Remove warnings in tests ⚠️

[wd60622]

If you are motivated to remove warnings from tests, we would appreciate it!

Here are warnings:

test_slow (tests/clv/models/test_beta_geo_beta_binom.py)

tests/clv/models/test_beta_geo_beta_binom.py::TestBetaGeoBetaBinomModel::test_model_convergence[mcmc-0.3]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

test_slow (tests/clv/models/test_pareto_nbd.py)

tests/clv/models/test_pareto_nbd.py: 4000 warnings
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/backends/ndarray.py:116: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
    data[key][draw_idx] = val

test (3.10, false, --ignore tests/mmm --ignore tests/clv)

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_config.py:295
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_config.py:295: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)

pymc_marketing/mlflow.py:180
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mlflow.py:180: FutureWarning: This functionality is experimental and subject to change. If you encounter any issues or have suggestions, please raise them at: https://github.com/pymc-labs/pymc-marketing/issues/new
    warnings.warn(warning_msg, FutureWarning, stacklevel=1)

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011: PydanticDeprecatedSince20: `min_items` is deprecated and will be removed, use `min_length` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/mlflow/pyfunc/utils/data_validation.py:168
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/mlflow/pyfunc/utils/data_validation.py:168: UserWarning: �[33mAdd type hints to the `predict` method to enable data validation and automatic signature inference during model logging. Check https://mlflow.org/docs/latest/model/python_model.html#type-hint-usage-in-pythonmodel for more details.�[0m
    color_warning(

tests/test_mlflow.py::test_autolog_mmm
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
    varsd = varvar / evar / 4

tests/test_mlflow.py::test_clv_fit_mcmc[BetaGeoModel]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
    self.pid = os.fork()

tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
    (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)

tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/arviz/stats/diagnostics.py:992: RuntimeWarning: invalid value encountered in sqrt
    mcse_sd_value = np.sqrt(varsd)

tests/test_prior.py::test_sample_prior
tests/test_prior.py::test_censored_sample_prior
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=25 going forward.
    warnings.warn(

tests/test_prior.py::test_custom_transform
tests/test_prior.py::test_custom_transform_comes_first
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=10 going forward.
    warnings.warn(

test_slow (--ignore tests/clv/models/test_beta_geo_beta_binom.py --ignore tests/clv/models/test_p...

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'arbitrary_types_allowed'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn(

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_config.py:295
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_config.py:295: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1011: PydanticDeprecatedSince20: `min_items` is deprecated and will be removed, use `min_length` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/mlflow/pyfunc/utils/data_validation.py:168
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/mlflow/pyfunc/utils/data_validation.py:168: UserWarning: �[33mAdd type hints to the `predict` method to enable data validation and automatic signature inference during model logging. Check https://mlflow.org/docs/latest/model/python_model.html#type-hint-usage-in-pythonmodel for more details.�[0m
    color_warning(

tests/clv/models/test_beta_geo.py::TestBetaGeoModel::test_model_convergence[mcmc-0.1]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

test (3.10, false, tests/mmm --ignore tests/mmm/test_tvp.py --ignore tests/mmm/test_budget_optimi...

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'arbitrary_types_allowed'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn(

pymc_marketing/mmm/multidimensional.py:58
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/multidimensional.py:58: FutureWarning: This functionality is experimental and subject to change. If you encounter any issues or have suggestions, please raise them at: https://github.com/pymc-labs/pymc-marketing/issues/new
    warnings.warn(warning_msg, FutureWarning, stacklevel=1)

tests/mmm/components/test_adstock.py::test_apply[x0-None-weibull_pdf]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[simple_backdoor_path]
tests/mmm/test_causal.py::test_compute_adjustment_sets[relevant_control]
tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
tests/mmm/test_causal.py::test_compute_adjustment_sets[no_controls]
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dowhy/causal_model.py:583: UserWarning: 3 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py: 8 warnings
tests/mmm/test_mmm.py: 4 warnings
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dowhy/causal_model.py:588: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
    logger.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[multiple_confounders]
tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[multiple_treatments]
tests/mmm/test_causal.py::test_compute_adjustment_sets[mixed_controls]
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dowhy/causal_model.py:583: UserWarning: 4 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[no_confounders]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/dowhy/causal_model.py:583: UserWarning: 2 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Columns {'W'} are not in the adjustment set. Controls are being modified.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Column Z in adjustment set not found in data.
                      Not controlling for this may induce bias in treatment effect estimates.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_causal.py::test_compute_adjustment_sets[mixed_controls]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Columns {'W', 'V'} are not in the adjustment set. Controls are being modified.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_fourier.py: 21 warnings
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=10 going forward.
    warnings.warn(

