def cli_preproc(command, raw_args):
"""Preprocess:
- Run the dataloader and store the results to a (hdf5) file
"""
assert command == "preproc"
parser = argparse.ArgumentParser(
'kipoi {}'.format(command),
description='Run the dataloader and save the output to an hdf5 file.')
add_dataloader_main(parser, with_args=True)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in data loading')
parser.add_argument("-i",
"--install_req",
action='store_true',
help="Install required packages from requirements.txt")
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-o",
"--output",
required=True,
help="Output hdf5 file")
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
dir_exists(os.path.dirname(args.output), logger)
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_dataloader_requirements(args.dataloader,
args.source)
Dataloader = kipoi.get_dataloader_factory(args.dataloader, args.source)
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dataloader,
dataloader_kwargs)
dataloader = Dataloader(**dataloader_kwargs)
it = dataloader.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
logger.info("Writing to the hdf5 file: {0}".format(args.output))
writer = writers.HDF5BatchWriter(file_path=args.output)
for i, batch in enumerate(tqdm(it)):
# check that the first batch was indeed correct
if i == 0 and not Dataloader.output_schema.compatible_with_batch(
batch):
logger.warn(
"First batch of data is not compatible with the dataloader schema."
)
writer.batch_write(batch)
writer.close()
logger.info("Done!")
def cli_predict(command, raw_args):
"""CLI interface to predict
"""
assert command == "predict"
parser = argparse.ArgumentParser('kipoi {}'.format(command),
description='Run the model prediction.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('--batch_size', type=int, default=32,
help='Batch size to use in prediction')
parser.add_argument("-n", "--num_workers", type=int, default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-i", "--install_req", action='store_true',
help="Install required packages from requirements.txt")
parser.add_argument("-k", "--keep_inputs", action='store_true',
help="Keep the inputs in the output file. ")
parser.add_argument("-l", "--layer",
help="Which output layer to use to make the predictions. If specified," +
"`model.predict_activation_on_batch` will be invoked instead of `model.predict_on_batch`")
parser.add_argument('-o', '--output', required=True, nargs="+",
help="Output files. File format is inferred from the file path ending. Available file formats are: " +
", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".
format(ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_model_requirements(args.model,
args.source,
and_dataloaders=True)
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader, args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
ending = output.split('.')[-1]
W = writers.FILE_SUFFIX_MAP[ending]
logger.info("Using {0} for file {1}".format(W.__name__, output))
if ending == "tsv":
assert W == writers.TsvBatchWriter
use_writers.append(writers.TsvBatchWriter(file_path=output, nested_sep="/"))
elif ending == "bed":
assert W == writers.BedBatchWriter
use_writers.append(writers.BedBatchWriter(file_path=output,
dataloader_schema=dl.output_schema.metadata,
header=True))
elif ending in ["hdf5", "h5"]:
assert W == writers.HDF5BatchWriter
use_writers.append(writers.HDF5BatchWriter(file_path=output))
else:
logger.error("Unknown file format: {0}".format(ending))
sys.exit(1)
# Loop through the data, make predictions, save the output
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.output_schema.compatible_with_batch(batch):
logger.warn("First batch of data is not compatible with the dataloader schema.")
# make the prediction
if args.layer is None:
pred_batch = model.predict_on_batch(batch['inputs'])
else:
pred_batch = model.predict_activation_on_batch(batch['inputs'], layer=args.layer)
# write out the predictions, metadata (, inputs, targets)
output_batch = prepare_batch(batch, pred_batch, keep_inputs=args.keep_inputs)
for writer in use_writers:
writer.batch_write(output_batch)
for writer in use_writers:
writer.close()
logger.info('Done! Predictions stored in {0}'.format(",".join(args.output)))
def cli_plot_mutation_map(command, raw_args):
"""CLI interface to plot mutation map
"""
assert command == "plot_mutation_map"
parser = argparse.ArgumentParser(
'kipoi postproc {}'.format(command),
description='Plot mutation map in a file.')
# TODO - rename path to fpath
parser.add_argument(
'-f',
'--input_file',
required=False,
help="Input HDF5 file produced from `create_mutation_map`")
parser.add_argument('-o',
'--output',
required=False,
help="Output image file")
parser.add_argument(
'--input_entry',
required=True,
type=int,
help="Input line for which the plot should be generated")
parser.add_argument(
'--model_seq_input',
required=True,
help=
"Model input name to be used for plotting. As defined in model.yaml.")
parser.add_argument(
'--scoring_key',
required=True,
help=
"Variant score label to be used for plotting. As defined when running "
"`create_mutation_map`.")
parser.add_argument(
'--model_output',
required=True,
help="Model output to be used for plotting. As defined in model.yaml.")
parser.add_argument(
'--limit_region_genomic',
required=False,
nargs=2,
type=int,
default=None,
help="Limit to genomic region. Given as tuple without chromosome, "
"eg: `--limit_region_genomic 13245 12347`")
parser.add_argument('--rc_plot',
required=False,
action="store_true",
help="Make reverse-complement plot.")
args = parser.parse_args(raw_args)
# Check that all the folders exist
dir_exists(os.path.dirname(args.output), logger)
# --------------------------------------------
# install args
import matplotlib.pyplot
matplotlib.pyplot.switch_backend('agg')
import matplotlib.pylab as plt
from kipoi.postprocessing.variant_effects.mutation_map import MutationMapPlotter
logger.info('Loading mutation map file...')
mutmap = MutationMapPlotter(fname=args.input_file)
fig = plt.figure(figsize=(20, 2))
ax = plt.subplot(1, 1, 1)
logger.info('Plotting...')
