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Python Experiment Suite on Python 3
# PyExperimentSuite
# Derive your experiment from the PyExperimentSuite, fill in the reset() and
# iterate() methods, and define your defaults and experiments variables
# in a config file.
# PyExperimentSuite will create directories, run the experiments and store the
# logged data. An aborted experiment can be resumed at any time. If you want
# to resume it on iteration level (instead of repetition level) you need to
# implement the restore_state and save_state method and make sure the
# restore_supported variable is set to True.
# For more information, consult the included documentation.pdf file.
# Licensed under the modified BSD License. See LICENSE file in same folder.
# Copyright 2010 - Thomas Rueckstiess
from configparser import ConfigParser
from multiprocessing import Process, Pool, cpu_count
from numpy import *
import os, sys, time, itertools, re, optparse, types
def mp_runrep(args):
""" Helper function to allow multiprocessing support. """
return PyExperimentSuite.run_rep(*args)
def progress(params, rep):
""" Helper function to calculate the progress made on one experiment. """
name = params['name']
fullpath = os.path.join(params['path'], params['name'])
logname = os.path.join(fullpath, '%i.log'%rep)
if os.path.exists(logname):
logfile = open(logname, 'r')
lines = logfile.readlines()
return int(100 * len(lines) / params['iterations'])
return 0
def convert_param_to_dirname(param):
""" Helper function to convert a parameter value to a valid directory name. """
if type(param) == str:
return param
return re.sub("0+$", '0', '%f'%param)
class PyExperimentSuite(object):
# change this in subclass, if you support restoring state on iteration level
restore_supported = False
def __init__(self):
# list of keys, that had to be renamed because they contained spaces
self.key_warning_issued = []
def parse_opt(self):
""" parses the command line options for different settings. """
optparser = optparse.OptionParser()
optparser.add_option('-c', '--config',
action='store', dest='config', type='string', default='experiments.cfg',
help="your experiments config file")
optparser.add_option('-n', '--numcores',
action='store', dest='ncores', type='int', default=cpu_count(),
help="number of processes you want to use, default is %i"%cpu_count())
optparser.add_option('-d', '--del',
action='store_true', dest='delete', default=False,
help="delete experiment folder if it exists")
optparser.add_option('-e', '--experiment',
action='append', dest='experiments', type='string',
help="run only selected experiments, by default run all experiments in config file.")
optparser.add_option('-b', '--browse',
action='store_true', dest='browse', default=False,
help="browse existing experiments.")
optparser.add_option('-B', '--Browse',
action='store_true', dest='browse_big', default=False,
help="browse existing experiments, more verbose than -b")
optparser.add_option('-p', '--progress',
action='store_true', dest='progress', default=False,
help="like browse, but only shows name and progress bar")
options, args = optparser.parse_args()
self.options = options
return options, args
def parse_cfg(self):
""" parses the given config file for experiments. """
self.cfgparser = ConfigParser()
if not
raise SystemExit('config file %s not found.'%self.options.config)
def mkdir(self, path):
""" create a directory if it does not exist. """
if not os.path.exists(path):
def get_exps(self, path='.'):
""" go through all subdirectories starting at path and return the experiment
identifiers (= directory names) of all existing experiments. A directory
is considered an experiment if it contains a experiment.cfg file.
exps = []
for dp, dn, fn in os.walk(path):
if 'experiment.cfg' in fn:
subdirs = [os.path.join(dp, d) for d in os.listdir(dp) if os.path.isdir(os.path.join(dp, d))]
if all([self.get_exps(s) == [] for s in subdirs]):
return exps
def items_to_params(self, items):
""" evaluate the found items (strings) to become floats, ints or lists.
params = {}
for t,v in items:
# try to evaluate parameter (float, int, list)
if v in ['grid', 'list']:
params[t] = v
params[t] = eval(v)
if isinstance(params[t], ndarray):
params[t] = params[t].tolist()
except (NameError, SyntaxError):
# otherwise assume string
params[t] = v
return params
def get_params(self, exp, cfgname='experiment.cfg'):
""" reads the parameters of the experiment (= path) given.
cfgp = ConfigParser(), cfgname))
section = cfgp.sections()[0]
params = self.items_to_params(cfgp.items(section))
params['name'] = section
return params
def get_exp(self, name, path='.'):
""" given an experiment name (used in section titles), this function
returns the correct path of the experiment.
