Repository: online-ml/river (branch: main)
Commit date: 2023-12-08T08:07:15Z

Workflow file: .github/workflows/unit-tests.yml (unit-tests)
name: unit-tests

on:
  pull_request:
    branches:
      - "*"
  push:
    branches:
      - main

jobs:
  run:
    strategy:
      matrix:
        os: [ubuntu-latest]
        python-version: ["3.12", "3.11", "3.10"]

    runs-on: ${{ matrix.os }}

    services:

      pypi_wayback:

        image: ghcr.io/waleko/pypi-wayback

        ports:

          - 8629:8080

    steps:
      - uses: actions/checkout@v3

      - name: Build River
        uses: ./.github/actions/install-env
        with:
          python-version: "3.12"

      - name: Cache River datasets
        uses: actions/cache@v3
        with:
          path: ~/river_data
          key: ${{ runner.os }}

      - name: Cache scikit-learn datasets
        uses: actions/cache@v3
        with:
          path: ~/scikit_learn_data
          key: ${{ runner.os }}

      - name: Download datasets
        run: |
          poetry run python -c "from river import datasets; datasets.CreditCard().download(); datasets.Elec2().download(); datasets.SMSSpam().download()"
          poetry run python -c "from river import bandit; bandit.datasets.NewsArticles().download()"

      - name: pytest
        run: |
          poetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores

env:
  PIP_INDEX_URL: http://localhost:8629/2023-12-08
  UV_INDEX_URL: http://localhost:8629/2023-12-08


Failed build 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
[36;1mpoetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores[0m
shell: /usr/bin/bash -e {0}
env:
  pythonLocation: /opt/hostedtoolcache/Python/3.12.0/x64
  PKG_CONFIG_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib/pkgconfig
  Python_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  Python2_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  Python3_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib
  VENV: .venv/bin/activate
##[endgroup]
[1m============================= test session starts ==============================[0m
platform linux -- Python 3.12.0, pytest-7.4.3, pluggy-1.3.0 -- /home/runner/work/river/river/.venv/bin/python
cachedir: .pytest_cache
rootdir: /home/runner/work/river/river
configfile: pyproject.toml
plugins: anyio-4.1.0, xdist-3.5.0
created: 4/4 workers
4 workers [3407 items]

scheduling tests via LoadScheduling

README.md::README.md 
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=================================== FAILURES ===================================
[31m[1m_____________________ test_covariance_update_many[ddof=0] ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 0

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m1[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p)))[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.021115787946478254, 0.036773566919091685)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.021115787946478254 = <bound method Cov.get of Cov: 0.021116>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.021116> = Cov: 0.021116.get[0m

[1m[31mriver/covariance/test_emp.py[0m:110: AssertionError
[31m[1m_____________________ test_covariance_update_many[ddof=1] ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 1

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m1[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p)))[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0, nan)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0 = <bound method Cov.get of Cov: 0.>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.> = Cov: 0..get[0m

[1m[31mriver/covariance/test_emp.py[0m:110: AssertionError
[31m[1m_________________ test_covariance_update_many_shuffled[ddof=0] _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 0

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many_shuffled[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.012307077362927493, 0.008910410592255408)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.012307077362927493 = <bound method Cov.get of Cov: 0.012307>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.012307> = Cov: 0.012307.get[0m

[1m[31mriver/covariance/test_emp.py[0m:138: AssertionError
[31m[1m_________________ test_covariance_update_many_shuffled[ddof=1] _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 1

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many_shuffled[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.008130659727412096, -0.01863436424026853)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.008130659727412096 = <bound method Cov.get of Cov: 0.008131>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.008131> = Cov: 0.008131.get[0m

[1m[31mriver/covariance/test_emp.py[0m:138: AssertionError
[31m[1m_____________________ test_covariance_update_many_sampled ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_covariance_update_many_sampled[39;49;00m():[90m[39;49;00m
        [90m# NOTE: this test only works with ddof=1 because pandas ignores it if there are missing values[39;49;00m[90m[39;49;00m
        ddof = [94m1[39;49;00m[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p - [94m1[39;49;00m, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.015212479685753347, 0.0013097742126793048)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.015212479685753347 = <bound method Cov.get of Cov: 0.015212>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.015212> = Cov: 0.015212.get[0m

[1m[31mriver/covariance/test_emp.py[0m:158: AssertionError
[31m[1m_________________________ test_with_two_micro_clusters _________________________[0m
[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_with_two_micro_clusters[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream()[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m2[39;49;00m[90m[39;49;00m
>       assert_micro_cluster_properties([90m[39;49;00m
            dbstream.micro_clusters[[94m0[39;49;00m], center={[94m1[39;49;00m: [94m1.597322[39;49;00m, [94m2[39;49;00m: [94m1.597322[39;49;00m}, last_update=[94m56[39;49;00m[90m[39;49;00m
        )[90m[39;49;00m

[1m[31mriver/cluster/test_dbstream.py[0m:67: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cdf0dee0>
center = {1: 1.597322, 2: 1.597322}, last_update = 56

    [94mdef[39;49;00m [92massert_micro_cluster_properties[39;49;00m(cluster, center, last_update=[94mNone[39;49;00m):[90m[39;49;00m
>       [94massert[39;49;00m cluster.center == pytest.approx(center)[90m[39;49;00m
[1m[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})[0m
[1m[31mE         comparison failed. Mismatched elements: 2 / 2:[0m
[1m[31mE         Max absolute difference: 0.4026780000000001[0m
[1m[31mE         Max relative difference: 0.2520956951697905[0m
[1m[31mE         Index | Obtained | Expected          [0m
[1m[31mE         1     | 2.0      | 1.597322 ± 1.6e-06[0m
[1m[31mE         2     | 2.0      | 1.597322 ± 1.6e-06[0m

[1m[31mriver/cluster/test_dbstream.py[0m:24: AssertionError
[31m[1m_________________ test_density_graph_with_three_micro_clusters _________________[0m
[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_density_graph_with_three_micro_clusters[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream()[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m4[39;49;00m, [94m2[39;49;00m: [94m4[39;49;00m}, move_towards={[94m1[39;49;00m: [94m3.3[39;49;00m, [94m2[39;49;00m: [94m3.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of second and third micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m4[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m3[39;49;00m[90m[39;49;00m
    [90m[39;49;00m
>       assert_micro_cluster_properties([90m[39;49;00m
            dbstream.micro_clusters[[94m0[39;49;00m], center={[94m1[39;49;00m: [94m1.597322[39;49;00m, [94m2[39;49;00m: [94m1.597322[39;49;00m}, last_update=[94m56[39;49;00m[90m[39;49;00m
        )[90m[39;49;00m

