# -*- coding: utf-8 -*-
"""
Extension template for series annotation.

Purpose of this implementation template:
    quick implementation of new estimators following the template
    NOT a concrete class to import! This is NOT a base class or concrete class!
    This is to be used as a "fill-in" coding template.

How to use this implementation template to implement a new estimator:
- make a copy of the template in a suitable location, give it a descriptive name.
- work through all the "todo" comments below
- fill in code for mandatory methods, and optionally for optional methods
- you can add more private methods, but do not override BaseEstimator's private methods
    an easy way to be safe is to prefix your methods with "_custom"
- change docstrings for functions and the file
- ensure interface compatibility by aeon.utils.estimator_checks.check_estimator
- once complete: use as a local library, or contribute to aeon via PR
- more details:
    https://www.aeon-toolkit.org/en/stable/developer_guide/add_estimators.html

Mandatory implements:
    fitting         - _fit(self, X, Y=None)
    annotating     - _predict(self, X)

Optional implements:
    updating        - _update(self, X, Y=None)

Testing - implement if aeon forecaster (not needed locally):
    get default parameters for test instance(s) - get_test_params()
"""

from aeon.annotation.base import BaseSeriesAnnotator

# todo: add any necessary imports here


class MySeriesAnnotator(BaseSeriesAnnotator):
    """Custom series annotator.

    todo: write docstring, describing your custom forecaster

    Parameters
    ----------
    fmt : str {"dense", "sparse"}, optional (default="dense")
        annotation output format:
        * If "sparse", a sub-series of labels for only the outliers in X is returned,
        * If "dense", a series of labels for all values in X is returned.
    labels : str {"indicator", "score"}, optional (default="indicator")
        annotation output labels:
        * If "indicator", returned values are boolean, indicating whether a value is an
        outlier,
        * If "score", returned values are floats, giving the outlier score.

    parama : int
        descriptive explanation of parama
    paramb : string, optional (default='default')
        descriptive explanation of paramb
    paramc : boolean, optional (default= whether paramb is not the default)
        descriptive explanation of paramc
    and so on

    Components
    ----------
    est : aeon.estimator, BaseEstimator descendant
        descriptive explanation of est
    est2: another estimator
        descriptive explanation of est2
    and so on
    """

    # todo: add any hyper-parameters and components to constructor
    def __init__(
        self,
        est,
        parama,
        est2=None,
        paramb="default",
        paramc=None,
        fmt="dense",
        labels="indicator",
    ):
        # estimators should precede parameters
        #  if estimators have default values, set None and initalize below

        # todo: write any hyper-parameters and components to self
        self.est = est
        self.parama = parama
        self.paramb = paramb
        self.paramc = paramc

        # todo: change "MySeriesAnnotator" to the name of the class
        super(MySeriesAnnotator, self).__init__(fmt=fmt, labels=labels)

        # todo: optional, parameter checking logic (if applicable) should happen here
        # if writes derived values to self, should *not* overwrite self.parama etc
        # instead, write to self._parama, self._newparam (starting with _)

        # todo: default estimators should have None arg defaults
        #  and be initialized here
        #  do this only with default estimators, not with parameters
        # if est2 is None:
        #     self.estimator = MyDefaultEstimator()

        # todo: if tags of estimator depend on component tags, set these here
        #  only needed if estimator is a composite
        #  tags set in the constructor apply to the object and override the class
        #
        # example 1: conditional setting of a tag
        # if est.foo == 42:
        #   self.set_tags(capability:missing_values=True)
        # example 2: cloning tags from component
        #   self.clone_tags(est2, ["enforce_index_type", "capability:missing_values"])

    # todo: implement this, mandatory
    def _fit(self, X, Y=None):
        """Fit to training data.

        core logic

        Parameters
        ----------
        X : pd.DataFrame
            training data to fit model to, time series
        Y : pd.Series, optional
            ground truth annotations for training if annotator is supervised
        Returns
        -------
        self : returns a reference to self

        State change
        ------------
        creates fitted model (attributes ending in "_")
        """

        # implement here
        # IMPORTANT: avoid side effects to y, X, fh

    # todo: implement this, mandatory
    def _predict(self, X):
        """Create annotations on test/deployment data.

        core logic

        Parameters
        ----------
        X : pd.DataFrame - data to annotate, time series

        Returns
        -------
        Y : pd.Series - annotations for sequence X
            exact format depends on annotation type
        """

        # implement here
        # IMPORTANT: avoid side effects to X, fh

    # todo: consider implementing this, optional
    # if not implementing, delete the _update method
    def _update(self, X, Y=None):
        """Update model with new data and optional ground truth annotations.

        core logic

        Parameters
        ----------
        X : pd.DataFrame
            training data to update model with, time series
        Y : pd.Series, optional
            ground truth annotations for training if annotator is supervised
        Returns
        -------
        self : returns a reference to self

        State change
        ------------
        updates fitted model (attributes ending in "_")
        """

        # implement here
        # IMPORTANT: avoid side effects to X, fh

    # todo: return default parameters, so that a test instance can be created
    #   required for automated unit and integration testing of estimator
    @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.
            There are currently no reserved values for annotators.

        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`
        """

        # todo: set the testing parameters for the estimators
        # Testing parameters can be dictionary or list of dictionaries
        # Testing parameter choice should cover internal cases well.
        #
        # this method can, if required, use:
        #   class properties (e.g., inherited); parent class test case
        #   imported objects such as estimators from aeon or sklearn
        # important: all such imports should be *inside get_test_params*, not at the top
        #            since imports are used only at testing time
        #
        # The parameter_set argument is not used for automated, module level tests.
        #   It can be used in custom, estimator specific tests, for "special" settings.
        # A parameter dictionary must be returned *for all values* of parameter_set,
        #   i.e., "parameter_set not available" errors should never be raised.
        #
        # A good parameter set should primarily satisfy two criteria,
        #   1. Chosen set of parameters should have a low testing time,
        #      ideally in the magnitude of few seconds for the entire test suite.
        #       This is vital for the cases where default values result in
        #       "big" models which not only increases test time but also
        #       run into the risk of test workers crashing.
        #   2. There should be a minimum two such parameter sets with different
        #      sets of values to ensure a wide range of code coverage is provided.
        #
        # example 1: specify params as dictionary
        # any number of params can be specified
        # params = {"est": value0, "parama": value1, "paramb": value2}
        #
        # example 2: specify params as list of dictionary
        # note: Only first dictionary will be used by create_test_instance
        # params = [{"est": value1, "parama": value2},
        #           {"est": value3, "parama": value4}]
        # return params
        #
        # example 3: parameter set depending on param_set value
        #   note: only needed if a separate parameter set is needed in tests
        # if parameter_set == "special_param_set":
        #     params = {"est": value1, "parama": value2}
        #     return params
        #
        # # "default" params - always returned except for "special_param_set" value
        # params = {"est": value3, "parama": value4}
        # return params