if args.limit_region_genomic is not None:
args.limit_region_genomic = tuple(args.limit_region_genomic)
mutmap.plot_mutmap(args.input_entry,
args.model_seq_input,
args.scoring_key,
args.model_output,
ax=ax,
limit_region_genomic=args.limit_region_genomic,
rc_plot=args.rc_plot)
fig.savefig(args.output)
logger.info('Successfully plotted mutation map')
def cli_predict(command, raw_args):
"""CLI interface to predict
"""
assert command == "predict"
parser = argparse.ArgumentParser('kipoi {}'.format(command),
description='Run the model prediction.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('--batch_size', type=int, default=32,
help='Batch size to use in prediction')
parser.add_argument("-n", "--num_workers", type=int, default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-k", "--keep_inputs", action='store_true',
help="Keep the inputs in the output file. ")
parser.add_argument("-l", "--layer",
help="Which output layer to use to make the predictions. If specified," +
"`model.predict_activation_on_batch` will be invoked instead of `model.predict_on_batch`")
parser.add_argument("--singularity", action='store_true',
help="Run `kipoi predict` in the appropriate singularity container. "
"Containters will get downloaded to ~/.kipoi/envs/ or to "
"$SINGULARITY_CACHEDIR if set")
parser.add_argument('-o', '--output', required=True, nargs="+",
help="Output files. File format is inferred from the file path ending. Available file formats are: " +
", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str_or_arglist(args.dataloader_args, parser)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".
format(ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# singularity_command
if args.singularity:
from kipoi.cli.singularity import singularity_command
logger.info("Running kipoi predict in the singularity container")
# Drop the singularity flag
raw_args = [x for x in raw_args if x != '--singularity']
singularity_command(['kipoi', command] + raw_args,
args.model,
dataloader_kwargs,
output_files=args.output,
source=args.source,
dry_run=False)
return None
# --------------------------------------------
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader, args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
writer = writers.get_writer(output, metadata_schema=dl.get_output_schema().metadata)
if writer is None:
logger.error("Unknown file format: {0}".format(ending))
sys.exit()
else:
use_writers.append(writer)
output_writers = writers.MultipleBatchWriter(use_writers)
# Loop through the data, make predictions, save the output
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.get_output_schema().compatible_with_batch(batch):
logger.warning("First batch of data is not compatible with the dataloader schema.")
# make the prediction
if args.layer is None:
pred_batch = model.predict_on_batch(batch['inputs'])
else:
pred_batch = model.predict_activation_on_batch(batch['inputs'], layer=args.layer)
# write out the predictions, metadata (, inputs, targets)
output_batch = prepare_batch(batch, pred_batch, keep_inputs=args.keep_inputs)
output_writers.batch_write(output_batch)
output_writers.close()
logger.info('Done! Predictions stored in {0}'.format(",".join(args.output)))
def cli_create_mutation_map(command, raw_args):
"""CLI interface to calculate mutation map data
"""
assert command == "create_mutation_map"
parser = argparse.ArgumentParser(
'kipoi postproc {}'.format(command),
description='Predict effect of SNVs using ISM.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument(
'-r',
'--regions_file',
help='Region definition as VCF or bed file. Not a required input.')
# TODO - rename path to fpath
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-i",
"--install_req",
action='store_true',
help="Install required packages from requirements.txt")
parser.add_argument(
'-o',
'--output',
required=True,
help="Output HDF5 file. To be used as input for plotting.")
parser.add_argument(
'-s',
"--scores",
default="diff",
nargs="+",
help=
"Scoring method to be used. Only scoring methods selected in the model yaml file are"
"available except for `diff` which is always available. Select scoring function by the"
"`name` tag defined in the model yaml file.")
parser.add_argument(
'-k',
"--score_kwargs",
default="",
nargs="+",
help=
"JSON definition of the kwargs for the scoring functions selected in --scores. The "
"definiton can either be in JSON in the command line or the path of a .json file. The "
"individual JSONs are expected to be supplied in the same order as the labels defined in "
"--scores. If the defaults or no arguments should be used define '{}' for that respective "
"scoring method.")
parser.add_argument(
'-l',
"--seq_length",
type=int,
default=None,
help=
"Optional parameter: Model input sequence length - necessary if the model does not have a "
"pre-defined input sequence length.")
args = parser.parse_args(raw_args)
# extract args for kipoi.variant_effects.predict_snvs
dataloader_arguments = parse_json_file_str(args.dataloader_args)
if args.output is None:
raise Exception("Output file `--output` has to be set!")
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_model_requirements(args.model,
args.source,
and_dataloaders=True)
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
regions_file = os.path.realpath(args.regions_file)
output = os.path.realpath(args.output)
with cd(model.source_dir):
if not os.path.exists(regions_file):
raise Exception("Regions inputs file does not exist: %s" %
args.regions_file)
# Check that all the folders exist
file_exists(regions_file, logger)
dir_exists(os.path.dirname(output), logger)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
if not isinstance(args.scores, list):
args.scores = [args.scores]
dts = get_scoring_fns(model, args.scores, args.score_kwargs)
# Load effect prediction related model info
model_info = kipoi.postprocessing.variant_effects.ModelInfoExtractor(
model, Dl)
manual_seq_len = args.seq_length
# Select the appropriate region generator and vcf or bed file input
args.file_format = regions_file.split(".")[-1]
bed_region_file = None
vcf_region_file = None
bed_to_region = None
vcf_to_region = None
if args.file_format == "vcf" or regions_file.endswith("vcf.gz"):
vcf_region_file = regions_file
if model_info.requires_region_definition:
# Select the SNV-centered region generator
vcf_to_region = kipoi.postprocessing.variant_effects.SnvCenteredRg(
model_info, seq_length=manual_seq_len)
logger.info('Using variant-centered sequence generation.')
elif args.file_format == "bed":
if model_info.requires_region_definition:
# Select the SNV-centered region generator
bed_to_region = kipoi.postprocessing.variant_effects.BedOverlappingRg(
model_info, seq_length=manual_seq_len)
logger.info('Using bed-file based sequence generation.')
bed_region_file = regions_file
else:
raise Exception("")
if model_info.use_seq_only_rc:
logger.info(
'Model SUPPORTS simple reverse complementation of input DNA sequences.'
)
else:
logger.info(
'Model DOES NOT support simple reverse complementation of input DNA sequences.'
