FABBA

Schnellstart

from sktime.transformations.fabba import FABBA

estimator = FABBA(method: str='agg', tolerance: float=0.2, alpha: float | None=0.5, auto_digitize: bool=False, sorting: str | None='norm', k: int=2, batch_size: int=1024, scl: float=1.0, max_iter: int=2, if_partition: bool=False, partition_rate: float | None=None, num_partition: int | None=None, partition_idx: list [int ] | int | None=None, max_len: int | None=None, random_state: int | None=None, return_as_strings: bool=False, return_start_values: bool=False, alphabet_set: int | list=0, n_jobs: int=-1)

Parameter(19)

methodstr, optional (default=”agg”, {“agg”, “kmeans”, “mini-kmeans”})

clustering method to use for symbolization

tolerancefloat, optional (default=0.2, greater than 0)

tolerance for polygonal chain approximation

alphafloat, optional (default=0.5, greater than 0)

distance threshold for aggregation used only if method is “agg” and auto_digitize is False if auto_digitize is True, alpha is determined automatically

auto_digitizebool, optional (default=False)

whether to automatically determine alpha for aggregation used only if method is “agg” if auto_digitize is True, alpha is determined automatically and input alpha is ignored

sortingstr, optional (default=”norm”, {“norm”, “pca”, None})

sorting method to use for aggregation/clustering used only if method is “agg”

kint, optional (default=2, greater equal 2)

number of clusters to form, used only if method is “kmeans” or “mini-kmeans”

batch_sizeint, optional (default=1024, greater equal 1)

number of samples per batch for MiniBatchKMeans used only if method is “mini-kmeans”

max_iterint, optional (default=2, greater equal 1)

maximum number of iterations for clustering methods used only if method is “kmeans” or “mini-kmeans”

sclfloat, optional (default=1, greater equal 0)

scaling factor for length in clustering

if_parition: bool, optinal(default=False)

determines if to parition series for parallel processing used only if input is a single series uses parition_rate or num_partition or parition_idx

partition_ratefloat, optional (default=None, greater equal 0)

rate to determine number of partitions for parallel processing used only if input is a single series and if_parition is True if partition_rate is None, number of partitions is set to number of processors

num_partitionint, optional (default=None, greater equal 1)

number of partitions for parallel processing used only if input is a single series and if_parition is True if partition is None, number of partitions is set based on partition_rate

partition_idxlist(int), optional (default=None, list of int greater equal 0)

indexes to partition on for parallel processing used only if input is a single series and if_parition is True if partition_idx is None, number of partitions is set based on parition

max_lenint, optional (default=np.inf, greater equal 1 or -1)

maximum length of segments for polygonal chain approximation -1 means no limit

random_stateint, optional (default=None)

random state for clustering methods used only if method is “kmeans” or “mini-kmeans”

return_as_stringsbool, optional (default=False)

whether to return symbolized series as strings or integer labels

return_start_valuesbool, optional (default=False)

whether to return the start values of each series start value is returned as first value in transformed data necessary for inverse transform

alphabet_setint or list, optional (default=0, {0, 1} or list of strings)

alphabet set to use for symbolization if int 0 uses mixed case, if int 1 uses upper case then lower case if list uses the provided list of strings as alphabet set

n_jobsint, optional (default=-1)

number of parallel jobs to run, -1 means using all processors

Beispiele

>>> from sktime.transformations.fabba import FABBA
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = FABBA (
... n_jobs = 1,
... random_state = 42,
... return_start_values = True,
... method = "agg",
... tolerance = 0.001,
... alpha = 0.05,
... )
>>> y_transformed = transformer. fit_transform (y)
>>> y_reconstructed = transformer. inverse_transform (y_transformed)

Referenzen