp_initialization(n_layers, n_units, activation): + """Test whether MLPNetwork initializes correctly with different configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(n_layers=n_layers, n_units=n_units, activation=activation) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + + # Check dropout and dense layers ordering + for i in range(n_layers): + dropout_layer = layers[2 + 2 * i] # Dropout before Dense + dense_layer = layers[3 + 2 * i] # Dense comes after Dropout + + assert isinstance( + dropout_layer, Dropout + ), f"Expected Dropout at index {2 + 2 * i}" + assert isinstance(dense_layer, Dense), f"Expected Dense at index {3 + 2 * i}" + + # Assert activation function + expected_activation = ( + activation[i] if isinstance(activation, list) else activation + ) + assert dense_layer.activation.__name__ == expected_activation, ( + f"Expected activation {expected_activation}, " + f"got {dense_layer.activation.__name__}" + ) + + # Assert number of units + expected_units = n_units[i] if isinstance(n_units, list) else n_units + assert ( + dense_layer.units == expected_units + ), f"Expected {expected_units} units, got {dense_layer.units}" + + # Check last layer is Dropout + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + # Assert model parameters (Just for show) + assert mlp.n_layers == n_layers + assert mlp.n_units == n_units + assert mlp.activation == activation + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dropout_rate, n_layers", + [ + (0.2, 3), + ([0.1, 0.2, 0.3], 3), + pytest.param([0.1, 0.2], 3, marks=pytest.mark.xfail(raises=AssertionError)), + ], +) +def test_mlp_dropout_rate(dropout_rate, n_layers): + """Test MLPNetwork dropout_rate configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(n_layers=n_layers, dropout_rate=dropout_rate) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + # Check first two layers + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + + # Check dropout and dense layers ordering + for i in range(n_layers): + dropout_layer = layers[2 + 2 * i] + dense_layer = layers[3 + 2 * i] + + assert isinstance( + dropout_layer, Dropout + ), f"Expected Dropout at index {2 + 2 * i}" + assert isinstance(dense_layer, Dense), f"Expected Dense at index {3 + 2 * i}" + + # Assert dropout rates match expected values + expected_dropout = ( + dropout_rate[i] if isinstance(dropout_rate, list) else dropout_rate + ) + assert ( + dropout_layer.rate == expected_dropout + ), f"Expected {expected_dropout},got {dropout_layer.rate}" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dropout_last", + [0.3, 0.5, pytest.param(1.2, marks=pytest.mark.xfail(raises=AssertionError))], +) +def test_mlp_dropout_last(dropout_last): + """Test MLPNetwork dropout_last configurations.""" + from tensorflow.keras.layers import Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(dropout_last=dropout_last) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + assert ( + layers[-1].rate == dropout_last + ), f"Expected {dropout_last}, got {layers[-1].rate}" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("use_bias", [True, False]) +def test_mlp_use_bias(use_bias): + """Test MLPNetwork use_bias configurations.""" + from tensorflow.keras.layers import Dense, Dropout, Flatten, InputLayer + from tensorflow.keras.models import Model + + mlp = MLPNetwork(use_bias=use_bias) + input_layer, output_layer = mlp.build_network((1000, 5)) + + # Wrap in a Model to access internal layers + model = Model(inputs=input_layer, outputs=output_layer) + layers = model.layers + + assert isinstance(layers[0], InputLayer), "Expected first layer to be InputLayer" + assert isinstance(layers[1], Flatten), "Expected second layer to be Flatten" + assert isinstance(layers[-1], Dropout), "Expected final layer to be Dropout" + + # Find the last Dense layer before the final Dropout layer + last_dense_layer = next( + (layer for layer in reversed(layers) if isinstance(layer, Dense)), None + ) + + assert last_dense_layer is not None, "No Dense layer found before final Dropout" + assert isinstance(last_dense_layer, Dense), "Expected last layer to be Dense" + + assert ( + last_dense_layer.use_bias == use_bias + ), f"Expected use_bias {use_bias}, got {last_dense_layer.use_bias}" From 0dfe3caebd01a87c323ee8b23de17719ec876373 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Tue, 25 Feb 2025 11:07:00 +0530 Subject: [PATCH 08/14] test file for FCNNetwork added (#2559) --- aeon/networks/tests/test_fcn.py | 196 ++++++++++++++++++++++++++++++++ 1 file changed, 196 insertions(+) create mode 100644 aeon/networks/tests/test_fcn.py diff --git a/aeon/networks/tests/test_fcn.py b/aeon/networks/tests/test_fcn.py new file mode 100644 index 0000000000..60c1cd42f5 --- /dev/null +++ b/aeon/networks/tests/test_fcn.py @@ -0,0 +1,196 @@ +"""Test for the FCNNetwork class.""" + +import pytest + +from aeon.networks import FCNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_fcnnetwork_valid(): + """Test FCNNetwork with valid configurations.""" + input_shape = (100, 5) + model = FCNNetwork(n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, should_raise", + [ + (["relu", "sigmoid", "tanh"], False), + (["relu", "sigmoid"], True), + ( + ["relu", "sigmoid", "tanh", "softmax"], + True, + ), + ("relu", False), + ("sigmoid", False), + ("tanh", False), + ("softmax", False), + ], +) +def test_fcnnetwork_activation(activation, should_raise): + """Test FCNNetwork with valid and invalid activation configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(activation=activation) + model.build_network(input_shape) + else: + model = FCNNetwork(activation=activation) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, should_raise", + [ + ([3, 1, 2], False), + ([1, 3], True), + ([3, 1, 1, 3], True), + (3, False), + ], +) +def test_fcnnetwork_kernel_size(kernel_size, should_raise): + """Test FCNNetwork with valid and invalid kernel_size configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(kernel_size=kernel_size, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(kernel_size=kernel_size, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation_rate, should_raise", + [ + ([1, 2, 1], False), + ([1, 4], True), + ([1, 2, 4, 1], True), + (1, False), + ], +) +def test_fcnnetwork_dilation_rate(dilation_rate, should_raise): + """Test FCNNetwork with valid and invalid dilation_rate configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(dilation_rate=dilation_rate, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, should_raise", + [ + ([1, 2, 3], False), + ([1, 1], True), + ([1, 2, 2, 1], True), + (1, False), + ], +) +def test_fcnnetwork_strides(strides, should_raise): + """Test FCNNetwork with valid and invalid strides configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(strides=strides, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(strides=strides, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, should_raise", + [ + (["same", "same", "valid"], False), + (["valid", "same"], True), + (["same", "valid", "same", "valid"], True), + ("same", False), + ("valid", False), + ], +) +def test_fcnnetwork_padding(padding, should_raise): + """Test FCNNetwork with valid and invalid padding configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(padding=padding, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(padding=padding, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_filters, should_raise", + [ + ([32, 64, 128], False), # Valid case with a list of filters + ([32, 64], True), # Invalid case with fewer filters than layers + ([32, 64, 128, 256], True), # Invalid case with more filters than layers + (32, False), # Valid case with a single filter value + ], +) +def test_fcnnetwork_n_filters(n_filters, should_raise): + """Test FCNNetwork with valid and invalid n_filters configurations.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + model = FCNNetwork(n_filters=n_filters, n_layers=3) + model.build_network(input_shape) + else: + model = FCNNetwork(n_filters=n_filters, n_layers=3) + input_layer, output_layer = model.build_network(input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") From ed287324a571a60eb306289e0256322707f0a0ef Mon Sep 17 00:00:00 2001 From: Kevin Shah <161136814+kevinzb56@users.noreply.github.com> Date: Tue, 25 Feb 2025 15:21:23 +0530 Subject: [PATCH 09/14] Documentation improvement of certain BaseClasses (#2516) Co-authored-by: Antoine Guillaume --- aeon/base/_base_collection.py | 22 +++++++++++++++++++++- aeon/classification/deep_learning/base.py | 17 +++++++++++++++++ 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/aeon/base/_base_collection.py b/aeon/base/_base_collection.py index ea3b21ed32..4d7f4b4564 100644 --- a/aeon/base/_base_collection.py +++ b/aeon/base/_base_collection.py @@ -1,4 +1,24 @@ -"""Base class for estimators that fit collections of time series.""" +""" +Base class for estimators that fit collections of time series. + + class name: BaseCollectionEstimator + +Defining methods: + preprocessing - _preprocess_collection(self, X, store_metadata=True) + input checking - _check_X(self, X) + input conversion - _convert_X(self, X) + shape checking - _check_shape(self, X) + +Inherited inspection methods: + hyper-parameter inspection - get_params() + fitted parameter inspection - get_fitted_params() + +State: + fitted model/strategy - by convention, any attributes ending in "_" + fitted state flag - is_fitted (property) + fitted state inspection - check_is_fitted() + +""" from abc import abstractmethod diff --git a/aeon/classification/deep_learning/base.py b/aeon/classification/deep_learning/base.py index 2ed56bc0bc..61ddeb3a72 100644 --- a/aeon/classification/deep_learning/base.py +++ b/aeon/classification/deep_learning/base.py @@ -1,6 +1,23 @@ """ Abstract base class for the Keras neural network classifiers. + class name: BaseDeepClassifier + +Defining methods: + fitting - fit(self, X, y) + predicting - predict(self, X) + - predict_proba(self, X) + model building - build_model(self, input_shape, n_classes) (abstract method) + +Inherited inspection methods: + hyper-parameter inspection - get_params() + fitted parameter inspection - get_fitted_params() + +State: + fitted model/strategy - by convention, any attributes ending in "_" + fitted state flag - is_fitted (property) + fitted state inspection - check_is_fitted() + The reason for this class between BaseClassifier and deep_learning classifiers is because we can generalise tags, _predict and _predict_proba """ From 6642bab1ddf5a57269621117116c6c4ee036f31c Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Tue, 25 Feb 2025 21:51:32 +0530 Subject: [PATCH 10/14] [ENH] Test coverage for AEFCNNetwork Improved (#2558) * test file added for aefcn * Test file for aefcn added * Test file reforammted * soft dependency added * name issues resolved --- aeon/networks/tests/test_ae_fcn.py | 288 +++++++++++++++++++++++++++++ 1 file changed, 288 insertions(+) create mode 100644 aeon/networks/tests/test_ae_fcn.py diff --git a/aeon/networks/tests/test_ae_fcn.py b/aeon/networks/tests/test_ae_fcn.py new file mode 100644 index 0000000000..6f19820d02 --- /dev/null +++ b/aeon/networks/tests/test_ae_fcn.py @@ -0,0 +1,288 @@ +"""Test for the AEFCNNetwork class.""" + +import pytest + +from aeon.networks import AEFCNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_aefcn_default(): + """Default testing for aefcn.""" + model = AEFCNNetwork() + assert model.latent_space_dim == 128 + assert model.temporal_latent_space is False + assert model.n_layers == 3 + assert model.n_filters is None + assert model.kernel_size is None + assert model.activation == "relu" + assert model.padding == "same" + assert model.strides == 1 + assert model.dilation_rate == 1 + assert model.use_bias is True + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("latent_space_dim", [64, 128, 256]) +def test_aefcn_latent_space(latent_space_dim): + """Test AEFCNNetwork with different latent space dimensions.""" + import tensorflow as tf + + aefcn = AEFCNNetwork(latent_space_dim=latent_space_dim) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, should_raise", + [ + ([8, 5, 3], False), + (3, False), + ([5, 5], True), + ([3, 3, 3, 3], True), + ], +) +def test_aefcnnetwork_kernel_size(kernel_size, should_raise): + """Test AEFCNNetwork with different kernel sizes.