Repository: online-ml/river (branch: main)
Commit date: 2023-11-29T13:09:40Z

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:8629/2023-11-29
  UV_INDEX_URL: http://localhost:8629/2023-11-29


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-7.4.2, 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: xdist-3.3.1, anyio-4.0.0
created: 4/4 workers
4 workers [3416 items]

scheduling tests via LoadScheduling

README.md::README.md 
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=================================== FAILURES ===================================
[31m[1m____________ [doctest] river.anomaly.pad.PredictiveAnomalyDetection ____________[0m
[gw1] linux -- Python 3.12.0 /home/runner/work/river/river/.venv/bin/python
070     ... )
071 
072     >>> PAD = anomaly.PredictiveAnomalyDetection(
073     ...     predictive_model,
074     ...     horizon=1,
075     ...     n_std=3.5,
076     ...     warmup_period=15
077     ... )
078 
079     >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()):
Differences (unified diff with -expected +actual):
    @@ -1,12 +1,5040 @@
    +PredictiveAnomalyDetection (
    +  predictive_model=SNARIMAX (
    +    p=12
    +    d=1
    +    q=12
    +    m=12
    +    sp=0
    +    sd=1
    +    sq=0
    +    regressor=Pipeline (
    +      steps=OrderedDict({'StandardScaler': StandardScaler (
    +  with_std=True
    +), 'LinearRegression': LinearRegression (
    +  optimizer=SGD (
    +    lr=Constant (
    +      learning_rate=0.005
    +    )
    +  )
    +  loss=Squared ()
    +  l2=0.
    +  l1=0.
    +  intercept_init=0.
    +  intercept_lr=Constant (
    +    learning_rate=0.01
    +  )
    +  clip_gradient=1e+12
    +  initializer=Zeros ()
    +)})
    +    )
    +  )
    +  horizon=1
    +  n_std=3.5
    +  warmup_period=15
    +)
     0.0
    +PredictiveAnomalyDetection (
    +  predictive_model=SNARIMAX (
    +    p=12
    +    d=1
    +    q=12
    +    m=12
    +    sp=0
    +    sd=1
    +    sq=0
    +    regressor=Pipeline (
    +      steps=OrderedDict({'StandardScaler': StandardScaler (
    +  with_std=True
    +), 'LinearRegression': LinearRegression (
    +  optimizer=SGD (
    +    lr=Constant (
    +      learning_rate=0.005
    +    )
    +  )
    +  loss=Squared ()
    +  l2=0.
    +  l1=0.
    +  intercept_init=0.
    +  intercept_lr=Constant (
    +    learning_rate=0.01
    +  )
    +  clip_gradient=1e+12
    +  initializer=Zeros ()
    +)})
    +    )
    +  )
    +  horizon=1
    +  n_std=3.5
    +  warmup_period=15
    +)
     0.0
    +PredictiveAnomalyDetection (
    +  predictive_model=SNARIMAX (
    +    p=12
    +    d=1
    +    q=12
    +    m=12
    +    sp=0
    +    sd=1
    +    sq=0
    +    regressor=Pipeline (
    +      steps=OrderedDict({'StandardScaler': StandardScaler (
    +  with_std=True
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/home/runner/work/river/river/river/anomaly/pad.py:79: DocTestFailure
[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_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/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
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
  /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/ucb.py: 1 warning
river/bandit/envs/candy_cane.py: 1 warning
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
  /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/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/bernoulli.py: 4 warnings
river/naive_bayes/test_naive_bayes.py: 108 warnings
  /home/runner/work/river/river/river/naive_bayes/bernoulli.py:288: 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/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()

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)

-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
============================= slowest 10 durations =============================
13.01s call     river/multioutput/chain.py::river.multioutput.chain.ClassifierChain
11.68s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_shuffle_features_no_impact]
10.78s call     river/anomaly/lof.py::river.anomaly.lof.LocalOutlierFactor
8.83s call     river/anomaly/test_lof.py::test_batch_lof_scores
7.19s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_pickling]
6.72s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_disappearing_features]
5.96s call     river/test_estimators.py::test_check_estimator[ProbabilisticClassifierChain(KNNClassifier):check_emerging_features]
5.94s call     river/stats/test_kolmogorov_smirnov.py::test_incremental_ks_statistics
5.90s call     river/ensemble/boosting.py::river.ensemble.boosting.BOLEClassifier
5.87s call     river/neighbors/knn_classifier.py::river.neighbors.knn_classifier.KNNClassifier
[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/anomaly/pad.py::[1mriver.anomaly.pad.PredictiveAnomalyDetection[0m
[31m===== [31m[1m1 failed[0m, [32m3373 passed[0m, [33m42 skipped[0m, [33m179 warnings[0m[31m in 89.35s (0:01:29)[0m[31m ======[0m
##[error]Process completed with exit code 1.


Changed files:
river/anomaly/pad.py

Fix diff:
diff --git a/river/anomaly/pad.py b/river/anomaly/pad.py
index e91f05ed55..c98f4c307f 100644
--- a/river/anomaly/pad.py
+++ b/river/anomaly/pad.py
@@ -76,23 +76,15 @@ class PredictiveAnomalyDetection(anomaly.base.SupervisedAnomalyDetector):
     ...     warmup_period=15
     ... )
 
+    >>> scores = []
+
     >>> for t, (x, y) in enumerate(datasets.AirlinePassengers()):
     ...     score = PAD.score_one(None, y)
-    ...     PAD.learn_one(None, y)
-    ...     print(score)
-    0.0
-    0.0
-    0.0
-    0.0
-    ...
-    5.477831890312668e-05
-    0.07305562392710468
-    0.030122505497227493
-    0.04803795404401492
-    0.014216675596576562
-    0.04789677144570603
-    0.003410489566495498
+    ...     PAD = PAD.learn_one(None, y)
+    ...     scores.append(score)
 
+    >>> print(scores[-3:])
+    [0.014216675596576562, 0.04789677144570603, 0.003410489566495498]
 
     References
     ----------