Failing to generate scalar predictions from NuPIC CLA model - nupic

Question

I'm failing to get scalar predictions out of a CLA model.
Here's a self-contained example. It uses config to create a model using the ModelFactory. Then it trains it with a simple data set ({input_field=X, output_field=X} where X is random between 0-1). Then it attempts to extract predictions with input of the form {input_field=X, output_field=None}.
#!/usr/bin/python
import random
from nupic.frameworks.opf.modelfactory import ModelFactory
config = {
'model': "CLA",
'version': 1,
'modelParams': {
'inferenceType': 'NontemporalClassification',
'sensorParams': {
'verbosity' : 0,
'encoders': {
'_classifierInput': {
'classifierOnly': True,
'clipInput': True,
'fieldname': u'output_field',
'maxval': 1.0,
'minval': 0.0,
'n': 100,
'name': '_classifierInput',
'type': 'ScalarEncoder',
'w': 21},
u'input_field': {
'clipInput': True,
'fieldname': u'input_field',
'maxval': 1.0,
'minval': 0.0,
'n': 100,
'name': u'input_field',
'type': 'ScalarEncoder',
'w': 21},
},
},
'spEnable': False,
'tpEnable' : False,
'clParams': {
'regionName' : 'CLAClassifierRegion',
'clVerbosity' : 0,
'alpha': 0.001,
'steps': '0',
},
},
}
model = ModelFactory.create(config)
ROWS = 100
def sample():
return random.uniform(0.0, 1.0)
# training data is {input_field: X, output_field: X}
def training():
for r in range(ROWS):
value = sample()
yield {"input_field": value, "output_field": value}
# testing data is {input_field: X, output_field: None} (want output_field predicted)
def testing():
for r in range(ROWS):
value = sample()
yield {"input_field": value, "output_field": None}
model.enableInference({"predictedField": "output_field"})
model.enableLearning()
for row in training():
model.run(row)
#model.finishLearning() fails in clamodel.py
model.disableLearning()
for row in testing():
result = model.run(row)
print result.inferences # Shows None as value
The output I see is high confidence None rather than what I expect, which is something close to the input value (since the model was trained on input==output).
{'multiStepPredictions': {0: {None: 1.0}}, 'multiStepBestPredictions': {0: None}, 'anomalyScore': None}
{'multiStepPredictions': {0: {None: 0.99999999999999978}}, 'multiStepBestPredictions': {0: None}, 'anomalyScore': None}
{'multiStepPredictions': {0: {None: 1.0000000000000002}}, 'multiStepBestPredictions': {0: None}, 'anomalyScore': None}
{'multiStepPredictions': {0: {None: 1.0}}, 'multiStepBestPredictions': {0: None}, 'anomalyScore': None}
'NontemporalClassification' seems to be the right inferenceType, because it's a simple classification. But does that work with scalars?
Is there a different way of expressing that I want a prediction other than output_field=None?
I need output_field to be classifierOnly=True. Is there related configuration missing or wrong?
Thanks for your help.

Answer 1

Here's the working example. The key changes were
Use TemporalMultiStep as recommended by #matthew-taylor (adding required parameters)
Use "implementation": "py" in clParams. My values are in the range 0.0-1.0. The fast classifier always returns None for values in that range. The same code with "py" implementation returns valid values. Change the range to 10-100 and the fast algorithm returns valid values also. It was this change that finally produced non-None results.
Less significant than #2, in order to improve the results I repeat each training row in order to let it sink in, which makes sense for training.
To see the classifier bug, comment out line 19 "implementation": "py". The results will be None. Then change MIN_VAL to 10 and MAX_VAL to 100 and watch the results come back.
#!/usr/bin/python
import random
from nupic.frameworks.opf.modelfactory import ModelFactory
from nupic.support import initLogging
from nupic.encoders import ScalarEncoder
import numpy
MIN_VAL = 0.0
MAX_VAL = 1.0
config = {
'model': "CLA",
'version': 1,
'predictAheadTime': None,
'modelParams': {
'clParams': {
"implementation": "py", # cpp version fails with small numbers
'regionName' : 'CLAClassifierRegion',
'clVerbosity' : 0,
'alpha': 0.001,
'steps': '1',
},
'inferenceType': 'TemporalMultiStep',
'sensorParams': {
'encoders': {
'_classifierInput': {
'classifierOnly': True,
'clipInput': True,
'fieldname': 'output_field',
'maxval': MAX_VAL,
'minval': MIN_VAL,
'n': 200,
'name': '_classifierInput',
'type': 'ScalarEncoder',
'w': 21},
u'input_field': {
'clipInput': True,
'fieldname': 'input_field',
'maxval': MAX_VAL,
'minval': MIN_VAL,
'n': 100,
'name': 'input_field',
'type': 'ScalarEncoder',
'w': 21},
},
'sensorAutoReset' : None,
'verbosity' : 0,
},
'spEnable': True,
'spParams': {
'columnCount': 2048,
'globalInhibition': 1,
'spatialImp': 'cpp',
},
'tpEnable' : True,
'tpParams': { 'activationThreshold': 12,
'cellsPerColumn': 32,
'columnCount': 2048,
'temporalImp': 'cpp',
},
'trainSPNetOnlyIfRequested': False,
},
}
# end of config dictionary
model = ModelFactory.create(config)
TRAINING_ROWS = 100
TESTING_ROWS = 100
def sample(r = 0.0):
return random.uniform(MIN_VAL, MAX_VAL)
def training():
for r in range(TRAINING_ROWS):
value = sample(r / TRAINING_ROWS)
for rd in range(5):
yield {
"input_field": value,
"output_field": value,
'_reset': 1 if (rd==0) else 0,
}
def testing():
for r in range(TESTING_ROWS):
value = sample()
yield {
"input_field": value,
"output_field": None,
}
model.enableInference({"predictedField": "output_field"})
for row in training():
model.run(row)
for row in testing():
result = model.run(row)
prediction = result.inferences['multiStepBestPredictions'][1]
if prediction==None:
print "Input %f, Output None" % (row['input_field'])
else:
print "Input %f, Output %f (err %f)" % (row['input_field'], prediction, prediction - row['input_field'])

Answer 2

The inferenceType you want is TemporalMultistep.
See this example for a complete walkthrough.