def main():
benchmark.benchmark(
get_X_y=functools.partial(stream.iter_sklearn_dataset,
datasets.load_boston()),
n=506,
get_pp=preprocessing.StandardScaler,
models=[
('creme', 'LinReg',
linear_model.LinearRegression(optimizer=optim.VanillaSGD(0.01),
l2=0.)),
('creme', 'GLM',
linear_model.GLMRegressor(optimizer=optim.VanillaSGD(0.01),
l2=0.)),
('creme', 'GLM detrend',
meta.Detrender(
linear_model.GLMRegressor(optimizer=optim.VanillaSGD(0.01),
l2=0.,
intercept_lr=0.))),
('sklearn', 'SGD',
compat.CremeRegressorWrapper(
sklearn_estimator=sk_linear_model.SGDRegressor(
learning_rate='constant',
eta0=0.01,
fit_intercept=True,
penalty='none'), )),
],
get_metric=metrics.MSE)
def main():
def add_hour(x):
x['hour'] = x['moment'].hour
return x
benchmark.benchmark(
get_X_y=datasets.fetch_bikes,
n=182470,
get_pp=lambda:
(compose.Whitelister('clouds', 'humidity', 'pressure', 'temperature',
'wind') +
(add_hour | feature_extraction.TargetAgg(by=['station', 'hour'],
how=stats.Mean())
) | preprocessing.StandardScaler()),
models=[
# ('creme', 'LinReg', linear_model.LinearRegression(
# optimizer=optim.VanillaSGD(0.01),
# l2=0.
# )),
('creme', 'GLM',
linear_model.GLMRegressor(optimizer=optim.VanillaSGD(0.01),
l2=0.)),
('creme', 'GLM',
meta.Detrender(
linear_model.GLMRegressor(optimizer=optim.VanillaSGD(0.01),
l2=0.))),
# ('sklearn', 'SGD', compat.CremeRegressorWrapper(
# sklearn_estimator=sk_linear_model.SGDRegressor(
# learning_rate='constant',
# eta0=0.01,
# fit_intercept=True,
# penalty='none'
# ),
# )),
# ('sklearn', 'SGD no intercept', compat.CremeRegressorWrapper(
# sklearn_estimator=sk_linear_model.SGDRegressor(
# learning_rate='constant',
# eta0=0.01,
# fit_intercept=False,
# penalty='none'
# ),
# )),
],
get_metric=metrics.MSE)
if (opt == "AdaBound"):
optimizer = optim.AdaBound(lr, beta_1, beta_2, eps, gamma, final_lr)
elif (opt == "AdaDelta"):
optimizer = optim.AdaDelta(rho, eps)
elif (opt == "AdaGrad"):
optimizer = optim.AdaGrad(lr, eps)
elif (opt == "Adam"):
optimizer = optim.Adam(lr, beta_1, beta_2, eps)
elif (opt == "FTRLProximal"):
optimizer = optim.FTRLProximal(alpha, beta, l1, l2)
elif (opt == "Momentum"):
optimizer = optim.Momentum(lr, rho)
elif (opt == "RMSProp"):
optimizer = optim.RMSProp(lr, rho, eps)
elif (opt == "VanillaSGD"):
optimizer = optim.VanillaSGD(lr)
elif (opt == "NesterovMomentum"):
optimizer = optim.NesterovMomentum(lr, rho)
else:
optimizer = None
output = {}
while True:
#wait request
data = input()
if (init == 0):
MNlog_reg = linear_model.SoftmaxRegression(optimizer, l2=l2)
init = 1
def main():
benchmark.benchmark(
get_X_y=datasets.fetch_electricity,
n=45312,
get_pp=preprocessing.StandardScaler,
models=[
# ('No-change', 'No-change', dummy.NoChangeClassifier()),
('creme', 'Logistic regression',
linear_model.LogisticRegression(optimizer=optim.VanillaSGD(0.05),
l2=0,
intercept_lr=0.05)),
# ('creme', 'PA-I', linear_model.PAClassifier(C=1, mode=1)),
# ('creme', 'PA-II', linear_model.PAClassifier(C=1, mode=2)),
('sklearn', 'Logistic regression',
compat.CremeClassifierWrapper(
sklearn_estimator=sk_linear_model.SGDClassifier(
loss='log',
learning_rate='constant',
eta0=0.05,
penalty='none'),
classes=[False, True])),
# ('sklearn', 'PA-I', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='hinge'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-II', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='squared_hinge'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'Logistic regression NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.SGDClassifier(
# loss='log',
# learning_rate='constant',
# eta0=0.01,
# fit_intercept=True,
# penalty='none'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-I NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='hinge',
# fit_intercept=False
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-II NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='squared_hinge',
