diff --git "a/data/python/4921af9.json" "b/data/python/4921af9.json"
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-{"language": "Python", "id": 97, "repo_owner": "online-ml", "repo_name": "river", "head_branch": "main", "workflow_name": "unit-tests", "workflow_filename": "unit-tests.yml", "workflow_path": ".github/workflows/unit-tests.yml", "contributor": "MarekWadinger", "sha_fail": "4921af92f5ec9d61f4ebefb8d2810b1f05e8045b", "sha_success": "7233a750b50604f72f9d69e75904c0142ddb048a", "workflow": "name: unit-tests\n\non:\n  pull_request:\n    branches:\n      - \"*\"\n  push:\n    branches:\n      - main\n\njobs:\n  run:\n    strategy:\n      matrix:\n        os: [ubuntu-latest]\n        python-version: [\"3.12\", \"3.11\", \"3.10\"]\n\n    runs-on: ${{ matrix.os }}\n\n    steps:\n      - uses: actions/checkout@v3\n\n      - name: Build River\n        uses: ./.github/actions/install-env\n        with:\n          python-version: \"3.12\"\n\n      - name: Cache River datasets\n        uses: actions/cache@v3\n        with:\n          path: ~/river_data\n          key: ${{ runner.os }}\n\n      - name: Cache scikit-learn datasets\n        uses: actions/cache@v3\n        with:\n          path: ~/scikit_learn_data\n          key: ${{ runner.os }}\n\n      - name: Download datasets\n        run: |\n          poetry run python -c \"from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()\"\n          poetry run python -c \"from river import bandit; bandit.datasets.NewsArticles().download()\"\n\n      - name: pytest\n        run: |\n          poetry run pytest -m \"not datasets\" --durations=10 -n logical # Run pytest on all logical CPU cores\n", "logs": [{"step_name": "run (ubuntu-latest, 3.11)/7_pytest.txt", "log": "##[group]Run poetry run pytest -m \"not datasets\" --durations=10 -n logical # Run pytest on all logical CPU cores\n\u001b[36;1mpoetry run pytest -m \"not datasets\" --durations=10 -n logical # Run pytest on all logical CPU cores\u001b[0m\nshell: /usr/bin/bash -e {0}\nenv:\n  pythonLocation: /opt/hostedtoolcache/Python/3.12.0/x64\n  PKG_CONFIG_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib/pkgconfig\n  Python_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  Python2_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  Python3_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib\n  VENV: .venv/bin/activate\n##[endgroup]\n\u001b[1m============================= test session starts ==============================\u001b[0m\nplatform linux -- Python 3.12.0, pytest-7.4.3, pluggy-1.3.0 -- /home/runner/work/river/river/.venv/bin/python\ncachedir: .pytest_cache\nrootdir: /home/runner/work/river/river\nconfigfile: pyproject.toml\nplugins: anyio-4.1.0, xdist-3.5.0\ncreated: 4/4 workers\n4 workers [3407 items]\n\nscheduling tests via LoadScheduling\n\nREADME.md::README.md \nriver/test_estimators.py::test_check_estimator[~[]:check_mutate_can_be_idempotent] 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river/datasets/synth/anomaly_sine.py::river.datasets.synth.anomaly_sine.AnomalySine \nriver/datasets/synth/concept_drift_stream.py::river.datasets.synth.concept_drift_stream.ConceptDriftStream \n[gw2]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/concept_drift_stream.py::river.datasets.synth.concept_drift_stream.ConceptDriftStream \nriver/datasets/synth/friedman.py::river.datasets.synth.friedman.Friedman \n[gw2]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/friedman.py::river.datasets.synth.friedman.Friedman \nriver/datasets/synth/friedman.py::river.datasets.synth.friedman.FriedmanDrift \n[gw2]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/friedman.py::river.datasets.synth.friedman.FriedmanDrift \nriver/datasets/synth/hyper_plane.py::river.datasets.synth.hyper_plane.Hyperplane \n[gw2]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m 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river/drift/adwin.py::river.drift.adwin.ADWIN \nriver/test_estimators.py::test_check_estimator[PolynomialExtender | StandardScaler | LinearRegression:check_debug_one] \nriver/drift/dummy.py::river.drift.dummy.DummyDriftDetector \n[gw1]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[PolynomialExtender | StandardScaler | LinearRegression:check_debug_one] \nriver/test_estimators.py::test_check_estimator[MinMaxScaler | HalfSpaceTrees:check_repr] \n[gw1]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[MinMaxScaler | HalfSpaceTrees:check_repr] \nriver/test_estimators.py::test_check_estimator[MinMaxScaler | HalfSpaceTrees:check_str] \n[gw2]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m river/drift/dummy.py::river.drift.dummy.DummyDriftDetector \nriver/drift/kswin.py::river.drift.kswin.KSWIN \n[gw1]\u001b[36m [ 66%] \u001b[0m\u001b[32mPASSED\u001b[0m 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\nriver/bandit/test_policies.py::test_better_than_random_policy[LinUCBDisjoint-CandyCaneContest] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[LinUCBDisjoint-CandyCaneContest] \nriver/bandit/test_policies.py::test_better_than_random_policy[LinUCBDisjoint-KArmedTestbed] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[LinUCBDisjoint-KArmedTestbed] \nriver/bandit/test_policies.py::test_better_than_random_policy[RandomPolicy-CandyCaneContest] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[RandomPolicy-CandyCaneContest] \nriver/bandit/test_policies.py::test_better_than_random_policy[RandomPolicy-KArmedTestbed] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[RandomPolicy-KArmedTestbed] \nriver/bandit/test_policies.py::test_better_than_random_policy[ThompsonSampling-CandyCaneContest] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[ThompsonSampling-CandyCaneContest] \nriver/bandit/test_policies.py::test_better_than_random_policy[ThompsonSampling-KArmedTestbed] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[ThompsonSampling-KArmedTestbed] \nriver/bandit/test_policies.py::test_better_than_random_policy[UCB-CandyCaneContest] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[UCB-CandyCaneContest] \nriver/bandit/test_policies.py::test_better_than_random_policy[UCB-KArmedTestbed] \n[gw1]\u001b[36m [ 84%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[UCB-KArmedTestbed] \nriver/bandit/thompson.py::river.bandit.thompson.ThompsonSampling \n[gw2]\u001b[36m [ 84%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/bayesian_lin_reg.py::river.linear_model.bayesian_lin_reg.BayesianLinearRegression \nriver/linear_model/lin_reg.py::river.linear_model.lin_reg.LinearRegression \n[gw2]\u001b[36m [ 84%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/lin_reg.py::river.linear_model.lin_reg.LinearRegression \nriver/linear_model/log_reg.py::river.linear_model.log_reg.LogisticRegression \n[gw2]\u001b[36m [ 84%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/log_reg.py::river.linear_model.log_reg.LogisticRegression \nriver/linear_model/pa.py::river.linear_model.pa.PAClassifier \n[gw2]\u001b[36m [ 84%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/pa.py::river.linear_model.pa.PAClassifier \nriver/linear_model/pa.py::river.linear_model.pa.PARegressor \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/pa.py::river.linear_model.pa.PARegressor \nriver/linear_model/perceptron.py::river.linear_model.perceptron.Perceptron \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/perceptron.py::river.linear_model.perceptron.Perceptron \nriver/linear_model/softmax.py::river.linear_model.softmax.SoftmaxRegression \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/thompson.py::river.bandit.thompson.ThompsonSampling \nriver/bandit/ucb.py::river.bandit.ucb.UCB \n[gw3]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier \nriver/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/ucb.py::river.bandit.ucb.UCB \nriver/bandit/datasets/news.py::river.bandit.datasets.news.NewsArticles \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/datasets/news.py::river.bandit.datasets.news.NewsArticles \nriver/bandit/envs/candy_cane.py::river.bandit.envs.candy_cane.CandyCaneContest \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/envs/candy_cane.py::river.bandit.envs.candy_cane.CandyCaneContest \nriver/base/base.py::river.base.base.Base.clone \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/base/base.py::river.base.base.Base.clone \nriver/optim/test_.py::test_loss_batch_online_equivalence[Absolute] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Absolute] \nriver/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] \nriver/optim/test_.py::test_loss_batch_online_equivalence[EpsilonInsensitiveHinge] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[EpsilonInsensitiveHinge] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Hinge] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Hinge] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Huber] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Huber] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Log] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Log] \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/softmax.py::river.linear_model.softmax.SoftmaxRegression \nriver/optim/test_.py::test_loss_batch_online_equivalence[Poisson] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaBound - Zeros] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Poisson] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Quantile] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Quantile] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Squared] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Squared] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AMSGrad] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AMSGrad] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaBound] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaBound] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaDelta] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaDelta] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaGrad] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaGrad] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaMax] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[AdaMax] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Adam] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Adam] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Averager] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Averager] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[FTRLProximal] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[FTRLProximal] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Momentum] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Momentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Nadam] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Nadam] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[NesterovMomentum] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[NesterovMomentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[RMSProp] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[RMSProp] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[SGD] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[SGD] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AMSGrad] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AMSGrad] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaBound] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaBound] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaDelta] \n[gw1]\u001b[36m [ 85%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaDelta] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaGrad] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaBound - Zeros] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaBound - Normal] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaGrad] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaBound - Normal] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaMax] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaDelta - Zeros] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaDelta - Zeros] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaMax] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaDelta - Normal] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Adam] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaDelta - Normal] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaGrad - Zeros] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Adam] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaGrad - Zeros] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Averager] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaGrad - Normal] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaGrad - Normal] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Averager] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaMax - Zeros] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[FTRLProximal] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AdaMax - Zeros] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[FTRLProximal] \nriver/misc/skyline.py::river.misc.skyline.Skyline \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Momentum] \n[gw1]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Momentum] 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river/tree/hoeffding_adaptive_tree_classifier.py::river.tree.hoeffding_adaptive_tree_classifier.HoeffdingAdaptiveTreeClassifier \nriver/tree/hoeffding_adaptive_tree_regressor.py::river.tree.hoeffding_adaptive_tree_regressor.HoeffdingAdaptiveTreeRegressor \n[gw2]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multiclass/ovo.py::river.multiclass.ovo.OneVsOneClassifier \nriver/multiclass/ovr.py::river.multiclass.ovr.OneVsRestClassifier \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/neural_net/mlp.py::river.neural_net.mlp.MLPRegressor \nriver/optim/ada_bound.py::river.optim.ada_bound.AdaBound \n[gw1]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/hoeffding_adaptive_tree_regressor.py::river.tree.hoeffding_adaptive_tree_regressor.HoeffdingAdaptiveTreeRegressor \nriver/tree/hoeffding_tree.py::river.tree.hoeffding_tree.HoeffdingTree.draw \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/ada_bound.py::river.optim.ada_bound.AdaBound \nriver/optim/ada_delta.py::river.optim.ada_delta.AdaDelta \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/ada_delta.py::river.optim.ada_delta.AdaDelta \nriver/optim/ada_grad.py::river.optim.ada_grad.AdaGrad \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/ada_grad.py::river.optim.ada_grad.AdaGrad \nriver/optim/ada_max.py::river.optim.ada_max.AdaMax \n[gw1]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/hoeffding_tree.py::river.tree.hoeffding_tree.HoeffdingTree.draw \nriver/tree/hoeffding_tree_classifier.py::river.tree.hoeffding_tree_classifier.HoeffdingTreeClassifier \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/ada_max.py::river.optim.ada_max.AdaMax \nriver/optim/adam.py::river.optim.adam.Adam \n[gw3]\u001b[36m [ 96%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/adam.py::river.optim.adam.Adam 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98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/test_stats.py::test_pickling[Link] \nriver/tree/test_base.py::test_iter_leaves \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_iter_leaves \nriver/tree/test_base.py::test_iter_branches \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_iter_branches \nriver/tree/test_base.py::test_iter_edges \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_iter_edges \nriver/tree/test_splitter.py::test_class_splitter[dataset0-splitter0] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/rms_prop.py::river.optim.rms_prop.RMSProp \nriver/optim/sgd.py::river.optim.sgd.SGD \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_class_splitter[dataset0-splitter0] \nriver/tree/test_splitter.py::test_class_splitter[dataset1-splitter1] \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_class_splitter[dataset1-splitter1] \nriver/tree/test_splitter.py::test_class_splitter[dataset2-splitter2] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/sgd.py::river.optim.sgd.SGD \nriver/tree/test_base.py::test_size \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_size \nriver/tree/test_base.py::test_height \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_height \nriver/tree/test_base.py::test_iter_dfs \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_iter_dfs \nriver/tree/test_base.py::test_iter_bfs \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_base.py::test_iter_bfs \nriver/tree/test_splitter.py::test_reg_splitter[dataset2-splitter2] \n[gw1]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/stochastic_gradient_tree.py::river.tree.stochastic_gradient_tree.SGTClassifier \nriver/tree/stochastic_gradient_tree.py::river.tree.stochastic_gradient_tree.SGTRegressor \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset2-splitter2] \nriver/tree/test_splitter.py::test_reg_splitter[dataset3-splitter3] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset3-splitter3] \nriver/tree/test_splitter.py::test_nominal_reg_splitter \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_class_splitter[dataset2-splitter2] \nriver/tree/test_splitter.py::test_reg_splitter[dataset0-splitter0] \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset0-splitter0] \nriver/tree/test_splitter.py::test_reg_splitter[dataset1-splitter1] \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset1-splitter1] \nriver/tree/test_trees.py::test_memory_usage_reg[dataset1-model1] \n[gw1]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/stochastic_gradient_tree.py::river.tree.stochastic_gradient_tree.SGTRegressor \nriver/tree/test_trees.py::test_memory_usage_class[dataset1-model1] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_nominal_reg_splitter \nriver/tree/test_trees.py::test_memory_usage_class[dataset0-model0] \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_reg[dataset1-model1] \nriver/tree/test_trees.py::test_memory_usage_multitarget \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_class[dataset0-model0] \nriver/tree/test_trees.py::test_drift_adaptation_hatc \n[gw1]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_class[dataset1-model1] \nriver/tree/test_trees.py::test_memory_usage_class[dataset2-model2] \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_multitarget \nriver/tree/test_trees.py::test_efdt_split_reevaluation \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_drift_adaptation_hatc \nriver/tree/test_trees.py::test_drift_adaptation_hatr \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_efdt_split_reevaluation \nriver/tree/mondrian/mondrian_tree_classifier.py::river.tree.mondrian.mondrian_tree_classifier.MondrianTreeClassifier \n[gw0]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/mondrian/mondrian_tree_classifier.py::river.tree.mondrian.mondrian_tree_classifier.MondrianTreeClassifier \nriver/tree/splitter/histogram_splitter.py::river.tree.splitter.histogram_splitter.decimal_range 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\n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/math.py::river.utils.math.outer \nriver/utils/param_grid.py::river.utils.param_grid.expand_param_grid \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_drift_adaptation_hatr \nriver/tree/utils.py::river.tree.utils.round_sig_fig \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/utils.py::river.tree.utils.round_sig_fig \nriver/utils/rolling.py::river.utils.rolling.TimeRolling \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/param_grid.py::river.utils.param_grid.expand_param_grid \nriver/utils/rolling.py::river.utils.rolling.Rolling \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/rolling.py::river.utils.rolling.TimeRolling \nriver/utils/sorted_window.py::river.utils.sorted_window.SortedWindow \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/rolling.py::river.utils.rolling.Rolling 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\nriver/utils/test_math.py::test_dotvecmat_vector_times_identity_matrix \nriver/utils/test_math.py::test_dotvecmat_vector_times_anti_diagonal_identity_matrix \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_vector_times_identity_matrix \nriver/utils/test_math.py::test_dotvecmat_three_dimensional_vector_times_non_quadratic_matrix \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_vector_times_anti_diagonal_identity_matrix \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_three_dimensional_vector_times_non_quadratic_matrix \nriver/utils/test_param_grid.py::test_expand_param_grid_count[model0-param_grid0-14] \nriver/utils/test_param_grid.py::test_expand_param_grid_count[model1-param_grid1-14] \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model0-param_grid0-14] \nriver/utils/test_param_grid.py::test_expand_param_grid_count[model2-param_grid2-9] \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model1-param_grid1-14] \nriver/utils/test_param_grid.py::test_decision_tree_max_depth \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_param_grid.py::test_decision_tree_max_depth \nriver/utils/test_rolling.py::test_with_counter \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_rolling.py::test_with_counter \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model2-param_grid2-9] \nriver/utils/test_rolling.py::river.utils.test_rolling.test_with_counter \nriver/utils/test_rolling.py::test_rolling_with_not_rollable \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_rolling.py::test_rolling_with_not_rollable \nriver/utils/test_rolling.py::test_issue_1343 \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_rolling.py::river.utils.test_rolling.test_with_counter \nriver/utils/test_rolling.py::test_time_rolling_with_not_rollable \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_rolling.py::test_issue_1343 \nriver/utils/test_vectordict.py::test_vectordict \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_rolling.py::test_time_rolling_with_not_rollable \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_vectordict.py::test_vectordict \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_class[dataset2-model2] \nriver/tree/test_trees.py::test_memory_usage_reg[dataset0-model0] \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_memory_usage_reg[dataset0-model0] \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/chain.py::river.multioutput.chain.ClassifierChain \nriver/multioutput/chain.py::river.multioutput.chain.MonteCarloClassifierChain \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/chain.py::river.multioutput.chain.MonteCarloClassifierChain \nriver/multioutput/chain.py::river.multioutput.chain.ProbabilisticClassifierChain \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/chain.py::river.multioutput.chain.ProbabilisticClassifierChain \nriver/multioutput/chain.py::river.multioutput.chain.RegressorChain \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/chain.py::river.multioutput.chain.RegressorChain \nriver/multioutput/encoder.py::river.multioutput.encoder.MultiClassEncoder \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/encoder.py::river.multioutput.encoder.MultiClassEncoder \nriver/naive_bayes/bernoulli.py::river.naive_bayes.bernoulli.BernoulliNB \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/bernoulli.py::river.naive_bayes.bernoulli.BernoulliNB \nriver/naive_bayes/complement.py::river.naive_bayes.complement.ComplementNB \n[gw2]\u001b[36m [100%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/complement.py::river.naive_bayes.complement.ComplementNB \n\n=================================== FAILURES ===================================\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many[ddof=0] ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 0\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m1\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p)))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.021115787946478254, 0.036773566919091685)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.021115787946478254 = <bound method Cov.get of Cov: 0.021116>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.021116> = Cov: 0.021116.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:110: AssertionError\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many[ddof=1] ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 1\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m1\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p)))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0, nan)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0 = <bound method Cov.get of Cov: 0.>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.