gdb7=>qm7

Giving regression performances of:

    MultitaskDNN(tf_regression),

    Graph convolution regression(graphconvreg)

on datasets: delaney(ESOL), nci, kaggle, pdbbind, gdb7, 

on datasets: delaney(ESOL), nci, kaggle, pdbbind, qm7, 

             chembl, sampl(FreeSolv)

time estimation listed in README file

from delaney.delaney_datasets import load_delaney

from pdbbind.pdbbind_datasets import load_pdbbind_grid

from chembl.chembl_datasets import load_chembl

from gdb7.gdb7_datasets import load_gdb7

from qm7.qm7_datasets import load_qm7

from sampl.sampl_datasets import load_sampl

from clintox.clintox_datasets import load_clintox

  Parameters

  ----------

  hyper_parameters: dict of list

  hyper_parameters: dict

      hyper parameters including layer size, dropout, learning rate, etc.

  dataset: string, optional (default='tox21')

      choice of which dataset to use, should be: tox21, muv, sider, 

      toxcast, pcba, delaney, kaggle, nci, clintox, pdbbind, chembl,

      gdb7, sampl

      qm7, sampl

  model: string,  optional (default='tf')

      choice of which model to use, should be: rf, tf, tf_robust, logreg,

      irv, graphconv, tf_regression, graphconvreg

  if dataset in ['muv', 'pcba', 'tox21', 'sider', 'toxcast', 'clintox']:

    mode = 'classification'

  elif dataset in [

      'kaggle', 'delaney', 'nci', 'pdbbind', 'chembl', 'gdb7', 'sampl'

      'kaggle', 'delaney', 'nci', 'pdbbind', 'chembl', 'qm7', 'sampl'

  ]:

    mode = 'regression'

  else:

    if not model in ['tf_regression']:

      return

  if dataset in ['gdb7']:

    featurizer = None

    if split in ['scaffold'

                ]:  # gdb7 supports index, random and indice splitting

      return

    if not model in ['tf_regression']:

      return

  if split in ['year']:

    if not dataset in ['chembl']:

      return

  elif split in ['indice']:

    if not dataset in ['gdb7']:

      return

  elif not split in [None, 'index', 'random', 'scaffold']:

    raise ValueError('Splitter function not supported')

      'delaney': load_delaney,

      'pdbbind': load_pdbbind_grid,

      'chembl': load_chembl,

      'gdb7': load_gdb7,

      'qm7': load_qm7,

      'sampl': load_sampl,

      'clintox': load_clintox

  }

  train_dataset, valid_dataset, test_dataset = all_dataset

  time_finish_loading = time.time()

  # time_finish_loading-time_start is the time(s) used for dataset loading

  if dataset in ['kaggle', 'pdbbind', 'gdb7']:

  if dataset in ['kaggle', 'pdbbind', 'qm7']:

    n_features = train_dataset.get_data_shape()[0]

    # dataset has customized features

      list of targets(tasks, datasets)

  transformers: BalancingTransformer struct

      loaded properties of dataset from load_* function

  hyper_parameters: dict of list

  hyper_parameters: dict

      hyper parameters including layer size, dropout, learning rate, etc.

  n_features: integer

      number of features, or length of binary fingerprints

      list of targets(tasks, datasets)

  transformers: BalancingTransformer struct

      loaded properties of dataset from load_* function

  hyper_parameters: dict of list

  hyper_parameters: dict

      hyper parameters including layer size, dropout, learning rate, etc.

  n_features: integer

      number of features, or length of binary fingerprints

      dest='dataset_args',

      default=[],

      help='Choice of dataset: tox21, sider, muv, toxcast, pcba, ' +

      'kaggle, delaney, nci, pdbbind, chembl, sampl, gdb7, clintox')

      'kaggle, delaney, nci, pdbbind, chembl, sampl, qm7, clintox')

  parser.add_argument(

      '-t',

      action='store_true',

  if len(datasets) == 0:

    datasets = [

        'tox21', 'sider', 'muv', 'toxcast', 'pcba', 'clintox', 'sampl',

        'delaney', 'nci', 'kaggle', 'pdbbind', 'chembl', 'gdb7'

        'delaney', 'nci', 'kaggle', 'pdbbind', 'chembl', 'qm7'

    ]

  #input hyperparameters