if __name__ == "__main__":
"""Crude, prototypical approach to fake data table generation."""
# smoke test
import candidate_test_pruners
nodes, head = buildRandomModel(
50,
4,
1,
0.5,
0.3,
2,
["U:\\mercurial\\fake-data-generator\\src\\ModelBehaviors"],
candidate_test_pruners.bigDelta(),
)
with open("E:\\debris\\whatever.gv", "w") as gvfile:
gvfile.write(graphvizEntireThing(head))
gvfile.flush()
import csv
with open("E:\\debris\\fakeData.tsv", "wb") as tsvfile:
cleanWriter = csv.writer(tsvfile, dialect="excel-tab")
cleanWriter.writerow(["{0}:{1}".format(node.name, node.genName(4)) for node in nodes])
for x in range(200):
key = object()
cleanWriter.writerow([str(node.calculate(key)) for node in nodes])
if not x % 100:
print x, "rows done"
tsvfile.flush()
Created on Mar 7, 2012
@author: anorberg
"""
import os.path
import sys
import candidate_test_pruners
import optparse
import ConfigParser
NULL = candidate_test_pruners.nullPruner()
UNIFOUR = candidate_test_pruners.uniformThroughFour()
GLOBALCUT = candidate_test_pruners.globalCutoff()
MINFRAC = candidate_test_pruners.minimalistFraction()
BIGDELTA = candidate_test_pruners.bigDelta()
PRUNER_LUT = {
"null": NULL,
"nullpruner": NULL,
"n": NULL,
"uniform": UNIFOUR,
"uniformthroughfour": UNIFOUR,
"u": UNIFOUR,
"minfrac": MINFRAC,
"minimalist": MINFRAC,
"m": MINFRAC,
"fraction": MINFRAC,
"fractional": MINFRAC,
"minimalistfraction": MINFRAC,
"f": MINFRAC,
class Config(object):
'''
A struct-like class that holds the configuration for the fake data generator.
Designed to build itself from a command line and/or a config file.
'''
outputRoot = os.path.join(os.path.curdir, "generatedData")
nGraphs = 1
graphSize = 50
nSeeds = 4
gvRecursion = 1
tsvRecursion = 3
tsvColRate = 1.0
behaviorPaths = [
os.path.join(os.path.dirname(__file__), "..", "ModelBehaviors")
]
pruner = candidate_test_pruners.bigDelta()
samples = 500
addIdentity = 3
def __init__(self, relevant_argv=None):
'''
Constructor. Read settings out of relevant_argv with optparse, gating to ConfigParser in case of the --config option
'''
if relevant_argv is None:
relevant_argv = sys.argv[1:]
parser = optparse.OptionParser()
parser.add_option("-?",
dest="stopAndHelp",
action="store_true",
help="View this help")
parser.add_option("-c",
"--config",
dest="configFile",
help="Load a configuration file")
parser.add_option("-g",
"--graphs",
dest="graphs",
type="int",
help="Number of separate graphs to generate")
parser.add_option("-n",
"--graphSize",
dest="graphSize",
type="int",
help="Number of nodes per graph (including seeds)")
parser.add_option("-s",
"--seeds",
dest="seeds",
type="int",
help="Number of seeds per graph")
parser.add_option(
"-r",
"--graphvizRecursion",
dest="gvRecursion",
type="int",
help=
"Depth of recursion for procedural name generation in Graphviz diagram"
)
parser.add_option(
"-t",
"--tsvRecursion",
dest="tsvRecursion",
type="int",
help="Depth of recursion for name generation in generated data file"
)
parser.add_option(
"-p",
"--pickRate",
dest="pickRate",
type="float",
help=
"Fraction of nodes to place in the output file; selected randomly")
parser.add_option(
"-b",
"--behaviors",
dest="behaviors",
help="Paths to search (use OS path separator) for behavior plugins"
)
parser.add_option("-x",
"--pruner",
dest="pruner",
help="Name of graph pruning algorithm to use")
parser.add_option("-m",
"--samples",
dest="samples",
type="int",
help="Number of rows of data to output")
parser.add_option(
"-o",
"--output",
dest="outputRoot",
help=
"Output file name without extension; .gv or .txt will be appended")
parser.add_option(
"-u",
"--unnoisiness",
dest="unNoisiness",
help=
"Number of times to add the identity function to the pool of noise functions"
)
(options, args) = parser.parse_args(relevant_argv)
if options.stopAndHelp:
parser.print_help()
sys.exit()
if options.configFile:
self._parse_config_file(options.configFile)
#all settings on the CLI override settings in the file, so just blindly write from here on out
if options.graphs:
self.nGraphs = options.graphs
if options.graphSize:
self.graphSize = options.graphSize
if options.seeds:
self.nSeeds = options.seeds
if options.samples:
self.samples = options.samples
if options.gvRecursion is not None:
#because zero is a legal value
self.gvRecursion = options.gvRecursion
if options.tsvRecursion is not None:
self.tsvRecursion = options.tsvRecursion
if options.pickRate is not None:
self.tsvColRate = options.pickRate
if options.behaviors:
self.behaviorPaths = options.behaviors.split(os.path.pathsep)
if options.pruner:
self.pruner = PRUNER_LUT[options.pruner.lower()]
if options.outputRoot:
self.outputRoot = options.outputRoot
if options.unNoisiness:
self.addIdentity = options.unNoisiness
def _parse_config_file(self, filePath):
"""
Use a ConfigParser to load settings.
