From 28f22e7c02e2f1283edfe329a8860f39dc43f18b Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Fri, 30 Apr 2021 16:12:32 +0200 Subject: [PATCH 01/11] Fix ASKL2 link in the docs --- doc/index.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/index.rst b/doc/index.rst index b6c331c50f..f74ac157cc 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -96,7 +96,7 @@ reference to the following paper: If you are using Auto-sklearn 2.0, please also cite - Auto-Sklearn 2.0: The Next Generation, Feurer *et al.*, to appear (2020). + `Auto-Sklearn 2.0: The Next Generation `_, Feurer *et al.*, (arXiv, 2020). Bibtex entry:: @@ -106,7 +106,7 @@ If you are using Auto-sklearn 2.0, please also cite Falkner, Stefan and Lindauer, Marius and Hutter, Frank}, booktitle = {Advances in Neural Information Processing Systems 28}, year = {2020}, - journal = {arXiv:2006.???? [cs.LG]}, + journal = {arXiv:2007.04074 [cs.LG]}, } Contributing From 0690a4dee3a310b0638c9ecfd7c8f7070bdd204b Mon Sep 17 00:00:00 2001 From: Francisco Rivera Valverde <44504424+franchuterivera@users.noreply.github.com> Date: Tue, 4 May 2021 21:28:14 +0200 Subject: [PATCH 02/11] [Fix] stale only-labels (#1134) * [FIX] clone test of dummy estimator (#1129) * [FIX] clone test of dummy estimator * [FIX] flake8 * [Fix] stale only-labels * [Fix] Mark as stale if any of the provided labels --- .github/workflows/stale.yaml | 4 ++- test/test_automl/test_automl.py | 11 ------- test/test_evaluation/test_dummy_pipelines.py | 31 ++++++++++++++++++++ 3 files changed, 34 insertions(+), 12 deletions(-) create mode 100644 test/test_evaluation/test_dummy_pipelines.py diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index 45422d04eb..bf2d62a628 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -18,5 +18,7 @@ jobs: close-issue-message: > This issue has been automatically closed due to inactivity. stale-issue-label: 'stale' - only-issue-labels: 'Answered,Feedback-Required,invalid,wontfix' + # Only issues with ANY of these labels are checked. + # Separate multiple labels with commas (eg. "incomplete,waiting-feedback"). + any-of-issue-labels: 'Answered,Feedback-Required,invalid,wontfix' exempt-all-milestones: true diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index e80f12de71..6f105e09b9 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -12,7 +12,6 @@ import pandas as pd import pytest import sklearn.datasets -from sklearn.base import clone from smac.scenario.scenario import Scenario from smac.facade.roar_facade import ROAR @@ -427,16 +426,6 @@ def test_do_dummy_prediction(backend, dask_client, datasets): 'predictions_ensemble_1_1_0.0.npy') ) - model_path = os.path.join(backend.temporary_directory, '.auto-sklearn', - 'runs', '1_1_0.0', - '1.1.0.0.model') - - # Make sure the dummy model complies with scikit learn - # get/set params - assert os.path.exists(model_path) - with open(model_path, 'rb') as model_handler: - clone(pickle.load(model_handler)) - auto._clean_logger() del auto diff --git a/test/test_evaluation/test_dummy_pipelines.py b/test/test_evaluation/test_dummy_pipelines.py new file mode 100644 index 0000000000..d71a6c1b69 --- /dev/null +++ b/test/test_evaluation/test_dummy_pipelines.py @@ -0,0 +1,31 @@ +import numpy as np + +import pytest + +from sklearn.base import clone +from sklearn.datasets import make_classification, make_regression +from sklearn.utils.validation import check_is_fitted + +from autosklearn.evaluation.abstract_evaluator import MyDummyClassifier, MyDummyRegressor + + +@pytest.mark.parametrize("task_type", ['classification', 'regression']) +def test_dummy_pipeline(task_type): + if task_type == 'classification': + estimator_class = MyDummyClassifier + data_maker = make_classification + elif task_type == 'regression': + estimator_class = MyDummyRegressor + data_maker = make_regression + else: + pytest.fail(task_type) + + estimator = estimator_class(config=1, random_state=np.random.RandomState(42)) + X, y = data_maker() + estimator.fit(X, y) + check_is_fitted(estimator) + + assert np.shape(X)[0] == np.shape(estimator.predict(X))[0] + + # make sure we comply with scikit-learn estimator API + clone(estimator) From bbdde940d860b40bf70fd4b19baa47a6a1aa5d4e Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Wed, 5 May 2021 12:59:28 +0200 Subject: [PATCH 03/11] Update stale.yaml (#1142) --- .github/workflows/stale.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index bf2d62a628..21d9198ac7 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -20,5 +20,5 @@ jobs: stale-issue-label: 'stale' # Only issues with ANY of these labels are checked. # Separate multiple labels with commas (eg. "incomplete,waiting-feedback"). - any-of-issue-labels: 'Answered,Feedback-Required,invalid,wontfix' + any-of-labels: 'Answered,Feedback-Required,invalid,wontfix' exempt-all-milestones: true From 52dd9a1747152d5494812d81dd32b7519ba790e5 Mon Sep 17 00:00:00 2001 From: Francisco Rivera Valverde <44504424+franchuterivera@users.noreply.github.com> Date: Fri, 14 May 2021 08:46:51 +0200 Subject: [PATCH 04/11] [Fix] from 60 to 30 days to stale (#1143) --- .github/workflows/stale.