本文整理汇总了Python中Utils.Parameters.toId方法的典型用法代码示例。如果您正苦于以下问题：Python Parameters.toId方法的具体用法？Python Parameters.toId怎么用？Python Parameters.toId使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类Utils.Parameters的用法示例。
在下文中一共展示了Parameters.toId方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: doGrid
# 需要导入模块: from Utils import Parameters [as 别名]
# 或者: from Utils.Parameters import toId [as 别名]
def doGrid(self):
print >> sys.stderr, "--------- Parameter grid search ---------"
# Build trigger examples
self.triggerDetector.buildExamples(self.model, [self.optData], [self.workDir+"grid-trigger-examples.gz"])
if self.fullGrid:
stepParams = {
"trigger":Parameters.get(self.model.getStr(self.triggerDetector.tag+"classifier-parameters-train", defaultIfNotExist=""), valueListKey="c"),
"booster":[float(i) for i in self.recallAdjustParameters.split(",")],
"edge":Parameters.get(self.model.getStr(self.edgeDetector.tag+"classifier-parameters-train", defaultIfNotExist=""), valueListKey="c")}
else:
stepParams = {
"trigger":Parameters.get(self.model.getStr(self.triggerDetector.tag+"classifier-parameter", defaultIfNotExist=""), valueListKey="c"),
"booster":[float(i) for i in self.recallAdjustParameters.split(",")],
"edge":Parameters.get(self.model.getStr(self.edgeDetector.tag+"classifier-parameter", defaultIfNotExist=""), valueListKey="c")}
for step in ["trigger", "edge"]:
stepParams[step] = Parameters.getCombinations(stepParams[step])
for i in range(len(stepParams[step])):
stepParams[step][i] = Parameters.toString(stepParams[step][i])
print >> sys.stderr, [stepParams[x] for x in ["trigger", "booster", "edge"]]
paramCombinations = combine(*[stepParams[x] for x in ["trigger", "booster", "edge"]])
print >> sys.stderr, paramCombinations
for i in range(len(paramCombinations)):
paramCombinations[i] = {"trigger":paramCombinations[i][0], "booster":paramCombinations[i][1], "edge":paramCombinations[i][2]}
#paramCombinations = Parameters.getCombinations(ALL_PARAMS, ["trigger", "booster", "edge"])
prevParams = None
EDGE_MODEL_STEM = os.path.join(self.edgeDetector.workDir, os.path.normpath(self.model.path)+"-edge-models/model")
TRIGGER_MODEL_STEM = os.path.join(self.triggerDetector.workDir, os.path.normpath(self.model.path)+"-trigger-models/model")
self.structureAnalyzer.load(self.model)
bestResults = None
for i in range(len(paramCombinations)):
params = paramCombinations[i]
print >> sys.stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
print >> sys.stderr, "Processing params", str(i+1) + "/" + str(len(paramCombinations)), params
print >> sys.stderr, "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
# Triggers and Boost
if prevParams == None or prevParams["trigger"] != params["trigger"] or prevParams["trigger"] != params["trigger"]:
print >> sys.stderr, "Classifying trigger examples for parameters", "trigger:" + str(params["trigger"]), "booster:" + str(params["booster"])
xml = self.triggerDetector.classifyToXML(self.optData, self.model, self.workDir+"grid-trigger-examples", self.workDir+"grid-", classifierModel=TRIGGER_MODEL_STEM + Parameters.toId(params["trigger"]), recallAdjust=params["booster"])
prevParams = params
## Build edge examples
#self.edgeDetector.buildExamples(self.model, [xml], [self.workDir+"grid-edge-examples"], [self.optData])
# Classify with pre-defined model
edgeClassifierModel = EDGE_MODEL_STEM + Parameters.toId(params["edge"])
xml = self.edgeDetector.classifyToXML(xml, self.model, self.workDir+"grid-edge-examples", self.workDir+"grid-", classifierModel=edgeClassifierModel, goldData=self.optData)
bestResults = self.evaluateGrid(xml, params, bestResults)
# Remove remaining intermediate grid files
for tag1 in ["edge", "trigger", "unmerging"]:
for tag2 in ["examples", "pred.xml.gz"]:
if os.path.exists(self.workDir+"grid-"+tag1+"-"+tag2):
os.remove(self.workDir+"grid-"+tag1+"-"+tag2)
print >> sys.stderr, "Parameter grid search complete"
print >> sys.stderr, "Tested", len(paramCombinations), "combinations"
print >> sys.stderr, "Best parameters:", bestResults[0]
print >> sys.stderr, "Best result:", bestResults[2] # f-score
# Save grid model
self.saveStr("recallAdjustParameter", str(bestResults[0]["booster"]), self.model)
self.saveStr("recallAdjustParameter", str(bestResults[0]["booster"]), self.combinedModel, False)
if self.fullGrid: # define best models
self.triggerDetector.addClassifierModel(self.model, TRIGGER_MODEL_STEM+str(bestResults[0]["trigger"]), bestResults[0]["trigger"])
self.edgeDetector.addClassifierModel(self.model, EDGE_MODEL_STEM+str(bestResults[0]["edge"]), bestResults[0]["edge"])
# Remove work files
for stepTag in [self.workDir+"grid-trigger", self.workDir+"grid-edge", self.workDir+"grid-unmerging"]:
for fileStem in ["-classifications", "-classifications.log", "examples.gz", "pred.xml.gz"]:
if os.path.exists(stepTag+fileStem):
os.remove(stepTag+fileStem)