def __init__(self, losses, size, reps=1000, block_size=None, method='R',
bootstrap='stationary'):
super(MCS, self).__init__()
self.losses = ensure2d(losses, 'losses')
self._losses_arr = np.asarray(self.losses)
if self._losses_arr.shape[1] < 2:
raise ValueError('losses must have at least two columns')
self.size = size
self.reps = reps
if block_size is None:
self.block_size = int(np.sqrt(losses.shape[0]))
else:
self.block_size = block_size
self.t, self.k = losses.shape
self.method = method
# Bootstrap indices since the same bootstrap should be used in the
# repeated steps
indices = np.arange(self.t)
bootstrap = bootstrap.lower().replace(' ', '_')
if bootstrap in ('stationary', 'sb'):
bootstrap = StationaryBootstrap(self.block_size, indices)
elif bootstrap in ('circular', 'cbb'):
bootstrap = CircularBlockBootstrap(self.block_size, indices)
elif bootstrap in ('moving_block', 'mbb'):
bootstrap = MovingBlockBootstrap(self.block_size, indices)
else:
raise ValueError('Unknown bootstrap:' + bootstrap)
self.bootstrap = bootstrap
self._bootsrap_indices = [] # For testing
self._model = 'MCS'
self._info = OrderedDict([('size', '{0:0.2f}'.format(self.size)),
('bootstrap', str(bootstrap)),
('ID', hex(id(self)))])
self._results_computed = False
def __init__(
self,
benchmark: ArrayLike,
models: ArrayLike,
block_size: Optional[int] = None,
reps: int = 1000,
bootstrap: str = "stationary",
studentize: bool = True,
nested: bool = False,
) -> None:
super().__init__()
self.benchmark: np.ndarray[Any, np.dtype[np.float64]] = ensure2d(
benchmark, "benchmark")
self.models: np.ndarray[Any, np.dtype[np.float64]] = ensure2d(
models, "models")
self.reps: int = reps
if block_size is None:
self.block_size = int(np.sqrt(benchmark.shape[0]))
else:
self.block_size = block_size
self.studentize: bool = studentize
self.nested: bool = nested
self._loss_diff = np.asarray(self.benchmark) - np.asarray(self.models)
self._loss_diff_var = np.empty(0)
self.t: int = self._loss_diff.shape[0]
self.k: int = self._loss_diff.shape[1]
bootstrap = bootstrap.lower().replace(" ", "_")
if bootstrap in ("circular", "cbb"):
bootstrap_inst = CircularBlockBootstrap(self.block_size,
self._loss_diff)
elif bootstrap in ("stationary", "sb"):
bootstrap_inst = StationaryBootstrap(self.block_size,
self._loss_diff)
elif bootstrap in ("moving_block", "mbb"):
bootstrap_inst = MovingBlockBootstrap(self.block_size,
self._loss_diff)
else:
raise ValueError("Unknown bootstrap:" + bootstrap)
self.bootstrap: CircularBlockBootstrap = bootstrap_inst
self._pvalues: Dict[str, float] = {}
self._simulated_vals: Optional[NDArray] = None
self._selector = np.ones(self.k, dtype=np.bool_)
self._model = "SPA"
if self.studentize:
method = "bootstrap" if self.nested else "asymptotic"
else:
method = "none"
self._info = dict([
("studentization", method),
("bootstrap", str(self.bootstrap)),
("ID", hex(id(self))),
])
def __init__(
self,
losses: ArrayLike,
size: float,
reps: int = 1000,
block_size: Optional[int] = None,
method: Literal["R", "max"] = "R",
bootstrap: Literal[
"stationary", "sb", "circular", "cbb", "moving block", "mbb"
] = "stationary",
) -> None:
super().__init__()
self.losses: np.ndarray[Any, np.dtype[np.float64]] = ensure2d(losses, "losses")
self._losses_arr = np.asarray(self.losses)
if self._losses_arr.shape[1] < 2:
raise ValueError("losses must have at least two columns")
self.size: float = size
self.reps: int = reps
if block_size is None:
self.block_size = int(np.sqrt(losses.shape[0]))
else:
self.block_size = block_size
self.t: int = losses.shape[0]
self.k: int = losses.shape[1]
self.method: Literal["R", "max"] = method
# Bootstrap indices since the same bootstrap should be used in the
# repeated steps
indices = np.arange(self.t)
bootstrap_meth = bootstrap.lower().replace(" ", "_")
if bootstrap_meth in ("circular", "cbb"):
bootstrap_inst = CircularBlockBootstrap(self.block_size, indices)
elif bootstrap_meth in ("stationary", "sb"):
bootstrap_inst = StationaryBootstrap(self.block_size, indices)
elif bootstrap_meth in ("moving_block", "mbb"):
bootstrap_inst = MovingBlockBootstrap(self.block_size, indices)
else:
raise ValueError(f"Unknown bootstrap: {bootstrap_meth}")
self.bootstrap: CircularBlockBootstrap = bootstrap_inst
self._bootstrap_indices: List[NDArray] = [] # For testing
self._model = "MCS"
self._info = dict(
[
("size", "{0:0.2f}".format(self.size)),
("bootstrap", str(bootstrap_inst)),
("ID", hex(id(self))),
]
)
self._results_computed = False
def __init__(self,
benchmark,
models,
block_size=None,
reps=1000,
bootstrap='stationary',
studentize=True,
nested=False):
super(SPA, self).__init__()
self.benchmark = ensure2d(benchmark, 'benchmark')
self.models = ensure2d(models, 'models')
self.reps = reps
if block_size is None:
self.block_size = int(np.sqrt(benchmark.shape[0]))
else:
self.block_size = block_size
self.studentize = studentize
self.nested = nested
self._loss_diff = np.asarray(self.benchmark) - np.asarray(self.models)
self._loss_diff_var = None
self.t, self.k = self._loss_diff.shape
bootstrap = bootstrap.lower().replace(' ', '_')
if bootstrap in ('stationary', 'sb'):
bootstrap = StationaryBootstrap(self.block_size, self._loss_diff)
elif bootstrap in ('circular', 'cbb'):
bootstrap = CircularBlockBootstrap(self.block_size,
self._loss_diff)
elif bootstrap in ('moving_block', 'mbb'):
bootstrap = MovingBlockBootstrap(self.block_size, self._loss_diff)
else:
raise ValueError('Unknown bootstrap:' + bootstrap)
self.bootstrap = bootstrap
self._pvalues = None
self._simulated_vals = None
self._selector = np.ones(self.k, dtype=np.bool)
self._model = 'SPA'
if self.studentize:
method = 'bootstrap' if self.nested else 'asymptotic'
else:
method = 'none'
self._info = OrderedDict([('studentization', method),
('bootstrap', str(self.bootstrap)),
('ID', hex(id(self)))])