def predict(sensor_id, user_id, end_time=None):
'''Makes a prediction with a virtual sensor
Makes a prediciton using a pre-trained classifer with timeseries data in a range
[end time - sampling period, end time]. sampling period is an average of sampling
durations in a traing set, which is stored as a part of a classifier.
When end_time is not specifiec, current time is used as end_time.
This function generated a sample on timestamps passed to this function. Then,
makes a prediction using a pre-trained classifier.
Actual feature extraction and prediction are implemented in a classifier class.
Check random_forest.py for current the implementation of the current classifier.
Args:
sensor_id: An object ID of a virtual sensor.
user_id: A user ID of a user who own the virtual sensor.
Returns:
cls_result: An instance of a container class MLClassifierResult
'''
clf_result = MLClassifierResult()
classifier = db_manager.classifier(sensor_id, user_id)
if classifier is None: # classifier not found in the database
clf_result.result = 'error'
clf_result.message = 'A classifier for the sensor not found.'
return clf_result
sensor = db_manager.sensor(sensor_id, user_id)
if end_time is not None:
end_time = float(end_time)
timeseries = db_manager.timeseries_for_inputs(
sensor.inputs, end_time - classifier.sampling_period, end_time)
else:
timeseries = db_manager.latest_timeseries_for_inputs(
sensor.inputs, classifier.sampling_period)
prediction = classifier.predict(timeseries) # make prediction
if prediction is None: # cannot make a prediction
clf_result.result = 'error'
clf_result.message = 'A classification error occurred.'
return clf_result
clf_result.prediction = prediction
print prediction
return clf_result
def predict(sensor_id, user_id, end_time=None):
'''Makes a prediction with a virtual sensor
Makes a prediciton using a pre-trained classifer with timeseries data in a range
[end time - sampling period, end time]. sampling period is an average of sampling
durations in a traing set, which is stored as a part of a classifier.
When end_time is not specifiec, current time is used as end_time.
This function generated a sample on timestamps passed to this function. Then,
makes a prediction using a pre-trained classifier.
Actual feature extraction and prediction are implemented in a classifier class.
Check random_forest.py for current the implementation of the current classifier.
Args:
sensor_id: An object ID of a virtual sensor.
user_id: A user ID of a user who own the virtual sensor.
Returns:
cls_result: An instance of a container class MLClassifierResult
'''
clf_result = MLClassifierResult()
classifier = db_manager.classifier(sensor_id, user_id)
if classifier is None: # classifier not found in the database
clf_result.result = 'error'
clf_result.message = 'A classifier for the sensor not found.'
return clf_result
sensor = db_manager.sensor(sensor_id, user_id)
if end_time is not None:
end_time = float(end_time)
timeseries = db_manager.timeseries_for_inputs(sensor.inputs, end_time-classifier.sampling_period, end_time)
else:
timeseries = db_manager.latest_timeseries_for_inputs(sensor.inputs, classifier.sampling_period)
prediction = classifier.predict(timeseries) # make prediction
if prediction is None: # cannot make a prediction
clf_result.result = 'error'
clf_result.message = 'A classification error occurred.'
return clf_result
clf_result.prediction = prediction
print prediction
return clf_result
def train(sensor_id, user_id):
'''Trains a classifier for a virtual sensor
Trains a classifier for a virtual sensor using its samples as a training set.
A trained classifier is stored in a database and can be used to make predictions.
This function generated a training set based on timestamps and labels obtained
from samples. Then, pass the training set to classifier.train.
Actual feature extraction and training are implemented in a classifier class.
Check random_forest.py for current the implementation of the current classifier.
Args:
sensor_id: An object ID of a virtual sensor
user_id: A user ID of a user who own this virtual sensor
Returns:
cls_result: An instance of a container class MLClassifierResult
'''
clf_result = MLClassifierResult()
# Load classifier. If no classifier is stored, create a new one
classifier = db_manager.classifier(sensor_id, user_id)
if classifier is None:
classifier = MLRandomForest()
classifier.sensor_id = sensor_id
classifier.user_id = user_id
# Load a training set from a database
dataset = db_manager.dataset(sensor_id, user_id)
if dataset is None:
clf_result.result = 'error'
clf_result.message = 'No samples in a training set'
return clf_result
# Train a classifier and store it in a database
classifier.train(dataset)
result = db_manager.store_classifier(classifier)
if result is None:
clf_result.result = 'error'
clf_result.message = 'Could not store a classifier in database'
return clf_result
return clf_result
def train(sensor_id, user_id):
'''Trains a classifier for a virtual sensor
Trains a classifier for a virtual sensor using its samples as a training set.
A trained classifier is stored in a database and can be used to make predictions.
This function generated a training set based on timestamps and labels obtained
from samples. Then, pass the training set to classifier.train.
Actual feature extraction and training are implemented in a classifier class.
Check random_forest.py for current the implementation of the current classifier.
Args:
sensor_id: An object ID of a virtual sensor
user_id: A user ID of a user who own this virtual sensor
Returns:
cls_result: An instance of a container class MLClassifierResult
'''
clf_result = MLClassifierResult()
# Load classifier. If no classifier is stored, create a new one
classifier = db_manager.classifier(sensor_id, user_id)
if classifier is None:
classifier = MLRandomForest()
classifier.sensor_id = sensor_id
classifier.user_id = user_id
# Load a training set from a database
dataset = db_manager.dataset(sensor_id, user_id)
if dataset is None:
clf_result.result = 'error'
clf_result.message = 'No samples in a training set'
return clf_result
# Train a classifier and store it in a database
classifier.train(dataset)
result = db_manager.store_classifier(classifier)
if result is None:
clf_result.result = 'error'
clf_result.message = 'Could not store a classifier in database'
return clf_result
return clf_result