[1m[31mriver/cluster/test_dbstream.py[0m:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f69cdf0d040>
center = {1: 1.597322, 2: 1.597322}, last_update = 56

    [94mdef[39;49;00m [92massert_micro_cluster_properties[39;49;00m(cluster, center, last_update=[94mNone[39;49;00m):[90m[39;49;00m
>       [94massert[39;49;00m cluster.center == pytest.approx(center)[90m[39;49;00m
[1m[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})[0m
[1m[31mE         comparison failed. Mismatched elements: 2 / 2:[0m
[1m[31mE         Max absolute difference: 0.4026780000000001[0m
[1m[31mE         Max relative difference: 0.2520956951697905[0m
[1m[31mE         Index | Obtained | Expected          [0m
[1m[31mE         1     | 2.0      | 1.597322 ± 1.6e-06[0m
[1m[31mE         2     | 2.0      | 1.597322 ± 1.6e-06[0m

[1m[31mriver/cluster/test_dbstream.py[0m:24: AssertionError
[31m[1m_________________ test_density_graph_with_removed_microcluster _________________[0m
[gw3] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_density_graph_with_removed_microcluster[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream(fading_factor=[94m0.1[39;49;00m, intersection_factor=[94m0.3[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m4[39;49;00m, [94m2[39;49;00m: [94m4[39;49;00m}, move_towards={[94m1[39;49;00m: [94m3.3[39;49;00m, [94m2[39;49;00m: [94m3.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of second and third micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m4[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
>       [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m2[39;49;00m[90m[39;49;00m
[1m[31mE       assert 3 == 2[0m
[1m[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>})[0m
[1m[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[0m

[1m[31mriver/cluster/test_dbstream.py[0m:131: AssertionError
[33m=============================== warnings summary ===============================[0m
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
  /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).
    EPOCH = datetime.datetime.utcfromtimestamp(0)

river/covariance/test_emp.py::test_covariance_update_many[ddof=0]
river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /home/runner/work/river/river/river/covariance/emp.py:175: RuntimeWarning: Degrees of freedom <= 0 for slice
    cov_arr = np.cov(X_arr.T, ddof=self.ddof)

river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/lib/function_base.py:2748: RuntimeWarning: divide by zero encountered in divide
    c *= np.true_divide(1, fact)

river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/numpy/lib/function_base.py:2748: RuntimeWarning: invalid value encountered in multiply
    c *= np.true_divide(1, fact)

river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/pandas/core/frame.py:10866: RuntimeWarning: Degrees of freedom <= 0 for slice
    base_cov = np.cov(mat.T, ddof=ddof)

river/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=0]
river/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=1]
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_covariance_update_many_sampled
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_precision_update_many_mini_batches
river/linear_model/test_glm.py::test_one_many_consistent
river/linear_model/test_glm.py::test_shuffle_columns
river/linear_model/test_glm.py::test_add_remove_columns
river/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent
river/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent
river/preprocessing/test_scale.py::test_standard_scaler_shuffle_columns
river/preprocessing/test_scale.py::test_standard_scaler_add_remove_columns
  /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.
    return bound(*args, **kwds)

river/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]
river/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]
  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py:245: RuntimeWarning: invalid value encountered in cast
    return x.astype(dtype, copy=copy, casting=casting)

river/linear_model/test_glm.py::test_one_many_consistent
river/linear_model/test_glm.py::test_shuffle_columns
river/linear_model/test_glm.py::test_add_remove_columns
  /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.
    return bound(*args, **kwds)

river/naive_bayes/test_naive_bayes.py: 108 warnings
river/naive_bayes/bernoulli.py: 4 warnings
  /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.
    X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values,

river/bandit/bayes_ucb.py: 1 warning
river/bandit/epsilon_greedy.py: 1 warning
river/bandit/evaluate.py: 11 warnings
river/bandit/exp3.py: 1 warning
river/bandit/random.py: 1 warning
river/bandit/test_envs.py: 2 warnings
river/bandit/thompson.py: 1 warning
river/bandit/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
  /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)
    if not isinstance(terminated, (bool, np.bool8)):

river/bandit/bayes_ucb.py: 1 warning
river/bandit/epsilon_greedy.py: 1 warning
river/bandit/evaluate.py: 11 warnings
river/bandit/exp3.py: 1 warning
river/bandit/random.py: 1 warning
river/bandit/test_envs.py: 2 warnings
river/bandit/thompson.py: 1 warning
river/bandit/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
  /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)
    if not isinstance(truncated, (bool, np.bool8)):

river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
  /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.
    rv = reductor(4)

river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
  /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.
    y.__setstate__(state)

river/utils/test_rolling.py::test_issue_1343
  /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).
    t = dt.datetime.utcnow()

