Added phenotype classification to train_model.py

deepof/train_model.py

@@ -514,7 +514,7 @@ if not tune:
             history = gmvaep.fit(
                 x=Xs,
                 y=ys,
-                epochs=35,
+                epochs=1,
                 batch_size=batch_size,
                 verbose=1,
                 validation_data=(
@@ -534,16 +534,54 @@ if not tune:
                 )
             )
 
+            # noinspection PyUnboundLocalVariable
+            def tensorboard_metric_logging(run_dir: str, hpms: Any):
+                output = gmvaep.predict(X_val)
+                if pheno_class or predictor:
+                    reconstruction = output[0]
+                    prediction = output[1]
+                    pheno = output[-1]
+                else:
+                    reconstruction = output
+
+                with tf.summary.create_file_writer(run_dir).as_default():
+                    hp.hparams(hpms)  # record the values used in this trial
+                    val_mae = tf.reduce_mean(
+                        tf.keras.metrics.mean_absolute_error(X_val, reconstruction)
+                    )
+                    val_mse = tf.reduce_mean(
+                        tf.keras.metrics.mean_squared_error(X_val, reconstruction)
+                    )
+                    tf.summary.scalar("val_{}mae".format(rec), val_mae, step=1)
+                    tf.summary.scalar("val_{}mse".format(rec), val_mse, step=1)
+
+                    if predictor:
+                        pred_mae = tf.reduce_mean(
+                            tf.keras.metrics.mean_absolute_error(X_val, prediction)
+                        )
+                        pred_mse = tf.reduce_mean(
+                            tf.keras.metrics.mean_squared_error(X_val, prediction)
+                        )
+                        tf.summary.scalar(
+                            "val_prediction_mae".format(rec), pred_mae, step=1
+                        )
+                        tf.summary.scalar(
+                            "val_prediction_mse".format(rec), pred_mse, step=1
+                        )
+
+                    if pheno_class:
+                        pheno_acc = tf.keras.metrics.Accuracy(y_val, pheno)
+                        pheno_auc = tf.keras.metrics.AUC(y_val, pheno)
+
+                        tf.summary.scalar(
+                            "phenotype_prediction_accuracy", pheno_acc, step=1
+                        )
+                        tf.summary.scalar("phenotype_prediction_auc", pheno_auc, step=1)
+
             # Logparams to tensorboard
             tensorboard_metric_logging(
                 os.path.join(output_path, "hparams", run_ID),
                 logparam,
-                X_val,
-                y_val,
-                gmvaep,
-                predictor,
-                pheno_class,
-                rec,
             )
 
         # To avoid stability issues

deepof/train_utils.py

@@ -137,52 +137,6 @@ def get_callbacks(
     return callbacks
 
 
-# noinspection PyUnboundLocalVariable
-def tensorboard_metric_logging(
-    run_dir: str,
-    hpms: Any,
-    X: np.ndarray,
-    y: np.ndarray,
-    model: tf.python.keras.engine.functional.Functional,
-    predictor: float,
-    pheno_class: float,
-    rec: str,
-):
-    output = model.predict(X)
-    if pheno_class or predictor:
-        reconstruction = output[0]
-        prediction = output[1]
-        pheno = output[-1]
-    else:
-        reconstruction = output
-
-    with tf.summary.create_file_writer(run_dir).as_default():
-        hp.hparams(hpms)  # record the values used in this trial
-        val_mae = tf.reduce_mean(
-            tf.keras.metrics.mean_absolute_error(X, reconstruction)
-        )
-        val_mse = tf.reduce_mean(tf.keras.metrics.mean_squared_error(X, reconstruction))
-        tf.summary.scalar("val_{}mae".format(rec), val_mae, step=1)
-        tf.summary.scalar("val_{}mse".format(rec), val_mse, step=1)
-
-        if predictor:
-            pred_mae = tf.reduce_mean(
-                tf.keras.metrics.mean_absolute_error(X, prediction)
-            )
-            pred_mse = tf.reduce_mean(
-                tf.keras.metrics.mean_squared_error(X, prediction)
-            )
-            tf.summary.scalar("val_prediction_mae".format(rec), pred_mae, step=1)
-            tf.summary.scalar("val_prediction_mse".format(rec), pred_mse, step=1)
-
-        if pheno_class:
-            pheno_acc = tf.keras.metrics.Accuracy(y, pheno)
-            pheno_auc = tf.keras.metrics.AUC(y, pheno)
-
-            tf.summary.scalar("phenotype_prediction_accuracy", pheno_acc, step=1)
-            tf.summary.scalar("phenotype_prediction_auc", pheno_auc, step=1)
-
-
 def tune_search(
     data: List[np.array],
     encoding_size: int,