Some fixes to tests (e5736aa7) · Commits · 钟慕尧 / deepchem

deepchem/featurizers/featurize.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -17,7 +17,6 @@ from deepchem.utils.save import log
		from deepchem.utils.save import save_to_disk
		from deepchem.utils.save import load_from_disk
		from deepchem.utils.save import load_pandas_from_disk
		from deepchem.utils import ScaffoldGenerator
		from deepchem.featurizers.nnscore import NNScoreComplexFeaturizer
		import multiprocessing as mp
		from functools import partial

deepchem/models/test/init.py

+12 −2

Original line number	Diff line number	Diff line
		@@ -28,6 +28,9 @@ from deepchem.transformers import LogTransformer
		from deepchem.transformers import ClippingTransformer
		from deepchem.hyperparameters import HyperparamOpt
		from sklearn.ensemble import RandomForestRegressor
		from deepchem.splits import RandomSplitter
		from deepchem.splits import ScaffoldSplitter
		from deepchem.splits import SpecifiedSplitter

		class TestAPI(unittest.TestCase):
		"""
		@@ -114,8 +117,15 @@ class TestAPI(unittest.TestCase):
		shard_size=shard_size)

		# Splits featurized samples into train/test
		train_samples, test_samples = samples.train_test_split(
		splittype, self.train_dir, self.test_dir)
		assert splittype in ["random", "specified", "scaffold"]
		if splittype == "random":
		splitter = RandomSplitter()
		elif splittype == "specified":
		splitter = SpecifiedSplitter()
		elif splittype == "scaffold":
		splitter = ScaffoldSplitter()
		train_samples, test_samples = splitter.train_test_split(
		samples, self.train_dir, self.test_dir)

		use_user_specified_features = (user_specified_features is not None)
		train_dataset = Dataset(data_dir=self.train_dir, samples=train_samples,

deepchem/splits/init.py

+7 −4

Original line number	Diff line number	Diff line
		@@ -12,6 +12,8 @@ __license__ = "LGPL"
		import os
		import numpy as np
		from rdkit import Chem
		from deepchem.utils import ScaffoldGenerator
		from deepchem.utils.save import log
		from deepchem.featurizers.featurize import FeaturizedSamples

		def generate_scaffold(smiles, include_chirality=False):
		@@ -73,7 +75,7 @@ class Splitter(object):

		return train_samples, valid_samples, test_samples

		def train_test_split(self, samples, splittype, train_dir, test_dir, seed=None,
		def train_test_split(self, samples, train_dir, test_dir, seed=None,
		frac_train=.8, reload=False):
		"""
		Splits self into train/test sets.
		@@ -81,7 +83,7 @@ class Splitter(object):
		Returns FeaturizedDataset objects.
		"""
		train_samples, _, test_samples = self.train_valid_test_split(
		samples, splittype, train_dir, valid_dir=None, test_dir=test_dir,
		samples, train_dir, valid_dir=None, test_dir=test_dir,
		frac_train=frac_train, frac_test=1-frac_train, frac_valid=0.,
		reload=False)
		return train_samples, test_samples
		@@ -114,7 +116,8 @@ class ScaffoldSplitter(Splitter):
		"""
		Class for doing data splits based on the scaffold of small molecules.
		"""
		def split(self, samples, frac_train=.8, frac_valid=.1, frac_test=.1, log_every_n=1000):
		def split(self, samples, frac_train=.8, frac_valid=.1, frac_test=.1,
		log_every_n=1000):
		"""
		Splits internal compounds into train/validation/test by scaffold.
		"""
		@@ -147,7 +150,7 @@ class ScaffoldSplitter(Splitter):
		train_inds += scaffold_set
		return train_inds, valid_inds, test_inds

		class SpecifiedSplit(Splitter):
		class SpecifiedSplitter(Splitter):
		"""
		Class that splits data according to user specification.
		"""

deepchem/splits/tests/init.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -91,7 +91,7 @@ class TestSplitAPI(unittest.TestCase):
		input_transformer_classes, output_transformer_classes,
		input_file, task_types.keys())

		def _load_multitask_data(self):
		def _load_multitask_samples(self):
		"""Load example multitask data."""
		compound_featurizers = [CircularFingerprint(size=1024)]
		complex_featurizers = []
		@@ -102,7 +102,7 @@ class TestSplitAPI(unittest.TestCase):
		"task13", "task14", "task15", "task16"]
		task_types = {task: "classification" for task in tasks}
		input_file = "multitask_example.csv"
		return self._create_dataset(
		return self._gen_samples(
		compound_featurizers, complex_featurizers,
		input_transformer_classes, output_transformer_classes,
		input_file, task_types.keys())

deepchem/splits/tests/test_splitter.py

+41 −1

Original line number	Diff line number	Diff line
		@@ -12,6 +12,7 @@ __license__ = "LGPL"
		import os
		import unittest
		from deepchem.splits import RandomSplitter
		from deepchem.splits import ScaffoldSplitter
		from deepchem.splits.tests import TestSplitAPI

		class TestSplitters(TestSplitAPI):
		@@ -19,7 +20,7 @@ class TestSplitters(TestSplitAPI):
		Test some basic splitters.
		"""
		def test_singletask_random_split(self):
		"""Test RandomSplitter class."""
		"""Test singletask RandomSplitter class."""
		solubility_samples = self._load_solubility_samples()
		random_splitter = RandomSplitter()
		train_data, valid_data, test_data = \
		@@ -30,3 +31,42 @@ class TestSplitters(TestSplitAPI):
		assert len(train_data) == 8
		assert len(valid_data) == 1
		assert len(test_data) == 1

		def test_singletask_scaffold_split(self):
		"""Test singletask ScaffoldSplitter class."""
		solubility_samples = self._load_solubility_samples()
		scaffold_splitter = ScaffoldSplitter()
		train_data, valid_data, test_data = \
		scaffold_splitter.train_valid_test_split(
		solubility_samples,
		self.train_dir, self.valid_dir, self.test_dir,
		frac_train=0.8, frac_valid=0.1, frac_test=0.1, reload=False)
		assert len(train_data) == 8
		assert len(valid_data) == 1
		assert len(test_data) == 1

		def test_multitask_random_split(self):
		"""Test multitask RandomSplitter class."""
		multitask_samples = self._load_multitask_samples()
		random_splitter = RandomSplitter()
		train_data, valid_data, test_data = \
		random_splitter.train_valid_test_split(
		multitask_samples,
		self.train_dir, self.valid_dir, self.test_dir,
		frac_train=0.8, frac_valid=0.1, frac_test=0.1, reload=False)
		assert len(train_data) == 8
		assert len(valid_data) == 1
		assert len(test_data) == 1

		def test_multitask_scaffold_split(self):
		"""Test multitask ScaffoldSplitter class."""
		multitask_samples = self._load_multitask_samples()
		scaffold_splitter = ScaffoldSplitter()
		train_data, valid_data, test_data = \
		scaffold_splitter.train_valid_test_split(
		multitask_samples,
		self.train_dir, self.valid_dir, self.test_dir,
		frac_train=0.8, frac_valid=0.1, frac_test=0.1, reload=False)
		assert len(train_data) == 8
		assert len(valid_data) == 1
		assert len(test_data) == 1

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