class Model_Generate(Base):
"""@Model_Generate
This class provides an interface to generate svm model(s), stored within a
NoSQL datastore.
Note: inherit base methods from superclass 'Base'
"""
def __init__(self, svm_data):
"""@__init__
This constructor is responsible for defining class variables, using the
superclass 'Base' constructor, along with the
constructor in this subclass.
@super(), implement 'Base', and 'Base_Data' superclass constructor
within this child class constructor.
Note: the superclass constructor expects the same 'svm_data' argument.
"""
super(Model_Generate, self).__init__(svm_data)
self.svm_data = svm_data
self.session_id = self.svm_data['data']['settings']['svm_session_id']
self.feature_request = Retrieve_Feature()
self.list_error = []
def generate_model(self):
"""@generate_model
This method generates an svm model, using a chosen dataset from the SQL
database. The resulting model is stored into a NoSQL datastore.
@grouped_features, a matrix of observations, where each nested vector,
or python list, is a collection of features within the containing
observation.
@encoded_labels, observation labels (dependent variable labels),
encoded into a unique integer representation.
"""
# local variables
dataset = self.feature_request.get_dataset(self.session_id)
feature_count = self.feature_request.get_count(self.session_id)
label_encoder = preprocessing.LabelEncoder()
# get dataset
if dataset['error']:
print dataset['error']
self.list_error.append(dataset['error'])
dataset = None
else:
dataset = numpy.asarray(dataset['result'])
# get feature count
if feature_count['error']:
print feature_count['error']
self.list_error.append(feature_count['error'])
feature_count = None
else:
feature_count = feature_count['result'][0][0]
# check dataset integrity, build model
if len(dataset) % feature_count == 0:
features_list = dataset[:, [[0], [2], [1]]]
current_features = []
grouped_features = []
observation_labels = []
feature_labels = []
# group features into observation instances, record labels
for index, feature in enumerate(features_list):
if not (index + 1) % feature_count == 0:
# observation labels
current_features.append(feature[1][0])
# general feature labels in every observation
if not len(feature_labels) == feature_count:
feature_labels.append(feature[2][0])
else:
# general feature labels in every observation
if not len(feature_labels) == feature_count:
feature_labels.append(feature[2][0])
current_features.append(feature[1][0])
grouped_features.append(current_features)
observation_labels.append(feature[0][0])
current_features = []
# convert observation labels to a unique integer representation
label_encoder = preprocessing.LabelEncoder()
label_encoder.fit(dataset[:, 0])
encoded_labels = label_encoder.transform(observation_labels)
# create svm model
clf = svm.SVC()
clf.fit(grouped_features, encoded_labels)
# get svm title, and cache (model, encoded labels, title)
entity = Retrieve_Entity()
title = entity.get_title(self.session_id)['result'][0][0]
Cache_Model(clf).cache('svm_rbf_model',
str(self.session_id) + '_' + title)
Cache_Model(label_encoder).cache('svm_rbf_labels', self.session_id)
Cache_Hset().cache('svm_rbf_title', self.session_id, title)
# cache svm feature labels, with respect to given session id
Cache_Hset().cache('svm_rbf_feature_labels', str(self.session_id),
json.dumps(feature_labels))
def return_error(self):
"""@return_error
This method returns all errors corresponding to this class instance.
"""
return self.list_error
class Model_Generate(Base):
"""@Model_Generate
This class provides an interface to generate svm model(s), stored within a
NoSQL datastore.
Note: inherit base methods from superclass 'Base'
"""
def __init__(self, svm_data):
"""@__init__
This constructor is responsible for defining class variables, using the
superclass 'Base' constructor, along with the
constructor in this subclass.
@super(), implement 'Base', and 'Base_Data' superclass constructor
within this child class constructor.
Note: the superclass constructor expects the same 'svm_data' argument.
"""
super(Model_Generate, self).__init__(svm_data)
self.svm_data = svm_data
self.session_id = self.svm_data['data']['settings']['svm_session_id']
self.feature_request = Retrieve_Feature()
self.list_error = []
def generate_model(self):
"""@generate_model
This method generates an svm model, using a chosen dataset from the SQL
database. The resulting model is stored into a NoSQL datastore.
@grouped_features, a matrix of observations, where each nested vector,
or python list, is a collection of features within the containing
observation.
@encoded_labels, observation labels (dependent variable labels),
encoded into a unique integer representation.
"""
# local variables
dataset = self.feature_request.get_dataset(self.session_id)
feature_count = self.feature_request.get_count(self.session_id)
label_encoder = preprocessing.LabelEncoder()
# get dataset
if dataset['error']:
print dataset['error']
self.list_error.append(dataset['error'])
dataset = None
else:
dataset = numpy.asarray(dataset['result'])
# get feature count
if feature_count['error']:
print feature_count['error']
self.list_error.append(feature_count['error'])
feature_count = None
else:
feature_count = feature_count['result'][0][0]
# check dataset integrity, build model
if len(dataset) % feature_count == 0:
features_list = dataset[:, [[0], [2], [1]]]
current_features = []
grouped_features = []
observation_labels = []
feature_labels = []
# group features into observation instances, record labels
for index, feature in enumerate(features_list):
if not (index+1) % feature_count == 0:
# observation labels
current_features.append(feature[1][0])
# general feature labels in every observation
if not len(feature_labels) == feature_count:
feature_labels.append(feature[2][0])
else:
# general feature labels in every observation
if not len(feature_labels) == feature_count:
feature_labels.append(feature[2][0])
current_features.append(feature[1][0])
grouped_features.append(current_features)
observation_labels.append(feature[0][0])
current_features = []
# convert observation labels to a unique integer representation
label_encoder = preprocessing.LabelEncoder()
label_encoder.fit(dataset[:, 0])
encoded_labels = label_encoder.transform(observation_labels)
# create svm model
clf = svm.SVC()
clf.fit(grouped_features, encoded_labels)
# get svm title, and cache (model, encoded labels, title)
entity = Retrieve_Entity()
title = entity.get_title(self.session_id)['result'][0][0]
Cache_Model(clf).cache(
'svm_rbf_model',
str(self.session_id) + '_' + title
)
Cache_Model(label_encoder).cache('svm_rbf_labels', self.session_id)
Cache_Hset().cache('svm_rbf_title', self.session_id, title)
# cache svm feature labels, with respect to given session id
Cache_Hset().cache(
'svm_rbf_feature_labels',
str(self.session_id),
json.dumps(feature_labels)
)
def return_error(self):
"""@return_error
This method returns all errors corresponding to this class instance.
"""
return self.list_error