def testClassifyHTMUsingTPAsExpectedWithKNN(self):
"""
Tests ClassificationModelHTM using TP region.
Training on the first five samples of the dataset, and testing on the rest,
the model's classifications should match those in the expected classes
data file.
"""
modelName = "HTMNetwork"
runner = HTMRunner(dataPath=os.path.join(DATA_DIR, "responses_network.csv"),
networkConfigPath=os.path.join(
DATA_DIR, "network_config_tp_knn.json"),
resultsDir="",
experimentName="htm_test",
experimentType="incremental",
loadPath=None,
modelName=modelName,
numClasses=3,
plots=0,
orderedSplit=True,
trainSizes=[5],
verbosity=0,
generateData=False,
votingMethod="last",
classificationFile=os.path.join(
DATA_DIR, "responses_categories.json"))
runner.initModel(0)
runner.runExperiment()
expectedClasses, resultClasses = self.getExpectedClassifications(runner,
os.path.join(DATA_DIR, "responses_expected_classes_htm_tp.csv"))
[self.assertEqual(sorted(e), sorted(r),
"HTM model predicted classes other than what we expect.")
for e, r in zip(expectedClasses, resultClasses)]
def run(args):
start = time.time()
root = os.path.dirname(os.path.realpath(__file__))
args.resultsDir = os.path.join(root, args.resultsDir)
if args.modelName == "HTMNetwork":
runner = HTMRunner(**args.__dict__)
runner.initModel(0)
else:
runner = Runner(**args.__dict__)
runner.initModel(args.modelName)
print "Reading in data and preprocessing."
dataTime = time.time()
runner.setupData(args.textPreprocess)
print(
"Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding "
"the data".format(time.time() - dataTime))
encodeTime = time.time()
runner.encodeSamples(args.writeEncodings)
print(
"Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the "
"experiment.".format(time.time() - encodeTime))
runner.runExperiment(args.seed)
runner.writeOutClassifications()
resultCalcs = runner.calculateResults()
runner.evaluateCumulativeResults(resultCalcs)
print "Saving..."
runner.saveModel()
print "Experiment complete in {0:.2f} seconds.".format(time.time() - start)
if args.validation:
print "Validating experiment against expected classifications..."
print runner.validateExperiment(args.validation)
def run(args):
start = time.time()
root = os.path.dirname(os.path.realpath(__file__))
args.resultsDir = os.path.join(root, args.resultsDir)
if args.modelName == "HTMNetwork":
runner = HTMRunner(**args.__dict__)
runner.initModel(0)
else:
runner = Runner(**args.__dict__)
runner.initModel(args.modelName)
print "Reading in data and preprocessing."
dataTime = time.time()
runner.setupData(args.textPreprocess)
print ("Data setup complete; elapsed time is {0:.2f} seconds.\nNow encoding "
"the data".format(time.time() - dataTime))
encodeTime = time.time()
runner.encodeSamples(args.writeEncodings)
print ("Encoding complete; elapsed time is {0:.2f} seconds.\nNow running the "
"experiment.".format(time.time() - encodeTime))
runner.runExperiment(args.seed)
runner.writeOutClassifications()
resultCalcs = runner.calculateResults()
runner.evaluateCumulativeResults(resultCalcs)
print "Saving..."
runner.saveModel()
print "Experiment complete in {0:.2f} seconds.".format(time.time() - start)
if args.validation:
print "Validating experiment against expected classifications..."
print runner.validateExperiment(args.validation)
def testClassifyHTMAsExpectedWithKNN(self):
"""
Tests ClassificationModelHTM, where the network is
LanguageSensor->KNNClassifier.
Training on the first five samples of the dataset, and testing on the rest,
the model's classifications should match those in the expected classes
data file.
"""
modelName = "HTMNetwork"
runner = HTMRunner(dataPath=os.path.join(DATA_DIR, "responses.csv"),
networkConfigPath=os.path.join(
DATA_DIR, "network_config_ls_knn.json"),
resultsDir="",
experimentName="htm_test",
experimentType="incremental",
loadPath=None,
modelName=modelName,
numClasses=3,
plots=0,
orderedSplit=True,
trainSizes=[5],
verbosity=0,
generateData=True,
votingMethod="most")
runner.initModel(0)
runner.runExperiment()
expectedClasses, resultClasses = self.getExpectedClassifications(
runner, os.path.join(DATA_DIR,
"responses_expected_classes_htm.csv"))
[
self.assertEqual(
sorted(e), sorted(r),
"HTM model predicted classes other than what we expect.")
for e, r in zip(expectedClasses, resultClasses)
]
loadPath=args.loadPath,
modelName=args.modelName,
retinaScaling=args.retinaScaling,
retina=args.retina,
apiKey=args.apiKey,
numClasses=args.numClasses,
plots=args.plots,
orderedSplit=args.orderedSplit,
folds=args.folds,
trainSizes=args.trainSizes,
verbosity=args.verbosity,
generateData=args.generateData,
votingMethod=args.votingMethod,
classificationFile=args.classificationFile,
seed=args.seed)
runner.initModel(0)
runner.setupData(args.textPreprocess)
runner.encodeSamples()
runner.partitionIndices(args.seed)
sensorRegion, spRegion, tmRegion, tpRegion, knnRegion = getNupicRegions(runner.model.network)
tmRegion.learningMode = True
tmRegion.computePredictedActiveCellIndices = True
categoryList = ['animal-eats-vegetable', 'vegetable-eats-animal']
animals, vegetables = getAnimalVegetableList()
vegetable = {}
animal = {}
tmCellUnion = []
tmInputUnion = []
tpOutput = []