Refactored train_utils.py

deepof/train_utils.py

@@ -12,6 +12,7 @@ from datetime import date, datetime
 from kerastuner import BayesianOptimization, Hyperband
 from kerastuner import HyperParameters
 from kerastuner_tensorboard_logger import TensorBoardLogger
+from sklearn.metrics import roc_auc_score
 from tensorboard.plugins.hparams import api as hp
 from typing import Tuple, Union, Any, List
 import deepof.hypermodels
@@ -139,7 +140,7 @@ def get_callbacks(
     return callbacks
 
 
-def log_hyperparameters(phenotype_class):
+def log_hyperparameters(phenotype_class: float, rec: str):
     """Blueprint for hyperparameter and metric logging in tensorboard during hyperparameter tuning"""
 
     logparams = [
@@ -163,7 +164,6 @@ def log_hyperparameters(phenotype_class):
         ),
     ]
 
-    rec = "reconstruction_" if phenotype_class else ""
     metrics = [
         hp.Metric("val_{}mae".format(rec), display_name="val_{}mae".format(rec)),
         hp.Metric("val_{}mse".format(rec), display_name="val_{}mse".format(rec)),
@@ -192,7 +192,16 @@ def log_hyperparameters(phenotype_class):
 
 
 # noinspection PyUnboundLocalVariable
-def tensorboard_metric_logging(run_dir: str, hpms: Any):
+def tensorboard_metric_logging(
+    run_dir: str,
+    hpms: Any,
+    ae: Any,
+    X_val: np.ndarray,
+    y_val: np.ndarray,
+    phenotype_class: float,
+    predictor: float,
+    rec: str,
+):
     """Autoencoder metric logging in tensorboard"""
 
     output = ae.predict(X_val)
@@ -262,7 +271,6 @@ def autoencoder_fitting(
     tf.keras.backend.clear_session()
 
     # Defines what to log on tensorboard (useful for trying out different models)
-
     logparam = {
         "encoding": encoding_size,
         "k": n_components,
@@ -287,8 +295,9 @@ def autoencoder_fitting(
         cbacks = cbacks[1:]
 
     # Logs hyperparameters to tensorboard
+    rec = "reconstruction_" if phenotype_class else ""
     if log_hparams:
-        logparams, metrics = log_hyperparameters(phenotype_class)
+        logparams, metrics = log_hyperparameters(phenotype_class, rec)
 
         with tf.summary.create_file_writer(
             os.path.join(output_path, "hparams", run_ID)
@@ -411,6 +420,12 @@ def autoencoder_fitting(
                 tensorboard_metric_logging(
                     os.path.join(output_path, "hparams", run_ID),
                     logparam,
+                    ae,
+                    X_val,
+                    y_val,
+                    phenotype_class,
+                    predictor,
+                    rec,
                 )
 
     return return_list

tests/test_train_utils.py

@@ -9,12 +9,13 @@ Testing module for deepof.train_utils
 """
 
 from hypothesis import given
-from hypothesis import settings, reproduce_failure
+from hypothesis import settings
 from hypothesis import strategies as st
 from hypothesis.extra.numpy import arrays
 import deepof.data
 import deepof.model_utils
 import deepof.train_utils
+import numpy as np
 import os
 import tensorflow as tf
 
@@ -113,7 +114,8 @@ def test_autoencoder_fitting(
     predictor,
     variational,
 ):
-    preprocessed_data = (X_train, [], X_train, [])
+    y_train = np.round(np.random.uniform(0, 1, X_train.shape[0]))
+    preprocessed_data = (X_train, y_train, X_train, y_train)
 
     prun = deepof.data.project(
         path=os.path.join(".", "tests", "test_examples", "test_single_topview"),
@@ -131,7 +133,7 @@ def test_autoencoder_fitting(
         log_hparams=True,
         n_components=k,
         loss=loss,
-        phenotype_class=0,
+        phenotype_class=pheno_class,
         predictor=predictor,
         variational=variational,
     )