def load(self, modelID):
""" Retrieve a model instance from checkpoint.
param modelID: unique model ID
retval: an OPF model instance
raises:
ModelNotFound if the model checkpoint hasn't been saved yet or if this
model's entry doesn't exist in the checkpoint archive
"""
startTime = time.time()
checkpointStoreDirPath = self._getCurrentCheckpointRealPath(modelID)
modelInstanceDirPath = os.path.join(checkpointStoreDirPath,
self._CHECKPOINT_INSTANCE_DIR_NAME)
model = ModelFactory.loadFromCheckpoint(modelInstanceDirPath)
self._logger.info(
"{TAG:MCKPT.LOAD} Loaded model=%s: duration=%ss; directory=%s",
modelID, time.time() - startTime, checkpointStoreDirPath)
return model
def load_model(self, modelSaveDir):
try:
print "Loading model from %s..." % modelSaveDir
model = ModelFactory.loadFromCheckpoint(modelSaveDir)
self._init_model(model)
except:
print "ERR", "load_model", sys.exc_info()
traceback.print_exc()
raise
def initModel(self):
if os.path.exists(os.path.abspath(self.model_path)):
self.model = ModelFactory.loadFromCheckpoint(os.path.relpath(self.model_path))
else:
self.model = ModelFactory.create(self.model_params)
predicted_field = self.model_params["predictedField"]
if predicted_field:
self.model.enableInference({"predictedField": predicted_field})
def initModel(self):
if os.path.exists(os.path.abspath(self.model_path)):
self.model = ModelFactory.loadFromCheckpoint(
os.path.relpath(self.model_path))
else:
self.model = ModelFactory.create(self.model_params)
predicted_field = self.model_params['predictedField']
if predicted_field:
self.model.enableInference({'predictedField': predicted_field})
def __init__(self,name,admin_in,admin_out,sensor_spec, sensors_dir,sensor_in,store,swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor. __init__(self,name=name,admin_in=admin_in, admin_out=admin_out,sensor_spec=sensor_spec, sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in +'/stores/' + store + '/swarms/' + swarm +'/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json')as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference({"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(name=self.name, lstnr=AbstractSensorListener(self.name,topic = '/topic/' +self.sensor_in,config=self.swarm_config_ng,model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +self.sensor_in, id=2, ack='auto')
self.values = []
self.self_announcement()
def createModel(modelParams):
#model = ModelFactory.create(modelParams)
#model.save('/Users/manpreet.singh/Sandbox/codehub/github/datascience/workspace/cortical/harddrive_lifeguard/model/model0')
model = ModelFactory.loadFromCheckpoint('/Users/manpreet.singh/Sandbox/codehub/github/datascience/workspace/cortical/harddrive_lifeguard/model/model0')
model.enableInference({'predictedField': 'class'})
#model.disableLearning()
# model = ModelFactory.create(modelParams)
# model.enableInference({'predictedField': 'class'})
return model
def runHospitalModel(inputFilePath):
model = None
if model_exists(mind_palaces+"weight"+"/model.pkl"):
print "using existing model"
model = ModelFactory.loadFromCheckpoint(mind_palaces+"weight")
else:
print "creating new model"
model = createModel()
runModel(model, inputFilePath)
print "======> updating model"
model.save(mind_palaces+"weight")
def runHospitalModel(inputFilePath, run_count):
model = None
if model_exists(mind_palaces+"vaccination_present_fresh"+"/model.pkl"):
print "using existing model"
model = ModelFactory.loadFromCheckpoint(mind_palaces+"vaccination_present_fresh")
else:
print "creating new model"
model = createModel()
runModel(model, inputFilePath, run_count)
print "======> updating model"
model.save(mind_palaces+"vaccination_present_fresh")
def runHospitalModel(inputFilePath, run_count):
model = None
if model_exists(mind_palaces + "vaccination_present_fresh" + "/model.pkl"):
print "using existing model"
model = ModelFactory.loadFromCheckpoint(mind_palaces +
"vaccination_present_fresh")
else:
print "creating new model"
model = createModel()
runModel(model, inputFilePath, run_count)
print "======> updating model"
model.save(mind_palaces + "vaccination_present_fresh")
def createModel(modelParams):
#model = ModelFactory.create(modelParams)
#model.save('/Users/manpreet.singh/Sandbox/codehub/github/datascience/workspace/cortical/harddrive_lifeguard/model/model0')
model = ModelFactory.loadFromCheckpoint(
'/Users/manpreet.singh/Sandbox/codehub/github/datascience/workspace/cortical/harddrive_lifeguard/model/model0'
)
model.enableInference({'predictedField': 'class'})
#model.disableLearning()
# model = ModelFactory.create(modelParams)
# model.enableInference({'predictedField': 'class'})
return model
def run():
if SMOOTH:
inputDataDir = "../preprocessing/smoothed_data"
else:
inputDataDir = "../preprocessing/formatted_data"
for channel in CHANNELS:
for movement in MOTOR:
outputName = "%s_%s" % (movement, channel)
if PLOT:
output = NuPICPlotOutput("output_%s" % outputName,
show_anomaly_score=True)
else:
output = NuPICFileOutput("data_output_%s" % outputName,
show_anomaly_score=True)
# load model from checkpoint
modelDir = "%s/%s_%s_%s" % (MODEL_PARENT_DIR, MODEL_PREFIX,
movement, channel)
model = ModelFactory.loadFromCheckpoint(modelDir)
model.enableInference({"predictedField": "channel_value"})
# disable learning
model.disableLearning()
# get input file
inputFile = "%s/%s_%s.csv" % (inputDataDir, movement, channel)
with open(inputFile, "rb") as input:
csvReader = csv.reader(input)
# skip header rows
csvReader.next()
csvReader.next()
csvReader.next()
# the real data
timestamp = 0
for row in csvReader:
channel_value = float(row[0])
result = model.run({"channel_value": channel_value})
output.write(timestamp,
"channel_value",
channel_value,
result,
prediction_step=1)
timestamp += 1
output.close()
def getModel(flag):
modelDir = os.getcwd() + '/model/%s' % flag
_LOGGER.info(modelDir)
if os.path.exists(modelDir):
_LOGGER.info('model exists')
model = ModelFactory.loadFromCheckpoint(modelDir)
return model
else:
_LOGGER.info('creating new model')
model = ModelFactory.create(MODEL_PARAMS)
model.save(modelDir)
return model
def new_abstract_sensor(base_sensor_name, admin_in, admin_out,store,swarm):
print "create abstract sensor...."