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/stale.yaml b/.github/workflows/stale.yaml index 21d9198ac7..a1449f10e7 100644 --- a/.github/workflows/stale.yaml +++ b/.github/workflows/stale.yaml @@ -9,7 +9,7 @@ jobs: steps: - uses: actions/stale@v3 with: - days-before-stale: 60 + days-before-stale: 30 days-before-close: 7 stale-issue-message: > This issue has been automatically marked as stale because it has not had From b34e3d82c3e7850eee28bc947d9a12747ed85b09 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Mon, 19 Jul 2021 21:07:04 +0200 Subject: [PATCH 05/11] Create Question issue template --- .github/ISSUE_TEMPLATE/question.md | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/question.md diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md new file mode 100644 index 0000000000..a5039ad854 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/question.md @@ -0,0 +1,29 @@ +--- +name: Question +about: Ask a question! +title: "[Question] My Question?" +labels: '' +assignees: '' + +--- + +# Short Question Description +A clear single sentence question we can try to help with? + +With some extra context to follow it up. This way the question is clear for both you and us without it being lost in the paragraph. +Some useful information to help us with your question: +* How did this question come about? +* Would a small code snippet help? +* What have you already looked at? + +Before you ask, please have a look at the +* [Documentation](https://automl.github.io/auto-sklearn/master/manual.html) + * If it's related but not clear, please include it in your question with a link, we'll try to make it better! +* [Examples](https://automl.github.io/auto-sklearn/master/examples/index.html) + * Likewise, an example can answer many questions! However we can't cover all question with examples but if you think your question would benefit from an example, let us know! +* [Issues](https://github.com/automl/auto-sklearn/issues?q=label%3Aquestion+) + * We try to label all questions with the label `Question`, maybe someone has already asked. If the question is about a feature, try searching more of the issues. If you find something related but doesn't directly answer your question, please link to it with #(issue number)! + +# System Details (if relevant) +* Which version of `auto-sklearn` are you using? +* Are you running this on Linux / Mac / ... ? From 7809d2775939d7b16dce74b05e070bc3ef9b5725 Mon Sep 17 00:00:00 2001 From: olehb007 Date: Tue, 20 Jul 2021 11:42:58 +0200 Subject: [PATCH 06/11] Last scipy breaks dependency (#1168) Fix ModuleNotFoundError: No module named 'scipy.optimize._shgo_lib.sobol_seq' https://stackoverflow.com/questions/68176331/from-scipy-optimize-shgo-lib-sobol-seq-import-sobol-does-not-work --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a29774a201..d7294cef97 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ setuptools numpy>=1.9.0 -scipy>=0.14.1 +scipy>=0.14.1,<1.7 joblib scikit-learn>=0.24.0,<0.25.0 From c86928feff942e223b8cb613dfcacc0876231dcf Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 20 Jul 2021 13:02:50 +0200 Subject: [PATCH 07/11] Update License files (#1173) * Delete COPYING * Create LICENSE * Delete LICENSE.txt * Update setup.py * Rename LICENSE to LICENSE.txt * bundle license --- COPYING | 31 ------------------------------- LICENSE.txt | 43 ++++++++++++++++++++++++------------------- MANIFEST.in | 1 + setup.py | 2 +- 4 files changed, 26 insertions(+), 51 deletions(-) delete mode 100644 COPYING diff --git a/COPYING b/COPYING deleted file mode 100644 index 2338f96c1b..0000000000 --- a/COPYING +++ /dev/null @@ -1,31 +0,0 @@ -New BSD License - -Copyright (c) 2015 The auto-sklearn developers. -All rights reserved. - - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - - a. Redistributions of source code must retain the above copyright notice, - this list of conditions and the following disclaimer. - b. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - c. Neither the name of the auto-sklearn Developers nor the names of - its contributors may be used to endorse or promote products - derived from this software without specific prior written - permission. - - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE -ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR -ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT -LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY -OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -DAMAGE. \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt index e1f17e32cb..7f50676480 100644 --- a/LICENSE.txt +++ b/LICENSE.txt @@ -1,24 +1,29 @@ -Copyright (c) 2014, Matthias Feurer +BSD 3-Clause License + +Copyright (c) 2014-2021, AutoML Freiburg All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - * Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - * Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - * Neither the name of the nor the - names of its contributors may be used to endorse or promote products - derived from this software without specific prior written permission. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND -ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED -WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL BE LIABLE FOR ANY -DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES -(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT -(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS -SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/MANIFEST.in b/MANIFEST.in index e3afa02b6a..4740ecd407 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -6,3 +6,4 @@ include requirements.txt include autosklearn/requirements.txt recursive-include autosklearn/experimental/askl2_portfolios *.json include autosklearn/experimental/askl2_training_data.json +include LICENSE.txt diff --git a/setup.py b/setup.py index 5833a190c7..c9620e8302 100644 --- a/setup.py +++ b/setup.py @@ -64,7 +64,7 @@ extras_require=extras_reqs, install_requires=install_reqs, include_package_data=True, - license='BSD', + license='BSD3', platforms=['Linux'], classifiers=[ "Environment :: Console", From 674eee4defbff5d7db90997bb6524950ef7c4ca3 Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 20 Jul 