Loading deepchem/molnet/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,8 @@ from deepchem.molnet.load_function.chembl25_datasets import load_chembl25 from deepchem.molnet.load_function.zinc15_datasets import load_zinc15 from deepchem.molnet.load_function.material_datasets.load_bandgap import load_bandgap from deepchem.molnet.load_function.material_datasets.load_perovskite import load_perovskite from deepchem.molnet.load_function.material_datasets.load_Pt_NO_surface_adsorbate_energy import load_Platinum_Adsorption from deepchem.molnet.load_function.material_datasets.load_mp_formation_energy import load_mp_formation_energy from deepchem.molnet.load_function.material_datasets.load_mp_metallicity import load_mp_metallicity Loading deepchem/molnet/load_function/material_datasets/load_Pt_NO_surface_adsorbate_energy.py +1 −3 Original line number Diff line number Diff line Loading @@ -94,7 +94,7 @@ def load_Platinum_Adsorption(featurizer=DEFAULT_FEATURIZERS['LCNNFeaturizer'], Examples -------- >> import deepchem as dc >> feat_args = {"cutoff": np.around(6.00, 2), "input_file_path": os.join.path(data_path,'input.in') } >> feat_args = {"cutoff": np.around(6.00, 2), "input_file_path": os.path.join(data_path,'input.in') } >> tasks, datasets, transformers = load_Platinum_Adsorption( reload=True, Loading Loading @@ -193,5 +193,3 @@ def load_Platinum_Adsorption(featurizer=DEFAULT_FEATURIZERS['LCNNFeaturizer'], save_folder, train_dataset, valid_dataset, test_dataset, transformers) return my_tasks, (train_dataset, valid_dataset, test_dataset), transformers deepchem/molnet/load_function/material_datasets/tests/test_load_Pt_NO_surface_adsorbate_energy.py 0 → 100644 +39 −0 Original line number Diff line number Diff line import os import numpy as np from deepchem.molnet import load_Platinum_Adsorption def test_Platinum_Adsorption_loader(): current_dir = os.path.dirname(os.path.abspath(__file__)) feat_args = { "cutoff": np.around(6.00, 2), "input_file_path": os.path.join(current_dir, 'input.in') } tasks, datasets, transformers = load_Platinum_Adsorption( reload=False, data_dir=current_dir, featurizer_kwargs=feat_args, splitter_kwargs={ 'seed': 42, 'frac_train': 0.5, 'frac_valid': 0.3, 'frac_test': 0.2 }) assert tasks[0] == "Formation Energy" assert datasets[0].X[0]['X_Sites'].shape[1] == 3 assert datasets[0].X[0]['X_NSs'].shape[3] == 19 assert datasets[0].X[0]['X_NSs'].shape[2] == 6 assert datasets[0].X[0]['X_NSs'].shape[1] == datasets[0].X[0][ 'X_Sites'].shape[0] if os.path.exists(os.path.join(current_dir, 'Platinum_Adsorption.json')): os.remove(os.path.join(current_dir, 'Platinum_Adsorption.json')) if os.path.exists(os.path.join(current_dir, 'input.in')): os.remove(os.path.join(current_dir, 'input.in')) if os.path.exists(os.path.join(current_dir, 'platinum_adsorption.tar.gz')): os.remove(os.path.join(current_dir, 'platinum_adsorption.tar.gz')) Loading
deepchem/molnet/__init__.py +2 −0 Original line number Diff line number Diff line Loading @@ -34,6 +34,8 @@ from deepchem.molnet.load_function.chembl25_datasets import load_chembl25 from deepchem.molnet.load_function.zinc15_datasets import load_zinc15 from deepchem.molnet.load_function.material_datasets.load_bandgap import load_bandgap from deepchem.molnet.load_function.material_datasets.load_perovskite import load_perovskite from deepchem.molnet.load_function.material_datasets.load_Pt_NO_surface_adsorbate_energy import load_Platinum_Adsorption from deepchem.molnet.load_function.material_datasets.load_mp_formation_energy import load_mp_formation_energy from deepchem.molnet.load_function.material_datasets.load_mp_metallicity import load_mp_metallicity Loading
deepchem/molnet/load_function/material_datasets/load_Pt_NO_surface_adsorbate_energy.py +1 −3 Original line number Diff line number Diff line Loading @@ -94,7 +94,7 @@ def load_Platinum_Adsorption(featurizer=DEFAULT_FEATURIZERS['LCNNFeaturizer'], Examples -------- >> import deepchem as dc >> feat_args = {"cutoff": np.around(6.00, 2), "input_file_path": os.join.path(data_path,'input.in') } >> feat_args = {"cutoff": np.around(6.00, 2), "input_file_path": os.path.join(data_path,'input.in') } >> tasks, datasets, transformers = load_Platinum_Adsorption( reload=True, Loading Loading @@ -193,5 +193,3 @@ def load_Platinum_Adsorption(featurizer=DEFAULT_FEATURIZERS['LCNNFeaturizer'], save_folder, train_dataset, valid_dataset, test_dataset, transformers) return my_tasks, (train_dataset, valid_dataset, test_dataset), transformers
deepchem/molnet/load_function/material_datasets/tests/test_load_Pt_NO_surface_adsorbate_energy.py 0 → 100644 +39 −0 Original line number Diff line number Diff line import os import numpy as np from deepchem.molnet import load_Platinum_Adsorption def test_Platinum_Adsorption_loader(): current_dir = os.path.dirname(os.path.abspath(__file__)) feat_args = { "cutoff": np.around(6.00, 2), "input_file_path": os.path.join(current_dir, 'input.in') } tasks, datasets, transformers = load_Platinum_Adsorption( reload=False, data_dir=current_dir, featurizer_kwargs=feat_args, splitter_kwargs={ 'seed': 42, 'frac_train': 0.5, 'frac_valid': 0.3, 'frac_test': 0.2 }) assert tasks[0] == "Formation Energy" assert datasets[0].X[0]['X_Sites'].shape[1] == 3 assert datasets[0].X[0]['X_NSs'].shape[3] == 19 assert datasets[0].X[0]['X_NSs'].shape[2] == 6 assert datasets[0].X[0]['X_NSs'].shape[1] == datasets[0].X[0][ 'X_Sites'].shape[0] if os.path.exists(os.path.join(current_dir, 'Platinum_Adsorption.json')): os.remove(os.path.join(current_dir, 'Platinum_Adsorption.json')) if os.path.exists(os.path.join(current_dir, 'input.in')): os.remove(os.path.join(current_dir, 'input.in')) if os.path.exists(os.path.join(current_dir, 'platinum_adsorption.tar.gz')): os.remove(os.path.join(current_dir, 'platinum_adsorption.tar.gz'))
