add trainer tests for testing without defining the dataset_train

tests/test_trainer.py

@@ -7,6 +7,7 @@
 import warnings
 
 import h5py
+import pandas as pd
 import pytest
 import torch
 
@@ -706,6 +707,89 @@ def test_invalid_no_cuda_available(self):
             warnings.warn('CUDA is available; test_invalid_no_cuda_available was skipped')
             _log.info('CUDA is available; test_invalid_no_cuda_available was skipped')
 
+    def test_train_method_no_train(self):
+
+        # Graphs data
+        test_data_graph = "tests/data/hdf5/test.hdf5"
+        pretrained_model_graph = "tests/data/pretrained/testing_graph_model.pth.tar"
+
+        dataset_test = GraphDataset(
+            hdf5_path = test_data_graph,
+            train = False,
+            train_data = pretrained_model_graph
+        )
+        trainer = Trainer(
+            neuralnet = NaiveNetwork,
+            dataset_test = dataset_test,
+            pretrained_model = pretrained_model_graph
+        )
+
+        with pytest.raises(ValueError):
+            trainer.train()
+
+        # Grids data
+        test_data_grid = "tests/data/hdf5/1ATN_ppi.hdf5"
+        pretrained_model_grid = "tests/data/pretrained/testing_grid_model.pth.tar"
+
+        dataset_test = GridDataset(
+            hdf5_path = test_data_grid,
+            train = False,
+            train_data = pretrained_model_grid
+        )
+        trainer = Trainer(
+            neuralnet = CnnClassification,
+            dataset_test = dataset_test,
+            pretrained_model = pretrained_model_grid
+        )
+
+        with pytest.raises(ValueError):
+            trainer.train()
+
+    def test_test_method_pretrained_model_on_dataset_with_target(self):
+
+        # Graphs data
+        test_data_graph = "tests/data/hdf5/test.hdf5"
+        pretrained_model_graph = "tests/data/pretrained/testing_graph_model.pth.tar"
+
+        dataset_test = GraphDataset(
+            hdf5_path = test_data_graph,
+            train = False,
+            train_data = pretrained_model_graph
+        )
+
+        trainer = Trainer(
+            neuralnet = NaiveNetwork,
+            dataset_test = dataset_test,
+            pretrained_model = pretrained_model_graph,
+            output_exporters = [HDF5OutputExporter("./")]
+            )
+
+        trainer.test()
+
+        output = pd.read_hdf("output_exporter.hdf5", key="testing")
+        assert len(output) == len(dataset_test)
+
+        # Grids data
+        test_data_grid = "tests/data/hdf5/1ATN_ppi.hdf5"
+        pretrained_model_grid = "tests/data/pretrained/testing_grid_model.pth.tar"
+
+        dataset_test = GridDataset(
+            hdf5_path = test_data_grid,
+            train = False,
+            train_data = pretrained_model_grid
+        )
+
+        trainer = Trainer(
+            neuralnet = CnnClassification,
+            dataset_test = dataset_test,
+            pretrained_model = pretrained_model_grid,
+            output_exporters = [HDF5OutputExporter("./")]
+            )
+
+        trainer.test()
+
+        output = pd.read_hdf("output_exporter.hdf5", key="testing")
+        assert len(output) == len(dataset_test)
 
 
 if __name__ == "__main__":