def run(args):
""" Run the 'query' test.
This method handles scenarios for running a single model or all of them.
"""
(trainingData, labelRefs, _, documentTextMap) = readDataAndReshuffle(args)
if args.modelName == "all":
modelNames = NLP_MODEL_TYPES
runningAllModels = True
else:
modelNames = [args.modelName]
runningAllModels = False
accuracies = {}
for name in modelNames:
# Setup args
args.modelName = name
args.modelDir = os.path.join(args.experimentDir, name)
if name == "htm":
if runningAllModels:
# Need to specify network config for htm models
try:
htmModelInfo = HTM_CONFIGS.pop()
except KeyError:
print "Not enough HTM configs, so skipping the HTM model."
continue
name = htmModelInfo[0]
args.networkConfigPath = htmModelInfo[1]
else:
# Get the specific model name from the config path
for (modelName, configPath) in HTM_CONFIGS:
if configPath == args.networkConfigPath:
name = modelName
# Create a model, train it, save it, reload it
_, model = executeModelLifecycle(args, trainingData, labelRefs)
# Now query the model using some example HR complaints about managers
queryModel(model,
"Begin by treating the employees of the department with the "
"respect they deserve. Halt the unfair practices "
"that they are aware of doing. There is no compassion "
"or loyalty to its senior employees",
documentTextMap)
queryModel(model,
"My manager is really incompetent. He has no clue how to "
"properly supervise his employees and keep them motivated.",
documentTextMap)
queryModel(model,
"I wish I had a lot more vacation and much more flexibility "
"in how I manage my own time. I should be able to choose "
"when I come in as long as I manage to get all my tasks done.",
documentTextMap)
if args.verbosity > 0:
# Print profile information
print
model.dumpProfile()
resultsCheck(name)
def run(args):
""" Run the classification test.
This method handles scenarios for running a single model or all of them.
Also tests serialization by checking the a model's results match before and
after saving/loading.
"""
if args.hello:
args = _setupHelloTest(args)
(dataset, labelRefs, documentCategoryMap, _) = readDataAndReshuffle(args)
if args.modelName == "all":
modelNames = NLP_MODEL_TYPES
runningAllModels = True
else:
modelNames = [args.modelName]
runningAllModels = False
accuracies = {}
for name in modelNames:
# Setup args
args.modelName = name
args.modelDir = os.path.join(args.experimentName, name)
if name == "htm":
if runningAllModels:
# Need to specify network config for htm models
try:
htmModelInfo = HTM_CONFIGS.pop()
except KeyError:
print "Not enough HTM configs, so skipping the HTM model."
continue
name = htmModelInfo[0]
args.networkConfigPath = htmModelInfo[1]
else:
# Get the specific model name from the config path
for (modelName, configPath) in HTM_CONFIGS:
if configPath == args.networkConfigPath:
name = modelName
# Split data for train/test (We still test on the training data!)
if args.split:
split = int(len(dataset) * args.split)
trainingData = dataset[:split]
else:
trainingData = dataset
# Create a model, train it, save it, reload it
_, model = executeModelLifecycle(args, trainingData, labelRefs)
# Test the model
accuracies[name] = testModel(model,
dataset,
labelRefs,
documentCategoryMap,
args.verbosity)
if args.verbosity > 0:
# Print profile information
print
model.dumpProfile()
printSummary(args.experimentName, accuracies)
if args.hello:
assertResults("hello_classification", accuracies)
def run(args):
""" Run the classification test.
This method handles scenarios for running a single model or all of them.
Also tests serialization by checking the a model's results match before and
after saving/loading.
"""
if args.hello:
args = _setupHelloTest(args)
(dataset, labelRefs, documentCategoryMap, _) = readDataAndReshuffle(args)
if args.modelName == "all":
modelNames = NLP_MODEL_TYPES
runningAllModels = True
else:
modelNames = [args.modelName]
runningAllModels = False
accuracies = {}
for name in modelNames:
# Setup args
args.modelName = name
args.modelDir = os.path.join(args.experimentName, name)
if name == "htm":
if runningAllModels:
# Need to specify network config for htm models
try:
htmModelInfo = HTM_CONFIGS.pop()
except KeyError:
print "Not enough HTM configs, so skipping the HTM model."
continue
name = htmModelInfo[0]
args.networkConfigPath = htmModelInfo[1]
else:
# Get the specific model name from the config path
for (modelName, configPath) in HTM_CONFIGS:
if configPath == args.networkConfigPath:
name = modelName
# Split data for train/test (We still test on the training data!)
if args.split:
split = int(len(dataset) * args.split)
trainingData = dataset[:split]
else:
trainingData = dataset
# Create a model, train it, save it, reload it
_, model = executeModelLifecycle(args, trainingData, labelRefs)
# Test the model
accuracies[name] = testModel(model, dataset, labelRefs,
documentCategoryMap, args.verbosity)
if args.verbosity > 0:
# Print profile information
print
model.dumpProfile()
printSummary(args.experimentName, accuracies)
if args.hello:
assertResults("hello_classification", accuracies)
def run(args):
""" Run the 'query' test.
This method handles scenarios for running a single model or all of them.
"""
(trainingData, labelRefs, _, documentTextMap) = readDataAndReshuffle(args)
if args.modelName == "all":
modelNames = NLP_MODEL_TYPES
runningAllModels = True
else:
modelNames = [args.modelName]
runningAllModels = False
accuracies = {}
for name in modelNames:
# Setup args
args.modelName = name
args.modelDir = os.path.join(args.experimentDir, name)
if name == "htm":
if runningAllModels:
# Need to specify network config for htm models
try:
htmModelInfo = HTM_CONFIGS.pop()
except KeyError:
print "Not enough HTM configs, so skipping the HTM model."
continue
name = htmModelInfo[0]
args.networkConfigPath = htmModelInfo[1]
else:
# Get the specific model name from the config path
for (modelName, configPath) in HTM_CONFIGS:
if configPath == args.networkConfigPath:
name = modelName
# Create a model, train it, save it, reload it
_, model = executeModelLifecycle(args, trainingData, labelRefs)
# Now query the model using some example HR complaints about managers
queryModel(
model,
"Begin by treating the employees of the department with the "
"respect they deserve. Halt the unfair practices "
"that they are aware of doing. There is no compassion "
"or loyalty to its senior employees", documentTextMap)
queryModel(
model, "My manager is really incompetent. He has no clue how to "
"properly supervise his employees and keep them motivated.",
documentTextMap)
queryModel(
model,
"I wish I had a lot more vacation and much more flexibility "
"in how I manage my own time. I should be able to choose "
"when I come in as long as I manage to get all my tasks done.",
documentTextMap)
if args.verbosity > 0:
# Print profile information
print
model.dumpProfile()
resultsCheck(name)