def run(args):
start = time.time()
if (not isinstance(args.kFolds, int)) or (args.kFolds < 1):
raise ValueError("Invalid value for number of cross-validation folds.")
root = os.path.dirname(os.path.realpath(__file__))
resultsDir = os.path.join(root, args.resultsDir)
if args.modelName == "HTMNetwork":
runner = HTMRunner(dataPath=args.dataPath,
networkConfigPath=args.networkConfigPath,
resultsDir=resultsDir,
experimentName=args.experimentName,
loadPath=args.loadPath,
modelName=args.modelName,
numClasses=args.numClasses,
plots=args.plots,
orderedSplit=args.orderedSplit,
trainSizes=[],
verbosity=args.verbosity,
generateData=args.generateData,
votingMethod=args.votingMethod,
classificationFile=args.classificationFile,
classifierType=args.classifierType)
else:
runner = Runner(dataPath=args.dataPath,
resultsDir=resultsDir,
experimentName=args.experimentName,
loadPath=args.loadPath,
modelName=args.modelName,
numClasses=args.numClasses,
plots=args.plots,
orderedSplit=args.orderedSplit,
trainSizes=[],
verbosity=args.verbosity)
# HTM network data isn't ready yet to initialize the model
runner.initModel(args.modelName)
print "Reading in data and preprocessing."
dataTime = time.time()
runner.setupData(args.textPreprocess)
# TODO: move kfolds splitting to Runner
random = False if args.orderedSplit else True
runner.partitions = KFolds(args.kFolds).split(
range(len(runner.samples)), randomize=random)
runner.trainSizes = [len(x[0]) for x in runner.partitions]
print ("Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding "
"the data".format(time.time() - dataTime))
encodeTime = time.time()
runner.encodeSamples()
print ("Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the "
"experiment.".format(time.time() - encodeTime))
runner.runExperiment()
print "Experiment complete in {0:.2f} seconds.".format(time.time() - start)
resultCalcs = runner.calculateResults()
_ = runner.evaluateCumulativeResults(resultCalcs)
print "Saving..."
runner.saveModel()
if args.validation:
print "Validating experiment against expected classifications..."
print runner.validateExperiment(args.validation)
def run(args):
start = time.time()
if (not isinstance(args.kFolds, int)) or (args.kFolds < 1):
raise ValueError("Invalid value for number of cross-validation folds.")
root = os.path.dirname(os.path.realpath(__file__))
resultsDir = os.path.join(root, args.resultsDir)
if args.modelName == "HTMNetwork":
runner = HTMRunner(dataPath=args.dataPath,
networkConfigPath=args.networkConfigPath,
resultsDir=resultsDir,
experimentName=args.experimentName,
loadPath=args.loadPath,
modelName=args.modelName,
numClasses=args.numClasses,
plots=args.plots,
orderedSplit=args.orderedSplit,
trainSizes=[],
verbosity=args.verbosity,
generateData=args.generateData,
votingMethod=args.votingMethod,
classificationFile=args.classificationFile,
classifierType=args.classifierType)
else:
runner = Runner(dataPath=args.dataPath,
resultsDir=resultsDir,
experimentName=args.experimentName,
loadPath=args.loadPath,
modelName=args.modelName,
numClasses=args.numClasses,
plots=args.plots,
orderedSplit=args.orderedSplit,
trainSizes=[],
verbosity=args.verbosity)
# HTM network data isn't ready yet to initialize the model
runner.initModel(args.modelName)
print "Reading in data and preprocessing."
dataTime = time.time()
runner.setupData(args.textPreprocess)
# TODO: move kfolds splitting to Runner
random = False if args.orderedSplit else True
runner.partitions = KFolds(args.kFolds).split(range(len(runner.samples)),
randomize=random)
runner.trainSizes = [len(x[0]) for x in runner.partitions]
print(
"Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding "
"the data".format(time.time() - dataTime))
encodeTime = time.time()
runner.encodeSamples()
print(
"Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the "
"experiment.".format(time.time() - encodeTime))
runner.runExperiment()
print "Experiment complete in {0:.2f} seconds.".format(time.time() - start)
resultCalcs = runner.calculateResults()
_ = runner.evaluateCumulativeResults(resultCalcs)
print "Saving..."
runner.saveModel()
if args.validation:
print "Validating experiment against expected classifications..."
print runner.validateExperiment(args.validation)
