Added extra branch to main autoencoder for rule_based prediction

deepof/data.py

@@ -892,8 +892,9 @@ class coordinates:
         montecarlo_kl: int = 10,
         n_components: int = 25,
         output_path: str = ".",
-        phenotype_class: float = 0,
-        predictor: float = 0,
+        next_sequence_prediction: float = 0,
+        phenotype_prediction: float = 0,
+        rule_based_prediction: float = 0,
         pretrained: str = False,
         save_checkpoints: bool = False,
         save_weights: bool = True,
@@ -954,8 +955,9 @@ class coordinates:
             montecarlo_kl=montecarlo_kl,
             n_components=n_components,
             output_path=output_path,
-            phenotype_class=phenotype_class,
-            predictor=predictor,
+            next_sequence_prediction=next_sequence_prediction,
+            phenotype_prediction=phenotype_prediction,
+            rule_based_prediction=rule_based_prediction,
             pretrained=pretrained,
             save_checkpoints=save_checkpoints,
             save_weights=save_weights,

deepof/train_model.py

@@ -373,8 +373,8 @@ if not tune:
         montecarlo_kl=mc_kl,
         n_components=k,
         output_path=output_path,
-        phenotype_class=pheno_class,
-        predictor=predictor,
+        phenotype_prediction=pheno_class,
+        next_sequence_prediction=predictor,
         save_checkpoints=False,
         save_weights=True,
         variational=variational,

deepof/train_utils.py

@@ -259,8 +259,9 @@ def autoencoder_fitting(
     montecarlo_kl: int,
     n_components: int,
     output_path: str,
-    phenotype_class: float,
-    predictor: float,
+    next_sequence_prediction: float,
+    phenotype_prediction: float,
+    rule_based_prediction: float,
     pretrained: str,
     save_checkpoints: bool,
     save_weights: bool,
@@ -284,8 +285,8 @@ def autoencoder_fitting(
         "k": n_components,
         "loss": loss,
     }
-    if phenotype_class:
-        logparam["pheno_weight"] = phenotype_class
+    if phenotype_prediction:
+        logparam["pheno_weight"] = phenotype_prediction
 
     # Load callbacks
     run_ID, *cbacks = get_callbacks(
@@ -294,8 +295,8 @@ def autoencoder_fitting(
         batch_size=batch_size,
         cp=save_checkpoints,
         variational=variational,
-        phenotype_class=phenotype_class,
-        predictor=predictor,
+        phenotype_class=phenotype_prediction,
+        predictor=next_sequence_prediction,
         loss=loss,
         entropy_samples=entropy_samples,
         entropy_knn=entropy_knn,
@@ -308,9 +309,9 @@ def autoencoder_fitting(
         cbacks = cbacks[1:]
 
     # Logs hyperparameters to tensorboard
-    rec = "reconstruction_" if phenotype_class else ""
+    rec = "reconstruction_" if phenotype_prediction else ""
     if log_hparams:
-        logparams, metrics = log_hyperparameters(phenotype_class, rec)
+        logparams, metrics = log_hyperparameters(phenotype_prediction, rec)
 
         with tf.summary.create_file_writer(
             os.path.join(output_path, "hparams", run_ID)
@@ -347,8 +348,12 @@ def autoencoder_fitting(
             neuron_control=False,
             number_of_components=n_components,
             overlap_loss=False,
-            phenotype_prediction=phenotype_class,
-            predictor=predictor,
+            next_sequence_prediction=next_sequence_prediction,
+            phenotype_prediction=phenotype_prediction,
+            rule_based_prediction=rule_based_prediction,
+            rule_based_features=(
+                y_train.shape[1] if not phenotype_prediction else y_train.shape[1] - 1
+            ),
             reg_cat_clusters=reg_cat_clusters,
             reg_cluster_variance=reg_cluster_variance,
         ).build(
@@ -399,11 +404,19 @@ def autoencoder_fitting(
             Xs, ys = [X_train], [X_train]
             Xvals, yvals = [X_val], [X_val]
 
-            if predictor > 0.0:
+            if next_sequence_prediction > 0.0:
                 Xs, ys = X_train[:-1], [X_train[:-1], X_train[1:]]
                 Xvals, yvals = X_val[:-1], [X_val[:-1], X_val[1:]]
 
-            if phenotype_class > 0.0:
+            if phenotype_prediction > 0.0:
+                ys += [y_train[:, 0]]
+                yvals += [y_val[:, 0]]
+
+                # Remove the used column (phenotype) from both y arrays
+                y_train = y_train[:, 1:]
+                y_val = y_val[:, 1:]
+
+            if rule_based_prediction > 0.0:
                 ys += [y_train]
                 yvals += [y_val]
 
@@ -440,8 +453,8 @@ def autoencoder_fitting(
                     ae,
                     Xvals,
                     yvals[-1],
-                    phenotype_class,
-                    predictor,
+                    phenotype_prediction,
+                    next_sequence_prediction,
                     rec,
                 )
 

tests/test_train_utils.py

@@ -118,8 +118,8 @@ def test_autoencoder_fitting(
         mmd_warmup=1,
         n_components=2,
         loss=loss,
-        phenotype_class=pheno_class,
-        predictor=predictor,
+        phenotype_prediction=pheno_class,
+        next_sequence_prediction=predictor,
         variational=variational,
         entropy_samples=10,
         entropy_knn=5,