| dataset_from_gen | R Documentation |
Collect samples from generator and store in rds or pickle file.
Description
Repeatedly generate samples with data generator and store output. Creates a separate rds or pickle file in output_path for each
batch.
Usage
dataset_from_gen(
output_path,
iterations = 10,
train_type = "lm",
output_format = "target_right",
path_corpus,
batch_size = 32,
maxlen = 250,
step = NULL,
vocabulary = c("a", "c", "g", "t"),
shuffle = FALSE,
set_learning = NULL,
seed = NULL,
random_sampling = FALSE,
store_format = "rds",
file_name_start = "batch_",
masked_lm = NULL,
...
)
Arguments
output_path |
Output directory. Output files will be named output_path + file_name_start + x + ".rds" or ".pickle", where x is an index (from 1 to
iterations) and file ending depends on store_format argument.
|
iterations |
Number of batches (output files) to create.
|
train_type |
Either "lm", "lm_rds", "masked_lm" for language model; "label_header", "label_folder", "label_csv", "label_rds" for classification or "dummy_gen".
Language model is trained to predict character(s) in a sequence.
-
"label_header"/"label_folder"/"label_csv" are trained to predict a corresponding class given a sequence as input.
If "label_header", class will be read from fasta headers.
If "label_folder", class will be read from folder, i.e. all files in one folder must belong to the same class.
If "label_csv", targets are read from a csv file. This file should have one column named "file". The targets then correspond to entries in that row (except "file"
column). Example: if we are currently working with a file called "a.fasta" and corresponding label is "label_1", there should be a row in our csv file
|
file | label_1 | label_2 |
|
"a.fasta" | 1 | 0 |
|
|
If "label_rds", generator will iterate over set of .rds files containing each a list of input and target tensors. Not implemented for model
with multiple inputs.
If "lm_rds", generator will iterate over set of .rds files and will split tensor according to target_len argument
(targets are last target_len nucleotides of each sequence).
If "dummy_gen", generator creates random data once and repeatedly feeds these to model.
If "masked_lm", generator maskes some parts of the input. See masked_lm argument for details.
|
output_format |
Determines shape of output tensor for language model.
Either "target_right", "target_middle_lstm", "target_middle_cnn" or "wavenet".
Assume a sequence "AACCGTA". Output correspond as follows
-
"target_right": X = "AACCGT", Y = "A"
-
"target_middle_lstm": X = (X_1 = "AAC", X_2 = "ATG"), Y = "C" (note reversed order of X_2)
-
"target_middle_cnn": X = "AACGTA", Y = "C"
-
"wavenet": X = "AACCGT", Y = "ACCGTA"
|
path_corpus |
Input directory where fasta files are located or path to single file ending with fasta or fastq
(as specified in format argument). Can also be a list of directories and/or files.
|
batch_size |
Number of samples in one batch.
|
maxlen |
Length of predictor sequence.
|
step |
How often to take a sample.
|
vocabulary |
Vector of allowed characters. Characters outside vocabulary get encoded as specified in ambiguous_nuc.
|
shuffle |
Whether to shuffle samples within each batch.
|
set_learning |
When you want to assign one label to set of samples. Only implemented for train_type = "label_folder".
Input is a list with the following parameters
-
samples_per_target: how many samples to use for one target.
-
maxlen: length of one sample.
-
reshape_mode: "time_dist", "multi_input" or "concat".
-
If reshape_mode is "multi_input", generator will produce samples_per_target separate inputs, each of length maxlen (model should have
samples_per_target input layers).
If reshape_mode is "time_dist", generator will produce a 4D input array. The dimensions correspond to
(batch_size, samples_per_target, maxlen, length(vocabulary)).
If reshape_mode is "concat", generator will concatenate samples_per_target sequences
of length maxlen to one long sequence.
If reshape_mode is "concat", there is an additional buffer_len
argument. If buffer_len is an integer, the subsequences are interspaced with buffer_len rows. The input length is
(maxlen * samples_per_target) + buffer_len * (samples_per_target - 1).
|
seed |
Sets seed for set.seed function for reproducible results.
|
random_sampling |
Whether samples should be taken from random positions when using max_samples argument. If FALSE random
samples are taken from a consecutive subsequence.
|
store_format |
Either "rds" or "pickle".
|
file_name_start |
Start of output file names.
|
masked_lm |
If not NULL, input and target are equal except some parts of the input are masked or random.
Must be list with the following arguments:
-
mask_rate: Rate of input to mask (rate of input to replace with mask token).
-
random_rate: Rate of input to set to random token.
-
identity_rate: Rate of input where sample weights are applied but input and output are identical.
-
include_sw: Whether to include sample weights.
-
block_len (optional): Masked/random/identity regions appear in blocks of size block_len.
|
... |
further generator options. See get_generator.
|
Value
None. Function writes data to files and does not return a value.
Examples
# create dummy fasta files
temp_dir <- tempfile()
dir.create(temp_dir)
create_dummy_data(file_path = temp_dir,
num_files = 3,
seq_length = 8,
num_seq = 2)
# extract samples
out_dir <- tempfile()
dir.create(out_dir)
dataset_from_gen(output_path = out_dir,
iterations = 10,
train_type = "lm",
output_format = "target_right",
path_corpus = temp_dir,
batch_size = 32,
maxlen = 5,
step = 1,
file_name_start = "batch_")
list.files(out_dir)