> = Cov: 0..get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:110: AssertionError\n\u001b[31m\u001b[1m_________________ test_covariance_update_many_shuffled[ddof=0] _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 0\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_shuffled\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.012307077362927493, 0.008910410592255408)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.012307077362927493 = <bound method Cov.get of Cov: 0.012307>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.012307> = Cov: 0.012307.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:138: AssertionError\n\u001b[31m\u001b[1m_________________ test_covariance_update_many_shuffled[ddof=1] _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 1\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_shuffled\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.008130659727412096, -0.01863436424026853)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.008130659727412096 = <bound method Cov.get of Cov: 0.008131>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.008131> = Cov: 0.008131.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:138: AssertionError\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many_sampled ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_sampled\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        \u001b[90m# NOTE: this test only works with ddof=1 because pandas ignores it if there are missing values\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        ddof = \u001b[94m1\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p - \u001b[94m1\u001b[39;49;00m, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.015212479685753347, 0.0013097742126793048)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.015212479685753347 = <bound method Cov.get of Cov: 0.015212>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.015212> = Cov: 0.015212.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:158: AssertionError\n\u001b[31m\u001b[1m_________________________ test_with_two_micro_clusters _________________________\u001b[0m\n[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_with_two_micro_clusters\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream()\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m2\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n>       assert_micro_cluster_properties(\u001b[90m\u001b[39;49;00m\n            dbstream.micro_clusters[\u001b[94m0\u001b[39;49;00m], center={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m}, last_update=\u001b[94m56\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        )\u001b[90m\u001b[39;49;00m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:67: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cdf0dee0>\ncenter = {1: 1.597322, 2: 1.597322}, last_update = 56\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92massert_micro_cluster_properties\u001b[39;49;00m(cluster, center, last_update=\u001b[94mNone\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m cluster.center == pytest.approx(center)\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\u001b[0m\n\u001b[1m\u001b[31mE         comparison failed. Mismatched elements: 2 / 2:\u001b[0m\n\u001b[1m\u001b[31mE         Max absolute difference: 0.4026780000000001\u001b[0m\n\u001b[1m\u001b[31mE         Max relative difference: 0.2520956951697905\u001b[0m\n\u001b[1m\u001b[31mE         Index | Obtained | Expected          \u001b[0m\n\u001b[1m\u001b[31mE         1     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\u001b[1m\u001b[31mE         2     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:24: AssertionError\n\u001b[31m\u001b[1m_________________ test_density_graph_with_three_micro_clusters _________________\u001b[0m\n[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_density_graph_with_three_micro_clusters\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream()\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of second and third micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m4\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m3\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n>       assert_micro_cluster_properties(\u001b[90m\u001b[39;49;00m\n            dbstream.micro_clusters[\u001b[94m0\u001b[39;49;00m], center={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m}, last_update=\u001b[94m56\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        )\u001b[90m\u001b[39;49;00m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:98: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cdf0d040>\ncenter = {1: 1.597322, 2: 1.597322}, last_update = 56\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92massert_micro_cluster_properties\u001b[39;49;00m(cluster, center, last_update=\u001b[94mNone\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m cluster.center == pytest.approx(center)\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\u001b[0m\n\u001b[1m\u001b[31mE         comparison failed. Mismatched elements: 2 / 2:\u001b[0m\n\u001b[1m\u001b[31mE         Max absolute difference: 0.4026780000000001\u001b[0m\n\u001b[1m\u001b[31mE         Max relative difference: 0.2520956951697905\u001b[0m\n\u001b[1m\u001b[31mE         Index | Obtained | Expected          \u001b[0m\n\u001b[1m\u001b[31mE         1     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\u001b[1m\u001b[31mE         2     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:24: AssertionError\n\u001b[31m\u001b[1m_________________ test_density_graph_with_removed_microcluster _________________\u001b[0m\n[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_density_graph_with_removed_microcluster\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream(fading_factor=\u001b[94m0.1\u001b[39;49;00m, intersection_factor=\u001b[94m0.3\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of second and third micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m4\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m2\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert 3 == 2\u001b[0m\n\u001b[1m\u001b[31mE        +  where 3 = len({0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd284bf0>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd285af0>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd286330>})\u001b[0m\n\u001b[1m\u001b[31mE        +    where {0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd284bf0>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd285af0>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd286330>} = DBSTREAM (\\n  clustering_threshold=1\\n  fading_factor=0.1\\n  cleanup_interval=1\\n  intersection_factor=0.3\\n  minimum_weight=1.\\n)._micro_clusters\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:131: AssertionError\n\u001b[33m=============================== warnings summary ===============================\u001b[0m\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).\n    EPOCH = datetime.datetime.utcfromtimestamp(0)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=0]\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/river/covariance/test_emp.py:106: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/river/covariance/emp.py:175: RuntimeWarning: Degrees of freedom <= 0 for slice\n    cov_arr = np.cov(X_arr.T, ddof=self.ddof)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/lib/function_base.py:2748: RuntimeWarning: divide by zero encountered in divide\n    c *= np.true_divide(1, fact)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/lib/function_base.py:2748: RuntimeWarning: invalid value encountered in multiply\n    c *= np.true_divide(1, fact)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/pandas/core/frame.py:10866: RuntimeWarning: Degrees of freedom <= 0 for slice\n    base_cov = np.cov(mat.T, ddof=ddof)\n\nriver/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=0]\nriver/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=1]\n  /home/runner/work/river/river/river/covariance/test_emp.py:134: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_covariance_update_many_sampled\n  /home/runner/work/river/river/river/covariance/test_emp.py:154: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_precision_update_many_mini_batches\nriver/linear_model/test_glm.py::test_one_many_consistent\nriver/linear_model/test_glm.py::test_shuffle_columns\nriver/linear_model/test_glm.py::test_add_remove_columns\nriver/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent\nriver/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent\nriver/preprocessing/test_scale.py::test_standard_scaler_shuffle_columns\nriver/preprocessing/test_scale.py::test_standard_scaler_add_remove_columns\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n    return bound(*args, **kwds)\n\nriver/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]\nriver/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py:245: RuntimeWarning: invalid value encountered in cast\n    return x.astype(dtype, copy=copy, casting=casting)\n\nriver/linear_model/test_glm.py::test_one_many_consistent\nriver/linear_model/test_glm.py::test_shuffle_columns\nriver/linear_model/test_glm.py::test_add_remove_columns\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n    return bound(*args, **kwds)\n\nriver/naive_bayes/test_naive_bayes.py: 108 warnings\nriver/naive_bayes/bernoulli.py: 4 warnings\n  /home/runner/work/river/river/river/naive_bayes/bernoulli.py:276: FutureWarning: Allowing arbitrary scalar fill_value in SparseDtype is deprecated. In a future version, the fill_value must be a valid value for the SparseDtype.subtype.\n    X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values,\n\nriver/bandit/bayes_ucb.py: 1 warning\nriver/bandit/epsilon_greedy.py: 1 warning\nriver/bandit/evaluate.py: 11 warnings\nriver/bandit/exp3.py: 1 warning\nriver/bandit/random.py: 1 warning\nriver/bandit/test_envs.py: 2 warnings\nriver/bandit/thompson.py: 1 warning\nriver/bandit/ucb.py: 1 warning\nriver/bandit/envs/candy_cane.py: 1 warning\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n    if not isinstance(terminated, (bool, np.bool8)):\n\nriver/bandit/bayes_ucb.py: 1 warning\nriver/bandit/epsilon_greedy.py: 1 warning\nriver/bandit/evaluate.py: 11 warnings\nriver/bandit/exp3.py: 1 warning\nriver/bandit/random.py: 1 warning\nriver/bandit/test_envs.py: 2 warnings\nriver/bandit/thompson.py: 1 warning\nriver/bandit/ucb.py: 1 warning\nriver/bandit/envs/candy_cane.py: 1 warning\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/gym/utils/passive_env_checker.py:237: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n    if not isinstance(truncated, (bool, np.bool8)):\n\nriver/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\nriver/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\n  /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/copy.py:151: DeprecationWarning: Pickle, copy, and deepcopy support will be removed from itertools in Python 3.14.\n    rv = reductor(4)\n\nriver/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\nriver/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\n  /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/copy.py:261: DeprecationWarning: Pickle, copy, and deepcopy support will be removed from itertools in Python 3.14.\n    y.__setstate__(state)\n\nriver/utils/test_rolling.py::test_issue_1343\n  /home/runner/work/river/river/river/utils/test_rolling.py:50: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n    t = dt.datetime.utcnow()\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n============================= slowest 10 durations =============================\n12.76s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_shuffle_features_no_impact]\n10.51s call     river/anomaly/test_lof.py::test_batch_lof_scores\n10.25s call     river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor\n7.25s call     river/ensemble/boosting.py::river.ensemble.boosting.BOLEClassifier\n6.51s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_emerging_features]\n6.44s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_disappearing_features]\n6.39s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_pickling]\n6.35s call     river/stats/test_kolmogorov_smirnov.py::test_incremental_ks_statistics\n6.21s call     river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\n5.91s call     river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\n\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n\u001b[33mSKIPPED\u001b[0m [22] river/bandit/test_policies.py:72: flaky\n\u001b[33mSKIPPED\u001b[0m [10] river/optim/test_.py:63: step_with_vector not implemented\n\u001b[33mSKIPPED\u001b[0m [10] river/optim/test_.py:84: step_with_vector not implemented\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many[ddof=0]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.021115787946478254, 0.036773566919091685)\n +    where <built-in function isclose> = math.isclose\n +    and   0.021115787946478254 = <bound method Cov.get of Cov: 0.021116>()\n +      where <bound method Cov.get of Cov: 0.021116> = Cov: 0.021116.