tests/mmm/test_fourier.py::test_sample_curve_use_dates[yearly]
tests/mmm/test_fourier.py::test_sample_curve_use_dates[monthly]
tests/mmm/test_fourier.py::test_sample_curve_use_dates[weekly]
tests/mmm/test_fourier.py::test_sample_curve_same_size[yearly]
tests/mmm/test_fourier.py::test_sample_curve_same_size[monthly]
tests/mmm/test_fourier.py::test_sample_curve_same_size[weekly]
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/fourier.py:519: FutureWarning: Non-integer 'periods' in pd.date_range, pd.timedelta_range, pd.period_range, and pd.interval_range are deprecated and will raise in a future version.
    date_range = pd.date_range(

tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/mmm.py:223: UserWarning: Columns {'control_2'} are not in the adjustment set. Controls are being modified.
    self.control_columns = self.causal_graphical_model.compute_adjustment_sets(

tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: UserWarning: Yearly seasonality excluded as it's not required for adjustment.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:537: DeprecationWarning: This method is deprecated and will be removed in a future version. Use optimize_budget() instead.
    inference_data = mmm_fitted.allocate_budget_to_maximize_response(

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/budget_optimizer.py:215: UserWarning: Using default equality constraint
    self.set_constraints(

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response_bad_noise_level
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:583: DeprecationWarning: This method is deprecated and will be removed in a future version. Use optimize_budget() instead.
    mmm_fitted.allocate_budget_to_maximize_response(

tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[scaled-prior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[scaled-posterior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[original-scale-prior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[original-scale-posterior_predictive]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:780: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
    assert dataset.dims["date"] == 135

tests/mmm/test_multidimensional.py::test_fit[tvp intercept only, no fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvp intercept only, no fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvp media only with fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvp media only with fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-County model]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/gp/hsgp_approx.py:297: DeprecationWarning: The drop_first argument will be deprecated in future versions. See https://github.com/pymc-devs/pymc/pull/6877
    warnings.warn(

tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-default_transform-seed_42-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-default_transform-seed_0-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-target_transform-seed_42-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-target_transform-seed_0-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_plotting.py:285: UserWarning: 
                  Each contribution value must be either all positive or all negative.
                  Try deselecting variables with negative contributions.
                  
    assert isinstance(func(**kwargs_plot), plt.Figure)

test (3.10, false, tests/mmm/test_tvp.py tests/mmm/test_budget_optimizer.py tests/mmm/test_hsgp.p...

../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'arbitrary_types_allowed'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn(

tests/mmm/test_tvp.py::test_time_varying_prior
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/mmm/test_budget_optimizer.py::test_allocate_budget[default_minimizer_kwargs]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:184: UserWarning: No budget bounds provided. Using default bounds (0, total_budget) for each channel.
    optimal_budgets, optimization_res = optimizer.allocate_budget(

tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_response_constraint[budget=10->resp=5]
tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_response_constraint[budget=50->resp=10]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:393: UserWarning: No budget bounds provided. Using default bounds (0, total_budget) for each channel.
    allocation, res = optimizer.allocate_budget(

tests/mmm/test_hsgp.py::test_curve_workflow[HSGP]
tests/mmm/test_hsgp.py::test_higher_dimension_hsgp
tests/mmm/test_hsgp.py::test_hsgp_with_shared_data
tests/mmm/test_hsgp.py::test_soft_plus_hsgp_is_centered_around_1
tests/mmm/test_hsgp.py::test_soft_plus_hsgp_continous_with_new_data
tests/mmm/test_hsgp.py::test_hsgp_with_transform
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/gp/hsgp_approx.py:297: DeprecationWarning: The drop_first argument will be deprecated in future versions. See https://github.com/pymc-devs/pymc/pull/6877
    warnings.warn(

test (3.10, false, tests/clv/models/test_pareto_nbd.py)

