def _svm_hp_space(
name_func,
kernel,
n_features=1,
C=None,
gamma=None,
coef0=None,
degree=None,
shrinking=None,
tol=None,
max_iter=None,
probability=False,
verbose=False,
cache_size=_svm_default_cache_size):
'''Generate SVM hyperparamters search space
'''
if kernel in ['linear', 'rbf', 'sigmoid']:
degree_ = 1
else:
degree_ = (_svm_degree(name_func('degree'))
if degree is None else degree)
if kernel in ['linear']:
gamma_ = 'auto'
else:
gamma_ = (_svm_gamma(name_func('gamma'), n_features=1)
if gamma is None else gamma)
gamma_ /= n_features # make gamma independent of n_features.
if kernel in ['linear', 'rbf']:
coef0_ = 0.0
elif coef0 is None:
if kernel == 'poly':
coef0_ = hp.pchoice(name_func('coef0'), [
(0.3, 0),
(0.7, gamma_ * hp.uniform(name_func('coef0val'), 0., 10.))
])
elif kernel == 'sigmoid':
coef0_ = hp.pchoice(name_func('coef0'), [
(0.3, 0),
(0.7, gamma_ * hp.uniform(name_func('coef0val'), -10., 10.))
])
else:
pass
else:
coef0_ = coef0
hp_space = dict(
kernel=kernel,
C=_svm_C(name_func('C')) if C is None else C,
gamma=gamma_,
coef0=coef0_,
degree=degree_,
shrinking=(hp_bool(name_func('shrinking'))
if shrinking is None else shrinking),
tol=_svm_tol(name_func('tol')) if tol is None else tol,
max_iter=(_svm_max_iter(name_func('maxiter'))
if max_iter is None else max_iter),
probability=probability,
verbose=verbose,
cache_size=cache_size)
return hp_space
def hyperopt_search(variant: cco.Variant,
data,
max_evals,
mix_algo_ratio=None,
random_state=None):
timer = stopwatch.Timer()
if variant.is_gb():
clf_hp_gb.do_fmin(
data,
max_evals,
mix_algo_ratio=mix_algo_ratio,
max_depth_space=hp.pchoice("max_depth", [(0.40, 1), (0.60, 2)]),
random_state=random_state,
)
elif variant.is_rf():
clf_hp_rf.do_fmin(
data,
max_evals,
mix_algo_ratio=mix_algo_ratio,
max_depth_space=hp.pchoice("max_depth", [(0.45, 1), (0.55, 2)]),
random_state=random_state,
)
elif variant.is_cb_native():
pools = clf_cat_tools.make_pools(data)
clf_hp_cb.do_fmin(
pools,
max_evals,
mix_algo_ratio=mix_algo_ratio,
random_state=random_state,
how="native",
)
sec_elapsed = timer.elapsed
cco.out("{} done for {}".format(variant.name, cco.fmt(sec_elapsed)))
def test_basic3(self):
space = hp.pchoice('something', [
(.2, hp.pchoice('number', [(.8, 2), (.2, 1)])),
(.8, hp.pchoice('number1', [(.7, 5), (.3, 6)]))
])
a, b, c, d = 0, 0, 0, 0
rng = np.random.RandomState(123)
for i in range(0, 2000):
nesto = hyperopt.pyll.stochastic.sample(space, rng=rng)
if nesto == 2:
a += 1
elif nesto == 1:
b += 1
elif nesto == 5:
c += 1
elif nesto == 6:
d += 1
else:
assert 0, nesto
print((a, b, c, d))
assert a + b + c + d == 2000
assert 300 < a + b < 500
assert 1500 < c + d < 1700
assert a * .3 > b # a * 1.2 > 4 * b
assert c * 3 * 1.2 > d * 7
def ridge(name,
alpha=None, #default - 1.0
normalize=None, #default - False,
tol=None, #default - 0.001
solver=None, #default - 'auto'
fit_intercept=None, #default - True
):
def _name(msg):
return '%s.%s_%s' % (name, 'sgd', msg)
rval = scope.sklearn_Ridge(
alpha=hp.loguniform(
_name('alpha'),
np.log(1e-3),
np.log(1e3)) if alpha is None else alpha,
normalize=hp.pchoice(
_name('normalize'),
[ (0.8, True), (0.2, False) ]) if normalize is None else normalize,
fit_intercept=hp.pchoice(
_name('fit_intercept'),
[ (0.8, True), (0.2, False) ]) if fit_intercept is None else fit_intercept,
tol=0.001 if tol is None else tol,
solver="auto" if solver is None else solver,
)
return rval
def test_basic3(self):
space = hp.pchoice(
"something",
[
(0.2, hp.pchoice("number", [(0.8, 2), (0.2, 1)])),
(0.8, hp.pchoice("number1", [(0.7, 5), (0.3, 6)])),
],
)
a, b, c, d = 0, 0, 0, 0
rng = np.random.RandomState(123)
for i in range(0, 2000):
nesto = hyperopt.pyll.stochastic.sample(space, rng=rng)
if nesto == 2:
a += 1
elif nesto == 1:
b += 1
elif nesto == 5:
c += 1
elif nesto == 6:
d += 1
else:
assert 0, nesto
print((a, b, c, d))
assert a + b + c + d == 2000
assert 300 < a + b < 500
assert 1500 < c + d < 1700
assert a * 0.3 > b # a * 1.2 > 4 * b
assert c * 3 * 1.2 > d * 7
class DecisionTreeModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return DecisionTreeRegressor(random_state=RANDOM_STATE, presort=True, **args)
hp_space = {
"criterion": hp.choice("criterion", ["mse", "friedman_mse", "mae"]),
"max_depth": hp.pchoice(
"max_depth_enabled",
[
(0.7, None),
(0.3, 1 + scope.int(hp.qlognormal("max_depth", np.log(30), 0.5, 3))),
],
),
"splitter": hp.choice("splitter_str", ["best", "random"]),
"max_features": hp.pchoice(
"max_features_str",
[
(0.2, "sqrt"), # most common choice.
