First version that should be runnable on sherlock.

    else:

      raise ValueError("Invalid model type provided.")

    model.fit(X_train, y_train.ravel())

    # TODO(rbharath): This breaks on regression datasets

    results = eval_model(test, model, {target: task_types[target]},

        desc_transforms, modeltype="sklearn", add_descriptors=add_descriptors)

def parse_args(input_args=None):

  """Parse command-line arguments."""

  parser = argparse.ArgumentParser()

  parser.add_argument('--dataset', required=1, nargs="+",

  parser.add_argument('--datasets', required=1, nargs="+",

                      choices=['muv', 'pcba', 'dude', 'pfizer'],

                      help='Name of dataset to process.')

  parser.add_argument("--paths", required=1, nargs="+",

                      help = "Paths to input datasets.")

  parser.add_argument('--model', required=1,

                      choices=["logistic", "rf_classifier", "rf_regressor",

                      "linear", "ridge", "lasso", "lasso_lars", "elastic_net",

                  help="Learning rate for NN models.")

  parser.add_argument("--n-epochs", type=int, default=50,

                  help="Number of epochs for NN models.")

  parser.add_argument("--batchsize", type=int, default=32,

  parser.add_argument("--batch-size", type=int, default=32,

                  help="Number of examples per minibatch for NN models.")

  parser.add_argument("--decay", type=float, default=1e-4,

                  help="Learning rate decay for NN models.")

def main():

  args = parse_args()

  paths = {}

  if "muv" in args.dataset:

    paths["muv"] = "/home/rbharath/vs-datasets/muv"

  elif "pcba" in args.dataset:

    paths["pcba"] = "/home/rbharath/vs-datasets/pcba"

  elif "dude" in args.dataset:

    paths["dude"]= "/home/rbharath/vs-datasets/dude"

  # TODO(rbharath): The pfizer dataset is currently private. Remove this before

  # the public release of the code.

  elif "pfizer" in args.dataset:

    paths["pfizer"] = "/home/rbharath/private-datasets/pfizer"

  for dataset, path in zip(args.datasets, args.paths):

    paths[dataset] = path

  task_types, task_transforms = get_default_task_types_and_transforms(paths)

  desc_transforms = get_default_descriptor_transforms()

      desc_transforms, splittype=args.splittype, add_descriptors=False,

      n_hidden=args.n_hidden, learning_rate=args.learning_rate,

      dropout=args.dropout, nb_epoch=args.n_epochs, decay=args.decay,

      batch_size=args.batch_size,

      validation_split=args.validation_split)

  elif args.model == "multitask_deep_network":

    fit_multitask_mlp(paths.values(), task_types, task_transforms,

      desc_transforms, splittype=args.splittype, add_descriptors=False,

      n_hidden=args.n_hidden, learning_rate = args.learning_rate, dropout = args.dropout,

      batch_size=args.batch_size,

      nb_epoch=args.n_epochs, decay=args.decay, validation_split=args.validation_split)

  else:

    fit_singletask_models(paths.values(), args.model, task_types,

import numpy as np

from deep_chem.utils.preprocess import dataset_to_numpy

from deep_chem.utils.preprocess import labels_to_weights

from sklearn.metrics import mean_squared_error

from sklearn.metrics import roc_auc_score

from sklearn.metrics import r2_score

def model_predictions(test_set, model, n_targets, n_descriptors=0,

    add_descriptors=False, modeltype="sklearn"):