Full Code of zygmuntz/phraug for AI

Repository: zygmuntz/phraug
Branch: master
Commit: 06a7e54fb5ad
Files: 21
Total size: 23.6 KB

Directory structure:
gitextract_5og_t45i/

├── .gitattributes
├── .gitignore
├── LICENSE
├── README.md
├── chunk.py
├── colstats.py
├── count.py
├── csv2libsvm.py
├── csv2vw.py
├── delete_cols.py
├── f_is_headers.py
├── libsvm2csv.py
├── libsvm2vw.py
├── pivotedcsv2libsvm.py
├── sample.py
├── shuffle.py
├── split.py
├── standardize.py
├── subset.py
├── tsv2csv.py
└── unshuffle.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .gitattributes
================================================
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================================================
FILE: .gitignore
================================================
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#################

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================================================
FILE: LICENSE
================================================
Copyright (c) 2013 Zygmunt Zając
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:

* Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

* Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


================================================
FILE: README.md
================================================
phraug
======

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/](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](https://github.com/zygmuntz/phraug2) is available. It offers improved handling of command line arguments.__ Check it out.

Format conversion
-----------------

`[...]` means that the parameter is optional.

`csv2libsvm.py <input file> <output file> [<label index = 0>] [<skip headers = 0>]`

Convert CSV to the 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.

`pivotedcsv2libsvm.py <input file> <output file> [<skip headers = 0>]`

Convert pivoted CSV (each line contains sample id, feature index and feature value) to the LIBSVM format. 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 standardization
--------------------------------------------------

How do you 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.

`standardize.py <stats file> <input file> <output file> [<label index>]`

Standardize (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.


Other operations
----------------

`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 `data_0.csv`, `data_1.csv` etc.

`count.py <input file>`

Count lines in a file. On Unix you can do it with `wc -l`

`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.



================================================
FILE: chunk.py
================================================
'''
split a file into a given number of chunks randomly, line by line. 
Usage: chunk.py <input file> <number of chunks> [<seed>]'
'''

import sys, random, os

input_file = sys.argv[1]
num_chunks = int( sys.argv[2] )

try:
	seed = sys.argv[3]
except IndexError:
	seed = None
	
if seed:
	print "seeding: %s" % ( seed )
	random.seed( seed )

basename = os.path.basename( input_file )
basename, ext = os.path.splitext( basename )

i = open( input_file )

os = {}
for n in range( num_chunks ):
	output_file = "%s_%s%s" % ( basename, n, ext )
	os[n] = open( output_file, 'wb' )

counter = 0

for line in i:
	n = random.randint( 0, num_chunks - 1 )
	os[n].write( line )
	
	counter += 1
	if counter % 100000 == 0:
		print counter
	

		
		
		
		
		
		
		

================================================
FILE: colstats.py
================================================
"""
compute column means and standard deviations from data in csv file
colstats.py <input file> <output file> [<label index>]
"""

import sys, csv
import numpy as np
from f_is_headers import *

print __doc__

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	label_index = int( sys.argv[3] )
except IndexError:
	label_index = False
	
i = open( input_file )
reader = csv.reader( i )
writer = csv.writer( open( output_file, 'wb' ))

# check headers

first_line = reader.next()
if not is_headers( first_line ):
	# rewind
	i.seek( 0 )		
	
n = 0
sums_x = 0		# will be a np array
sums_x2 = 0		# will be a np array

for line in reader:
	n += 1
	
	if not label_index is False:
		line.pop( label_index )
	
	x = np.array( map( float, line ))
	x2 = np.square( x )

	sums_x += x
	sums_x2 += x2


# preparation

print n	
print sums_x
print sums_x2
	
means = sums_x / n	
sums2_x = np.square( sums_x )

#print means
#print sums2_x

variances = sums_x2 / n - sums2_x / ( n ** 2 )
standard_deviations = np.sqrt( variances )