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= slowest 10 durations =============================
12.76s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_shuffle_features_no_impact]
10.51s call     river/anomaly/test_lof.py::test_batch_lof_scores
10.25s call     river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor
7.25s call     river/ensemble/boosting.py::river.ensemble.boosting.BOLEClassifier
6.51s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_emerging_features]
6.44s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_disappearing_features]
6.39s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_pickling]
6.35s call     river/stats/test_kolmogorov_smirnov.py::test_incremental_ks_statistics
6.21s call     river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
5.91s call     river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
[36m[1m=========================== short test summary info ============================[0m
[33mSKIPPED[0m [22] river/bandit/test_policies.py:72: flaky
[33mSKIPPED[0m [10] river/optim/test_.py:63: step_with_vector not implemented
[33mSKIPPED[0m [10] river/optim/test_.py:84: step_with_vector not implemented
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many[ddof=0][0m - assert False
 +  where False = <built-in function isclose>(0.021115787946478254, 0.036773566919091685)
 +    where <built-in function isclose> = math.isclose
 +    and   0.021115787946478254 = <bound method Cov.get of Cov: 0.021116>()
 +      where <bound method Cov.get of Cov: 0.021116> = Cov: 0.021116.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many[ddof=1][0m - assert False
 +  where False = <built-in function isclose>(0, nan)
 +    where <built-in function isclose> = math.isclose
 +    and   0 = <bound method Cov.get of Cov: 0.>()
 +      where <bound method Cov.get of Cov: 0.> = Cov: 0..get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_shuffled[ddof=0][0m - assert False
 +  where False = <built-in function isclose>(0.012307077362927493, 0.008910410592255408)
 +    where <built-in function isclose> = math.isclose
 +    and   0.012307077362927493 = <bound method Cov.get of Cov: 0.012307>()
 +      where <bound method Cov.get of Cov: 0.012307> = Cov: 0.012307.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_shuffled[ddof=1][0m - assert False
 +  where False = <built-in function isclose>(0.008130659727412096, -0.01863436424026853)
 +    where <built-in function isclose> = math.isclose
 +    and   0.008130659727412096 = <bound method Cov.get of Cov: 0.008131>()
 +      where <bound method Cov.get of Cov: 0.008131> = Cov: 0.008131.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_sampled[0m - assert False
 +  where False = <built-in function isclose>(0.015212479685753347, 0.0013097742126793048)
 +    where <built-in function isclose> = math.isclose
 +    and   0.015212479685753347 = <bound method Cov.get of Cov: 0.015212>()
 +      where <bound method Cov.get of Cov: 0.015212> = Cov: 0.015212.get
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_with_two_micro_clusters[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})
  comparison failed. Mismatched elements: 2 / 2:
  Max absolute difference: 0.4026780000000001
  Max relative difference: 0.2520956951697905
  Index | Obtained | Expected          
  1     | 2.0      | 1.597322 ± 1.6e-06
  2     | 2.0      | 1.597322 ± 1.6e-06
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_density_graph_with_three_micro_clusters[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})
  comparison failed. Mismatched elements: 2 / 2:
  Max absolute difference: 0.4026780000000001
  Max relative difference: 0.2520956951697905
  Index | Obtained | Expected          
  1     | 2.0      | 1.597322 ± 1.6e-06
  2     | 2.0      | 1.597322 ± 1.6e-06
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_density_graph_with_removed_microcluster[0m - assert 3 == 2
 +  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>})
 +    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
[31m===== [31m[1m8 failed[0m, [32m3357 passed[0m, [33m42 skipped[0m, [33m185 warnings[0m[31m in 88.61s (0:01:28)[0m[31m ======[0m
##[error]Process completed with exit code 1.

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
[36;1mpoetry run pytest -m "not datasets" --durations=10 -n logical # Run pytest on all logical CPU cores[0m
shell: /usr/bin/bash -e {0}
env:
  pythonLocation: /opt/hostedtoolcache/Python/3.12.0/x64
  PKG_CONFIG_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib/pkgconfig
  Python_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  Python2_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  Python3_ROOT_DIR: /opt/hostedtoolcache/Python/3.12.0/x64
  LD_LIBRARY_PATH: /opt/hostedtoolcache/Python/3.12.0/x64/lib
  VENV: .venv/bin/activate
##[endgroup]
[1m============================= test session starts ==============================[0m
platform linux -- Python 3.12.0, pytest-7.4.3, pluggy-1.3.0 -- /home/runner/work/river/river/.venv/bin/python
cachedir: .pytest_cache
rootdir: /home/runner/work/river/river
configfile: pyproject.toml
plugins: anyio-4.1.0, xdist-3.5.0
created: 4/4 workers
4 workers [3407 items]

scheduling tests via LoadScheduling

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[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_math.py::test_dotvecmat_three_dimensional_vector_times_non_quadratic_matrix 
river/utils/test_param_grid.py::test_expand_param_grid_count[model0-param_grid0-14] 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model0-param_grid0-14] 
river/utils/test_param_grid.py::test_expand_param_grid_count[model1-param_grid1-14] 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model1-param_grid1-14] 
river/utils/test_param_grid.py::test_expand_param_grid_count[model2-param_grid2-9] 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_param_grid.py::test_expand_param_grid_count[model2-param_grid2-9] 
river/utils/test_param_grid.py::test_decision_tree_max_depth 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_param_grid.py::test_decision_tree_max_depth 
river/utils/test_rolling.py::river.utils.test_rolling.test_with_counter 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_rolling.py::river.utils.test_rolling.test_with_counter 
river/utils/test_rolling.py::test_with_counter 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_rolling.py::test_with_counter 
river/utils/test_rolling.py::test_rolling_with_not_rollable 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_rolling.py::test_rolling_with_not_rollable 
river/utils/test_rolling.py::test_time_rolling_with_not_rollable 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_rolling.py::test_time_rolling_with_not_rollable 
river/utils/test_rolling.py::test_issue_1343 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_rolling.py::test_issue_1343 
river/utils/test_vectordict.py::test_vectordict 
[gw0][36m [ 99%] [0m[32mPASSED[0m river/tree/test_trees.py::test_memory_usage_class[dataset2-model2] 
[gw3][36m [ 99%] [0m[32mPASSED[0m river/utils/test_vectordict.py::test_vectordict 
river/tree/utils.py::river.tree.utils.round_sig_fig 
[gw0][36m [ 99%] [0m[32mPASSED[0m river/tree/utils.py::river.tree.utils.round_sig_fig 
river/tree/mondrian/mondrian_tree_classifier.py::river.tree.mondrian.mondrian_tree_classifier.MondrianTreeClassifier 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/momentum.py::river.optim.momentum.Momentum 
river/optim/nadam.py::river.optim.nadam.Nadam 
[gw0][36m [ 99%] [0m[32mPASSED[0m river/tree/mondrian/mondrian_tree_classifier.py::river.tree.mondrian.mondrian_tree_classifier.MondrianTreeClassifier 
[gw2][36m [ 99%] [0m[32mPASSED[0m river/tree/test_trees.py::test_drift_adaptation_hatr 
river/utils/math.py::river.utils.math.dotvecmat 
[gw2][36m [ 99%] [0m[32mPASSED[0m river/utils/math.py::river.utils.math.dotvecmat 
river/utils/math.py::river.utils.math.matmul2d 
[gw2][36m [ 99%] [0m[32mPASSED[0m river/utils/math.py::river.utils.math.matmul2d 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/nadam.py::river.optim.nadam.Nadam 
river/optim/nesterov.py::river.optim.nesterov.NesterovMomentum 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/nesterov.py::river.optim.nesterov.NesterovMomentum 
river/optim/newton.py::river.optim.newton.sherman_morrison 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/newton.py::river.optim.newton.sherman_morrison 
river/optim/rms_prop.py::river.optim.rms_prop.RMSProp 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/rms_prop.py::river.optim.rms_prop.RMSProp 
river/optim/sgd.py::river.optim.sgd.SGD 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/sgd.py::river.optim.sgd.SGD 
river/optim/test_.py::test_loss_batch_online_equivalence[Absolute] 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/test_.py::test_loss_batch_online_equivalence[Absolute] 
river/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] 
[gw1][36m [ 99%] [0m[32mPASSED[0m river/optim/test_.py::test_loss_batch_online_equivalence[BinaryFocalLoss] 
river/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] 
[gw1][36m [100%] [0m[32mPASSED[0m river/optim/test_.py::test_loss_batch_online_equivalence[Cauchy] 