tests/clv/models/test_pareto_nbd.py::TestParetoNBDModelWithCovariates::test_extract_predictive_covariates
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_pareto_nbd.py::TestParetoNBDModelWithCovariates::test_covariate_model_convergence
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=1 going forward.
    warnings.warn(

test (3.10, false, tests/clv/models/test_beta_geo_beta_binom.py)

tests/clv/models/test_beta_geo_beta_binom.py::TestBetaGeoBetaBinomModel::test_distribution_new_customer
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

test (3.10, false, tests/clv/models/test_beta_geo.py)

tests/clv/models/test_beta_geo.py::TestBetaGeoModel::test_numerically_stable_logp[200-38-100.7957]
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'a_prior' in model_config is deprecated and will be removed in future versions.Use 'a' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'b_prior' in model_config is deprecated and will be removed in future versions.Use 'b' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'r_prior' in model_config is deprecated and will be removed in future versions.Use 'r' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'alpha_prior' in model_config is deprecated and will be removed in future versions.Use 'alpha' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'purchase_coefficient_prior' in model_config is deprecated and will be removed in future versions.Use 'purchase_coefficient' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'dropout_coefficient_prior' in model_config is deprecated and will be removed in future versions.Use 'dropout_coefficient' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/clv/models/basic.py:134: DeprecationWarning: 'fit_method' is deprecated and will be removed in a future release. Use 'method' instead.
    warnings.warn(

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'phi_dropout_prior' in model_config is deprecated and will be removed in future versions.Use 'phi_dropout' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'kappa_dropout_prior' in model_config is deprecated and will be removed in future versions.Use 'kappa_dropout' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

test (3.10, false, tests/clv/models/test_shifted_beta_geo.py)

test (3.10, false, tests/clv/models/test_basic.py)

tests/clv/models/test_basic.py::TestCLVModel::test_fit_advi
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/clv/models/basic.py:151: UserWarning: The 'chains' parameter must be 1 with 'advi'. Sampling only 1 chain despite the provided parameter.
    approx, idata = self._fit_approx(method="advi", **kwargs)

tests/clv/models/test_basic.py::TestCLVModel::test_fit_advi
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_basic.py::TestCLVModel::test_deprecation_warning_on_old_config
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/model_config.py:156: DeprecationWarning: x is automatically converted to Prior("Normal", mu=0, sigma=1). Use the Prior class to avoid this warning.
    name: handle_prior_config(name, prior_config)

test (3.10, false, tests/clv/test_distributions.py)

test (3.10, false, tests/clv/ --ignore=tests/clv/models/test_pareto_nbd.py --ignore=tests/clv/mod...

tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_new_customer_spend[True]
tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_new_customer_spend[False]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/clv/models/test_gamma_gamma.py:244: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    fake_fit = pm.sample_prior_predictive(

tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_save_load
  /home/runner/work/pymc-marketing/pymc-marketing/tests/conftest.py:134: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    idata: InferenceData = pm.sample_prior_predictive(

tests/clv/models/test_modified_beta_geo.py::TestModifiedBetaGeoModel::test_distribution_new_customer
  /opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/test_utils.py::TestCustomerLifetimeValue::test_customer_lifetime_value_as_gg_method[fitted_bg]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/clv/test_utils.py:78: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    fake_fit = pm.sample_prior_predictive(

test (3.12, true, --ignore tests/mmm --ignore tests/clv)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_config.py:295
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_config.py:295: PydanticDeprecatedSince20: Support for class-based `config` is deprecated, use ConfigDict instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warnings.warn(DEPRECATION_MESSAGE, DeprecationWarning)

pymc_marketing/mlflow.py:180
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mlflow.py:180: FutureWarning: This functionality is experimental and subject to change. If you encounter any issues or have suggestions, please raise them at: https://github.com/pymc-labs/pymc-marketing/issues/new
    warnings.warn(warning_msg, FutureWarning, stacklevel=1)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1011
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1011
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1011: PydanticDeprecatedSince20: `min_items` is deprecated and will be removed, use `min_length` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn('`min_items` is deprecated and will be removed, use `min_length` instead', DeprecationWarning)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/mlflow/pyfunc/utils/data_validation.py:168
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/mlflow/pyfunc/utils/data_validation.py:168: UserWarning: �[33mAdd type hints to the `predict` method to enable data validation and automatic signature inference during model logging. Check https://mlflow.org/docs/latest/model/python_model.html#type-hint-usage-in-pythonmodel for more details.�[0m
    color_warning(