#print variances
#print standard_deviations

# save stats

writer.writerow( means )
writer.writerow( standard_deviations )


================================================
FILE: count.py
================================================
'Count lines in a file'

import sys

file_path = sys.argv[1]
f = open( file_path )

count =  0
for line in f:
	count += 1
	
	if count % 100000 == 0:
		print count
	
print count
		
		
		
		
		
		
		

================================================
FILE: csv2libsvm.py
================================================
#!/usr/bin/env python

"""
Convert CSV file to libsvm format. Works only with numeric variables.
Put -1 as label index (argv[3]) if there are no labels in your file.
Expecting no headers. If present, headers can be skipped with argv[4] == 1.

"""

import sys
import csv
from collections import defaultdict

def construct_line( label, line ):
	new_line = []
	if float( label ) == 0.0:
		label = "0"
	new_line.append( label )

	for i, item in enumerate( line ):
		if item == '' or float( item ) == 0.0:
			continue
		new_item = "%s:%s" % ( i + 1, item )
		new_line.append( new_item )
	new_line = " ".join( new_line )
	new_line += "\n"
	return new_line

# ---

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	label_index = int( sys.argv[3] )
except IndexError:
	label_index = 0

try:
	skip_headers = sys.argv[4]
except IndexError:
	skip_headers = 0

i = open( input_file, 'rb' )
o = open( output_file, 'wb' )

reader = csv.reader( i )

if skip_headers:
	headers = reader.next()

for line in reader:
	if label_index == -1:
		label = '1'
	else:
		label = line.pop( label_index )

	new_line = construct_line( label, line )
	o.write( new_line )



================================================
FILE: csv2vw.py
================================================
"""
Convert CSV file to vw format. Headers can be skipped with argv[4] == true.
Use -1 for label index if there no labels in the input file

phraug2 version has an option to ignore columns:
https://github.com/zygmuntz/phraug2/blob/master/csv2vw.py
"""

import sys
import csv

def construct_line( label, line ):
	new_line = []
	if float( label ) == 0.0:
		label = "0"
	new_line.append( "%s |n " % ( label ))
	
	for i, item in enumerate( line ):
		if float( item ) == 0.0:
			continue	# sparse!!!
		new_item = "%s:%s" % ( i + 1, item )
		new_line.append( new_item )
	new_line = " ".join( new_line )
	new_line += "\n"
	return new_line

# ---

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	label_index = int( sys.argv[3] )
except IndexError:
	label_index = 0
	
try:
	skip_headers = sys.argv[4]
except IndexError:
	skip_headers = 0	

i = open( input_file )
o = open( output_file, 'w' )

reader = csv.reader( i )
if skip_headers:
	headers = reader.next()

n = 0

for line in reader:
	if label_index == -1:
		label = 1
	else:
		label = line.pop( label_index )
		
	new_line = construct_line( label, line )
	o.write( new_line )
	
	n += 1
	if n % 10000 == 0:
		print n
		
		

================================================
FILE: delete_cols.py
================================================
'delete some columns from file, given by their indexes'

import csv
import sys

input_file = sys.argv[1]
output_file = sys.argv[2]
headers = sys.argv[3:]

headers = map( int, headers )
headers.sort( reverse = True )

print "%s ---> %s" % ( input_file, output_file )
print "header indices: %s" % ( headers )

i = open( input_file )
o = open( output_file, 'wb' )

reader = csv.reader( i )
writer = csv.writer( o )

counter = 0
for line in reader:

	for h in headers:
		del line[h]

	writer.writerow( line )
	
	counter += 1
	if counter % 10000 == 0:
		print counter


		
		
		
		
		
		
		

================================================
FILE: f_is_headers.py
================================================
import re

def is_headers( line ):
	line = ''.join( line )
	if re.match( '.*[a-df-zA-Z].*', line ):
		return True
		
		

================================================
FILE: libsvm2csv.py
================================================
#!/usr/bin/env python