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of kernels .* should be the same as number of layers", + ): + AEFCNNetwork(kernel_size=kernel_size, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(kernel_size=kernel_size, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_filters, should_raise", + [ + ([128, 256, 128], False), + (32, False), + ([32, 64], True), + ([16, 32, 64, 128], True), + ], +) +def test_aefcnnetwork_n_filters(n_filters, should_raise): + """Test AEFCNNetwork with different number of filters.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of filters .* should be the same as number of layers", + ): + AEFCNNetwork(n_filters=n_filters, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(n_filters=n_filters, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation_rate, should_raise", + [ + ([1, 2, 1], False), + (2, False), + ([1, 2], True), + ([1, 2, 2, 1], True), + ], +) +def test_aefcnnetwork_dilation_rate(dilation_rate, should_raise): + """Test AEFCNNetwork with different dilation rates.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of dilations .* should be the same as number of layers", + ): + AEFCNNetwork(dilation_rate=dilation_rate, n_layers=3).build_network( + (1000, 5) + ) + else: + aefcn = AEFCNNetwork(dilation_rate=dilation_rate, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, should_raise", + [ + ([1, 2, 1], False), + (2, False), + ([1, 2], True), + ([1, 2, 2, 1], True), + ], +) +def test_aefcnnetwork_strides(strides, should_raise): + """Test AEFCNNetwork with different strides.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of strides .* should be the same as number of layers", + ): + AEFCNNetwork(strides=strides, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(strides=strides, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, should_raise", + [ + (["same", "valid", "same"], False), + ("same", False), + (["same", "valid"], True), + ( + ["same", "valid", "same", "valid"], + True, + ), + ], +) +def test_aefcnnetwork_padding(padding, should_raise): + """Test AEFCNNetwork with different paddings.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of paddings .* should be the same as number of layers", + ): + AEFCNNetwork(padding=padding, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(padding=padding, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, should_raise", + [ + (["relu", "sigmoid", "tanh"], False), + ("sigmoid", False), + (["relu", "sigmoid"], True), + ( + ["relu", "sigmoid", "tanh", "softmax"], + True, + ), + ], +) +def test_aefcnnetwork_activation(activation, should_raise): + """Test AEFCNNetwork with different activations.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of activations .* should be the same as number of layers", + ): + AEFCNNetwork(activation=activation, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(activation=activation, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "use_bias, should_raise", + [ + ([True, False, True], False), + (True, False), + ([True, False], True), + ([True, False, True, False], True), + ], +) +def test_aefcnnetwork_use_bias(use_bias, should_raise): + """Test AEFCNNetwork with different use_bias values.""" + import tensorflow as tf + + if should_raise: + with pytest.raises( + ValueError, + match="Number of biases .* should be the same as number of layers", + ): + AEFCNNetwork(use_bias=use_bias, n_layers=3).build_network((1000, 5)) + else: + aefcn = AEFCNNetwork(use_bias=use_bias, n_layers=3) + encoder, decoder = aefcn.build_network((1000, 5)) + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize("temporal_latent_space", [True, False]) +def test_aefcnnetwork_temporal_latent_space(temporal_latent_space): + """Test for temporal latent space.""" + import tensorflow as tf + + input_shape = (1000, 5) + + aefcn = AEFCNNetwork( + latent_space_dim=128, temporal_latent_space=temporal_latent_space + ) + + encoder, decoder = aefcn.build_network(input_shape) + + assert isinstance(encoder, tf.keras.models.Model) + assert isinstance(decoder, tf.keras.models.Model) + + if temporal_latent_space: + assert any( + isinstance(layer, tf.keras.layers.Conv1D) for layer in encoder.layers + ), "Expected Conv1D layer in encoder but not found." + else: + assert any( + isinstance(layer, tf.keras.layers.Dense) for layer in decoder.layers + ), "Expected Dense layer in decoder but not found." From 7518feb7840fba1c5fa8e6aea1810baf7c44d5a8 Mon Sep 17 00:00:00 2001 From: Balgopal Moharana <99070111+lucifer4073@users.noreply.github.com> Date: Wed, 26 Feb 2025 18:14:12 +0530 Subject: [PATCH 11/14] [ENH] Test coverage for TimeCNNNetwork Improved (#2534) * Test coverage improved for cnn network * assertion changed for test_cnn * coverage improved along with naming --- aeon/networks/tests/test_cnn.py | 22 --- aeon/networks/tests/test_time_cnn.py | 274 +++++++++++++++++++++++++++ 2 files changed, 274 insertions(+), 22 deletions(-) delete mode 100644 aeon/networks/tests/test_cnn.py create mode 100644 aeon/networks/tests/test_time_cnn.py diff --git a/aeon/networks/tests/test_cnn.py b/aeon/networks/tests/test_cnn.py deleted file mode 100644 index c859397b34..0000000000 --- a/aeon/networks/tests/test_cnn.py +++ /dev/null @@ -1,22 +0,0 @@ -"""Tests for the CNN Model.""" - -import pytest - -from aeon.networks import TimeCNNNetwork -from aeon.utils.validation._dependencies import _check_soft_dependencies - -__maintainer__ = [] - - -@pytest.mark.skipif( - not _check_soft_dependencies(["tensorflow"], severity="none"), - reason="Tensorflow soft dependency unavailable.", -) -def test_cnn_input_shape_padding(): - """Test of CNN network with input_shape < 60.""" - input_shape = (40, 2) - network = TimeCNNNetwork() - input_layer, output_layer = network.build_network(input_shape=input_shape) - - assert input_layer is not None - assert output_layer is not None diff --git a/aeon/networks/tests/test_time_cnn.py b/aeon/networks/tests/test_time_cnn.py new file mode 100644 index 0000000000..3f31f1db10 --- /dev/null +++ b/aeon/networks/tests/test_time_cnn.py @@ -0,0 +1,274 @@ +"""Tests for the TimeCNNNetwork Model.""" + +import pytest + +from aeon.networks import TimeCNNNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +def test_time_cnn_input_shape_padding(): + """Test of CNN network with input_shape < 60.""" + input_shape = (40, 2) + network = TimeCNNNetwork() + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "activation, n_layers, should_raise", + [ + ("relu", 2, False), + ("sigmoid", 2, False), + ("tanh", 2, False), + (["relu", "sigmoid", "tanh"], 2, True), + (["relu"], 2, True), + ], +) +def test_time_cnn_activation(activation, n_layers, should_raise): + """Test activation configuration handling.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(activation=activation, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(activation=activation, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "kernel_size, n_layers, should_raise", + [ + (7, 2, False), + ([5, 3], 2, False), + ([5, 3, 2], 2, True), + ([5], 2, True), + ], +) +def test_time_cnn_kernel_size(kernel_size, n_layers, should_raise): + """Test kernel size configuration with different layer counts.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "n_layers,n_filters,should_raise", + [ + (2, [8, 16], False), + (1, [12, 10, 4], True), + (2, 8, False), + (3, [8], True), + ], +) +def test_time_cnn_n_filters(n_layers, n_filters, should_raise): + """Test filter configuration handling.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "avg_pool_size, n_layers, should_raise", + [ + (3, 2, False), + ([2, 3], 2, False), + ([2, 3, 4], 2, True), + ([2], 2, True), + ], +) +def test_time_cnn_avg_pool_size(avg_pool_size, n_layers, should_raise): + """Test average pool size configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides_pooling, n_layers, should_raise", + [ + (None, 2, False), + (2, 2, False), + ([2, 3], 2, False), + ([2, 3, 4], 2, True), + ([2], 2, True), + ], +) +def test_time_cnn_strides_pooling(strides_pooling, n_layers, should_raise): + """Test strides pooling configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "padding, n_layers, should_raise", + [ + ("valid", 2, False), + ("same", 2, False), + (["same", "valid"], 2, False), + (["same", "valid", "same"], 2, True), + (["same"], 2, True), + ], +) +def test_time_cnn_padding(padding, n_layers, should_raise): + """Test padding override behavior for different inputs.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(padding=padding, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(padding=padding, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "dilation, n_layers, should_raise", + [ + (2, 2, False), + ([1, 2], 2, False), + ([1, 2, 3], 2, True), + ([1], 2, True), + ], +) +def test_time_cnn_dilation_rate(dilation, n_layers, should_raise): + """Test dilation rate configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "strides, n_layers, should_raise", + [ + (1, 2, False), + ([1, 2], 2, False), + ([1, 2, 3], 2, True), + ([1], 2, True), + ], +) +def test_time_cnn_strides(strides, n_layers, should_raise): + """Test strides configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(strides=strides, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(strides=strides, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="Tensorflow soft dependency unavailable.", +) +@pytest.mark.parametrize( + "use_bias, n_layers, should_raise", + [ + (True, 2, False), + ([True, False], 2, False), + ([True, False, True], 2, True), + ([True], 2, True), + ], +) +def test_time_cnn_use_bias(use_bias, n_layers, should_raise): + """Test bias usage configuration.""" + input_shape = (100, 5) + if should_raise: + with pytest.raises(ValueError): + network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers) + network.build_network(input_shape=input_shape) + else: + network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers) + input_layer, output_layer = network.build_network(input_shape=input_shape) + + assert hasattr(input_layer, "shape") + assert hasattr(output_layer, "shape") From a13d06393025f935881a108a089e3318a685c79d Mon Sep 17 00:00:00 2001 From: Kavya Rambhia <161142013+kavya-r30@users.noreply.github.com> Date: Wed, 26 Feb 2025 18:15:04 +0530 Subject: [PATCH 12/14] [ENH] Test coverage for Resnet Network (#2553) * Resnet pytest * Resnet pytest * Fixed tensorflow failing * Added Resnet in function name --- aeon/networks/tests/test_resnet.py | 109 +++++++++++++++++++++++++++++ 1 file changed, 109 insertions(+) create mode 100644 aeon/networks/tests/test_resnet.py diff --git a/aeon/networks/tests/test_resnet.py b/aeon/networks/tests/test_resnet.py new file mode 100644 index 0000000000..4d5c58c5b9 --- /dev/null +++ b/aeon/networks/tests/test_resnet.py @@ -0,0 +1,109 @@ +"""Tests for the ResNet Model.""" + +import pytest + +from aeon.networks import ResNetNetwork +from aeon.utils.validation._dependencies import _check_soft_dependencies + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_default_initialization(): + """Test if the network initializes with proper attributes.""" + model = ResNetNetwork() + assert isinstance( + model, ResNetNetwork + ), "Model initialization failed: Incorrect type" + assert model.n_residual_blocks == 3, "Default residual blocks count mismatch" + assert ( + model.n_conv_per_residual_block == 3 + ), "Default convolution blocks count mismatch" + assert model.n_filters is None, "Default n_filters should be None" + assert model.kernel_size is None, "Default kernel_size should be None" + assert model.strides == 1, "Default strides value mismatch" + assert model.dilation_rate == 1, "Default dilation rate mismatch" + assert model.activation == "relu", "Default activation mismatch" + assert