tests/test_mlflow.py::test_autolog_pymc_model[pymc]
tests/test_mlflow.py::test_autolog_pymc_model[pymc]
tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=2799) is multi-threaded, use of fork() may lead to deadlocks in the child.
    self.pid = os.fork()

tests/test_mlflow.py::test_autolog_mmm
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/arviz/stats/diagnostics.py:991: RuntimeWarning: invalid value encountered in scalar divide
    varsd = varvar / evar / 4

tests/test_mlflow.py::test_clv_fit_mcmc[BetaGeoModel]
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
    self.pid = os.fork()

tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/arviz/stats/diagnostics.py:596: RuntimeWarning: invalid value encountered in scalar divide
    (between_chain_variance / within_chain_variance + num_samples - 1) / (num_samples)

tests/test_mlflow.py::test_logging_callback
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/arviz/stats/diagnostics.py:992: RuntimeWarning: invalid value encountered in sqrt
    mcse_sd_value = np.sqrt(varsd)

tests/test_prior.py::test_sample_prior
tests/test_prior.py::test_censored_sample_prior
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=25 going forward.
    warnings.warn(

tests/test_prior.py::test_custom_transform
tests/test_prior.py::test_custom_transform_comes_first
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=10 going forward.
    warnings.warn(

test (3.12, true, tests/mmm --ignore tests/mmm/test_tvp.py --ignore tests/mmm/test_budget_optimiz...

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'arbitrary_types_allowed'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn(

pymc_marketing/mmm/multidimensional.py:58
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/multidimensional.py:58: FutureWarning: This functionality is experimental and subject to change. If you encounter any issues or have suggestions, please raise them at: https://github.com/pymc-labs/pymc-marketing/issues/new
    warnings.warn(warning_msg, FutureWarning, stacklevel=1)

tests/mmm/components/test_adstock.py::test_apply[x0-None-weibull_pdf]
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[simple_backdoor_path]
tests/mmm/test_causal.py::test_compute_adjustment_sets[relevant_control]
tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
tests/mmm/test_causal.py::test_compute_adjustment_sets[no_controls]
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dowhy/causal_model.py:583: UserWarning: 3 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py: 8 warnings
tests/mmm/test_mmm.py: 4 warnings
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dowhy/causal_model.py:588: DeprecationWarning: The 'warn' method is deprecated, use 'warning' instead
    logger.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[multiple_confounders]
tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[multiple_treatments]
tests/mmm/test_causal.py::test_compute_adjustment_sets[mixed_controls]
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dowhy/causal_model.py:583: UserWarning: 4 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py::test_get_unique_adjustment_nodes[no_confounders]
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/dowhy/causal_model.py:583: UserWarning: 2 variables are assumed unobserved because they are not in the dataset. Configure the logging level to `logging.WARNING` or higher for additional details.
    warnings.warn(

tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Columns {'W'} are not in the adjustment set. Controls are being modified.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_causal.py::test_compute_adjustment_sets[irrelevant_control]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Column Z in adjustment set not found in data.
                      Not controlling for this may induce bias in treatment effect estimates.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_causal.py::test_compute_adjustment_sets[mixed_controls]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_causal.py:170: UserWarning: Columns {'V', 'W'} are not in the adjustment set. Controls are being modified.
    adjusted_controls = causal_model.compute_adjustment_sets(

tests/mmm/test_fourier.py: 21 warnings
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=10 going forward.
    warnings.warn(

tests/mmm/test_fourier.py::test_sample_curve_use_dates[yearly]
tests/mmm/test_fourier.py::test_sample_curve_use_dates[monthly]
tests/mmm/test_fourier.py::test_sample_curve_same_size[yearly]
tests/mmm/test_fourier.py::test_sample_curve_same_size[monthly]
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/fourier.py:519: FutureWarning: Non-integer 'periods' in pd.date_range, pd.timedelta_range, pd.period_range, and pd.interval_range are deprecated and will raise in a future version.
    date_range = pd.date_range(

tests/mmm/test_lift_test.py::test_works_with_negative_delta
tests/mmm/test_lift_test.py::test_works_with_negative_delta
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/multiprocessing/popen_fork.py:66: DeprecationWarning: This process (pid=2760) is multi-threaded, use of fork() may lead to deadlocks in the child.
    self.pid = os.fork()

tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/mmm.py:223: UserWarning: Columns {'control_2'} are not in the adjustment set. Controls are being modified.
    self.control_columns = self.causal_graphical_model.compute_adjustment_sets(

tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_serializes_and_deserializes_dag_and_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_initialization
tests/mmm/test_mmm.py::TestMMM::test_mmm_causal_attributes_default_treatment_nodes
tests/mmm/test_mmm.py::TestMMM::test_mmm_adjustment_set_updates_control_columns
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: UserWarning: Yearly seasonality excluded as it's not required for adjustment.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/mmm/test_mmm.py::TestMMM::test_channel_contributions_forward_pass_recovers_contribution
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/contextlib.py:81: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    return func(*args, **kwds)

tests/mmm/test_mmm.py::TestMMM::test_channel_contributions_forward_pass_recovers_contribution
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/contextlib.py:81: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    return func(*args, **kwds)

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:537: DeprecationWarning: This method is deprecated and will be removed in a future version. Use optimize_budget() instead.
    inference_data = mmm_fitted.allocate_budget_to_maximize_response(

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/mmm/budget_optimizer.py:215: UserWarning: Using default equality constraint
    self.set_constraints(

tests/mmm/test_mmm.py::TestMMM::test_allocate_budget_to_maximize_response_bad_noise_level
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:583: DeprecationWarning: This method is deprecated and will be removed in a future version. Use optimize_budget() instead.
    mmm_fitted.allocate_budget_to_maximize_response(

tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[scaled-prior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[scaled-posterior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[original-scale-prior_predictive]
tests/mmm/test_mmm.py::TestMMM::test_get_group_predictive_data[original-scale-posterior_predictive]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_mmm.py:780: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
    assert dataset.dims["date"] == 135

tests/mmm/test_multidimensional.py::test_fit[tvp intercept only, no fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvp intercept only, no fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvp media only with fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvp media only with fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-Marginal model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-County model]
tests/mmm/test_multidimensional.py::test_fit[tvps and fourier-County model]
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/gp/hsgp_approx.py:297: DeprecationWarning: The drop_first argument will be deprecated in future versions. See https://github.com/pymc-devs/pymc/pull/6877
    warnings.warn(

tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-default_transform-seed_42-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-default_transform-seed_0-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-target_transform-seed_42-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
tests/mmm/test_plotting.py::TestBasePlotting::test_plots[with_controls-target_transform-seed_0-plot_grouped_contribution_breakdown_over_time-kwargs_plot35]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_plotting.py:285: UserWarning: 
                  Each contribution value must be either all positive or all negative.
                  Try deselecting variables with negative contributions.
                  
    assert isinstance(func(**kwargs_plot), plt.Figure)

test (3.12, true, tests/mmm/test_tvp.py tests/mmm/test_budget_optimizer.py tests/mmm/test_hsgp.py...

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/fields.py:1042: PydanticDeprecatedSince20: Using extra keyword arguments on `Field` is deprecated and will be removed. Use `json_schema_extra` instead. (Extra keys: 'arbitrary_types_allowed'). Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.10/migration/
    warn(

tests/mmm/test_tvp.py::test_time_varying_prior
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/mmm/test_budget_optimizer.py::test_allocate_budget[default_minimizer_kwargs]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:184: UserWarning: No budget bounds provided. Using default bounds (0, total_budget) for each channel.
    optimal_budgets, optimization_res = optimizer.allocate_budget(

tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_minimize_args
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:235: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=kwargs["x0"], y=np.array([50.0, 50.0]))

tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_minimize_args
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:235: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=kwargs["x0"], y=np.array([50.0, 50.0]))

tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_response_constraint[budget=10->resp=5]
tests/mmm/test_budget_optimizer.py::test_allocate_budget_custom_response_constraint[budget=50->resp=10]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_budget_optimizer.py:393: UserWarning: No budget bounds provided. Using default bounds (0, total_budget) for each channel.
    allocation, res = optimizer.allocate_budget(