(0.1, "log2"), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform("max_features_str_frac", 0.0, 1.0)),
],
),
"min_samples_split": scope.int(hp.quniform("min_samples_split_str", 2, 10, 1)),
"min_samples_leaf": hp.choice(
"min_samples_leaf_enabled",
[
1,
scope.int(
hp.qloguniform("min_samples_leaf", np.log(1.5), np.log(50.5), 1)
),
],
),
}
class SVMModel(NonTreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None, test=False):
return SVC(
random_state=RANDOM_STATE,
# Prevent very long training time for some hyper-parameters
max_iter=int(1e3) if test else int(2e6),
# Use 4GB of cache to speed up
cache_size=4096,
**args)
# Hyper-parameters distributions
C_hp = hp.loguniform('C', np.log(1e-5), np.log(1e4))
gamma_hp = hp.pchoice(
'gamma_choice',
[(0.3, 'auto'), (0.3, 'scale'),
(0.2, hp.loguniform('gamma', np.log(1e-4), np.log(1e2)))])
class_weight_hp = hp.pchoice('class_weight', [
(0.5, None),
(0.5, 'balanced'),
])
coef0_hp = hp.pchoice(
'coef0_null',
[(0.3, 0.0), (0.7, hp.loguniform('coef0', np.log(1e-3), np.log(1e3)))])
# Defintions of hyper-parameters spaces for each kernel
linear_hp_space = {
'kernel': 'linear',
'C': C_hp,
'class_weight': class_weight_hp
}
poly_hp_space = {
'kernel': 'poly',
'C': C_hp,
'degree': hp.quniform('degree', 1.5, 6.5, 1),
'gamma': gamma_hp,
'coef0': coef0_hp,
'class_weight': class_weight_hp
}
rbf_hp_space = {
'kernel': 'rbf',
'C': C_hp,
'gamma': gamma_hp,
'class_weight': class_weight_hp
}
sigmoid_hp_space = {
'kernel': 'sigmoid',
'C': C_hp,
'gamma': gamma_hp,
'coef0': coef0_hp,
'class_weight': class_weight_hp
}
# Final hyper-parameters space
hp_space = hp.pchoice('kernel', [(0.5, rbf_hp_space),
(0.2, linear_hp_space),
(0.2, poly_hp_space),
(0.1, sigmoid_hp_space)])
def _svm_hp_space(
name_func,
kernel,
n_features=1,
C=None,
gamma=None,
coef0=None,
degree=None,
shrinking=None,
tol=None,
max_iter=None,
verbose=False,
cache_size=_svm_default_cache_size):
'''Generate SVM hyperparamters search space
'''
if kernel in ['linear', 'rbf', 'sigmoid']:
degree_ = 1
else:
degree_ = (_svm_degree(name_func('degree'))
if degree is None else degree)
if kernel in ['linear']:
gamma_ = 'auto'
else:
gamma_ = (_svm_gamma(name_func('gamma'), n_features=1)
if gamma is None else gamma)
gamma_ /= n_features # make gamma independent of n_features.
if kernel in ['linear', 'rbf']:
coef0_ = 0.0
elif coef0 is None:
if kernel == 'poly':
coef0_ = hp.pchoice(name_func('coef0'), [
(0.3, 0),
(0.7, gamma_ * hp.uniform(name_func('coef0val'), 0., 10.))
])
elif kernel == 'sigmoid':
coef0_ = hp.pchoice(name_func('coef0'), [
(0.3, 0),
(0.7, gamma_ * hp.uniform(name_func('coef0val'), -10., 10.))
])
else:
pass
else:
coef0_ = coef0
hp_space = dict(
kernel=kernel,
C=_svm_C(name_func('C')) if C is None else C,
gamma=gamma_,
coef0=coef0_,
degree=degree_,
shrinking=(hp_bool(name_func('shrinking'))
if shrinking is None else shrinking),
tol=_svm_tol(name_func('tol')) if tol is None else tol,
max_iter=(_svm_max_iter(name_func('maxiter'))
if max_iter is None else max_iter),
verbose=verbose,
cache_size=cache_size)
return hp_space
def sgd_regression(name,
loss=None, #default - 'hinge'
penalty=None, #default - 'l2'
alpha=None, #default - 0.0001
l1_ratio=None, #default - 0.15, must be within [0, 1]
fit_intercept=None, #default - True
n_iter=None, #default - 5
shuffle=None, #default - False
random_state=None, #default - None
epsilon=None, #default - 0.1
learning_rate=None, #default - 'invscaling'
eta0=None, #default - 0.01
power_t=None, #default - 0.5
warm_start=False,
verbose=0,
):
def _name(msg):
return '%s.%s_%s' % (name, 'sgd', msg)
rval = scope.sklearn_SGDRegressor(
loss=hp.pchoice(
_name('loss'),
[ (0.25, 'squared_loss'),
(0.25, 'huber'),
(0.25, 'epsilon_insensitive'),
(0.25, 'squared_epsilon_insensitive') ] ) if loss is None else loss,
penalty=hp.pchoice(
_name('penalty'),
[ (0.40, 'l2'),
(0.35, 'l1'),
(0.25, 'elasticnet') ] ) if penalty is None else penalty,
alpha=hp.loguniform(
_name('alpha'),
np.log(1e-7),
np.log(1)) if alpha is None else alpha,
l1_ratio=hp.uniform(
_name('l1_ratio'),
0, 1 ) if l1_ratio is None else l1_ratio,
fit_intercept=hp.pchoice(
_name('fit_intercept'),
[ (0.8, True), (0.2, False) ]) if fit_intercept is None else fit_intercept,
epsilon=hp.loguniform(
_name('epsilon'),
np.log(1e-7),
np.log(1)) if epsilon is None else epsilon,
learning_rate='invscaling' if learning_rate is None else learning_rate,
eta0=hp.loguniform(
_name('eta0'),
np.log(1e-5),
np.log(1e-1)) if eta0 is None else eta0,
power_t=hp.uniform(
_name('power_t'),
0, 1) if power_t is None else power_t,
verbose=verbose,
random_state=random_state,
)
return rval
class GradientBoostingModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return GradientBoostingRegressor(random_state=RANDOM_STATE,
presort=True,
**args)
loss_alpha = hp.choice('loss_alpha',
[('ls', 0.9), ('lad', 0.9),
('huber', hp.uniform('gbr_alpha', 0.85, 0.95)),
('quantile', 0.5)])
hp_space = {
'n_estimators':
scope.int(
hp.qloguniform('n_estimators', np.log(10.5), np.log(1000.5), 1)),
'learning_rate':
hp.lognormal('learning_rate', np.log(0.01), np.log(10.0)),
'criterion':
hp.choice('criterion', ['mse', 'friedman_mse', 'mae']),
'max_depth':
hp.pchoice('max_depth', [(0.2, 2), (0.5, 3), (0.2, 4), (0.1, 5)]),
'min_samples_leaf':
hp.choice(
'min_samples_leaf_enabled',
[
1, # most common choice.
scope.int(
hp.qloguniform('min_samples_leaf', np.log(1.5),
np.log(50.5), 1))
]),
'subsample':
hp.pchoice(
'subsample_enabled',
[
(0.2, 1.0), # default choice.
(0.8, hp.uniform('subsample', 0.5, 1.0)
) # stochastic grad boosting.
