Loading examples/gdb7/get_gdb7.sh→examples/gdb7/get_qm7.sh +0 −0 File moved. View file examples/gdb7/gdb7_datasets.py→examples/gdb7/qm7_datasets.py +37 −12 Original line number Diff line number Diff line """ gdb7 dataset loader. qm7 dataset loader. """ from __future__ import print_function from __future__ import division Loading @@ -12,7 +12,7 @@ import deepchem as dc import scipy.io import csv def load_gdb7_from_mat(split=0): def load_qm7_from_mat(featurizer=None, split=0): if not os.path.exists('qm7.mat'): os.system('wget http://www.quantum-machine.org/data/qm7.mat') dataset = scipy.io.loadmat('qm7.mat') Loading @@ -35,20 +35,45 @@ def load_gdb7_from_mat(split=0): train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) gdb7_tasks = ["atomization_energy"] return gdb7_tasks, (train_dataset, test_dataset), transformers qm7_tasks = ["atomization_energy"] return qm7_tasks, (train_dataset, test_dataset), transformers def load_gdb7(featurizer=None, split='random'): """Load gdb7 datasets.""" # Featurize gdb7 dataset print("About to featurize gdb7 dataset.") def load_qm7b_from_mat(featurizer=None, split='stratified'): if not os.path.exists('qm7b.mat'): os.system('wget http://www.quantum-machine.org/data/qm7b.mat') dataset_b = scipy.io.loadmat('qm7b.mat') X = dataset_b['X'] y = dataset_b['T'] w = np.ones_like(y) dataset = dc.data.NumpyDataset(X, y, w, ids=None) transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=dataset)] for transformer in transformers: dataset = transformer.transform(dataset) splitters = {'index': dc.splits.IndexSplitter(), 'random': dc.splits.RandomSplitter(), 'stratified': dc.splits.SingletaskStratifiedSplitter()} splitter = splitters[split] train_dataset, test_dataset = splitter.train_test_split(dataset) qm7_tasks = np.arange(y.shape[1]) return qm7_tasks, (train_dataset, test_dataset), transformers def load_qm7(featurizer=None, split='random'): """Load qm7 datasets.""" # Featurize qm7 dataset print("About to featurize qm7 dataset.") current_dir = os.path.dirname(os.path.realpath(__file__)) dataset_file = os.path.join( current_dir, "./gdb7.sdf") gdb7_tasks = ["u0_atom"] qm7_tasks = ["u0_atom"] if featurizer is None: featurizer = dc.feat.CoulombMatrixEig(23) loader = dc.data.SDFLoader(tasks=gdb7_tasks, smiles_field="smiles", loader = dc.data.SDFLoader(tasks=qm7_tasks, smiles_field="smiles", mol_field="mol", featurizer=featurizer) dataset = loader.featurize(dataset_file) Loading @@ -61,7 +86,7 @@ def load_gdb7(featurizer=None, split='random'): dataset = transformer.transform(dataset) split_file = os.path.join( current_dir, "./gdb7_splits.csv") current_dir, "./qm7_splits.csv") split_indices = [] with open(split_file, 'r') as f: Loading @@ -76,4 +101,4 @@ def load_gdb7(featurizer=None, split='random'): 'indice': dc.splits.IndiceSplitter(valid_indices=split_indices[1])} splitter = splitters[split] train, valid, test = splitter.train_valid_test_split(dataset) return gdb7_tasks, (train, valid, test), transformers return qm7_tasks, (train, valid, test), transformers examples/gdb7/gdb7_sklearn.py→examples/gdb7/qm7_sklearn.py +0 −0 File moved. View file examples/gdb7/gdb7_splits.csv→examples/gdb7/qm7_splits.csv +0 −0 File moved. View file examples/gdb7/gdb7_tf_model.py→examples/gdb7/qm7_tf_model.py +3 −3 Original line number Diff line number Diff line """ Script that trains Tensorflow singletask models on GDB7 dataset. Script that trains Tensorflow singletask models on QM7 dataset. """ from __future__ import print_function from __future__ import division Loading @@ -8,13 +8,13 @@ from __future__ import unicode_literals import os import deepchem as dc import numpy as np from gdb7_datasets import load_gdb7_from_mat from qm7_datasets import load_qm7_from_mat np.random.seed(123) split = 0 num_atoms = 23 gdb7_tasks, datasets, transformers = load_gdb7_from_mat(split) qm7_tasks, datasets, transformers = load_qm7_from_mat(split) train_dataset, test_dataset = datasets fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)] Loading Loading