"""
convert libsvm file to csv'
libsvm2csv.py <input file> <output file> <X dimensionality>
"""

import sys
import csv

input_file = sys.argv[1]
output_file = sys.argv[2]

d = int( sys.argv[3] )
assert ( d > 0 )

reader = csv.reader( open( input_file ), delimiter = " " )
writer = csv.writer( open( output_file, 'wb' ))

for line in reader:
	label = line.pop( 0 )
	if line[-1].strip() == '':
		line.pop( -1 )
		
	# print line
	
	line = map( lambda x: tuple( x.split( ":" )), line )
	#print line
	# ('1', '0.194035105364'), ('2', '0.186042408882'), ('3', '-0.148706067206'), ...
	
	new_line = [ label ] + [ 0 ] * d
	for i, v in line:
		i = int( i )
		if i <= d:
			new_line[i] = v
		
	writer.writerow( new_line )


================================================
FILE: libsvm2vw.py
================================================
"convert a libsvm file to VW format"
"skip malformed lines"
"in case of binary classification with 0/1 labels set the third argument to True"
"this will convert labels to -1/1"

import sys

input_file = sys.argv[1]
output_file = sys.argv[2]
try:
	convert_zero_to_negative_one = bool( sys.argv[3] )
except IndexError:
	convert_zero_to_negative_one = False

i = open( input_file )
o = open( output_file, 'wb' )

for line in i:
	try:
		y, x = line.split( " ", 1 )
	# ValueError: need more than 1 value to unpack
	except ValueError:
		print "line with ValueError (skipping):"
		print line
		continue
		
	if convert_zero_to_negative_one and y == '0':
		y = '-1'
	new_line = y + " |n " + x
	o.write( new_line )
	


================================================
FILE: pivotedcsv2libsvm.py
================================================
#!/usr/bin/env python

"""
Convert pivoted CSV file to libsvm format. Works only with numeric variables.
Expecting no labels and no headers. If present, headers can be skipped with argv[3] == 1.

Example call: python pivotedlibsvm2csv.py pivoted.csv output.txt
format: row_id, zero_based_feature_index [, value = 1]

input example:
id1,1
id1,2
id1,3
id2,2,0.5
id2,3,0.6
id2,4,0.7

output example:
1 2:1 3:1 4:1
1 3:0.5 4:0.6 5:0.7

"""

import sys
import csv

def construct_line( label, line ):
	new_line = []
	if float( label ) == 0.0:
		label = "0"
	new_line.append( label )

	for i_item in line:
		i, item = i_item
		if item == '' or float( item ) == 0.0:
			continue
		new_item = "%s:%s" % ( i + 1, item )
		new_line.append( new_item )
		
	new_line = " ".join( new_line )
	new_line += "\n"
	return new_line

# ---

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	skip_headers = sys.argv[3]
except IndexError:
	skip_headers = 0

i = open( input_file, 'rb' )
o = open( output_file, 'wb' )

reader = csv.reader( i )

if skip_headers:
	headers = reader.next()


line = reader.next()
current_row = line[0].strip()
current_feature = int( line[1].strip())
current_value = line[2].strip() if len( line ) > 2 else '1'
current_line = [ ( current_feature, current_value ) ]

for line in reader:
	row_id = line[0]
	feature_index = int( line[1].strip())
	feature_value = line[2].strip() if len( line ) > 2 else '1'
	
	if row_id != current_row:
		new_line = construct_line( '1', current_line )
		o.write( new_line )	
		
		current_row = row_id
		current_line = [( feature_index, feature_value )]
	else:
		current_line.append(( feature_index, feature_value ))

# the last row
new_line = construct_line( '1', current_line )
o.write( new_line )	


================================================
FILE: sample.py
================================================
'sample lines from input file with probability P, save them to output file'

import csv
import sys
import random

try:
	P = float( sys.argv[3] )
except IndexError:
	P = 0.5
	
print "P = %s" % ( P )

input_file = sys.argv[1]
output_file = sys.argv[2]

i = open( input_file )
o = open( output_file, 'w' )

reader = csv.reader( i )
writer = csv.writer( o )

headers = reader.next()
writer.writerow( headers )

for line in reader:
	r = random.random()
	if r > P:
		continue

	writer.writerow( line )
	

