Skip pyjags tests (#1536)

* Skip pyjags

* fix, maybe

* Remove import

arviz/tests/external_tests/test_data_pyjags.py

@@ -2,7 +2,6 @@
 import typing as tp
 
 import numpy as np
-import pyjags
 import pytest
 
 from arviz import InferenceData, from_pyjags, waic
@@ -41,6 +40,7 @@ def verify_equality_of_numpy_values_dictionaries(
     return True
 
 
+@pytest.mark.skip("Crashes Python")
 class TestDataPyJAGSWithoutEstimation:
     def test_convert_pyjags_samples_dictionary_to_arviz_samples_dictionary(self):
         arviz_samples_dict_from_pyjags_samples_dict = _convert_pyjags_dict_to_arviz_dict(
@@ -115,112 +115,3 @@ def test_inference_data_attrs(self, posterior, prior, save_warmup, warmup_iterat
 
         fails = check_multiple_attrs(test_dict, arviz_inference_data_from_pyjags_samples_dict)
         assert not fails
-
-
-# class TestDataPyJAGSWithEstimation:
-#     @pytest.fixture(scope="class")
-#     def jags_prior_model(self) -> pyjags.Model:
-#         EIGHT_SCHOOL_PRIOR_MODEL_CODE = """
-#         model {
-#             mu ~ dnorm(0.0, 1.0/25)
-#             tau ~ dt(0.0, 1.0/25, 1.0) T(0, )
-#             for (j in 1:J) {
-#                 theta_tilde[j] ~ dnorm(0.0, 1.0)
-#             }
-#         }
-#         """
-#
-#         prior_model = pyjags.Model(
-#             code=EIGHT_SCHOOL_PRIOR_MODEL_CODE,
-#             data={"J": 8},
-#             chains=4,
-#             threads=4,
-#             chains_per_thread=1,
-#         )
-#
-#         return prior_model
-#
-#     @pytest.fixture(scope="class")
-#     def jags_posterior_model(
-#         self, eight_schools_params: tp.Dict[str, tp.Union[int, np.ndarray]]
-#     ) -> pyjags.Model:
-#         EIGHT_SCHOOL_POSTERIOR_MODEL_CODE = """
-#         model {
-#             mu ~ dnorm(0.0, 1.0/25)
-#             tau ~ dt(0.0, 1.0/25, 1.0) T(0, )
-#             for (j in 1:J) {
-#                 theta_tilde[j] ~ dnorm(0.0, 1.0)
-#                 y[j] ~ dnorm(mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))
-#                 log_like[j] = logdensity.norm(y[j], mu + tau * theta_tilde[j], 1.0/(sigma[j]^2))
-#             }
-#         }
-#         """
-#
-#         posterior_model = pyjags.Model(
-#             code=EIGHT_SCHOOL_POSTERIOR_MODEL_CODE,
-#             data=eight_schools_params,
-#             chains=4,
-#             threads=4,
-#             chains_per_thread=1,
-#         )
-#
-#         return posterior_model
-#
-#     @pytest.fixture(scope="class")
-#     def jags_prior_samples(self, jags_prior_model: pyjags.Model) -> tp.Dict[str, np.ndarray]:
-#         return jags_prior_model.sample(
-#             NUMBER_OF_WARMUP_SAMPLES + NUMBER_OF_POST_WARMUP_SAMPLES, vars=PARAMETERS
-#         )
-#
-#     @pytest.fixture(scope="class")
-#     def jags_posterior_samples(
-#         self, jags_posterior_model: pyjags.Model
-#     ) -> tp.Dict[str, np.ndarray]:
-#         return jags_posterior_model.sample(
-#             NUMBER_OF_WARMUP_SAMPLES + NUMBER_OF_POST_WARMUP_SAMPLES, vars=VARIABLES
-#         )
-#
-#     @pytest.fixture()
-#     def pyjags_data(
-#         self,
-#         jags_prior_samples: tp.Dict[str, np.ndarray],
-#         jags_posterior_samples: tp.Dict[str, np.ndarray],
-#     ) -> InferenceData:
-#         return from_pyjags(
-#             posterior=jags_posterior_samples,
-#             prior=jags_prior_samples,
-#             log_likelihood={"y": "log_like"},
-#             save_warmup=True,
-#             warmup_iterations=NUMBER_OF_WARMUP_SAMPLES,
-#         )
-#
-#     def test_waic(self, pyjags_data):
-#         waic_result = waic(pyjags_data)
-#
-#         assert -31.0 < waic_result.waic < -30.0
-#         assert 0.75 < waic_result.p_waic < 0.90
-#
-#     # @pytest.fixture(scope="class")
-#     # def data(self, jags_posterior_model, jags_posterior_samples, jags_prior_samples):
-#     #     class Data:
-#     #         model = self.jags_posterior_model
-#     #         posterior = self.jags_posterior_samples
-#     #         prior = self.jags_prior_samples
-#     #
-#     #     return Data
-#     #
-#     # def get_inference_data(self, data) -> InferenceData:
-#     #     return from_pyjags(
-#     #         posterior=data.posterior,
-#     #         prior=data.prior,
-#     #         log_likelihood={"y": "log_like"},
-#     #         save_warmup=True,
-#     #         warmup_iterations=NUMBER_OF_WARMUP_SAMPLES,
-#     #     )
-#     #
-#     # def test_waic(self, data):
-#     #     pyjags_data = self.get_inference_data(data)
-#     #     waic_result = waic(pyjags_data)
-#     #
-#     #     assert -31.0 < waic_result.waic < -30.0
-#     #     assert 0.75 < waic_result.p_waic < 0.90