examples/gdb7/gdb7_datasets.py→examples/gdb7/qm7_datasets.py +37 −12 Original line number Diff line number Diff line """ gdb7 dataset loader. qm7 dataset loader. """ from __future__ import print_function from __future__ import division Loading @@ -12,7 +12,7 @@ import deepchem as dc import scipy.io import csv def load_gdb7_from_mat(split=0): def load_qm7_from_mat(featurizer=None, split=0): if not os.path.exists('qm7.mat'): os.system('wget http://www.quantum-machine.org/data/qm7.mat') dataset = scipy.io.loadmat('qm7.mat') Loading @@ -35,20 +35,45 @@ def load_gdb7_from_mat(split=0): train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) gdb7_tasks = ["atomization_energy"] return gdb7_tasks, (train_dataset, test_dataset), transformers qm7_tasks = ["atomization_energy"] return qm7_tasks, (train_dataset, test_dataset), transformers def load_gdb7(featurizer=None, split='random'): """Load gdb7 datasets.""" # Featurize gdb7 dataset print("About to featurize gdb7 dataset.") def load_qm7b_from_mat(featurizer=None, split='stratified'): if not os.path.exists('qm7b.mat'): os.system('wget http://www.quantum-machine.org/data/qm7b.mat') dataset_b = scipy.io.loadmat('qm7b.mat') X = dataset_b['X'] y = dataset_b['T'] w = np.ones_like(y) dataset = dc.data.NumpyDataset(X, y, w, ids=None) transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=dataset)] for transformer in transformers: dataset = transformer.transform(dataset) splitters = {'index': dc.splits.IndexSplitter(), 'random': dc.splits.RandomSplitter(), 'stratified': dc.splits.SingletaskStratifiedSplitter()} splitter = splitters[split] train_dataset, test_dataset = splitter.train_test_split(dataset) qm7_tasks = np.arange(y.shape[1]) return qm7_tasks, (train_dataset, test_dataset), transformers def load_qm7(featurizer=None, split='random'): """Load qm7 datasets.""" # Featurize qm7 dataset print("About to featurize qm7 dataset.") current_dir = os.path.dirname(os.path.realpath(__file__)) dataset_file = os.path.join( current_dir, "./gdb7.sdf") gdb7_tasks = ["u0_atom"] qm7_tasks = ["u0_atom"] if featurizer is None: featurizer = dc.feat.CoulombMatrixEig(23) loader = dc.data.SDFLoader(tasks=gdb7_tasks, smiles_field="smiles", loader = dc.data.SDFLoader(tasks=qm7_tasks, smiles_field="smiles", mol_field="mol", featurizer=featurizer) dataset = loader.featurize(dataset_file) Loading @@ -61,7 +86,7 @@ def load_gdb7(featurizer=None, split='random'): dataset = transformer.transform(dataset) split_file = os.path.join( current_dir, "./gdb7_splits.csv") current_dir, "./qm7_splits.csv") split_indices = [] with open(split_file, 'r') as f: Loading @@ -76,4 +101,4 @@ def load_gdb7(featurizer=None, split='random'): 'indice': dc.splits.IndiceSplitter(valid_indices=split_indices[1])} splitter = splitters[split] train, valid, test = splitter.train_valid_test_split(dataset) return gdb7_tasks, (train, valid, test), transformers return qm7_tasks, (train, valid, test), transformers
examples/gdb7/gdb7_tf_model.py→examples/gdb7/qm7_tf_model.py +3 −3 Original line number Diff line number Diff line """ Script that trains Tensorflow singletask models on GDB7 dataset. Script that trains Tensorflow singletask models on QM7 dataset. """ from __future__ import print_function from __future__ import division Loading @@ -8,13 +8,13 @@ from __future__ import unicode_literals import os import deepchem as dc import numpy as np from gdb7_datasets import load_gdb7_from_mat from qm7_datasets import load_qm7_from_mat np.random.seed(123) split = 0 num_atoms = 23 gdb7_tasks, datasets, transformers = load_gdb7_from_mat(split) qm7_tasks, datasets, transformers = load_qm7_from_mat(split) train_dataset, test_dataset = datasets fit_transformers = [dc.trans.CoulombFitTransformer(train_dataset)] Loading
