changes (cb8a0ad6) · Commits · 钟慕尧 / deepchem

deepchem/molnet/load_function/bace_features.py

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		bace_user_specified_features = [
		'MW', 'AlogP', 'HBA', 'HBD', 'RB', 'HeavyAtomCount', 'ChiralCenterCount',
		'ChiralCenterCountAllPossible', 'RingCount', 'PSA', 'Estate', 'MR', 'Polar',
		'sLi_Key', 'ssBe_Key', 'ssssBem_Key', 'sBH2_Key', 'ssBH_Key', 'sssB_Key',
		'ssssBm_Key', 'sCH3_Key', 'dCH2_Key', 'ssCH2_Key', 'tCH_Key', 'dsCH_Key',
		'aaCH_Key', 'sssCH_Key', 'ddC_Key', 'tsC_Key', 'dssC_Key', 'aasC_Key',
		'aaaC_Key', 'ssssC_Key', 'sNH3_Key', 'sNH2_Key', 'ssNH2_Key', 'dNH_Key',
		'ssNH_Key', 'aaNH_Key', 'tN_Key', 'sssNH_Key', 'dsN_Key', 'aaN_Key',
		'sssN_Key', 'ddsN_Key', 'aasN_Key', 'ssssN_Key', 'daaN_Key', 'sOH_Key',
		'dO_Key', 'ssO_Key', 'aaO_Key', 'aOm_Key', 'sOm_Key', 'sF_Key', 'sSiH3_Key',
		'ssSiH2_Key', 'sssSiH_Key', 'ssssSi_Key', 'sPH2_Key', 'ssPH_Key',
		'sssP_Key', 'dsssP_Key', 'ddsP_Key', 'sssssP_Key', 'sSH_Key', 'dS_Key',
		'ssS_Key', 'aaS_Key', 'dssS_Key', 'ddssS_Key', 'ssssssS_Key', 'Sm_Key',
		'sCl_Key', 'sGeH3_Key', 'ssGeH2_Key', 'sssGeH_Key', 'ssssGe_Key',
		'sAsH2_Key', 'ssAsH_Key', 'sssAs_Key', 'dsssAs_Key', 'ddsAs_Key',
		'sssssAs_Key', 'sSeH_Key', 'dSe_Key', 'ssSe_Key', 'aaSe_Key', 'dssSe_Key',
		'ssssssSe_Key', 'ddssSe_Key', 'sBr_Key', 'sSnH3_Key', 'ssSnH2_Key',
		'sssSnH_Key', 'ssssSn_Key', 'sI_Key', 'sPbH3_Key', 'ssPbH2_Key',
		'sssPbH_Key', 'ssssPb_Key', 'sLi_Cnt', 'ssBe_Cnt', 'ssssBem_Cnt',
		'sBH2_Cnt', 'ssBH_Cnt', 'sssB_Cnt', 'ssssBm_Cnt', 'sCH3_Cnt', 'dCH2_Cnt',
		'ssCH2_Cnt', 'tCH_Cnt', 'dsCH_Cnt', 'aaCH_Cnt', 'sssCH_Cnt', 'ddC_Cnt',
		'tsC_Cnt', 'dssC_Cnt', 'aasC_Cnt', 'aaaC_Cnt', 'ssssC_Cnt', 'sNH3_Cnt',
		'sNH2_Cnt', 'ssNH2_Cnt', 'dNH_Cnt', 'ssNH_Cnt', 'aaNH_Cnt', 'tN_Cnt',
		'sssNH_Cnt', 'dsN_Cnt', 'aaN_Cnt', 'sssN_Cnt', 'ddsN_Cnt', 'aasN_Cnt',
		'ssssN_Cnt', 'daaN_Cnt', 'sOH_Cnt', 'dO_Cnt', 'ssO_Cnt', 'aaO_Cnt',
		'aOm_Cnt', 'sOm_Cnt', 'sF_Cnt', 'sSiH3_Cnt', 'ssSiH2_Cnt', 'sssSiH_Cnt',
		'ssssSi_Cnt', 'sPH2_Cnt', 'ssPH_Cnt', 'sssP_Cnt', 'dsssP_Cnt', 'ddsP_Cnt',
		'sssssP_Cnt', 'sSH_Cnt', 'dS_Cnt', 'ssS_Cnt', 'aaS_Cnt', 'dssS_Cnt',
		'ddssS_Cnt', 'ssssssS_Cnt', 'Sm_Cnt', 'sCl_Cnt', 'sGeH3_Cnt', 'ssGeH2_Cnt',
		'sssGeH_Cnt', 'ssssGe_Cnt', 'sAsH2_Cnt', 'ssAsH_Cnt', 'sssAs_Cnt',
		'dsssAs_Cnt', 'ddsAs_Cnt', 'sssssAs_Cnt', 'sSeH_Cnt', 'dSe_Cnt', 'ssSe_Cnt',
		'aaSe_Cnt', 'dssSe_Cnt', 'ssssssSe_Cnt', 'ddssSe_Cnt', 'sBr_Cnt',
		'sSnH3_Cnt', 'ssSnH2_Cnt', 'sssSnH_Cnt', 'ssssSn_Cnt', 'sI_Cnt',
		'sPbH3_Cnt', 'ssPbH2_Cnt', 'sssPbH_Cnt', 'ssssPb_Cnt', 'sLi_Sum',
		'ssBe_Sum', 'ssssBem_Sum', 'sBH2_Sum', 'ssBH_Sum', 'sssB_Sum', 'ssssBm_Sum',
		'sCH3_Sum', 'dCH2_Sum', 'ssCH2_Sum', 'tCH_Sum', 'dsCH_Sum', 'aaCH_Sum',
		'sssCH_Sum', 'ddC_Sum', 'tsC_Sum', 'dssC_Sum', 'aasC_Sum', 'aaaC_Sum',
