def __init__(self,
dataPath,
resultsDir,
experimentName,
modelName,
loadPath=None,
numClasses=3,
plots=0,
orderedSplit=False,
trainSizes=None,
verbosity=0):
"""
@param dataPath (str) Path to raw data file for the experiment.
@param resultsDir (str) Directory where for the results metrics.
@param experimentName (str) Experiment name, used for saving results.
@param loadPath (str) Path to serialized model for loading.
@param modelName (str) Name of nupic.fluent Model subclass.
@param numClasses (int) Number of classes (labels) per sample.
@param plots (int) Specifies plotting of evaluation metrics.
@param orderedSplit (bool) Indicates method for splitting train/test
samples; False is random, True is ordered.
@param trainSizes (list) Number of samples to use in training, per
trial.
@param verbosity (int) Greater value prints out more progress.
"""
self.dataPath = dataPath
self.resultsDir = resultsDir
self.experimentName = experimentName
self.loadPath = loadPath
self.modelName = modelName
self.numClasses = numClasses
self.plots = plots
self.orderedSplit = orderedSplit
self.trainSizes = trainSizes if trainSizes else []
self.verbosity = verbosity
self.modelDir = os.path.join(self.resultsDir, self.experimentName,
self.modelName)
if not os.path.exists(self.modelDir):
os.makedirs(self.modelDir)
if self.plots:
from fluent.utils.plotting import PlotNLP
self.plotter = PlotNLP()
self.dataDict = None
self.labels = None
self.labelRefs = None
self.partitions = []
self.samples = {}
self.patterns = None
self.results = []
self.model = None
def __init__(self, dataPath, resultsDir, experimentName, load, modelName,
modelModuleName, numClasses, plots, orderedSplit, trainSize,
verbosity):
"""
@param dataPath (str) Path to raw data file for the experiment.
@param resultsDir (str) Directory where for the results metrics.
@param experimentName (str) Experiment name, used for saving results.
@param load (bool) True if a serialized model is to be
loaded.
@param modelName (str) Name of nupic.fluent Model subclass.
@param modeModuleName (str) Model module -- location of the subclass.
@param numClasses (int) Number of classes (labels) per sample.
@param plots (int) Specifies plotting of evaluation metrics.
@param orderedSplit (bool) Indicates method for splitting train/test
samples; False is random, True is ordered.
@param trainSize (str) Number of samples to use in training.
@param verbosity (int) Greater value prints out more progress.
"""
self.dataPath = dataPath
self.resultsDir = resultsDir
self.experimentName = experimentName
self.load = load
self.modelName = modelName
self.modelModuleName = modelModuleName
self.numClasses = numClasses
self.plots = plots
self.orderedSplit = orderedSplit
self.trainSize = trainSize
self.verbosity = verbosity
self.modelPath = os.path.join(self.resultsDir, self.experimentName,
self.modelName)
if not os.path.exists(self.modelPath):
os.makedirs(self.modelPath)
if self.plots:
self.plotter = PlotNLP()
self.dataDict = None
self.labels = None
self.labelRefs = None
self.partitions = []
self.samples = None
self.patterns = None
self.results = []
self.model = None
