def yield_datasets(model):
from creme import base
from creme import datasets
from creme import stream
from sklearn import datasets as sk_datasets
model = guess_model(model)
if isinstance(model, (base.BinaryClassifier, base.MultiClassifier)):
yield datasets.Phishing()
if isinstance(model, base.MultiClassifier):
yield datasets.ImageSegments().take(500)
if isinstance(model, base.Regressor):
yield datasets.TrumpApproval()
if isinstance(model, base.MultiOutputRegressor):
yield stream.iter_sklearn_dataset(sk_datasets.load_linnerud())
if isinstance(model, base.MultiOutputClassifier):
yeast = stream.iter_sklearn_dataset(sk_datasets.fetch_openml('yeast', version=4))
yield itertools.islice(yeast, 100)
def yield_datasets(model):
from creme import base
from creme import datasets
from creme import stream
from sklearn import datasets as sk_datasets
model = guess_model(model)
if isinstance(model, (base.BinaryClassifier, base.MultiClassifier)):
yield datasets.Phishing()
if isinstance(model, base.MultiClassifier):
yield datasets.ImageSegments().take(500)
if isinstance(model, base.Regressor):
yield datasets.TrumpApproval()
if isinstance(model, base.MultiOutputRegressor):
yield stream.iter_sklearn_dataset(sk_datasets.load_linnerud())
if isinstance(model, base.MultiOutputClassifier):
yield datasets.Music()
def yield_datasets(model):
from creme import base
from creme import compose
from creme import datasets
from creme import preprocessing
from creme import stream
from sklearn import datasets as sk_datasets
model = guess_model(model)
# Classification
if isinstance(model, (base.BinaryClassifier, base.MultiClassifier)):
yield datasets.Phishing()
# Multi-class classification
if isinstance(model, base.MultiClassifier):
yield datasets.ImageSegments().take(500)
# Regression
if isinstance(model, base.Regressor):
yield datasets.TrumpApproval()
# Multi-output regression
if isinstance(model, base.MultiOutputRegressor):
# 1
yield stream.iter_sklearn_dataset(sk_datasets.load_linnerud())
# 2
class SolarFlare:
"""One-hot encoded version of `datasets.SolarFlare`."""
def __iter__(self):
oh = (compose.SelectType(str) |
preprocessing.OneHotEncoder()) + compose.SelectType(int)
for x, y in datasets.SolarFlare():
yield oh.transform_one(x), y
yield SolarFlare()
# Multi-output classification
if isinstance(model, base.MultiOutputClassifier):
yield datasets.Music()
def yield_datasets(model):
from creme import compose
from creme import datasets
from creme import preprocessing
from creme import stream
from creme import utils
from sklearn import datasets as sk_datasets
# Classification
if utils.inspect.isclassifier(model):
yield datasets.Phishing()
# Multi-class classification
if model._multiclass:
yield datasets.ImageSegments().take(500)
# Regression
if utils.inspect.isregressor(model):
yield datasets.TrumpApproval()
# Multi-output regression
if utils.inspect.ismoregressor(model):
# 1
yield stream.iter_sklearn_dataset(sk_datasets.load_linnerud())
# 2
class SolarFlare:
"""One-hot encoded version of `datasets.SolarFlare`."""
def __iter__(self):
oh = (compose.SelectType(str) | preprocessing.OneHotEncoder()) + compose.SelectType(int)
for x, y in datasets.SolarFlare():
yield oh.transform_one(x), y
yield SolarFlare()
# Multi-output classification
if utils.inspect.ismoclassifier(model):
yield datasets.Music()
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))
}
def add_intercept(x):
return {**x, 'intercept': 1.}
for name, (creme_optim, torch_optim, keras_optim) in OPTIMIZERS.items():
X_y = stream.iter_sklearn_dataset(dataset=datasets.load_boston(),
shuffle=True,
random_state=42)
n_features = 13
creme_lin_reg = (compose.FuncTransformer(add_intercept)
| linear_model.LinearRegression(
optimizer=creme_optim, l2=0, intercept_lr=0))
torch_model = PyTorchNet(n_features=n_features)
torch_lin_reg = PyTorchRegressor(network=torch_model,
loss_fn=torch.nn.MSELoss(),
optimizer=torch_optim(
torch_model.parameters()))
inputs = layers.Input(shape=(n_features, ))
predictions = layers.Dense(1,