[ENH] adding back MrSEQL to sktime

docs/source/api_reference/classification.rst

@@ -177,6 +177,7 @@ Shapelet-based
     :template: class.rst
 
     ShapeletTransformClassifier
+    MrSEQL
     MrSQM
 
 sklearn

pyproject.toml

@@ -125,7 +125,9 @@ all_extras_pandas2 = [
 ]
 
 cython_extras = [
+    "mrseql",
     "mrsqm; python_version < '3.11'",
+    "numba",
 ]
 
 dev = [

sktime/classification/shapelet_based/__init__.py

@@ -1,5 +1,6 @@
 """Shapelet based time series classifiers."""
-__all__ = ["MrSQM", "ShapeletTransformClassifier"]
+__all__ = ["MrSEQL", "MrSQM", "ShapeletTransformClassifier"]
 
+from sktime.classification.shapelet_based._mrseql import MrSEQL
 from sktime.classification.shapelet_based._mrsqm import MrSQM
 from sktime.classification.shapelet_based._stc import ShapeletTransformClassifier

sktime/classification/shapelet_based/_mrseql.py

@@ -0,0 +1,128 @@
+"""Interface for MrSEQL classifier."""
+
+__authors__ = ["lnthach", "heerme"]  # fkiraly for the wrapper
+
+from sktime.classification._delegate import _DelegatedClassifier
+
+
+class MrSEQL(_DelegatedClassifier):
+    """MrSEQL = Multiple Representations Sequence Learning classification model.
+
+    Direct Interface to MrSEQLClassifier from mrseql.
+    Note: mrseql itself is copyleft (GPL3). This interface is permissive license (BSD3).
+
+    MrSEQL is an efficient time series classifier utilizing symbolic representations of
+    time series, using SAX and SFA features.
+
+    Parameters
+    ----------
+    seql_mode : str, either 'clf' or 'fs' (default).
+        In the 'clf' mode, Mr-SEQL is an ensemble of SEQL models, while in the 'fs'
+        mode Mr-SEQL, trains a logistic regression model with features extracted
+        by SEQL from symbolic representations of time series.
+    symrep : str, or list or tuple of string, strings being 'sax' or 'sfa'.
+        default = "sax", i.e., only SAX features, no SFA features.
+        The symbolic representations to be used to transform the input time series.
+    custom_config : dict, optional, default=None
+        Customized parameters for the symbolic transformation.
+        If defined, symrep will be ignored.
+        (no documentation of this parameter is provided in the original mrseql code)
+
+    References
+    ----------
+    .. [1] Thach Le Nguyen, Severin Gsponer, Iulia Ilie, Martin O'Reilly,
+       Georgiana Ifrim.
+       "Interpretable Time Series Classification Using Linear Models and
+       Multi-resolution Multi-domain Symbolic Representations",
+       Data Mining and Knowledge Discovery, 2019.
+    """
+
+    _tags = {
+        "X_inner_mtype": "nested_univ",
+        "python_dependencies": "mrseql",
+        "requires_cython": True,
+    }
+
+    def __init__(
+        self,
+        seql_mode="fs",
+        symrep="sax",
+        custom_config=None,
+    ):
+        self.seql_mode = seql_mode
+        self.symrep = symrep
+        self.custom_config = custom_config
+
+        if not isinstance(symrep, (list, tuple)):
+            self._symrep = [symrep]
+        else:
+            self._symrep = symrep
+
+        if "sfa" in self._symrep:
+            self.set_tags(**{"python_dependencies": ["mrseql", "numba"]})
+
+        super().__init__()
+
+        # construct the delegate - direct delegation to MrSEQLClassifier
+        from mrseql import MrSEQLClassifier
+
+        kwargs = self.get_params(deep=False)
+        kwargs["symrep"] = self._symrep
+
+        self.estimator_ = MrSEQLClassifier(**kwargs)
+
+    # temporary workaround - delegate is not sktime interface compliant,
+    # does not implement get_fitted_params
+    # see https://github.com/mlgig/mrseql/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 {}
+
+    @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`.
+        """
+        from sktime.utils.validation._dependencies import _check_soft_dependencies
+
+        params1 = {}
+
+        if not _check_soft_dependencies("numba", severity="none"):
+            symrep = ["sax"]
+        else:
+            symrep = ["sax", "sfa"]
+
+        params2 = {
+            "seql_mode": "fs",
+            "symrep": symrep,
+        }
+
+        return [params1, params2]