add rudimentary test for loss plot

Author: LarsKue

tests/test_diagnostics/conftest.py

@@ -1,3 +1,4 @@
+import keras
 import numpy as np
 import pytest
 from bayesflow.utils.numpy_utils import softmax
@@ -61,3 +62,19 @@ def pred_models(true_models):
     pred_models = np.random.normal(loc=true_models)
     pred_models = softmax(pred_models, axis=-1)
     return pred_models
+
+
+@pytest.fixture()
+def history():
+    h = keras.callbacks.History()
+
+    step = np.linspace(0, 1, 10_000)
+    train_loss = (1.0 - step) ** 2 + np.random.normal(loc=0, scale=0.02, size=step.shape)
+    validation_loss = 0.1 + (0.75 - step) ** 2 + np.random.normal(loc=0, scale=0.02, size=step.shape)
+
+    h.history = {
+        "loss": train_loss.tolist(),
+        "val_loss": validation_loss.tolist(),
+    }
+
+    return h

tests/test_diagnostics/test_diagnostics_plots.py

@@ -45,6 +45,15 @@ def test_calibration_histogram(random_estimates, random_targets):
     assert out.axes[0].title._text == "beta_0"
 
 
+def test_loss(history):
+    import matplotlib.pyplot as plt
+
+    out = bf.diagnostics.loss(history)
+    plt.show()
+    assert len(out.axes) == 1
+    assert out.axes[0].title._text == "Loss Trajectory"
+
+
 def test_recovery(random_estimates, random_targets):
     # basic functionality: automatic variable names
     out = bf.diagnostics.plots.recovery(random_estimates, random_targets)