def model_complexity_exp3(self):
#TODO should we create a new learner object??
self.learner = KNNLearner(weights='distance')
self.expHelper = ExperimentHelper(self.splitter, self.learner,
'distance weight')
param_range = np.array([1, 2, 3, 4, 5])
self.expHelper.model_complexity_exp('n_neighbors', param_range)
def model_complexity_exp2(self):
#TODO should we create a new learner object??
self.learner = KNNLearner(metric='euclidean')
self.expHelper = ExperimentHelper(self.splitter, self.learner,
'euclidean')
param_range = np.array([1, 2, 3, 4, 5])
self.expHelper.model_complexity_exp('n_neighbors', param_range)
def learning_curve_iter2_bank(self):
self.learner = KNNLearner(metric='euclidean',
n_neighbors=38,
weights='uniform')
self.expHelper = ExperimentHelper(self.splitter, self.learner,
'-iter-2')
self.expHelper.learning_curve_exp()
def model_complexity_exp4(self):
#TODO should we create a new learner object??
self.learner = KNNLearner(metric='euclidean', weights='distance')
self.expHelper = ExperimentHelper(self.splitter, self.learner,
'distance weight')
param_range = np.arange(1, 40, 2)
print(param_range)
self.expHelper.model_complexity_exp('n_neighbors', param_range)
def model_complexity_exp11(self):
#TODO should we create a new learner object??
self.learner = KNNLearner()
self.expHelper = ExperimentHelper(self.splitter, self.learner,
'euclidean', '1')
#param_range = np.array([1,2,3,4,5])
param_range = np.arange(40, 60, 2)
print(param_range)
self.expHelper.model_complexity_exp('n_neighbors', param_range)
def __init__(self, reader, helper, splitter):
self.reader = reader
self.helper = helper
self.learner = KNNLearner()
self.splitter = splitter
self.expHelper = ExperimentHelper(self.splitter, self.learner)
def experiment_run_test_bank(self):
self.learner = KNNLearner(metric='euclidean',
n_neighbors=38,
weights='uniform')
self.expHelper = ExperimentHelper(self.splitter, self.learner)
self.expHelper.experiment_run_test()