def get_space(cache):
space = HyperSpace()
with space.as_default():
name_prefix = 'test_'
filters = 64
in1 = Input(shape=(
28,
28,
1,
))
in2 = Input(shape=(
28,
28,
1,
))
ic1 = InputChoice([in1, in2], 1)([in1, in2])
or1 = ModuleChoice([
sepconv5x5(name_prefix, filters),
sepconv3x3(name_prefix, filters),
avgpooling3x3(name_prefix, filters),
maxpooling3x3(name_prefix, filters),
identity(name_prefix)
])(ic1)
space.set_inputs([in1, in2])
space.weights_cache = cache
return space
def get_space():
space = HyperSpace()
with space.as_default():
filters = 64
in1 = Input(shape=(
28,
28,
1,
))
conv_layer(hp_dict, 'normal', 0, [in1, in1], filters, 5)
space.set_inputs(in1)
return space
def get_space():
space = HyperSpace()
with space.as_default():
filters = 64
in1 = Input(shape=(
28,
28,
1,
), dtype='float32')
conv_node(hp_dict, 'normal', 0, 0, [in1, in1], filters)
space.set_inputs(in1)
return space
def enas_micro_search_space(arch='NRNR',
input_shape=(28, 28, 1),
init_filters=64,
node_num=4,
data_format=None,
classes=10,
classification_dropout=0,
hp_dict={},
use_input_placeholder=True,
weights_cache=None):
space = HyperSpace()
with space.as_default():
if use_input_placeholder:
input = Input(shape=input_shape, name='0_input')
else:
input = None
stem, input = stem_op(input, init_filters, data_format)
node0 = stem
node1 = stem
reduction_no = 0
normal_no = 0
for l in arch:
if l == 'N':
normal_no += 1
type = 'normal'
cell_no = normal_no
is_reduction = False
else:
reduction_no += 1
type = 'reduction'
cell_no = reduction_no
is_reduction = True
filters = (2**reduction_no) * init_filters
if is_reduction:
node0 = FactorizedReduction(
filters, f'{normal_no + reduction_no}_{type}_C{cell_no}_0',
data_format)(node0)
node1 = FactorizedReduction(
filters, f'{normal_no + reduction_no}_{type}_C{cell_no}_1',
data_format)(node1)
x = conv_layer(hp_dict, f'{normal_no + reduction_no}_{type}',
cell_no, [node0, node1], filters, node_num,
is_reduction)
node0 = node1
node1 = x
logit = classification(x, classes, classification_dropout, data_format)
space.set_inputs(input)
if weights_cache is not None:
space.weights_cache = weights_cache
return space
def get_space():
space = HyperSpace()
with space.as_default():
filters = 64
in1 = Input(shape=(
28,
28,
1,
))
conv = conv_cell(hp_dict, 'normal', 0, 0, 'L', [in1, in1],
filters)
space.set_inputs([in1, in1])
space.set_outputs(conv)
return space
def get_space_p_in_p():
space = HyperSpace()
with space.as_default():
p1 = Pipeline(
[SimpleImputer(name='imputer1'),
StandardScaler(name='scaler1')],
name='p1')
p2 = Pipeline(
[SimpleImputer(name='imputer2'),
StandardScaler(name='scaler2')],
name='p2')
input = HyperInput(name='input1')
p3 = Pipeline([p1, p2], name='p3')(input)
space.set_inputs(input)
return space
def get_space_num_cat_pipeline_complex(dataframe_mapper_default=False,
lightgbm_fit_kwargs={},
xgb_fit_kwargs={},
catboost_fit_kwargs={}):
space = HyperSpace()
with space.as_default():
input = HyperInput(name='input1')
p1 = numeric_pipeline_complex()(input)
p2 = categorical_pipeline_complex()(input)
# p2 = categorical_pipeline_simple()(input)
p3 = DataFrameMapper(default=dataframe_mapper_default,
input_df=True,
df_out=True,
df_out_dtype_transforms=[(column_object, 'int')
])([p1, p2])
lightgbm_init_kwargs = {
'boosting_type': Choice(['gbdt', 'dart', 'goss']),
'num_leaves': Choice([11, 31, 101, 301, 501]),
'learning_rate': Real(0.001, 0.1, step=0.005),
'n_estimators': 100,
'max_depth': -1,
'tree_learner': 'data' # add for dask
# subsample_for_bin = 200000, objective = None, class_weight = None,
# min_split_gain = 0., min_child_weight = 1e-3, min_child_samples = 20,
}
lightgbm_est = LightGBMDaskEstimator(task='binary',
fit_kwargs=lightgbm_fit_kwargs,
**lightgbm_init_kwargs)
xgb_init_kwargs = {
'tree_method': 'approx' # add for dask
}
xgb_est = XGBoostDaskEstimator(task='binary',
fit_kwargs=xgb_fit_kwargs,
**xgb_init_kwargs)
# catboost_init_kwargs = {
# 'silent': True
# }
# catboost_est = CatBoostEstimator(task='binary', fit_kwargs=catboost_fit_kwargs, **catboost_init_kwargs)
# or_est = ModuleChoice([lightgbm_est, xgb_est, catboost_est], name='estimator_options')(p3)
or_est = ModuleChoice([lightgbm_est, xgb_est],
name='estimator_options')(p3)
space.set_inputs(input)
return space
def get_space_column_transformer():
space = HyperSpace()
with space.as_default():
input = HyperInput(name='input1')
p1 = Pipeline(
[SimpleImputer(name='imputer1'),
StandardScaler(name='scaler1')],
columns=['a', 'b', 'c'],
name='p1')(input)
p2 = Pipeline(
[SimpleImputer(name='imputer2'),
StandardScaler(name='scaler2')],
columns=['c', 'd'],
name='p2')(input)
p3 = ColumnTransformer()([p1, p2])
space.set_inputs(input)
return space
def __call__(self, *args, **kwargs):
space = HyperSpace()
with space.as_default():
hyper_input = HyperInput(name='input1')
estimators = []
if self.enable_dt:
estimators.append(self.dt)
if self.enable_dtr:
estimators.append(self.dtr)
if self.enable_lr:
estimators.append(self.lr)
if self.enable_nn:
estimators.append(self.nn)
modules = [ModuleSpace(name=f'{e["cls"].__name__}', **e) for e in estimators]
outputs = ModuleChoice(modules)(hyper_input)
space.set_inputs(hyper_input)
return space