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import argparse
import json
import os
import sys
import warnings
from goby.SequenceSegmentInformation import SequenceSegmentInformationGenerator
from goby.pyjavaproperties import Properties
import numpy as np
def get_metadata_value(base):
# Metadata value 0: ref
# Metadata value 1: SNP
# Metadata value 2: indel
if not base.isVariant and not base.hasTrueIndel:
return 0
elif base.isVariant and not base.hasTrueIndel:
return 1
elif base.hasTrueIndel:
return 2
else:
raise Exception("Invalid isVariant {} and hasTrueIndel {} combination for a base".format(base.isVariant,
base.hasTrueIndel))
def vectorize_segment_info(segment_info, max_base_count, max_feature_count, max_label_count, padding="pre"):
# Only look at first segment in sample for now
sample = segment_info.sample[0]
feature_array = []
label_array = []
metadata_array = []
ref_array = []
location_array = []
for base in sample.base:
feature_array.append(np.pad(np.array(list(base.features)), (0, max_feature_count - len(base.features)),
mode='constant'))
# Reserve label index 0 for padding/masking timesteps
labels_to_append = [0] + list(base.labels)
label_array.append(np.pad(np.array(labels_to_append),
(0, max_label_count - len(labels_to_append)),
mode='constant'))
metadata_array.append(get_metadata_value(base))
ref_array.append(base.referenceAllele)
location_array.append(base.location)
amount_to_pad = max(0, max_base_count - len(sample.base))
timestep_padding = (amount_to_pad, 0) if padding == "pre" else (0, amount_to_pad)
padding_shape = (timestep_padding, (0, 0))
feature_array = np.pad(np.array(feature_array), padding_shape, mode='constant')
label_array = np.pad(np.array(label_array), padding_shape, mode='constant')
metadata_array = np.pad(np.array(metadata_array), timestep_padding, mode='constant')
ref_array = np.pad(np.array(ref_array), timestep_padding, mode='constant')
location_array = np.pad(np.array(location_array), timestep_padding, mode='constant')
if max_base_count < len(sample.base):
if padding == "post":
feature_array = feature_array[:max_base_count, :]
label_array = label_array[:max_base_count, :]
metadata_array = metadata_array[:max_base_count]
ref_array = ref_array[:max_base_count]
location_array = location_array[:max_base_count]
else:
start_base = feature_array.shape[1] - max_base_count
feature_array = feature_array[start_base:, :]
label_array = label_array[start_base:, :]
metadata_array = metadata_array[start_base:]
ref_array = ref_array[start_base:]
location_array = location_array[start_base:]
return feature_array, label_array, metadata_array, ref_array, location_array
def minimal_vectorize_segment(segment_info, padding="pre"):
num_bases = len(segment_info.sample[0].base)
# Add 1 to labels because 0 reserved for padding/masking
num_features_set, num_labels_set = map(frozenset,
zip(*[(len(base.features), len(base.labels) + 1)
for base in segment_info.sample[0].base]))
feature_mismatch = len(num_features_set) > 1
label_mismatch = len(num_labels_set) > 1
num_features = max(num_features_set)
num_labels = max(num_labels_set)
return vectorize_segment_info(segment_info, num_bases, num_features, num_labels,
padding), feature_mismatch, label_mismatch
def vectorize_by_mini_batch(segment_info_generator, mini_batch_size, num_segments, max_base_count, padding="pre",
limit=None):
segments_processed_in_batch = 0
segments_processed_total = 0
feature_mismatch = False
label_mismatch = False
max_bases_mini_batch = -sys.maxsize
max_features_mini_batch = -sys.maxsize
max_labels_mini_batch = -sys.maxsize
mini_batch_segment_data = []
for segment_info in segment_info_generator:
if limit is None or segments_processed_total < limit:
segments_processed_in_batch += 1
segments_processed_total += 1
for base_idx in range(len(segment_info.sample[0].base)):
if len(segment_info.sample[0].base[base_idx].features) == 0:
continue
if len(segment_info.sample) == 0:
continue
if len(segment_info.sample[0].base) == 0:
continue
segment_info_data, segment_feature_mismatch, segment_label_mismatch = minimal_vectorize_segment(
segment_info, padding)