model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
abstract_sensor = AbstractSensor(
name='%s_predict' % base_sensor_name,
admin_in= admin_in,
admin_out=admin_out,
sensor_spec=["consolidate"], #not needed anyway
sensors_dir=sensors_dir_ng,
sensor_in=base_sensor_name,
store = store,
swarm= swarm
)
abstract_sensor.start()
print "new abstract sensor created"
def getModel(flag):
modelDir = os.getcwd() + "/model/%s" % flag
_LOGGER.info(modelDir)
if os.path.exists(modelDir):
_LOGGER.info("model exists")
model = ModelFactory.loadFromCheckpoint(modelDir)
return model
else:
_LOGGER.info("creating new model")
model = ModelFactory.create(MODEL_PARAMS)
model.save(modelDir)
return model
def getModel(self):
"""
Get the existing model from file or create
from model params if not already existing
:return:
"""
# Check if the dir is empty
if os.path.exists(self.savedModelsPath) and os.listdir(
self.savedModelsPath):
log.info("Loading model from checkpoint %s" % self.savedModelsPath)
model = ModelFactory.loadFromCheckpoint(self.savedModelsPath)
else:
log.info("Creating model from %s..." % self.modelParamsPath)
model = self.createModelFromParams(self.getModelParams())
return model
def new_abstract_sensor(base_sensor_name, admin_in, admin_out, store, swarm):
print "create abstract sensor...."
model = ModelFactory.loadFromCheckpoint(
'/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
)
abstract_sensor = AbstractSensor(
name='%s_predict' % base_sensor_name,
admin_in=admin_in,
admin_out=admin_out,
sensor_spec=["consolidate"], #not needed anyway
sensors_dir=sensors_dir_ng,
sensor_in=base_sensor_name,
store=store,
swarm=swarm)
abstract_sensor.start()
print "new abstract sensor created"
def get_online(number_of_records=20):# 0 means forever
model = ModelFactory.loadFromCheckpoint(os.getcwd() + "/model_save")
count=0
ser.flushInput()
while (count < number_of_records) or (number_of_records == 0):
count = count + 1
text = ser.readline()
if (len(text.split(",")) == 4):
result = model.run({
"s1": float(text.split(",")[0]),
"s2": float(text.split(",")[1]),
"s3": float(text.split(",")[2]),
"s4": float(text.split(",")[3])
})
prediction = int(result.inferences['multiStepBestPredictions'][4])
sys.stdout.write("\r"+ str(prediction))
sys.stdout.write("\t"+ text)
ser.write(str(prediction)+ '\n')
def run():
if SMOOTH:
inputDataDir = "../preprocessing/smoothed_data"
else:
inputDataDir = "../preprocessing/formatted_data"
for channel in CHANNELS:
for movement in MOTOR:
outputName = "%s_%s" % (movement, channel)
if PLOT:
output = NuPICPlotOutput("output_%s" % outputName, show_anomaly_score=True)
else:
output = NuPICFileOutput("data_output_%s" %outputName, show_anomaly_score=True)
# load model from checkpoint
modelDir = "%s/%s_%s_%s" % (MODEL_PARENT_DIR, MODEL_PREFIX, movement, channel)
model = ModelFactory.loadFromCheckpoint(modelDir)
model.enableInference({"predictedField": "channel_value"})
# disable learning
model.disableLearning()
# get input file
inputFile = "%s/%s_%s.csv" % (inputDataDir, movement, channel)
with open(inputFile, "rb") as input:
csvReader = csv.reader(input)
# skip header rows
csvReader.next()
csvReader.next()
csvReader.next()
# the real data
timestamp = 0
for row in csvReader:
channel_value = float(row[0])
result = model.run({"channel_value": channel_value})
output.write(timestamp, "channel_value", channel_value, result, prediction_step=1)
timestamp +=1
output.close()
def testCheckpoint(self):
tmpDir = tempfile.mkdtemp()
model = ModelFactory.create(MODEL_PARAMS)
model.enableInference({'predictedField': 'consumption'})
headers = ['timestamp', 'consumption']
# Now do a bunch of small load/train/save batches
for _ in range(20):
for _ in range(2):
record = [datetime.datetime(2013, 12, 12), numpy.random.uniform(100)]
modelInput = dict(zip(headers, record))
model.run(modelInput)
# Save and load a checkpoint after each batch. Clean up.
tmpBundleName = os.path.join(tmpDir, "test_checkpoint")
self.assertIs(model.save(tmpBundleName), None, "Save command failed.")
model = ModelFactory.loadFromCheckpoint(tmpBundleName)
shutil.rmtree(tmpBundleName)
def clone(self):
"""
Returns a deep copy of this object.
@return: a deep copy of this object.