=================================== FAILURES ===================================
[31m[1m_________________________ test_with_two_micro_clusters _________________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_with_two_micro_clusters[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream()[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m2[39;49;00m[90m[39;49;00m
>       assert_micro_cluster_properties([90m[39;49;00m
            dbstream.micro_clusters[[94m0[39;49;00m], center={[94m1[39;49;00m: [94m1.597322[39;49;00m, [94m2[39;49;00m: [94m1.597322[39;49;00m}, last_update=[94m56[39;49;00m[90m[39;49;00m
        )[90m[39;49;00m

[1m[31mriver/cluster/test_dbstream.py[0m:67: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015e9e20>
center = {1: 1.597322, 2: 1.597322}, last_update = 56

    [94mdef[39;49;00m [92massert_micro_cluster_properties[39;49;00m(cluster, center, last_update=[94mNone[39;49;00m):[90m[39;49;00m
>       [94massert[39;49;00m cluster.center == pytest.approx(center)[90m[39;49;00m
[1m[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})[0m
[1m[31mE         comparison failed. Mismatched elements: 2 / 2:[0m
[1m[31mE         Max absolute difference: 0.4026780000000001[0m
[1m[31mE         Max relative difference: 0.2520956951697905[0m
[1m[31mE         Index | Obtained | Expected          [0m
[1m[31mE         1     | 2.0      | 1.597322 ± 1.6e-06[0m
[1m[31mE         2     | 2.0      | 1.597322 ± 1.6e-06[0m

[1m[31mriver/cluster/test_dbstream.py[0m:24: AssertionError
[31m[1m_________________ test_density_graph_with_three_micro_clusters _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_density_graph_with_three_micro_clusters[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream()[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m4[39;49;00m, [94m2[39;49;00m: [94m4[39;49;00m}, move_towards={[94m1[39;49;00m: [94m3.3[39;49;00m, [94m2[39;49;00m: [94m3.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of second and third micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m4[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m3[39;49;00m[90m[39;49;00m
    [90m[39;49;00m
>       assert_micro_cluster_properties([90m[39;49;00m
            dbstream.micro_clusters[[94m0[39;49;00m], center={[94m1[39;49;00m: [94m1.597322[39;49;00m, [94m2[39;49;00m: [94m1.597322[39;49;00m}, last_update=[94m56[39;49;00m[90m[39;49;00m
        )[90m[39;49;00m

[1m[31mriver/cluster/test_dbstream.py[0m:98: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cluster = <river.cluster.dbstream.DBSTREAMMicroCluster object at 0x7f0e015f5d60>
center = {1: 1.597322, 2: 1.597322}, last_update = 56

    [94mdef[39;49;00m [92massert_micro_cluster_properties[39;49;00m(cluster, center, last_update=[94mNone[39;49;00m):[90m[39;49;00m
>       [94massert[39;49;00m cluster.center == pytest.approx(center)[90m[39;49;00m
[1m[31mE       assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})[0m
[1m[31mE         comparison failed. Mismatched elements: 2 / 2:[0m
[1m[31mE         Max absolute difference: 0.4026780000000001[0m
[1m[31mE         Max relative difference: 0.2520956951697905[0m
[1m[31mE         Index | Obtained | Expected          [0m
[1m[31mE         1     | 2.0      | 1.597322 ± 1.6e-06[0m
[1m[31mE         2     | 2.0      | 1.597322 ± 1.6e-06[0m

[1m[31mriver/cluster/test_dbstream.py[0m:24: AssertionError
[31m[1m_________________ test_density_graph_with_removed_microcluster _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_density_graph_with_removed_microcluster[39;49;00m():[90m[39;49;00m
        dbstream = build_dbstream(fading_factor=[94m0.1[39;49;00m, intersection_factor=[94m0.3[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m1[39;49;00m, [94m2[39;49;00m: [94m1[39;49;00m}, move_towards={[94m1[39;49;00m: [94m1.7[39;49;00m, [94m2[39;49;00m: [94m1.7[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m}, move_towards={[94m1[39;49;00m: [94m2.3[39;49;00m, [94m2[39;49;00m: [94m2.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of first and second micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m5[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m2[39;49;00m, [94m2[39;49;00m: [94m2[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
        add_cluster(dbstream, initial_point={[94m1[39;49;00m: [94m4[39;49;00m, [94m2[39;49;00m: [94m4[39;49;00m}, move_towards={[94m1[39;49;00m: [94m3.3[39;49;00m, [94m2[39;49;00m: [94m3.3[39;49;00m}, times=[94m25[39;49;00m)[90m[39;49;00m
        [90m# Points in the middle of second and third micro-clusters[39;49;00m[90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m([94m4[39;49;00m):[90m[39;49;00m
            dbstream.learn_one({[94m1[39;49;00m: [94m3[39;49;00m, [94m2[39;49;00m: [94m3[39;49;00m})[90m[39;49;00m
    [90m[39;49;00m
>       [94massert[39;49;00m [96mlen[39;49;00m(dbstream._micro_clusters) == [94m2[39;49;00m[90m[39;49;00m
[1m[31mE       assert 3 == 2[0m
[1m[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>})[0m
[1m[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[0m

[1m[31mriver/cluster/test_dbstream.py[0m:131: AssertionError
[31m[1m_____________________ test_covariance_update_many[ddof=0] ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 0

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m1[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p)))[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(-0.013429458902475771, -0.01913291331931292)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   -0.013429458902475771 = <bound method Cov.get of Cov: -0.013429>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: -0.013429> = Cov: -0.013429.get[0m

