Minimal Fix for Broken Tests (#130)

* typo

* Minimal Fix for Tests

Author: LarsKue

.github/workflows/docs.yml

@@ -1,7 +1,7 @@
 # From https://github.com/eeholmes/readthedoc-test/blob/main/.github/workflows/docs_pages.yml
 name: docs
 
-# execute this workflow automatically when a we push to master
+# execute this workflow automatically when we push to master
 on:
   push:
     branches:

tests/test_sensitivity.py

@@ -1,12 +1,11 @@
+from copy import deepcopy
 from functools import partial
 from unittest.mock import MagicMock, Mock
 
 import numpy as np
 import pytest
 import tensorflow as tf
 
-from copy import deepcopy
-
 from bayesflow import computational_utilities, sensitivity, simulation
 from tests.test_trainers import _create_training_setup, _prior, _simulator
 
@@ -26,7 +25,7 @@ def _trainer_amortizer_sample_mock(input_dict, n_samples):
 
 
 class TestMisspecificationExperiment:
-    def setup(self):
+    def setup_method(self):
         self.trainer = _create_training_setup(mode="posterior")
 
         # Mock the approximate posterior sampling of the amortizer, return np.ones of shape (n_sim, n_samples)

tests/test_trainers.py

@@ -68,8 +68,9 @@ def _create_training_setup(mode):
     trainer = Trainer(generative_model=model, amortizer=amortizer)
     return trainer
 
+
 class TestTrainer:
-    def setup(self):
+    def setup_method(self):
         trainer_posterior = _create_training_setup("posterior")
         trainer_likelihood = _create_training_setup("likelihood")
         trainer_joint = _create_training_setup("joint")
@@ -111,7 +112,6 @@ def test_train_online(self, mode, reuse_optimizer, validation_sims):
             assert type(h["train_losses"]) is DataFrame
             assert type(h["val_losses"]) is DataFrame
 
-
     @pytest.mark.parametrize("mode", ["posterior", "joint"])
     @pytest.mark.parametrize("reuse_optimizer", [True, False])
     @pytest.mark.parametrize("validation_sims", [20, None])
@@ -202,34 +202,3 @@ def test_train_rounds(self, mode, reuse_optimizer, validation_sims):
             assert type(h) is dict
             assert type(h["train_losses"]) is DataFrame
             assert type(h["val_losses"]) is DataFrame
-
-    @pytest.mark.parametrize("reference_data", [None, "dict", "numpy"])
-    @pytest.mark.parametrize("observed_data_type", ["dict", "numpy"])
-    @pytest.mark.parametrize("bootstrap", [True, False])
-    def mmd_hypothesis_test_no_reference(self, reference_data, observed_data_type, bootstrap):
-        trainer = self.trainers["posterior"]
-        _ = trainer.train_online(epochs=1, iterations_per_epoch=1, batch_size=4)
-
-        num_reference_simulations = 10
-        num_observed_simulations = 2
-        num_null_samples = 5
-
-        if reference_data is None:
-            if reference_data == "dict":
-                reference_data = trainer.configurator(trainer.generative_model(num_reference_simulations))
-            elif reference_data == "numpy":
-                reference_data = trainer.configurator(trainer.generative_model(num_reference_simulations))['summary_conditions']
-
-        if observed_data_type == "dict":
-            observed_data = trainer.configurator(trainer.generative_model(num_observed_simulations))
-        elif observed_data_type == "numpy":
-            observed_data = trainer.configurator(trainer.generative_model(num_observed_simulations))['summary_conditions']
-
-        MMD_sampling_distribution, MMD_observed = trainer.mmd_hypothesis_test(observed_data=observed_data,
-                                                                              reference_data=reference_data,
-                                                                              num_reference_simulations=num_reference_simulations,
-                                                                              num_null_samples=num_null_samples,
-                                                                              bootstrap=bootstrap)
-
-        assert MMD_sampling_distribution.shape[0] == num_reference_simulations
-        assert np.all(MMD_sampling_distribution > 0)