		'ssssC_Sum', 'sNH3_Sum', 'sNH2_Sum', 'ssNH2_Sum', 'dNH_Sum', 'ssNH_Sum',
		'aaNH_Sum', 'tN_Sum', 'sssNH_Sum', 'dsN_Sum', 'aaN_Sum', 'sssN_Sum',
		'ddsN_Sum', 'aasN_Sum', 'ssssN_Sum', 'daaN_Sum', 'sOH_Sum', 'dO_Sum',
		'ssO_Sum', 'aaO_Sum', 'aOm_Sum', 'sOm_Sum', 'sF_Sum', 'sSiH3_Sum',
		'ssSiH2_Sum', 'sssSiH_Sum', 'ssssSi_Sum', 'sPH2_Sum', 'ssPH_Sum',
		'sssP_Sum', 'dsssP_Sum', 'ddsP_Sum', 'sssssP_Sum', 'sSH_Sum', 'dS_Sum',
		'ssS_Sum', 'aaS_Sum', 'dssS_Sum', 'ddssS_Sum', 'ssssssS_Sum', 'Sm_Sum',
		'sCl_Sum', 'sGeH3_Sum', 'ssGeH2_Sum', 'sssGeH_Sum', 'ssssGe_Sum',
		'sAsH2_Sum', 'ssAsH_Sum', 'sssAs_Sum', 'dsssAs_Sum', 'ddsAs_Sum',
		'sssssAs_Sum', 'sSeH_Sum', 'dSe_Sum', 'ssSe_Sum', 'aaSe_Sum', 'dssSe_Sum',
		'ssssssSe_Sum', 'ddssSe_Sum', 'sBr_Sum', 'sSnH3_Sum', 'ssSnH2_Sum',
		'sssSnH_Sum', 'ssssSn_Sum', 'sI_Sum', 'sPbH3_Sum', 'ssPbH2_Sum',
		'sssPbH_Sum', 'ssssPb_Sum', 'sLi_Avg', 'ssBe_Avg', 'ssssBem_Avg',
		'sBH2_Avg', 'ssBH_Avg', 'sssB_Avg', 'ssssBm_Avg', 'sCH3_Avg', 'dCH2_Avg',
		'ssCH2_Avg', 'tCH_Avg', 'dsCH_Avg', 'aaCH_Avg', 'sssCH_Avg', 'ddC_Avg',
		'tsC_Avg', 'dssC_Avg', 'aasC_Avg', 'aaaC_Avg', 'ssssC_Avg', 'sNH3_Avg',
		'sNH2_Avg', 'ssNH2_Avg', 'dNH_Avg', 'ssNH_Avg', 'aaNH_Avg', 'tN_Avg',
		'sssNH_Avg', 'dsN_Avg', 'aaN_Avg', 'sssN_Avg', 'ddsN_Avg', 'aasN_Avg',
		'ssssN_Avg', 'daaN_Avg', 'sOH_Avg', 'dO_Avg', 'ssO_Avg', 'aaO_Avg',
		'aOm_Avg', 'sOm_Avg', 'sF_Avg', 'sSiH3_Avg', 'ssSiH2_Avg', 'sssSiH_Avg',
		'ssssSi_Avg', 'sPH2_Avg', 'ssPH_Avg', 'sssP_Avg', 'dsssP_Avg', 'ddsP_Avg',
		'sssssP_Avg', 'sSH_Avg', 'dS_Avg', 'ssS_Avg', 'aaS_Avg', 'dssS_Avg',
		'ddssS_Avg', 'ssssssS_Avg', 'Sm_Avg', 'sCl_Avg', 'sGeH3_Avg', 'ssGeH2_Avg',
		'sssGeH_Avg', 'ssssGe_Avg', 'sAsH2_Avg', 'ssAsH_Avg', 'sssAs_Avg',
		'dsssAs_Avg', 'ddsAs_Avg', 'sssssAs_Avg', 'sSeH_Avg', 'dSe_Avg', 'ssSe_Avg',
		'aaSe_Avg', 'dssSe_Avg', 'ssssssSe_Avg', 'ddssSe_Avg', 'sBr_Avg',
		'sSnH3_Avg', 'ssSnH2_Avg', 'sssSnH_Avg', 'ssssSn_Avg', 'sI_Avg',
		'sPbH3_Avg', 'ssPbH2_Avg', 'sssPbH_Avg', 'ssssPb_Avg', 'First Zagreb (ZM1)',
		'First Zagreb index by valence vertex degrees (ZM1V)',
		'Second Zagreb (ZM2)',
		'Second Zagreb index by valence vertex degrees (ZM2V)', 'Polarity (Pol)',
		'Narumi Simple Topological (NST)', 'Narumi Harmonic Topological (NHT)',
		'Narumi Geometric Topological (NGT)', 'Total structure connectivity (TSC)',
		'Wiener (W)', 'Mean Wiener (MW)', 'Xu (Xu)', 'Quadratic (QIndex)',
		'Radial centric (RC)', 'Mean Square Distance Balaban (MSDB)',
		'Superpendentic (SP)', 'Harary (Har)', 'Log of product of row sums (LPRS)',
		'Pogliani (Pog)', 'Schultz Molecular Topological (SMT)',
		'Schultz Molecular Topological by valence vertex degrees (SMTV)',
		'Mean Distance Degree Deviation (MDDD)', 'Ramification (Ram)',