exps = []
for dp, dn, df in os.walk(path):
if 'experiment.cfg' in df:
cfgp = ConfigParser(), 'experiment.cfg'))
if name in cfgp.sections():
return exps
def write_config_file(self, params, path):
""" write a config file for this single exp in the folder path.
cfgp = ConfigParser()
for p in params:
if p == 'name':
#cfgp[params['name']][p] = str(params[p])
cfgp.set(params['name'], p, str(params[p]))
f = open(os.path.join(path, 'experiment.cfg'), 'w')
def get_history(self, exp, rep, tags):
""" returns the whole history for one experiment and one repetition.
tags can be a string or a list of strings. if tags is a string,
the history is returned as list of values, if tags is a list of
strings or 'all', history is returned as a dictionary of lists
of values.
params = self.get_params(exp)
if params == None:
raise SystemExit('experiment %s not found.'%exp)
# make list of tags, even if it is only one
if tags != 'all' and ((not hasattr(tags, '__iter__')) or isinstance(tags, str)):
tags = [tags]
results = {}
logfile = os.path.join(exp, '%i.log'%rep)
f = open(logfile)
except IOError:
if len(tags) == 1:
return []
return {}
for line in f:
pairs = line.split()
for pair in pairs:
tag,val = pair.split(':')
if tags == 'all' or tag in tags:
if not tag in results:
results[tag] = [eval(val)]
except (NameError, SyntaxError):
results[tag] = [val]
except (NameError, SyntaxError):
if len(results) == 0:
if len(tags) == 1:
return []
return {}
# raise ValueError('tag(s) not found: %s'%str(tags))
if len(tags) == 1:
return results[list(results.keys())[0]]
return results
def get_history_tags(self, exp, rep=0):
""" returns all available tags (logging keys) of the given experiment
Note: Technically, each repetition could have different
tags, therefore the rep number can be passed in as parameter,
even though usually all repetitions have the same tags. The default
repetition is 0 and in most cases, can be omitted.
history = self.get_history(exp, rep, 'all')
return list(history.keys())
def get_value(self, exp, rep, tags, which='last'):
""" Like get_history(..) but returns only one single value rather
than the whole list.
tags can be a string or a list of strings. if tags is a string,
the history is returned as a single value, if tags is a list of
strings, history is returned as a dictionary of values.
'which' can be one of the following:
last: returns the last value of the history
min: returns the minimum value of the history
max: returns the maximum value of the history
#: (int) returns the value at that index
history = self.get_history(exp, rep, tags)
# empty histories always return None
if len(history) == 0:
return None
# distinguish dictionary (several tags) from list
if type(history) == dict:
for h in history:
if which == 'last':
history[h] = history[h][-1]
if which == 'min':
history[h] = min(history[h])
if which == 'max':
history[h] = max(history[h])
if type(which) == int:
history[h] = history[h][which]
return history
if which == 'last':
return history[-1]
if which == 'min':
return min(history)
if which == 'max':
return max(history)
if type(which) == int:
return history[which]
return None
def get_values_fix_params(self, exp, rep, tag, which='last', **kwargs):
""" this function uses get_value(..) but returns all values where the
subexperiments match the additional kwargs arguments. if alpha=1.0,
beta=0.01 is given, then only those experiment values are returned,
as a list.
subexps = self.get_exps(exp)
tagvalues = ['%s%s'%(k, convert_param_to_dirname(kwargs[k])) for k in kwargs]
values = [self.get_value(se, rep, tag, which) for se in subexps if all([tv in se for tv in tagvalues])]
params = [self.get_params(se) for se in subexps if all([tv in se for tv in tagvalues])]
return values, params
def get_histories_fix_params(self, exp, rep, tag, **kwargs):
""" this function uses get_history(..) but returns all histories where the
subexperiments match the additional kwargs arguments. if alpha=1.0,
beta = 0.01 is given, then only those experiment histories are returned,
as a list.