"""
tmpDirLocation = os.path.expanduser("~/buffer-htm")
# Make sure the directory exists and it is empty
if os.path.exists(tmpDirLocation):
shutil.rmtree(tmpDirLocation)
#os.makedirs(tmpDirLocation)
# Save the model in the tmp folder
self.model.save(tmpDirLocation)
# load from tmp location
newModel = ModelFactory.loadFromCheckpoint(tmpDirLocation)
result = HTMWrapper()
result.model = newModel
return result
def createModel(intersection):
modelDir = getModelDir(intersection)
if CACHE_MODELS and os.path.isdir(modelDir):
# Read in the cached model
print "Loading cached model for {} from {}...".format(intersection, modelDir)
return ModelFactory.loadFromCheckpoint(modelDir)
else:
start = time.time()
# redo the modelParams to use the actual sensor names
modelParams = getModelParamsFromName('3001')
sensor_counts = get_most_used_sensors(intersection)
# try:
# pField = getSwarmConfig(intersection)['inferenceArgs']['predictedField']
# except:
# print "Determining most used sensor for ", intersection
try:
counts = sensor_counts.most_common(1)
if counts[0][1] == 0:
return None
else:
pField = counts[0][0]
except:
return None
print "Using", pField, "as predictedField for", intersection
doc = readings_collection.find_one({'site_no': intersection})
for k in doc['readings']:
# don't model unused sensors
# could run into errors when the sensor
# was damaged for more than the sample period though
if sensor_counts[k] == 0 or k != pField:
continue
modelParams['modelParams']['sensorParams']['encoders'][k] = get_sensor_encoder(k)
for i in get_time_encoders():
modelParams['modelParams']['sensorParams']['encoders'][i['name']] = i
model = ModelFactory.create(modelParams)
model.enableInference({'predictedField': pField})
print "Creating model for {}, in {}s".format(intersection, time.time() - start)
return model
def clone(self):
"""
Returns a deep copy of this object.
@return: a deep copy of this object.
"""
tmpDirLocation = os.path.expanduser("~/buffer-htm")
# Make sure the directory exists and it is empty
if os.path.exists(tmpDirLocation):
shutil.rmtree(tmpDirLocation)
#os.makedirs(tmpDirLocation)
# Save the model in the tmp folder
self.model.save(tmpDirLocation)
# load from tmp location
newModel = ModelFactory.loadFromCheckpoint(tmpDirLocation)
result = HTMWrapper()
result.model = newModel
return result
def createModel(intersection):
modelDir = getModelDir(intersection)
if CACHE_MODELS and os.path.isdir(modelDir):
# Read in the cached model
print "Loading cached model for {} from {}...".format(intersection, modelDir)
return ModelFactory.loadFromCheckpoint(modelDir)
else:
start = time.time()
# redo the modelParams to use the actual sensor names
modelParams = getModelParamsFromName("3001")
sensor_counts = get_most_used_sensors(intersection)
# try:
# pField = getSwarmConfig(intersection)['inferenceArgs']['predictedField']
# except:
# print "Determining most used sensor for ", intersection
try:
counts = sensor_counts.most_common(1)
if counts[0][1] == 0:
return None
else:
pField = counts[0][0]
except:
return None
print "Using", pField, "as predictedField for", intersection
doc = readings_collection.find_one({"site_no": intersection})
for k in doc["readings"]:
# don't model unused sensors
# could run into errors when the sensor
# was damaged for more than the sample period though
if sensor_counts[k] == 0 or k != pField:
continue
modelParams["modelParams"]["sensorParams"]["encoders"][k] = get_sensor_encoder(k)
for i in get_time_encoders():
modelParams["modelParams"]["sensorParams"]["encoders"][i["name"]] = i
model = ModelFactory.create(modelParams)
model.enableInference({"predictedField": pField})
print "Creating model for {}, in {}s".format(intersection, time.time() - start)
return model
def createModel(intersection):
modelDir = getModelDir(intersection)
if CACHE_MODELS and os.path.isdir(modelDir):
# Read in the cached model
print "Loading cached model for {} from {}...".format(intersection, modelDir)
return ModelFactory.loadFromCheckpoint(modelDir)
else:
start = time.time()
# redo the modelParams to use the actual sensor names
modelParams = getModelParamsFromName('3001')
sensor_counts = get_most_used_sensors(intersection)
# try:
# pField = getSwarmConfig(intersection)['inferenceArgs']['predictedField']
# except:
# print "Determining most used sensor for ", intersection
try:
counts = sensor_counts.most_common(1)
if counts[0][1] == 0:
return None
else:
pField = counts[0][0]
except:
return None
print "Using", pField, "as predictedField for", intersection
location = locations_collection.find_one({'intersection_number': intersection})
for k in location['sensors']:
modelParams['modelParams']['sensorParams']['encoders'][k] = get_sensor_encoder(k)
for i in get_time_encoders():
modelParams['modelParams']['sensorParams']['encoders'][i['name']] = i
model = ModelFactory.create(modelParams)
model.enableInference({'predictedField': pField})
print "Creating model for {}, in {}s".format(intersection, time.time() - start)
return model
def testCheckpoint(self):
tmpDir = tempfile.mkdtemp()
model = ModelFactory.create(MODEL_PARAMS)
model.enableInference({'predictedField': 'consumption'})
headers = ['timestamp', 'consumption']
# Now do a bunch of small load/train/save batches
for _ in range(20):
for _ in range(2):
record = [
datetime.datetime(2013, 12, 12),
numpy.random.uniform(100)
]
modelInput = dict(zip(headers, record))
model.run(modelInput)
# Save and load a checkpoint after each batch. Clean up.
tmpBundleName = os.path.join(tmpDir, "test_checkpoint")
self.assertIs(model.save(tmpBundleName), None,
"Save command failed.")
model = ModelFactory.loadFromCheckpoint(tmpBundleName)
shutil.rmtree(tmpBundleName)
def runModel(gymName, plot=False, load=False):
"""
Assumes the gynName corresponds to both a like-named model_params file in the
model_params directory, and that the data exists in a like-named CSV file in
the current directory.