)
from kipoi.postprocessing.variant_effects.mutation_map import _generate_mutation_map
mdmm = _generate_mutation_map(
model,
Dl,
vcf_fpath=vcf_region_file,
bed_fpath=bed_region_file,
batch_size=args.batch_size,
num_workers=args.num_workers,
dataloader_args=dataloader_arguments,
vcf_to_region=vcf_to_region,
bed_to_region=bed_to_region,
evaluation_function_kwargs={'diff_types': dts},
)
mdmm.save_to_file(output)
logger.info('Successfully generated mutation map data')
def cli_score_variants(command, raw_args):
"""CLI interface to score variants
"""
# Updated argument names:
# - scoring -> scores
# - --vcf_path -> --input_vcf, -i
# - --out_vcf_fpath -> --output_vcf, -o
# - --output -> -e, --extra_output
# - remove - -install_req
# - scoring_kwargs -> score_kwargs
AVAILABLE_FORMATS = ["tsv", "hdf5", "h5"]
assert command == "score_variants"
parser = argparse.ArgumentParser(
'kipoi postproc {}'.format(command),
description='Predict effect of SNVs using ISM.')
parser.add_argument('model', help='Model name.', nargs="+")
parser.add_argument(
'--source',
default=["kipoi"],
nargs="+",
choices=list(kipoi.config.model_sources().keys()),
help='Model source to use. Specified in ~/.kipoi/config.yaml' +
" under model_sources. " +
"'dir' is an additional source referring to the local folder.")
parser.add_argument(
'--dataloader',
nargs="+",
default=[],
help="Dataloader name. If not specified, the model's default" +
"DataLoader will be used")
parser.add_argument('--dataloader_source',
nargs="+",
default=["kipoi"],
help="Dataloader source")
parser.add_argument('--dataloader_args',
nargs="+",
default=[],
help="Dataloader arguments either as a json string:" +
"'{\"arg1\": 1} or as a file path to a json file")
parser.add_argument('-i', '--input_vcf', help='Input VCF.')
parser.add_argument('-o',
'--output_vcf',
help='Output annotated VCF file path.',
default=None)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument(
'-r',
'--restriction_bed',
default=None,
help="Regions for prediction can only be subsets of this bed file")
parser.add_argument(
'-e',
'--extra_output',
required=False,
help=
"Additional output file. File format is inferred from the file path ending"
+ ". Available file formats are: {0}".format(
",".join(AVAILABLE_FORMATS)))
parser.add_argument(
'-s',
"--scores",
default="diff",
nargs="+",
help=
"Scoring method to be used. Only scoring methods selected in the model yaml file are"
"available except for `diff` which is always available. Select scoring function by the"
"`name` tag defined in the model yaml file.")
parser.add_argument(
'-k',
"--score_kwargs",
default="",
nargs="+",
help=
"JSON definition of the kwargs for the scoring functions selected in --scoring. The "
"definiton can either be in JSON in the command line or the path of a .json file. The "
"individual JSONs are expected to be supplied in the same order as the labels defined in "
"--scoring. If the defaults or no arguments should be used define '{}' for that respective "
"scoring method.")
parser.add_argument(
'-l',
"--seq_length",
type=int,
nargs="+",
default=[],
help=
"Optional parameter: Model input sequence length - necessary if the model does not have a "
"pre-defined input sequence length.")
parser.add_argument(
'--std_var_id',
action="store_true",
help="If set then variant IDs in the annotated"
" VCF will be replaced with a standardised, unique ID.")
args = parser.parse_args(raw_args)
# Make sure all the multi-model arguments like source, dataloader etc. fit together
_prepare_multi_model_args(args)
# Check that all the folders exist
file_exists(args.input_vcf, logger)
dir_exists(os.path.dirname(args.output_vcf), logger)
if args.extra_output is not None:
dir_exists(os.path.dirname(args.extra_output), logger)
# infer the file format
args.file_format = args.extra_output.split(".")[-1]
if args.file_format not in AVAILABLE_FORMATS:
logger.error("File ending: {0} for file {1} not from {2}".format(
args.file_format, args.extra_output, AVAILABLE_FORMATS))
sys.exit(1)
if args.file_format in ["hdf5", "h5"]:
# only if hdf5 output is used
import deepdish
if not isinstance(args.scores, list):
args.scores = [args.scores]
score_kwargs = []
if len(args.score_kwargs) > 0:
score_kwargs = args.score_kwargs
if len(args.scores) >= 1:
# Check if all scoring functions should be used:
if args.scores == ["all"]:
if len(score_kwargs) >= 1:
raise ValueError(
"`--score_kwargs` cannot be defined in combination will `--scoring all`!"
)
else:
score_kwargs = [parse_json_file_str(el) for el in score_kwargs]
if not len(args.score_kwargs) == len(score_kwargs):
raise ValueError(
"When defining `--score_kwargs` a JSON representation of arguments (or the "
"path of a file containing them) must be given for every "
"`--scores` function.")
keep_predictions = args.extra_output is not None
n_models = len(args.model)
res = {}
for model_name, model_source, dataloader, dataloader_source, dataloader_args, seq_length in zip(
args.model, args.source, args.dataloader, args.dataloader_source,
args.dataloader_args, args.seq_length):
model_name_safe = model_name.replace("/", "_")
output_vcf_model = None
if args.output_vcf is not None:
output_vcf_model = args.output_vcf
# If multiple models are to be analysed then vcfs need renaming.
if n_models > 1:
if output_vcf_model.endswith(".vcf"):
output_vcf_model = output_vcf_model[:-4]
output_vcf_model += model_name_safe + ".vcf"
dataloader_arguments = parse_json_file_str(dataloader_args)
# --------------------------------------------
# load model & dataloader
model = kipoi.get_model(model_name, model_source)
if dataloader is not None:
Dl = kipoi.get_dataloader_factory(dataloader, dataloader_source)
else:
Dl = model.default_dataloader
# Load effect prediction related model info
model_info = kipoi.postprocessing.variant_effects.ModelInfoExtractor(
model, Dl)
if model_info.use_seq_only_rc:
logger.info(
'Model SUPPORTS simple reverse complementation of input DNA sequences.'
)
else:
logger.info(
'Model DOES NOT support simple reverse complementation of input DNA sequences.'
