Collect samples from generator and store in rds or pickle file.

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

if (FALSE) { # reticulate::py_module_available("tensorflow")
# 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)
}

file	label_1	label_2
"a.fasta"	1	0