:param gymName: Important for finding model params and input CSV file
:param plot: Plot in matplotlib? Don't use this unless matplotlib is
installed.
"""
if load:
print "Loading model from %s..." % MODEL_DIR
model = ModelFactory.loadFromCheckpoint(MODEL_DIR)
model.disableLearning()
f = open(DATA_DIR + "/learning_list.txt")
filename = f.readline().strip()
inputData = "%s/%s.csv" % (DATA_DIR, filename)
runIoThroughNupic(inputData, model, filename, plot, load)
else:
print "Creating model from %s..." % gymName
model = createModel(getModelParamsFromName(gymName))
model.enableLearning()
# read learning file list from learning_list.txt
f = open(DATA_DIR + "/learning_list.txt")
files = f.readlines()
f.close()
for file in files:
model.resetSequenceStates()
filename = file.strip()
print(filename)
inputData = "%s/%s.csv" % (DATA_DIR, filename)
model = runIoThroughNupic(inputData, model, filename, plot, load)
def runModel(gymName, plot=False, load=False):
"""
Assumes the gynName corresponds to both a like-named model_params file in the
model_params directory, and that the data exists in a like-named CSV file in
the current directory.
:param gymName: Important for finding model params and input CSV file
:param plot: Plot in matplotlib? Don't use this unless matplotlib is
installed.
"""
if load:
print "Loading model from %s..." % MODEL_DIR
model = ModelFactory.loadFromCheckpoint(MODEL_DIR)
model.disableLearning()
f = open(DATA_DIR + "/learning_list.txt")
filename = f.readline().strip()
inputData = "%s/%s.csv" % (DATA_DIR, filename)
runIoThroughNupic(inputData, model, filename, plot, load)
else:
print "Creating model from %s..." % gymName
model = createModel(getModelParamsFromName(gymName))
model.enableLearning()
# read learning file list from learning_list.txt
f = open(DATA_DIR + "/learning_list.txt")
files = f.readlines()
f.close()
for file in files:
model.resetSequenceStates()
filename = file.strip()
print(filename)
inputData = "%s/%s.csv" % (DATA_DIR, filename)
model = runIoThroughNupic(inputData, model, filename, plot, load)
inp.seek(0)
next(inp)
if CLUSTERING:
print "Max. Long: %s ; Min. Long: %s\n" % str(maxLongitude) % str(
minLongitude)
print "Max. Lat: %s ; Min. Lat: %s\n" % str(maxLatitude) % str(
minLatitude)
# FIFO
events = reversed(events)
if PREDICT: import PREDICTmodel_params as model_params
else: import model_params as model_params
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"})
print "Model created!\n"
# Get the Model-Classes:
anomalyLikelihood = AnomalyLikelihood()
if PREDICT:
from nupic.data.inference_shifter import InferenceShifter
shifter = InferenceShifter()
if (WINDOWSIZE != None):
AnomalyScores = deque(numpy.ones(WINDOWSIZE), maxlen=WINDOWSIZE)
else:
AnomalyScores = deque() # numpy.ones(len(events)), maxlen=len(events) ?
def _runExperimentImpl(options, model=None):
"""Creates and runs the experiment
Args:
options: namedtuple ParseCommandLineOptionsResult
model: For testing: may pass in an existing OPF Model instance
to use instead of creating a new one.
Returns: referece to OPFExperiment instance that was constructed (this
is provided to aid with debugging) or None, if none was
created.
"""
jsonhelpers.validate(options.privateOptions, schemaDict=g_parsedPrivateCommandLineOptionsSchema)
# Load the experiment's description.py module
experimentDir = options.experimentDir
descriptionPyModule = opfhelpers.loadExperimentDescriptionScriptFromDir(experimentDir)
expIface = opfhelpers.getExperimentDescriptionInterfaceFromModule(descriptionPyModule)
# Handle "list checkpoints" request
if options.privateOptions["listAvailableCheckpoints"]:
_printAvailableCheckpoints(experimentDir)
return None
# Load experiment tasks
experimentTasks = expIface.getModelControl().get("tasks", [])
# If the tasks list is empty, and this is a nupic environment description
# file being run from the OPF, convert it to a simple OPF description file.
if len(experimentTasks) == 0 and expIface.getModelControl()["environment"] == OpfEnvironment.Nupic:
expIface.convertNupicEnvToOPF()
experimentTasks = expIface.getModelControl().get("tasks", [])
# Handle listTasks
if options.privateOptions["listTasks"]:
print "Available tasks:"
for label in [t["taskLabel"] for t in experimentTasks]:
print "\t", label
return None
# Construct the experiment instance
if options.privateOptions["runCheckpointName"]:
assert model is None
checkpointName = options.privateOptions["runCheckpointName"]
model = ModelFactory.loadFromCheckpoint(savedModelDir=_getModelCheckpointDir(experimentDir, checkpointName))
elif model is not None:
print "Skipping creation of OPFExperiment instance: caller provided his own"
else:
modelDescription = expIface.getModelDescription()
model = ModelFactory.create(modelDescription)
# Handle "create model" request
if options.privateOptions["createCheckpointName"]:
checkpointName = options.privateOptions["createCheckpointName"]
_saveModel(model=model, experimentDir=experimentDir, checkpointLabel=checkpointName)
return model
# Build the task list
# Default task execution index list is in the natural list order of the tasks
taskIndexList = range(len(experimentTasks))
customTaskExecutionLabelsList = options.privateOptions["taskLabels"]
if customTaskExecutionLabelsList:
taskLabelsList = [t["taskLabel"] for t in experimentTasks]
taskLabelsSet = set(taskLabelsList)
customTaskExecutionLabelsSet = set(customTaskExecutionLabelsList)
assert customTaskExecutionLabelsSet.issubset(taskLabelsSet), (
"Some custom-provided task execution labels don't correspond "
"to actual task labels: mismatched labels: %r; actual task "
"labels: %r."