)
if output_vcf_model is not None:
logger.info('Annotated VCF will be written to %s.' %
str(output_vcf_model))
res[model_name_safe] = kipoi.postprocessing.variant_effects.score_variants(
model,
dataloader_arguments,
args.input_vcf,
output_vcf_model,
scores=args.scores,
score_kwargs=score_kwargs,
num_workers=args.num_workers,
batch_size=args.batch_size,
seq_length=seq_length,
std_var_id=args.std_var_id,
restriction_bed=args.restriction_bed,
return_predictions=keep_predictions)
# tabular files
if keep_predictions:
if args.file_format in ["tsv"]:
for model_name in res:
for i, k in enumerate(res[model_name]):
# Remove an old file if it is still there...
if i == 0:
try:
os.unlink(args.extra_output)
except Exception:
pass
with open(args.extra_output, "w") as ofh:
ofh.write("KPVEP_%s:%s\n" % (k.upper(), model_name))
res[model_name][k].to_csv(args.extra_output,
sep="\t",
mode="a")
if args.file_format in ["hdf5", "h5"]:
deepdish.io.save(args.extra_output, res)
logger.info('Successfully predicted samples')
def cli_grad(command, raw_args):
"""CLI interface to predict
"""
from .main import prepare_batch
from kipoi.model import GradientMixin
assert command == "grad"
from tqdm import tqdm
parser = argparse.ArgumentParser(
'kipoi {}'.format(command),
description='Save gradients and inputs to a hdf5 file.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-i",
"--install_req",
action='store_true',
help="Install required packages from requirements.txt")
parser.add_argument(
"-l",
"--layer",
default=None,
help="Which output layer to use to make the predictions. If specified,"
+
"`model.predict_activation_on_batch` will be invoked instead of `model.predict_on_batch`",
required=False)
parser.add_argument(
"--final_layer",
help=
"Alternatively to `--layer` this flag can be used to indicate that the last layer should "
"be used.",
action='store_true')
parser.add_argument(
"--pre_nonlinearity",
help=
"Flag indicating that it should checked whether the selected output is post activation "
"function. If a non-linear activation function is used attempt to use its input. This "
"feature is not available for all models.",
action='store_true')
parser.add_argument(
"-f",
"--filter_idx",
help=
"Filter index that should be inspected with gradients. If not set all filters will "
+ "be used.",
default=None)
parser.add_argument(
"-a",
"--avg_func",
help=
"Averaging function to be applied across selected filters (`--filter_idx`) in "
+ "layer `--layer`.",
choices=GradientMixin.allowed_functions,
default="sum")
parser.add_argument(
'--selected_fwd_node',
help="If the selected layer has multiple inbound connections in "
"the graph then those can be selected here with an integer "
"index. Not necessarily supported by all models.",
default=None,
type=int)
parser.add_argument(
'-o',
'--output',
required=True,
nargs="+",
help=
"Output files. File format is inferred from the file path ending. Available file formats are: "
+ ", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".format(
ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_model_requirements(args.model,
args.source,
and_dataloaders=True)
layer = args.layer
if layer is None and not args.final_layer:
raise Exception(
"A layer has to be selected explicitely using `--layer` or implicitely by using the"
"`--final_layer` flag.")
# Not a good idea
# if layer is not None and isint(layer):
# logger.warn("Interpreting `--layer` value as integer layer index!")
# layer = int(args.layer)
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if not isinstance(model, GradientMixin):
raise Exception("Model does not support gradient calculation.")
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
filter_idx_parsed = None
if args.filter_idx is not None:
filter_idx_parsed = parse_filter_slice(args.filter_idx)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
ending = output.split('.')[-1]
W = writers.FILE_SUFFIX_MAP[ending]
logger.info("Using {0} for file {1}".format(W.__name__, output))
if ending == "tsv":
assert W == writers.TsvBatchWriter
use_writers.append(
writers.TsvBatchWriter(file_path=output, nested_sep="/"))
elif ending == "bed":
raise Exception("Please use tsv or hdf5 output format.")
elif ending in ["hdf5", "h5"]:
assert W == writers.HDF5BatchWriter
use_writers.append(writers.HDF5BatchWriter(file_path=output))
else:
logger.error("Unknown file format: {0}".format(ending))
sys.exit(1)
# Loop through the data, make predictions, save the output
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.output_schema.compatible_with_batch(batch):
logger.warn(
"First batch of data is not compatible with the dataloader schema."
)
# make the prediction
pred_batch = model.input_grad(batch['inputs'],
filter_idx=filter_idx_parsed,
avg_func=args.avg_func,
layer=layer,
final_layer=args.final_layer,
selected_fwd_node=args.selected_fwd_node,
pre_nonlinearity=args.pre_nonlinearity)
# write out the predictions, metadata (, inputs, targets)
# always keep the inputs so that input*grad can be generated!
# output_batch = prepare_batch(batch, pred_batch, keep_inputs=True)
output_batch = batch
output_batch["grads"] = pred_batch
for writer in use_writers:
writer.batch_write(output_batch)
for writer in use_writers:
writer.close()
logger.info('Done! Gradients stored in {0}'.format(",".join(args.output)))
def cli_grad_to_file(command, raw_args):
""" CLI to save seq inputs of grad*input to a bigwig file
"""
assert command == "gr_inp_to_file"
parser = argparse.ArgumentParser('kipoi postproc {}'.format(command),
description='Save grad*input in a file.')
add_model(parser)
add_dataloader(parser, with_args=True)
# TODO - rename path to fpath
parser.add_argument('-f',
'--input_file',
required=False,
help="Input HDF5 file produced from `grad`")
parser.add_argument('-o',
'--output',
required=False,
help="Output bigwig for bedgraph file")
parser.add_argument(
'--sample',
required=False,
type=int,
default=None,
help=
"Input line for which the BigWig file should be generated. If not defined all"
"samples will be written.")
parser.add_argument(
'--model_input',
required=False,
default=None,
help=
"Model input name to be used for plotting. As defined in model.yaml. Can be omitted if"
"model only has one input.")
args = parser.parse_args(raw_args)
# Check that all the folders exist
dir_exists(os.path.dirname(args.output), logger)
# --------------------------------------------
# install args
import matplotlib.pyplot
matplotlib.pyplot.switch_backend('agg')
import matplotlib.pylab as plt
from kipoi.postprocessing.variant_effects.mutation_map import MutationMapPlotter
from kipoi.postprocessing.gradient_vis.vis import GradPlotter
from kipoi.writers import BedGraphWriter
logger.info('Loading gradient results file and model info...')