segment_info_chromosome = [str(segment_info.start_position.reference_id)]
segment_input, segment_label, _, _, _ = segment_info_data
feature_mismatch |= segment_feature_mismatch
label_mismatch |= segment_label_mismatch
segment_num_bases, segment_num_features = segment_input.shape
_, segment_num_labels = segment_label.shape
max_bases_mini_batch = max(max_bases_mini_batch, segment_num_bases)
max_features_mini_batch = max(max_features_mini_batch, segment_num_features)
max_labels_mini_batch = max(max_labels_mini_batch, segment_num_labels)
mini_batch_segment_data.append((segment_info_data, segment_info_chromosome))
if ((segments_processed_in_batch == mini_batch_size)
or (segments_processed_total == num_segments)
or (limit is not None and segments_processed_total == limit)):
mini_batch_input_ndarray = []
mini_batch_label_ndarray = []
mini_batch_metadata_ndarray = []
mini_batch_ref_ndarray = []
mini_batch_location_ndarray = []
mini_batch_chromosome_ndarray = []
for segment_data_batch, segment_chromosome_batch in mini_batch_segment_data:
(segment_input_batch, segment_label_batch, segment_metadata_batch,
segment_ref_batch, segment_location_batch) = segment_data_batch
segment_num_bases_batch, segment_num_features_batch = segment_input_batch.shape
_, segment_num_labels_batch = segment_label_batch.shape
# Prepad timesteps, postpad features/labels (if there's a shape mismatch)
num_base_diff = max_bases_mini_batch - segment_num_bases_batch
timestep_padding = (num_base_diff, 0) if padding == "pre" else (0, num_base_diff)
mini_batch_input_ndarray.append(np.pad(
segment_input_batch,
pad_width=(timestep_padding, (0, max_features_mini_batch - segment_num_features_batch)),
mode='constant'))
segment_label_ndarray = np.pad(
segment_label_batch,
pad_width=(timestep_padding, (0, max_labels_mini_batch - segment_num_labels_batch)),
mode='constant')
if padding == "post":
segment_label_ndarray[segment_num_bases_batch:, 0] = 1.
else:
segment_label_ndarray[:segment_num_bases_batch, 0] = 1.
mini_batch_label_ndarray.append(segment_label_ndarray)
mini_batch_metadata_ndarray.append(np.pad(
segment_metadata_batch,
pad_width=timestep_padding,
mode='constant'))
mini_batch_ref_ndarray.append(np.pad(
segment_ref_batch,
pad_width=timestep_padding,
mode='constant'))
mini_batch_location_ndarray.append(np.pad(
segment_location_batch,
pad_width=timestep_padding,
mode='constant'))
mini_batch_chromosome_ndarray.append(segment_chromosome_batch)
mini_batch_input_ndarray = np.array(mini_batch_input_ndarray)
mini_batch_label_ndarray = np.array(mini_batch_label_ndarray)
mini_batch_metadata_ndarray = np.array(mini_batch_metadata_ndarray)
mini_batch_ref_ndarray = np.array(mini_batch_ref_ndarray)
mini_batch_chromosome_ndarray = np.array(mini_batch_chromosome_ndarray)
mini_batch_location_ndarray = np.array(mini_batch_location_ndarray)
if max_base_count < mini_batch_input_ndarray.shape[1]:
if padding == "post":
mini_batch_input_ndarray = mini_batch_input_ndarray[:, :max_base_count, :]
mini_batch_label_ndarray = mini_batch_label_ndarray[:, :max_base_count, :]
mini_batch_metadata_ndarray = mini_batch_metadata_ndarray[:, :max_base_count]
mini_batch_ref_ndarray = mini_batch_ref_ndarray[:, :max_base_count]
mini_batch_location_ndarray = mini_batch_location_ndarray[:, :max_base_count]
else:
start_base = mini_batch_input_ndarray.shape[1] - max_base_count
mini_batch_input_ndarray = mini_batch_input_ndarray[:, start_base:, :]
mini_batch_label_ndarray = mini_batch_label_ndarray[:, start_base:, :]
mini_batch_metadata_ndarray = mini_batch_metadata_ndarray[:, start_base:]
mini_batch_ref_ndarray = mini_batch_ref_ndarray[:, start_base:]
mini_batch_location_ndarray = mini_batch_location_ndarray[:, start_base:]
mini_batch_data = (mini_batch_input_ndarray, mini_batch_label_ndarray,
mini_batch_metadata_ndarray, mini_batch_ref_ndarray,
mini_batch_chromosome_ndarray, mini_batch_location_ndarray)
yield mini_batch_data, feature_mismatch, label_mismatch
segments_processed_in_batch = 0
mini_batch_segment_data = []
max_bases_mini_batch = -sys.maxsize
max_features_mini_batch = -sys.maxsize
max_labels_mini_batch = -sys.maxsize