) % (customTaskExecutionLabelsSet - taskLabelsSet, customTaskExecutionLabelsList)
taskIndexList = [taskLabelsList.index(label) for label in customTaskExecutionLabelsList]
print "#### Executing custom task list: %r" % [taskLabelsList[i] for i in taskIndexList]
# Run all experiment tasks
for taskIndex in taskIndexList:
task = experimentTasks[taskIndex]
# Create a task runner and run it!
taskRunner = _TaskRunner(model=model, task=task, cmdOptions=options)
taskRunner.run()
del taskRunner
if options.privateOptions["checkpointModel"]:
_saveModel(model=model, experimentDir=experimentDir, checkpointLabel=task["taskLabel"])
return model
def _runExperimentImpl(options, model=None):
"""Creates and runs the experiment
Args:
options: namedtuple ParseCommandLineOptionsResult
model: For testing: may pass in an existing OPF Model instance
to use instead of creating a new one.
Returns: reference to OPFExperiment instance that was constructed (this
is provided to aid with debugging) or None, if none was
created.
"""
jsonhelpers.validate(options.privateOptions,
schemaDict=g_parsedPrivateCommandLineOptionsSchema)
# Load the experiment's description.py module
experimentDir = options.experimentDir
descriptionPyModule = opfhelpers.loadExperimentDescriptionScriptFromDir(
experimentDir)
expIface = opfhelpers.getExperimentDescriptionInterfaceFromModule(
descriptionPyModule)
# Handle "list checkpoints" request
if options.privateOptions['listAvailableCheckpoints']:
_printAvailableCheckpoints(experimentDir)
return None
# Load experiment tasks
experimentTasks = expIface.getModelControl().get('tasks', [])
# If the tasks list is empty, and this is a nupic environment description
# file being run from the OPF, convert it to a simple OPF description file.
if (len(experimentTasks) == 0 and
expIface.getModelControl()['environment'] == OpfEnvironment.Nupic):
expIface.convertNupicEnvToOPF()
experimentTasks = expIface.getModelControl().get('tasks', [])
# Ensures all the source locations are either absolute paths or relative to
# the nupic.datafiles package_data location.
expIface.normalizeStreamSources()
# Handle listTasks
if options.privateOptions['listTasks']:
print "Available tasks:"
for label in [t['taskLabel'] for t in experimentTasks]:
print "\t", label
return None
# Construct the experiment instance
if options.privateOptions['runCheckpointName']:
assert model is None
checkpointName = options.privateOptions['runCheckpointName']
model = ModelFactory.loadFromCheckpoint(
savedModelDir=_getModelCheckpointDir(experimentDir, checkpointName))
elif model is not None:
print "Skipping creation of OPFExperiment instance: caller provided his own"
else:
modelDescription = expIface.getModelDescription()
model = ModelFactory.create(modelDescription)
# Handle "create model" request
if options.privateOptions['createCheckpointName']:
checkpointName = options.privateOptions['createCheckpointName']
_saveModel(model=model,
experimentDir=experimentDir,
checkpointLabel=checkpointName)
return model
# Build the task list
# Default task execution index list is in the natural list order of the tasks
taskIndexList = range(len(experimentTasks))
customTaskExecutionLabelsList = options.privateOptions['taskLabels']
if customTaskExecutionLabelsList:
taskLabelsList = [t['taskLabel'] for t in experimentTasks]
taskLabelsSet = set(taskLabelsList)
customTaskExecutionLabelsSet = set(customTaskExecutionLabelsList)
assert customTaskExecutionLabelsSet.issubset(taskLabelsSet), \
("Some custom-provided task execution labels don't correspond "
"to actual task labels: mismatched labels: %r; actual task "
"labels: %r.") % (customTaskExecutionLabelsSet - taskLabelsSet,
customTaskExecutionLabelsList)
taskIndexList = [taskLabelsList.index(label) for label in
customTaskExecutionLabelsList]
print "#### Executing custom task list: %r" % [taskLabelsList[i] for
i in taskIndexList]
# Run all experiment tasks
for taskIndex in taskIndexList:
task = experimentTasks[taskIndex]
# Create a task runner and run it!
taskRunner = _TaskRunner(model=model,
task=task,
cmdOptions=options)
taskRunner.run()
del taskRunner
if options.privateOptions['checkpointModel']:
_saveModel(model=model,
experimentDir=experimentDir,
checkpointLabel=task['taskLabel'])
return model
def run():
print "loading models ..."
start = time.time()
models = {}
for motor in MOTOR:
if not motor in models:
models[motor] = {}
for channel in CHANNELS:
modelDir = "%s/%s_%s_%s" % (MODEL_PARENT_DIR, MODEL_PREFIX, motor, channel)
model = ModelFactory.loadFromCheckpoint(modelDir)
models[motor][channel] = model
print "loaded model %s" % modelDir
end = time.time()
print "loaded model in % s" % (end - start)
if SMOOTH:
inputDataDir = "../preprocessing/smoothed_data"
else:
inputDataDir = "../preprocessing/formatted_data"
# keeps track of the 3 different likelihoods per movement
likelihoods = {}
for motor in MOTOR:
likelihoods[motor] = anomaly_likelihood.AnomalyLikelihood(0,1)
for movementToClassify in MOTOR:
for channel in CHANNELS:
print ""
print "Incoming EGG signal on channel %s (Patient is in the '%s' phase)" % (channel, movementToClassify)
print "-> Let's classify this EEG signal!"