model.use_bias is True, "Default use_bias mismatch" + assert model.padding == "same", "Default padding mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_custom_initialization(): + """Test whether custom kwargs are correctly set.""" + model = ResNetNetwork( + n_residual_blocks=3, + n_conv_per_residual_block=3, + n_filters=[64, 128, 128], + kernel_size=[8, 5, 3], + activation="relu", + strides=1, + padding="same", + ) + model.build_network((128, 1)) + assert isinstance( + model, ResNetNetwork + ), "Custom initialization failed: Incorrect type" + assert model._n_filters == [64, 128, 128], "n_filters list mismatch" + assert model._kernel_size == [8, 5, 3], "kernel_size list mismatch" + assert model._activation == ["relu", "relu", "relu"], "activation list mismatch" + assert model._strides == [1, 1, 1], "strides list mismatch" + assert model._padding == ["same", "same", "same"], "padding list mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_invalid_initialization(): + """Test if the network raises valid exceptions for invalid configurations.""" + with pytest.raises(ValueError, match=".*same as number of residual blocks.*"): + ResNetNetwork(n_filters=[64, 128], n_residual_blocks=3).build_network((128, 1)) + + with pytest.raises(ValueError, match=".*same as number of convolution layers.*"): + ResNetNetwork(kernel_size=[8, 5], n_conv_per_residual_block=3).build_network( + (128, 1) + ) + + with pytest.raises(ValueError, match=".*same as number of convolution layers.*"): + ResNetNetwork(strides=[1, 2], n_conv_per_residual_block=3).build_network( + (128, 1) + ) + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_build_network(): + """Test network building with various input shapes.""" + model = ResNetNetwork() + + input_shapes = [(128, 1), (256, 3), (512, 1)] + for shape in input_shapes: + input_layer, output_layer = model.build_network(shape) + assert hasattr(input_layer, "shape"), "Input layer type mismatch" + assert hasattr(output_layer, "shape"), "Output layer type mismatch" + assert input_layer.shape[1:] == shape, "Input shape mismatch" + assert output_layer.shape[-1] == 128, "Output layer mismatch" + + +@pytest.mark.skipif( + not _check_soft_dependencies(["tensorflow"], severity="none"), + reason="skip test if required soft dependency not available", +) +def test_resnet_shortcut_layer(): + """Test the shortcut layer functionality.""" + model = ResNetNetwork() + + input_shape = (128, 64) + input_layer, output_layer = model.build_network(input_shape) + + shortcut = model._shortcut_layer(input_layer, output_layer) + + assert hasattr(shortcut, "shape"), "Shortcut layer output type mismatch" + assert shortcut.shape[-1] == 128, "Shortcut output shape mismatch" From f2c2aad00bf0b075e2b1558b9bf8aea463f38058 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Sat, 1 Mar 2025 22:22:59 +0100 Subject: [PATCH 13/14] =?UTF-8?q?=F0=9F=93=9D=20Add=20shinymack=20as=20a?= =?UTF-8?q?=20contributor=20for=20code=20(#2577)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++ CONTRIBUTORS.md | 65 +++++++++++++++++++++++---------------------- 2 files changed, 42 insertions(+), 32 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 859fb1e5e8..30f63a762a 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2656,6 +2656,15 @@ "contributions": [ "doc" ] + }, + { + "login": "shinymack", + "name": "Akash Kawle", + "avatar_url": "https://avatars.githubusercontent.com/u/128881349?v=4", + "profile": "https://github.com/shinymack", + "contributions": [ + "code" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 91c7468d42..9ad2fffcbb 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-259-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-260-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -28,12 +28,13 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Afzal Ansari
Afzal Ansari
💻 📖 Ahmed Bilal
Ahmed Bilal
📖 AidenRushbrooke
AidenRushbrooke
💻 ⚠️ + Akash Kawle
Akash Kawle
💻 Akhil Jasson
Akhil Jasson
📖 Akshat Nayak
Akshat Nayak
💻 Akshat Rampuria
Akshat Rampuria
📖 - Aleksandr Grekov
Aleksandr Grekov
📖 + Aleksandr Grekov
Aleksandr Grekov
📖 Alex Hawkins-Hooker
Alex Hawkins-Hooker
💻 Alexandra Amidon
Alexandra Amidon
📝 📖 🤔 Ali Ismail-Fawaz
Ali Ismail-Fawaz
💻 🐛 📖 ⚠️ 🚧 👀 📢 ✅ 🧑‍🏫 💡 @@ -41,9 +42,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ali Yazdizadeh
Ali Yazdizadeh
📖 Alwin
Alwin
📖 💻 🚧 An Hoang
An Hoang
🐛 💻 - Andreas Kanz
Andreas Kanz
✅ + Andreas Kanz
Andreas Kanz
✅ André Guarnier De Mitri
André Guarnier De Mitri
💻 Angus Dempster
Angus Dempster
💻 ⚠️ ✅ Antoine Guillaume
Antoine Guillaume
💻 📖 @@ -51,9 +52,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Aparna Sakshi
Aparna Sakshi
💻 Arelo Tanoh
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📖 Arepalli Yashwanth Reddy
Arepalli Yashwanth Reddy
💻 🐛 📖 - Arik Ermshaus
Arik Ermshaus
💻 + Arik Ermshaus
Arik Ermshaus
💻 Arnav
Arnav
💻 Aryan Pola
Aryan Pola
💻 📖 Ayushmaan Seth
Ayushmaan Seth
💻 👀 ⚠️ 📖 📋 ✅ @@ -61,9 +62,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Badr-Eddine Marani
Badr-Eddine Marani
💻 Benedikt Heidrich
Benedikt Heidrich
💻 Benjamin Bluhm
Benjamin Bluhm
💻 📖 💡 - Bhaskar Dhariyal
Bhaskar Dhariyal
💻 ⚠️ + Bhaskar Dhariyal
Bhaskar Dhariyal
💻 ⚠️ Binay Kumar
Binay Kumar
💻 📖 ⚠️ Bohan Zhang
Bohan Zhang
💻 Bouke Postma
Bouke Postma
💻 🐛 🤔 @@ -71,9 +72,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Carlos Borrajo
Carlos Borrajo
💻 📖 Carlos Ramos Carreño
Carlos Ramos Carreño
📖 Chang Wei Tan
Chang Wei Tan
💻 - Cheuk Ting Ho
Cheuk Ting Ho
💻 + Cheuk Ting Ho
Cheuk Ting Ho
💻 Christian Kastner
Christian Kastner
💻 🐛 Christopher Dahlin
Christopher Dahlin
💻 Christopher Lo
Christopher Lo
💻 🤔 @@ -81,9 +82,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ciaran Gilbert
Ciaran Gilbert
🐛 💻 📖 ⚠️ 🤔 ClaudiaSanches
ClaudiaSanches
💻 ⚠️ Corvin Paul
Corvin Paul
📖 - Cyril Meyer
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⚠️ 📖 💻 + Cyril Meyer
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⚠️ 📖 💻 Daniel Burkhardt Cerigo
Daniel Burkhardt Cerigo
💻 Daniel L.
Daniel L.
