"--num_workers",
type=int,
default=0,
help="Number of parallel workers for loading the dataset")
parser.add_argument('-p',
'--patience',
default=10,
type=int,
help='Early stopping patience')
parser.add_argument(
'--gpu',
default=-1,
type=int,
help='Which gpu to use. If -1, determine automatically')
args = parser.parse_args()
dl_kwargs_train = parse_json_file_str(args.dl_kwargs_train)
dl_kwargs_eval = parse_json_file_str(args.dl_kwargs_eval)
if args.add_n_hidden == "":
hidden = []
else:
hidden = [int(x) for x in args.add_n_hidden.split(",")]
# -------
odir = Path(args.output)
odir.mkdir(parents=True, exist_ok=True)
if args.gpu == -1:
gpu = GPUtil.getFirstAvailable(attempts=3, includeNan=True)[0]
else:
gpu = args.gpu
create_tf_session(gpu)
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)))
help="Number of workers used to load the data")
parser.add_argument("--tf",
required=True,
help="Transcription factor to benchmark")
parser.add_argument("--output",
"-o",
required=True,
help="Transcription factor to benchmark")
args = parser.parse_args()
model = kipoi.get_model(args.model)
print("Obtaining a batch of data, using {} workers".format(
args.num_workers))
dl_kwargs = kipoi.pipeline.validate_kwargs(
model.default_dataloader, parse_json_file_str(args.dl_kwargs))
print("Used kwargs: {}".format(dl_kwargs))
dl = model.default_dataloader(**dl_kwargs)
# batch = numpy_collate([dl[0]]*args.batch_size)
it = dl.batch_iter(args.batch_size, num_workers=args.num_workers)
batch = next(it)
print("Measuring the forward time pass")
times = []
for i in range(args.num_runs):
start_time = time.time()
model.predict_on_batch(batch['inputs'])
duration = time.time() - start_time
times.append(duration)
print("Writing results to a json file")
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')
def _get_scoring_fns(model, sel_scoring_labels, sel_scoring_kwargs):
# get the scoring methods according to the model definition
avail_scoring_fns, avail_scoring_fn_def_args, avail_scoring_fn_names, \
default_scoring_fns = get_avail_scoring_methods(model)
errmsg_scoring_kwargs = "When defining `--scoring_kwargs` a JSON representation of arguments (or the path of a" \
" file containing them) must be given for every `--scoring` function."
dts = {}
if len(sel_scoring_labels) >= 1:
# Check if all scoring functions should be used:
if sel_scoring_labels == ["all"]:
if len(sel_scoring_kwargs) >= 1:
raise ValueError(
"`--scoring_kwargs` cannot be defined in combination will `--scoring all`!"
)
for arg_iter, k in enumerate(avail_scoring_fn_names):
si = avail_scoring_fn_names.index(k)
# get the default kwargs
kwargs = avail_scoring_fn_def_args[si]
if kwargs is None:
raise ValueError(
"No default kwargs for scoring function: %s"
" `--scoring all` cannot be used. "
"Please also define `--scoring_kwargs`." % (k))
# instantiate the scoring fn
dts[k] = avail_scoring_fns[si](**kwargs)
else:
# if -k set check that length matches with -s
if len(sel_scoring_kwargs) >= 1:
if not len(sel_scoring_labels) == len(sel_scoring_kwargs):
raise ValueError(errmsg_scoring_kwargs)
for arg_iter, k in enumerate(sel_scoring_labels):
# if -s set check is available for model
if k in avail_scoring_fn_names:
si = avail_scoring_fn_names.index(k)
# get the default kwargs
kwargs = avail_scoring_fn_def_args[si]
# if the user has set scoring function kwargs then load them here.
if len(sel_scoring_kwargs) >= 1:
# all the {}s in -k replace by their defaults, if the default is None
# raise exception with the corrsponding scoring function label etc.
defined_kwargs = parse_json_file_str(
sel_scoring_kwargs[si])
if len(defined_kwargs) != 0:
kwargs = defined_kwargs
if kwargs is None:
raise ValueError(
"No kwargs were given for scoring function %s"
" with no defaults but required argmuents. "
"Please also define `--scoring_kwargs`." % (k))
# instantiate the scoring fn
dts[k] = avail_scoring_fns[si](**kwargs)
else:
logger.warn("Cannot choose scoring function %s. "
"Model only supports: %s." %
(k, str(avail_scoring_fn_names)))
# if -s not set use all defaults
elif len(default_scoring_fns) != 0:
for arg_iter, k in enumerate(default_scoring_fns):
si = avail_scoring_fn_names.index(k)
kwargs = avail_scoring_fn_def_args[si]
dts[k] = avail_scoring_fns[si](**kwargs)
if len(dts) == 0:
raise Exception("No scoring method was chosen!")
return dts
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)))