tests/mmm/test_hsgp.py::test_curve_workflow[HSGP]
tests/mmm/test_hsgp.py::test_higher_dimension_hsgp
tests/mmm/test_hsgp.py::test_hsgp_with_shared_data
tests/mmm/test_hsgp.py::test_soft_plus_hsgp_is_centered_around_1
tests/mmm/test_hsgp.py::test_soft_plus_hsgp_continous_with_new_data
tests/mmm/test_hsgp.py::test_hsgp_with_transform
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/gp/hsgp_approx.py:297: DeprecationWarning: The drop_first argument will be deprecated in future versions. See https://github.com/pymc-devs/pymc/pull/6877
    warnings.warn(

tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[After]
tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[Before]
tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[Overlap]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:132: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=x, y=y.eval())

tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[After]
tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[Before]
tests/mmm/test_transformers.py::TestsAdstockTransformers::test_geometric_adstock_x_zero[Overlap]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:132: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=x, y=y.eval())

tests/mmm/test_transformers.py::TestsAdstockTransformers::test_delayed_adstock_x_zero
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:183: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=x, y=y.eval())

tests/mmm/test_transformers.py::TestsAdstockTransformers::test_delayed_adstock_x_zero
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:183: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=x, y=y.eval())

tests/mmm/test_transformers.py::TestSaturationTransformers::test_logistic_saturation_lam_zero
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:330: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=np.zeros(shape=(100)), y=y.eval())

tests/mmm/test_transformers.py::TestSaturationTransformers::test_logistic_saturation_lam_zero
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:330: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(x=np.zeros(shape=(100)), y=y.eval())

tests/mmm/test_transformers.py::TestSaturationTransformers::test_logistic_saturation_lam_one
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:335: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(

tests/mmm/test_transformers.py::TestSaturationTransformers::test_logistic_saturation_lam_one
  /home/runner/work/pymc-marketing/pymc-marketing/tests/mmm/test_transformers.py:335: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    np.testing.assert_array_equal(

tests/mmm/test_transformers.py: 10 warnings
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/contextlib.py:81: DeprecationWarning: Use of keyword argument `x` is deprecated and replaced by `actual`. Support for `x` will be removed in NumPy 2.2.0.
    return func(*args, **kwds)

tests/mmm/test_transformers.py: 10 warnings
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/contextlib.py:81: DeprecationWarning: Use of keyword argument `y` is deprecated and replaced by `desired`. Support for `y` will be removed in NumPy 2.2.0.
    return func(*args, **kwds)

test (3.12, true, tests/clv/models/test_pareto_nbd.py)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7: DeprecationWarning: numpy.core.einsumfunc is deprecated and has been renamed to numpy._core.einsumfunc. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.einsumfunc._parse_einsum_input.
    from numpy.core.einsumfunc import _parse_einsum_input

tests/clv/models/test_pareto_nbd.py::TestParetoNBDModelWithCovariates::test_extract_predictive_covariates
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_pareto_nbd.py::TestParetoNBDModelWithCovariates::test_covariate_model_convergence
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pymc/sampling/forward.py:427: DeprecationWarning: The samples argument has been deprecated in favor of draws. Use draws=1 going forward.
    warnings.warn(

test (3.12, true, tests/clv/models/test_beta_geo_beta_binom.py)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7: DeprecationWarning: numpy.core.einsumfunc is deprecated and has been renamed to numpy._core.einsumfunc. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.einsumfunc._parse_einsum_input.
    from numpy.core.einsumfunc import _parse_einsum_input

tests/clv/models/test_beta_geo_beta_binom.py::TestBetaGeoBetaBinomModel::test_distribution_new_customer
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

test (3.12, true, tests/clv/models/test_beta_geo.py)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7: DeprecationWarning: numpy.core.einsumfunc is deprecated and has been renamed to numpy._core.einsumfunc. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.einsumfunc._parse_einsum_input.
    from numpy.core.einsumfunc import _parse_einsum_input

tests/clv/models/test_beta_geo.py::TestBetaGeoModel::test_numerically_stable_logp[200-38-100.7957]
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'a_prior' in model_config is deprecated and will be removed in future versions.Use 'a' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_extract_predictive_covariates
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'b_prior' in model_config is deprecated and will be removed in future versions.Use 'b' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'r_prior' in model_config is deprecated and will be removed in future versions.Use 'r' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'alpha_prior' in model_config is deprecated and will be removed in future versions.Use 'alpha' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'purchase_coefficient_prior' in model_config is deprecated and will be removed in future versions.Use 'purchase_coefficient' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'dropout_coefficient_prior' in model_config is deprecated and will be removed in future versions.Use 'dropout_coefficient' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_a_b
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/clv/models/basic.py:134: DeprecationWarning: 'fit_method' is deprecated and will be removed in a future release. Use 'method' instead.
    warnings.warn(