gp = GradPlotter.from_hdf5(args.input_file,
model=args.model,
source=args.source)
if args.sample is not None:
samples = [args.sample]
else:
samples = list(range(gp.get_num_samples(args.model_input)))
if args.output.endswith(".bed") or args.output.endswith(".bedgraph"):
of_obj = BedGraphWriter(args.output)
else:
raise Exception("Output file format not supported!")
logger.info('Writing...')
for sample in samples:
gp.write(sample, model_input=args.model_input, writer_obj=of_obj)
logger.info('Saving...')
of_obj.close()
logger.info('Successfully wrote grad*input to file.')
def cli_score_variants(command, raw_args):
"""CLI interface to score variants
"""
# Updated argument names:
# - scoring -> scores
# - --vcf_path -> --input_vcf, -i
# - --out_vcf_fpath -> --output_vcf, -o
# - --output -> -e, --extra_output
# - remove - -install_req
# - scoring_kwargs -> score_kwargs
AVAILABLE_FORMATS = [k for k in writers.FILE_SUFFIX_MAP if k != 'bed']
assert command == "score_variants"
parser = argparse.ArgumentParser(
'kipoi veff {}'.format(command),
description='Predict effect of SNVs using ISM.')
parser.add_argument('model', help='Model name.')
parser.add_argument(
'--source',
default="kipoi",
choices=list(kipoi.config.model_sources().keys()),
help='Model source to use. Specified in ~/.kipoi/config.yaml' +
" under model_sources. " +
"'dir' is an additional source referring to the local folder.")
add_dataloader(parser=parser, with_args=True)
parser.add_argument('-i', '--input_vcf', required=True, help='Input VCF.')
parser.add_argument('-o',
'--output_vcf',
help='Output annotated VCF file path.',
default=None)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument(
'-r',
'--restriction_bed',
default=None,
help="Regions for prediction can only be subsets of this bed file")
parser.add_argument(
'-e',
'--extra_output',
type=str,
default=None,
required=False,
help=
"Additional output files in other (non-vcf) formats. File format is inferred from the file path ending"
+ ". Available file formats are: {0}".format(", ".join(
["." + k for k in AVAILABLE_FORMATS])))
parser.add_argument(
'-s',
"--scores",
default="diff",
nargs="+",
help=
"Scoring method to be used. Only scoring methods selected in the model yaml file are"
"available except for `diff` which is always available. Select scoring function by the"
"`name` tag defined in the model yaml file.")
parser.add_argument(
'-k',
"--score_kwargs",
default="",
nargs="+",
help=
"JSON definition of the kwargs for the scoring functions selected in --scoring. The "
"definiton can either be in JSON in the command line or the path of a .json file. The "
"individual JSONs are expected to be supplied in the same order as the labels defined in "
"--scoring. If the defaults or no arguments should be used define '{}' for that respective "
"scoring method.")
parser.add_argument(
'-l',
"--seq_length",
type=int,
default=None,
help=
"Optional parameter: Model input sequence length - necessary if the model does not have a "
"pre-defined input sequence length.")
parser.add_argument(
'--std_var_id',
action="store_true",
help="If set then variant IDs in the annotated"
" VCF will be replaced with a standardised, unique ID.")
parser.add_argument(
"--model_outputs",
type=str,
default=None,
nargs="+",
help=
"Optional parameter: Only return predictions for the selected model outputs. Naming"
"according to the definition in model.yaml > schema > targets > column_labels"
)
parser.add_argument(
"--model_outputs_i",
type=int,
default=None,
nargs="+",
help=
"Optional parameter: Only return predictions for the selected model outputs. Give integer"
"indices of the selected model output(s).")
parser.add_argument(
"--singularity",
action='store_true',
help="Run `kipoi predict` in the appropriate singularity container. "
"Containters will get downloaded to ~/.kipoi/envs/ or to "
"$SINGULARITY_CACHEDIR if set")
args = parser.parse_args(raw_args)
# OBSOLETE
# Make sure all the multi-model arguments like source, dataloader etc. fit together
#_prepare_multi_model_args(args)
# Check that all the folders exist
file_exists(args.input_vcf, logger)
if args.output_vcf is None and args.extra_output is None:
logger.error(
"One of the two needs to be specified: --output_vcf or --extra_output"
)
sys.exit(1)
if args.extra_output is not None:
dir_exists(os.path.dirname(args.extra_output), logger)
ending = args.extra_output.split('.')[-1]
if ending not in AVAILABLE_FORMATS:
logger.error("File ending: {0} for file {1} not from {2}".format(
ending, args.extra_output, AVAILABLE_FORMATS))
sys.exit(1)
# singularity_command
if args.singularity:
from kipoi.cli.singularity import singularity_command
logger.info(
"Running kipoi veff {} in the singularity container".format(
command))
# Drop the singularity flag
raw_args = [x for x in raw_args if x != '--singularity']
dataloader_kwargs = parse_json_file_str_or_arglist(
args.dataloader_args)
# create output files
output_files = []
if args.output_vcf is not None:
output_files.append(args.output_vcf)
if args.extra_output is not None:
output_files.append(args.extra_output)
singularity_command(['kipoi', 'veff', command] + raw_args,
model=args.model,
dataloader_kwargs=dataloader_kwargs,
output_files=output_files,
source=args.source,
dry_run=False)
return None
if not isinstance(args.scores, list):
args.scores = [args.scores]
score_kwargs = []
if len(args.score_kwargs) > 0:
score_kwargs = args.score_kwargs
if len(args.scores) >= 1:
# Check if all scoring functions should be used:
if args.scores == ["all"]:
if len(score_kwargs) >= 1:
raise ValueError(
"`--score_kwargs` cannot be defined in combination will `--scoring all`!"
)
else:
score_kwargs = [parse_json_file_str(el) for el in score_kwargs]
if not len(args.score_kwargs) == len(score_kwargs):
raise ValueError(
"When defining `--score_kwargs` a JSON representation of arguments (or the "
"path of a file containing them) must be given for every "
"`--scores` function.")
