Added MolNet Loader for Chembl25_dataset

from deepchem.molnet.load_function.kinase_datasets import load_kinase

from deepchem.molnet.load_function.thermosol_datasets import load_thermosol

from deepchem.molnet.load_function.hppb_datasets import load_hppb

from deepchem.molnet.load_function.chembl25_datasets import load_chembl25

from deepchem.molnet.dnasim import simulate_motif_density_localization

from deepchem.molnet.dnasim import simulate_motif_counting

"""

ChEMBL dataset loader, for training ChemNet

"""

from __future__ import print_function

from __future__ import unicode_literals

import os

import numpy as np

import logging

import gzip

import shutil

import deepchem as dc

import pickle

from deepchem.feat import SmilesToSeq, SmilesToImage

from deepchem.feat.smiles_featurizers import create_char_to_idx

CHEMBL_URL = "https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/chembl_25.csv.gz"

DEFAULT_DIR = dc.utils.get_data_dir()

logger = logging.getLogger(__name__)

chembl25_tasks = [

    "MolWt", "HeavyAtomMolWt", "MolLogP", "MolMR", "TPSA", "LabuteASA",

    "HeavyAtomCount", "NHOHCount", "NOCount", "NumHAcceptors", "NumHDonors",

    "NumHeteroatoms", "NumRotatableBonds", "NumRadicalElectrons",

    "NumValenceElectrons", "NumAromaticRings", "NumSaturatedRings",

    "NumAliphaticRings", "NumAromaticCarbocycles", "NumSaturatedCarbocycles",

    "NumAliphaticCarbocycles", "NumAromaticHeterocycles",

    "NumSaturatedHeterocycles", "NumAliphaticHeterocycles", "PEOE_VSA1",

    "PEOE_VSA2", "PEOE_VSA3", "PEOE_VSA4", "PEOE_VSA5", "PEOE_VSA6",

    "PEOE_VSA7", "PEOE_VSA8", "PEOE_VSA9", "PEOE_VSA10", "PEOE_VSA11",

    "PEOE_VSA12", "PEOE_VSA13", "PEOE_VSA14", "SMR_VSA1", "SMR_VSA2",

    "SMR_VSA3", "SMR_VSA4", "SMR_VSA5", "SMR_VSA6", "SMR_VSA7", "SMR_VSA8",

    "SMR_VSA9", "SMR_VSA10", "SlogP_VSA1", "SlogP_VSA2", "SlogP_VSA3",

    "SlogP_VSA4", "SlogP_VSA5", "SlogP_VSA6", "SlogP_VSA7", "SlogP_VSA8",

    "SlogP_VSA9", "SlogP_VSA10", "SlogP_VSA11", "SlogP_VSA12", "EState_VSA1",

    "EState_VSA2", "EState_VSA3", "EState_VSA4", "EState_VSA5", "EState_VSA6",

    "EState_VSA7", "EState_VSA8", "EState_VSA9", "EState_VSA10", "EState_VSA11",

    "VSA_EState1", "VSA_EState2", "VSA_EState3", "VSA_EState4", "VSA_EState5",

    "VSA_EState6", "VSA_EState7", "VSA_EState8", "VSA_EState9", "VSA_EState10",

    "BalabanJ", "BertzCT", "Ipc", "Kappa1", "Kappa2", "Kappa3", "HallKierAlpha",

    "Chi0", "Chi1", "Chi0n", "Chi1n", "Chi2n", "Chi3n", "Chi4n", "Chi0v",

    "Chi1v", "Chi2v", "Chi3v", "Chi4v"

]

def load_chembl25(featurizer="smiles2seq",

                  split="random",

                  data_dir=None,

                  save_dir=None,

                  split_seed=None,

                  reload=True,

                  **kwargs):

  """Loads the ChEMBL25 dataset, featurizes it, and does a split.

  Parameters

  ----------

  featurizer: str, default smiles2seq

    Featurizer to use

  split: str, default None

    Splitter to use

  data_dir: str, default None

    Directory to download data to, or load dataset from. (TODO: If None, make tmp)

  save_dir: str, default None

    Directory to save the featurized dataset to. (TODO: If None, make tmp)

  split_seed: int, default None

    Seed to be used for splitting the dataset

  reload: bool, default True

    Whether to reload saved dataset

  """

  if data_dir is None:

    data_dir = DEFAULT_DIR

  if save_dir is None:

    save_dir = DEFAULT_DIR

  save_folder = os.path.join(save_dir, "chembl_25-featurized", str(featurizer))

  if reload:

    if not os.path.exists(save_folder):

      logger.warning(

          "{} does not exist. Reconstructing dataset.".format(save_folder))

    else:

      logger.info("{} exists. Restoring dataset.".format(save_folder))

      loaded, dataset, transformers = dc.utils.save.load_dataset_from_disk(

            save_folder)

      if loaded:

          return chembl25_tasks, dataset, transformers

  dataset_file = os.path.join(data_dir, "chembl_25.csv.gz")

  if not os.path.exists(dataset_file):

    logger.warning("File {} not found. Downloading dataset. (~555 MB)".format(

        dataset_file))

    dc.utils.download_url(url=CHEMBL_URL, dest_dir=data_dir)

  if featurizer == "smiles2seq":

    max_len = kwargs.get('max_len', 250)

    pad_len = kwargs.get('pad_len', 10)

    char_to_idx = create_char_to_idx(

        dataset_file, max_len=max_len, smiles_field="smiles")

    featurizer = SmilesToSeq(

        char_to_idx=char_to_idx, max_len=max_len, pad_len=pad_len)

  elif featurizer == "smiles2img":

    img_size = kwargs.get("img_size", 80)

    img_mode = kwargs.get("img_mode", "engd")

    res = kwargs.get("res", 0.5)

    featurizer = SmilesToImage(img_size=img_size, img_mode=img_mode, res=res)

  else:

    raise ValueError(

        "Featurizer of type {} is not supported".format(featurizer))

  loader = dc.data.CSVLoader(

      tasks=chembl25_tasks, smiles_field='smiles', featurizer=featurizer)

  dataset = loader.featurize(

      input_files=[dataset_file], shard_size=10000, data_dir=save_folder)

  if split is None:

    transformer = dc.trans.NormalizationTransformer(

        transform_X=False, transform_y=True, dataset=dataset)

    dataset = transformer.transform(dataset)

    return chembl25_tasks, (dataset, None, None), transformers

  splitters = {

      'index': dc.splits.IndexSplitter(),

      'random': dc.splits.RandomSplitter(),

      'scaffold': dc.splits.ScaffoldSplitter(),

  }

  logger.info("About to split data.")

  splitter = splitters[split]

  train, valid, test = splitter.train_valid_test_split(

      dataset, seed=split_seed)

  transformers = [

      dc.trans.NormalizationTransformer(

        transform_X=False, transform_y=True, dataset=train)

  ]

  for transformer in transformers:

    train = transformer.transform(train)

    valid = transformer.transform(valid)

    test = transformer.transform(test)

  if reload:

    dc.utils.save.save_dataset_to_disk(save_folder, train, valid, test,

                                       transformers)

  return chembl25_tasks, (train, valid, test), transformers