subexps = self.get_exps(exp)
tagvalues = [re.sub("0+$", '0', '%s%f'%(k, kwargs[k])) for k in kwargs]
histories = [self.get_history(se, rep, tag) for se in subexps if all([tv in se for tv in tagvalues])]
params = [self.get_params(se) for se in subexps if all([tv in se for tv in tagvalues])]
return histories, params
def get_histories_over_repetitions(self, exp, tags, aggregate):
""" this function gets all histories of all repetitions using get_history() on the given
tag(s), and then applies the function given by 'aggregate' to all corresponding values
in each history over all iterations. Typical aggregate functions could be 'mean' or
params = self.get_params(exp)
# explicitly make tags list in case of 'all'
if tags == 'all':
tags = list(self.get_history(exp, 0, 'all').keys())
# make list of tags if it is just a string
if isinstance(tags, str) or not hasattr(tags, '__iter__'):
tags = [tags]
results = {}
for tag in tags:
# get all histories
histories = zeros((params['repetitions'], params['iterations']))
skipped = []
for i in range(params['repetitions']):
histories[i, :] = self.get_history(exp, i, tag)
except ValueError:
h = self.get_history(exp, i, tag)
if len(h) == 0:
# history not existent, skip it
print(('warning: history %i has length 0 (expected: %i). it will be skipped.'%(i, params['iterations'])))
elif len(h) > params['iterations']:
# if history too long, crop it
print(('warning: history %i has length %i (expected: %i). it will be truncated.'%(i, len(h), params['iterations'])))
h = h[:params['iterations']]
histories[i,:] = h
elif len(h) < params['iterations']:
# if history too short, crop everything else
print(('warning: history %i has length %i (expected: %i). all other histories will be truncated.'%(i, len(h), params['iterations'])))
params['iterations'] = len(h)
histories = histories[:,:params['iterations']]
histories[i, :] = h
# remove all rows that have been skipped
histories = delete(histories, skipped, axis=0)
params['repetitions'] -= len(skipped)
# calculate result from each column with aggregation function
aggregated = zeros(params['iterations'])
for i in range(params['iterations']):
aggregated[i] = aggregate(histories[:, i])
# if only one tag is requested, return list immediately, otherwise append to dictionary
if len(tags) == 1:
return aggregated
results[tag] = aggregated
return results
def browse(self):
""" go through all subfolders (starting at '.') and return information
about the existing experiments. if the -B option is given, all
parameters are shown, -b only displays the most important ones.
this function does *not* execute any experiments.
for d in self.get_exps('.'):
params = self.get_params(d)
name = params['name']
basename = name.split('/')[0]
# if -e option is used, only show requested experiments
if self.options.experiments and basename not in self.options.experiments:
fullpath = os.path.join(params['path'], name)
# calculate progress
prog = 0
for i in range(params['repetitions']):
prog += progress(params, i)
prog /= params['repetitions']
# if progress flag is set, only show the progress bars
if self.options.progress:
bar = "["
bar += "="*int(prog/4)
bar += " "*int(25-prog/4)
bar += "]"
print('%3i%% %27s %s'%(prog,bar,d))
print('%16s %s'%('experiment', d))
minfile = min(
(os.path.join(dirname, filename)
for dirname, dirnames, filenames in os.walk(fullpath)
for filename in filenames
if filename.endswith(('.log', '.cfg'))),
key=lambda fn: os.stat(fn).st_mtime)
maxfile = max(
(os.path.join(dirname, filename)
for dirname, dirnames, filenames in os.walk(fullpath)
for filename in filenames
if filename.endswith(('.log', '.cfg'))),
key=lambda fn: os.stat(fn).st_mtime)
except ValueError:
print(' started %s'%'not yet')
print(' started %s'%time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(os.stat(minfile).st_mtime)))
print(' ended %s'%time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(os.stat(maxfile).st_mtime)))
for k in ['repetitions', 'iterations']:
print('%16s %s'%(k, params[k]))
print('%16s %i%%'%('progress', prog))
if self.options.browse_big:
# more verbose output
for p in [p for p in params if p not in ('repetitions', 'iterations', 'path', 'name')]:
print('%16s %s'%(p, params[p]))
def expand_param_list(self, paramlist):
""" expands the parameters list according to one of these schemes:
grid: every list item is combined with every other list item
list: every n-th list item of parameter lists are combined
# for one single experiment, still wrap it in list
if type(paramlist) == dict:
paramlist = [paramlist]
# get all options that are iteratable and build all combinations (grid) or tuples (list)
iparamlist = []
for params in paramlist:
if ('experiment' in params and params['experiment'] == 'single'):
iterparams = [p for p in params if (hasattr(params[p], '__iter__') and not isinstance(params[p], str))]
if len(iterparams) > 0:
# write intermediate config file
self.mkdir(os.path.join(params['path'], params['name']))
self.write_config_file(params, os.path.join(params['path'], params['name']))
# create sub experiments (check if grid or list is requested)
if 'experiment' in params and params['experiment'] == 'list':
iterfunc = itertools.izip
elif ('experiment' not in params) or ('experiment' in params and params['experiment'] == 'grid'):
iterfunc = itertools.product
raise SystemExit("unexpected value '%s' for parameter 'experiment'. Use 'grid', 'list' or 'single'."%params['experiment'])
for il in iterfunc(*[params[p] for p in iterparams]):
par = params.copy()
converted = str(list(zip(iterparams, list(map(convert_param_to_dirname, il)))))
par['name'] = par['name'] + '/' + re.sub("[' \[\],()]", '', converted)
for i, ip in enumerate(iterparams):
par[ip] = il[i]
return iparamlist
def create_dir(self, params, delete=False):
""" creates a subdirectory for the experiment, and deletes existing
files, if the delete flag is true. then writes the current
experiment.cfg file in the folder.