# VCF writer
output_vcf_model = None
if args.output_vcf is not None:
dir_exists(os.path.dirname(args.output_vcf), logger)
output_vcf_model = args.output_vcf
# Other writers
if args.extra_output is not None:
dir_exists(os.path.dirname(args.extra_output), logger)
extra_output = args.extra_output
writer = writers.get_writer(extra_output, metadata_schema=None)
assert writer is not None
extra_writers = [SyncBatchWriter(writer)]
else:
extra_writers = []
dataloader_arguments = parse_json_file_str_or_arglist(args.dataloader_args)
# --------------------------------------------
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
# Load effect prediction related model info
model_info = kipoi_veff.ModelInfoExtractor(model, Dl)
if model_info.use_seq_only_rc:
logger.info(
'Model SUPPORTS simple reverse complementation of input DNA sequences.'
)
else:
logger.info(
'Model DOES NOT support simple reverse complementation of input DNA sequences.'
)
if output_vcf_model is not None:
logger.info('Annotated VCF will be written to %s.' %
str(output_vcf_model))
model_outputs = None
if args.model_outputs is not None:
model_outputs = args.model_outputs
elif args.model_outputs_i is not None:
model_outputs = args.model_outputs_i
kipoi_veff.score_variants(model,
dataloader_arguments,
args.input_vcf,
output_vcf=output_vcf_model,
output_writers=extra_writers,
scores=args.scores,
score_kwargs=score_kwargs,
num_workers=args.num_workers,
batch_size=args.batch_size,
seq_length=args.seq_length,
std_var_id=args.std_var_id,
restriction_bed=args.restriction_bed,
return_predictions=False,
model_outputs=model_outputs)
logger.info('Successfully predicted samples')
def cli_ism(command, raw_args):
# TODO: find a way to define the model output selection
"""CLI interface to predict
"""
# from .main import prepare_batch
assert command == "ism"
from tqdm import tqdm
from .ism import Mutation
parser = argparse.ArgumentParser('kipoi interpret {}'.format(command),
description='Calculate DeepLIFT scores.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("--model_input",
help="Name of the model input that should be scored.",
required=True)
parser.add_argument(
'-s',
"--scores",
default="diff",
nargs="+",
help=
"Scoring method to be used. Only scoring methods selected in the model yaml file are"
"available except for `diff` which is always available. Select scoring function by the"
"`name` tag defined in the model yaml file.")
parser.add_argument(
'-k',
"--score_kwargs",
default=None,
nargs="+",
help=
"JSON definition of the kwargs for the scoring functions selected in --scores. The "
"definiton can either be in JSON in the command line or the path of a .json file. The "
"individual JSONs are expected to be supplied in the same order as the labels defined in "
"--scores. If the defaults or no arguments should be used define '{}' for that respective "
"scoring method.")
parser.add_argument(
"-c",
"--category_axis",
help="Using the selected model input with `--model_input`: Which "
"dimension of that array contains the one-hot encoded categories?"
" e.g. for a one-hot encoded DNA-sequence"
"array with input shape (1000, 4) for a single sample, "
"`category_dim` is 1, for (4, 1000) `category_dim`"
"is 0.",
default=1,
type=int,
required=False)
parser.add_argument(
"-f",
"--output_sel_fn",
help="Define an output selection function in order to return effects"
"on the output of the function. example definitoin: "
"`--output_sel_fn my_file.py::my_sel_fn`",
default=None,
required=False)
parser.add_argument(
'-o',
'--output',
required=True,
nargs="+",
help="Output files. File format is inferred from the file path ending. "
"Available file formats are: " +
", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".format(
ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# --------------------------------------------
if not isinstance(args.scores, list):
args.scores = [args.scores]
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
ending = output.split('.')[-1]
W = writers.FILE_SUFFIX_MAP[ending]
logger.info("Using {0} for file {1}".format(W.__name__, output))
if ending == "tsv":
assert W == writers.TsvBatchWriter
use_writers.append(
writers.TsvBatchWriter(file_path=output, nested_sep="/"))
elif ending == "bed":
raise Exception("Please use tsv or hdf5 output format.")
elif ending in ["hdf5", "h5"]:
assert W == writers.HDF5BatchWriter
use_writers.append(writers.HDF5BatchWriter(file_path=output))
else:
logger.error("Unknown file format: {0}".format(ending))
sys.exit(1)
output_sel_fn = None
if args.output_sel_fn is not None:
file_path, obj_name = tuple(args.output_sel_fn.split("::"))
output_sel_fn = getattr(load_module(file_path), obj_name)
m = Mutation(model,
args.model_input,
scores=args.scores,
score_kwargs=args.score_kwargs,
batch_size=args.batch_size,
output_sel_fn=output_sel_fn,
category_axis=args.category_axis,
test_ref_ref=True)
out_batches = {}
# Loop through the data, make predictions, save the output..
# TODO: batch writer fails because it tries to concatenate on highest dimension rather than the lowest!
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.output_schema.compatible_with_batch(batch):
logger.warn(
"First batch of data is not compatible with the dataloader schema."
)
# calculate scores without reference for the moment.
pred_batch = m.score(batch['inputs'])
# with the current writers it's not possible to store the scores and the model inputs in the same file
output_batch = {}
output_batch["scores"] = pred_batch
for k in output_batch:
if k not in out_batches:
out_batches[k] = []
out_batches[k].append(output_batch[k])
# concatenate batches:
full_output = {
k: np.concatenate([np.array(el) for el in v])
for k, v in out_batches.items()
}
logger.info('Full output shape: {0}'.format(
str(full_output["scores"].shape)))
for writer in use_writers:
writer.batch_write(full_output)
for writer in use_writers:
writer.close()
logger.info('Done! ISM stored in {0}'.format(",".join(args.output)))
def cli_feature_importance(command, raw_args):
"""CLI interface to predict
"""
# from .main import prepare_batch
assert command == "feature_importance"
parser = argparse.ArgumentParser(
'kipoi {}'.format(command),
description='Save gradients and inputs to a hdf5 file.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument("--imp_score",
help="Importance score name",
choices=available_importance_scores())
parser.add_argument("--imp_score_kwargs", help="Importance score kwargs")