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many[ddof=1]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0, nan)\n +    where <built-in function isclose> = math.isclose\n +    and   0 = <bound method Cov.get of Cov: 0.>()\n +      where <bound method Cov.get of Cov: 0.> = Cov: 0..get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_shuffled[ddof=0]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.012307077362927493, 0.008910410592255408)\n +    where <built-in function isclose> = math.isclose\n +    and   0.012307077362927493 = <bound method Cov.get of Cov: 0.012307>()\n +      where <bound method Cov.get of Cov: 0.012307> = Cov: 0.012307.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_shuffled[ddof=1]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.008130659727412096, -0.01863436424026853)\n +    where <built-in function isclose> = math.isclose\n +    and   0.008130659727412096 = <bound method Cov.get of Cov: 0.008131>()\n +      where <bound method Cov.get of Cov: 0.008131> = Cov: 0.008131.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_sampled\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.015212479685753347, 0.0013097742126793048)\n +    where <built-in function isclose> = math.isclose\n +    and   0.015212479685753347 = <bound method Cov.get of Cov: 0.015212>()\n +      where <bound method Cov.get of Cov: 0.015212> = Cov: 0.015212.get\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_with_two_micro_clusters\u001b[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\n  comparison failed. Mismatched elements: 2 / 2:\n  Max absolute difference: 0.4026780000000001\n  Max relative difference: 0.2520956951697905\n  Index | Obtained | Expected          \n  1     | 2.0      | 1.597322 \u00b1 1.6e-06\n  2     | 2.0      | 1.597322 \u00b1 1.6e-06\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_density_graph_with_three_micro_clusters\u001b[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\n  comparison failed. Mismatched elements: 2 / 2:\n  Max absolute difference: 0.4026780000000001\n  Max relative difference: 0.2520956951697905\n  Index | Obtained | Expected          \n  1     | 2.0      | 1.597322 \u00b1 1.6e-06\n  2     | 2.0      | 1.597322 \u00b1 1.6e-06\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_density_graph_with_removed_microcluster\u001b[0m - assert 3 == 2\n +  where 3 = len({0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd284bf0>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd285af0>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd286330>})\n +    where {0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd284bf0>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd285af0>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cd286330>} = DBSTREAM (\\n  clustering_threshold=1\\n  fading_factor=0.1\\n  cleanup_interval=1\\n  intersection_factor=0.3\\n  minimum_weight=1.\\n)._micro_clusters\n\u001b[31m===== \u001b[31m\u001b[1m8 failed\u001b[0m, \u001b[32m3357 passed\u001b[0m, \u001b[33m42 skipped\u001b[0m, \u001b[33m185 warnings\u001b[0m\u001b[31m in 88.61s (0:01:28)\u001b[0m\u001b[31m ======\u001b[0m\n##[error]Process completed with exit code 1.\n"}, {"step_name": "run (ubuntu-latest, 3.10)/7_pytest.txt", "log": "##[group]Run poetry run pytest -m \"not datasets\" --durations=10 -n logical # Run pytest on all logical CPU cores\n\u001b[36;1mpoetry run pytest -m \"not datasets\" --durations=10 -n logical # Run pytest on all logical CPU cores\u001b[0m\nshell: /usr/bin/bash -e {0}\nenv:\n  pythonLocation: /opt/hostedtoolcache/Python/3.12.0/x64\n  PKG_CONFIG_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib/pkgconfig\n  Python_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  Python2_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  Python3_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64\n  LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib\n  VENV: .venv/bin/activate\n##[endgroup]\n\u001b[1m============================= test session starts ==============================\u001b[0m\nplatform linux -- Python 3.12.0, pytest-7.4.3, pluggy-1.3.0 -- /home/runner/work/river/river/.venv/bin/python\ncachedir: .pytest_cache\nrootdir: /home/runner/work/river/river\nconfigfile: pyproject.toml\nplugins: anyio-4.1.0, xdist-3.5.0\ncreated: 4/4 workers\n4 workers [3407 items]\n\nscheduling tests via LoadScheduling\n\nriver/test_estimators.py::test_check_estimator[~[]:check_mutate_can_be_idempotent] \nriver/test_estimators.py::test_check_estimator[StackingClassifier:check_shuffle_features_no_impact1] \nriver/test_estimators.py::test_check_estimator[ADWINBoostingClassifier(LogisticRegression):check_clone_same_class] \nREADME.md::README.md \n[gw1]\u001b[36m [  0%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[~[]:check_mutate_can_be_idempotent] 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river/datasets/test_datasets.py::test_synth_non_idempotent[Agrawal9] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_repr] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[Agrawal10] \n[gw1]\u001b[36m [ 63%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_repr] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_str] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_str] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_tags] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/test_datasets.py::test_synth_non_idempotent[Agrawal10] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_tags] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_clone_same_class] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[AnomalySine] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_clone_same_class] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_clone_is_idempotent] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/test_datasets.py::test_synth_non_idempotent[AnomalySine] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_clone_is_idempotent] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[ConceptDriftStream] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_init_has_default_params_for_tests] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_init_has_default_params_for_tests] \n[gw2]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_init_default_params_are_not_mutable] \nriver/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[StandardScaler] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_init_default_params_are_not_mutable] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_doc] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m 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river/datasets/test_datasets.py::test_synth_non_idempotent[FriedmanDrift] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[Hyperplane] \n[gw1]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_mutate_can_be_idempotent] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/test_datasets.py::test_synth_non_idempotent[Hyperplane] \nriver/test_estimators.py::test_check_estimator[StandardScaler | SKL2RiverRegressor:check_learn_one] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[LED] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/test_datasets.py::test_synth_non_idempotent[LED] \nriver/datasets/test_datasets.py::test_synth_non_idempotent[LEDDrift] \n[gw3]\u001b[36m [ 64%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/test_datasets.py::test_synth_non_idempotent[LEDDrift] 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river/datasets/synth/friedman.py::river.datasets.synth.friedman.FriedmanDrift \nriver/datasets/synth/hyper_plane.py::river.datasets.synth.hyper_plane.Hyperplane \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/hyper_plane.py::river.datasets.synth.hyper_plane.Hyperplane \nriver/datasets/synth/led.py::river.datasets.synth.led.LED \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/led.py::river.datasets.synth.led.LED \nriver/datasets/synth/led.py::river.datasets.synth.led.LEDDrift \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/led.py::river.datasets.synth.led.LEDDrift \nriver/datasets/synth/logical.py::river.datasets.synth.logical.Logical \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/logical.py::river.datasets.synth.logical.Logical \nriver/datasets/synth/mixed.py::river.datasets.synth.mixed.Mixed \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/mixed.py::river.datasets.synth.mixed.Mixed \nriver/datasets/synth/mv.py::river.datasets.synth.mv.Mv \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/mv.py::river.datasets.synth.mv.Mv \nriver/datasets/synth/planes_2d.py::river.datasets.synth.planes_2d.Planes2D \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/planes_2d.py::river.datasets.synth.planes_2d.Planes2D \nriver/datasets/synth/random_rbf.py::river.datasets.synth.random_rbf.RandomRBF \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/random_rbf.py::river.datasets.synth.random_rbf.RandomRBF \nriver/datasets/synth/random_rbf.py::river.datasets.synth.random_rbf.RandomRBFDrift \n[gw3]\u001b[36m [ 65%] \u001b[0m\u001b[32mPASSED\u001b[0m river/datasets/synth/random_rbf.py::river.datasets.synth.random_rbf.RandomRBFDrift \nriver/datasets/synth/random_tree.py::river.datasets.synth.random_tree.RandomTree \n[gw3]\u001b[36m [ 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river/metrics/test_metrics.py::test_rolling_metric[WeightedFBeta] \nriver/metrics/test_metrics.py::test_rolling_metric[F1] \n[gw2]\u001b[36m [ 77%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[F1] \nriver/metrics/test_metrics.py::test_rolling_metric[MacroF1] \n[gw2]\u001b[36m [ 77%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[MacroF1] \nriver/metrics/test_metrics.py::test_rolling_metric[MicroF1] \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[MicroF1] \nriver/metrics/test_metrics.py::test_rolling_metric[WeightedF1] \n[gw1]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor \nriver/anomaly/sad.py::river.anomaly.sad.StandardAbsoluteDeviation \n[gw1]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/anomaly/sad.py::river.anomaly.sad.StandardAbsoluteDeviation 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river/ensemble/stacking.py::river.ensemble.stacking.StackingClassifier \nriver/ensemble/streaming_random_patches.py::river.ensemble.streaming_random_patches.SRPClassifier \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[FowlkesMallows] \nriver/metrics/test_metrics.py::test_rolling_metric[Rand] \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[Rand] \nriver/metrics/test_metrics.py::test_rolling_metric[AdjustedRand] \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[AdjustedRand] \nriver/metrics/test_metrics.py::test_rolling_metric[MutualInfo] \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[MutualInfo] \nriver/metrics/test_metrics.py::test_rolling_metric[NormalizedMutualInfo0] \n[gw2]\u001b[36m [ 78%] \u001b[0m\u001b[32mPASSED\u001b[0m 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river/ensemble/streaming_random_patches.py::river.ensemble.streaming_random_patches.SRPRegressor \nriver/ensemble/voting.py::river.ensemble.voting.VotingClassifier \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_rolling_metric[RollingROCAUC] \nriver/metrics/test_metrics.py::test_compose \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_metrics.py::test_compose \nriver/metrics/test_r2.py::test_r2 \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_r2.py::test_r2 \nriver/metrics/test_r2.py::test_rolling_r2 \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/test_r2.py::test_rolling_r2 \nriver/metrics/vbeta.py::river.metrics.vbeta.Completeness \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/vbeta.py::river.metrics.vbeta.Completeness \nriver/metrics/vbeta.py::river.metrics.vbeta.Homogeneity \n[gw2]\u001b[36m [ 79%] 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river/metrics/multioutput/sample_average.py::river.metrics.multioutput.sample_average.SampleAverage \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[ExactMatch] \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[ExactMatch] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MacroAverage(Precision)] \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MacroAverage(Precision)] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MicroAverage(Precision)] \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/ensemble/voting.py::river.ensemble.voting.VotingClassifier \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MicroAverage(Precision)] \nriver/evaluate/progressive_validation.py::river.evaluate.progressive_validation.iter_progressive_val_score \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[SampleAverage(Precision)] \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[SampleAverage(Precision)] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MacroAverage(Recall)] \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/evaluate/progressive_validation.py::river.evaluate.progressive_validation.iter_progressive_val_score \nriver/evaluate/progressive_validation.py::river.evaluate.progressive_validation.progressive_val_score \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MacroAverage(Recall)] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MicroAverage(Recall)] \n[gw2]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MicroAverage(Recall)] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[SampleAverage(Recall)] \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/evaluate/progressive_validation.py::river.evaluate.progressive_validation.progressive_val_score \nriver/facto/ffm.py::river.facto.ffm.FFMClassifier \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/facto/ffm.py::river.facto.ffm.FFMClassifier \nriver/facto/ffm.py::river.facto.ffm.FFMRegressor \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/facto/ffm.py::river.facto.ffm.FFMRegressor 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river/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[SampleAverage(Recall)] \nriver/metrics/multioutput/test_multioutput_metrics.py::test_multiout_binary_clf[MacroAverage(F1)] \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/facto/hofm.py::river.facto.hofm.HOFMRegressor \nriver/feature_extraction/agg.py::river.feature_extraction.agg.Agg \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/feature_extraction/agg.py::river.feature_extraction.agg.Agg \nriver/feature_extraction/agg.py::river.feature_extraction.agg.TargetAgg \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/feature_extraction/agg.py::river.feature_extraction.agg.TargetAgg \nriver/feature_extraction/kernel_approx.py::river.feature_extraction.kernel_approx.RBFSampler \n[gw3]\u001b[36m [ 79%] \u001b[0m\u001b[32mPASSED\u001b[0m river/feature_extraction/kernel_approx.py::river.feature_extraction.kernel_approx.RBFSampler 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river/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed0] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest1] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest1] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed1] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed1] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest2] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest2] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed2] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed2] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest3] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest3] \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed3] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-KArmedTestbed3] \n[gw3]\u001b[36m [ 82%] \u001b[0m\u001b[32mPASSED\u001b[0m river/imblearn/random.py::river.imblearn.random.RandomSampler \nriver/imblearn/random.py::river.imblearn.random.RandomUnderSampler \nriver/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest4] \n[gw1]\u001b[36m [ 82%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/bandit/test_policies.py::test_better_than_random_policy[Exp3-CandyCaneContest4] 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river/bandit/ucb.py::river.bandit.ucb.UCB \nriver/bandit/datasets/news.py::river.bandit.datasets.news.NewsArticles \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/datasets/news.py::river.bandit.datasets.news.NewsArticles \nriver/bandit/envs/candy_cane.py::river.bandit.envs.candy_cane.CandyCaneContest \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/bandit/envs/candy_cane.py::river.bandit.envs.candy_cane.CandyCaneContest \nriver/base/base.py::river.base.base.Base.clone \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/base/base.py::river.base.base.Base.clone \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - Adam - Zeros] \n[gw3]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/perceptron.py::river.linear_model.perceptron.Perceptron \nriver/linear_model/softmax.py::river.linear_model.softmax.SoftmaxRegression \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - Adam - Zeros] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - Adam - Normal] \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - Adam - Normal] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AMSGrad - Zeros] \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AMSGrad - Zeros] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - AMSGrad - Normal] \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m river/linear_model/test_glm.py::test_finite_differences[LinearRegression - AMSGrad - Normal] \nriver/linear_model/test_glm.py::test_finite_differences[LinearRegression - RMSProp - Zeros] \n[gw1]\u001b[36m [ 83%] \u001b[0m\u001b[32mPASSED\u001b[0m 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85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/multioutput/encoder.py::river.multioutput.encoder.MultiClassEncoder \nriver/naive_bayes/bernoulli.py::river.naive_bayes.bernoulli.BernoulliNB \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/bernoulli.py::river.naive_bayes.bernoulli.BernoulliNB \nriver/naive_bayes/complement.py::river.naive_bayes.complement.ComplementNB \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/complement.py::river.naive_bayes.complement.ComplementNB \nriver/naive_bayes/gaussian.py::river.naive_bayes.gaussian.GaussianNB \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/gaussian.py::river.naive_bayes.gaussian.GaussianNB \nriver/naive_bayes/multinomial.py::river.naive_bayes.multinomial.MultinomialNB \n[gw2]\u001b[36m [ 85%] \u001b[0m\u001b[32mPASSED\u001b[0m river/naive_bayes/multinomial.py::river.naive_bayes.multinomial.MultinomialNB 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river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Momentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Nadam] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[Nadam] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[NesterovMomentum] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[NesterovMomentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[RMSProp] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[RMSProp] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_vector_dict[SGD] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m 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river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaGrad] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaMax] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[AdaMax] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Adam] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Adam] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Averager] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Averager] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[FTRLProximal] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[FTRLProximal] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Momentum] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Momentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Nadam] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[Nadam] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[NesterovMomentum] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[33mSKIPPED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[NesterovMomentum] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[RMSProp] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[RMSProp] \nriver/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[SGD] \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_optimizer_step_with_dict_same_as_step_with_numpy_array[SGD] \nriver/preprocessing/feature_hasher.py::river.preprocessing.feature_hasher.FeatureHasher \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/preprocessing/feature_hasher.py::river.preprocessing.feature_hasher.FeatureHasher \nriver/preprocessing/impute.py::river.preprocessing.impute.PreviousImputer \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/preprocessing/impute.py::river.preprocessing.impute.PreviousImputer \nriver/preprocessing/impute.py::river.preprocessing.impute.StatImputer \n[gw2]\u001b[36m [ 86%] \u001b[0m\u001b[32mPASSED\u001b[0m river/preprocessing/impute.py::river.preprocessing.impute.StatImputer 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river/stats/kolmogorov_smirnov.py::river.stats.kolmogorov_smirnov.KolmogorovSmirnov \nriver/stats/kurtosis.py::river.stats.kurtosis.Kurtosis \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/kurtosis.py::river.stats.kurtosis.Kurtosis \nriver/stats/link.py::river.stats.link.Link \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/link.py::river.stats.link.Link \nriver/stats/mad.py::river.stats.mad.MAD \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/mad.py::river.stats.mad.MAD \nriver/stats/maximum.py::river.stats.maximum.AbsMax \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/maximum.py::river.stats.maximum.AbsMax \nriver/stats/maximum.py::river.stats.maximum.Max \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m river/stats/maximum.py::river.stats.maximum.Max \nriver/stats/maximum.py::river.stats.maximum.RollingAbsMax \n[gw2]\u001b[36m [ 88%] \u001b[0m\u001b[32mPASSED\u001b[0m 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river/tree/test_splitter.py::test_class_splitter[dataset0-splitter0] \nriver/tree/test_splitter.py::test_class_splitter[dataset1-splitter1] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset1-splitter1] \nriver/tree/test_splitter.py::test_reg_splitter[dataset2-splitter2] \n[gw1]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/ada_max.py::river.optim.ada_max.AdaMax \nriver/optim/adam.py::river.optim.adam.Adam \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset2-splitter2] \nriver/tree/test_splitter.py::test_reg_splitter[dataset3-splitter3] \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_reg_splitter[dataset3-splitter3] \nriver/tree/test_splitter.py::test_nominal_reg_splitter \n[gw2]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_splitter.py::test_class_splitter[dataset1-splitter1] 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river/optim/losses.py::river.optim.losses.Squared \nriver/optim/momentum.py::river.optim.momentum.Momentum \n[gw2]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_drift_adaptation_hatc \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/splitter/histogram_splitter.py::river.tree.splitter.histogram_splitter.decimal_range \nriver/utils/inspect.py::river.utils.inspect.ischildobject \nriver/tree/test_trees.py::test_drift_adaptation_hatr \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/inspect.py::river.utils.inspect.ischildobject \nriver/utils/math.py::river.utils.math.chain_dot \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/math.py::river.utils.math.chain_dot \nriver/utils/math.py::river.utils.math.dot \n[gw3]\u001b[36m [ 98%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/math.py::river.utils.math.dot \nriver/utils/math.py::river.utils.math.outer \n[gw3]\u001b[36m [ 98%] 