2021 17:25:35 +0200 Subject: [PATCH 08/11] Add precision reduction (#1178) * Add precision reduction in case of data being too large * reduce scipy dependency --- autosklearn/automl.py | 27 ++++++++++++++-- test/test_automl/test_automl.py | 57 ++++++++++++++++++++++----------- 2 files changed, 64 insertions(+), 20 deletions(-) diff --git a/autosklearn/automl.py b/autosklearn/automl.py index 636762b6ff..c5001c53f9 100644 --- a/autosklearn/automl.py +++ b/autosklearn/automl.py @@ -849,17 +849,40 @@ def subsample_if_too_large( task: int, ): if memory_limit and isinstance(X, np.ndarray): + if X.dtype == np.float32: multiplier = 4 - elif X.dtype in (np.float64, np.float): + elif X.dtype in (np.float64, float): multiplier = 8 - elif X.dtype == np.float128: + elif ( + # In spite of the names, np.float96 and np.float128 + # provide only as much precision as np.longdouble, + # that is, 80 bits on most x86 machines and 64 bits + # in standard Windows builds. + (hasattr(np, 'float128') and X.dtype == np.float128) + or (hasattr(np, 'float96') and X.dtype == np.float96) + ): multiplier = 16 else: # Just assuming some value - very unlikely multiplier = 8 logger.warning('Unknown dtype for X: %s, assuming it takes 8 bit/number', str(X.dtype)) + + megabytes = X.shape[0] * X.shape[1] * multiplier / 1024 / 1024 + if memory_limit <= megabytes * 10 and X.dtype != np.float32: + cast_to = { + 8: np.float32, + 16: np.float64, + }.get(multiplier, np.float32) + logger.warning( + 'Dataset too large for memory limit %dMB, reducing the precision from %s to %s', + memory_limit, + X.dtype, + cast_to, + ) + X = X.astype(cast_to) + megabytes = X.shape[0] * X.shape[1] * multiplier / 1024 / 1024 if memory_limit <= megabytes * 10: new_num_samples = int( diff --git a/test/test_automl/test_automl.py b/test/test_automl/test_automl.py index 6f105e09b9..13e118cdd2 100644 --- a/test/test_automl/test_automl.py +++ b/test/test_automl/test_automl.py @@ -660,24 +660,45 @@ def test_fail_if_feat_type_on_pandas_input(backend, dask_client): @pytest.mark.parametrize( - 'memory_limit,task', + 'memory_limit,precision,task', [ - (memory_limit, task) + (memory_limit, precision, task) for task in itertools.chain(CLASSIFICATION_TASKS, REGRESSION_TASKS) - for memory_limit in (1, 10, None) + for precision in (float, np.float32, np.float64, np.float128) + for memory_limit in (1, 100, None) ] ) -def test_subsample_if_too_large(memory_limit, task): +def test_subsample_if_too_large(memory_limit, precision, task): fixture = { - BINARY_CLASSIFICATION: {1: 436, 10: 569, None: 569}, - MULTICLASS_CLASSIFICATION: {1: 204, 10: 1797, None: 1797}, - MULTILABEL_CLASSIFICATION: {1: 204, 10: 1797, None: 1797}, - REGRESSION: {1: 1310, 10: 1326, None: 1326}, - MULTIOUTPUT_REGRESSION: {1: 1310, 10: 1326, None: 1326} + BINARY_CLASSIFICATION: { + 1: {float: 1310, np.float32: 2621, np.float64: 1310, np.float128: 655}, + 100: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + None: {float: 12000, np.float32: 12000, np.float64: 12000, np.float128: 12000}, + }, + MULTICLASS_CLASSIFICATION: { + 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + MULTILABEL_CLASSIFICATION: { + 1: {float: 204, np.float32: 409, np.float64: 204, np.float128: 102}, + 100: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + None: {float: 1797, np.float32: 1797, np.float64: 1797, np.float128: 1797}, + }, + REGRESSION: { + 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + }, + MULTIOUTPUT_REGRESSION: { + 1: {float: 655, np.float32: 1310, np.float64: 655, np.float128: 327}, + 100: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + None: {float: 5000, np.float32: 5000, np.float64: 5000, np.float128: 5000}, + } } mock = unittest.mock.Mock() if task == BINARY_CLASSIFICATION: - X, y = sklearn.datasets.load_breast_cancer(return_X_y=True) + X, y = sklearn.datasets.make_hastie_10_2() elif task == MULTICLASS_CLASSIFICATION: X, y = sklearn.datasets.load_digits(return_X_y=True) elif task == MULTILABEL_CLASSIFICATION: @@ -686,22 +707,22 @@ def test_subsample_if_too_large(memory_limit, task): for i, j in enumerate(y_): y[i, j] = 1 elif task == REGRESSION: - X, y = sklearn.datasets.load_diabetes(return_X_y=True) - X = np.vstack((X, X, X)) - y = np.vstack((y.reshape((-1, 1)), y.reshape((-1, 1)), y.reshape((-1, 1)))) + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) elif task == MULTIOUTPUT_REGRESSION: - X, y = sklearn.datasets.load_diabetes(return_X_y=True) + X, y = sklearn.datasets.make_friedman1(n_samples=5000, n_features=20) y = np.vstack((y, y)).transpose() - X = np.vstack((X, X, X)) - y = np.vstack((y, y, y)) else: raise ValueError(task) + X = X.astype(precision) assert X.shape[0] == y.shape[0] X_new, y_new = AutoML.subsample_if_too_large(X, y, mock, 1, memory_limit, task) - assert X_new.shape[0] == fixture[task][memory_limit] + assert X_new.shape[0] == fixture[task][memory_limit][precision] if memory_limit == 1: - assert mock.warning.call_count == 1 + if precision in (np.float128, np.float64, float): + assert mock.warning.call_count == 2 + else: + assert mock.warning.call_count == 1 else: assert mock.warning.call_count == 0 From 73ab8c38c36066efe8eeea210ab941fa67c0a6ab Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 20 Jul 2021 22:41:51 +0200 Subject: [PATCH 09/11] Askl2 more metadata (#1179) * update askl2 metadata * updates * update Auto-sklearn 2.0 data and selector * fix rebase error --- MANIFEST.in | 2 +- autosklearn/experimental/askl2.py | 115 +- .../RF_None_10CV_iterative_es_if.json | 1107 --- .../RF_None_3CV_iterative_es_if.json | 1110 --- .../RF_None_5CV_iterative_es_if.json | 1132 --- .../RF_None_holdout_iterative_es_if.json | 1112 --- .../experimental/askl2_training_data.json | 6124 --------------- .../RF_None_10CV_iterative_es_if.json} | 1105 +-- .../RF_None_3CV_iterative_es_if.json | 1173 +++ .../RF_None_5CV_iterative_es_if.json} | 1186 +-- .../RF_None_holdout_iterative_es_if.json | 1188 +++ .../RF_SH-eta4-i_10CV_iterative_es_if.json | 1100 +-- .../RF_SH-eta4-i_3CV_iterative_es_if.json | 1177 +++ .../RF_SH-eta4-i_5CV_iterative_es_if.json | 1125 +-- .../RF_SH-eta4-i_holdout_iterative_es_if.json | 1165 +++ .../askl2_training_data.json | 6699 +++++++++++++++++ .../RF_None_10CV_iterative_es_if.json | 1218 +++ .../RF_None_3CV_iterative_es_if.json | 1183 +++ .../RF_None_5CV_iterative_es_if.json | 1202 +++ .../RF_None_holdout_iterative_es_if.json | 1204 +++ .../RF_SH-eta4-i_10CV_iterative_es_if.json | 1191 +++ .../RF_SH-eta4-i_3CV_iterative_es_if.json | 1196 +++ .../RF_SH-eta4-i_5CV_iterative_es_if.json | 1191 +++ .../RF_SH-eta4-i_holdout_iterative_es_if.json | 1202 +++ .../log_loss/askl2_training_data.json | 6699 +++++++++++++++++ .../RF_None_10CV_iterative_es_if.json | 1186 +++ .../RF_None_3CV_iterative_es_if.json | 1201 +++ .../RF_None_5CV_iterative_es_if.json | 1224 +++ .../RF_None_holdout_iterative_es_if.json | 1188 +++ .../RF_SH-eta4-i_10CV_iterative_es_if.json | 1190 +++ .../RF_SH-eta4-i_3CV_iterative_es_if.json | 1205 +++ .../RF_SH-eta4-i_5CV_iterative_es_if.json | 1223 +++ .../RF_SH-eta4-i_holdout_iterative_es_if.json | 1198 +++ .../roc_auc/askl2_training_data.json | 4043 ++++++++++ autosklearn/experimental/selector.py | 389 +- test/test_automl/test_estimators.py | 19 +- 36 files changed, 44041 insertions(+), 12931 deletions(-) delete mode 100644 autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json delete mode 100644 autosklearn/experimental/askl2_portfolios/RF_None_3CV_iterative_es_if.json delete mode 100644 autosklearn/experimental/askl2_portfolios/RF_None_5CV_iterative_es_if.json delete mode 100644 autosklearn/experimental/askl2_portfolios/RF_None_holdout_iterative_es_if.json delete mode 100644 autosklearn/experimental/askl2_training_data.json rename autosklearn/experimental/{askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json => balanced_accuracy/askl2_portfolios/RF_None_10CV_iterative_es_if.json} (66%) create mode 100644 autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_3CV_iterative_es_if.json rename autosklearn/experimental/{askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json => balanced_accuracy/askl2_portfolios/RF_None_5CV_iterative_es_if.json} (71%) create mode 100644 autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_None_holdout_iterative_es_if.json rename autosklearn/experimental/{ => balanced_accuracy}/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json (65%) create mode 100644 autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json rename autosklearn/experimental/{ => balanced_accuracy}/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json (68%) create mode 100644 autosklearn/experimental/balanced_accuracy/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json create mode 100644 autosklearn/experimental/balanced_accuracy/askl2_training_data.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_None_10CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_None_3CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_None_5CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_None_holdout_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json create mode 100644 autosklearn/experimental/log_loss/askl2_training_data.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_10CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_3CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_5CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_None_holdout_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_10CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_3CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_5CV_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_portfolios/RF_SH-eta4-i_holdout_iterative_es_if.json create mode 100644 autosklearn/experimental/roc_auc/askl2_training_data.json diff --git a/MANIFEST.in b/MANIFEST.in index 4740ecd407..e76cdbb0ea 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -4,6 +4,6 @@ recursive-include autosklearn/metalearning/files *.txt include autosklearn/util/logging.yaml include requirements.txt include autosklearn/requirements.txt -recursive-include autosklearn/experimental/askl2_portfolios *.json +recursive-include autosklearn/experimental/ *.json include autosklearn/experimental/askl2_training_data.json include LICENSE.txt diff --git a/autosklearn/experimental/askl2.py b/autosklearn/experimental/askl2.py index 4dfbf4c981..31f87c97e4 100644 --- a/autosklearn/experimental/askl2.py +++ b/autosklearn/experimental/askl2.py @@ -15,53 +15,60 @@ import autosklearn from autosklearn.classification import AutoSklearnClassifier import autosklearn.experimental.selector -from autosklearn.metrics import Scorer +from autosklearn.metrics import Scorer, balanced_accuracy, roc_auc, log_loss, accuracy +metrics = (balanced_accuracy, roc_auc, log_loss) +selector_files = {} this_directory = pathlib.Path(__file__).resolve().parent -training_data_file = this_directory / 'askl2_training_data.json' -with open(training_data_file) as fh: - training_data = json.load(fh) - fh.seek(0) - m = hashlib.md5() - m.update(fh.read().encode('utf8')) -training_data_hash = m.hexdigest()[:10] -selector_filename = "askl2_selector_%s_%s_%s.pkl" % ( - autosklearn.__version__, - sklearn.