def commit(self, store, model):
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, minScore, maxScore, mapName, setLen,
outputName, outputFunction, **csvValues):
line = csvFile.newLine()
for k, v in csvValues.iteritems():
line[k] = v
line["score"] = score[0]
line["min_score"] = minScore[0]
line["min_score_commit"] = minScore[1]
line["max_score"] = maxScore[0]
line["max_score_commit"] = maxScore[1]
line["set"] = "%s" % (mapName)
line["set_size"] = "%s" % (setLen)
line["output_layer"] = outputName
line["output_function"] = outputFunction
line.commit()
self.length += 1
if self.csvLegend is None:
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend([
"score", "min_score", "min_score_commit", "max_score",
"max_score_commit", "set", "set_size", "output_layer",
"output_function"
])
self.csvFile = CSVFile(legend=self.csvLegend, separator="\t")
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
self.scores.update(store["scores"], store["runInfos"]["epoch"])
for mapName, os in store["scores"].iteritems():
for outputName, fs in os.iteritems():
for functionName in fs:
_fillLine(
self.csvFile,
self.scores.getScore(mapName, outputName,
functionName),
self.scores.getMinScore(mapName, outputName,
functionName),
self.scores.getMaxScore(mapName, outputName,
functionName), mapName,
store["setSizes"][mapName], outputName, functionName,
**muchData)
if self.printRate > 0 and (self.length % self.printRate) == 0:
self.printCurrentState()
for w in self.whenToSave:
if w._shouldISave(self):
model.save(w.getFilename(self))
def _importSNPs_AgnosticSNP(setName, species, genomeSource, snpsFile):
"This function will also create an index on start->chromosomeNumber->setName. Warning : pyGeno wil interpret all positions as 0 based"
printf('importing SNP set %s for species %s...' % (setName, species))
snpData = CSVFile()
snpData.parse(snpsFile, separator="\t")
AgnosticSNP.dropIndex(('start', 'chromosomeNumber', 'setName'))
conf.db.beginTransaction()
pBar = ProgressBar(len(snpData))
pLabel = ''
currChrNumber = None
for snpEntry in snpData:
tmpChr = snpEntry['chromosomeNumber']
if tmpChr != currChrNumber:
currChrNumber = tmpChr
pLabel = 'Chr %s...' % currChrNumber
snp = AgnosticSNP()
snp.species = species
snp.setName = setName
for f in snp.getFields():
try:
setattr(snp, f, snpEntry[f])
except KeyError:
if f != 'species' and f != 'setName':
printf("Warning filetype as no key %s", f)
snp.quality = float(snp.quality)
snp.start = int(snp.start)
snp.end = int(snp.end)
snp.save()
pBar.update(label=pLabel)
pBar.close()
snpMaster = SNPMaster()
snpMaster.set(setName=setName, SNPType='AgnosticSNP', species=species)
snpMaster.save()
printf('saving...')
conf.db.endTransaction()
printf('creating indexes...')
AgnosticSNP.ensureGlobalIndex(('start', 'chromosomeNumber', 'setName'))
printf('importation of SNP set %s for species %s done.' %
(setName, species))
return True
def commit(self, store, model) : """Appends the current state of the store to the CSV. This one is meant to be called by the trainer""" def _fillLine(csvFile, score, minScore, maxScore, mapName, setLen, outputName, outputFunction, **csvValues) : line = csvFile.newLine() for k, v in csvValues.iteritems() : line[k] = v line["score"] = score[0] line["min_score"] = minScore[0] line["min_score_commit"] = minScore[1] line["max_score"] = maxScore[0] line["max_score_commit"] = maxScore[1] line["set"] = "%s" %(mapName) line["set_size"] = "%s" %(setLen) line["output_layer"] = outputName line["output_function"] = outputFunction line.commit() self.length += 1 if self.csvLegend is None : self.csvLegend = store["hyperParameters"].keys() self.csvLegend.extend(store["runInfos"].keys()) self.csvLegend.extend( ["score", "min_score", "min_score_commit", "max_score", "max_score_commit", "set", "set_size", "output_layer", "output_function"] ) self.csvFile = CSVFile(legend = self.csvLegend, separator = "\t") self.csvFile.streamToFile( self.filename, writeRate = self.writeRate ) muchData = store["hyperParameters"] muchData.update(store["runInfos"]) self.scores.update(store["scores"], store["runInfos"]["epoch"]) for mapName, os in store["scores"].iteritems() : for outputName, fs in os.iteritems() : for functionName in fs : _fillLine( self.csvFile, self.scores.getScore(mapName, outputName, functionName), self.scores.getMinScore(mapName, outputName, functionName), self.scores.getMaxScore(mapName, outputName, functionName), mapName, store["setSizes"][mapName], outputName, functionName, **muchData ) self.trainerStore = store self.epoch = store["runInfos"]["epoch"] if self.printRate > 0 and (self.length%self.printRate) == 0: self.printCurrentState() for w in self.whenToSave : if w._shouldISave(self) : model.save(w.getFilename(self))
def commit(self, store, model):