# try with all 3 models
averageAnomalyScores = []
anomalyLikelihoods = []
anomalyCount = 0
for movement in MOTOR:
# load model from checkpoint
print " * Trying with %s model : %s" % (channel, movement)
model = models[movementToClassify][channel]
model.enableInference({"predictedField": "channel_value"})
# disable learning
model.disableLearning()
# keep tack of anomaly scores
anomalyScores = []
# Stream data
inputFile = "%s/%s_%s.csv" % (inputDataDir, movement, channel)
with open(inputFile, "rb") as input:
csvReader = csv.reader(input)
# skip 3 header rows
for i in range (0,3):
csvReader.next()
# stream in the data
rowCount = 0
for row in csvReader:
if rowCount < 100:
channel_value = float(row[0])
result = model.run({"channel_value": channel_value})
anomalyScore = result.inferences['anomalyScore']
anomalyScores.append(anomalyScore)
# Compute the Anomaly Likelihood
timestamp = datetime.datetime.now()
likelihood = likelihoods[movement].anomalyProbability(channel_value, anomalyScore, timestamp)
print "likelihood: %s" % likelihood
# compute the log likelihood
logLikelihood = likelihoods[movement].computeLogLikelihood(likelihood)
anomalyLikelihoods.append(logLikelihood)
print "loglikelihood: %s" % logLikelihood
# count anomalies
if logLikelihood > 0.9999:
anomalyCount += 1
print "anomaly count : %s " % anomalyCount
# just stream a bit of points
rowCount += 1
print " ... streamed %s data points" % rowCount
averageAnomalyScore = sum(anomalyScores) / len(anomalyScores)
averageAnomalyScores.append(averageAnomalyScore)
print " ... anomaly scores : %s" % anomalyScores
print " ... Average anomaly score : %s" % averageAnomalyScore
# find out who had the lowest anomaly score. This is the prediction.
# TODO: I should do that with the anomaly likelyhood instead
minAnomalyScore = min(averageAnomalyScores)
predictedMovement = MOTOR[averageAnomalyScores.index(minAnomalyScore)]
print " ==> Predicted movement : %s" % predictedMovement
"""Utility script for checking the in-memory size of a model checkpoint."""
import resource
import sys
import time
from nupic.frameworks.opf.modelfactory import ModelFactory
if __name__ == "__main__":
helpMessage = "Usage: check_model_mem_size.py <path/to/checkpointDir>"
if len(sys.argv) != 2:
print helpMessage
sys.exit(-1)
elif sys.argv[1].strip() == "--help":
print helpMessage
sys.exit()
originalMemory = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
print "Original memory usage: ", originalMemory
models = []
for _ in xrange(10):
s = time.time()
models.append(ModelFactory.loadFromCheckpoint(sys.argv[1].strip()))
print "Loaded model in %f seconds" % (time.time() - s)
diff = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss - originalMemory
average = float(diff) / len(models)
print "Using %i bytes for %i models (%f average)." % (diff, len(models), average)
next(inp)
# LIFO - important for learning the correct sequences!
events = reversed(events)
# Cluster the events:
if CLUSTERING:
from clustering import cluster, scipy_cluster
events, minLatitudes, maxLatitudes, minLongitudes, maxLongitudes = cluster(events)
if PREDICT: import PREDICTmodel_params as model_params
else: import TESTmodel_params as model_params
if LOAD: model = ModelFactory.loadFromCheckpoint(MODELSTATE)
else: model = ModelFactory.create(model_params.MODEL_PARAMS)
if VISUALIZE: Patcher().patchCLAModel(model)
model.enableInference({"predictedField": "event"}) # Predict not only event but also scalar! TODO
# model.enableInference({"predictedField": "scalar"})
# model.enableInference({"predictedField": "timestamp"})
print "Model created!\n"
# Get the Model-Classes:
if PREDICT:
from nupic.data.inference_shifter import InferenceShifter
shifter = InferenceShifter()
anomalyLikelihood = AnomalyLikelihood()
# ----------------------------------------------------------------------
"""Utility script for checking the in-memory size of a model checkpoint."""
import resource
import sys
import time
from nupic.frameworks.opf.modelfactory import ModelFactory
if __name__ == "__main__":
helpMessage = "Usage: check_model_mem_size.py <path/to/checkpointDir>"
if len(sys.argv) != 2:
print helpMessage
sys.exit(-1)
elif sys.argv[1].strip() == "--help":
print helpMessage
sys.exit()
originalMemory = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss
print "Original memory usage: ", originalMemory
models = []
for _ in xrange(10):
s = time.time()
models.append(ModelFactory.loadFromCheckpoint(sys.argv[1].strip()))
print "Loaded model in %f seconds" % (time.time() - s)
diff = resource.getrusage(
resource.RUSAGE_SELF).ru_maxrss - originalMemory
average = float(diff) / len(models)
print "Using %i bytes for %i models (%f average)." % (
diff, len(models), average)
def runGame():
global games
global yards
global slopesize
global slopewidth
global slopewidthmin
global slopewidthmax
global variablewidth
global savefile
tree = "|"
skier = "H"
minpadding = 0
maxpadding = slopesize - slopewidth
#choicelist_drift = [-2,-1,0,1,2]
#choicelist_width = [-2,0,2]
choicelist_drift = [-1,0,1]
choicelist_width = [-1,0,1]
games = games + 1
change = 0
padding = 14
skierposition = (padding + (slopewidth/2))
# See if we have a saved model to load
if os.path.exists(savefile):
print "Loading game history from " + savefile
model = ModelFactory.loadFromCheckpoint(savefile)
else:
print "Creating new game model"
model = createModel()
model.enableInference({'predictionSteps': [1], 'predictedField': 'pos', 'numRecords': 4000})
inf_shift = InferenceShifter();
# - Train on a perfect run
print
print "================================= Start Training ================================="
print
for i in xrange(slopesize - slopewidth):
record = print_slopeline_perfect(i,tree,skier,slopewidth)