[1m[31mriver/covariance/test_emp.py[0m:110: AssertionError
[31m[1m_____________________ test_covariance_update_many[ddof=1] ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 1

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m1[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p)))[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.008778377636804292, 0.006898690912795011)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.008778377636804292 = <bound method Cov.get of Cov: 0.008778>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.008778> = Cov: 0.008778.get[0m

[1m[31mriver/covariance/test_emp.py[0m:110: AssertionError
[31m[1m_________________ test_covariance_update_many_shuffled[ddof=0] _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 0

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many_shuffled[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.0021320924551367216, 0.008581090035300476)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.0021320924551367216 = <bound method Cov.get of Cov: 0.002132>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.002132> = Cov: 0.002132.get[0m

[1m[31mriver/covariance/test_emp.py[0m:138: AssertionError
[31m[1m_________________ test_covariance_update_many_shuffled[ddof=1] _________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

ddof = 1

    [37m@pytest[39;49;00m.mark.parametrize([90m[39;49;00m
        [33m"[39;49;00m[33mddof[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
        [[90m[39;49;00m
            pytest.param([90m[39;49;00m
                ddof,[90m[39;49;00m
                [96mid[39;49;00m=[33mf[39;49;00m[33m"[39;49;00m[33m{[39;49;00mddof[33m=}[39;49;00m[33m"[39;49;00m,[90m[39;49;00m
            )[90m[39;49;00m
            [94mfor[39;49;00m ddof [95min[39;49;00m [[94m0[39;49;00m, [94m1[39;49;00m][90m[39;49;00m
        ],[90m[39;49;00m
    )[90m[39;49;00m
    [94mdef[39;49;00m [92mtest_covariance_update_many_shuffled[39;49;00m(ddof):[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(0.0027720586589603507, -0.008617735050464837)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   0.0027720586589603507 = <bound method Cov.get of Cov: 0.002772>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: 0.002772> = Cov: 0.002772.get[0m

[1m[31mriver/covariance/test_emp.py[0m:138: AssertionError
[31m[1m_____________________ test_covariance_update_many_sampled ______________________[0m
[gw2] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python

    [94mdef[39;49;00m [92mtest_covariance_update_many_sampled[39;49;00m():[90m[39;49;00m
        [90m# NOTE: this test only works with ddof=1 because pandas ignores it if there are missing values[39;49;00m[90m[39;49;00m
        ddof = [94m1[39;49;00m[90m[39;49;00m
        cov = covariance.EmpiricalCovariance(ddof=ddof)[90m[39;49;00m
        p = [94m5[39;49;00m[90m[39;49;00m
        X_all = pd.DataFrame(columns=[96mrange[39;49;00m(p))[90m[39;49;00m
    [90m[39;49;00m
        [94mfor[39;49;00m _ [95min[39;49;00m [96mrange[39;49;00m(p):[90m[39;49;00m
            n = np.random.randint([94m5[39;49;00m, [94m31[39;49;00m)[90m[39;49;00m
            X = pd.DataFrame(np.random.random((n, p))).sample(p - [94m1[39;49;00m, axis=[33m"[39;49;00m[33mcolumns[39;49;00m[33m"[39;49;00m)[90m[39;49;00m
    [90m[39;49;00m
            cov.update_many(X)[90m[39;49;00m
    [90m[39;49;00m
            X_all = pd.concat((X_all, X)).astype([96mfloat[39;49;00m)[90m[39;49;00m
            pd_cov = X_all.cov(ddof=ddof)[90m[39;49;00m
    [90m[39;49;00m
            [94mfor[39;49;00m i, j [95min[39;49;00m cov._cov:[90m[39;49;00m
>               [94massert[39;49;00m math.isclose(cov[i, j].get(), pd_cov.loc[i, j])[90m[39;49;00m
[1m[31mE               assert False[0m
[1m[31mE                +  where False = <built-in function isclose>(-0.037234744053568615, -0.021211995100492684)[0m
[1m[31mE                +    where <built-in function isclose> = math.isclose[0m
[1m[31mE                +    and   -0.037234744053568615 = <bound method Cov.get of Cov: -0.037235>()[0m
[1m[31mE                +      where <bound method Cov.get of Cov: -0.037235> = Cov: -0.037235.get[0m

[1m[31mriver/covariance/test_emp.py[0m:158: AssertionError
[33m=============================== warnings summary ===============================[0m
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
.venv/lib/python3.12/site-packages/dateutil/tz/tz.py:37
  /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).
    EPOCH = datetime.datetime.utcfromtimestamp(0)

river/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]
river/compat/test_sklearn.py::test_river_to_sklearn_check_estimator[LogisticRegression]
  /home/runner/work/river/river/.venv/lib/python3.12/site-packages/sklearn/utils/_array_api.py:245: RuntimeWarning: invalid value encountered in cast
    return x.astype(dtype, copy=copy, casting=casting)

river/covariance/test_emp.py::test_covariance_update_many[ddof=0]
river/covariance/test_emp.py::test_covariance_update_many[ddof=1]
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=0]
river/covariance/test_emp.py::test_covariance_update_many_shuffled[ddof=1]
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_covariance_update_many_sampled
  /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.
    X_all = pd.concat((X_all, X)).astype(float)

river/covariance/test_emp.py::test_precision_update_many_mini_batches
river/linear_model/test_glm.py::test_one_many_consistent
river/linear_model/test_glm.py::test_shuffle_columns
river/linear_model/test_glm.py::test_add_remove_columns
river/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent
river/preprocessing/test_scale.py::test_standard_scaler_one_many_consistent
river/preprocessing/test_scale.py::test_standard_scaler_shuffle_columns
river/preprocessing/test_scale.py::test_standard_scaler_add_remove_columns
  /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.
    return bound(*args, **kwds)

river/bandit/bayes_ucb.py: 1 warning
river/bandit/epsilon_greedy.py: 1 warning
river/bandit/evaluate.py: 11 warnings
river/bandit/exp3.py: 1 warning
river/bandit/random.py: 1 warning
river/bandit/test_envs.py: 2 warnings
river/bandit/thompson.py: 1 warning
river/bandit/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
  /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)
    if not isinstance(terminated, (bool, np.bool8)):

