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plant-genomic-benchmark / plant-genomic-benchmark.py

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	# See the License for the specific language governing permissions and
	# limitations under the License.


	import os
	from typing import List
	import datasets


	_CITATION = '''\
	@article{mendoza2023foundational,
	title={A Foundational Large Language Model for Edible Plant Genomes},
	author={Mendoza-Revilla, Javier and Trop, Evan and Gonzalez, Liam and Roller, Masa and Dalla-Torre, Hugo and de Almeida, Bernardo P and Richard, Guillaume and Caton, Jonathan and Lopez Carranza, Nicolas and Skwark, Marcin and others},
	journal={bioRxiv},
	pages={2023--10},
	year={2023},
	publisher={Cold Spring Harbor Laboratory}
	}
	'''

	_DESCRIPTION = """\
	This dataset comprises the various supervised learning tasks considered in the agro-nt
	paper. The task types include binary classification,multi-label classification,
	regression,and multi-output regression. The actual underlying genomic tasks range from
	predicting regulatory features, RNA processing sites, and gene expression values.
	"""


	_LICENSE = "https://huggingface.co/datasets/InstaDeepAI/plant-genomic-benchmark/blob/main/LICENSE.md"

	_TASK_NAMES = ['poly_a.arabidopsis_thaliana',
	'poly_a.oryza_sativa_indica_group',
	'poly_a.trifolium_pratense',
	'poly_a.medicago_truncatula',
	'poly_a.chlamydomonas_reinhardtii',
	'poly_a.oryza_sativa_japonica_group',
	'splicing.arabidopsis_thaliana_donor',
	'splicing.arabidopsis_thaliana_acceptor',
	'lncrna.m_esculenta',
	'lncrna.z_mays',
	'lncrna.g_max',
	'lncrna.s_lycopersicum',
	'lncrna.t_aestivum',
	'lncrna.s_bicolor',
	'promoter_strength.leaf',
	'promoter_strength.protoplast',
	'terminator_strength.leaf',
	'terminator_strength.protoplast',
	'gene_exp.glycine_max',
	'gene_exp.oryza_sativa',
	'gene_exp.solanum_lycopersicum',
	'gene_exp.zea_mays',
	'gene_exp.arabidopsis_thaliana',
	'chromatin_access.oryza_sativa_MH63_RS2',
	'chromatin_access.setaria_italica',
	'chromatin_access.oryza_sativa_ZS97_RS2',
	'chromatin_access.arabidopis_thaliana',
	'chromatin_access.brachypodium_distachyon',
	'chromatin_access.sorghum_bicolor',
	'chromatin_access.zea_mays',
	'pro_seq.m_esculenta']


	_TASK_INFO = {'poly_a':{'type': 'binary', 'val_set':False},
	'splicing':{'type': 'binary', 'val_set':False},
	'lncrna':{'type': 'binary', 'val_set':False},
	'promoter_strength': {'type': 'regression', 'val_set': True},
	'terminator_strength': {'type': 'regression', 'val_set': True},
	'gene_exp':{'type':'multi_regression','val_set':True},
	'chromatin_access':{'type':'multi_label','val_set':True},
	'pro_seq':{'type':'binary','val_set':True}
	}


	# This function is a basic reimplementation of SeqIO's parse method. This allows the
	# dataset viewer to work as it does not require an external package.
	def parse_fasta(fp):
	name, seq = None, []
	for line in fp:
	line = line.rstrip()
	if line.startswith(">"):
	if name:
	# Slice to remove '>'
	yield (name[1:], "".join(seq))
	name, seq = line, []
	else:
	seq.append(line)
	if name:
	# Slice to remove '>'
	yield (name[1:], "".join(seq))


	class AgroNtTasksConfig(datasets.BuilderConfig):
	"""BuilderConfig for the Agro NT supervised learning tasks dataset."""

	def __init__(self, args, task_name: str, *kwargs):
	"""BuilderConfig downstream tasks dataset.
	Args:
	task (:obj:`str`): Task name.
	**kwargs: keyword arguments forwarded to super.
	"""

	self.task,self.sub_task = task_name.split(".")

	self.task_type = _TASK_INFO[self.task]['type']
	self.val_set = _TASK_INFO[self.task]['val_set']

	super().__init__(
	*args,
	name=f"{task_name}",
	**kwargs,
	)


	class AgroNtTasks(datasets.GeneratorBasedBuilder):
	"""GeneratorBasedBuilder for the Agro NT supervised learning tasks dataset."""

	BUILDER_CONFIG_CLASS = AgroNtTasksConfig
	VERSION = datasets.Version("1.1.0")

	BUILDER_CONFIGS = [AgroNtTasksConfig(task_name=TASK_NAME) for TASK_NAME
	in _TASK_NAMES]

	def _info(self):

	feature_dit = {"sequence": datasets.Value("string"),
	"name": datasets.Value("string")}

	if self.config.task_type == 'binary':
	feature_dit["label"] = datasets.Value("int8")
	elif self.config.task_type == 'regression':
	feature_dit["label"] = datasets.Value("float32")
	elif self.config.task_type == 'multi_regression':
	feature_dit['labels'] = [datasets.Value("float32")]
	elif self.config.task_type == 'multi_label':
	feature_dit['labels'] = [datasets.Value("int8")]

	features = datasets.Features(feature_dit)

	return datasets.DatasetInfo(
	# This is the description that will appear on the datasets page.
	description=_DESCRIPTION,
	# This defines the different columns of the dataset and their types
	features=features,
	# License for the dataset if available
	license=_LICENSE,
	# Citation for the dataset
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:

	train_file = dl_manager.download_and_extract(
	os.path.join(self.config.task, self.config.sub_task + "_train.fa"))
	test_file = dl_manager.download_and_extract(
	os.path.join(self.config.task, self.config.sub_task + "_test.fa"))

	generator_list = [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": train_file,
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": test_file,
	},
	),
	]

	if self.config.val_set:
	validation_file = dl_manager.download_and_extract(
	os.path.join(self.config.task, self.config.sub_task + "_validation.fa"))

	generator_list += datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": validation_file,
	},
	),

	return generator_list

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
	def _generate_examples(self, filepath):

	key = 0

	with open(filepath, "rt") as f:
	fasta_sequences = parse_fasta(f)

	for name, seq in fasta_sequences:
	# Yields examples as (key, example) tuples
	sequence, name = str(seq), str(name)

	split_name = name.split("\|")
	name = split_name[0]
	labels = split_name[1:]

	if 'multi' in self.config.task_type:
	yield key, {
	"sequence": sequence,
	"name": name,
	"labels": labels
	}
	else:
	yield key, {
	"sequence": sequence,
	"name": name,
	"label": labels[0],
	}
	key += 1