]),
'max_features':
hp.pchoice(
'max_features_str',
[
(0.1, 'sqrt'), # most common choice.
(0.2, 'log2'), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform('max_features_str_frac', 0., 1.))
]),
'loss':
loss_alpha[0],
'alpha':
loss_alpha[1]
}
class GradientBoostingModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return GradientBoostingRegressor(
random_state=RANDOM_STATE, presort=True, **args
)
loss_alpha = hp.choice(
"loss_alpha",
[
("ls", 0.9),
("lad", 0.9),
("huber", hp.uniform("gbr_alpha", 0.85, 0.95)),
("quantile", 0.5),
],
)
hp_space = {
"n_estimators": scope.int(
hp.qloguniform("n_estimators", np.log(10.5), np.log(1000.5), 1)
),
"learning_rate": hp.lognormal("learning_rate", np.log(0.01), np.log(10.0)),
"criterion": hp.choice("criterion", ["mse", "friedman_mse", "mae"]),
"max_depth": hp.pchoice("max_depth", [(0.2, 2), (0.5, 3), (0.2, 4), (0.1, 5)]),
"min_samples_leaf": hp.choice(
"min_samples_leaf_enabled",
[
1, # most common choice.
scope.int(
hp.qloguniform("min_samples_leaf", np.log(1.5), np.log(50.5), 1)
),
],
),
"subsample": hp.pchoice(
"subsample_enabled",
[
(0.2, 1.0), # default choice.
(0.8, hp.uniform("subsample", 0.5, 1.0)), # stochastic grad boosting.
],
),
"max_features": hp.pchoice(
"max_features_str",
[
(0.1, "sqrt"), # most common choice.
(0.2, "log2"), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform("max_features_str_frac", 0.0, 1.0)),
],
),
"loss": loss_alpha[0],
"alpha": loss_alpha[1],
}
def sgd(
name,
loss=None, #default - 'hinge'
penalty=None, #default - 'l2'
alpha=None, #default - 0.0001
l1_ratio=None, #default - 0.15, must be within [0, 1]
fit_intercept=None, #default - True
n_iter=None, #default - 5
shuffle=None, #default - False
random_state=None, #default - None
epsilon=None,
n_jobs=1, #default - 1 (-1 means all CPUs)
learning_rate=None, #default - 'invscaling'
eta0=None, #default - 0.01
power_t=None, #default - 0.5
class_weight=None,
warm_start=False,
verbose=False,
):
def _name(msg):
return '%s.%s_%s' % (name, 'sgd', msg)
rval = scope.sklearn_SGDClassifier(
loss=hp.pchoice(_name('loss'), [(0.25, 'hinge'), (0.25, 'log'),
(0.25, 'modified_huber'),
(0.05, 'squared_hinge'),
(0.05, 'perceptron'),
(0.05, 'squared_loss'),
(0.05, 'huber'),
(0.03, 'epsilon_insensitive'),
(0.02, 'squared_epsilon_insensitive')])
if loss is None else loss,
penalty=hp.pchoice(_name('penalty'), [(0.40, 'l2'), (0.35, 'l1'),
(0.25, 'elasticnet')])
if penalty is None else penalty,
alpha=hp.loguniform(_name('alpha'), np.log(1e-7), np.log(1))
if alpha is None else alpha,
l1_ratio=hp.uniform(_name('l1_ratio'), 0, 1)
if l1_ratio is None else l1_ratio,
fit_intercept=hp.pchoice(_name('fit_intercept'), [(0.8, True),
(0.2, False)])
if fit_intercept is None else fit_intercept,
learning_rate='invscaling' if learning_rate is None else learning_rate,
eta0=hp.loguniform(_name('eta0'), np.log(1e-5), np.log(1e-1))
if eta0 is None else eta0,
power_t=hp.uniform(_name('power_t'), 0, 1)
if power_t is None else power_t,
n_jobs=n_jobs,
verbose=verbose,
)
return rval
def _trees_max_features(name):
return hp.pchoice(name, [
(0.2, 'sqrt'), # most common choice.
(0.1, 'log2'), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform(name + '.frac', 0., 1.))
])
def _trees_max_features(name):
return hp.pchoice(name, [
(0.2, 'sqrt'), # most common choice.
(0.1, 'log2'), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform(name + '.frac', 0., 1.))
])
def setUp(self):
self.space = hp.pchoice('a', [
(.1, 0),
(.2, 1),
(.3, 2),
(.4, 3)])
self.trials = Trials()
def test_basic2(self):
space = hp.choice('normal_choice', [
hp.pchoice('fsd',
[(.1, 'first'),
(.8, 'second'),
(.1, 2)]),
hp.choice('something_else', [10, 20])
])
a, b, c = 0, 0, 0
rng=np.random.RandomState(123)
for i in range(0, 1000):
nesto = hyperopt.pyll.stochastic.sample(space, rng=rng)
if nesto == 'first':
a += 1
elif nesto == 'second':
b += 1
elif nesto == 2:
c += 1
elif nesto in (10, 20):
pass
else:
assert 0, nesto
print((a, b, c))
assert b > 2 * a
assert b > 2 * c
def test_parse_hyperopt_param_error():
"""Test invalid input to parse_hyperopt_param.
"""
# This will raise an error since pchoice is a categorical distribution
parameter = hp.pchoice('test', [(0.5, 'no'), (0.5, 'yes')])
with pytest.raises(ValueError):
parse_hyperopt_param(str(parameter))
def test_basic2(self):
space = hp.choice(
"normal_choice",
[
hp.pchoice("fsd", [(0.1, "first"), (0.8, "second"), (0.1, 2)]),
hp.choice("something_else", [10, 20]),
],
)
a, b, c = 0, 0, 0
rng = np.random.RandomState(123)
for i in range(0, 1000):
nesto = hyperopt.pyll.stochastic.sample(space, rng=rng)
if nesto == "first":
a += 1
elif nesto == "second":
b += 1
elif nesto == 2:
c += 1
elif nesto in (10, 20):
pass
else:
assert 0, nesto
print((a, b, c))
assert b > 2 * a
assert b > 2 * c
class BasicNeuralNetworkModel(NonTreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None, test=False):
return MLPClassifier(random_state=RANDOM_STATE,
max_iter=(1 if test else 300),
**args)
"""
In order to facilitate hyper-parameter tuning, we choose not to tune betas hyperparameters
used by the Adam optimizer, the power exponent used by the invscaling learning rate
schedule and the batch size.
We choose to use only stochastic gradient-based optimizers because other optimizers would not
work with some of the big datasets.