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, bestScore, setName, setLen, outputName,
**csvValues):
line = csvFile.newLine()
for k, v in csvValues.iteritems():
line[k] = v
line["score"] = score
line["best_score"] = bestScore[0]
line["best_score_commit"] = bestScore[1]
line["set"] = "%s(%s)" % (setName, setLen)
line["output"] = outputName
line.commit()
self.length += 1
if self.csvLegend is None:
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend(
["score", "best_score", "best_score_commit", "set", "output"])
self.csvFile = CSVFile(legend=self.csvLegend)
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
for theSet, scores in store["scores"].iteritems():
self.currentScores[theSet] = {}
if theSet not in self.bestScores:
self.bestScores[theSet] = {}
for outputName, score in scores.iteritems():
self.currentScores[theSet][outputName] = score
if outputName not in self.bestScores[
theSet] or score < self.bestScores[theSet][outputName][
0]:
self.bestScores[theSet][outputName] = (score, self.length)
model.save(self.getBestModelFilename(outputName, theSet))
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
_fillLine(self.csvFile, self.currentScores[theSet][outputName],
self.bestScores[theSet][outputName], theSet,
store["setSizes"][theSet], outputName, **muchData)
if self.printRate > 0 and (self.length % self.printRate) == 0:
self.printCurrentState()
class CSV(Writer_ABC):
"""This training recorder will create a nice CSV (or tab delimited) file fit for using with ggplot2 and will update
it as the training goes. It will also save the best model for each set of the trainer, and print
regular reports on the console.
:param string filename: The filename of the tsv to be generated. the extension '.ggplot2.tsv' will be added automatically
:param int write: The rate at which the status is written on disk
"""
def __init__(self, filename, loggers, separator="\t", writeRate=1):
self.filename = filename.replace(".tsv", "") + ".ggplot.tsv"
self.writeRate = writeRate
self.loggers = loggers
self.csvFile = None
self.nbCommits = 0
self.separator = separator
def commit(self, trainer):
""""""
if self.csvFile is not None:
line = self.csvFile.newLine()
else:
line = {}
for log in self.loggers:
for k, v in log.log(trainer):
line[k] = v
if self.csvFile is None:
self.csvFile = CSVFile(legend=line.keys(),
separator=self.separator)
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
newLine = self.csvFile.newLine()
for k, v in line.iteritems():
newLine[k] = v
line = newLine
self.nbCommits += 1
line.commit()
def __len__(self):
"""returns the number of commits performed"""
return self.nbCommits
def commit(self, trainer):
""""""
if self.csvFile is not None:
line = self.csvFile.newLine()
else:
line = {}
for log in self.loggers:
for k, v in log.log(trainer):
line[k] = v
if self.csvFile is None:
self.csvFile = CSVFile(legend=line.keys(),
separator=self.separator)
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
newLine = self.csvFile.newLine()
for k, v in line.iteritems():
newLine[k] = v
line = newLine
self.nbCommits += 1
line.commit()
def commit(self, store, model) :
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, bestScore, setName, setLen, outputName, **csvValues) :
line = csvFile.newLine()
for k, v in csvValues.iteritems() :
line[k] = v
line["score"] = score
line["best_score"] = bestScore[0]
line["best_score_commit"] = bestScore[1]
line["set"] = "%s(%s)" %(setName, setLen)
line["output"] = outputName
line.commit()
self.length += 1
if self.csvLegend is None :
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend( ["score", "best_score", "best_score_commit", "set", "output"] )
self.csvFile = CSVFile(legend = self.csvLegend)
self.csvFile.streamToFile( self.filename, writeRate = 1 )
for theSet, scores in store["scores"].iteritems() :
self.currentScores[theSet] = {}
if theSet not in self.bestScores :
self.bestScores[theSet] = {}
for outputName, score in scores.iteritems() :
self.currentScores[theSet][outputName] = score
if outputName not in self.bestScores[theSet] or score < self.bestScores[theSet][outputName][0] :
self.bestScores[theSet][outputName] = (score, self.length)
model.save("best-%s-%s" % (theSet, self.filename))
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
_fillLine(
self.csvFile,
self.currentScores[theSet][outputName],
self.bestScores[theSet][outputName],
theSet,
store["setSizes"][theSet],
outputName,
**muchData
)
if self.verbose :
self.printCurrentState()
class GGPlot2(Recorder_ABC):
"""This training recorder will create a nice CSV file fit for using with ggplot2 and will update
it as the training gos. It will also save the best model for each set of the trainer, and print
regular reports if you tell it to be verbose."""