📖 Daniel Martín Martínez
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📖 🐛 @@ -91,9 +92,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Daniele Carli
Daniele Carli
📖 Dave Hirschfeld
Dave Hirschfeld
🚇 David Buchaca Prats
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💻 - David Guijo-Rubio
David Guijo-Rubio
💻 🤔 + David Guijo-Rubio
David Guijo-Rubio
💻 🤔 Divya Tiwari
Divya Tiwari
💻 🔣 Dmitriy Valetov
Dmitriy Valetov
💻 ✅ Doug Ollerenshaw
Doug Ollerenshaw
📖 @@ -101,9 +102,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Dylan Sherry
Dylan Sherry
🚇 Emilia Rose
Emilia Rose
💻 ⚠️ Emmanuel Ferdman
Emmanuel Ferdman
📖 - Er Jie Yong
Er Jie Yong
🐛 💻 + Er Jie Yong
Er Jie Yong
🐛 💻 Evan Miller
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✅ Eyal Shafran
Eyal Shafran
💻 Federico Garza
Federico Garza
💻 💡 @@ -111,9 +112,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Ferdinand Rewicki
Ferdinand Rewicki
💻 🐛 Florian Stinner
Florian Stinner
💻 ⚠️ Francesco Spinnato
Francesco Spinnato
💻 - Franz Kiraly
Franz Kiraly
🐛 💼 💻 📖 🎨 📋 💡 💵 🔍 🤔 🚧 🧑‍🏫 📆 💬 👀 📢 ⚠️ ✅ 📹 + Franz Kiraly
Franz Kiraly
🐛 💼 💻 📖 🎨 📋 💡 💵 🔍 🤔 🚧 🧑‍🏫 📆 💬 👀 📢 ⚠️ ✅ 📹 Freddy A Boulton
Freddy A Boulton
🚇 ⚠️ Futuer
Futuer
📖 Gabriel Riegner
Gabriel Riegner
📖 @@ -121,9 +122,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d George Langley
George Langley
📖 George Oastler
George Oastler
💻 ⚠️ 📦 💡 📖 Gilberto Barbosa
Gilberto Barbosa
💻 - Grace Gao
Grace Gao
💻 🐛 + Grace Gao
Grace Gao
💻 🐛 Guilherme Arcencio
Guilherme Arcencio
💻 ⚠️ Guzal Bulatova
Guzal Bulatova
🐛 💻 📋 🧑‍🏫 📆 👀 ⚠️ HYang1996
HYang1996
💻 ⚠️ 📖 ✅ @@ -131,9 +132,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Hedeer El Showk
Hedeer El Showk
🐛 📖 💻 Huayi Wei
Huayi Wei
✅ Ifeanyi30
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💻 - Ilja Maurer
Ilja Maurer
💻 + Ilja Maurer
Ilja Maurer
💻 Ilyas Moutawwakil
Ilyas Moutawwakil
💻 📖 Ireoluwatomiwa
Ireoluwatomiwa
📖 Ishan Nangia
Ishan Nangia
🤔 @@ -141,9 +142,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Jack Russon
Jack Russon
💻 James Large
James Large
💻 📖 ⚠️ 🚇 🚧 James Morrill
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💻 - Jasmine Liaw
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💻 + Jasmine Liaw
Jasmine Liaw
💻 Jason Lines
Jason Lines
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 💡 Jason Mok
Jason Mok
📖 Jason Pong
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💻 ⚠️ @@ -151,9 +152,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d JonathanBechtel
JonathanBechtel
💻 🤔 ⚠️ Joren Hammudoglu
Joren Hammudoglu
🚇 Juan Orduz
Juan Orduz
✅ 📖 - Julian Cooper
Julian Cooper
💻 🤔 + Julian Cooper
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💻 🤔 Juliana
Juliana
💻 Justin Shenk
Justin Shenk
📖 Kai Lion
Kai Lion
💻 ⚠️ 📖 @@ -161,9 +162,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kavya Rambhia
Kavya Rambhia
💻 Kejsi Take
Kejsi Take
💻 Kevin Lam
Kevin Lam
💻 💡 ⚠️ - Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 + Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 Kishan Manani
Kishan Manani
💻 📖 ⚠️ 🐛 🤔 Krum Arnaudov
Krum Arnaudov
🐛 💻 Kutay Koralturk
Kutay Koralturk
💻 🐛 @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Lielle Ravid
Lielle Ravid
💻 📖 Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 Lorena Pantano
Lorena Pantano
🤔 - Lorenzo Toniazzi
Lorenzo Toniazzi
💻 + Lorenzo Toniazzi
Lorenzo Toniazzi
💻 Lovkush
Lovkush
💻 ⚠️ 🤔 🧑‍🏫 📆 Luca Bennett
Luca Bennett
💻 📖 ⚠️ Luis Ventura
Luis Ventura
💻 @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 Marcelo Trylesinski
Marcelo Trylesinski
📖 Marco Gorelli
Marco Gorelli
🚇 - Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ + Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ Mariam Jabara
Mariam Jabara
💻 Marielle
Marielle
📖 💻 🤔 Markus Löning
Markus Löning
💻 ⚠️ 🚧 📦 👀 🚇 💡 🐛 ✅ 💼 📖 🎨 📋 🔍 🤔 📆 💬 📢 🧑‍🏫 📹 @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Martina G. Vilas
Martina G. Vilas
👀 🤔 Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 Max Patzelt
Max Patzelt
💻 - Miao Cai
Miao Cai
🐛 💻 + Miao Cai
Miao Cai
🐛 💻 Michael F. Mbouopda
Michael F. Mbouopda
💻 🐛 📖 Michael Feil
Michael Feil
💻 ⚠️ 🤔 Michal Chromcak
Michal Chromcak
💻 📖 ⚠️ ✅ @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 Morad :)
Morad :)
💻 ⚠️ 📖 Multivin12
Multivin12
💻 ⚠️ - Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 + Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 Niek van der Laan
Niek van der Laan
💻 Nikhil Gupta
Nikhil Gupta
💻 🐛 📖 Nikola Shahpazov
Nikola Shahpazov
📖 @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Nima Nooshiri
Nima Nooshiri
📖 Ninnart Fuengfusin
Ninnart Fuengfusin
💻 Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 - Oleksandr Shchur
Oleksandr Shchur
🐛 💻 + Oleksandr Shchur
Oleksandr Shchur
🐛 💻 Oleksii Kachaiev
Oleksii Kachaiev
💻 ⚠️ Oliver Matthews
Oliver Matthews
💻 Patrick Müller
Patrick Müller
💻 @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Patrick Schäfer
Patrick Schäfer
💻 ✅ Paul
Paul
📖 Paul Rabich
Paul Rabich
💻 - Paul Yim
Paul Yim
💻 💡 ⚠️ + Paul Yim
Paul Yim
💻 💡 ⚠️ Philip
Philip
📖 Philipp Kortmann
Philipp Kortmann
💻 📖 Phillip Wenig
Phillip Wenig