		'Gutman Molecular Topological (GMT)',
		'Gutman MTI by valence vertex degrees (GMTV)',
		'Average vertex distance degree (AVDD)', 'Unipolarity (UP)',
		'Centralization (CENT)', 'Variation (VAR)',
		'Molecular electrotopological variation (MEV)',
		'Maximal electrotopological positive variation (MEPV)',
		'Maximal electrotopological negative variation (MENV)',
		'Eccentric connectivity (ECCc)', 'Eccentricity (ECC)',
		'Average eccentricity (AECC)', 'Eccentric (DECC)',
		'Valence connectivity index chi-0 (vX0)',
		'Valence connectivity index chi-1 (vX1)',
		'Valence connectivity index chi-2 (vX2)',
		'Valence connectivity index chi-3 (vX3)',
		'Valence connectivity index chi-4 (vX4)',
		'Valence connectivity index chi-5 (vX5)',
		'Average valence connectivity index chi-0 (AvX0)',
		'Average valence connectivity index chi-1 (AvX1)',
		'Average valence connectivity index chi-2 (AvX2)',
		'Average valence connectivity index chi-3 (AvX3)',
		'Average valence connectivity index chi-4 (AvX4)',
		'Average valence connectivity index chi-5 (AvX5)', 'Quasi Wiener (QW)',
		'First Mohar (FM)', 'Second Mohar (SM)', 'Spanning tree number (STN)',
		'Kier benzene-likeliness index (KBLI)',
		'Topological charge index of order 1 (TCI1)',
		'Topological charge index of order 2 (TCI2)',
		'Topological charge index of order 3 (TCI3)',
		'Topological charge index of order 4 (TCI4)',
		'Topological charge index of order 5 (TCI5)',
		'Topological charge index of order 6 (TCI6)',
		'Topological charge index of order 7 (TCI7)',
		'Topological charge index of order 8 (TCI8)',
		'Topological charge index of order 9 (TCI9)',
		'Topological charge index of order 10 (TCI10)',
		'Mean topological charge index of order 1 (MTCI1)',
		'Mean topological charge index of order 2 (MTCI2)',
		'Mean topological charge index of order 3 (MTCI3)',
		'Mean topological charge index of order 4 (MTCI4)',
		'Mean topological charge index of order 5 (MTCI5)',
		'Mean topological charge index of order 6 (MTCI6)',
		'Mean topological charge index of order 7 (MTCI7)',
		'Mean topological charge index of order 8 (MTCI8)',
		'Mean topological charge index of order 9 (MTCI9)',
		'Mean topological charge index of order 10 (MTCI10)',
		'Global topological charge (GTC)', 'Hyper-distance-path index (HDPI)',
		'Reciprocal hyper-distance-path index (RHDPI)',
		'Square reciprocal distance sum (SRDS)',
		'Modified Randic connectivity (MRC)', 'Balaban centric (BC)',
		'Lopping centric (LC)', 'Kier Hall electronegativity (KHE)',
		'Sum of topological distances between N..N (STD(N N))',
		'Sum of topological distances between N..O (STD(N O))',
		'Sum of topological distances between N..S (STD(N S))',
		'Sum of topological distances between N..P (STD(N P))',
		'Sum of topological distances between N..F (STD(N F))',
		'Sum of topological distances between N..Cl (STD(N Cl))',
		'Sum of topological distances between N..Br (STD(N Br))',
		'Sum of topological distances between N..I (STD(N I))',
		'Sum of topological distances between O..O (STD(O O))',
		'Sum of topological distances between O..S (STD(O S))',
		'Sum of topological distances between O..P (STD(O P))',
		'Sum of topological distances between O..F (STD(O F))',
		'Sum of topological distances between O..Cl (STD(O Cl))',
		'Sum of topological distances between O..Br (STD(O Br))',
		'Sum of topological distances between O..I (STD(O I))',
		'Sum of topological distances between S..S (STD(S S))',
		'Sum of topological distances between S..P (STD(S P))',
		'Sum of topological distances between S..F (STD(S F))',
		'Sum of topological distances between S..Cl (STD(S Cl))',
		'Sum of topological distances between S..Br (STD(S Br))',
		'Sum of topological distances between S..I (STD(S I))',
		'Sum of topological distances between P..P (STD(P P))',
		'Sum of topological distances between P..F (STD(P F))',
		'Sum of topological distances between P..Cl (STD(P Cl))',
		'Sum of topological distances between P..Br (STD(P Br))',
		'Sum of topological distances between P..I (STD(P I))',
		'Sum of topological distances between F..F (STD(F F))',
		'Sum of topological distances between F..Cl (STD(F Cl))',
		'Sum of topological distances between F..Br (STD(F Br))',
		'Sum of topological distances between F..I (STD(F I))',
		'Sum of topological distances between Cl..Cl (STD(Cl Cl))',
		'Sum of topological distances between Cl..Br (STD(Cl Br))',
		'Sum of topological distances between Cl..I (STD(Cl I))',
		'Sum of topological distances between Br..Br (STD(Br Br))',
		'Sum of topological distances between Br..I (STD(Br I))',
		'Sum of topological distances between I..I (STD(I I))',
		'Wiener-type index from Z weighted distance matrix - Barysz matrix (WhetZ)',
		'Wiener-type index from electronegativity weighted distance matrix (Whete)',
		'Wiener-type index from mass weighted distance matrix (Whetm)',
		'Wiener-type index from van der waals weighted distance matrix (Whetv)',
		'Wiener-type index from polarizability weighted distance matrix (Whetp)',
		'Balaban-type index from Z weighted distance matrix - Barysz matrix (JhetZ)',
		'Balaban-type index from electronegativity weighted distance matrix (Jhete)',
		'Balaban-type index from mass weighted distance matrix (Jhetm)',
		'Balaban-type index from van der waals weighted distance matrix (Jhetv)',
		'Balaban-type index from polarizability weighted distance matrix (Jhetp)',
		'Topological diameter (TD)', 'Topological radius (TR)',
		'Petitjean 2D shape (PJ2DS)', 'Balaban distance connectivity index (J)',
		'Solvation connectivity index chi-0 (SCIX0)',
		'Solvation connectivity index chi-1 (SCIX1)',
		'Solvation connectivity index chi-2 (SCIX2)',
		'Solvation connectivity index chi-3 (SCIX3)',
		'Solvation connectivity index chi-4 (SCIX4)',
		'Solvation connectivity index chi-5 (SCIX5)',
		'Connectivity index chi-0 (CIX0)',
		'Connectivity chi-1 [Randic connectivity] (CIX1)',
		'Connectivity index chi-2 (CIX2)', 'Connectivity index chi-3 (CIX3)',
		'Connectivity index chi-4 (CIX4)', 'Connectivity index chi-5 (CIX5)',
		'Average connectivity index chi-0 (ACIX0)',
		'Average connectivity index chi-1 (ACIX1)',
		'Average connectivity index chi-2 (ACIX2)',
		'Average connectivity index chi-3 (ACIX3)',
		'Average connectivity index chi-4 (ACIX4)',
		'Average connectivity index chi-5 (ACIX5)',
		'reciprocal distance Randic-type index (RDR)',
		'reciprocal distance square Randic-type index (RDSR)',
		'1-path Kier alpha-modified shape index (KAMS1)',
		'2-path Kier alpha-modified shape index (KAMS2)',
		'3-path Kier alpha-modified shape index (KAMS3)', 'Kier flexibility (KF)',
		'path/walk 2 - Randic shape index (RSIpw2)',
		'path/walk 3 - Randic shape index (RSIpw3)',
		'path/walk 4 - Randic shape index (RSIpw4)',
		'path/walk 5 - Randic shape index (RSIpw5)',
		'E-state topological parameter (ETP)', 'Ring Count 3 (RNGCNT3)',
		'Ring Count 4 (RNGCNT4)', 'Ring Count 5 (RNGCNT5)',
		'Ring Count 6 (RNGCNT6)', 'Ring Count 7 (RNGCNT7)',
		'Ring Count 8 (RNGCNT8)', 'Ring Count 9 (RNGCNT9)',
		'Ring Count 10 (RNGCNT10)', 'Ring Count 11 (RNGCNT11)',
		'Ring Count 12 (RNGCNT12)', 'Ring Count 13 (RNGCNT13)',
		'Ring Count 14 (RNGCNT14)', 'Ring Count 15 (RNGCNT15)',
		'Ring Count 16 (RNGCNT16)', 'Ring Count 17 (RNGCNT17)',
		'Ring Count 18 (RNGCNT18)', 'Ring Count 19 (RNGCNT19)',
		'Ring Count 20 (RNGCNT20)', 'Atom Count (ATMCNT)', 'Bond Count (BNDCNT)',
		'Atoms in Ring System (ATMRNGCNT)', 'Bonds in Ring System (BNDRNGCNT)',
		'Cyclomatic number (CYCLONUM)', 'Number of ring systems (NRS)',
		'Normalized number of ring systems (NNRS)', 'Ring Fusion degree (RFD)',
		'Ring perimeter (RNGPERM)', 'Ring bridge count (RNGBDGE)',
		'Molecule cyclized degree (MCD)', 'Ring Fusion density (RFDELTA)',
		'Ring complexity index (RCI)', 'Van der Waals surface area (VSA)',
		'MR1 (MR1)', 'MR2 (MR2)', 'MR3 (MR3)', 'MR4 (MR4)', 'MR5 (MR5)',
		'MR6 (MR6)', 'MR7 (MR7)', 'MR8 (MR8)', 'ALOGP1 (ALOGP1)', 'ALOGP2 (ALOGP2)',
		'ALOGP3 (ALOGP3)', 'ALOGP4 (ALOGP4)', 'ALOGP5 (ALOGP5)', 'ALOGP6 (ALOGP6)',
		'ALOGP7 (ALOGP7)', 'ALOGP8 (ALOGP8)', 'ALOGP9 (ALOGP9)',
		'ALOGP10 (ALOGP10)', 'PEOE1 (PEOE1)', 'PEOE2 (PEOE2)', 'PEOE3 (PEOE3)',
		'PEOE4 (PEOE4)', 'PEOE5 (PEOE5)', 'PEOE6 (PEOE6)', 'PEOE7 (PEOE7)',
		'PEOE8 (PEOE8)', 'PEOE9 (PEOE9)', 'PEOE10 (PEOE10)', 'PEOE11 (PEOE11)',
		'PEOE12 (PEOE12)', 'PEOE13 (PEOE13)', 'PEOE14 (PEOE14)'
		]

deepchem/molnet/load_function/chembl_datasets.py

deleted100644 → 0

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		"""
		ChEMBL dataset loader.
		"""
		import os
		import logging
		import deepchem
		from deepchem.molnet.load_function.chembl_tasks import chembl_tasks

		logger = logging.getLogger(__name__)

		DEFAULT_DIR = deepchem.utils.get_data_dir()


		def load_chembl(shard_size=2000,
		featurizer="ECFP",
		set="5thresh",
		split="random",
		reload=True,
		data_dir=None,
		save_dir=None,
		**kwargs):

		if data_dir is None:
		data_dir = DEFAULT_DIR
		if save_dir is None:
		save_dir = DEFAULT_DIR

		logger.info("About to load ChEMBL dataset.")

		if reload:
		save_folder = os.path.join(save_dir, "chembl-featurized", featurizer)
		if featurizer == "smiles2img":
		img_spec = kwargs.get("img_spec", "std")
		save_folder = os.path.join(save_folder, img_spec)
		save_folder = os.path.join(save_folder, str(split))

		loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
		save_folder)
		if loaded:
		return chembl_tasks, all_dataset, transformers

		dataset_path = os.path.join(data_dir, "chembl_%s.csv.gz" % set)
		if not os.path.exists(dataset_path):
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_5thresh.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_sparse.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_5thresh_ts_test.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_5thresh_ts_train.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_5thresh_ts_valid.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_sparse_ts_test.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_sparse_ts_train.csv.gz',
		dest_dir=data_dir)
		deepchem.utils.download_url(
		url=
		'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/chembl_year_sets/chembl_sparse_ts_valid.csv.gz',
		dest_dir=data_dir)

		if split == "year":
		train_files = os.path.join(
		data_dir, "./chembl_year_sets/chembl_%s_ts_train.csv.gz" % set)
		valid_files = os.path.join(
		data_dir, "./chembl_year_sets/chembl_%s_ts_valid.csv.gz" % set)
		test_files = os.path.join(
		data_dir, "./chembl_year_sets/chembl_%s_ts_test.csv.gz" % set)

		# Featurize ChEMBL dataset
		logger.info("About to featurize ChEMBL dataset.")
		if featurizer == 'ECFP':
		featurizer = deepchem.feat.CircularFingerprint(size=1024)
		elif featurizer == 'GraphConv':
		featurizer = deepchem.feat.ConvMolFeaturizer()
		elif featurizer == 'Weave':
		featurizer = deepchem.feat.WeaveFeaturizer()
		elif featurizer == 'Raw':
		featurizer = deepchem.feat.RawFeaturizer()
		elif featurizer == "smiles2img":
		img_spec = kwargs.get("img_spec", "std")
		img_size = kwargs.get("img_size", 80)
		featurizer = deepchem.feat.SmilesToImage(
		img_size=img_size, img_spec=img_spec)

		loader = deepchem.data.CSVLoader(
		tasks=chembl_tasks, smiles_field="smiles", featurizer=featurizer)

		if split == "year":
		logger.info("Featurizing train datasets")
		train = loader.featurize(train_files, shard_size=shard_size)
		logger.info("Featurizing valid datasets")
		valid = loader.featurize(valid_files, shard_size=shard_size)
		logger.info("Featurizing test datasets")
		test = loader.featurize(test_files, shard_size=shard_size)
		else:
		dataset = loader.featurize(dataset_path, shard_size=shard_size)

		if split is None:
		transformers = [
		deepchem.trans.NormalizationTransformer(
		transform_y=True, dataset=dataset)
		]

		logger.info("Split is None, about to transform data.")
		for transformer in transformers:
		dataset = transformer.transform(dataset)

		return chembl_tasks, (dataset, None, None), transformers

		if split != "year":
		splitters = {
		'index': deepchem.splits.IndexSplitter(),
		'random': deepchem.splits.RandomSplitter(),
		'scaffold': deepchem.splits.ScaffoldSplitter(),
		}

		splitter = splitters[split]
		logger.info("Performing new split.")
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)

		transformers = [
		deepchem.trans.NormalizationTransformer(transform_y=True, dataset=train)
		]

		logger.info("About to transform data.")
		for transformer in transformers:
		train = transformer.transform(train)
		valid = transformer.transform(valid)
		test = transformer.transform(test)

		if reload:
		deepchem.utils.save.save_dataset_to_disk(save_folder, train, valid, test,
		transformers)
		return chembl_tasks, (train, valid, test), transformers

deepchem/molnet/load_function/chembl_tasks.py

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deepchem/molnet/load_function/clearance_datasets.py

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		"""
		clearance dataset loader.
		"""
		import os
		import logging
		import deepchem

		logger = logging.getLogger(__name__)

		DEFAULT_DIR = deepchem.utils.get_data_dir()
		CLEARANCE_URL = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/clearance.csv'


		def load_clearance(featurizer='ECFP',
		split='random',
		reload=True,
		move_mean=True,
		data_dir=None,
		save_dir=None,
		**kwargs):
		"""Load clearance datasets."""
		# Featurize clearance dataset
		logger.info("About to featurize clearance dataset.")
		logger.info("About to load clearance dataset.")
		if data_dir is None:
		data_dir = DEFAULT_DIR
		if save_dir is None:
		save_dir = DEFAULT_DIR

		clearance_tasks = ['exp']

		if reload:
		save_folder = os.path.join(save_dir, "clearance-featurized")
		if not move_mean:
		save_folder = os.path.join(save_folder, str(featurizer) + "_mean_unmoved")
		else:
		save_folder = os.path.join(save_folder, str(featurizer))

		if featurizer == "smiles2img":
		img_spec = kwargs.get("img_spec", "std")
		save_folder = os.path.join(save_folder, img_spec)
		save_folder = os.path.join(save_folder, str(split))

		loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
		save_folder)
		if loaded:
		return clearance_tasks, all_dataset, transformers

		dataset_file = os.path.join(data_dir, "clearance.csv")
		if not os.path.exists(dataset_file):
		deepchem.utils.download_url(url=CLEARANCE_URL, dest_dir=data_dir)

		if featurizer == 'ECFP':
		featurizer = deepchem.feat.CircularFingerprint(size=1024)
		elif featurizer == 'GraphConv':
		featurizer = deepchem.feat.ConvMolFeaturizer()
		elif featurizer == 'Weave':
		featurizer = deepchem.feat.WeaveFeaturizer()
		elif featurizer == 'Raw':
		featurizer = deepchem.feat.RawFeaturizer()
		elif featurizer == "smiles2img":
		img_spec = kwargs.get("img_spec", "std")
		img_size = kwargs.get("img_size", 80)
		featurizer = deepchem.feat.SmilesToImage(
		img_size=img_size, img_spec=img_spec)

		loader = deepchem.data.CSVLoader(
		tasks=clearance_tasks, smiles_field="smiles", featurizer=featurizer)
		dataset = loader.featurize(dataset_file, shard_size=8192)

		if split is None:
		# Initialize transformers
		transformers = [
		deepchem.trans.NormalizationTransformer(
		transform_y=True, dataset=dataset, move_mean=move_mean)
		]

		logger.info("Split is None, about to transform data")
		for transformer in transformers:
		dataset = transformer.transform(dataset)

		return clearance_tasks, (dataset, None, None), transformers

		splitters = {
		'index': deepchem.splits.IndexSplitter(),
		'random': deepchem.splits.RandomSplitter(),
		'scaffold': deepchem.splits.ScaffoldSplitter(),
		'stratified': deepchem.splits.SingletaskStratifiedSplitter()
		}
		splitter = splitters[split]
		logger.info("About to split data with {} splitter.".format(split))
		frac_train = kwargs.get("frac_train", 0.8)
		frac_valid = kwargs.get('frac_valid', 0.1)
		frac_test = kwargs.get('frac_test', 0.1)

		train, valid, test = splitter.train_valid_test_split(
		dataset,
		frac_train=frac_train,
		frac_valid=frac_valid,
		frac_test=frac_test)

		transformers = [
		deepchem.trans.NormalizationTransformer(
		transform_y=True, dataset=train, move_mean=move_mean)
		]

		logger.info("About to transform data")
		for transformer in transformers:
		train = transformer.transform(train)
		valid = transformer.transform(valid)
		test = transformer.transform(test)

		if reload:
		deepchem.utils.save.save_dataset_to_disk(save_folder, train, valid, test,
		transformers)
		return clearance_tasks, (train, valid, test), transformers

deepchem/molnet/load_function/clintox_datasets.py

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