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'phi_dropout_prior' in model_config is deprecated and will be removed in future versions.Use 'phi_dropout' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

tests/clv/models/test_beta_geo.py::TestBetaGeoModelWithCovariates::test_covariate_model_convergence_phi_kappa
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pydantic/_internal/_validate_call.py:111: DeprecationWarning: The key 'kappa_dropout_prior' in model_config is deprecated and will be removed in future versions.Use 'kappa_dropout' instead.
    res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))

test (3.12, true, tests/clv/models/test_shifted_beta_geo.py)

test (3.12, true, tests/clv/models/test_basic.py)

tests/clv/models/test_basic.py::TestCLVModel::test_fit_advi
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/clv/models/basic.py:151: UserWarning: The 'chains' parameter must be 1 with 'advi'. Sampling only 1 chain despite the provided parameter.
    approx, idata = self._fit_approx(method="advi", **kwargs)

tests/clv/models/test_basic.py::TestCLVModel::test_fit_advi
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/models/test_basic.py::TestCLVModel::test_deprecation_warning_on_old_config
  /home/runner/work/pymc-marketing/pymc-marketing/pymc_marketing/model_config.py:156: DeprecationWarning: x is automatically converted to Prior("Normal", mu=0, sigma=1). Use the Prior class to avoid this warning.
    name: handle_prior_config(name, prior_config)

test (3.12, true, tests/clv/test_distributions.py)

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7: DeprecationWarning: numpy.core.einsumfunc is deprecated and has been renamed to numpy._core.einsumfunc. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.einsumfunc._parse_einsum_input.
    from numpy.core.einsumfunc import _parse_einsum_input

test (3.12, true, tests/clv/ --ignore=tests/clv/models/test_pareto_nbd.py --ignore=tests/clv/mode...

../../../../../opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/autograd/numpy/numpy_wrapper.py:7: DeprecationWarning: numpy.core.einsumfunc is deprecated and has been renamed to numpy._core.einsumfunc. The numpy._core namespace contains private NumPy internals and its use is discouraged, as NumPy internals can change without warning in any release. In practice, most real-world usage of numpy.core is to access functionality in the public NumPy API. If that is the case, use the public NumPy API. If not, you are using NumPy internals. If you would still like to access an internal attribute, use numpy._core.einsumfunc._parse_einsum_input.
    from numpy.core.einsumfunc import _parse_einsum_input

tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_new_customer_spend[True]
tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_new_customer_spend[False]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/clv/models/test_gamma_gamma.py:244: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    fake_fit = pm.sample_prior_predictive(

tests/clv/models/test_gamma_gamma.py::TestGammaGammaModel::test_save_load
  /home/runner/work/pymc-marketing/pymc-marketing/tests/conftest.py:134: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    idata: InferenceData = pm.sample_prior_predictive(

tests/clv/models/test_modified_beta_geo.py::TestModifiedBetaGeoModel::test_distribution_new_customer
  /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pytensor/link/c/cmodule.py:2959: UserWarning: PyTensor could not link to a BLAS installation. Operations that might benefit from BLAS will be severely degraded.
  This usually happens when PyTensor is installed via pip. We recommend it be installed via conda/mamba/pixi instead.
  Alternatively, you can use an experimental backend such as Numba or JAX that perform their own BLAS optimizations, by setting `pytensor.config.mode == 'NUMBA'` or passing `mode='NUMBA'` when compiling a PyTensor function.
  For more options and details see https://pytensor.readthedocs.io/en/latest/troubleshooting.html#how-do-i-configure-test-my-blas-library
    warnings.warn(

tests/clv/test_utils.py::TestCustomerLifetimeValue::test_customer_lifetime_value_as_gg_method[fitted_bg]
  /home/runner/work/pymc-marketing/pymc-marketing/tests/clv/test_utils.py:78: UserWarning: The effect of Potentials on other parameters is ignored during prior predictive sampling. This is likely to lead to invalid or biased predictive samples.
    fake_fit = pm.sample_prior_predictive(

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Latest run date: 2025-03-15