:recycle: execuate doctest using pytest (8740b502) · Commits · 钟慕尧 / deepchem

.travis.yml

+2 −2

Original line number	Diff line number	Diff line
		@@ -37,13 +37,13 @@ script:
		- bash devtools/run_yapf.sh
		- bash devtools/run_flake8.sh
		- mypy -p deepchem
		- pytest -m "not slow" --cov=deepchem deepchem
		- pytest -v -m "not slow" --cov=deepchem deepchem
		- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then
		cd docs && pip install -r requirements.txt;
		make clean html && cd ..;
		fi
		- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then
		find ./deepchem -name ".py" ! -name 'load_dataset_template.py' \| xargs python -m doctest -v;
		pytest -v --doctest-modules deepchem
		fi
		after_success:
		- echo $TRAVIS_SECURE_ENV_VARS

deepchem/molnet/load_function/load_dataset_template.py

+12 −15

Original line number	Diff line number	Diff line
		@@ -22,7 +22,7 @@ MYDATASET_CSV_URL = "https://deepchemdata.s3-us-west-1.amazonaws.com/datasets/my
		DEFAULT_FEATURIZERS = get_defaults("feat")

		# Names of supported featurizers
		mydataset_featurizers = ['Featurizer1', 'Featurizer2', 'Featurizer3']
		mydataset_featurizers = ['CircularFingerprint', 'ConvMolFeaturizer']
		DEFAULT_FEATURIZERS = {k: DEFAULT_FEATURIZERS[k] for k in mydataset_featurizers}

		# dict of accepted transformers
		@@ -32,14 +32,14 @@ DEFAULT_TRANSFORMERS = get_defaults("trans")
		DEFAULT_SPLITTERS = get_defaults("splits")

		# names of supported splitters
		mydataset_splitters = ['Splitter1', 'Splitter2', 'Splitter3']
		mydataset_splitters = ['RandomSplitter', 'RandomStratifiedSplitter']
		DEFAULT_SPLITTERS = {k: DEFAULT_SPLITTERS[k] for k in mydataset_splitters}


		def load_mydataset(
		featurizer: Featurizer = DEFAULT_FEATURIZERS['RawFeaturizer'],
		featurizer: Featurizer = DEFAULT_FEATURIZERS['CircularFingerprint'],
		transformers: List[Transformer] = [
		DEFAULT_TRANSFORMERS['PowerTransformer']
		DEFAULT_TRANSFORMERS['NormalizationTransformer']
		],
		splitter: Splitter = DEFAULT_SPLITTERS['RandomSplitter'],
		reload: bool = True,
		@@ -77,16 +77,16 @@ def load_mydataset(
		----------
		featurizer : {List of allowed featurizers for this dataset}
		A featurizer that inherits from deepchem.feat.Featurizer.
		transformers : List{List of allowed transformers for this dataset}
		transformers : {List of allowed transformers for this dataset}
		A transformer that inherits from deepchem.trans.Transformer.
		splitter : {List of allowed splitters for this dataset}
		A splitter that inherits from deepchem.splits.splitters.Splitter.
		reload : bool (default True)
		Try to reload dataset from disk if already downloaded. Save to disk
		after featurizing.
		data_dir : str, optional
		data_dir : str, optional (default None)
		Path to datasets.
		save_dir : str, optional
		save_dir : str, optional (default None)
		Path to featurized datasets.
		featurizer_kwargs : dict
		Specify parameters to featurizer, e.g. {"size": 1024}
		@@ -111,13 +111,11 @@ def load_mydataset(

		References
		----------
		MLA style references for this dataset. E.g.
		Wu, Zhenqin et al. "MoleculeNet: a benchmark for molecular
		machine learning." Chemical Science, vol. 9, 2018,
		pp. 513-530, 10.1039/c7sc02664a.
		MLA style references for this dataset.
		Last, First et al. "Article title." Journal name, vol. #, no. #, year, pp. page range, DOI.

		Last, First et al. "Article title." Journal name, vol. #,
		no. #, year, pp. page range, DOI.
		...[1] Wu, Zhenqin et al. "MoleculeNet: a benchmark for molecular machine learning."
		Chemical Science, vol. 9, 2018, pp. 513-530, 10.1039/c7sc02664a.

		Examples
		--------
		@@ -127,7 +125,6 @@ def load_mydataset(
		>> n_tasks = len(tasks)
		>> n_features = train_dataset.get_data_shape()[0]
		>> model = dc.models.MultitaskClassifier(n_tasks, n_features)

		"""

		# Warning message about this template

Admin message