Added extra branch to main autoencoder for rule_based prediction

deepof/train_utils.py

@@ -100,7 +100,7 @@ def get_callbacks(
     elif reg_cat_clusters and reg_cluster_variance:
         latreg = "categorical+variance"
 
-    run_ID = "{}{}{}{}{}{}{}{}{}{}{}{}".format(
+    run_ID = "{}{}{}{}{}{}{}{}{}{}{}{}{}".format(
         ("GMVAE" if variational else "AE"),
         ("_input_type={}".format(input_type) if input_type else "coords"),
         ("_window_size={}", format(X_train.shape[1])),
@@ -116,8 +116,6 @@ def get_callbacks(
         ("_{}".format(datetime.now().strftime("%Y%m%d-%H%M%S")) if not run else ""),
     )
 
-    print(run_ID)
-
     log_dir = os.path.abspath(os.path.join(outpath, "fit", run_ID))
     tensorboard_callback = tf.keras.callbacks.TensorBoard(
         log_dir=log_dir,

tests/test_train_utils.py

@@ -33,7 +33,7 @@ def test_load_treatments():
 
 @given(
     X_train=arrays(
-        shape=st.tuples(st.integers(min_value=1, max_value=1000)),
+        shape=st.tuples(st.integers(min_value=1, max_value=1000), st.just(24)),
         dtype=float,
         elements=st.floats(
             min_value=0.0,