💻 @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Pulkit Verma
Pulkit Verma
📖 Quaterion
Quaterion
🐛 Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 - Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 + Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 RavenRudi
RavenRudi
💻 Raya Chakravarty
Raya Chakravarty
📖 Rick van Hattem
Rick van Hattem
🚇 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Rishav Kumar Sinha
Rishav Kumar Sinha
📖 Rishi Kumar Ray
Rishi Kumar Ray
🚇 Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 - Ronnie Llamado
Ronnie Llamado
📖 + Ronnie Llamado
Ronnie Llamado
📖 Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ Sagar Mishra
Sagar Mishra
⚠️ Sajaysurya Ganesh
Sajaysurya Ganesh
💻 📖 🎨 💡 🤔 ⚠️ ✅ @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 Sebastiaan Koel
Sebastiaan Koel
💻 📖 - Sebastian Hagn
Sebastian Hagn
📖 + Sebastian Hagn
Sebastian Hagn
📖 Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 Sharathchenna
Sharathchenna
💻 Shivansh Subramanian
Shivansh Subramanian
📖 💻 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 Svea Marie Meyer
Svea Marie Meyer
📖 💻 - Sylvain Combettes
Sylvain Combettes
💻 🐛 + Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 Thach Le Nguyen
Thach Le Nguyen
💻 ⚠️ @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tom Xu
Tom Xu
💻 📖 Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 - Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 + Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 Utsav Kumar Tiwari
Utsav Kumar Tiwari
💻 📖 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ - Wayne Adams
Wayne Adams
📖 + Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 William Zeng
William Zeng
🐛 William Zheng
William Zheng
💻 ⚠️ @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 Ziyao Wei
Ziyao Wei
💻 - aa25desh
aa25desh
💻 🐛 + aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 alexbanwell1
alexbanwell1
💻 🎨 📖 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 brett koonce
brett koonce
📖 - btrtts
btrtts
📖 + btrtts
btrtts
📖 chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 danbartl
danbartl
🐛 💻 👀 📢 ⚠️ ✅ 📹 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 jschemm
jschemm
💻 - julu98
julu98
🐛 + julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 neuron283
neuron283
💻 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ patiently pending world peace
patiently pending world peace
💻 - raishubham1
raishubham1
📖 + raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 sri1419
sri1419
💻 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 vedazeren
vedazeren
💻 ⚠️ - vincent-nich12
vincent-nich12
💻 + vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 xiaobenbenecho
xiaobenbenecho
💻 xiaopu222
xiaopu222
📖 From 3106722308c06550cc834a93342919edafe39051 Mon Sep 17 00:00:00 2001 From: "allcontributors[bot]" <46447321+allcontributors[bot]@users.noreply.github.com> Date: Mon, 3 Mar 2025 22:13:31 +0000 Subject: [PATCH 14/14] =?UTF-8?q?=F0=9F=93=9D=20Add=20kevinzb56=20as=20a?= =?UTF-8?q?=20contributor=20for=20doc=20(#2588)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 📝 Update CONTRIBUTORS.md [skip ci] * 📝 Update .all-contributorsrc [skip ci] --------- Co-authored-by: allcontributors[bot] <46447321+allcontributors[bot]@users.noreply.github.com> --- .all-contributorsrc | 9 +++++++++ CONTRIBUTORS.md | 37 +++++++++++++++++++------------------ 2 files changed, 28 insertions(+), 18 deletions(-) diff --git a/.all-contributorsrc b/.all-contributorsrc index 30f63a762a..10fb4bd7ed 100644 --- a/.all-contributorsrc +++ b/.all-contributorsrc @@ -2665,6 +2665,15 @@ "contributions": [ "code" ] + }, + { + "login": "kevinzb56", + "name": "Kevin Shah", + "avatar_url": "https://avatars.githubusercontent.com/u/161136814?v=4", + "profile": "https://github.com/kevinzb56", + "contributions": [ + "doc" + ] } ], "commitType": "docs" diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 9ad2fffcbb..1c9b7a4d12 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -1,7 +1,7 @@ # Contributors -[![All Contributors](https://img.shields.io/badge/all_contributors-260-orange.svg)](#contributors) +[![All Contributors](https://img.shields.io/badge/all_contributors-261-orange.svg)](#contributors) This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome! @@ -164,6 +164,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Kevin Lam
Kevin Lam
💻 💡 ⚠️ + Kevin Shah
Kevin Shah
📖 Kirstie Whitaker
Kirstie Whitaker
🤔 🔍 Kishan Manani
Kishan Manani
💻 📖 ⚠️ 🐛 🤔 Krum Arnaudov
Krum Arnaudov
🐛 💻 @@ -171,9 +172,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Leonidas Tsaprounis
Leonidas Tsaprounis
💻 🐛 🧑‍🏫 👀 Lielle Ravid
Lielle Ravid
💻 📖 Logan Duffy
Logan Duffy
💻 📖 ⚠️ 🐛 🤔 - Lorena Pantano
Lorena Pantano
🤔 + Lorena Pantano
Lorena Pantano
🤔 Lorenzo Toniazzi
Lorenzo Toniazzi
💻 Lovkush
Lovkush
💻 ⚠️ 🤔 🧑‍🏫 📆 Luca Bennett
Luca Bennett
💻 📖 ⚠️ @@ -181,9 +182,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Luis Zugasti
Luis Zugasti
📖 Lukasz Mentel
Lukasz Mentel
💻 📖 🚇 ⚠️ 🐛 🚧 🧑‍🏫 Marcelo Trylesinski
Marcelo Trylesinski
📖 - Marco Gorelli
Marco Gorelli
🚇 + Marco Gorelli