def __init__(self, filename, verbose = True):
self.filename = filename.replace(".csv", "") + ".ggplot2.csv"
self.verbose = verbose
self.bestScores = {}
self.currentScores = {}
self.csvLegend = None
self.csvFile = None
self.length = 0
def commit(self, store, model) :
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, bestScore, setName, setLen, outputName, **csvValues) :
line = csvFile.newLine()
for k, v in csvValues.iteritems() :
line[k] = v
line["score"] = score
line["best_score"] = bestScore[0]
line["best_score_commit"] = bestScore[1]
line["set"] = "%s(%s)" %(setName, setLen)
line["output"] = outputName
line.commit()
self.length += 1
if self.csvLegend is None :
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend( ["score", "best_score", "best_score_commit", "set", "output"] )
self.csvFile = CSVFile(legend = self.csvLegend)
self.csvFile.streamToFile( self.filename, writeRate = 1 )
for theSet, scores in store["scores"].iteritems() :
self.currentScores[theSet] = {}
if theSet not in self.bestScores :
self.bestScores[theSet] = {}
for outputName, score in scores.iteritems() :
self.currentScores[theSet][outputName] = score
if outputName not in self.bestScores[theSet] or score < self.bestScores[theSet][outputName][0] :
self.bestScores[theSet][outputName] = (score, self.length)
model.save("best-%s-%s" % (theSet, self.filename))
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
_fillLine(
self.csvFile,
self.currentScores[theSet][outputName],
self.bestScores[theSet][outputName],
theSet,
store["setSizes"][theSet],
outputName,
**muchData
)
if self.verbose :
self.printCurrentState()
def printCurrentState(self) :
"""prints the current state stored in the recorder"""
if self.length > 0 :
print "\n==>rec: ggplot2, commit %s, pid: %s:" % (self.length, os.getpid())
for setName, scores in self.bestScores.iteritems() :
print " |-%s set" % setName
for outputName in scores :
if self.currentScores[setName][outputName] == self.bestScores[setName][outputName][0] :
highlight = "+best+"
else :
score, epoch = self.bestScores[setName][outputName]
highlight = "(best: %s @ commit: %s)" % (score, epoch)
print " |->%s: %s %s" % (outputName, self.currentScores[setName][outputName], highlight)
else :
print "==>rec: ggplot2, nothing to show yet"
sys.stdout.flush()
def __len__(self) :
"""returns the number of commits performed"""
return self.length
class GGPlot2(Recorder_ABC):
"""This training recorder will create a nice CSV file fit for using with ggplot2 and will update
it as the training goes. It will also save the best model for each set of the trainer, and print
regular reports on the console.
:param int printRate: The rate at which the status is printed on the console. If set to <= to 0, will never print.
