Loading examples/tox21/tox21_tensorgraph_graphconv_sluice.py→contrib/sluice/tox21_tensorgraph_graphconv_sluice.py +0 −0 File moved. View file examples/benchmark_low_data.pydeleted 100644 → 0 +0 −62 Original line number Diff line number Diff line #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Dec 8 16:48:05 2016 @author: Michael Wu Low data benchmark test Giving performances of: Siamese, attention-based embedding, residual embedding on datasets: muv, sider, tox21 time estimation listed in README file """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np import deepchem as dc import argparse np.random.seed(123) parser = argparse.ArgumentParser( description='Deepchem benchmark: ' + 'giving performances of different learning models on datasets') parser.add_argument( '-m', action='append', dest='model_args', default=[], help='Choice of model: siamese, attn, res') parser.add_argument( '-d', action='append', dest='dataset_args', default=[], help='Choice of dataset: tox21, sider, muv') parser.add_argument( '--cv', action='store_true', dest='cross_valid', default=False, help='whether to implement cross validation') args = parser.parse_args() #Datasets and models used in the benchmark test models = args.model_args datasets = args.dataset_args cross_valid = args.cross_valid if len(models) == 0: models = ['siamese', 'attn', 'res'] if len(datasets) == 0: datasets = ['tox21', 'sider', 'muv'] for dataset in datasets: for model in models: dc.molnet.run_benchmark_low_data( [dataset], str(model), cross_valid=cross_valid) examples/tox21/tox21_tensorgraph_DAG.py→examples/tox21/tox21_DAG.py +0 −0 File moved. View file Loading
examples/tox21/tox21_tensorgraph_graphconv_sluice.py→contrib/sluice/tox21_tensorgraph_graphconv_sluice.py +0 −0 File moved. View file
examples/benchmark_low_data.pydeleted 100644 → 0 +0 −62 Original line number Diff line number Diff line #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Dec 8 16:48:05 2016 @author: Michael Wu Low data benchmark test Giving performances of: Siamese, attention-based embedding, residual embedding on datasets: muv, sider, tox21 time estimation listed in README file """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np import deepchem as dc import argparse np.random.seed(123) parser = argparse.ArgumentParser( description='Deepchem benchmark: ' + 'giving performances of different learning models on datasets') parser.add_argument( '-m', action='append', dest='model_args', default=[], help='Choice of model: siamese, attn, res') parser.add_argument( '-d', action='append', dest='dataset_args', default=[], help='Choice of dataset: tox21, sider, muv') parser.add_argument( '--cv', action='store_true', dest='cross_valid', default=False, help='whether to implement cross validation') args = parser.parse_args() #Datasets and models used in the benchmark test models = args.model_args datasets = args.dataset_args cross_valid = args.cross_valid if len(models) == 0: models = ['siamese', 'attn', 'res'] if len(datasets) == 0: datasets = ['tox21', 'sider', 'muv'] for dataset in datasets: for model in models: dc.molnet.run_benchmark_low_data( [dataset], str(model), cross_valid=cross_valid)