result = inf_shift.shift(model.run(record))
while i > 0:
record = print_slopeline_perfect(i,tree,skier,slopewidth)
result = inf_shift.shift(model.run(record))
i = i - 1
while i < padding:
record = print_slopeline_perfect(i,tree,skier,slopewidth)
result = inf_shift.shift(model.run(record))
i = i + 1
for i in xrange(_NUM_RECORDS):
yards = yards + 1
if (variablewidth):
change = generate_random(choicelist_width)
slopewidth = slopewidth + change
if slopewidth > slopewidthmax:
slopewidth = slopewidthmax
if slopewidth < slopewidthmin:
slopewidth = slopewidthmin
drift = generate_random(choicelist_drift)
padding = padding + drift
if padding > maxpadding:
padding = maxpadding
if padding < minpadding:
padding = minpadding
padding = padding - (change/2)
if padding < 0:
padding = 0
if padding + slopewidth > slopesize:
padding = slopesize - slopewidth
record = print_slopeline_perfect(padding,tree,skier,slopewidth)
result = inf_shift.shift(model.run(record))
# - Then set it free to run on it's own
# model.disableLearning()
print
print "=================================== Begin Game ==================================="
print
yards = 0
padding = 14
skierposition = (padding + (slopewidth/2))
while True:
yards = yards + 1
if (variablewidth):
change = generate_random(choicelist_width)
slopewidth = slopewidth + change
if slopewidth > slopewidthmax:
slopewidth = slopewidthmax
if slopewidth < slopewidthmin:
slopewidth = slopewidthmin
drift = generate_random(choicelist_drift)
padding = padding + drift
if padding > maxpadding:
padding = maxpadding
if padding < minpadding:
padding = minpadding
padding = padding - (change/2)
if padding < 0:
padding = 0
if padding + slopewidth > slopesize:
padding = slopesize - slopewidth
record = print_slopeline(padding,tree,skier,slopewidth,skierposition)
if ((skierposition - padding) < 1) or ((skierposition - padding) > slopewidth):
break
model.save(savefile)
result = inf_shift.shift(model.run(record))
#inferred = result.inferences['multiStepPredictions'][1]
#predicted = sorted(inferred.items(), key=lambda x: x[1])[-1][0]
predicted = 0.0
total_probability = 0.0
for key, value in result.inferences['multiStepPredictions'][1].iteritems():
predicted += float(key) * float(value)
total_probability += float(value)
predicted = predicted / total_probability
skierposition = calc_skier_position(skierposition, predicted)
# reload if we made a bad prediction
perfect = padding + slopewidth/2
if abs(perfect - skierposition) > 2:
model = ModelFactory.loadFromCheckpoint(savefile)
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This script is main module of this repository.
# Predict next word's POS in the sentence.
#
import nltk
from nupic.frameworks.opf.modelfactory import ModelFactory
from nupic.data.inference_shifter import InferenceShifter
model = ModelFactory.loadFromCheckpoint("model/")
model.disableLearning()
shifter = InferenceShifter()
def predictPOS(target_str):
pos_list = nltk.pos_tag(nltk.word_tokenize(target_str))
ret = []
for row in pos_list:
model_input = {"token": row[1]}
result = shifter.shift(model.run(model_input))
dic = result.inferences["multiStepPredictions"][1]
if type(dic) == type({}):
if dic.has_key(row[1]):
ret.append((row[0], row[1], dic[row[1]]))
else:
ret.append((row[0], row[1], 0.0))
else:
ret.append((row[0], row[1], 0.0))
def resurrect_model(saved_model): return ModelFactory.loadFromCheckpoint(saved_model)
def load_model(self, name):
name = os.path.abspath(name)
self.model = ModelFactory.loadFromCheckpoint(name)
def resurrect_model(saved_model): return ModelFactory.loadFromCheckpoint(saved_model)
import cmd
import logging
from nupic.data.datasethelpers import findDataset
from nupic.frameworks.opf.modelfactory import ModelFactory
from nupic.frameworks.opf.predictionmetricsmanager import MetricsManager
from urls import loadTlds
from urls import parseUrl
import datetime
import texttable
scriptDir = os.path.dirname(os.path.realpath(__file__))
_LOGGER = logging.getLogger(__name__)
_DATA_PATH = os.path.join(scriptDir, "..", "data", "sanitized.csv")
_MODEL_PATH = os.path.join(scriptDir, "..", "savedModel", "checkpoint")
model = ModelFactory.loadFromCheckpoint(_MODEL_PATH)
model.disableLearning()
tlds = loadTlds()
class UrlShell(cmd.Cmd):
intro = 'Enter URL to predict next hostname.\n'
prompt = 'url> '
file = None
def default(self, line):
processOneUrl(line)
def processOneUrl(url):
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%s")
def __init__(self, name, admin_in, admin_out, sensor_spec, sensors_dir,
sensor_in, store, swarm):
threading.Thread.__init__(self)
#self.config = config
self.sensor_in = sensor_in
self.store = store
self.swarm = swarm
self.name = name
self.brain_available = False
threading.Thread.__init__(self)
Sensor.__init__(self,
name=name,
admin_in=admin_in,
admin_out=admin_out,
sensor_spec=sensor_spec,
sensors_dir=sensors_dir)
swarm_config_path = sensors_dir + sensor_in + '/stores/' + store + '/swarms/' + swarm + '/'
#store_path = sensors_dir + sensor_in +'/stores/' + store + '/out.csv'
#model = ModelFactory.loadFromCheckpoint('/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save')
print swarm_config_path
#load original swarm config file
with open(swarm_config_path + 'swarm_config.json') as json_file:
self.swarm_config = json.load(json_file)
print(self.swarm_config)
self.swarm_config_ng = SwarmConfig(self.swarm_config)
print self.swarm_config_ng.get_predicted_field()
#if there is a 'brain', then tae the existing brain
self.possible_brain_path = str(swarm_config_path + 'model_save')
if os.path.exists(self.possible_brain_path):
possible_brain_2 = '/home/hans/cortical_one_var/sensors/cpu/stores/store_3/swarms/swarm_1/model_save'
print "load existing brain..."
