Added tests for model_utils.py and models.py

.gitlab-ci.yml

@@ -9,7 +9,7 @@ test:
     - pip install -r ./deepof/requirements.txt
     - pip install -e deepof/
     - coverage run --source deepof -m pytest
-    - coverage report -m --include deepof/utils.py,deepof/preprocess.py,deepof/model_utils.py,deepof/visuals.py
+    - coverage report -m --include deepof/utils.py,deepof/preprocess.py,deepof/model_utils.py,deepof/visuals.py,deepof/models.py
     - coverage xml -o deepof_cov.xml
   artifacts:
     reports:

deepof/model_utils.py

@@ -351,7 +351,7 @@ class MMDiscrepancyLayer(Layer):
         return z
 
 
-class Gaussian_mixture_overlap(Layer):  # pragma: no cover
+class Gaussian_mixture_overlap(Layer):
     """
     Identity layer that measures the overlap between the components of the latent Gaussian Mixture
     using a specified metric (MMD, Wasserstein, Fischer-Rao)
@@ -381,7 +381,7 @@ class Gaussian_mixture_overlap(Layer):  # pragma: no cover
         dists = []
         for k in range(self.n_components):
             locs = (target[..., : self.lat_dims, k],)
-            scales = tf.keras.activations.softplus(target[..., self.lat_dims:, k])
+            scales = tf.keras.activations.softplus(target[..., self.lat_dims :, k])
 
             dists.append(
                 tfd.BatchReshape(tfd.MultivariateNormalDiag(locs, scales), [-1])

tests/test_build_models.py

+# @author lucasmiranda42
+# encoding: utf-8
+# module deepof
+
+"""
+
+Testing module for deepof.models
+
+"""
+
+from hypothesis import given
+from hypothesis import settings
+from hypothesis import strategies as st
+from hypothesis.extra.numpy import arrays
+import deepof.models
+import deepof.model_utils
+import numpy as np
+import tensorflow as tf
+import tensorflow_probability as tfp
+from tensorflow.python.framework.ops import EagerTensor
+
+
+@settings(deadline=None)
+@given(
+    input_shape=st.tuples(
+        st.integers(min_value=100, max_value=1000),
+        st.integers(min_value=5, max_value=15),
+        st.integers(min_value=5, max_value=15),
+    )
+)
+def test_SEQ_2_SEQ_AE_build(input_shape):
+    deepof.models.SEQ_2_SEQ_AE(input_shape=input_shape)
+
+
+@settings(deadline=None)
+@given(
+    input_shape=st.tuples(
+        st.integers(min_value=100, max_value=1000),
+        st.integers(min_value=5, max_value=15),
+        st.integers(min_value=5, max_value=15),
+    ),
+    loss=st.one_of(st.just("ELBO"), st.just("MMD"), st.just("ELBO+MMD")),
+    kl_warmup_epochs=st.integers(min_value=0, max_value=5),
+    mmd_warmup_epochs=st.integers(min_value=0, max_value=5),
+    number_of_components=st.integers(min_value=1, max_value=5),
+    predictor=st.booleans(),
+    overlap_loss=st.booleans(),
+    entropy_reg_weight=st.floats(min_value=0.0, max_value=1.0),
+)
+def test_SEQ_2_SEQ_GMVAE_build(
+    input_shape,
+    loss,
+    kl_warmup_epochs,
+    mmd_warmup_epochs,
+    number_of_components,
+    predictor,
+    overlap_loss,
+    entropy_reg_weight,
+):
+    deepof.models.SEQ_2_SEQ_GMVAE(
+        input_shape=input_shape,
+        loss=loss,
+        kl_warmup_epochs=kl_warmup_epochs,
+        mmd_warmup_epochs=mmd_warmup_epochs,
+        number_of_components=number_of_components,
+        predictor=predictor,
+        overlap_loss=overlap_loss,
+        entropy_reg_weight=entropy_reg_weight,
+    )

tests/test_model_utils.py

@@ -230,5 +230,17 @@ def test_dead_neuron_control():
     assert type(fit) == tf.python.keras.callbacks.History
 
 
-# def test_entropy_regulariser():
-#     pass
+def test_entropy_regulariser():
+    X = np.random.uniform(0, 10, [1500, 5])
+    y = np.random.randint(0, 2, [1500, 1])
+
+    test_model = tf.keras.Sequential()
+    test_model.add(tf.keras.layers.Dense(1))
+    test_model.add(deepof.model_utils.Entropy_regulariser(1.0))
+
+    test_model.compile(
+        loss=tf.keras.losses.binary_crossentropy, optimizer=tf.keras.optimizers.SGD(),
+    )
+
+    fit = test_model.fit(X, y, epochs=10, batch_size=100)
+    assert type(fit) == tf.python.keras.callbacks.History