"""Interface for MrSQM classifier."""
__authors__ = ["lnthach", "heerme"] # fkiraly for the wrapper
from sktime.classification._delegate import _DelegatedClassifier
[文档]class MrSQM(_DelegatedClassifier):
"""MrSQM = Multiple Representations Sequence Miner.
Direct Interface to MrSQMClassifier from mrsqm.
Note: mrsqm itself is copyleft (GPL3). This interface is permissive license (BSD3).
MrSQM is an efficient time series classifier utilizing symbolic representations of
time series. MrSQM implements four different feature selection strategies =
(R,S,RS,SR) that can quickly select subsequences from multiple symbolic
representations of time series data.
Parameters
----------
strat : str, one of 'R','S','SR', or 'RS', default="RS"
feature selection strategy. By default set to 'RS'.
R and S are single-stage filters while RS and SR are two-stage filters.
features_per_rep : int, default=500
(maximum) number of features selected per representation.
selection_per_rep : int, default=2000
(maximum) number of candidate features selected per representation.
Only applied in two stages strategies (RS and SR), otherwise ignored.
nsax : int, default=1
number of representations produced by sax transformation.
nsfa : int, default=0
number of representations produced by sfa transformation.
custom_config : dict, default=None
customized parameters for the symbolic transformation.
random_state : int, default=None.
random seed for the classifier.
sfa_norm : bool, default=True.
whether to apply time series normalisation (standardisation).
References
----------
.. [1] Thach Le Nguyen and Georgiana Ifrim.
"MrSQM: Fast Time Series Classification with Symbolic Representations
and Efficient Sequence Mining" arXiv preprint arXiv:2109.01036 (2021).
.. [2] Thach Le Nguyen and Georgiana Ifrim.
"Fast Time Series Classification with Random Symbolic Subsequences".
AALTD 2022.
"""
_tags = {
# packaging info
# --------------
"authors": ["lnthach", "heerme", "fkiraly"],
"maintainers": ["lnthach", "heerme", "fkiraly"],
"python_dependencies": "mrsqm",
"requires_cython": True,
# estimator type
# --------------
"X_inner_mtype": "nested_univ",
}
def __init__(
self,
strat="RS",
features_per_rep=500,
selection_per_rep=2000,
nsax=1,
nsfa=0,
custom_config=None,
random_state=None,
sfa_norm=True,
):
self.strat = strat
self.features_per_rep = features_per_rep
self.selection_per_rep = selection_per_rep
self.nsax = nsax
self.nsfa = nsfa
self.custom_config = custom_config
self.random_state = random_state
self.sfa_norm = sfa_norm
super().__init__()
# construct the delegate - direct delegation to MrSQMClassifier
from mrsqm import MrSQMClassifier
kwargs = self.get_params(deep=False)
self.estimator_ = MrSQMClassifier(**kwargs)
# temporary workaround - delegate is not sktime interface compliant,
# does not implement get_fitted_params
# see https://github.com/mlgig/mrsqm/issues/7
def _get_fitted_params(self):
"""Get fitted parameters.
private _get_fitted_params, called from get_fitted_params
State required:
Requires state to be "fitted".
Returns
-------
fitted_params : dict with str keys
fitted parameters, keyed by names of fitted parameter
"""
return {}
[文档] @classmethod
def get_test_params(cls, parameter_set="default"):
"""Return testing parameter settings for the estimator.
Parameters
----------
parameter_set : str, default="default"
Name of the set of test parameters to return, for use in tests. If no
special parameters are defined for a value, will return ``"default"`` set.
For classifiers, a "default" set of parameters should be provided for
general testing, and a "results_comparison" set for comparing against
previously recorded results if the general set does not produce suitable
probabilities to compare against.
Returns
-------
params : dict or list of dict, default={}
Parameters to create testing instances of the class.
Each dict are parameters to construct an "interesting" test instance, i.e.,
``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
instance.
``create_test_instance`` uses the first (or only) dictionary in ``params``.
"""
params1 = {}
params2 = {
"strat": "SR",
"features_per_rep": 200,
"selection_per_rep": 1000,
"nsax": 2,
"nsfa": 1,
"sfa_norm": False,
}
return [params1, params2]