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
# TODO - handle the reference-based importance scores...
# io
parser.add_argument(
'-o',
'--output',
required=True,
nargs="+",
help=
"Output files. File format is inferred from the file path ending. Available file formats are: "
+ ", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
imp_score_kwargs = parse_json_file_str(args.imp_score_kwargs)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".format(
ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_model_requirements(args.model,
args.source,
and_dataloaders=True)
# load model & dataloader
model = kipoi.get_model(args.model,
args.source,
with_dataloader=args.dataloader is None)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
# get_importance_score
ImpScore = get_importance_score(args.imp_score)
if not ImpScore.is_compatible(model):
raise ValueError("model not compatible with score: {0}".format(
args.imp_score))
impscore = ImpScore(model, **imp_score_kwargs)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
ending = output.split('.')[-1]
W = writers.FILE_SUFFIX_MAP[ending]
logger.info("Using {0} for file {1}".format(W.__name__, output))
if ending == "tsv":
assert W == writers.TsvBatchWriter
use_writers.append(
writers.TsvBatchWriter(file_path=output, nested_sep="/"))
elif ending == "bed":
raise Exception("Please use tsv or hdf5 output format.")
elif ending in ["hdf5", "h5"]:
assert W == writers.HDF5BatchWriter
use_writers.append(writers.HDF5BatchWriter(file_path=output))
else:
logger.error("Unknown file format: {0}".format(ending))
sys.exit(1)
# Loop through the data, make predictions, save the output
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.output_schema.compatible_with_batch(batch):
logger.warn(
"First batch of data is not compatible with the dataloader schema."
)
# make the prediction
# TODO - handle the reference-based importance scores...
importance_scores = impscore.score(batch['inputs'])
# write out the predictions, metadata (, inputs, targets)
# always keep the inputs so that input*grad can be generated!
# output_batch = prepare_batch(batch, pred_batch, keep_inputs=True)
output_batch = batch
output_batch["importance_scores"] = importance_scores
for writer in use_writers:
writer.batch_write(output_batch)
for writer in use_writers:
writer.close()
logger.info('Done! Importance scores stored in {0}'.format(",".join(
args.output)))
def cli_deeplift(command, raw_args):
"""CLI interface to predict
"""
# TODO: find a way to define the "reference" for a scored sequence.
# from .main import prepare_batch
assert command == "deeplift"
from tqdm import tqdm
from .referencebased import DeepLift
from .referencebased import get_mxts_modes
parser = argparse.ArgumentParser('kipoi interpret {}'.format(command),
description='Calculate DeepLIFT scores.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument(
"-l",
"--layer",
type=int,
default=None,
help="With respect to which layer the scores should be calculated.",
required=True)
parser.add_argument(
"--pre_nonlinearity",
help=
"Flag indicating that it should checked whether the selected output is post activation "
"function. If a non-linear activation function is used attempt to use its input. This "
"feature is not available for all models.",
action='store_true')
parser.add_argument(
"-f",
"--filter_idx",
help="Filter index that should be inspected with gradients",
default=None,
required=True,
type=int)
parser.add_argument("-m",
"--mxts_mode",
help="Deeplift score, allowed values are: %s" %
str(list(get_mxts_modes().keys())),
default='rescale_conv_revealcancel_fc')
parser.add_argument(
'-o',
'--output',
required=True,
nargs="+",
help=
"Output files. File format is inferred from the file path ending. Available file formats are: "
+ ", ".join(["." + k for k in writers.FILE_SUFFIX_MAP]))
args = parser.parse_args(raw_args)
dataloader_kwargs = parse_json_file_str(args.dataloader_args)
# setup the files
if not isinstance(args.output, list):
args.output = [args.output]
for o in args.output:
ending = o.split('.')[-1]
if ending not in writers.FILE_SUFFIX_MAP:
logger.error("File ending: {0} for file {1} not from {2}".format(
ending, o, writers.FILE_SUFFIX_MAP))
sys.exit(1)
dir_exists(os.path.dirname(o), logger)
# --------------------------------------------
layer = args.layer
if layer is None and not args.final_layer:
raise Exception(
"A layer has to be selected explicitely using `--layer` or implicitely by using the"
"`--final_layer` flag.")
# Not a good idea
# if layer is not None and isint(layer):
# logger.warn("Interpreting `--layer` value as integer layer index!")
# layer = int(args.layer)
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
dataloader_kwargs = kipoi.pipeline.validate_kwargs(Dl, dataloader_kwargs)
dl = Dl(**dataloader_kwargs)
# setup batching
it = dl.batch_iter(batch_size=args.batch_size,
num_workers=args.num_workers)
# Setup the writers
use_writers = []
for output in args.output:
ending = output.split('.')[-1]
W = writers.FILE_SUFFIX_MAP[ending]
logger.info("Using {0} for file {1}".format(W.__name__, output))
if ending == "tsv":
assert W == writers.TsvBatchWriter
use_writers.append(
writers.TsvBatchWriter(file_path=output, nested_sep="/"))
elif ending == "bed":
raise Exception("Please use tsv or hdf5 output format.")
elif ending in ["hdf5", "h5"]:
assert W == writers.HDF5BatchWriter
use_writers.append(writers.HDF5BatchWriter(file_path=output))
else:
logger.error("Unknown file format: {0}".format(ending))
sys.exit(1)
d = DeepLift(model,
output_layer=args.layer,
task_idx=args.filter_idx,
preact=args.pre_nonlinearity,
mxts_mode=args.mxts_mode,
batch_size=args.batch_size)
# Loop through the data, make predictions, save the output
for i, batch in enumerate(tqdm(it)):
# validate the data schema in the first iteration
if i == 0 and not Dl.output_schema.compatible_with_batch(batch):
logger.warn(
"First batch of data is not compatible with the dataloader schema."