river/bandit/bayes_ucb.py: 1 warning
river/bandit/epsilon_greedy.py: 1 warning
river/bandit/evaluate.py: 11 warnings
river/bandit/exp3.py: 1 warning
river/bandit/random.py: 1 warning
river/bandit/test_envs.py: 2 warnings
river/bandit/thompson.py: 1 warning
river/bandit/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
  /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)
    if not isinstance(truncated, (bool, np.bool8)):

river/linear_model/test_glm.py::test_one_many_consistent
river/linear_model/test_glm.py::test_shuffle_columns
river/linear_model/test_glm.py::test_add_remove_columns
  /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.
    return bound(*args, **kwds)

river/naive_bayes/test_naive_bayes.py: 108 warnings
river/naive_bayes/bernoulli.py: 4 warnings
  /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.
    X @ (flp - neg_p).T + (np.log(self.p_class_many()) + neg_p.sum(axis=1).T).values,

river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
  /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.
    rv = reductor(4)

river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
  /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.
    y.__setstate__(state)

river/utils/test_rolling.py::test_issue_1343
  /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).
    t = dt.datetime.utcnow()

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= slowest 10 durations =============================
12.89s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_shuffle_features_no_impact]
10.84s call     river/anomaly/test_lof.py::test_batch_lof_scores
10.46s call     river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor
7.08s call     river/ensemble/boosting.py::river.ensemble.boosting.BOLEClassifier
6.97s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_disappearing_features]
6.89s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_emerging_features]
6.39s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_pickling]
6.35s call     river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
6.26s call     river/stats/test_kolmogorov_smirnov.py::test_incremental_ks_statistics
5.56s call     river/neighbors/knn_regressor.py::river.neighbors.knn_regressor.KNNRegressor
[36m[1m=========================== short test summary info ============================[0m
[33mSKIPPED[0m [22] river/bandit/test_policies.py:72: flaky
[33mSKIPPED[0m [10] river/optim/test_.py:63: step_with_vector not implemented
[33mSKIPPED[0m [10] river/optim/test_.py:84: step_with_vector not implemented
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_with_two_micro_clusters[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})
  comparison failed. Mismatched elements: 2 / 2:
  Max absolute difference: 0.4026780000000001
  Max relative difference: 0.2520956951697905
  Index | Obtained | Expected          
  1     | 2.0      | 1.597322 ± 1.6e-06
  2     | 2.0      | 1.597322 ± 1.6e-06
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_density_graph_with_three_micro_clusters[0m - assert {1: 2.0, 2: 2.0} == approx({1: 1....22 ± 1.6e-06})
  comparison failed. Mismatched elements: 2 / 2:
  Max absolute difference: 0.4026780000000001
  Max relative difference: 0.2520956951697905
  Index | Obtained | Expected          
  1     | 2.0      | 1.597322 ± 1.6e-06
  2     | 2.0      | 1.597322 ± 1.6e-06
[31mFAILED[0m river/cluster/test_dbstream.py::[1mtest_density_graph_with_removed_microcluster[0m - assert 3 == 2
 +  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>})
 +    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
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many[ddof=0][0m - assert False
 +  where False = <built-in function isclose>(-0.013429458902475771, -0.01913291331931292)
 +    where <built-in function isclose> = math.isclose
 +    and   -0.013429458902475771 = <bound method Cov.get of Cov: -0.013429>()
 +      where <bound method Cov.get of Cov: -0.013429> = Cov: -0.013429.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many[ddof=1][0m - assert False
 +  where False = <built-in function isclose>(0.008778377636804292, 0.006898690912795011)
 +    where <built-in function isclose> = math.isclose
 +    and   0.008778377636804292 = <bound method Cov.get of Cov: 0.008778>()
 +      where <bound method Cov.get of Cov: 0.008778> = Cov: 0.008778.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_shuffled[ddof=0][0m - assert False
 +  where False = <built-in function isclose>(0.0021320924551367216, 0.008581090035300476)
 +    where <built-in function isclose> = math.isclose
 +    and   0.0021320924551367216 = <bound method Cov.get of Cov: 0.002132>()
 +      where <bound method Cov.get of Cov: 0.002132> = Cov: 0.002132.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_shuffled[ddof=1][0m - assert False
 +  where False = <built-in function isclose>(0.0027720586589603507, -0.008617735050464837)
 +    where <built-in function isclose> = math.isclose
 +    and   0.0027720586589603507 = <bound method Cov.get of Cov: 0.002772>()
 +      where <bound method Cov.get of Cov: 0.002772> = Cov: 0.002772.get
[31mFAILED[0m river/covariance/test_emp.py::[1mtest_covariance_update_many_sampled[0m - assert False
 +  where False = <built-in function isclose>(-0.037234744053568615, -0.021211995100492684)
 +    where <built-in function isclose> = math.isclose
 +    and   -0.037234744053568615 = <bound method Cov.get of Cov: -0.037235>()
 +      where <bound method Cov.get of Cov: -0.037235> = Cov: -0.037235.get
[31m===== [31m[1m8 failed[0m, [32m3357 passed[0m, [33m42 skipped[0m, [33m179 warnings[0m[31m in 87.55s (0:01:27)[0m[31m ======[0m
##[error]Process completed with exit code 1.


Changed files:
river/cluster/dbstream.py
river/cluster/test_dbstream.py
river/covariance/emp.py

Fix diff:
diff --git a/river/cluster/dbstream.py b/river/cluster/dbstream.py
index e098f0de2e..0b4408271f 100644
--- a/river/cluster/dbstream.py
+++ b/river/cluster/dbstream.py
@@ -227,10 +227,10 @@ class DBSTREAM(base.Clusterer):
                             self.s_t[i][j] = self._time_stamp
                         except KeyError:
                             try:
-                                self.s[i][j] = 0
+                                self.s[i][j] = 1
                                 self.s_t[i][j] = self._time_stamp
                             except KeyError:
-                                self.s[i] = {j: 0}
+                                self.s[i] = {j: 1}
                                 self.s_t[i] = {j: self._time_stamp}
 
             # prevent collapsing clusters
@@ -266,6 +266,15 @@ class DBSTREAM(base.Clusterer):
 
             if micro_cluster_i.weight * value < weight_weak:
                 micro_clusters.pop(i)
+                self.s.pop(i, None)
+                self.s_t.pop(i, None)
+                # Since self.s and self.s_t always have the same keys and are arranged in ascending orders
+                for j in self.s:
+                    if j < i:
+                        self.s[j].pop(i, None)
+                        self.s_t[j].pop(i, None)
+                    else:
+                        break
 