__version__, - training_data_hash -) -selector_directory = os.environ.get('XDG_CACHE_HOME') -if selector_directory is None: - selector_directory = pathlib.Path.home() -selector_directory = pathlib.Path(selector_directory).joinpath('auto-sklearn').expanduser() -selector_file = selector_directory / selector_filename -metafeatures = pd.DataFrame(training_data['metafeatures']) -y_values = np.array(training_data['y_values']) -strategies = training_data['strategies'] -minima_for_methods = training_data['minima_for_methods'] -maxima_for_methods = training_data['maxima_for_methods'] -if not selector_file.exists(): - selector = autosklearn.experimental.selector.OneVSOneSelector( - configuration=training_data['configuration'], - default_strategy_idx=strategies.index('RF_SH-eta4-i_holdout_iterative_es_if'), - rng=1, +for metric in metrics: + training_data_file = this_directory / metric.name / 'askl2_training_data.json' + with open(training_data_file) as fh: + training_data = json.load(fh) + fh.seek(0) + m = hashlib.md5() + m.update(fh.read().encode('utf8')) + training_data_hash = m.hexdigest()[:10] + selector_filename = "askl2_selector_%s_%s_%s_%s.pkl" % ( + autosklearn.__version__, + sklearn.__version__, + metric.name, + training_data_hash ) - selector.fit( - X=metafeatures, - y=y_values, - methods=strategies, - minima=minima_for_methods, - maxima=maxima_for_methods, - ) - selector_file.parent.mkdir(exist_ok=True, parents=True) - try: - with open(selector_file, 'wb') as fh: - pickle.dump(selector, fh) - except Exception as e: - print("AutoSklearn2Classifier needs to create a selector file under " - "the user's home directory or XDG_CACHE_HOME. Nevertheless " - "the path {} is not writable.".format(selector_file)) - raise e + selector_directory = os.environ.get('XDG_CACHE_HOME') + if selector_directory is None: + selector_directory = pathlib.Path.home() + selector_directory = pathlib.Path(selector_directory).joinpath('auto-sklearn').expanduser() + selector_files[metric.name] = selector_directory / selector_filename + metafeatures = pd.DataFrame(training_data['metafeatures']) + strategies = training_data['strategies'] + y_values = pd.DataFrame(training_data['y_values'], columns=strategies, index=metafeatures.index) + minima_for_methods = training_data['minima_for_methods'] + maxima_for_methods = training_data['maxima_for_methods'] + default_strategies = training_data['tie_break_order'] + if not selector_files[metric.name].exists(): + selector = autosklearn.experimental.selector.OVORF( + configuration=training_data['configuration'], + random_state=np.random.RandomState(1), + n_estimators=500, + tie_break_order=default_strategies, + ) + selector = autosklearn.experimental.selector.FallbackWrapper(selector, default_strategies) + selector.fit( + X=metafeatures, + y=y_values, + minima=minima_for_methods, + maxima=maxima_for_methods, + ) + selector_files[metric.name].parent.mkdir(exist_ok=True, parents=True) + + try: + with open(selector_files[metric.name], 'wb') as fh: + pickle.dump(selector, fh) + except Exception as e: + print("AutoSklearn2Classifier needs to create a selector file under " + "the user's home directory or XDG_CACHE_HOME. Nevertheless " + "the path {} is not writable.".format(selector_files[metric.name])) + raise e class SmacObjectCallback: @@ -296,7 +303,7 @@ def __init__( Attributes ---------- - cv_results\_ : dict of numpy (masked) ndarrays + cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. @@ -346,10 +353,22 @@ def fit(self, X, y, feat_type=None, dataset_name=None): + if self.metric is None: + if len(y.shape) == 1 or y.shape[1] == 1: + self.metric = accuracy + else: + self.metric = log_loss + + if self.metric in metrics: + metric_name = self.metric.name + selector_file = selector_files[metric_name] + else: + metric_name = 'balanced_accuracy' + selector_file = selector_files[metric_name] with open(selector_file, 'rb') as fh: selector = pickle.load(fh) - metafeatures = np.array([len(np.unique(y)), X.shape[1], X.shape[0]]) + metafeatures = pd.DataFrame({dataset_name: [X.shape[1], X.shape[0]]}).transpose() selection = np.argmax(selector.predict(metafeatures)) automl_policy = strategies[selection] @@ -400,7 +419,9 @@ def fit(self, X, y, else: resampling_strategy_kwargs = None - portfolio_file = this_directory / 'askl2_portfolios' / ('%s.json' % automl_policy) + portfolio_file = ( + this_directory / metric_name / 'askl2_portfolios' / ('%s.json' % automl_policy) + ) with open(portfolio_file) as fh: portfolio_json = json.load(fh) portfolio = portfolio_json['portfolio'] diff --git a/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json b/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json deleted file mode 100644 index ad0d429d77..0000000000 --- a/autosklearn/experimental/askl2_portfolios/RF_None_10CV_iterative_es_if.json +++ /dev/null @@ -1,1107 +0,0 @@ -{ - "portfolio": { - "6a746f5": { - "balancing:strategy": "none", - "classifier:__choice__": "sgd", - 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0.5061879978022417, + "RF_None_10CV_iterative_es_if": 0.5119685479586008, + "RF_SH-eta4-i_holdout_iterative_es_if": 0.5208296707115775, + "RF_SH-eta4-i_3CV_iterative_es_if": 0.5482769412662901, + "RF_SH-eta4-i_5CV_iterative_es_if": 0.5125078632221003, + "RF_SH-eta4-i_10CV_iterative_es_if": 0.51179813513461 + } + }, + "tie_break_order": [ + "RF_SH-eta4-i_holdout_iterative_es_if", + "RF_None_holdout_iterative_es_if", + "RF_SH-eta4-i_3CV_iterative_es_if", + "RF_None_3CV_iterative_es_if", + "RF_SH-eta4-i_5CV_iterative_es_if", + "RF_None_5CV_iterative_es_if", + "RF_SH-eta4-i_10CV_iterative_es_if", + "RF_None_10CV_iterative_es_if" + ], + "configuration": { + "bootstrap": "True", + "max_depth": 0, + "max_features": 2, + "min_samples_leaf": 5, + "min_samples_split": 