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river/utils/test_math.py::test_dotvecmat_zero_vector_times_matrix_of_ones \nriver/utils/test_math.py::test_dotvecmat_vector_of_ones_times_zero_matrix \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_vector_of_ones_times_zero_matrix \nriver/utils/test_math.py::test_dotvecmat_filter_first_matrix_row_with_vector \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_filter_first_matrix_row_with_vector \nriver/utils/test_math.py::test_dotvecmat_filter_second_matrix_row_with_vector \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_filter_second_matrix_row_with_vector \nriver/utils/test_math.py::test_dotvecmat_vector_times_identity_matrix \n[gw3]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/test_math.py::test_dotvecmat_vector_times_identity_matrix 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river/optim/momentum.py::river.optim.momentum.Momentum \nriver/optim/nadam.py::river.optim.nadam.Nadam \n[gw0]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/mondrian/mondrian_tree_classifier.py::river.tree.mondrian.mondrian_tree_classifier.MondrianTreeClassifier \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/tree/test_trees.py::test_drift_adaptation_hatr \nriver/utils/math.py::river.utils.math.dotvecmat \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/math.py::river.utils.math.dotvecmat \nriver/utils/math.py::river.utils.math.matmul2d \n[gw2]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/utils/math.py::river.utils.math.matmul2d \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/nadam.py::river.optim.nadam.Nadam \nriver/optim/nesterov.py::river.optim.nesterov.NesterovMomentum \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/nesterov.py::river.optim.nesterov.NesterovMomentum \nriver/optim/newton.py::river.optim.newton.sherman_morrison \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/newton.py::river.optim.newton.sherman_morrison \nriver/optim/rms_prop.py::river.optim.rms_prop.RMSProp \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/rms_prop.py::river.optim.rms_prop.RMSProp \nriver/optim/sgd.py::river.optim.sgd.SGD \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/sgd.py::river.optim.sgd.SGD \nriver/optim/test_.py::test_loss_batch_online_equivalence[Absolute] \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Absolute] \nriver/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] \n[gw1]\u001b[36m [ 99%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] \nriver/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] \n[gw1]\u001b[36m [100%] \u001b[0m\u001b[32mPASSED\u001b[0m river/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] \n\n=================================== FAILURES ===================================\n\u001b[31m\u001b[1m_________________________ test_with_two_micro_clusters _________________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_with_two_micro_clusters\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream()\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m2\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n>       assert_micro_cluster_properties(\u001b[90m\u001b[39;49;00m\n            dbstream.micro_clusters[\u001b[94m0\u001b[39;49;00m], center={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m}, last_update=\u001b[94m56\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        )\u001b[90m\u001b[39;49;00m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:67: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9e20>\ncenter = {1: 1.597322, 2: 1.597322}, last_update = 56\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92massert_micro_cluster_properties\u001b[39;49;00m(cluster, center, last_update=\u001b[94mNone\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m cluster.center == pytest.approx(center)\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\u001b[0m\n\u001b[1m\u001b[31mE         comparison failed. Mismatched elements: 2 / 2:\u001b[0m\n\u001b[1m\u001b[31mE         Max absolute difference: 0.4026780000000001\u001b[0m\n\u001b[1m\u001b[31mE         Max relative difference: 0.2520956951697905\u001b[0m\n\u001b[1m\u001b[31mE         Index | Obtained | Expected          \u001b[0m\n\u001b[1m\u001b[31mE         1     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\u001b[1m\u001b[31mE         2     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:24: AssertionError\n\u001b[31m\u001b[1m_________________ test_density_graph_with_three_micro_clusters _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_density_graph_with_three_micro_clusters\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream()\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of second and third micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m4\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m3\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n>       assert_micro_cluster_properties(\u001b[90m\u001b[39;49;00m\n            dbstream.micro_clusters[\u001b[94m0\u001b[39;49;00m], center={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.597322\u001b[39;49;00m}, last_update=\u001b[94m56\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        )\u001b[90m\u001b[39;49;00m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:98: \n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\ncluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015f5d60>\ncenter = {1: 1.597322, 2: 1.597322}, last_update = 56\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92massert_micro_cluster_properties\u001b[39;49;00m(cluster, center, last_update=\u001b[94mNone\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m cluster.center == pytest.approx(center)\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\u001b[0m\n\u001b[1m\u001b[31mE         comparison failed. Mismatched elements: 2 / 2:\u001b[0m\n\u001b[1m\u001b[31mE         Max absolute difference: 0.4026780000000001\u001b[0m\n\u001b[1m\u001b[31mE         Max relative difference: 0.2520956951697905\u001b[0m\n\u001b[1m\u001b[31mE         Index | Obtained | Expected          \u001b[0m\n\u001b[1m\u001b[31mE         1     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\u001b[1m\u001b[31mE         2     | 2.0      | 1.597322 \u00b1 1.6e-06\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:24: AssertionError\n\u001b[31m\u001b[1m_________________ test_density_graph_with_removed_microcluster _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_density_graph_with_removed_microcluster\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        dbstream = build_dbstream(fading_factor=\u001b[94m0.1\u001b[39;49;00m, intersection_factor=\u001b[94m0.3\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m1.7\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of first and second micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m5\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m2\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        add_cluster(dbstream, initial_point={\u001b[94m1\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m4\u001b[39;49;00m}, move_towards={\u001b[94m1\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3.3\u001b[39;49;00m}, times=\u001b[94m25\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n        \u001b[90m# Points in the middle of second and third micro-clusters\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(\u001b[94m4\u001b[39;49;00m):\u001b[90m\u001b[39;49;00m\n            dbstream.learn_one({\u001b[94m1\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m, \u001b[94m2\u001b[39;49;00m: \u001b[94m3\u001b[39;49;00m})\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n>       \u001b[94massert\u001b[39;49;00m \u001b[96mlen\u001b[39;49;00m(dbstream._micro_clusters) == \u001b[94m2\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE       assert 3 == 2\u001b[0m\n\u001b[1m\u001b[31mE        +  where 3 = len({0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8200>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9340>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8380>})\u001b[0m\n\u001b[1m\u001b[31mE        +    where {0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8200>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9340>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8380>} = DBSTREAM (\\n  clustering_threshold=1\\n  fading_factor=0.1\\n  cleanup_interval=1\\n  intersection_factor=0.3\\n  minimum_weight=1.\\n)._micro_clusters\u001b[0m\n\n\u001b[1m\u001b[31mriver/cluster/test_dbstream.py\u001b[0m:131: AssertionError\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many[ddof=0] ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 0\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m1\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p)))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(-0.013429458902475771, -0.01913291331931292)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   -0.013429458902475771 = <bound method Cov.get of Cov: -0.013429>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: -0.013429> = Cov: -0.013429.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:110: AssertionError\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many[ddof=1] ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 1\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m1\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p)))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.008778377636804292, 0.006898690912795011)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.008778377636804292 = <bound method Cov.get of Cov: 0.008778>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.008778> = Cov: 0.008778.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:110: AssertionError\n\u001b[31m\u001b[1m_________________ test_covariance_update_many_shuffled[ddof=0] _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 0\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_shuffled\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.0021320924551367216, 0.008581090035300476)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.0021320924551367216 = <bound method Cov.get of Cov: 0.002132>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.002132> = Cov: 0.002132.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:138: AssertionError\n\u001b[31m\u001b[1m_________________ test_covariance_update_many_shuffled[ddof=1] _________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\nddof = 1\n\n    \u001b[37m@pytest\u001b[39;49;00m.mark.parametrize(\u001b[90m\u001b[39;49;00m\n        \u001b[33m\"\u001b[39;49;00m\u001b[33mddof\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n        [\u001b[90m\u001b[39;49;00m\n            pytest.param(\u001b[90m\u001b[39;49;00m\n                ddof,\u001b[90m\u001b[39;49;00m\n                \u001b[96mid\u001b[39;49;00m=\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mddof\u001b[33m=}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[90m\u001b[39;49;00m\n            )\u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m ddof \u001b[95min\u001b[39;49;00m [\u001b[94m0\u001b[39;49;00m, \u001b[94m1\u001b[39;49;00m]\u001b[90m\u001b[39;49;00m\n        ],\u001b[90m\u001b[39;49;00m\n    )\u001b[90m\u001b[39;49;00m\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_shuffled\u001b[39;49;00m(ddof):\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(0.0027720586589603507, -0.008617735050464837)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   0.0027720586589603507 = <bound method Cov.get of Cov: 0.002772>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: 0.002772> = Cov: 0.002772.