"""
layer_size = scope.int(hp.quniform("layer_size", 10, 100, 5))
hp_space = {
"hidden_layer_sizes":
hp.choice("n_layers", [
[layer_size],
[layer_size] * 2,
[layer_size] * 3,
]),
"early_stopping":
True,
"activation":
hp.pchoice("activation", [(0.25, "logistic"), (0.25, "tanh"),
(0.5, "relu")]),
"solver":
hp.choice("solver", ["sgd", "adam"]),
"alpha":
hp.loguniform("alpha", np.log(1e-7), np.log(1e-2)),
"batch_size":
128,
"learning_rate":
hp.pchoice(
"learning_rate_schedule",
[(0.2, "constant"), (0.3, "invscaling"), (0.5, "adaptive")],
),
"learning_rate_init":
hp.loguniform("learning_rate", np.log(1e-4), np.log(1e-1)),
"momentum":
hp.uniform("momentum", 0.87, 0.99),
"nesterovs_momentum":
hp.pchoice("nesterovs_momentum", [(0.7, True), (0.3, False)]),
}
def _trees_max_depth(name):
return hp.pchoice(name, [
(0.7, None), # most common choice.
# Try some shallow trees.
(0.1, 2),
(0.1, 3),
(0.1, 4),
])
class BasicNeuralNetworkModel(NonTreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None, test=False):
return MLPClassifier(random_state=RANDOM_STATE,
max_iter=(1 if test else 300),
**args)
"""
In order to facilitate hyper-parameter tuning, we choose not to tune betas hyperparameters
used by the Adam optimizer, the power exponent used by the invscaling learning rate
schedule and the batch size.
We choose to use only stochastic gradient-based optimizers because other optimizers would not
work with some of the big datasets.
"""
layer_size = scope.int(hp.quniform('layer_size', 10, 100, 5))
hp_space = {
'hidden_layer_sizes':
hp.choice('n_layers', [
[layer_size],
[layer_size] * 2,
[layer_size] * 3,
]),
'early_stopping':
True,
'activation':
hp.pchoice('activation', [(0.25, 'logistic'), (0.25, 'tanh'),
(0.5, 'relu')]),
'solver':
hp.choice('solver', ['sgd', 'adam']),
'alpha':
hp.loguniform('alpha', np.log(1e-7), np.log(1e-2)),
'batch_size':
128,
'learning_rate':
hp.pchoice('learning_rate_schedule', [(0.2, 'constant'),
(0.3, 'invscaling'),
(0.5, 'adaptive')]),
'learning_rate_init':
hp.loguniform('learning_rate', np.log(1e-4), np.log(1e-1)),
'momentum':
hp.uniform('momentum', 0.87, 0.99),
'nesterovs_momentum':
hp.pchoice('nesterovs_momentum', [(0.7, True), (0.3, False)]),
}
def _trees_max_depth(name):
return hp.pchoice(name, [
(0.7, None), # most common choice.
# Try some shallow trees.
(0.1, 2),
(0.1, 3),
(0.1, 4),
])
def _grad_boosting_subsample(name):
return hp.pchoice(
name,
[
(0.2, 1.0), # default choice.
(0.8, hp.uniform(name + '.sgb', 0.5, 1.0)
) # stochastic grad boosting.
])
def generic_space(name='space'):
model = hp.pchoice('%s' % name, [
(.8, {'preprocessing': [pca(name + '.pca')],
'classifier': any_classifier(name + '.pca_clsf')
}),
(.2, {'preprocessing': [min_max_scaler(name + '.min_max_scaler')],
'classifier': any_classifier(name + '.min_max_clsf'),
}),
])
return as_apply({'model': model})
def space_rf(parts, n_features, max_depth_space, random_state):
if max_depth_space is not None:
max_depth = max_depth_space
else:
max_depth = hp.pchoice(
"max_depth",
[
(0.22, 1),
(0.22, 2),
(0.24, 3),
(0.22, 4),
(0.10, 5),
],
)
criterion = hp.pchoice("criterion", [(0.5, "gini"), (0.5, "entropy")])
max_features = hp.choice("max_features", cco.colsample_ratios(n_features))
min_samples_leaf = hp.choice("min_samples_leaf", [15, 20, 30, 40])
space = hp.pchoice(
"split_parts",
[(
part.pratio,
{
"random_state":
random_state,
"criterion":
criterion,
"max_depth":
max_depth,
"max_features":
max_features,
"min_samples_leaf":
min_samples_leaf,
"n_estimators":
hp.choice(
"estimators_" + str(idx),
list(range(part.n_estim1, part.n_estim2, part.n_estimq)),
),
},
) for idx, part in enumerate(parts)],
)
return space
def test_bug1_anneal():
space = hp.choice(
"preprocess_choice",
[
{"pwhiten": hp.pchoice("whiten_randomPCA", [(0.3, False), (0.7, True)])},
{"palgo": False},
{"pthree": 7},
],
)
fmin(fn=lambda x: 1, space=space, algo=anneal.suggest, max_evals=50)
def test_bug1_anneal():
space = hp.choice('preprocess_choice', [
{'pwhiten': hp.pchoice('whiten_randomPCA',
[(.3, False), (.7, True)])},
{'palgo': False},
{'pthree': 7}])
best = fmin(fn=lambda x: 1,
space=space,
algo=anneal.suggest,
max_evals=50)
def generic_space(name='space'):
model = hp.pchoice('%s' % name, [
(.8, {'preprocessing': [pca(name + '.pca')],
'classifier': any_classifier(name + '.pca_clsf')
}),
(.2, {'preprocessing': [min_max_scaler(name + '.min_max_scaler')],
'classifier': any_classifier(name + '.min_max_clsf'),
}),
])
return as_apply({'model': model})
def space_gboost(parts, n_features, max_depth_space=None, random_state=None):
# n_estimators = hp.choice('n_estimators', range(100, 1150, 10))
if max_depth_space is not None:
max_depth = max_depth_space
else:
max_depth = hp.pchoice(
"max_depth",
[
(0.29, 2),
(0.27, 3),
(0.24, 4),
(0.20, 5),
# (0.20, 6),
],
)
loss = hp.choice("loss", ["exponential", "deviance"])
max_features = hp.choice("max_features", cco.colsample_ratios(n_features))
space = hp.pchoice(
"split_parts",
[
(
part.pratio,
{
"random_state": random_state,
"max_depth": max_depth,
"loss": loss,
"max_features": max_features,
"n_estimators": hp.choice(
"estimators_" + str(idx),
list(range(part.n_estim1, part.n_estim2, part.n_estimq)),
),
"learning_rate": hp.uniform(
"learning_rate_" + str(idx), part.learn_rate1, part.learn_rate2
),
},
)
for idx, part in enumerate(parts)
],
)
return space
def _starting_space(self) -> Any:
"""Create starting space for sampler.