:param int write: The rate at which the status is written on disk
"""
def __init__(self, filename, printRate=1, writeRate=1):
self.filename = filename.replace(".csv", "") + ".ggplot2.csv"
self.bestScores = {}
self.currentScores = {}
self.csvLegend = None
self.csvFile = None
self.length = 0
self.printRate = printRate
self.writeRate = writeRate
def getBestModelFilename(self, outputName, theSet):
return "best-%s-%s-%s" % (outputName, theSet, self.filename)
def commit(self, store, model):
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, bestScore, setName, setLen, outputName,
**csvValues):
line = csvFile.newLine()
for k, v in csvValues.iteritems():
line[k] = v
line["score"] = score
line["best_score"] = bestScore[0]
line["best_score_commit"] = bestScore[1]
line["set"] = "%s(%s)" % (setName, setLen)
line["output"] = outputName
line.commit()
self.length += 1
if self.csvLegend is None:
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend(
["score", "best_score", "best_score_commit", "set", "output"])
self.csvFile = CSVFile(legend=self.csvLegend)
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
for theSet, scores in store["scores"].iteritems():
self.currentScores[theSet] = {}
if theSet not in self.bestScores:
self.bestScores[theSet] = {}
for outputName, score in scores.iteritems():
self.currentScores[theSet][outputName] = score
if outputName not in self.bestScores[
theSet] or score < self.bestScores[theSet][outputName][
0]:
self.bestScores[theSet][outputName] = (score, self.length)
model.save(self.getBestModelFilename(outputName, theSet))
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
_fillLine(self.csvFile, self.currentScores[theSet][outputName],
self.bestScores[theSet][outputName], theSet,
store["setSizes"][theSet], outputName, **muchData)
if self.printRate > 0 and (self.length % self.printRate) == 0:
self.printCurrentState()
def printCurrentState(self):
"""prints the current state stored in the recorder"""
if self.length > 0:
print "\n==>rec: ggplot2, commit %s, pid: %s:" % (self.length,
os.getpid())
for setName, scores in self.bestScores.iteritems():
print " |-%s set" % setName
for outputName in scores:
if self.currentScores[setName][
outputName] == self.bestScores[setName][
outputName][0]:
highlight = "+best+"
else:
score, epoch = self.bestScores[setName][outputName]
highlight = "(best: %s @ commit: %s)" % (score, epoch)
print " |->%s: %s %s" % (
outputName, self.currentScores[setName][outputName],
highlight)
else:
print "==>rec: ggplot2, nothing to show yet"
sys.stdout.flush()
def getBestModel(self, outputName, theSet):
import cPickle
f = open(
self.getBestModelFilename(outputName, theSet) + ".mariana.pkl")
model = cPickle.load(f)
f.close()
return model
def __len__(self):
"""returns the number of commits performed"""
return self.length
class GGPlot2(Recorder_ABC):
"""This training recorder will create a nice TSV (tab delimited) file fit for using with ggplot2 and will update
it as the training goes. It will also save the best model for each set of the trainer, and print
regular reports on the console.
:param string filename: The filename of the tsv to be generated. the extension '.ggplot2.tsv' will be added automatically
:param list whenToSave: List of saving rules.
:param int printRate: The rate at which the status is printed on the console. If set to <= to 0, will never print.
:param int write: The rate at which the status is written on disk
"""
def __init__(self, filename, whenToSave = [], printRate=1, writeRate=1):
self.filename = filename.replace(".tsv", "") + ".ggplot2.tsv"
self.scores = Scores()
self.csvLegend = None
self.csvFile = None
self.length = 0
self.printRate = printRate
self.writeRate = writeRate
self.whenToSave = whenToSave
def commit(self, store, model) :
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, minScore, maxScore, mapName, setLen, outputName, outputFunction, **csvValues) :
line = csvFile.newLine()
for k, v in csvValues.iteritems() :
line[k] = v
line["score"] = score[0]
line["min_score"] = minScore[0]
line["min_score_commit"] = minScore[1]
line["max_score"] = maxScore[0]
line["max_score_commit"] = maxScore[1]
line["set"] = "%s" %(mapName)
line["set_size"] = "%s" %(setLen)
line["output_layer"] = outputName
line["output_function"] = outputFunction
line.commit()
self.length += 1
if self.csvLegend is None :
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend( ["score", "min_score", "min_score_commit", "max_score", "max_score_commit", "set", "set_size", "output_layer", "output_function"] )
self.csvFile = CSVFile(legend = self.csvLegend, separator = "\t")
self.csvFile.streamToFile( self.filename, writeRate = self.writeRate )
