def loadResources(self):
"""
Loads the resources from the previously set resource pool.
@raise Exception: when some of the resources required by the learner is not available in the ResourcePool object.
"""
AbstractIterativeLearner.loadResources(self)
Y = self.resource_pool['train_labels']
self.Y = Y
#Number of training examples
self.size = Y.shape[0]
if not Y.shape[1] == 1:
raise Exception(
'GreedyRLS currently supports only one output at a time. The output matrix is now of shape '
+ str(Y.shape) + '.')
X = self.resource_pool['train_features']
self.setDataMatrix(X.T)
if self.resource_pool.has_key('bias'):
self.bias = float(self.resource_pool['bias'])
else:
self.bias = 0.
if self.resource_pool.has_key('measure'):
self.measure = self.resource_pool['measure']
else:
self.measure = None
qids = self.resource_pool['train_qids']
if not self.resource_pool.has_key('cross-validation_folds'):
self.resource_pool['cross-validation_folds'] = qids
self.setQids(qids)
self.results = {}
def loadResources(self):
"""
Loads the resources from the previously set resource pool.
@raise Exception: when some of the resources required by the learner is not available in the ResourcePool object.
"""
AbstractIterativeLearner.loadResources(self)
Y = self.resource_pool['train_labels']
self.Y = Y
#Number of training examples
self.size = Y.shape[0]
if not Y.shape[1] == 1:
raise Exception('GreedyRLS currently supports only one output at a time. The output matrix is now of shape ' + str(Y.shape) + '.')
X = self.resource_pool['train_features']
self.setDataMatrix(X.T)
if self.resource_pool.has_key('bias'):
self.bias = float(self.resource_pool['bias'])
else:
self.bias = 0.
if self.resource_pool.has_key('measure'):
self.measure = self.resource_pool['measure']
else:
self.measure = None
qids = self.resource_pool['train_qids']
if not self.resource_pool.has_key('cross-validation_folds'):
self.resource_pool['cross-validation_folds'] = qids
self.setQids(qids)
self.results = {}
def loadResources(self):
AbstractIterativeLearner.loadResources(self)
X = self.resource_pool[data_sources.TRAIN_FEATURES]
if isinstance(X, sp.base.spmatrix):
self.X = X.todense()
else:
self.X = X
self.X = self.X.T
self.Y = self.resource_pool[data_sources.TRAIN_LABELS]
#Number of training examples
self.size = self.Y.shape[0]
#if not self.Y.shape[1] == 1:
# raise Exception('GreedyRLS currently supports only one output at a time. The output matrix is now of shape ' + str(self.Y.shape) + '.')
if self.resource_pool.has_key('bias'):
self.bias = float(self.resource_pool['bias'])
else:
self.bias = 0.
if self.resource_pool.has_key(data_sources.PERFORMANCE_MEASURE):
self.measure = self.resource_pool[data_sources.PERFORMANCE_MEASURE]
else:
self.measure = None
self.results = {}
