return self
class KerasRegressor(KerasModel, base.Regressor):
def predict_one(self, x):
x = [[list(x.values())]]
return self.model.predict_on_batch(x)[0][0]
KERAS_EPS = K.epsilon()
LR = .01
OPTIMIZERS = {
'SGD': (optim.SGD(lr=LR), functools.partial(torch.optim.SGD,
lr=LR), optimizers.SGD(lr=LR)),
'Adam': (optim.Adam(lr=LR, beta_1=.9, beta_2=.999, eps=KERAS_EPS),
functools.partial(torch.optim.Adam,
lr=LR,
betas=(.9, .999),
eps=KERAS_EPS),
optimizers.Adam(lr=LR, beta_1=.9, beta_2=.999)),
'AdaDelta':
(optim.AdaDelta(rho=.95, eps=KERAS_EPS),
functools.partial(torch.optim.Adadelta, rho=.95,
eps=KERAS_EPS), optimizers.Adadelta(rho=.95)),
'AdaGrad': (optim.AdaGrad(lr=LR, eps=KERAS_EPS),
functools.partial(torch.optim.Adagrad,
lr=LR), optimizers.Adagrad(lr=LR)),
'Momentum': (optim.Momentum(lr=LR, rho=.1),
functools.partial(torch.optim.SGD, lr=LR, momentum=.1),
optimizers.SGD(lr=LR, momentum=.1))
pytest.param(
lm(optimizer=copy.deepcopy(optimizer), initializer=initializer, l2=0),
dataset,
id=f'{lm.__name__} - {optimizer} - {initializer}'
)
for lm, dataset in [
(lm.LinearRegression, datasets.TrumpApproval()),
(lm.LogisticRegression, datasets.Bananas())
]
for optimizer, initializer in itertools.product(
[
optim.AdaBound(),
optim.AdaDelta(),
optim.AdaGrad(),
optim.AdaMax(),
optim.Adam(),
optim.AMSGrad(),
# TODO: check momentum optimizers
# optim.Momentum(),
# optim.NesterovMomentum(),
optim.RMSProp(),
optim.SGD()
],
[
optim.initializers.Zeros(),
optim.initializers.Normal(mu=0, sigma=1, seed=42)
]
)
]
)
@pytest.mark.slow
beta_2 = config['beta_2']
eps = config['eps']
gamma = config['gamma']
final_lr = config['final_lr']
alpha = config['alpha']
FTRL_l1 = config['FTRL_l1']
FTRL_l2 = config['FTRL_l2']
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 = {}
def __init__(self, data_collector):
dc = data_collector
data = dc.get_data_frame()
metric = metrics.MAE()
# delete NA examples
data = data.dropna()
# shuffle data
X_y = data.sample(frac=1).reset_index(drop=True)
data = X_y[['x', 'y', 'theta']].to_dict('records')
target_1 = X_y[['sensor_1']]
target_2 = X_y[['sensor_3']]
target_3 = X_y[['sensor_5']]
target_4 = X_y[['sensor_7']]
print('constructing models')
# construct our pipeline
model_1 = Pipeline([
("scale", StandardScaler()),
("learn",
ensemble.HedgeRegressor([
linear_model.LinearRegression(optim.SGD()),
linear_model.LinearRegression(optim.RMSProp()),
linear_model.LinearRegression(optim.Adam())
]))
])
# construct our pipeline
model_2 = Pipeline([
("scale", StandardScaler()),
("learn",
ensemble.HedgeRegressor([
linear_model.LinearRegression(optim.SGD()),
linear_model.LinearRegression(optim.RMSProp()),
linear_model.LinearRegression(optim.Adam())
]))
])
# construct our pipeline
model_3 = Pipeline([
("scale", StandardScaler()),
("learn",
ensemble.HedgeRegressor([
linear_model.LinearRegression(optim.SGD()),
linear_model.LinearRegression(optim.RMSProp()),
linear_model.LinearRegression(optim.Adam())
]))
])
# construct our pipeline
model_4 = Pipeline([
("scale", StandardScaler()),
("learn",
ensemble.HedgeRegressor([
linear_model.LinearRegression(optim.SGD()),
linear_model.LinearRegression(optim.RMSProp()),
linear_model.LinearRegression(optim.Adam())
]))
])
print('start training')
for x, y_1, y_2, y_3, y_4 in zip(
data,
target_1.values,
target_2.values,
target_3.values,
target_4.values,
):
model_1, y_pred_1 = self._update_model(model_1, x, y_1)
model_2, y_pred_2 = self._update_model(model_2, x, y_2)
model_3, y_pred_3 = self._update_model(model_3, x, y_3)
model_4, y_pred_4 = self._update_model(model_4, x, y_4)
self.models = [model_1, model_2, model_3, model_4]
print('done...')
'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()))
])
}
fig, ax = plt.subplots(figsize=(10, 6))
for name, model in models.items():
print(name)