def knn(name, **kwargs):
'''
Return a pyll graph with hyperparamters that will construct
a sklearn.neighbors.KNeighborsClassifier model.
See help(hpsklearn.components._knn_hp_space) for info on available KNN
arguments.
'''
def _name(msg):
return '%s.%s_%s' % (name, 'knc', msg)
hp_space = _knn_hp_space(_name, **kwargs)
return scope.sklearn_KNeighborsClassifier(**hp_space)
def knn(name, **kwargs):
'''
Return a pyll graph with hyperparamters that will construct
a sklearn.neighbors.KNeighborsClassifier model.
See help(hpsklearn.components._knn_hp_space) for info on available KNN
arguments.
'''
def _name(msg):
return '%s.%s_%s' % (name, 'knc', msg)
hp_space = _knn_hp_space(_name, **kwargs)
return scope.sklearn_KNeighborsClassifier(**hp_space)
def knn(name,
sparse_data=False,
n_neighbors=None,
weights=None,
leaf_size=None,
metric=None,
p=None,
**kwargs):
def _name(msg):
return '%s.%s_%s' % (name, 'knn', msg)
if sparse_data:
metric_args = {'metric': 'euclidean'}
else:
metric_args = hp.pchoice(_name('metric'), [
(0.65, {
'metric': 'euclidean'
}),
(0.10, {
'metric': 'manhattan'
}),
(0.10, {
'metric': 'chebyshev'
}),
(0.10, {
'metric': 'minkowski',
'p': scope.int(hp.quniform(_name('minkowski_p'), 1, 5, 1))
}),
(0.05, {
'metric': 'wminkowski',
'p': scope.int(hp.quniform(_name('wminkowski_p'), 1, 5, 1)),
'w': hp.uniform(_name('wminkowski_w'), 0, 100)
}),
])
rval = scope.sklearn_KNeighborsClassifier(
n_neighbors=scope.int(hp.quniform(_name('n_neighbors'), 0.5, 50, 1))
if n_neighbors is None else n_neighbors,
weights=hp.choice(_name('weights'), ['uniform', 'distance'])
if weights is None else weights,
leaf_size=scope.int(hp.quniform(_name('leaf_size'), 0.51, 100, 1))
if leaf_size is None else leaf_size,
starstar_kwargs=metric_args)
return rval
def knn(name,
sparse_data=False,
n_neighbors=None,
weights=None,
leaf_size=None,
metric=None,
p=None,
**kwargs):
def _name(msg):
return '%s.%s_%s' % (name, 'knn', msg)
if sparse_data:
metric_args = { 'metric':'euclidean' }
else:
metric_args = hp.pchoice(_name('metric'), [
(0.65, { 'metric':'euclidean' }),
(0.10, { 'metric':'manhattan' }),
(0.10, { 'metric':'chebyshev' }),
(0.10, { 'metric':'minkowski',
'p':scope.int(hp.quniform(_name('minkowski_p'), 1, 5, 1))}),
(0.05, { 'metric':'wminkowski',
'p':scope.int(hp.quniform(_name('wminkowski_p'), 1, 5, 1)),
'w':hp.uniform( _name('wminkowski_w'), 0, 100 ) }),
] )
rval = scope.sklearn_KNeighborsClassifier(
n_neighbors=scope.int(hp.quniform(
_name('n_neighbors'),
0.5, 50, 1)) if n_neighbors is None else n_neighbors,
weights=hp.choice(
_name('weights'),
['uniform', 'distance']) if weights is None else weights,
leaf_size=scope.int(hp.quniform(
_name('leaf_size'),
0.51, 100, 1)) if leaf_size is None else leaf_size,
starstar_kwargs=metric_args
)
return rval
def knn(name,
n_neighbors=None,
weights=None,
algorithm=None,
leaf_size=None,
metric=None,
p=None,
**kwargs):
def _name(msg):
return '%s.%s_%s' % (name, 'knn', msg)
"""
metric_arg = hp.choice( _name('metric'), [
('euclidean', None, None, None ),
('manhattan', None, None, None ),
('chebyshev', None, None, None ),
('minkowski', hp.quniform(_name('minkowski_p'), 1, 5, 1 ), None, None),
('wminkowski', hp.quniform(_name('wminkowski_p'), 1, 5, 1 ),
hp.uniform(_name('wminkowski_w'), 0, 100 ), None ),
('seuclidean', None, None, hp.uniform(_name('seuclidean_V'), 0, 100)),
('mahalanobis', None, None, hp.uniform(_name('mahalanobis_V'), 0, 100)),
])
"""
"""
metric_args = hp.choice(_name('metric'), [
{ 'metric':'euclidean' },
{ 'metric':'manhattan' },
{ 'metric':'chebyshev' },
{ 'metric':'minkowski',
'p':scope.int(hp.quniform(_name('minkowski_p'), 1, 5, 1))},
{ 'metric':'wminkowski',
'p':scope.int(hp.quniform(_name('wminkowski_p'), 1, 5, 1)),
'w':hp.uniform( _name('wminkowski_w'), 0, 100 ) },
{ 'metric':'seuclidean',
'V':hp.uniform( _name('seuclidean_V'), 0, 100 ) },
{ 'metric':'mahalanobis',
'V':hp.uniform( _name('mahalanobis_V'), 0, 100 ) },
] )
"""
rval = scope.sklearn_KNeighborsClassifier(
n_neighbors=scope.int(hp.quniform(
_name('n_neighbors'),
0.5, 50, 1)) if n_neighbors is None else n_neighbors,
weights=hp.choice(
_name('weights'),
['uniform', 'distance']) if weights is None else weights,
algorithm=hp.choice(
_name('algorithm'),
['ball_tree', 'kd_tree',
'brute', 'auto']) if algorithm is None else algorithm,
leaf_size=scope.int(hp.quniform(
_name('leaf_size'),
0.51, 100, 1)) if leaf_size is None else leaf_size,
#TODO: more metrics available
###metric_args,
##metric=metric_arg[0] if metric is None else metric,
##p=metric_arg[1],
##w=metric_arg[2],
##V=metric_arg[3],
#metric=hp.choice(
# _name('metric'),
# [ 'euclidean', 'manhattan', 'chebyshev',
# 'minkowski' ] ) if metric is None else metric,
#p=hp.quniform(
# _name('p'),
# 1, 5, 1 ) if p is None else p,
)
return rval