# fit_intercept=False
# ),
# classes=[False, True]
# )),
],
get_metric=metrics.Accuracy)
preprocessing.StandardScaler(),
compat.CremeClassifierWrapper(
sklearn_estimator=PassiveAggressiveClassifier(),
classes=[False, True]
)
]),
'No-change classifier': dummy.NoChangeClassifier(),
'Passive-aggressive II': compose.Pipeline([
preprocessing.StandardScaler(),
linear_model.PAClassifier(C=1, mode=2)
]),
'Logistic regression w/ VanillaSGD': compose.Pipeline([
preprocessing.StandardScaler(),
linear_model.LogisticRegression(
optimizer=optim.VanillaSGD(
lr=optim.OptimalLR()
)
)
]),
'Logistic regression w/ Adam': compose.Pipeline([
preprocessing.StandardScaler(),
linear_model.LogisticRegression(optim.Adam(optim.OptimalLR()))
]),
'Logistic regression w/ AdaGrad': compose.Pipeline([
preprocessing.StandardScaler(),
linear_model.LogisticRegression(optim.AdaGrad(optim.OptimalLR()))
]),
'Logistic regression w/ RMSProp': compose.Pipeline([
preprocessing.StandardScaler(),
linear_model.LogisticRegression(optim.RMSProp(optim.OptimalLR()))
])
def main():
benchmark.benchmark(
get_X_y=functools.partial(stream.iter_sklearn_dataset, datasets.load_breast_cancer()),
n=569,
get_pp=preprocessing.StandardScaler,
models=[
('creme', 'Log reg', linear_model.LogisticRegression(
optimizer=optim.VanillaSGD(0.01),
l2=0,
intercept_lr=0.01
)),
('sklearn', 'SGD', compat.CremeClassifierWrapper(
sklearn_estimator=sk_linear_model.SGDClassifier(
loss='log',
learning_rate='constant',
eta0=0.01,
penalty='none'
),
classes=[False, True]
)),
('creme', 'PA-I', linear_model.PAClassifier(
C=0.01,
mode=1,
fit_intercept=True
)),
('sklearn', 'PA-I', compat.CremeClassifierWrapper(
sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
C=0.01,
loss='hinge',
fit_intercept=True
),
classes=[False, True]
)),
# ('creme', 'PA-I', linear_model.PAClassifier(C=1, mode=1)),
# ('creme', 'PA-II', linear_model.PAClassifier(C=1, mode=2)),
# ('sklearn', 'PA-I', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='hinge'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-II', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='squared_hinge'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'Logistic regression NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.SGDClassifier(
# loss='log',
# learning_rate='constant',
# eta0=0.01,
# fit_intercept=True,
# penalty='none'
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-I NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='hinge',
# fit_intercept=False
# ),
# classes=[False, True]
# )),
# ('sklearn', 'PA-II NI', compat.CremeClassifierWrapper(
# sklearn_estimator=sk_linear_model.PassiveAggressiveClassifier(
# C=1,
# loss='squared_hinge',
# fit_intercept=False
# ),
# classes=[False, True]
# )),
],
get_metric=metrics.Accuracy
)
'total_mastery_points_ratio': total_points_ratio,
'rank_ratio': rank_ratio
}
MODELS = {
'v0': (compose.FuncTransformer(process_match) | compose.TransformerUnion([
compose.Whitelister(
'champion_mastery_points_ratio',
'total_mastery_points_ratio',
'rank_ratio',
),
preprocessing.OneHotEncoder('mode', sparse=False),
preprocessing.OneHotEncoder('type', sparse=False)
]) | preprocessing.StandardScaler()
| linear_model.LinearRegression(optim.VanillaSGD(0.005)))
}
class Command(base.BaseCommand):
def handle(self, *args, **options):
print(f'Adding models with creme version {creme.__version__}')
for name, pipeline in MODELS.items():
if models.CremeModel.objects.filter(name=name).exists():
print(f'\t{name} has already been added')
continue
models.CremeModel(name=name, pipeline=pipeline).save()