Parameters
----------
None
Returns
-------
dict
Key/value pairs, key is hyperparameter name and value is statistical
distribution that can be sampled
"""
params: Dict[str, Any] = {}
params['n_hidden_layers'] = self.n_hidden_layers
# Add all hidden layer units
for i in range(1, self.n_hidden_layers + 1):
params['n_hidden%s' % i] = hp.quniform('mlp_h%s' % i, 1,
self.max_neurons, 1)
params.update({
'learning_rate':
hp.loguniform('mlp_lr', log(1e-4), log(1)),
'dropout':
hp.uniform('mlp_do', 0, 1),
'epochs':
hp.quniform('mlp_e', 1, self.max_epochs, 1),
'batch_size':
hp.quniform('mlp_bs', 2, 512, 1),
'batch_norm':
hp.pchoice('mlp_bn', [(0.5, 'no'), (0.5, 'yes')]),
'optimizer':
hp.pchoice('mlp_opt', [(0.5, 'adam'), (0.5, 'sgd')]),
'reg_l1':
hp.loguniform('mlp_l1', log(1e-4), log(1)),
'reg_l2':
hp.loguniform('mlp_l2', log(1e-4), log(1))
})
return params
def test_bug1_tpe():
space = hp.choice(
'preprocess_choice',
[{
'pwhiten': hp.pchoice('whiten_randomPCA', [(.3, False),
(.7, True)])
}, {
'palgo': False
}, {
'pthree': 7
}])
best = fmin(fn=lambda x: 1, space=space, algo=tpe.suggest, max_evals=50)
class DecisionTreeModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return DecisionTreeClassifier(random_state=RANDOM_STATE,
presort=True,
**args)
hp_space = {
'max_depth':
hp.pchoice(
'max_depth_enabled',
[(0.7, None),
(0.3,
1 + scope.int(hp.qlognormal('max_depth', np.log(30), 0.5, 3)))]),
'splitter':
hp.choice('splitter_str', ['best', 'random']),
'max_features':
hp.pchoice(
'max_features_str',
[
(0.2, 'sqrt'), # most common choice.
(0.1, 'log2'), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform('max_features_str_frac', 0., 1.))
]),
'min_samples_split':
scope.int(hp.quniform('min_samples_split_str', 2, 10, 1)),
'min_samples_leaf':
hp.choice('min_samples_leaf_enabled', [
1,
scope.int(
hp.qloguniform('min_samples_leaf', np.log(1.5), np.log(50.5),
1))
]),
'class_weight':
hp.pchoice('class_weight', [
(0.5, None),
(0.5, 'balanced'),
])
}
class RandomForestsModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return RandomForestRegressor(random_state=RANDOM_STATE,
n_jobs=-1,
**args)
hp_space = {
"max_depth":
hp.pchoice(
"max_depth_enabled",
[
(0.7, None),
(0.3, 1 +
scope.int(hp.qlognormal("max_depth", np.log(30), 0.5, 3))),
],
),
"n_estimators":
scope.int(hp.qloguniform("n_estimators", np.log(9.5), np.log(300), 1)),
"min_samples_leaf":
hp.choice(
"min_samples_leaf_enabled",
[
1,
scope.int(
hp.qloguniform("min_samples_leaf", np.log(1.5),
np.log(50.5), 1)),
],
),
"max_features":
hp.pchoice(
"max_features_str",
[
(0.1, "sqrt"), # most common choice.
(0.2, "log2"), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform("max_features_str_frac", 0.0, 1.0)),
],
),
}
def ridge(
name,
alpha=None, #default - 1.0
normalize=None, #default - False,
tol=None, #default - 0.001
solver=None, #default - 'auto'
fit_intercept=None, #default - True
):
def _name(msg):
return '%s.%s_%s' % (name, 'sgd', msg)
rval = scope.sklearn_Ridge(
alpha=hp.loguniform(_name('alpha'), np.log(1e-3), np.log(1e3))
if alpha is None else alpha,
normalize=hp.pchoice(_name('normalize'), [(0.8, True), (0.2, False)])
if normalize is None else normalize,
fit_intercept=hp.pchoice(_name('fit_intercept'), [(0.8, True),
(0.2, False)])
if fit_intercept is None else fit_intercept,
tol=0.001 if tol is None else tol,
solver="auto" if solver is None else solver,
)
return rval
def sgd(name,
loss=None, # default - 'hinge'
penalty=None, # default - 'l2'
alpha=None, # default - 0.0001
l1_ratio=None, # default - 0.15, must be within [0, 1]
fit_intercept=True, # default - True
n_iter=5, # default - 5
shuffle=True, # default - True
random_state=None, # default - None
epsilon=None,
n_jobs=1, # default - 1 (-1 means all CPUs)
learning_rate=None, # default - 'optimal'
eta0=None, # default - 0.0
power_t=None, # default - 0.5
class_weight='choose',
warm_start=False,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'sgdc', msg)
rval = scope.sklearn_SGDClassifier(
loss=hp.pchoice(_name('loss'), [
(0.25, 'hinge'),
(0.25, 'log'),
(0.25, 'modified_huber'),
(0.05, 'squared_hinge'),
(0.05, 'perceptron'),
(0.05, 'squared_loss'),
(0.05, 'huber'),
(0.03, 'epsilon_insensitive'),
(0.02, 'squared_epsilon_insensitive')
]) if loss is None else loss,
penalty=_sgd_penalty(_name('penalty')) if penalty is None else penalty,
alpha=_sgd_alpha(_name('alpha')) if alpha is None else alpha,
l1_ratio=(_sgd_l1_ratio(_name('l1ratio'))
if l1_ratio is None else l1_ratio),
fit_intercept=fit_intercept,
n_iter=n_iter,
learning_rate=(_sgdc_learning_rate(_name('learning_rate'))
if learning_rate is None else learning_rate),
eta0=_sgd_eta0(_name('eta0')) if eta0 is None else eta0,
power_t=_sgd_power_t(_name('power_t')) if power_t is None else power_t,
class_weight=(_class_weight(_name('clsweight'))
if class_weight == 'choose' else class_weight),
n_jobs=n_jobs,
verbose=verbose,
random_state=_random_state(_name('rstate'), random_state),
)
return rval
def sgd(name,
loss=None, # default - 'hinge'
penalty=None, # default - 'l2'
alpha=None, # default - 0.0001
l1_ratio=None, # default - 0.15, must be within [0, 1]
fit_intercept=True, # default - True
n_iter=5, # default - 5
shuffle=True, # default - True
random_state=None, # default - None
epsilon=None,
n_jobs=1, # default - 1 (-1 means all CPUs)
learning_rate=None, # default - 'optimal'
eta0=None, # default - 0.0
power_t=None, # default - 0.5
class_weight='choose',
warm_start=False,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'sgdc', msg)
rval = scope.sklearn_SGDClassifier(
