def test_pipeline_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) | b,
compose.FuncTransformer(a) | ('b', b),
compose.FuncTransformer(a) | ('b', compose.FuncTransformer(b)),
a | compose.FuncTransformer(b), ('a', a) | compose.FuncTransformer(b),
('a', compose.FuncTransformer(a)) | compose.FuncTransformer(b)
]
for pipeline in pipelines:
assert str(pipeline) == 'a | b'
def test_union_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) + b,
compose.FuncTransformer(a) + ('b', b),
compose.FuncTransformer(a) + ('b', compose.FuncTransformer(b)),
a + compose.FuncTransformer(b), ('a', a) + compose.FuncTransformer(b),
('a', compose.FuncTransformer(a)) + compose.FuncTransformer(b)
]
for pipeline in pipelines:
assert str(pipeline) == 'a + b'
def test_union_funcs():
def a(x):
pass
def b(x):
pass
pipelines = [
compose.FuncTransformer(a) + b,
compose.FuncTransformer(a) + ('b', b),
compose.FuncTransformer(a) + ('b', compose.FuncTransformer(b)),
a + compose.FuncTransformer(b), ('a', a) + compose.FuncTransformer(b),
('a', compose.FuncTransformer(a)) + compose.FuncTransformer(b)
]
for i, pipeline in enumerate(pipelines):
print(i, str(pipeline))
assert str(pipeline) == 'a + b'
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,
kernel_initializer='zeros',
bias_initializer='zeros')(inputs)
keras_model = models.Model(inputs=inputs, outputs=predictions)
keras_model.compile(optimizer=keras_optim, loss='mean_squared_error')
# Ranking ratio
blue_rank = safe_mean(filter(None, [get_ranking(p) for p in blue_side]))
red_rank = safe_mean(filter(None, [get_ranking(p) for p in red_side]))
rank_ratio = safe_ratio(max(blue_rank, red_rank), min(blue_rank, red_rank))
return {
'mode': match['gameMode'],
'type': match['gameType'],
'champion_mastery_points_ratio': champion_points_ratio,
'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):