5, + "normalization": "all", + "prediction": "hard" + } +} diff --git a/autosklearn/experimental/selector.py b/autosklearn/experimental/selector.py index 13364f9a29..3ef681bef8 100644 --- a/autosklearn/experimental/selector.py +++ b/autosklearn/experimental/selector.py @@ -1,134 +1,345 @@ +import copy +import itertools +import typing + import numpy as np +import pandas as pd +import scipy.stats import sklearn.ensemble -class OneVSOneSelector: - def __init__(self, configuration, default_strategy_idx, rng): +class AbstractSelector: + + def fit( + self, + X: pd.DataFrame, + y: pd.DataFrame, + minima: typing.Dict[int, typing.Dict[str, float]], + maxima: typing.Dict[int, typing.Dict[str, float]], + ) -> None: + raise NotImplementedError() + + def predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame] = None) -> pd.DataFrame: + prediction = self._predict(X, y) + for col, series in prediction.iteritems(): + assert series.dtype == float, (col, series) + np.testing.assert_array_almost_equal( + prediction.sum(axis='columns').to_numpy(), + np.ones(X.shape[0]), + err_msg=prediction.to_csv(), + ) + return prediction + + def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: + raise NotImplementedError() + + +class OneVSOneSelector(AbstractSelector): + def __init__(self, configuration, random_state, tie_break_order): self.configuration = configuration - self.default_strategy_idx = default_strategy_idx - self.rng = rng + self.single_strategy_idx = None + self.rng = random_state + self.tie_break_order = tie_break_order self.models = None self.target_indices = None self.selectors_ = None self.weights_ = {} - self.X_train = None + self.strategies_ = None - def fit(self, X, y, methods, minima, maxima): - self.X_train = X.copy() + def fit( + self, + X: pd.DataFrame, + y: pd.DataFrame, + minima: typing.Dict[int, typing.Dict[str, float]], + maxima: typing.Dict[int, typing.Dict[str, float]], + ) -> None: + + self.maxima = copy.deepcopy(maxima) target_indices = np.array(list(range(y.shape[1]))) models = dict() weights = dict() + + self.strategies_ = copy.deepcopy(y.columns.to_list()) + y_pd = y.copy() + y = y.to_numpy() + for i in range(len(target_indices)): models[i] = dict() weights[i] = dict() for j in range(i + 1, len(target_indices)): - y_i_j = y[:, i] < y[:, j] - min_i = np.array([minima[methods[i]][task_id] for task_id in X.index]) - max_i = np.array([maxima[methods[i]][task_id] for task_id in X.index]) - min_j = np.array([minima[methods[j]][task_id] for task_id in X.index]) - max_j = np.array([maxima[methods[j]][task_id] for task_id in X.index]) - - minimum = np.minimum(min_i, min_j) - maximum = np.maximum(max_i, max_j) - diff = maximum - minimum - diff[diff == 0] = 1 - normalized_y_i = (y[:, i].copy() - minimum) / diff - normalized_y_j = (y[:, j].copy() - minimum) / diff - - weights_i_j = np.abs(normalized_y_i - normalized_y_j) + + if self.configuration['normalization'] in ('all', 'binary', 'y', 'all1', + 'binary1'): + minimum2 = np.ones(len(X)) * np.inf + maximum2 = np.zeros(len(X)) + + if self.configuration['normalization'] in ('all', 'all1'): + for idx, task_id in enumerate(X.index): + for method_id in range(len(target_indices)): + minimum2[idx] = np.nanmin(( + minimum2[idx], + minima[task_id][self.strategies_[method_id]] + )) + maximum2[idx] = np.nanmax(( + maximum2[idx], + maxima[task_id][self.strategies_[method_id]] + )) + if self.configuration['normalization'] == 'all1': + maximum2 = np.ones_like(maximum2) + elif self.configuration['normalization'] in ('binary', 'binary1'): + for idx, task_id in enumerate(X.index): + for method_id in (i, j): + minimum2[idx] = np.nanmin(( + minimum2[idx], + minima[task_id][self.strategies_[method_id]] + )) + maximum2[idx] = np.nanmax(( + maximum2[idx], + maxima[task_id][self.strategies_[method_id]] + )) + if self.configuration['normalization'] == 'binary1': + maximum2 = np.ones_like(maximum2) + elif self.configuration['normalization'] == 'y': + for idx, task_id in enumerate(X.index): + minimum2[idx] = np.nanmin((minimum2[idx], y_pd.loc[task_id].min())) + maximum2[idx] = np.nanmax((maximum2[idx], y_pd.loc[task_id].max())) + else: + raise ValueError(self.configuration['normalization']) + + y_i_j = y[:, i] < y[:, j] + mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) + X_ = X.to_numpy()[mask] + y_i_j = y_i_j[mask] + + minimum = minimum2[mask] + maximum = maximum2[mask] + + diff = maximum - minimum + diff[diff == 0] = 1 + normalized_y_i = (y[:, i][mask].copy() - minimum) / diff + normalized_y_j = (y[:, j][mask].copy() - minimum) / diff + + weights_i_j = np.abs(normalized_y_i - normalized_y_j) + + elif self.configuration['normalization'] == 'rank': + y_i_j = y[:, i] < y[:, j] + mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) + X_ = X.to_numpy()[mask] + y_i_j = y_i_j[mask] + ranks = scipy.stats.rankdata(y[mask], axis=1) + weights_i_j = np.abs(ranks[:, i] - ranks[:, j]) + + elif self.configuration['normalization'] == 'None': + y_i_j = y[:, i] < y[:, j] + mask = np.isfinite(y[:, i]) & np.isfinite(y[:, j]) + X_ = X.to_numpy()[mask] + y_i_j = y_i_j[mask] + weights_i_j = np.ones_like(y_i_j).astype(int) + + else: + raise ValueError(self.configuration['normalization']) + + if len(y_i_j) == 0: + models[i][j] = None + weights[i][j] = None + continue + if np.all([target == y_i_j[0] for target in y_i_j]): n_zeros = int(np.ceil(len(y_i_j) / 2)) n_ones = int(np.floor(len(y_i_j) / 2)) - base_model = sklearn.dummy.DummyClassifier( - strategy='constant', constant=y_i_j[0], - ) + import sklearn.dummy + base_model = sklearn.dummy.DummyClassifier(strategy='constant', + constant=y_i_j[0]) base_model.fit( - X.values, + X_, np.array(([[0]] * n_zeros) + ([[1]] * n_ones)).flatten(), sample_weight=weights_i_j, ) else: - base_model = sklearn.ensemble.RandomForestClassifier( - random_state=self.rng, - n_estimators=500, - oob_score=True, - bootstrap=True, - min_samples_split=self.configuration['min_samples_split'], - min_samples_leaf=self.configuration['min_samples_leaf'], - max_features=int(np.rint(X.shape[1] ** self.configuration['max_features'])), - ) - base_model.fit(X.values, y_i_j, sample_weight=weights_i_j) + if self.configuration.get('max_depth') == 0: + import sklearn.dummy + loss_i = np.sum((y_i_j == 0) * weights_i_j) + loss_j = np.sum((y_i_j == 1) * weights_i_j) + + base_model = sklearn.dummy.DummyClassifier( + strategy='constant', constant=1 if loss_i < loss_j else 0, + ) + base_model.fit( + X_, + np.ones_like(y_i_j) * int(loss_i < loss_j), + sample_weight=weights_i_j, + ) + else: + base_model = self.fit_pairwise_model( + X_, y_i_j, weights_i_j, self.rng, self.configuration, + ) models[i][j] = base_model weights[i][j] = weights_i_j self.models = models self.weights_ = weights self.target_indices = target_indices - def predict(self, X): - - if self.default_strategy_idx is not None: - use_prediction = False - counter = 0 - te = X.copy().flatten() - assert len(te) == 3 - for _, tr in self.X_train.iterrows(): - tr = tr.to_numpy() - if tr[0] >= te[0] and tr[1] >= te[1] and tr[2] >= te[2]: - counter += 1 + def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: - if counter > 0: - use_prediction = True - - if not use_prediction: - print('Using Backup selector') - return np.array( - [1 if i == self.default_strategy_idx else 0 for i in self.target_indices] - ) - print('Using no backup selector') - - X = X.reshape((1, -1)) + if y is not None: + raise ValueError("y must not be provided") raw_predictions = dict() + raw_probas = dict() for i in range(len(self.target_indices)): for j in range(i + 1, len(self.target_indices)): - raw_predictions[(i, j)] = self.models[i][j].predict(X) + if self.models[i][j] is not None: + raw_predictions[(i, j)] = self.models[i][j].predict(X) + raw_probas[(i, j)] = self.models[i][j].predict_proba(X) - predictions = [] + if len(raw_predictions) == 0: + predictions = pd.DataFrame(0, index=X.index, columns=self.strategies_).astype(float) + predictions.iloc[:, self.single_strategy_idx] = 1.0 + return predictions + + predictions = {} for x_idx in range(X.shape[0]): wins = np.zeros(self.target_indices.shape) for i in range(len(self.target_indices)): for j in range(i + 1, len(self.target_indices)): - prediction = raw_predictions[(i, j)][x_idx] - if prediction == 1: - wins[i] += 1 - else: - wins[j] += 1 + if (i, j) in raw_predictions: + if self.configuration['prediction'] == 'soft': + if raw_probas[(i, j)].shape[1] == 1: + proba = raw_probas[(i, j)][x_idx][0] + else: + proba = raw_probas[(i, j)][x_idx][1] + wins[i] += proba + wins[j] += 1 - proba + elif self.configuration['prediction'] == 'hard': + prediction = raw_predictions[(i, j)][x_idx] + if prediction == 1: + wins[i] += 1 + else: + wins[j] += 1 + else: + raise ValueError(self.configuration['prediction']) + + n_prev = np.inf + # Tie breaking + while True: + most_wins = np.max(wins) + most_wins_mask = most_wins == wins + if n_prev == np.sum(most_wins_mask): + n_prev = np.sum(most_wins_mask) + hit = False + for method in self.tie_break_order: + if method not in self.strategies_: + continue + method_idx = self.strategies_.index(method) + if most_wins_mask[method_idx]: + wins[method_idx] += 1 + hit = True + break + if not hit: + wins[int(self.rng.choice(np.argwhere(most_wins_mask).flatten()))] += 1 + elif np.sum(most_wins_mask) > 1: + n_prev = np.sum(most_wins_mask) + where = np.argwhere(most_wins_mask).flatten() + for i, j in itertools.combinations(where, 2): + if (i, j) in raw_predictions: + prediction = raw_predictions[(i, j)][x_idx] + if prediction == 1: + wins[i] += 1 + else: + wins[j] += 1 + else: + method_i = self.strategies_[i] + method_j = self.strategies_[j] + if ( + self.tie_break_order.index(method_i) + < self.tie_break_order.index(method_j) + ): + wins[i] += 1 + else: + wins[j] += 1 + else: + break + wins = wins / np.sum(wins) - predictions.append(wins) - predictions = np.array([np.array(prediction) for prediction in predictions]) - return predictions + predictions[X.index[x_idx]] = wins - def predict_oob(self, X): + rval = { + task_id: { + strategy: predictions[task_id][strategy_idx] + for strategy_idx, strategy in enumerate(self.strategies_) + } + for task_id in X.index + } + rval = pd.DataFrame(rval).transpose().astype(float) + rval = rval[self.strategies_] + rval = rval.fillna(0.0) + return rval - raw_predictions = dict() - for i in range(len(self.target_indices)): - for j in range(i + 1, len(self.target_indices)): - rp = self.models[i][j].oob_decision_function_.copy() - rp[np.isnan(rp)] = 0 - rp = np.nanargmax(rp, axis=1) - raw_predictions[(i, j)] = rp + def fit_pairwise_model(self, X, y, weights, rng, configuration): + raise NotImplementedError() - predictions = [] - for x_idx in range(X.shape[0]): - wins = np.zeros(self.target_indices.shape) - for i in range(len(self.target_indices)): - for j in range(i + 1, len(self.target_indices)): - prediction = raw_predictions[(i, j)][x_idx] - if prediction == 1: - wins[i] += 1 - else: - wins[j] += 1 - wins = wins / np.sum(wins) - predictions.append(wins) - predictions = np.array([np.array(prediction) for prediction in predictions]) - return predictions + +class OVORF(OneVSOneSelector): + def __init__(self, n_estimators, **kwargs): + super().