continue keras core integration

kgcnn/data/base.py

@@ -1,8 +1,8 @@
 import logging
 import numpy as np
-import tensorflow as tf
 import pandas as pd
 import os
+from sklearn.model_selection import KFold
 # import typing as t
 from typing import Union, List, Callable, Dict, Optional
 # from collections.abc import MutableSequence
@@ -705,10 +705,10 @@ def get_train_test_indices(self,
           dataset belongs to that part of the split (train / test)
         - The property is a list containing integer split indices, where each split index present within
           that list implies that the corresponding dataset element is part of that particular split.
-          In this case the ``split_index`` parameter may also be a list of split indices that specifies
+          In this case the `split_index` parameter may also be a list of split indices that specify
           for which of these split indices the train test index split is to be returned by this method.
 
-        The return value of this method is a list with the same length as the ``split_index`` parameter,
+        The return value of this method is a list with the same length as the `split_index` parameter,
         which by default will be 1.
 
         Args:
@@ -808,5 +808,17 @@ def get_multi_target_indices(self, graph_labels: str = "graph_labels", multi_tar
         self.info("Labels '%s' in '%s' have shape '%s'." % (label_names, label_units, labels.shape))
         return labels, label_names, label_units
 
+    def set_train_test_indices_k_fold(self, n_splits: int = 5, shuffle: bool = False, random_state: int = None,
+                                      train: str = "train", test: str = "test"):
+        kf = KFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state)
+        for x in self:
+            x.set(train, [])
+            x.set(test, [])
+        for fold, (train_index, test_index) in enumerate(kf.split(np.expand_dims(np.arange(len(self)), axis=0))):
+            for i in train_index:
+                self[i].set(train, list(self[i].get(train)) + [fold])
+            for i in train_index:
+                self[i].set(test, list(self[i].get(test)) + [fold])
+
 
 MemoryGeometricGraphDataset = MemoryGraphDataset

training_core/train_graph.py

@@ -7,7 +7,6 @@
 from datetime import timedelta
 from kgcnn.training_core.history import save_history_score, load_history_list
 from kgcnn.metrics_core.metrics import ScaledMeanAbsoluteError, ScaledRootMeanSquaredError
-from sklearn.model_selection import KFold
 from sklearn.preprocessing import StandardScaler as StandardLabelScaler
 from kgcnn.utils_core.plots import plot_train_test_loss, plot_predict_true
 from kgcnn.model.serial import deserialize as deserialize_model
@@ -72,10 +71,6 @@
     data_unit=hyper["data"]["data_unit"] if "data_unit" in hyper["data"] else None
 )
 
-# Cross-validation via random KFold split form `sklearn.model_selection`. Other validation schemes could include
-# stratified k-fold cross-validation for `MoleculeNetDataset` but is not implemented yet.
-kf = KFold(**hyper["training"]["cross_validation"]["config"])
-
 # Iterate over the cross-validation splits.
 # Indices for train-test splits are stored in 'test_indices_list'.
 execute_folds = args["fold"] if "execute_folds" not in hyper["training"] else hyper["training"]["execute_folds"]
@@ -143,7 +138,7 @@
     save_pickle_file(hist.history, os.path.join(filepath, f"history{postfix_file}_fold_{current_split}.pickle"))
 
 # Plot training- and test-loss vs epochs for all splits.
-history_list = load_history_list(os.path.join(filepath, f"history{postfix_file}_fold_(i).pickle"), current_split)
+history_list = load_history_list(os.path.join(filepath, f"history{postfix_file}_fold_(i).pickle"), current_split+1)
 plot_train_test_loss(history_list, loss_name=None, val_loss_name=None,
                      model_name=hyper.model_name, data_unit=label_units, dataset_name=hyper.dataset_class,
                      filepath=filepath, file_name=f"loss{postfix_file}.png")