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
self.scores.update(store["scores"], store["runInfos"]["epoch"])
for mapName, os in store["scores"].iteritems() :
for outputName, fs in os.iteritems() :
for functionName in fs :
_fillLine(
self.csvFile,
self.scores.getScore(mapName, outputName, functionName),
self.scores.getMinScore(mapName, outputName, functionName),
self.scores.getMaxScore(mapName, outputName, functionName),
mapName,
store["setSizes"][mapName],
outputName,
functionName,
**muchData
)
if self.printRate > 0 and (self.length%self.printRate) == 0:
self.printCurrentState()
for w in self.whenToSave :
if w._shouldISave(self) :
model.save(w.getFilename(self))
def printCurrentState(self) :
"""prints the current state stored in the recorder"""
if self.length > 0 :
print "\n==>rec: ggplot2, commit %s, pid: %s:" % (self.length, os.getpid())
for mapName, outs in self.scores.currentScores.iteritems() :
print " |-%s set" % mapName
for outputName, fs in outs.iteritems() :
print " |-%s" % outputName
for functionName in fs :
s = self.scores.getScore(mapName, outputName, functionName)
mi = self.scores.getMinScore(mapName, outputName, functionName)
ma = self.scores.getMaxScore(mapName, outputName, functionName)
highlight = []
if s[0] == mi[0] :
highlight.append("+min+")
else :
highlight.append("%s@%s" % (mi[0], mi[1]))
if s[0] == ma[0] :
highlight.append("+max+")
else :
highlight.append("%s@%s" % (ma[0], ma[1]))
print " |-%s: %s [%s]" % (functionName, s[0], "; ".join(highlight))
else :
print "==>rec: ggplot2, nothing to show yet"
sys.stdout.flush()
def __repr__(self):
return "<recorder: %s, filename: %s>" % (self.__class__.__name__, self.filename)
def __len__(self) :
"""returns the number of commits performed"""
return self.length
class GGPlot2(Recorder_ABC):
"""This training recorder will create a nice CSV file fit for using with ggplot2. It will also print regular
reports if you tell it to be verbose and save the best models"""
def __init__(self, filename, verbose=True):
self.filename = filename.replace(".csv", "") + ".ggplot2.csv"
self.verbose = verbose
self.bestScores = {}
self.currentScores = {}
self.csvLegend = None
self.csvFile = None
self.length = 0
def commit(self, store, model):
"""Appends the current state of the store to the CSV """
def _fillLine(csvFile, score, bestScore, setName, setLen, outputName,
**csvValues):
line = csvFile.newLine()
for k, v in csvValues.iteritems():
line[k] = v
line["score"] = score
line["best_score"] = bestScore[0]
line["best_score_commit"] = bestScore[1]
line["set"] = "%s(%s)" % (setName, setLen)
line["output"] = outputName
line.commit()
self.length += 1
if self.csvLegend is None:
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend(
["score", "best_score", "best_score_commit", "set", "output"])
self.csvFile = CSVFile(legend=self.csvLegend)
self.csvFile.streamToFile(self.filename, writeRate=1)
for theSet, scores in store["scores"].iteritems():
self.currentScores[theSet] = {}
if theSet not in self.bestScores:
self.bestScores[theSet] = {}
for outputName, score in scores.iteritems():
self.currentScores[theSet][outputName] = score
if outputName not in self.bestScores[
theSet] or score < self.bestScores[theSet][outputName][
0]:
self.bestScores[theSet][outputName] = (score, self.length)
model.save("best-%s-%s" % (theSet, self.filename))
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
_fillLine(self.csvFile, self.currentScores[theSet][outputName],
self.bestScores[theSet][outputName], theSet,
store["setSizes"][theSet], outputName, **muchData)
if self.verbose:
self.printCurrentState()
def printCurrentState(self):
"""prints the current state stored in the recorder"""
if self.length > 0:
print "\n==>rec: ggplot2, commit %s, pid: %s:" % (self.length,
os.getpid())
for setName, scores in self.bestScores.iteritems():
print " |-%s set" % setName
for outputName in scores:
if self.currentScores[setName][
outputName] == self.bestScores[setName][
outputName][0]:
highlight = "+best+"
else:
score, epoch = self.bestScores[setName][outputName]
highlight = "(best: %s @ commit: %s)" % (score, epoch)
print " |->%s: %s %s" % (
outputName, self.currentScores[setName][outputName],
highlight)
else:
print "==>rec: ggplot2, nothing to show yet"
sys.stdout.flush()
def __len__(self):
"""returns the number of commits performed"""
return self.length
class GGPlot(Recorder_ABC):
"""This training recorder will create a nice TSV (tab delimited) file fit for using with ggplot2 and will update
it as the training goes. It will also save the best model for each set of the trainer, and print
regular reports on the console.
