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LihangLiuLihangLiu
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Merge pull request #29 from PaddlePaddle/pretrain_gnn
add unittest
2 parents dc63c94 + 272df79 commit c9538c0

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‎apps/pretrained_compound/pretrain_gnns/README.md

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‎apps/pretrained_compound/pretrain_gnns/README_cn.md

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‎pahelix/tests/import_test.py

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#!/usr/bin/python
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#-*-coding:utf-8-*-
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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class ImportTest(unittest.TestCase):
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def test_import_pahelix_alone(self):
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import pahelix
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if __name__ == '__main__':
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unittest.main()

‎pahelix/utils/tests/compound_tools_test.py

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#!/usr/bin/python
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#-*-coding:utf-8-*-
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import unittest
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from rdkit import Chem
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from rdkit.Chem import AllChem
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from pahelix.utils.compound_tools import smiles_to_graph_data
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from pahelix.utils.compound_tools import mol_to_graph_data
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from pahelix.utils.compound_tools import get_gasteiger_partial_charges
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from pahelix.utils.compound_tools import create_standardized_mol_id
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from pahelix.utils.compound_tools import split_rdkit_mol_obj
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from pahelix.utils.compound_tools import CompoundConstants
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class CompoundToolsTest(unittest.TestCase):
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def test_mol_to_graph_data(self, add_self_loop=True):
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smiles ='CCOc1ccc2nc(S(N)(=O)=O)sc2c1'
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mol = AllChem.MolFromSmiles(smiles)
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data = mol_to_graph_data(mol)
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self.assertTrue(data)
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def test_smiles_to_graph_data(self, add_self_loop=True):
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smiles ='CCOc1ccc2nc(S(N)(=O)=O)sc2c1'
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data = smiles_to_graph_data(smiles)
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self.assertTrue(data)
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def test_get_gasteiger_partial_charges(self, n_iter=12):
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smiles ='CCOc1ccc2nc(S(N)(=O)=O)sc2c1'
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mol = AllChem.MolFromSmiles(smiles)
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charges = get_gasteiger_partial_charges(mol)
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self.assertEqual(len(charges), 16)
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def test_create_standardized_mol_id(self):
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smiles ='CCOc1ccc2nc(S(N)(=O)=O)sc2c1'
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id1 = create_standardized_mol_id(smiles)
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id2 = create_standardized_mol_id(smiles)
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self.assertEqual(id1, id2)
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if __name__ == '__main__':
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unittest.main()

‎pahelix/utils/tests/data_utils_test.py

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#!/usr/bin/python
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#-*-coding:utf-8-*-
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import numpy as np
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import unittest
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from pahelix.utils.data_utils import load_npz_to_data_list
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from pahelix.utils.data_utils import save_data_list_to_npz
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class DataUtilsTest(unittest.TestCase):
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def test_data_list_to_npz(self):
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data_list = [
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{"a": np.array([1,23,4])},
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{"a": np.array([2,34,5])}
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]
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npz_file = 'tmp.npz'
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save_data_list_to_npz(data_list, npz_file)
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reload_data_list = load_npz_to_data_list(npz_file)
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self.assertEqual(len(data_list), len(reload_data_list))
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for d1, d2 in zip(data_list, reload_data_list):
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self.assertEqual(len(d1), len(d2))
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for key in d1:
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self.assertTrue((d1[key] == d2[key]).all())
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if __name__ == '__main__':
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unittest.main()

‎pahelix/utils/tests/splitters_test.py

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#!/usr/bin/python
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#-*-coding:utf-8-*-
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import numpy as np
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import unittest
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from pahelix.utils.splitters import \
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RandomSplitter, IndexSplitter, ScaffoldSplitter, RandomScaffoldSplitter
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from pahelix.datasets.inmemory_dataset import InMemoryDataset
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from pahelix.featurizers.featurizer import Featurizer
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class RandomSplitterTest(unittest.TestCase):
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def test_split(self):
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raw_data_list = [
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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]
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dataset = InMemoryDataset(raw_data_list)
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splitter = RandomSplitter()
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train_dataset, valid_dataset, test_dataset = splitter.split(
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dataset, frac_train=0.34, frac_valid=0.33, frac_test=0.33)
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n = len(train_dataset) + len(valid_dataset) + len(test_dataset)
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self.assertEqual(n, len(dataset))
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class IndexSplitterTest(unittest.TestCase):
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def test_split(self):
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raw_data_list = [
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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]
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dataset = InMemoryDataset(raw_data_list)
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splitter = IndexSplitter()
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train_dataset, valid_dataset, test_dataset = splitter.split(
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dataset, frac_train=0.34, frac_valid=0.33, frac_test=0.33)
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n = len(train_dataset) + len(valid_dataset) + len(test_dataset)
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self.assertEqual(n, len(dataset))
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class ScaffoldSplitterTest(unittest.TestCase):
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def test_split(self):
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raw_data_list = [
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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]
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dataset = InMemoryDataset(raw_data_list)
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splitter = ScaffoldSplitter()
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train_dataset, valid_dataset, test_dataset = splitter.split(
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dataset, frac_train=0.34, frac_valid=0.33, frac_test=0.33)
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n = len(train_dataset) + len(valid_dataset) + len(test_dataset)
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self.assertEqual(n, len(dataset))
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class RandomScaffoldSplitterTest(unittest.TestCase):
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def test_split(self):
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raw_data_list = [
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CCOc1ccc2nc(S(N)(=O)=O)sc2c1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CC(C)CCCCCCCOP(OCCCCCCCC(C)C)Oc1ccccc1'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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{'smiles': 'CCCCCCCCCCOCC(O)CN'},
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]
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dataset = InMemoryDataset(raw_data_list)
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splitter = RandomScaffoldSplitter()
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train_dataset, valid_dataset, test_dataset = splitter.split(
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dataset, frac_train=0.34, frac_valid=0.33, frac_test=0.33)
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n = len(train_dataset) + len(valid_dataset) + len(test_dataset)
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self.assertEqual(n, len(dataset))
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if __name__ == '__main__':
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unittest.main()

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