loss=hp.pchoice(_name('loss'), [
(0.25, 'hinge'),
(0.25, 'log'),
(0.25, 'modified_huber'),
(0.05, 'squared_hinge'),
(0.05, 'perceptron'),
(0.05, 'squared_loss'),
(0.05, 'huber'),
(0.03, 'epsilon_insensitive'),
(0.02, 'squared_epsilon_insensitive')
]) if loss is None else loss,
penalty=_sgd_penalty(_name('penalty')) if penalty is None else penalty,
alpha=_sgd_alpha(_name('alpha')) if alpha is None else alpha,
l1_ratio=(_sgd_l1_ratio(_name('l1ratio'))
if l1_ratio is None else l1_ratio),
fit_intercept=fit_intercept,
n_iter=n_iter,
learning_rate=(_sgdc_learning_rate(_name('learning_rate'))
if learning_rate is None else learning_rate),
eta0=_sgd_eta0(_name('eta0')) if eta0 is None else eta0,
power_t=_sgd_power_t(_name('power_t')) if power_t is None else power_t,
class_weight=(_class_weight(_name('clsweight'))
if class_weight == 'choose' else class_weight),
n_jobs=n_jobs,
verbose=verbose,
random_state=_random_state(_name('rstate'), random_state),
)
return rval
class RandomForestsModel(TreeBasedModel):
@staticmethod
def build_estimator(args, train_data=None):
return RandomForestClassifier(random_state=RANDOM_STATE,
n_jobs=-1,
**args)
hp_space = {
'max_depth':
hp.pchoice(
'max_depth_enabled',
[(0.7, None),
(0.3,
1 + scope.int(hp.qlognormal('max_depth', np.log(30), 0.5, 3)))]),
'n_estimators':
scope.int(hp.qloguniform('n_estimators', np.log(9.5), np.log(300), 1)),
'min_samples_leaf':
hp.choice('min_samples_leaf_enabled', [
1,
scope.int(
hp.qloguniform('min_samples_leaf', np.log(1.5), np.log(50.5),
1))
]),
'max_features':
hp.pchoice(
'max_features_str',
[
(0.2, 'sqrt'), # most common choice.
(0.1, 'log2'), # less common choice.
(0.1, None), # all features, less common choice.
(0.6, hp.uniform('max_features_str_frac', 0., 1.))
]),
'class_weight':
hp.pchoice('class_weight', [(0.5, None), (0.3, 'balanced'),
(0.2, 'balanced_subsample')])
}
def many_dists():
a = hp.choice('a', [0, 1, 2])
b = hp.randint('b', 10)
c = hp.uniform('c', 4, 7)
d = hp.loguniform('d', -2, 0)
e = hp.quniform('e', 0, 10, 3)
f = hp.qloguniform('f', 0, 3, 2)
g = hp.normal('g', 4, 7)
h = hp.lognormal('h', -2, 2)
i = hp.qnormal('i', 0, 10, 2)
j = hp.qlognormal('j', 0, 2, 1)
k = hp.pchoice('k', [(.1, 0), (.9, 1)])
z = a + b + c + d + e + f + g + h + i + j + k
return {'loss': scope.float(scope.log(1e-12 + z ** 2)),
'status': base.STATUS_OK}
def many_dists():
a = hp.choice("a", [0, 1, 2])
b = hp.randint("b", 10)
bb = hp.randint("bb", 12, 25)
c = hp.uniform("c", 4, 7)
d = hp.loguniform("d", -2, 0)
e = hp.quniform("e", 0, 10, 3)
f = hp.qloguniform("f", 0, 3, 2)
g = hp.normal("g", 4, 7)
h = hp.lognormal("h", -2, 2)
i = hp.qnormal("i", 0, 10, 2)
j = hp.qlognormal("j", 0, 2, 1)
k = hp.pchoice("k", [(0.1, 0), (0.9, 1)])
z = a + b + bb + c + d + e + f + g + h + i + j + k
return {"loss": scope.float(scope.log(1e-12 + z ** 2)), "status": base.STATUS_OK}
def knn_regression(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_regression', msg)
if sparse_data:
metric_args = {'metric': 'euclidean'}
else:
metric_args = hp.pchoice(
_name('metric'),
[
(0.05, {
'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_KNeighborsRegressor(
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 sgd_regression(name,
loss=None, # default - 'squared_loss'
penalty=None, # default - 'l2'
alpha=None, # default - 0.0001
l1_ratio=None, # default - 0.15, must be within [0, 1]
fit_intercept=True, # default - True
n_iter=5, # default - 5
shuffle=None, # default - False
random_state=None, # default - None
epsilon=None, # default - 0.1
learning_rate=None, # default - 'invscaling'
eta0=None, # default - 0.01
power_t=None, # default - 0.5
warm_start=False,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'sgdr', msg)
rval = scope.sklearn_SGDRegressor(
loss=hp.pchoice(_name('loss'), [
(0.25, 'squared_loss'),
(0.25, 'huber'),
(0.25, 'epsilon_insensitive'),
(0.25, 'squared_epsilon_insensitive')
]) if loss is None else loss,
penalty=_sgd_penalty(_name('penalty')) if penalty is None else penalty,
alpha=_sgd_alpha(_name('alpha')) if alpha is None else alpha,
l1_ratio=(_sgd_l1_ratio(_name('l1ratio'))
if l1_ratio is None else l1_ratio),
fit_intercept=fit_intercept,
n_iter=n_iter,
# For regression, use the SVM epsilon instead of the SGD one.
epsilon=_svm_epsilon(_name('epsilon')) if epsilon is None else epsilon,
learning_rate=(_sgdr_learning_rate(_name('learning_rate'))
if learning_rate is None else learning_rate),
eta0=_sgd_eta0(_name('eta0')) if eta0 is None else eta0,
power_t=_sgd_power_t(_name('power_t')) if power_t is None else power_t,
verbose=verbose,
random_state=_random_state(_name('rstate'), random_state),
)
return rval
def sgd_regression(name,
loss=None, # default - 'squared_loss'
penalty=None, # default - 'l2'
alpha=None, # default - 0.0001
l1_ratio=None, # default - 0.15, must be within [0, 1]
fit_intercept=True, # default - True
n_iter=5, # default - 5
shuffle=None, # default - False
random_state=None, # default - None
epsilon=None, # default - 0.1
learning_rate=None, # default - 'invscaling'
eta0=None, # default - 0.01
power_t=None, # default - 0.5
warm_start=False,
verbose=False):
def _name(msg):
return '%s.%s_%s' % (name, 'sgdr', msg)
rval = scope.sklearn_SGDRegressor(
loss=hp.pchoice(_name('loss'), [
(0.25, 'squared_loss'),
(0.25, 'huber'),
(0.25, 'epsilon_insensitive'),
(0.25, 'squared_epsilon_insensitive')
]) if loss is None else loss,
penalty=_sgd_penalty(_name('penalty')) if penalty is None else penalty,
alpha=_sgd_alpha(_name('alpha')) if alpha is None else alpha,
l1_ratio=(_sgd_l1_ratio(_name('l1ratio'))