	

		
		
		
		
		
		
		

================================================
FILE: shuffle.py
================================================
"""
Shuffle lines in a [big] file
shuffle.py <input_file> <output_file> [<preserve headers?>] [<max. lines in memory>] [<random seed>]

"""

import sys
import random

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	preserve_headers = int( sys.argv[3] )
except IndexError:
	preserve_headers = 0

try:
	lines_in_memory = int( sys.argv[4] )
except IndexError:
	lines_in_memory = 25000
	
print "caching %s lines at a time..." % ( lines_in_memory )
	
try:
	random_seed = sys.argv[5]
	random.seed( random_seed )
	print "random seed: %s" % ( random_seed )
except IndexError:
	pass
	
# first count

print "counting lines..."

i_f = open( input_file )
o_f = open( output_file, 'wb' )

if preserve_headers:
	headers = i_f.readline()
	o_f.write( headers )

counter =  0
for line in i_f:
	counter += 1
	
	if counter % 100000 == 0:
		print counter
	
print counter
		
print "shuffling..."

order = range( counter )
random.shuffle( order )

epoch = 0
	
while order:

	current_lines = {}
	current_lines_count = 0

	current_chunk = order[:lines_in_memory]
	current_chunk_dict = { x: 1 for x in current_chunk }		# faster "in"
	current_chunk_length = len( current_chunk )
	
	order = order[lines_in_memory:]
	
	i_f.seek( 0 )
	if preserve_headers:
		i_f.readline()
		
	count = 0
		
	for line in i_f:
		if count in current_chunk_dict:
			current_lines[count] = line
			current_lines_count += 1
			if current_lines_count == current_chunk_length:
				break
		count += 1	
		if count % 100000 == 0:
			print count		
	
	print "writing..."
	
	for l in current_chunk:
		o_f.write( current_lines[l] )
	
	lines_saved = current_chunk_length + epoch * lines_in_memory
	epoch += 1
	print "pass %s complete (%s lines saved)" % ( epoch, lines_saved )
		

================================================
FILE: split.py
================================================
'''
split a file into two randomly, line by line. 
Usage: split.py <input file> <output file 1> <output file 2> [<probability of writing to the first file>]'
'''

import csv
import sys
import random

input_file = sys.argv[1]
output_file1 = sys.argv[2]
output_file2 = sys.argv[3]

try:
	P = float( sys.argv[4] )
except IndexError:
	P = 0.9
	
try:
	seed = sys.argv[5]
except IndexError:
	seed = None

try:
	skip_headers = sys.argv[6]
except IndexError:
	skip_headers = False
	
try:
	skip_headers = sys.argv[6]
except IndexError:
	skip_headers = False	
	
print "P = %s" % ( P )

if seed:
	random.seed( seed )

i = open( input_file )
o1 = open( output_file1, 'wb' )
o2 = open( output_file2, 'wb' )

if skip_headers:
	i.readline()

counter = 0

for line in i:
	r = random.random()
	if r > P:
		o2.write( line )
	else:
		o1.write( line )
	
	counter += 1
	if counter % 100000 == 0:
		print counter
	

		
		
		
		
		
		
		


================================================
FILE: standardize.py
================================================
'standardize (shift and scale to zero mean and unit standard deviation) data from csv file'
'meant to be used together with colstats.py'
'standardize.py <stats file> <input file> <output file> [<label index>]'

import sys, csv
import numpy as np
from f_is_headers import *

stats_file = sys.argv[1]
input_file = sys.argv[2]
output_file = sys.argv[3]

try:
	label_index = int( sys.argv[4] )
except IndexError:
	label_index = False
	
i = open( input_file )	
stats_reader = csv.reader( open( stats_file ))	
reader = csv.reader( i )
writer = csv.writer( open( output_file, 'wb' ))