if segments_processed_total == limit:
break
def write_mini_batch_data(batch_input_to_write, batch_label_to_write, batch_metadata_to_write, batch_ref_to_write,
batch_chromosome_to_write, batch_location_to_write, output_path, compress):
save_fn = np.savez_compressed if compress else np.savez
save_fn(output_path, input=batch_input_to_write, label=batch_label_to_write, metadata=batch_metadata_to_write,
ref=batch_ref_to_write, chromosome=batch_chromosome_to_write, location=batch_location_to_write)
def main(args):
os.makedirs(args.output_dir, exist_ok=True)
with open("{}p".format(args.input), "r") as input_ssip:
input_properties = Properties()
input_properties.load(input_ssip)
max_base_count = int(input_properties.getProperty("maxNumOfBases"))
max_feature_count = int(input_properties.getProperty("maxNumOfFeatures"))
max_label_count = int(input_properties.getProperty("maxNumOfLabels")) + 1
num_segments = int(input_properties.getProperty("numSegments"))
output_path_and_prefix = os.path.join(args.output_dir, args.prefix)
feature_mismatch = False
label_mismatch = False
num_segments_in_last_data_set = 0
batches_written = 0
num_segments_written = 0
for batch_idx, (batch_data_set, batch_feature_mismatch, batch_label_mismatch) in enumerate(vectorize_by_mini_batch(
SequenceSegmentInformationGenerator(args.input), args.mini_batch_size, num_segments, max_base_count,
args.padding, args.limit)):
batch_input, batch_label, batch_metadata, batch_ref, batch_chromosome, batch_location = batch_data_set
num_segments_written_in_batch = batch_input.shape[0]
num_segments_written += num_segments_written_in_batch
num_segments_in_last_data_set = num_segments_written_in_batch
feature_mismatch |= batch_feature_mismatch
label_mismatch |= batch_label_mismatch
output_full_path = "{}_{}.npz".format(output_path_and_prefix, batch_idx)
write_mini_batch_data(batch_input, batch_label, batch_metadata, batch_ref, batch_chromosome,
batch_location, output_full_path, args.compress)
batches_written += 1
output_json_path = os.path.join(args.output_dir, "properties.json")
properties = {
"max_base_count": max_base_count,
"max_feature_count": max_feature_count,
"max_label_count": max_label_count,
"num_segments_in_last_data_set": num_segments_in_last_data_set,
"mini_batch_size": args.mini_batch_size,
"num_segments_written": num_segments_written,
"total_batches_written": batches_written,
"batch_prefix": args.prefix,
"padding": args.padding,
"genotype.segment.label_plus_one.0": "<PAD>",
}
for label in range(1, max_label_count):
label_name_in = "genotype.segment.label.{}".format(label - 1)
label_name_out = "genotype.segment.label_plus_one.{}".format(label)
properties[label_name_out] = input_properties.getProperty(label_name_in)
with open(output_json_path, "w") as output_json_file:
json.dump(properties, output_json_file, indent=2)
if feature_mismatch:
warnings.warn("Mismatched number of features in each base; training behavior will be undefined")
if label_mismatch:
warnings.warn("Mismatched number of labels in each base; training behavior will be undefined")
if __name__ == "__main__":
parser = argparse.ArgumentParser("Generate datasets from SSI files")
parser.add_argument("-i", "--input", type=str, required=True, help="SSI file with input segments.")
parser.add_argument("-o", "--output-dir", type=str, required=True,
help="Parent directory to put generated numpy files.")
parser.add_argument("-p", "--prefix", type=str, required=True,
help="Prefix to prepend all generated files with.")
parser.add_argument("-m", "--mini-batch-size", type=int, default=1,
help="Number of segments to put in each numpy array.")
parser.add_argument("--compress", dest="compress", action="store_true",
help="When set, compress npz files that are generated.")
parser.add_argument("--padding", type=str, choices=["pre", "post"], default="post",
help="Whether to pad timesteps before or after sequences.")
parser.add_argument("--limit", type=int, help="If present, only generate --limit segments.")
parser_args = parser.parse_args()
main(parser_args)
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