if l1_ratio is None else l1_ratio),
fit_intercept=fit_intercept,
n_iter=n_iter,
# For regression, use the SVM epsilon instead of the SGD one.
epsilon=_svm_epsilon(_name('epsilon')) if epsilon is None else epsilon,
learning_rate=(_sgdr_learning_rate(_name('learning_rate'))
if learning_rate is None else learning_rate),
eta0=_sgd_eta0(_name('eta0')) if eta0 is None else eta0,
power_t=_sgd_power_t(_name('power_t')) if power_t is None else power_t,
verbose=verbose,
random_state=_random_state(_name('rstate'), random_state),
)
return rval
def test_basic(self):
space = hp.pchoice('naive_type',
[(.14, 'gaussian'),
(.02, 'multinomial'),
(.84, 'bernoulli')])
a, b, c = 0, 0, 0
rng = np.random.RandomState(123)
for i in range(0, 1000):
nesto = hyperopt.pyll.stochastic.sample(space, rng=rng)
if nesto == 'gaussian':
a += 1
elif nesto == 'multinomial':
b += 1
elif nesto == 'bernoulli':
c += 1
print((a, b, c))
assert a + b + c == 1000
assert 120 < a < 160
assert 0 < b < 40
assert 800 < c < 900
def nystrom(name, n_components=None, kernel=None, max_components=np.Inf, copy=True):
def _name(msg):
return '%s.%s_%s' % (name, 'nystrom', msg)
rval = scope.sklearn_Nystrom(
n_components=4 * scope.int(
hp.qloguniform(
name + '.n_components',
low=np.log(0.51),
high=np.log(min(max_components / 4, 30.5)),
q=1.0)) if n_components is None else n_components,
kernel=hp.pchoice(
_name('kernel'),
[ (0.35, 'sigmoid'),
(0.35, 'rbf'),
(0.30, 'poly')]) if kernel is None else kernel,
gamma=_svc_gamma('gamma'),
coef0=hp.uniform(_name('coef0'), 0.0, 1.0)
)
return rval
def _knn_metric_p(name, sparse_data=False, metric=None, p=None):
if sparse_data:
return ('euclidean', 2)
elif metric == 'euclidean':
return (metric, 2)
elif metric == 'manhattan':
return (metric, 1)
elif metric == 'chebyshev':
return (metric, 0)
elif metric == 'minkowski':
assert p is not None
return (metric, p)
elif metric is None:
return hp.pchoice(name, [
(0.55, ('euclidean', 2)),
(0.15, ('manhattan', 1)),
(0.15, ('chebyshev', 0)),
(0.15, ('minkowski', _knn_p(name + '.p'))),
])
else:
return (metric, p) # undefined, simply return user input.
def knn_regression(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_regression', msg)
if sparse_data:
metric_args = { 'metric':'euclidean' }
else:
metric_args = hp.pchoice(_name('metric'), [
(0.05, { '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_KNeighborsRegressor(
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
'cd_epochs_2': hp.qloguniform('cd_epochs_2', np.log(1), np.log(1500), q=1),
'sample_v0s_2': hp.choice('sample_v0s_2', [0, 1]), # [False, True]
'lr_anneal_2': hp.qloguniform('lr_anneal_2',
np.log(10), np.log(10000), q=1)})
space = {'preproc': hp.choice('preproc', [{
'preproc': 0 }, { #'no'
'preproc': 1, #'zca'
'pca_energy': hp.uniform('pca_energy', .5, 1),
}]),
# Sqash is fixed at logistic, no need for a hyperparameter
'iseed': hp.choice('iseed', [0, 1, 2, 3]), # [5, 6, 7, 8]
# This was called nnet_features
'depth': hp.pchoice('depth', [(0.5, 0),
(0.25, layer1),
(0.125, layer2),
(0.125, layer3)]), # This is fine
#'nnet_features': hp.pchoice('nnet_features', [(.5, 0), (.25, 1),(.125, 2), (.125, 3)]),
'batch_size': hp.choice('batch_size', [0, 1]), # [20, 100]
'lr': hp.lognormal('lr', np.log(.01), 3.),
'lr_anneal_start': hp.qloguniform('lr_anneal_start', np.log(100),
np.log(10000),
q=1),
'l2_penalty': hp.choice('l2_penalty', [{
'l2_penalty' : 0}, { # Zero
'l2_penalty' : 1, # notzero
'l2_penalty_nz': hp.lognormal('l2_penalty_nz', np.log(1.0e-6), 2.)}])
}
def preproc_space(
sup_min_epochs=300,
sup_max_epochs=2000,
max_seconds=60 * 60,
):
"""
Return a hyperopt-compatible pyll expression for a trained neural network.
The trained neural network will have 0, 1, 2, or 3 hidden layers, and may
have an affine first layer that does column normalization or PCA
pre-processing.
Each layer of the network will be pre-trained by some amount of
contrastive divergence before being fine-tuning by SGD.
The training program is built using stub literals `pyll_stubs.train_task`
and `pyll_stubs.valid_task`. When evaluating the pyll program, these
literals must be replaced with skdata Task objects with
`vector_classification` semantics. See `skdata_learning_algo.py` for how
to use the `use_obj_for_literal_in_memo` function to swap live Task
objects in for these stubs.
The search space described by this function corresponds to the DBN model
used in [1] and [2].