print self.possible_brain_path
#model = ModelFactory.loadFromCheckpoint(possible_brain_2)
model = ModelFactory.loadFromCheckpoint(self.possible_brain_path)
#use this case to add the availabilty of a 'brain' (???!!!) to your annuncement
else:
#laod model configuration
model = ModelFactory.create(
getModelParamsFromFileNG(swarm_config_path))
#configure prediction
model.enableInference(
{"predictedField": self.swarm_config_ng.get_predicted_field()})
self.connection_sensor_in = stomp.Connection()
self.connection_sensor_in.set_listener(
name=self.name,
lstnr=AbstractSensorListener(self.name,
topic='/topic/' + self.sensor_in,
config=self.swarm_config_ng,
model=model))
self.connection_sensor_in.start()
self.connection_sensor_in.connect(self.user, self.password, wait=True)
#self.connection_sensor_in.connect('admin', 'password', wait=True)
self.abstract_listener = self.connection_sensor_in.get_listener(
name=self.name)
self.connection_sensor_in.subscribe(destination='/topic/' +
self.sensor_in,
id=2,
ack='auto')
self.values = []
self.self_announcement()
def run():
print "loading models ..."
start = time.time()
models = {}
for motor in MOTOR:
if not motor in models:
models[motor] = {}
for channel in CHANNELS:
modelDir = "%s/%s_%s_%s" % (MODEL_PARENT_DIR, MODEL_PREFIX, motor,
channel)
model = ModelFactory.loadFromCheckpoint(modelDir)
models[motor][channel] = model
print "loaded model %s" % modelDir
end = time.time()
print "loaded model in % s" % (end - start)
if SMOOTH:
inputDataDir = "../preprocessing/smoothed_data"
else:
inputDataDir = "../preprocessing/formatted_data"
# keeps track of the 3 different likelihoods per movement
likelihoods = {}
for motor in MOTOR:
likelihoods[motor] = anomaly_likelihood.AnomalyLikelihood(0, 1)
for movementToClassify in MOTOR:
for channel in CHANNELS:
print ""
print "Incoming EGG signal on channel %s (Patient is in the '%s' phase)" % (
channel, movementToClassify)
print "-> Let's classify this EEG signal!"
# try with all 3 models
averageAnomalyScores = []
anomalyLikelihoods = []
anomalyCount = 0
for movement in MOTOR:
# load model from checkpoint
print " * Trying with %s model : %s" % (channel, movement)
model = models[movementToClassify][channel]
model.enableInference({"predictedField": "channel_value"})
# disable learning
model.disableLearning()
# keep tack of anomaly scores
anomalyScores = []
# Stream data
inputFile = "%s/%s_%s.csv" % (inputDataDir, movement, channel)
with open(inputFile, "rb") as input:
csvReader = csv.reader(input)
# skip 3 header rows
for i in range(0, 3):
csvReader.next()
# stream in the data
rowCount = 0
for row in csvReader:
if rowCount < 100:
channel_value = float(row[0])
result = model.run(
{"channel_value": channel_value})
anomalyScore = result.inferences['anomalyScore']
anomalyScores.append(anomalyScore)
# Compute the Anomaly Likelihood
timestamp = datetime.datetime.now()
likelihood = likelihoods[
movement].anomalyProbability(
channel_value, anomalyScore, timestamp)
print "likelihood: %s" % likelihood
# compute the log likelihood
logLikelihood = likelihoods[
movement].computeLogLikelihood(likelihood)
anomalyLikelihoods.append(logLikelihood)
print "loglikelihood: %s" % logLikelihood
# count anomalies
if logLikelihood > 0.9999:
anomalyCount += 1
print "anomaly count : %s " % anomalyCount
# just stream a bit of points
rowCount += 1
print " ... streamed %s data points" % rowCount
averageAnomalyScore = sum(anomalyScores) / len(anomalyScores)
averageAnomalyScores.append(averageAnomalyScore)
print " ... anomaly scores : %s" % anomalyScores
print " ... Average anomaly score : %s" % averageAnomalyScore
# find out who had the lowest anomaly score. This is the prediction.
# TODO: I should do that with the anomaly likelyhood instead
minAnomalyScore = min(averageAnomalyScores)
predictedMovement = MOTOR[averageAnomalyScores.index(
minAnomalyScore)]
print " ==> Predicted movement : %s" % predictedMovement
import sys
sys.path.append('model_0')
import model_params
from nupic.frameworks.opf.modelfactory import ModelFactory
from scrape_data import getPlayersOnline
import time
import datetime
from Queue import Queue
import csv
predictionSteps = 15
# load prev model
if len(sys.argv) >= 2 and sys.argv[1] != "None":
print "loading old model"
model = ModelFactory.loadFromCheckpoint(sys.argv[1])
else:
model = ModelFactory.create(model_params.MODEL_PARAMS)
model.enableInference({'predictedField': 'players'})
# load initialization data
if len(sys.argv) >= 3 and sys.argv[2] != "None":
print "initializing model with data"
f = open(sys.argv[2], 'r')
csvReader = csv.reader(f)
# skip header rows
csvReader.next()
csvReader.next()
csvReader.next()
count = 0
for row in csvReader:
if count % 100 == 0: