merging (9aaff43b) · Commits · 钟慕尧 / deepchem

README.md

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		@@ -653,6 +653,7 @@ Ligand Based
		Approaches](http://pubs.acs.org/doi/abs/10.1021/acs.jcim.6b00290)
		2. [Low Data Drug Discovery with One-shot Learning](https://arxiv.org/abs/1611.03199)
		3. [MoleculeNet: A Benchmark for Molecular Machine Learning](https://arxiv.org/abs/1703.00564)
		4. [Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity](https://arxiv.org/abs/1703.10603)

		## About Us
		DeepChem is a package by the [Pande group](https://pande.stanford.edu/) at Stanford. DeepChem was originally created by [Bharath Ramsundar](http://rbharath.github.io/), and has grown through the contributions of a number of undergraduate, graduate, and postdoctoral researchers working with the Pande lab.

deepchem/models/xgboost_models/init.py

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		@@ -2,7 +2,7 @@
		Scikit-learn wrapper interface of xgboost
		"""

		import xgboost as xgb
		from deepchem.utils.dependencies import xgboost as xgb
		import numpy as np
		import os
		from deepchem.models import Model

deepchem/molnet/load_function/hopv_datasets.py

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		@@ -41,16 +41,16 @@ def load_hopv(featurizer='ECFP', split='index', reload=True):
		return hopv_tasks, all_dataset, transformers

		if featurizer == 'ECFP':
		featurizer_func = deepchem.feat.CircularFingerprint(size=1024)
		featurizer = deepchem.feat.CircularFingerprint(size=1024)
		elif featurizer == 'GraphConv':
		featurizer_func = deepchem.feat.ConvMolFeaturizer()
		featurizer = deepchem.feat.ConvMolFeaturizer()
		elif featurizer == 'Weave':
		featurizer = deepchem.feat.WeaveFeaturizer()
		elif featurizer == 'Raw':
		featurizer = deepchem.feat.RawFeaturizer()

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

		# Initialize transformers

deepchem/molnet/run_benchmark.py

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		@@ -226,22 +226,30 @@ def load_dataset(dataset, featurizer, split='random'):
		choice of splitter function, None = using the default splitter
		"""
		dataset_loading_functions = {
		'tox21': deepchem.molnet.load_tox21,
		'bace_c': deepchem.molnet.load_bace_classification,
		'bace_r': deepchem.molnet.load_bace_regression,
		'bbbp': deepchem.molnet.load_bbbp,
		'chembl': deepchem.molnet.load_chembl,
		'clearance': deepchem.molnet.load_clearance,
		'clintox': deepchem.molnet.load_clintox,
		'delaney': deepchem.molnet.load_delaney,
		'hiv': deepchem.molnet.load_hiv,
		'hopv': deepchem.molnet.load_hopv,
		'kaggle': deepchem.molnet.load_kaggle,
		'lipo': deepchem.molnet.load_lipo,
		'muv': deepchem.molnet.load_muv,
		'pcba': deepchem.molnet.load_pcba,
		'nci': deepchem.molnet.load_nci,
		'sider': deepchem.molnet.load_sider,
		'toxcast': deepchem.molnet.load_toxcast,
		'kaggle': deepchem.molnet.load_kaggle,
		'delaney': deepchem.molnet.load_delaney,
		'pcba': deepchem.molnet.load_pcba,
		'pdbbind': deepchem.molnet.load_pdbbind_grid,
		'chembl': deepchem.molnet.load_chembl,
		'ppb': deepchem.molnet.load_ppb,
		'qm7': deepchem.molnet.load_qm7_from_mat,
		'qm7b': deepchem.molnet.load_qm7b_from_mat,
		'qm8': deepchem.molnet.load_qm8,
		'qm9': deepchem.molnet.load_qm9,
		'sampl': deepchem.molnet.load_sampl,
		'clintox': deepchem.molnet.load_clintox,
		'hiv': deepchem.molnet.load_hiv
		'sider': deepchem.molnet.load_sider,
		'tox21': deepchem.molnet.load_tox21,
		'toxcast': deepchem.molnet.load_toxcast
		}
		print('-------------------------------------')
		print('Loading dataset: %s' % dataset)

deepchem/utils/dependencies.py

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		"""
		This file is for lazily importing optional dependencies
		"""
		try:
		import xgboost as xgboost
		except:
		print("Warning: No XGBOOST installed on your system")
		print(
		"Attempting to run models with XGBOOST dependencies will throw runtime errors"
		)
		xgboost = None

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