__init__(**kwargs) + self.n_estimators = n_estimators + + def fit_pairwise_model(self, X, y, weights, rng, configuration): + base_model = sklearn.ensemble.RandomForestClassifier( + random_state=rng, + n_estimators=self.n_estimators, + bootstrap=True if configuration['bootstrap'] == 'True' else False, + min_samples_split=configuration['min_samples_split'], + min_samples_leaf=configuration['min_samples_leaf'], + max_features=int(configuration['max_features']), + max_depth=configuration['max_depth'], + ) + base_model.fit(X, y, sample_weight=weights) + return base_model + + +class FallbackWrapper(AbstractSelector): + + def __init__(self, selector, default_strategies: typing.List[str]): + self.selector = selector + self.default_strategies = default_strategies + + def fit( + self, + X: pd.DataFrame, + y: pd.DataFrame, + minima: typing.Dict[int, typing.Dict[str, float]], + maxima: typing.Dict[int, typing.Dict[str, float]], + ) -> None: + self.X_ = X + self.strategies_ = y.columns + self.rval_ = np.array([ + ( + len(self.strategies_) - self.default_strategies.index(strategy) - 1 + ) / (len(self.strategies_) - 1) + for strategy in self.strategies_ + ]) + self.rval_ = self.rval_ / np.sum(self.rval_) + self.selector.fit(X, y, minima, maxima) + + def _predict(self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]) -> pd.DataFrame: + + if y is not None: + prediction = self.selector.predict(X, y) + else: + prediction = self.selector.predict(X) + for task_id, x in X.iterrows(): + counter = 0 + te = x.copy() + assert len(te) == self.X_.shape[1] + for _, tr in self.X_.iterrows(): + tr = tr.to_numpy() + if all([tr[i] >= te[i] for i in range(self.X_.shape[1])]): + counter += 1 + + if counter == 0: + prediction.loc[task_id] = pd.Series({ + strategy: value for strategy, value in zip(self.strategies_, self.rval_) + }) + + return prediction diff --git a/test/test_automl/test_estimators.py b/test/test_automl/test_estimators.py index e6b9ef3bd0..32ddf98537 100644 --- a/test/test_automl/test_estimators.py +++ b/test/test_automl/test_estimators.py @@ -682,13 +682,18 @@ def test_selector_file_askl2_can_be_created(selector_path): importlib.reload(autosklearn.experimental.askl2) else: importlib.reload(autosklearn.experimental.askl2) - assert os.path.exists(autosklearn.experimental.askl2.selector_file) - if selector_path is None or not os.access(selector_path, os.W_OK): - # We default to home in worst case - assert os.path.expanduser("~") in str(autosklearn.experimental.askl2.selector_file) - else: - # a dir provided via XDG_CACHE_HOME - assert selector_path in str(autosklearn.experimental.askl2.selector_file) + for metric in autosklearn.experimental.askl2.metrics: + assert os.path.exists(autosklearn.experimental.askl2.selector_files[metric.name]) + if selector_path is None or not os.access(selector_path, os.W_OK): + # We default to home in worst case + assert os.path.expanduser("~") in str( + autosklearn.experimental.askl2.selector_files[metric.name] + ) + else: + # a dir provided via XDG_CACHE_HOME + assert selector_path in str( + autosklearn.experimental.askl2.selector_files[metric.name] + ) # Re import it at the end so we do not affect other test importlib.reload(autosklearn.experimental.askl2) From bd480928e2345aca5e65fced345f2c638af39c0b Mon Sep 17 00:00:00 2001 From: Matthias Feurer Date: Tue, 20 Jul 2021 22:55:46 +0200 Subject: [PATCH 10/11] bump version for new release --- autosklearn/__version__.py | 2 +- doc/releases.rst | 17 +++++++++++++++++ 2 files changed, 18 insertions(+), 1 deletion(-) diff --git a/autosklearn/__version__.py b/autosklearn/__version__.py index cc1171f2e5..d2d1825e32 100644 --- a/autosklearn/__version__.py +++ b/autosklearn/__version__.py @@ -1,4 +1,4 @@ """Version information.""" # The following line *must* be the last in the module, exactly as formatted: -__version__ = "0.12.6" +__version__ = "0.12.7" diff --git a/doc/releases.rst b/doc/releases.rst index ab7a7198bd..c37709fc28 100644 --- a/doc/releases.rst +++ b/doc/releases.rst @@ -7,6 +7,23 @@ commit. From http://stackoverflow.com/questions/6482436/list-of-authors-in-git-since-a-given-commit git log 25d680d4e9520a661aae48ea4c7295c663b64df3.. --format="%aN <%aE>" --reverse | perl -e 'my %dedupe; while () { print unless $dedupe{$_}++}' +Version 0.12.7 +============== + +* ADD #1178: Reduce precision if dataset is too large for given memory limit. +* ADD #1179: Improve Auto-sklearn 2.0 meta-data by providing new meta-data for the metrics + `roc_auc` and `logloss`. +* DOC: Fix reference to arXiv paper +* MAINT #1134,#1142,#1143: Improvements to the stale bot - the stale bot now marks issues labeled + with `feedback required` as stale if there is nothing happening for 30 days. After another 7 + days it then closes the issue. +* MAINT: Added a new issue template for questions. +* MAINT #1168: Upper-bound scipy to `1.6.3` as `1.7.0` is incompatible with `SMAC`. +* MAINT #1173: Update the license files to be recognized by github. + +Contributors v0.12.7 +******************** + Version 0.12.6 ============== From 6f1e5c33bf3cec21a80bd88a531f6019e59e9586 Mon Sep 17 00:00:00 2001 From: Eddie Bergman Date: Tue, 27 Jul 2021 13:36:48 +0200 Subject: [PATCH 11/11] Fix test errors with SMAC caused by dask 2021.07 (#1183) * Fix test errors with SMAC caused by dask 2021.07 The latest version of dask (2021.07) does not play nicely with `lazy_import` in SMAC. Fixing the version to be `dask>=2021.06,<2021.07` fixes this. * Removed lower bound for dask, reset distributed's lower bound * Typo --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index b4a78f25e9..a8df33ca2f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,8 +6,8 @@ scipy>=0.14.1,<1.7.0 joblib scikit-learn>=0.24.0,<0.25.0 -dask -distributed>=2.2.0 +dask<2021.07 +distributed>=2.2.0,<2021.07 pyyaml pandas>=1.0 liac-arff