"""
train_task_x = scope.getattr(pyll_stubs.train_task, 'x')
nnet0 = scope.NNet([], n_out=scope.getattr(train_task_x, 'shape')[1])
nnet1 = hp.choice('preproc',
[
nnet0, # -- raw data
scope.nnet_add_layers( # -- ZCA of data
nnet0,
scope.zca_layer(
train_task_x,
energy=hp.uniform('pca_energy', .5, 1),
eps=1e-14,
)),
])
param_seed = hp.choice('iseed', [5, 6, 7, 8])
time_limit = scope.time() + max_seconds
nnets = [nnet1]
nnet_i_pt = nnet1
for ii, cd_epochs_max in enumerate([3000, 2000, 1500]):
layer = scope.random_sigmoid_layer(
# -- hack to get different seeds for dif't layers
seed=param_seed + cd_epochs_max,
n_in=scope.getattr(nnet_i_pt, 'n_out'),
n_out=hp.qloguniform('n_hid_%i' % ii,
np.log(2**7),
np.log(2**12),
q=16),
dist=hp.choice('W_idist_%i' % ii, ['uniform', 'normal']),
scale_heuristic=hp.choice(
'W_ialgo_%i' % ii, [
('old', hp.lognormal('W_imult_%i' % ii, 0, 1)),
('Glorot',)]),
squash='logistic',
)
nnet_i_raw = scope.nnet_add_layer(nnet_i_pt, layer)
# -- repeatedly calculating lower-layers wastes some CPU, but keeps
# memory usage much more stable across jobs (good for cluster)
# and the wasted CPU is not so much overall.
nnet_i_pt = scope.nnet_pretrain_top_layer_cd(
nnet_i_raw,
train_task_x,
lr=hp.lognormal('cd_lr_%i' % ii, np.log(.01), 2),
seed=1 + hp.randint('cd_seed_%i' % ii, 10),
n_epochs=hp.qloguniform('cd_epochs_%i' % ii,
np.log(1),
np.log(cd_epochs_max),
q=1),
# -- for whatever reason (?), this was fixed at 100
batchsize=100,
sample_v0s=hp.choice('sample_v0s_%i' % ii, [False, True]),
lr_anneal_start=hp.qloguniform('lr_anneal_%i' % ii,
np.log(10),
np.log(10000),
q=1),
time_limit=time_limit,
)
nnets.append(nnet_i_pt)
# this prior is not what I would do now, but it is what I did then...
nnet_features = hp.pchoice(
'depth',
[(.5, nnets[0]),
(.25, nnets[1]),
(.125, nnets[2]),
(.125, nnets[3])])
sup_nnet = scope.nnet_add_layer(
nnet_features,
scope.zero_softmax_layer(
n_in=scope.getattr(nnet_features, 'n_out'),
n_out=scope.getattr(pyll_stubs.train_task, 'n_classes')))
nnet4, report = scope.nnet_sgd_finetune_classifier(
sup_nnet,
pyll_stubs.train_task,
pyll_stubs.valid_task,
fixed_nnet=nnet1,
max_epochs=sup_max_epochs,
min_epochs=sup_min_epochs,
batch_size=hp.choice('batch_size', [20, 100]),
lr=hp.lognormal('lr', np.log(.01), 3.),
lr_anneal_start=hp.qloguniform(
'lr_anneal_start',
np.log(100),
np.log(10000),
q=1),
l2_penalty=hp.choice('l2_penalty', [
0,
hp.lognormal('l2_penalty_nz', np.log(1.0e-6), 2.)]),
time_limit=time_limit,
)
return nnet4, report
import time
import argparse
# remove headers, footers, and citations from 20 newsgroups data
REMOVE_HEADERS=False
# use the default settings up TfidfVectorizer before doing optimization
PRE_VECTORIZE=False
# Record the test score for every evaluation point
#TEST_ALL_EVALS=True
suppress_output = False
optional_pca = hp.pchoice('preproc', [
( 0.8, [pca('pca')]),
( 0.1, [min_max_scaler('mms')]),
( 0.1, [] ) ])
def score( y1, y2 ):
length = len( y1 )
correct = 0.0
for i in xrange(length):
if y1[i] == y2[i]:
correct += 1.0
return correct / length
# TODO: currently does not use seed for anything
def sklearn_newsgroups( classifier, algorithm, max_evals=100, seed=1,
filename='none', preproc=[], loss=None ):
global suppress_output
from hyperopt import hp
from hyperopt.pyll import scope
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier as DTree
if __name__ == '__main__':
scope.define(GaussianNB)
scope.define(SVC)
C = hp.lognormal('svm_C', 0, 1)
space = hp.pchoice('estimator', [
(0.1, scope.GaussianNB()),
(0.2, scope.SVC(C=C, kernel='linear'))])
def sgd(name,
loss=None, # default - 'hinge'
penalty=None, # default - 'l2'
alpha=None, # default - 0.0001
l1_ratio=None, # default - 0.15, must be within [0, 1]
fit_intercept=None, # default - True
n_iter=None, # default - 5
shuffle=None, # default - False
random_state=None, # default - None
epsilon=None,
n_jobs=1, # default - 1 (-1 means all CPUs)
learning_rate=None, # default - 'invscaling'
eta0=None, # default - 0.01
power_t=None, # default - 0.5
class_weight=None,
warm_start=False,
verbose=False,
):
def _name(msg):
return '%s.%s_%s' % (name, 'sgd', msg)
rval = scope.sklearn_SGDClassifier(
loss=hp.pchoice(
_name('loss'),
[ #(0.00, 'hinge'), # no probability
(0.5, 'log'),
(0.5, 'modified_huber'),
#(0.00, 'squared_hinge'), # no probability
#(0.05, 'perceptron'),
#(0.05, 'squared_loss'),
#(0.05, 'huber'),
#(0.03, 'epsilon_insensitive'),
#(0.02, 'squared_epsilon_insensitive'),
]) if loss is None else loss,
penalty=hp.pchoice(
_name('penalty'),
[ (0.60, 'l2'),
(0.15, 'l1'),
(0.25, 'elasticnet') ]) if penalty is None else penalty,
alpha=hp.loguniform(
_name('alpha'),
np.log(1e-5),
np.log(1)) if alpha is None else alpha,
l1_ratio=hp.uniform(
_name('l1_ratio'),
0, 1) if l1_ratio is None else l1_ratio,
fit_intercept=hp.pchoice(
_name('fit_intercept'),
[ (0.8, True), (0.2, False) ]) if fit_intercept is None else fit_intercept,
learning_rate='invscaling' if learning_rate is None else learning_rate,
eta0=hp.loguniform(
_name('eta0'),
np.log(1e-5),
np.log(1e-1)) if eta0 is None else eta0,
power_t=hp.uniform(
_name('power_t'),
0, 1) if power_t is None else power_t,
n_jobs=n_jobs,
verbose=verbose,
)
return rval
def _grad_boosting_subsample(name):
return hp.pchoice(name, [
(0.2, 1.0), # default choice.
(0.8, hp.uniform(name + '.sgb', 0.5, 1.0)) # stochastic grad boosting.
])
def _sgd_penalty(name):
return hp.pchoice(name, [
(0.40, 'l2'),
(0.35, 'l1'),
(0.25, 'elasticnet')
])
def _sgdr_learning_rate(name):
return hp.pchoice(name, [
(0.50, 'invscaling'),
(0.25, 'optimal'),
(0.25, 'constant')
])