         # Update microclusters
         self._micro_clusters = micro_clusters
diff --git a/river/cluster/test_dbstream.py b/river/cluster/test_dbstream.py
index 255c71e7a3..6b5e3776f4 100644
--- a/river/cluster/test_dbstream.py
+++ b/river/cluster/test_dbstream.py
@@ -1,11 +1,13 @@
 from __future__ import annotations
 
 import pytest
+from sklearn.datasets import make_blobs
 
+from river import metrics, stream, utils
 from river.cluster import DBSTREAM
 
 
-def build_dbstream(fading_factor=0.001, intersection_factor=0.05):
+def build_dbstream(fading_factor=0.01, intersection_factor=0.05):
     return DBSTREAM(
         fading_factor=fading_factor,
         clustering_threshold=1,
@@ -31,27 +33,44 @@ def test_cluster_formation_and_cleanup():
 
     X = [
         {1: 1},
+        {1: 2},
         {1: 3},
         {1: 3},
         {1: 3},
         {1: 5},
         {1: 7},
         {1: 9},
+        {1: 10},
         {1: 11},
         {1: 11},
+        {1: 12},
         {1: 13},
         {1: 11},
         {1: 15},
+        {1: 15},
+        {1: 16},
+        {1: 17},
+        {1: 17},
         {1: 17},
     ]
 
     for x in X:
         dbstream.learn_one(x)
 
-    assert len(dbstream._micro_clusters) == 3
-    assert_micro_cluster_properties(dbstream.micro_clusters[1], center={1: 3}, last_update=3)
-    assert_micro_cluster_properties(dbstream.micro_clusters[5], center={1: 11}, last_update=10)
-    assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 17}, last_update=12)
+    assert len(dbstream._micro_clusters) == 4
+    assert_micro_cluster_properties(dbstream.micro_clusters[2], center={1: 3}, last_update=4)
+    assert_micro_cluster_properties(dbstream.micro_clusters[7], center={1: 11}, last_update=13)
+    assert_micro_cluster_properties(dbstream.micro_clusters[8], center={1: 15}, last_update=15)
+    assert_micro_cluster_properties(dbstream.micro_clusters[10], center={1: 17}, last_update=19)
+
+    assert dbstream.predict_one({1: 2.0}) == 0
+    assert dbstream.predict_one({1: 13.0}) == 1
+    assert dbstream.predict_one({1: 13 + 1e-10}) == 2
+    assert dbstream.predict_one({1: 16 - 1e-10}) == 2
+    assert dbstream.predict_one({1: 18}) == 3
+
+    assert len(dbstream._clusters) == 4
+    assert dbstream.s == dbstream.s_t == {}
 
 
 def test_with_two_micro_clusters():
@@ -59,24 +78,21 @@ def test_with_two_micro_clusters():
 
     add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25)
     add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25)
-    # Points in the middle of first and second micro-clusters
-    for _ in range(5):
-        dbstream.learn_one({1: 2, 2: 2})
 
-    assert len(dbstream._micro_clusters) == 2
+    assert len(dbstream.micro_clusters) == 2
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56
+        dbstream.micro_clusters[0], center={1: 2.137623, 2: 2.137623}, last_update=51
     )
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[1], center={1: 2.402677, 2: 2.402677}, last_update=56
+        dbstream.micro_clusters[1], center={1: 2.914910, 2: 2.914910}, last_update=51
     )
 
-    assert dbstream.s == {0: {1: 3.995844478090532}}
-    assert dbstream.s_t == {0: {1: 56}}
+    assert dbstream.s == {0: {1: 23.033438964246173}}
+    assert dbstream.s_t == {0: {1: 51}}
 
     dbstream._recluster()
     assert len(dbstream.clusters) == 1
-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.003033, 2: 2.003033})
+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.415239, 2: 2.415239})
 
 
 def test_density_graph_with_three_micro_clusters():
@@ -88,34 +104,38 @@ def test_density_graph_with_three_micro_clusters():
     for _ in range(5):
         dbstream.learn_one({1: 2, 2: 2})
 
+    assert dbstream.s == {0: {1: 23.033438964246173}}
+    assert dbstream.s_t == {0: {1: 51}}
+
     add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25)
     # Points in the middle of second and third micro-clusters
     for _ in range(4):
         dbstream.learn_one({1: 3, 2: 3})
 
     assert len(dbstream._micro_clusters) == 3
-
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[0], center={1: 1.597322, 2: 1.597322}, last_update=56
+        dbstream.micro_clusters[0], center={1: 2.0, 2: 2.0}, last_update=56
     )
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86
+        dbstream.micro_clusters[1], center={1: 3.0, 2: 3.0}, last_update=86
     )
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86
+        dbstream.micro_clusters[2], center={1: 3.982141, 2: 3.982141}, last_update=82
     )
 
-    assert dbstream.s[0] == pytest.approx({1: 3.995844})
-    assert dbstream.s[1] == pytest.approx({2: 2.997921})
-    assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}}
+    assert dbstream.s[0] == pytest.approx({1: 23.033439})
+    assert dbstream.s[1] == pytest.approx({2: 23.033439})
+    assert dbstream.s_t == {0: {1: 51}, 1: {2: 82}}
 
     dbstream._recluster()
     assert len(dbstream.clusters) == 1
-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.489894, 2: 2.489894})
+    print(dbstream.clusters[0].center)
+    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 2.800788, 2: 2.800788})
 
 
 def test_density_graph_with_removed_microcluster():
-    dbstream = build_dbstream(fading_factor=0.1, intersection_factor=0.3)
+    dbstream = build_dbstream(fading_factor=0.1,
+                              intersection_factor=0.3)
 
     add_cluster(dbstream, initial_point={1: 1, 2: 1}, move_towards={1: 1.7, 2: 1.7}, times=25)
     add_cluster(dbstream, initial_point={1: 3, 2: 3}, move_towards={1: 2.3, 2: 2.3}, times=25)
@@ -123,23 +143,70 @@ def test_density_graph_with_removed_microcluster():
     for _ in range(5):
         dbstream.learn_one({1: 2, 2: 2})
 