Marco Gorelli
🚇 Margaret Gorlin
Margaret Gorlin
💻 💡 ⚠️ Mariam Jabara
Mariam Jabara
💻 Marielle
Marielle
📖 💻 🤔 @@ -191,9 +192,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Martin Walter
Martin Walter
💻 🐛 📆 🔍 🧑‍🏫 🤔 🎨 👀 📖 📢 Martina G. Vilas
Martina G. Vilas
👀 🤔 Matthew Middlehurst
Matthew Middlehurst
🐛 💻 🔣 📖 🎨 💡 🤔 🚇 🚧 🧑‍🏫 📣 💬 🔬 👀 ⚠️ ✅ 📢 - Max Patzelt
Max Patzelt
💻 + Max Patzelt
Max Patzelt
💻 Miao Cai
Miao Cai
🐛 💻 Michael F. Mbouopda
Michael F. Mbouopda
💻 🐛 📖 Michael Feil
Michael Feil
💻 ⚠️ 🤔 @@ -201,9 +202,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Mirae Parker
Mirae Parker
💻 ⚠️ Mohammed Saif Kazamel
Mohammed Saif Kazamel
🐛 Morad :)
Morad :)
💻 ⚠️ 📖 - Multivin12
Multivin12
💻 ⚠️ + Multivin12
Multivin12
💻 ⚠️ Márcio A. Freitas Jr
Márcio A. Freitas Jr
📖 Niek van der Laan
Niek van der Laan
💻 Nikhil Gupta
Nikhil Gupta
💻 🐛 📖 @@ -211,9 +212,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Nilesh Kumar
Nilesh Kumar
💻 Nima Nooshiri
Nima Nooshiri
📖 Ninnart Fuengfusin
Ninnart Fuengfusin
💻 - Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 + Noa Ben Ami
Noa Ben Ami
💻 ⚠️ 📖 Oleksandr Shchur
Oleksandr Shchur
🐛 💻 Oleksii Kachaiev
Oleksii Kachaiev
💻 ⚠️ Oliver Matthews
Oliver Matthews
💻 @@ -221,9 +222,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Patrick Rockenschaub
Patrick Rockenschaub
💻 🎨 🤔 ⚠️ Patrick Schäfer
Patrick Schäfer
💻 ✅ Paul
Paul
📖 - Paul Rabich
Paul Rabich
💻 + Paul Rabich
Paul Rabich
💻 Paul Yim
Paul Yim
💻 💡 ⚠️ Philip
Philip
📖 Philipp Kortmann
Philipp Kortmann
💻 📖 @@ -231,9 +232,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Piyush Gade
Piyush Gade
💻 👀 Pulkit Verma
Pulkit Verma
📖 Quaterion
Quaterion
🐛 - Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 + Rafael Ayllón-Gavilán
Rafael Ayllón-Gavilán
💻 Rakshitha Godahewa
Rakshitha Godahewa
💻 📖 RavenRudi
RavenRudi
💻 Raya Chakravarty
Raya Chakravarty
📖 @@ -241,9 +242,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Rishabh Bali
Rishabh Bali
💻 Rishav Kumar Sinha
Rishav Kumar Sinha
📖 Rishi Kumar Ray
Rishi Kumar Ray
🚇 - Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 + Riya Elizabeth John
Riya Elizabeth John
💻 ⚠️ 📖 Ronnie Llamado
Ronnie Llamado
📖 Ryan Kuhns
Ryan Kuhns
💻 📖 ✅ 💡 🤔 👀 ⚠️ Sagar Mishra
Sagar Mishra
⚠️ @@ -251,9 +252,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Saransh Chopra
Saransh Chopra
📖 🚇 Satya Prakash Pattnaik
Satya Prakash Pattnaik
📖 Saurabh Dasgupta
Saurabh Dasgupta
💻 - Sebastiaan Koel
Sebastiaan Koel
💻 📖 + Sebastiaan Koel
Sebastiaan Koel
💻 📖 Sebastian Hagn
Sebastian Hagn
📖 Sebastian Schmidl
Sebastian Schmidl
🐛 💻 📖 🔬 ⚠️ 👀 🔣 Sharathchenna
Sharathchenna
💻 @@ -261,9 +262,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Solomon Botchway
Solomon Botchway
🚧 Stanislav Khrapov
Stanislav Khrapov
💻 Stijn Rotman
Stijn Rotman
💻 - Svea Marie Meyer
Svea Marie Meyer
📖 💻 + Svea Marie Meyer
Svea Marie Meyer
📖 💻 Sylvain Combettes
Sylvain Combettes
💻 🐛 TNTran92
TNTran92
💻 Taiwo Owoseni
Taiwo Owoseni
💻 @@ -271,9 +272,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d TheMathcompay Widget Factory Team
TheMathcompay Widget Factory Team
📖 Thomas Buckley-Houston
Thomas Buckley-Houston
🐛 Tom Xu
Tom Xu
💻 📖 - Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 + Tomasz Chodakowski
Tomasz Chodakowski
💻 📖 🐛 Tony Bagnall
Tony Bagnall
💻 💼 📖 🎨 📋 🔍 🤔 📆 💬 👀 📢 🔣 Tvisha Vedant
Tvisha Vedant
💻 Utkarsh Kumar
Utkarsh Kumar
💻 📖 @@ -281,9 +282,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Vedant
Vedant
📖 Viktor Dremov
Viktor Dremov
💻 ViktorKaz
ViktorKaz
💻 📖 🎨 - Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ + Vyomkesh Vyas
Vyomkesh Vyas
💻 📖 💡 ⚠️ Wayne Adams
Wayne Adams
📖 William Templier
William Templier
📖 William Zeng
William Zeng
🐛 @@ -291,9 +292,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d Yair Beer
Yair Beer
💻 Yash Lamba
Yash Lamba
💻 Yi-Xuan Xu
Yi-Xuan Xu
💻 ⚠️ 🚧 📖 - Ziyao Wei
Ziyao Wei
💻 + Ziyao Wei
Ziyao Wei
💻 aa25desh
aa25desh
💻 🐛 abandus
abandus
🤔 💻 adoherty21
adoherty21
🐛 @@ -301,9 +302,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d bethrice44
bethrice44
🐛 💻 👀 ⚠️ big-o
big-o
💻 ⚠️ 🎨 🤔 👀 ✅ 🧑‍🏫 bobbys
bobbys
💻 - brett koonce
brett koonce
📖 + brett koonce
brett koonce
📖 btrtts
btrtts
📖 chizzi25
chizzi25
📝 chrisholder
chrisholder
💻 ⚠️ 📖 🎨 💡 🐛 @@ -311,9 +312,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d hamzahiqb
hamzahiqb
🚇 hiqbal2
hiqbal2
📖 jesellier
jesellier
💻 - jschemm
jschemm
💻 + jschemm
jschemm
💻 julu98
julu98
🐛 kkoziara
kkoziara
💻 🐛 matteogales
matteogales
💻 🎨 🤔 @@ -321,9 +322,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d nileenagp
nileenagp
💻 oleskiewicz
oleskiewicz
💻 📖 ⚠️ pabworks
pabworks
💻 ⚠️ - patiently pending world peace
patiently pending world peace
💻 + patiently pending world peace
patiently pending world peace
💻 raishubham1
raishubham1
📖 simone-pignotti
simone-pignotti
💻 🐛 sophijka
sophijka
📖 🚧 @@ -331,9 +332,9 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d tensorflow-as-tf
tensorflow-as-tf
💻 vNtzYy
vNtzYy
🐛 ved pawar
ved pawar
📖 - vedazeren
vedazeren
💻 ⚠️ + vedazeren
vedazeren
💻 ⚠️ vincent-nich12
vincent-nich12
💻 vollmersj
vollmersj
📖 xiaobenbenecho
xiaobenbenecho
💻