:param string filename: The filename of the tsv to be generated. the extension '.ggplot2.tsv' will be added automatically
:param list whenToSave: List of saving rules.
:param int printRate: The rate at which the status is printed on the console. If set to <= to 0, will never print.
:param int write: The rate at which the status is written on disk
"""
def __init__(self, filename, whenToSave=[], printRate=1, writeRate=1):
self.filename = filename.replace(".tsv", "") + ".ggplot.tsv"
self.scores = Scores()
self.csvLegend = None
self.csvFile = None
self.length = 0
self.epoch = 0
self.trainerStore = None
self.printRate = printRate
self.writeRate = writeRate
self.whenToSave = whenToSave
def commit(self, store, model):
"""Appends the current state of the store to the CSV. This one is meant to be called by the trainer"""
def _fillLine(csvFile, score, minScore, maxScore, mapName, setLen,
outputName, outputFunction, **csvValues):
line = csvFile.newLine()
for k, v in csvValues.iteritems():
line[k] = v
line["score"] = score[0]
line["min_score"] = minScore[0]
line["min_score_commit"] = minScore[1]
line["max_score"] = maxScore[0]
line["max_score_commit"] = maxScore[1]
line["set"] = "%s" % (mapName)
line["set_size"] = "%s" % (setLen)
line["output_layer"] = outputName
line["output_function"] = outputFunction
line.commit()
self.length += 1
if self.csvLegend is None:
self.csvLegend = store["hyperParameters"].keys()
self.csvLegend.extend(store["runInfos"].keys())
self.csvLegend.extend([
"score", "min_score", "min_score_commit", "max_score",
"max_score_commit", "set", "set_size", "output_layer",
"output_function"
])
self.csvFile = CSVFile(legend=self.csvLegend, separator="\t")
self.csvFile.streamToFile(self.filename, writeRate=self.writeRate)
muchData = store["hyperParameters"]
muchData.update(store["runInfos"])
self.scores.update(store["scores"], store["runInfos"]["epoch"])
for mapName, os in store["scores"].iteritems():
for outputName, fs in os.iteritems():
for functionName in fs:
_fillLine(
self.csvFile,
self.scores.getScore(mapName, outputName,
functionName),
self.scores.getMinScore(mapName, outputName,
functionName),
self.scores.getMaxScore(mapName, outputName,
functionName), mapName,
store["setSizes"][mapName], outputName, functionName,
**muchData)
self.trainerStore = store
self.epoch = store["runInfos"]["epoch"]
if self.printRate > 0 and (self.length % self.printRate) == 0:
self.printCurrentState()
for w in self.whenToSave:
if w._shouldISave(self):
model.save(w.getFilename(self))
def printCurrentState(self):
"""prints the current state stored in the recorder"""
if self.length > 0:
print "\n==>rec: ggplot2, epoch %s, commit %s, pid: %s:" % (
self.epoch, self.length, os.getpid())
for mapName, outs in self.scores.currentScores.iteritems():
print " |-%s set" % mapName
for outputName, fs in outs.iteritems():
print " |-%s" % outputName
for functionName in fs:
s = self.scores.getScore(mapName, outputName,
functionName)
mi = self.scores.getMinScore(mapName, outputName,
functionName)
ma = self.scores.getMaxScore(mapName, outputName,
functionName)
highlight = []
if s[0] == mi[0]:
highlight.append("+min+")
else:
highlight.append("%s@%s" % (mi[0], mi[1]))
if s[0] == ma[0]:
highlight.append("+max+")
else:
highlight.append("%s@%s" % (ma[0], ma[1]))
print " |-%s: %s [%s]" % (functionName, s[0],
"; ".join(highlight))
else:
print "==>rec: ggplot2, nothing to show yet"
sys.stdout.flush()
def __repr__(self):
return "<recorder: %s, filename: %s>" % (self.__class__.__name__,
self.filename)
def __len__(self):
"""returns the number of commits performed"""
return self.length