)
# calculate scores without reference for the moment.
pred_batch = d.score(batch['inputs'], None)
# write out the predictions, metadata (, inputs, targets)
# always keep the inputs so that input*grad can be generated!
# output_batch = prepare_batch(batch, pred_batch, keep_inputs=True)
output_batch = batch
output_batch["scores"] = pred_batch
for writer in use_writers:
writer.batch_write(output_batch)
for writer in use_writers:
writer.close()
logger.info('Done! Gradients stored in {0}'.format(",".join(args.output)))
def cli_score_variants(command, raw_args):
"""CLI interface to predict
"""
AVAILABLE_FORMATS = ["tsv", "hdf5", "h5"]
import pybedtools
assert command == "score_variants"
parser = argparse.ArgumentParser(
'kipoi postproc {}'.format(command),
description='Predict effect of SNVs using ISM.')
add_model(parser)
add_dataloader(parser, with_args=True)
parser.add_argument('-v', '--vcf_path', help='Input VCF.')
# TODO - rename path to fpath
parser.add_argument('-a',
'--out_vcf_fpath',
help='Output annotated VCF file path.',
default=None)
parser.add_argument('--batch_size',
type=int,
default=32,
help='Batch size to use in prediction')
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument("-i",
"--install_req",
action='store_true',
help="Install required packages from requirements.txt")
parser.add_argument(
'-r',
'--restriction_bed',
default=None,
help="Regions for prediction can only be subsets of this bed file")
parser.add_argument(
'-o',
'--output',
required=False,
help=
"Additional output file. File format is inferred from the file path ending"
+ ". Available file formats are: {0}".format(
",".join(AVAILABLE_FORMATS)))
parser.add_argument(
'-s',
"--scoring",
default="diff",
nargs="+",
help=
"Scoring method to be used. Only scoring methods selected in the model yaml file are"
"available except for `diff` which is always available. Select scoring function by the"
"`name` tag defined in the model yaml file.")
parser.add_argument(
'-k',
"--scoring_kwargs",
default="",
nargs="+",
help=
"JSON definition of the kwargs for the scoring functions selected in --scoring. The "
"definiton can either be in JSON in the command line or the path of a .json file. The "
"individual JSONs are expected to be supplied in the same order as the labels defined in "
"--scoring. If the defaults or no arguments should be used define '{}' for that respective "
"scoring method.")
args = parser.parse_args(raw_args)
# extract args for kipoi.variant_effects.predict_snvs
vcf_path = args.vcf_path
out_vcf_fpath = args.out_vcf_fpath
dataloader_arguments = parse_json_file_str(args.dataloader_args)
# infer the file format
args.file_format = args.output.split(".")[-1]
if args.file_format not in AVAILABLE_FORMATS:
logger.error("File ending: {0} for file {1} not from {2}".format(
args.file_format, args.output, AVAILABLE_FORMATS))
sys.exit(1)
if args.file_format in ["hdf5", "h5"]:
# only if hdf5 output is used
import deepdish
# Check that all the folders exist
file_exists(args.vcf_path, logger)
dir_exists(os.path.dirname(args.out_vcf_fpath), logger)
if args.output is not None:
dir_exists(os.path.dirname(args.output), logger)
# --------------------------------------------
# install args
if args.install_req:
kipoi.pipeline.install_model_requirements(args.model,
args.source,
and_dataloaders=True)
# load model & dataloader
model = kipoi.get_model(args.model, args.source)
if args.dataloader is not None:
Dl = kipoi.get_dataloader_factory(args.dataloader,
args.dataloader_source)
else:
Dl = model.default_dataloader
if not os.path.exists(vcf_path):
raise Exception("VCF file does not exist: %s" % vcf_path)
if not isinstance(args.scoring, list):
args.scoring = [args.scoring]
dts = _get_scoring_fns(model, args.scoring, args.scoring_kwargs)
# Load effect prediction related model info
model_info = kipoi.postprocessing.variant_effects.ModelInfoExtractor(
model, Dl)
# Select the appropriate region generator
if args.restriction_bed is not None:
# Select the restricted SNV-centered region generator
pbd = pybedtools.BedTool(args.restriction_bed)
vcf_to_region = kipoi.postprocessing.variant_effects.SnvPosRestrictedRg(
model_info, pbd)
logger.info(
'Restriction bed file defined. Only variants in defined regions will be tested.'
'Only defined regions will be tested.')
elif model_info.requires_region_definition:
# Select the SNV-centered region generator
vcf_to_region = kipoi.postprocessing.variant_effects.SnvCenteredRg(
model_info)
logger.info('Using variant-centered sequence generation.')
else:
# No regions can be defined for the given model, VCF overlap will be inferred, hence tabixed VCF is necessary
vcf_to_region = None
# Make sure that the vcf is tabixed
vcf_path = kipoi.postprocessing.variant_effects.ensure_tabixed_vcf(
vcf_path)
logger.info(
'Dataloader does not accept definition of a regions bed-file. Only VCF-variants that lie within'
'produced regions can be predicted')
if model_info.use_seq_only_rc:
logger.info(
'Model SUPPORTS simple reverse complementation of input DNA sequences.'
)
else:
logger.info(
'Model DOES NOT support simple reverse complementation of input DNA sequences.'
)
# Get a vcf output writer if needed
if out_vcf_fpath is not None:
logger.info('Annotated VCF will be written to %s.' %
str(out_vcf_fpath))
vcf_writer = kipoi.postprocessing.variant_effects.VcfWriter(
model, vcf_path, out_vcf_fpath)
else:
vcf_writer = None
keep_predictions = args.output is not None
res = kipoi.postprocessing.variant_effects.predict_snvs(
model,
Dl,
vcf_path,
batch_size=args.batch_size,
num_workers=args.num_workers,
dataloader_args=dataloader_arguments,
vcf_to_region=vcf_to_region,
evaluation_function_kwargs={"diff_types": dts},
sync_pred_writer=vcf_writer,
return_predictions=keep_predictions)
# tabular files
if args.output is not None:
if args.file_format in ["tsv"]:
for i, k in enumerate(res):
# Remove an old file if it is still there...
if i == 0:
try:
os.unlink(args.output)
except Exception:
pass
with open(args.output, "w") as ofh:
ofh.write("KPVEP_%s\n" % k.upper())
res[k].to_csv(args.output, sep="\t", mode="a")
if args.file_format in ["hdf5", "h5"]:
deepdish.io.save(args.output, res)
logger.info('Successfully predicted samples')