"""
parser = ConfigParser.SafeConfigParser({
"File":
self.outputRoot,
"PickRate":
self.tsvColRate,
"TsvRecursion":
self.tsvRecursion,
"GraphvizRecursion":
self.gvRecursion,
"Behaviors":
os.path.pathsep.join(self.behaviorPaths),
"Pruner":
self.pruner,
"Graphs":
self.nGraphs,
"GraphSize":
self.graphSize,
"Seeds":
self.nSeeds,
"Samples":
self.samples
})
parser.add_section("Output")
parser.add_section("Model")
parser.add_section("Generation")
parser.read(filePath)
self.outputRoot = parser.get("Output", "File")
self.tsvColRate = parser.getfloat("Output", "PickRate")
self.tsvRecursion = parser.getint("Output", "TsvRecursion")
self.gvRecursion = parser.getint("Output", "GraphvizRecursion")
self.samples = parser.getint("Output", "Samples")
self.behaviorPaths = parser.get("Model",
"Behaviors").split(os.path.pathsep)
self.pruner = PRUNER_LUT[parser.get("Model", "Pruner").lower()]
self.addIdentity = parser.getint("Model", "UnNoisiness")
self.nGraphs = parser.getint("Generation", "Graphs")
self.graphSize = parser.getint("Generation", "GraphSize")
self.nSeeds = parser.getint("Generation", "Seeds")
for pluginInfo in candidatePruners:
if pluginInfo.name == pruner:
pruner = pluginInfo.plugin_object
pruner.activate()
break
raise ValueError("No pruner by name {0} found in specified paths.".format(pruner))
trimmedGraph = pruner.prune(rawCompleteGraph)
function_plugins =modelBehaviorImplementations(behaviorPaths)
functions = [plugin.plugin_object for plugin in function_plugins]
return workingModelFromPygraph(trimmedGraph, functions, bonus_identity)
if __name__ == "__main__":
"""Crude, prototypical approach to fake data table generation."""
#smoke test
import candidate_test_pruners
nodes, head = buildRandomModel(50, 4, 1, 0.5, 0.3, 2, ['U:\\mercurial\\fake-data-generator\\src\\ModelBehaviors'], candidate_test_pruners.bigDelta())
with open("E:\\debris\\whatever.gv", "w") as gvfile:
gvfile.write(graphvizEntireThing(head))
gvfile.flush()
import csv
with open("E:\\debris\\fakeData.tsv", "wb") as tsvfile:
cleanWriter = csv.writer(tsvfile, dialect='excel-tab')
cleanWriter.writerow(["{0}:{1}".format(node.name, node.genName(4)) for node in nodes])
for x in range(200):
key = object()
cleanWriter.writerow([str(node.calculate(key)) for node in nodes])
if not x % 100:
print x, "rows done"
tsvfile.flush()
Created on Mar 7, 2012
@author: anorberg
'''
import os.path
import sys
import candidate_test_pruners
import optparse
import ConfigParser
NULL = candidate_test_pruners.nullPruner()
UNIFOUR = candidate_test_pruners.uniformThroughFour()
GLOBALCUT = candidate_test_pruners.globalCutoff()
MINFRAC = candidate_test_pruners.minimalistFraction()
BIGDELTA = candidate_test_pruners.bigDelta()
PRUNER_LUT = {
"null": NULL,
"nullpruner": NULL,
"n": NULL,
"uniform": UNIFOUR,
"uniformthroughfour": UNIFOUR,
"u": UNIFOUR,
"minfrac": MINFRAC,
"minimalist": MINFRAC,
"m": MINFRAC,
"fraction": MINFRAC,
"fractional": MINFRAC,
"minimalistfraction": MINFRAC,
"f": MINFRAC,
