Add .sdf reader test (485efaee) · Commits · 钟慕尧 / deepchem

deepchem/featurizers/tests/data/water.sdf

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		OpenBabel03241615583D

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		OpenBabel03241615583D

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		Generated by ForceBalance from calcs/cluster-02/VLE/250K/02/qchem.out: Frame 1 of 1
		OpenBabel03241615583D

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		Generated by ForceBalance from calcs/cluster-02/VLE/250K/03/qchem.out: Frame 1 of 1
		OpenBabel03241615583D

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		OpenBabel03241615583D

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		OpenBabel03241615583D

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		OpenBabel03241615583D

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		OpenBabel03241615583D

		6 4 0 0 0 0 0 0 0 0999 V2000
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		3 1 1 0 0 0 0
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		6 4 1 0 0 0 0
		M END
		$$$$
		Generated by ForceBalance from calcs/cluster-02/VLE/250K/08/qchem.out: Frame 1 of 1
		OpenBabel03241615583D

		6 4 0 0 0 0 0 0 0 0999 V2000
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		2 1 1 0 0 0 0
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		M END
		$$$$
		Generated by ForceBalance from calcs/cluster-02/VLE/250K/09/qchem.out: Frame 1 of 1
		OpenBabel03241615583D

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		M END
		$$$$

deepchem/featurizers/tests/data/water.sdf.csv

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Original line number	Diff line number	Diff line
		atomization_energy
		447.082359
		448.859851
		450.466600
		450.851977
		450.894234
		450.743387
		451.436905
		451.559751
		451.326782
		451.400550

deepchem/featurizers/tests/test_sdf_reader.py

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Original line number	Diff line number	Diff line
		"""
		Tests for importing .sdf files
		"""
		from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		__author__ = "Joseph Gomes"
		__copyright__ = "Copyright 2016, Stanford University"
		__license__ = "LGPL"

		import os
		import unittest
		import tempfile
		import shutil
		from deepchem.splits import RandomSplitter
		from deepchem.featurizers.featurize import DataFeaturizer
		from deepchem.featurizers.coulomb_matrices import CoulombMatrixEig

		class TestFeaturizedSamples(unittest.TestCase):
		"""
		Test Featurized Samples class.
		"""
		def setUp(self):
		self.current_dir = os.path.dirname(os.path.abspath(__file__))
		self.smiles_field = "smiles"
		self.mol_field = "mol"
		self.feature_dir = tempfile.mkdtemp()
		self.samples_dir = tempfile.mkdtemp()
		self.train_dir = tempfile.mkdtemp()
		self.valid_dir = tempfile.mkdtemp()
		self.test_dir = tempfile.mkdtemp()

		def _featurize_train_valid_test_split(self, splittype, input_file, tasks,
		frac_train, frac_valid, frac_test):
		# Featurize input
		compound_featurizers = [CoulombMatrixEig(6, remove_hydrogens=False)]
		complex_featurizers = []
		featurizers = compound_featurizers + complex_featurizers

		input_file = os.path.join(self.current_dir, input_file)
		featurizer = DataFeaturizer(tasks=tasks,
		smiles_field=self.smiles_field,
		mol_field=self.mol_field,
		compound_featurizers=compound_featurizers,
		complex_featurizers=complex_featurizers,
		verbosity="low")

		#Featurizes samples and transforms them into NumPy arrays suitable for ML.
		#returns an instance of class FeaturizedSamples()

		samples = featurizer.featurize(input_file, self.feature_dir, self.samples_dir)

		# Splits featurized samples into train/test
		splitter = RandomSplitter()
		if frac_valid > 0:
		train_samples, valid_samples, test_samples = splitter.train_valid_test_split(
		samples, train_dir=self.train_dir, valid_dir=self.valid_dir,
		test_dir=self.test_dir, frac_train=frac_train,
		frac_valid=frac_valid, frac_test=frac_test)

		return train_samples, valid_samples, test_samples
		else:
		train_samples, test_samples = splitter.train_test_split(
		samples, train_dir=self.train_dir, test_dir=self.test_dir,
		frac_train=frac_train)
		return train_samples, test_samples

		def random_test_train_valid_test_split_from_sdf(self):
		"""Test of singletask RF ECFP regression API when reading from .sdf file."""
		splittype = "random"
		input_transforms = []
		output_transforms = ["normalize"]
		model_params = {}
		tasks = ["atomization_energy"]
		task_type = "regression"
		task_types = {task: task_type for task in tasks}
		input_file = "data/water.sdf"
		train_samples, valid_samples, test_samples = (
		self._featurize_train_valid_test_split(
		splittype, input_file, tasks, frac_train=.8,
		frac_valid=.1, frac_test=.1))
		assert len(train_samples) == 8
		assert len(valid_samples) == 1
		assert len(test_samples) == 1

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