-    add_cluster(dbstream, initial_point={1: 4, 2: 4}, move_towards={1: 3.3, 2: 3.3}, times=25)
+    add_cluster(dbstream, initial_point={1: 3.5, 2: 3.5}, move_towards={1: 2.9, 2: 2.9}, times=25)
+
     # Points in the middle of second and third micro-clusters
     for _ in range(4):
-        dbstream.learn_one({1: 3, 2: 3})
+        dbstream.learn_one({1: 2.6, 2: 2.6})
 
     assert len(dbstream._micro_clusters) == 2
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[1], center={1: 2.461654, 2: 2.461654}, last_update=86
+        dbstream.micro_clusters[0], center={1: 2.023498, 2: 2.023498}, last_update=86
     )
     assert_micro_cluster_properties(
-        dbstream.micro_clusters[2], center={1: 3.430485, 2: 3.430485}, last_update=86
+        dbstream.micro_clusters[1], center={1: 2.766543, 2: 2.766543}, last_update=86
     )
 
-    assert dbstream.s[0] == pytest.approx({1: 3.615835})
-    assert dbstream.s[1] == pytest.approx({2: 2.803583})
-    assert dbstream.s_t == {0: {1: 56}, 1: {2: 86}}
+    assert dbstream.s == {0: {1: 4.702391097045977}}
+    assert dbstream.s_t == {0: {1: 86}}
 
     dbstream._recluster()
     assert len(dbstream.clusters) == 1
-    assert_micro_cluster_properties(dbstream.clusters[0], center={1: 3.152231, 2: 3.152231})
+    assert_micro_cluster_properties(
+        dbstream.clusters[0], center={1: 2.560647, 2: 2.560647}
+    )
+
+
+def test_dbstream_synthetic_sklearn():
+    centers = [(-10, -10), (-5, -5), (0, 0), (5, 5), (10, 10)]
+    cluster_std = [0.6] * 5
+
+    # Create a dataset with 15000 data points with 5 centers and cluster SD of 0.6 each
+    X, y = make_blobs(n_samples=15_000,
+                      cluster_std=cluster_std,
+                      centers=centers,
+                      n_features=2,
+                      random_state=42)
+
+    dbstream = DBSTREAM(
+        clustering_threshold=2,
+        fading_factor=0.05,
+        intersection_factor=0.1,
+        cleanup_interval=1.0,
+        minimum_weight=1.0,
+    )
+
+    # Use VBeta as the metric to investigate the performance of DBSTREAM
+    v_beta = metrics.VBeta(beta=1.0)
+
+    for x, y_true in stream.iter_array(X, y):
+        dbstream.learn_one(x)
+        y_pred = dbstream.predict_one(x)
+        v_beta.update(y_true, y_pred)
+
+    assert len(dbstream._micro_clusters) == 12
+    assert round(v_beta.get(), 4) == 0.9816
+
+    assert dbstream.s.keys() == dbstream.s_t.keys()
+
+    dbstream._recluster()
+
+    # Check that the resulted cluster centers are close to the expected centers
+    dbstream_expected_centers = {0: {0: 10, 1: 10},
+                                 1: {0: -5, 1: -5},
+                                 2: {0: 0, 1: 0},
+                                 3: {0: 5, 1: 5},
+                                 4: {0: -10, 1: -10}}
+
+    for i in dbstream.centers.keys():
+        assert utils.math.minkowski_distance(dbstream.centers[i], dbstream_expected_centers[i], 2) < 0.2
diff --git a/river/covariance/emp.py b/river/covariance/emp.py
index 2245a78291..b02b208126 100644
--- a/river/covariance/emp.py
+++ b/river/covariance/emp.py
@@ -183,79 +183,27 @@ class EmpiricalCovariance(SymmetricMatrix):
             )
         }
 
-        self._update_from_state(n=n, mean=mean, cov=cov)
-
-    def _update_from_state(self, n: int, mean: dict, cov: float | dict):
-        """Update from state information.
-
-        Parameters
-        ----------
-        n
-            The number of data points.
-        mean
-            A dictionary of variable means.
-        cov
-            A dictionary of covariance or variance values.
-        ddof
-            Degrees of freedom for covariance calculation. Defaults to 1.
-
-        Raises
-        ----------
-            KeyError: If an element in `mean` or `cov` is missing.
-        """
-        for i, j in itertools.combinations(mean.keys(), r=2):
+        for i, j in itertools.combinations(sorted(mean.keys()), r=2):
             try:
                 self[i, j]
             except KeyError:
                 self._cov[i, j] = stats.Cov(self.ddof)
-                if isinstance(cov, dict):
-                    cov_ = cov.get((i, j), cov.get((j, i)))
-                else:
-                    cov_ = cov
-                self._cov[i, j] += stats.Cov._from_state(
-                    n=n,
-                    mean_x=mean[i],
-                    mean_y=mean[j],
-                    cov=cov_,
-                    ddof=self.ddof,
-                )
+            self._cov[i, j] += stats.Cov._from_state(
+                n=n,
+                mean_x=mean[i],
+                mean_y=mean[j],
+                cov=cov.get((i, j), cov.get((j, i))),
+                ddof=self.ddof,
+            )
 
         for i in mean.keys():
             try:
                 self[i, i]
             except KeyError:
                 self._cov[i, i] = stats.Var(self.ddof)
-            if isinstance(cov, dict):
-                if isinstance(cov, dict):
-                    cov_ = cov[i, i]
-                else:
-                    cov_ = cov
-            self._cov[i, i] += stats.Var._from_state(n=n, m=mean[i], sig=cov_, ddof=self.ddof)
-
-    @classmethod
-    def _from_state(cls, n: int, mean: dict, cov: float | dict, *, ddof=1):
-        """Create a new instance from state information.
-
-        Parameters
-        ----------
-        cls
-            The class type.
-        n
-            The number of data points.
-        mean
-            A dictionary of variable means.
-        cov
-            A dictionary of covariance or variance values.
-        ddof
-            Degrees of freedom for covariance calculation. Defaults to 1.
-
-        Returns
-        ----------
-            cls: A new instance of the class with updated covariance matrix.
-        """
-        new = cls(ddof=ddof)
-        new._update_from_state(n=n, mean=mean, cov=cov)
-        return new
+            self._cov[i, i] += stats.Var._from_state(
+                n=n, m=mean[i], sig=cov[i, i], ddof=self.ddof
+            )
 
 
 class EmpiricalPrecision(SymmetricMatrix):