# create experiment path and subdir
fullpath = os.path.join(params['path'], params['name'])
# delete old histories if --del flag is active
if delete:
os.system('rm %s/*' % fullpath)
# write a config file for this single exp. in the folder
self.write_config_file(params, fullpath)
def start(self):
""" starts the experiments as given in the config file. """
# if -b, -B or -p option is set, only show information, don't
# start the experiments
if self.options.browse or self.options.browse_big or self.options.progress:
raise SystemExit
# read main configuration file
paramlist = []
for exp in self.cfgparser.sections():
if not self.options.experiments or exp in self.options.experiments:
params = self.items_to_params(self.cfgparser.items(exp))
params['name'] = exp
def do_experiment(self, params):
""" runs one experiment programatically and returns.
params: either parameter dictionary (for one single experiment) or a list of parameter
dictionaries (for several experiments).
paramlist = self.expand_param_list(params)
# create directories, write config files
for pl in paramlist:
# check for required param keys
if ('name' in pl) and ('iterations' in pl) and ('repetitions' in pl) and ('path' in pl):
self.create_dir(pl, self.options.delete)
print('Error: parameter set does not contain all required keys: name, iterations, repetitions, path')
return False
# create experiment list
explist = []
# expand paramlist for all repetitions and add self and rep number
for p in paramlist:
explist.extend(list(zip( [self]*p['repetitions'], [p]*p['repetitions'], list(range(p['repetitions'])) )))
# if only 1 process is required call each experiment seperately (no worker pool)
if self.options.ncores == 1:
for e in explist:
# create worker processes
pool = Pool(processes=self.options.ncores), explist)
return True
def run_rep(self, params, rep):
""" run a single repetition including directory creation, log files, etc. """
name = params['name']
fullpath = os.path.join(params['path'], params['name'])
logname = os.path.join(fullpath, '%i.log'%rep)
# check if repetition exists and has been completed
restore = 0
if os.path.exists(logname):
logfile = open(logname, 'r')
lines = logfile.readlines()
# if completed, continue loop
if 'iterations' in params and len(lines) == params['iterations']:
return False
# if not completed, check if restore_state is supported
if not self.restore_supported:
# not supported, delete repetition and start over
# print 'restore not supported, deleting %s' % logname
restore = 0
restore = len(lines)
self.reset(params, rep)
if restore:
logfile = open(logname, 'a')
self.restore_state(params, rep, restore)
logfile = open(logname, 'w')
# loop through iterations and call iterate
for it in range(restore, params['iterations']):
dic = self.iterate(params, rep, it)
if self.restore_supported:
self.save_state(params, rep, it)
# replace all spaces in keys with underscores
for k in dic:
if ' ' in k:
newk = k.replace(' ', '_')
dic[newk] = dic[k]
del dic[k]
# issue warning but only once per key
if k not in self.key_warning_issued:
print("warning: key '%s' contained spaces and was renamed to '%s'"%(k, newk))
# build string from dictionary
outstr = ' '.join(['%s:%s'%(x[0], str(x[1])) for x in list(dic.items())])
logfile.write(outstr + '\n')
self.finalize(params, rep)
def reset(self, params, rep):
""" needs to be implemented by subclass. """
def iterate(self, params, rep, n):
""" needs to be implemented by subclass. """
ret = {'iteration':n, 'repetition':rep}
return ret
def finalize(self, params, rep):
""" can be implemented by sublcass. """
def save_state(self, params, rep, n):
""" optionally can be implemented by subclass. """
def restore_state(self, params, rep, n):
""" if the experiment supports restarting within a repetition
(on iteration level), load necessary stored state in this
function. Otherwise, restarting will be done on repetition
level, deleting all unfinished repetitions and restarting
the experiments.
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