A set of simple Python scripts for pre-processing large files, things like splitting and format conversion. The names phraug comes from a great book, Made to Stick, by Chip and Dan Heath.
See http://fastml.com/processing-large-files-line-by-line/ for the basic idea.
There's always at least one input file and usually one or more output files. An input file always stays unchanged.
phraug2 is available, with improved command line arguments parsing. Check it out.
csv2libsvm.py <input file> <output file> [<label index = 0>] [<skip headers = 0>]
Convert CSV to LIBSVM format. If there are no labels in the input file, specify label index = -1. If there are headers in the input file, specify skip headers = 1.
csv2vw.py <input file> <output file> [<label index = 0>] [<skip headers = 0>]
Convert CSV to VW format. Arguments as above.
libsvm2csv.py <input file> <output file> <input file dimensionality>
Convert LIBSVM to CSV. You need to specify dimensionality, that is a number of columns (not counting a label).
libsvm2vw.py <input file> <output file>
Convert LIBSVM to VW.
tsv2csv.py <input file> <output file>
Convert tab-separated file to comma-separated file.
Column means, standard deviations and normalization
How do you normalize (or standardize or shift and scale) your data if it doesn't fit into memory? With these two scripts.
colstats.py <input file> <output file> [<label index>]
Compute column means and standard deviations from data in csv file. Can skip label if present. Numbers only. The first line of the output file contains means, the second one standard deviations.
This script uses f_is_headers module, which contains is_headers() function. The purpose of the function is to automatically define if the [first] line in file contains headers.
normalize.py <stats file> <input file> <output file> [<label index>]
Normalize (shift and scale to zero mean and unit standard deviation) data from csv file. Meant to be used with column stats file produced by colstats.py. Numbers only.
chunk.py <input file> <number of output files> [<random seed>]
Split a file randomly line by line into a number of smaller files. Might be useful for preparing cross-validation. Output files will have the base nume suffixed with a chunk number, for example
data.csv will be chunked into
count.py <input file>
Count lines in a file. On Unix you can do it with
delete_cols.py <input file> <output_file> <indices of columns to delete>
delete_cols.py train.csv train_del.csv 0 2 3
Delete some columns from a CSV file. Indexes start with 0. Separate them with whitespace.
sample.py <input file> <output file> [<P = 0.5>]
Sample lines from an input file with probability P. Similiar to
split.py, but there's only one output file. Useful for sampling large datasets.
shuffle.py <input file> <output file> [<max. lines in memory = 25000>] [<random seed>]
Shuffle (randomize order of) lines in a [big] file. Similiar to Unix'
shuf. Useful for files that don't fit in memory. For fastest operation, set max. lines in memory as big as possible - this will result in fewer passes over the input file.
split.py <input file> <output file 1> <output file 2> [<P = 0.9>] [<random seed>]
Split a file into two randomly. Default P (probability of writing to the first file) is 0.9. You can specify any string as a seed for random number generator.
subset.py <input file> <output file> [<offset = 0>] [<lines = 100>]
Save a subset of lines from an input file to an output file. Start at offset (default 0), save lines (default 100).
unshuffle.py <input file> <output file> <max. lines in memory> <random seed>
Unshuffle a previously shuffled file (or any file) to the original order. Syntax is the same as for
shuffle.py, but the seed is mandatory so max. lines in memory is mandatory also.