# get stats

means = stats_reader.next()
means = np.array( map( float, means ))

standard_deviations = stats_reader.next()
standard_deviations = np.array( map( float, standard_deviations ))

# check headers

first_line = reader.next()
if is_headers( first_line ):
	headers = first_line
else:
	headers = False
	i.seek( 0 )
	
# go

for line in reader:
	
	if not label_index is False:
		l = line.pop( label_index )	
		print l
		
	x = np.array( map( float, line ))
	
	# shift and scale
	x = x - means
	x = x / standard_deviations
	
	if not label_index is False:
		# -1.0,...
		#x = np.insert( x, 0, l )
		line = list( x )
		line.insert( 0, l )
	
	writer.writerow( line )
	


================================================
FILE: subset.py
================================================
'Save a subset of lines from an input file; start at offset and count n lines'
'default 100 lines starting from 0'

import sys

input_file = sys.argv[1]
output_file = sys.argv[2]
try:
	offset = int( sys.argv[3] )
except IndexError:
	offset = 0
	
try:
	lines = int( sys.argv[4] )
except IndexError:
	lines = 100	


i = open( input_file )
o = open( output_file, 'wb' )

count =  0
for line in i:

	if offset > 0:
		offset -= 1
		continue

	o.write( line )
	count += 1
	
	if count >= lines:
		break
	

		
		
		
		
		
		
		

================================================
FILE: tsv2csv.py
================================================
import csv
import sys

input_file = sys.argv[1]
output_file = sys.argv[2]

i = open( input_file )
o = open( output_file, 'wb' )

reader = csv.reader( i, delimiter = '\t' )
writer = csv.writer( o )

for line in reader:
	writer.writerow( line )


================================================
FILE: unshuffle.py
================================================
"""
Unshuffle previously shuffled file
unshuffle.py input_file.csv output_file.csv <max. lines in memory> <random seed>

"""

import sys
import random

input_file = sys.argv[1]
output_file = sys.argv[2]

try:
	lines_in_memory = int( sys.argv[3] )
except IndexError:
	lines_in_memory = 100000
	
print "caching %s lines at a time..." % ( lines_in_memory )
	
try:
	random_seed = sys.argv[4]
	random.seed( random_seed )
	print "random seed: %s" % ( random_seed )
except IndexError:
	print "need a seed..."
	sys.exit( 1 )
	
# first count

print "counting lines..."

f = open( input_file )

count =  0
for line in f:
	count += 1
	
	if count % 100000 == 0:
		print count
	
print count
		
# then shuffle		

print "(un)shuffling..."

o_f = open( output_file, 'wb' )
	
order = range( count )
random.shuffle( order )

# un-shuffle

order_dict = { shuf_i: orig_i for shuf_i, orig_i in enumerate( order ) }
# sort by original key asc, will get shuffled keys in the right order to unshuffle
order = sorted( order_dict, key = order_dict.get )

epoch = 0
	
while order:

	current_lines = {}
	current_lines_count = 0

	current_chunk = order[:lines_in_memory]
	current_chunk_dict = { x: 1 for x in current_chunk }		# faster "in"
	current_chunk_length = len( current_chunk )
	
	order = order[lines_in_memory:]
	
	f.seek( 0 )
	count = 0
		
	for line in f:
		if count in current_chunk_dict:
			current_lines[count] = line
			current_lines_count += 1
			if current_lines_count == current_chunk_length:
				break
		count += 1	
		if count % 100000 == 0:
			print count		
	
	print "writing..."
	
	for l in current_chunk:
		o_f.write( current_lines[l] )
	
	lines_saved = current_chunk_length + epoch * lines_in_memory
	epoch += 1
	print "pass %s complete (%s lines saved)" % ( epoch, lines_saved )