from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
# KNN for classification task
path = os.getcwd() + '/../dataset/electricity-normalized.arff'
loader = DataLoader(path)
dataset = loader.load(target_col_name='class')
trainset, testset = dataset.cross_split()
knn = KNNClassifier(search_mode='kd_tree')
knn.fit(trainset[0], trainset[1])
predict_kd_tree = scheduler.tic_tac('kd_tree', knn.predict, X=testset[0])
knn = KNNClassifier(search_mode='brutal')
knn.fit(trainset[0], trainset[1])
predict_brutal = scheduler.tic_tac('brutal', knn.predict, X=testset[0])
scheduler.print_task_schedule('brutal')
scheduler.print_task_schedule('kd_tree')
print accuracy_score(testset[1], predict_brutal), accuracy_score(testset[1], predict_kd_tree)
# KNN for regression task
# path = os.getcwd() + '/../dataset/winequality-white.csv'
# loader = DataLoader(path)
# dataset = loader.load(target_col_name='quality')
# trainset, testset = dataset.cross_split()
# knn = KNNRegressor(search_mode='brutal')
# knn.fit(trainset[0], trainset[1])
# predict_brutal = scheduler.tic_tac('brutal', knn.predict, X=testset[0])
# knn = KNNRegressor(search_mode='kd_tree')
# knn.fit(trainset[0], trainset[1])
# predict_kd_tree = scheduler.tic_tac('kd_tree', knn.predict, X=testset[0])
# scheduler.print_task_schedule('brutal')
from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
# KNN for classification task
path = os.getcwd() + '/../dataset/electricity-normalized.arff'
loader = DataLoader(path)
dataset = loader.load(target_col_name='class')
trainset, testset = dataset.cross_split()
knn = KNNClassifier(search_mode='kd_tree')
knn.fit(trainset[0], trainset[1])
predict_kd_tree = scheduler.tic_tac('kd_tree', knn.predict, X=testset[0])
knn = KNNClassifier(search_mode='brutal')
knn.fit(trainset[0], trainset[1])
predict_brutal = scheduler.tic_tac('brutal', knn.predict, X=testset[0])
scheduler.print_task_schedule('brutal')
scheduler.print_task_schedule('kd_tree')
print accuracy_score(testset[1], predict_brutal), accuracy_score(
testset[1], predict_kd_tree)
# KNN for regression task
# path = os.getcwd() + '/../dataset/winequality-white.csv'
# loader = DataLoader(path)
# dataset = loader.load(target_col_name='quality')
# trainset, testset = dataset.cross_split()
# knn = KNNRegressor(search_mode='brutal')
# knn.fit(trainset[0], trainset[1])
# predict_brutal = scheduler.tic_tac('brutal', knn.predict, X=testset[0])
# knn = KNNRegressor(search_mode='kd_tree')
# knn.fit(trainset[0], trainset[1])
# predict_kd_tree = scheduler.tic_tac('kd_tree', knn.predict, X=testset[0])
from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
# KNN for classification task
path = os.getcwd() + "/../dataset/electricity-normalized.arff"
loader = DataLoader(path)
dataset = loader.load(target_col_name="class")
trainset, testset = dataset.cross_split()
knn = KNNClassifier(search_mode="kd_tree")
knn.fit(trainset[0], trainset[1])
predict_kd_tree = scheduler.tic_tac("kd_tree", knn.predict, X=testset[0])
knn = KNNClassifier(search_mode="brutal")
knn.fit(trainset[0], trainset[1])
predict_brutal = scheduler.tic_tac("brutal", knn.predict, X=testset[0])
scheduler.print_task_schedule("brutal")
scheduler.print_task_schedule("kd_tree")
print accuracy_score(testset[1], predict_brutal), accuracy_score(testset[1], predict_kd_tree)
# KNN for regression task
# path = os.getcwd() + '/../dataset/winequality-white.csv'
# loader = DataLoader(path)
# dataset = loader.load(target_col_name='quality')
# trainset, testset = dataset.cross_split()
# knn = KNNRegressor(search_mode='brutal')
# knn.fit(trainset[0], trainset[1])
# predict_brutal = scheduler.tic_tac('brutal', knn.predict, X=testset[0])
# knn = KNNRegressor(search_mode='kd_tree')
# knn.fit(trainset[0], trainset[1])
# predict_kd_tree = scheduler.tic_tac('kd_tree', knn.predict, X=testset[0])
# scheduler.print_task_schedule('brutal')
break
return np.array(result)
def _fast_sample_by_proba(self, sample_weight):
nArray = 1e5
cdf_map = list()
start_ix = 0
end_ix = 0
for i in xrange(self._N):
if i == self._N:
cdf_map.extend([i] * (self._N - start_ix + 1))
break
end_ix += int(math.floor(sample_weight[i] * nArray))
cdf_map.extend([i] * (end_ix - start_ix + 1))
start_ix = end_ix
cdf_map = np.array(cdf_map)
indices = np.random.randint(0, nArray, self._K)
return cdf_map[indices]
if __name__ == '__main__':
from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
fast_sampler = Sampler(1e5, 1e3, mode='fast')
normal_sampler = Sampler(1e5, 1e3, mode='normal')
scheduler.tic_tac('sample_fast', fast_sampler.sample)
scheduler.tic_tac('sample_normal', normal_sampler.sample)
scheduler.print_task_schedule('sample_fast')
scheduler.print_task_schedule('sample_normal')
break
return np.array(result)
def _fast_sample_by_proba(self, sample_weight):
nArray = 1e5
cdf_map = list()
start_ix = 0
end_ix = 0
for i in xrange(self._N):
if i == self._N:
cdf_map.extend([i] * (self._N - start_ix + 1))
break
end_ix += int(math.floor(sample_weight[i] * nArray))
cdf_map.extend([i] * (end_ix - start_ix + 1))
start_ix = end_ix
cdf_map = np.array(cdf_map)
indices = np.random.randint(0, nArray, self._K)
return cdf_map[indices]
if __name__ == '__main__':
from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
fast_sampler = Sampler(1e5, 1e3, mode='fast')
normal_sampler = Sampler(1e5, 1e3, mode='normal')
scheduler.tic_tac('sample_fast', fast_sampler.sample)
scheduler.tic_tac('sample_normal', normal_sampler.sample)
scheduler.print_task_schedule('sample_fast')
scheduler.print_task_schedule('sample_normal')