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:138: AssertionError\n\u001b[31m\u001b[1m_____________________ test_covariance_update_many_sampled ______________________\u001b[0m\n[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python\n\n    \u001b[94mdef\u001b[39;49;00m \u001b[92mtest_covariance_update_many_sampled\u001b[39;49;00m():\u001b[90m\u001b[39;49;00m\n        \u001b[90m# NOTE: this test only works with ddof=1 because pandas ignores it if there are missing values\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        ddof = \u001b[94m1\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        cov = covariance.EmpiricalCovariance(ddof=ddof)\u001b[90m\u001b[39;49;00m\n        p = \u001b[94m5\u001b[39;49;00m\u001b[90m\u001b[39;49;00m\n        X_all = pd.DataFrame(columns=\u001b[96mrange\u001b[39;49;00m(p))\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n        \u001b[94mfor\u001b[39;49;00m _ \u001b[95min\u001b[39;49;00m \u001b[96mrange\u001b[39;49;00m(p):\u001b[90m\u001b[39;49;00m\n            n = np.random.randint(\u001b[94m5\u001b[39;49;00m, \u001b[94m31\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            X = pd.DataFrame(np.random.random((n, p))).sample(p - \u001b[94m1\u001b[39;49;00m, axis=\u001b[33m\"\u001b[39;49;00m\u001b[33mcolumns\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            cov.update_many(X)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            X_all = pd.concat((X_all, X)).astype(\u001b[96mfloat\u001b[39;49;00m)\u001b[90m\u001b[39;49;00m\n            pd_cov = X_all.cov(ddof=ddof)\u001b[90m\u001b[39;49;00m\n    \u001b[90m\u001b[39;49;00m\n            \u001b[94mfor\u001b[39;49;00m i, j \u001b[95min\u001b[39;49;00m cov._cov:\u001b[90m\u001b[39;49;00m\n>               \u001b[94massert\u001b[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])\u001b[90m\u001b[39;49;00m\n\u001b[1m\u001b[31mE               assert False\u001b[0m\n\u001b[1m\u001b[31mE                +  where False = <built-in function isclose>(-0.037234744053568615, -0.021211995100492684)\u001b[0m\n\u001b[1m\u001b[31mE                +    where <built-in function isclose> = math.isclose\u001b[0m\n\u001b[1m\u001b[31mE                +    and   -0.037234744053568615 = <bound method Cov.get of Cov: -0.037235>()\u001b[0m\n\u001b[1m\u001b[31mE                +      where <bound method Cov.get of Cov: -0.037235> = Cov: -0.037235.get\u001b[0m\n\n\u001b[1m\u001b[31mriver/covariance/test_emp.py\u001b[0m:158: AssertionError\n\u001b[33m=============================== warnings summary ===============================\u001b[0m\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).\n    EPOCH = datetime.datetime.utcfromtimestamp(0)\n\nriver/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]\nriver/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py:245: RuntimeWarning: invalid value encountered in cast\n    return x.astype(dtype, copy=copy, casting=casting)\n\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=0]\nriver/covariance/test_emp.py::test_covariance_update_many[ddof=1]\n  /home/runner/work/river/river/river/covariance/test_emp.py:106: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=0]\nriver/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=1]\n  /home/runner/work/river/river/river/covariance/test_emp.py:134: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_covariance_update_many_sampled\n  /home/runner/work/river/river/river/covariance/test_emp.py:154: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n    X_all = pd.concat((X_all, X)).astype(float)\n\nriver/covariance/test_emp.py::test_precision_update_many_mini_batches\nriver/linear_model/test_glm.py::test_one_many_consistent\nriver/linear_model/test_glm.py::test_shuffle_columns\nriver/linear_model/test_glm.py::test_add_remove_columns\nriver/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent\nriver/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent\nriver/preprocessing/test_scale.py::test_standard_scaler_shuffle_columns\nriver/preprocessing/test_scale.py::test_standard_scaler_add_remove_columns\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'DataFrame.swapaxes' is deprecated and will be removed in a future version. Please use 'DataFrame.transpose' instead.\n    return bound(*args, **kwds)\n\nriver/bandit/bayes_ucb.py: 1 warning\nriver/bandit/epsilon_greedy.py: 1 warning\nriver/bandit/evaluate.py: 11 warnings\nriver/bandit/exp3.py: 1 warning\nriver/bandit/random.py: 1 warning\nriver/bandit/test_envs.py: 2 warnings\nriver/bandit/thompson.py: 1 warning\nriver/bandit/ucb.py: 1 warning\nriver/bandit/envs/candy_cane.py: 1 warning\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/gym/utils/passive_env_checker.py:233: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n    if not isinstance(terminated, (bool, np.bool8)):\n\nriver/bandit/bayes_ucb.py: 1 warning\nriver/bandit/epsilon_greedy.py: 1 warning\nriver/bandit/evaluate.py: 11 warnings\nriver/bandit/exp3.py: 1 warning\nriver/bandit/random.py: 1 warning\nriver/bandit/test_envs.py: 2 warnings\nriver/bandit/thompson.py: 1 warning\nriver/bandit/ucb.py: 1 warning\nriver/bandit/envs/candy_cane.py: 1 warning\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/gym/utils/passive_env_checker.py:237: DeprecationWarning: `np.bool8` is a deprecated alias for `np.bool_`.  (Deprecated NumPy 1.24)\n    if not isinstance(truncated, (bool, np.bool8)):\n\nriver/linear_model/test_glm.py::test_one_many_consistent\nriver/linear_model/test_glm.py::test_shuffle_columns\nriver/linear_model/test_glm.py::test_add_remove_columns\n  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/core/fromnumeric.py:59: FutureWarning: 'Series.swapaxes' is deprecated and will be removed in a future version. Please use 'Series.transpose' instead.\n    return bound(*args, **kwds)\n\nriver/naive_bayes/test_naive_bayes.py: 108 warnings\nriver/naive_bayes/bernoulli.py: 4 warnings\n  /home/runner/work/river/river/river/naive_bayes/bernoulli.py:276: FutureWarning: Allowing arbitrary scalar fill_value in SparseDtype is deprecated. In a future version, the fill_value must be a valid value for the SparseDtype.subtype.\n    X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values,\n\nriver/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\nriver/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\n  /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/copy.py:151: DeprecationWarning: Pickle, copy, and deepcopy support will be removed from itertools in Python 3.14.\n    rv = reductor(4)\n\nriver/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\nriver/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\n  /opt/hostedtoolcache/Python/3.12.0/x64/lib/python3.12/copy.py:261: DeprecationWarning: Pickle, copy, and deepcopy support will be removed from itertools in Python 3.14.\n    y.__setstate__(state)\n\nriver/utils/test_rolling.py::test_issue_1343\n  /home/runner/work/river/river/river/utils/test_rolling.py:50: DeprecationWarning: datetime.datetime.utcnow() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.now(datetime.UTC).\n    t = dt.datetime.utcnow()\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n============================= slowest 10 durations =============================\n12.89s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_shuffle_features_no_impact]\n10.84s call     river/anomaly/test_lof.py::test_batch_lof_scores\n10.46s call     river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor\n7.08s call     river/ensemble/boosting.py::river.ensemble.boosting.BOLEClassifier\n6.97s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_disappearing_features]\n6.89s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_emerging_features]\n6.39s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_pickling]\n6.35s call     river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier\n6.26s call     river/stats/test_kolmogorov_smirnov.py::test_incremental_ks_statistics\n5.56s call     river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor\n\u001b[36m\u001b[1m=========================== short test summary info ============================\u001b[0m\n\u001b[33mSKIPPED\u001b[0m [22] river/bandit/test_policies.py:72: flaky\n\u001b[33mSKIPPED\u001b[0m [10] river/optim/test_.py:63: step_with_vector not implemented\n\u001b[33mSKIPPED\u001b[0m [10] river/optim/test_.py:84: step_with_vector not implemented\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_with_two_micro_clusters\u001b[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\n  comparison failed. Mismatched elements: 2 / 2:\n  Max absolute difference: 0.4026780000000001\n  Max relative difference: 0.2520956951697905\n  Index | Obtained | Expected          \n  1     | 2.0      | 1.597322 \u00b1 1.6e-06\n  2     | 2.0      | 1.597322 \u00b1 1.6e-06\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_density_graph_with_three_micro_clusters\u001b[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 \u00b1 1.6e-06})\n  comparison failed. Mismatched elements: 2 / 2:\n  Max absolute difference: 0.4026780000000001\n  Max relative difference: 0.2520956951697905\n  Index | Obtained | Expected          \n  1     | 2.0      | 1.597322 \u00b1 1.6e-06\n  2     | 2.0      | 1.597322 \u00b1 1.6e-06\n\u001b[31mFAILED\u001b[0m river/cluster/test_dbstream.py::\u001b[1mtest_density_graph_with_removed_microcluster\u001b[0m - assert 3 == 2\n +  where 3 = len({0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8200>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9340>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8380>})\n +    where {0: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8200>, 1: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9340>, 2: <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e8380>} = DBSTREAM (\\n  clustering_threshold=1\\n  fading_factor=0.1\\n  cleanup_interval=1\\n  intersection_factor=0.3\\n  minimum_weight=1.\\n)._micro_clusters\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many[ddof=0]\u001b[0m - assert False\n +  where False = <built-in function isclose>(-0.013429458902475771, -0.01913291331931292)\n +    where <built-in function isclose> = math.isclose\n +    and   -0.013429458902475771 = <bound method Cov.get of Cov: -0.013429>()\n +      where <bound method Cov.get of Cov: -0.013429> = Cov: -0.013429.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many[ddof=1]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.008778377636804292, 0.006898690912795011)\n +    where <built-in function isclose> = math.isclose\n +    and   0.008778377636804292 = <bound method Cov.get of Cov: 0.008778>()\n +      where <bound method Cov.get of Cov: 0.008778> = Cov: 0.008778.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_shuffled[ddof=0]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.0021320924551367216, 0.008581090035300476)\n +    where <built-in function isclose> = math.isclose\n +    and   0.0021320924551367216 = <bound method Cov.get of Cov: 0.002132>()\n +      where <bound method Cov.get of Cov: 0.002132> = Cov: 0.002132.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_shuffled[ddof=1]\u001b[0m - assert False\n +  where False = <built-in function isclose>(0.0027720586589603507, -0.008617735050464837)\n +    where <built-in function isclose> = math.isclose\n +    and   0.0027720586589603507 = <bound method Cov.get of Cov: 0.002772>()\n +      where <bound method Cov.get of Cov: 0.002772> = Cov: 0.002772.get\n\u001b[31mFAILED\u001b[0m river/covariance/test_emp.py::\u001b[1mtest_covariance_update_many_sampled\u001b[0m - assert False\n +  where False = <built-in function isclose>(-0.037234744053568615, -0.021211995100492684)\n +    where <built-in function isclose> = math.isclose\n +    and   -0.037234744053568615 = <bound method Cov.get of Cov: -0.037235>()\n +      where <bound method Cov.get of Cov: -0.037235> = Cov: -0.037235.get\n\u001b[31m===== \u001b[31m\u001b[1m8 failed\u001b[0m, \u001b[32m3357 passed\u001b[0m, \u001b[33m42 skipped\u001b[0m, \u001b[33m179 warnings\u001b[0m\u001b[31m in 87.55s (0:01:27)\u001b[0m\u001b[31m ======\u001b[0m\n##[error]Process completed with exit code 1.\n"}], "diff": "diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py\nindex e098f0de2e..0b4408271f 100644\n--- a/river/cluster/dbstream.py\n+++ b/river/cluster/dbstream.py\n@@ -227,10 +227,10 @@ class DBSTREAM(base.Clusterer):\n                             self.s_t[i][j] = self._time_stamp\n                         except KeyError:\n                             try:\n-                                self.s[i][j] = 0\n+                                self.s[i][j] = 1\n                                 self.s_t[i][j] = self._time_stamp\n                             except KeyError:\n-                                self.s[i] = {j: 0}\n+                                self.s[i] = {j: 1}\n                                 self.s_t[i] = {j: self._time_stamp}\n \n             # prevent collapsing clusters\n@@ -266,6 +266,15 @@ class DBSTREAM(base.Clusterer):\n \n             if micro_cluster_i.weight * value < weight_weak:\n                 micro_clusters.pop(i)\n+                self.s.pop(i, None)\n+                self.s_t.pop(i, None)\n+                # Since self.s and self.s_t always have the same keys and are arranged in ascending orders\n+                for j in self.s:\n+                    if j < i:\n+                        self.s[j].pop(i, None)\n+                        self.s_t[j].pop(i, None)\n+                    else:\n+                        break\n \n         # Update microclusters\n         self._micro_clusters = micro_clusters\ndiff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py\nindex 255c71e7a3..6b5e3776f4 100644\n--- a/river/cluster/test_dbstream.py\n+++ b/river/cluster/test_dbstream.py\n@@ -1,11 +1,13 @@\n from __future__ import annotations\n \n import pytest\n+from sklearn.datasets import make_blobs\n \n+from river import metrics, stream, utils\n from river.cluster import DBSTREAM\n \n \n-def build_dbstream(fading_factor=0.001, intersection_factor=0.05):\n+def build_dbstream(fading_factor=0.01, intersection_factor=0.05):\n     return DBSTREAM(\n         fading_factor=fading_factor,\n         clustering_threshold=1,\n@@ -31,27 +33,44 @@ def test_cluster_formation_and_cleanup():\n \n     X = [\n         {1: 1},\n+        {1: 2},\n         {1: 3},\n         {1: 3},\n         {1: 3},\n         {1: 5},\n         {1: 7},\n         {1: 9},\n+        {1: 10},\n         {1: 11},\n         {1: 11},\n+        {1: 12},\n         {1: 13},\n         {1: 11},\n         {1: 15},\n+        {1: 15},\n+        {1: 16},\n+        {1: 17},\n+        {1: 17},\n         {1: 17},\n     ]\n \n     for x in X:\n         dbstream.learn_one(x)\n \n-    assert len(dbstream._micro_clusters) == 3\n-    assert_micro_cluster_properties(dbstream.micro_clusters[1], center={1: 3}, last_update=3)\n-    assert_micro_cluster_properties(dbstream.micro_clusters[5], center={1: 11}, last_update=10)\n-    assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12)\n+    assert len(dbstream._micro_clusters) == 4\n+    assert_micro_cluster_properties(dbstream.micro_clusters[2], center={1: 3}, last_update=4)\n+    assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 11}, last_update=13)\n+    assert_micro_cluster_properties(dbstream.micro_clusters[8], center={1: 15}, last_update=15)\n+    assert_micro_cluster_properties(dbstream.micro_clusters[10], center={1: 17}, last_update=19)\n+\n+    assert dbstream.predict_one({1: 2.0}) == 0\n+    assert dbstream.predict_one({1: 13.0}) == 1\n+    assert dbstream.predict_one({1: 13 + 1e-10}) == 2\n+    assert dbstream.predict_one({1: 16 - 1e-10}) == 2\n+    assert dbstream.predict_one({1: 18}) == 3\n+\n+    assert len(dbstream._clusters) == 4\n+    assert dbstream.s == dbstream.s_t == {}\n \n \n def test_with_two_micro_clusters():\n@@ -59,24 +78,21 @@ def test_with_two_micro_clusters():\n \n     add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25)\n     add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25)\n-    # Points in the middle of first and second micro-clusters\n-    for _ in range(5):\n-        dbstream.learn_one({1: 2, 2: 2})\n \n-    assert len(dbstream._micro_clusters) == 2\n+    assert len(dbstream.micro_clusters) == 2\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56\n+        dbstream.micro_clusters[0], center={1: 2.137623, 2: 2.137623}, last_update=51\n     )\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[1], center={1: 2.402677, 2: 2.402677}, last_update=56\n+        dbstream.micro_clusters[1], center={1: 2.914910, 2: 2.914910}, last_update=51\n     )\n \n-    assert dbstream.s == {0: {1: 3.995844478090532}}\n-    assert dbstream.s_t == {0: {1: 56}}\n+    assert dbstream.s == {0: {1: 23.033438964246173}}\n+    assert dbstream.s_t == {0: {1: 51}}\n \n     dbstream._recluster()\n     assert len(dbstream.clusters) == 1\n-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033})\n+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.415239, 2: 2.415239})\n \n \n def test_density_graph_with_three_micro_clusters():\n@@ -88,34 +104,38 @@ def test_density_graph_with_three_micro_clusters():\n     for _ in range(5):\n         dbstream.learn_one({1: 2, 2: 2})\n \n+    assert dbstream.s == {0: {1: 23.033438964246173}}\n+    assert dbstream.s_t == {0: {1: 51}}\n+\n     add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25)\n     # Points in the middle of second and third micro-clusters\n     for _ in range(4):\n         dbstream.learn_one({1: 3, 2: 3})\n \n     assert len(dbstream._micro_clusters) == 3\n-\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56\n+        dbstream.micro_clusters[0], center={1: 2.0, 2: 2.0}, last_update=56\n     )\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86\n+        dbstream.micro_clusters[1], center={1: 3.0, 2: 3.0}, last_update=86\n     )\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86\n+        dbstream.micro_clusters[2], center={1: 3.982141, 2: 3.982141}, last_update=82\n     )\n \n-    assert dbstream.s[0] == pytest.approx({1: 3.995844})\n-    assert dbstream.s[1] == pytest.approx({2: 2.997921})\n-    assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}}\n+    assert dbstream.s[0] == pytest.approx({1: 23.033439})\n+    assert dbstream.s[1] == pytest.approx({2: 23.033439})\n+    assert dbstream.s_t == {0: {1: 51}, 1: {2: 82}}\n \n     dbstream._recluster()\n     assert len(dbstream.clusters) == 1\n-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894})\n+    print(dbstream.clusters[0].center)\n+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.800788, 2: 2.800788})\n \n \n def test_density_graph_with_removed_microcluster():\n-    dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3)\n+    dbstream = build_dbstream(fading_factor=0.1,\n+                              intersection_factor=0.3)\n \n     add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25)\n     add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25)\n@@ -123,23 +143,70 @@ def test_density_graph_with_removed_microcluster():\n     for _ in range(5):\n         dbstream.learn_one({1: 2, 2: 2})\n \n-    add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25)\n+    add_cluster(dbstream, initial_point={1: 3.5, 2: 3.5}, move_towards={1: 2.9, 2: 2.9}, times=25)\n+\n     # Points in the middle of second and third micro-clusters\n     for _ in range(4):\n-        dbstream.learn_one({1: 3, 2: 3})\n+        dbstream.learn_one({1: 2.6, 2: 2.6})\n \n     assert len(dbstream._micro_clusters) == 2\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86\n+        dbstream.micro_clusters[0], center={1: 2.023498, 2: 2.023498}, last_update=86\n     )\n     assert_micro_cluster_properties(\n-        dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86\n+        dbstream.micro_clusters[1], center={1: 2.766543, 2: 2.766543}, last_update=86\n     )\n \n-    assert dbstream.s[0] == pytest.approx({1: 3.615835})\n-    assert dbstream.s[1] == pytest.approx({2: 2.803583})\n-    assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}}\n+    assert dbstream.s == {0: {1: 4.702391097045977}}\n+    assert dbstream.s_t == {0: {1: 86}}\n \n     dbstream._recluster()\n     assert len(dbstream.clusters) == 1\n-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 3.152231, 2: 3.152231})\n+    assert_micro_cluster_properties(\n+        dbstream.clusters[0], center={1: 2.560647, 2: 2.560647}\n+    )\n+\n+\n+def test_dbstream_synthetic_sklearn():\n+    centers = [(-10, -10), (-5, -5), (0, 0), (5, 5), (10, 10)]\n+    cluster_std = [0.6] * 5\n+\n+    # Create a dataset with 15000 data points with 5 centers and cluster SD of 0.6 each\n+    X, y = make_blobs(n_samples=15_000,\n+                      cluster_std=cluster_std,\n+                      centers=centers,\n+                      n_features=2,\n+                      random_state=42)\n+\n+    dbstream = DBSTREAM(\n+        clustering_threshold=2,\n+        fading_factor=0.05,\n+        intersection_factor=0.1,\n+        cleanup_interval=1.0,\n+        minimum_weight=1.0,\n+    )\n+\n+    # Use VBeta as the metric to investigate the performance of DBSTREAM\n+    v_beta = metrics.VBeta(beta=1.0)\n+\n+    for x, y_true in stream.iter_array(X, y):\n+        dbstream.learn_one(x)\n+        y_pred = dbstream.predict_one(x)\n+        v_beta.update(y_true, y_pred)\n+\n+    assert len(dbstream._micro_clusters) == 12\n+    assert round(v_beta.get(), 4) == 0.9816\n+\n+    assert dbstream.s.keys() == dbstream.s_t.keys()\n+\n+    dbstream._recluster()\n+\n+    # Check that the resulted cluster centers are close to the expected centers\n+    dbstream_expected_centers = {0: {0: 10, 1: 10},\n+                                 1: {0: -5, 1: -5},\n+                                 2: {0: 0, 1: 0},\n+                                 3: {0: 5, 1: 5},\n+                                 4: {0: -10, 1: -10}}\n+\n+    for i in dbstream.centers.keys():\n+        assert utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) < 0.2\ndiff --git a/river/covariance/emp.py b/river/covariance/emp.py\nindex 2245a78291..b02b208126 100644\n--- a/river/covariance/emp.py\n+++ b/river/covariance/emp.py\n@@ -183,79 +183,27 @@ class EmpiricalCovariance(SymmetricMatrix):\n             )\n         }\n \n-        self._update_from_state(n=n, mean=mean, cov=cov)\n-\n-    def _update_from_state(self, n: int, mean: dict, cov: float | dict):\n-        \"\"\"Update from state information.\n-\n-        Parameters\n-        ----------\n-        n\n-            The number of data points.\n-        mean\n-            A dictionary of variable means.\n-        cov\n-            A dictionary of covariance or variance values.\n-        ddof\n-            Degrees of freedom for covariance calculation. Defaults to 1.\n-\n-        Raises\n-        ----------\n-            KeyError: If an element in `mean` or `cov` is missing.\n-        \"\"\"\n-        for i, j in itertools.combinations(mean.keys(), r=2):\n+        for i, j in itertools.combinations(sorted(mean.keys()), r=2):\n             try:\n                 self[i, j]\n             except KeyError:\n                 self._cov[i, j] = stats.Cov(self.ddof)\n-                if isinstance(cov, dict):\n-                    cov_ = cov.get((i, j), cov.get((j, i)))\n-                else:\n-                    cov_ = cov\n-                self._cov[i, j] += stats.Cov._from_state(\n-                    n=n,\n-                    mean_x=mean[i],\n-                    mean_y=mean[j],\n-                    cov=cov_,\n-                    ddof=self.ddof,\n-                )\n+            self._cov[i, j] += stats.Cov._from_state(\n+                n=n,\n+                mean_x=mean[i],\n+                mean_y=mean[j],\n+                cov=cov.get((i, j), cov.get((j, i))),\n+                ddof=self.ddof,\n+            )\n \n         for i in mean.keys():\n             try:\n                 self[i, i]\n             except KeyError:\n                 self._cov[i, i] = stats.Var(self.ddof)\n-            if isinstance(cov, dict):\n-                if isinstance(cov, dict):\n-                    cov_ = cov[i, i]\n-                else:\n-                    cov_ = cov\n-            self._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov_, ddof=self.ddof)\n-\n-    @classmethod\n-    def _from_state(cls, n: int, mean: dict, cov: float | dict, *, ddof=1):\n-        \"\"\"Create a new instance from state information.\n-\n-        Parameters\n-        ----------\n-        cls\n-            The class type.\n-        n\n-            The number of data points.\n-        mean\n-            A dictionary of variable means.\n-        cov\n-            A dictionary of covariance or variance values.\n-        ddof\n-            Degrees of freedom for covariance calculation. Defaults to 1.\n-\n-        Returns\n-        ----------\n-            cls: A new instance of the class with updated covariance matrix.\n-        \"\"\"\n-        new = cls(ddof=ddof)\n-        new._update_from_state(n=n, mean=mean, cov=cov)\n-        return new\n+            self._cov[i, i] += stats.Var._from_state(\n+                n=n, m=mean[i], sig=cov[i, i], ddof=self.ddof\n+            )\n \n \n class EmpiricalPrecision(SymmetricMatrix):\n", "difficulty": 3, "changed_files": ["river/cluster/dbstream.py", "river/cluster/test_dbstream.py", "river/covariance/emp.py"], "commit_link": "https://github.com/online-ml/river/tree/4921af92f5ec9d61f4ebefb8d2810b1f05e8045b"}
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