[ENH] probability distributions - boilerplate refactor

extension_templates/distributions.py

@@ -0,0 +1,379 @@
+"""Extension template for probability distributions - simple pattern."""
+# todo: write an informative docstring for the file or module, remove the above
+# todo: add an appropriate copyright notice for your estimator
+#       estimators contributed to skpro should have the copyright notice at the top
+#       estimators of your own do not need to have permissive or BSD-3 copyright
+
+# todo: uncomment the following line, enter authors' GitHub IDs
+# __author__ = [authorGitHubID, anotherAuthorGitHubID]
+
+from skpro.distributions.base import BaseDistribution
+
+# todo: add any necessary imports here - no soft dependency imports
+
+# todo: for imports of skpro soft dependencies:
+# make sure to fill in the "python_dependencies" tag with the package import name
+# import soft dependencies only inside methods of the class, not at the top of the file
+
+
+# todo: change class name and write docstring
+class ClassName(BaseDistribution):
+    """Custom probability distribution. todo: write docstring.
+
+    todo: describe your custom probability distribution here
+
+    Parameters
+    ----------
+    parama : float or np.ndarray
+        descriptive explanation of parama
+    paramb : float or np.ndarray, optional (default='default')
+        descriptive explanation of paramb
+    """
+
+    # todo: fill out estimator tags here
+    #  tags are inherited from parent class if they are not set
+    # tags inherited from base are "safe defaults" which can usually be left as-is
+    _tags = {
+        # packaging info
+        # --------------
+        "authors": ["author1", "author2"],  # authors, GitHub handles
+        "maintainers": ["maintainer1", "maintainer2"],  # maintainers, GitHub handles
+        # author = significant contribution to code at some point
+        # maintainer = algorithm maintainer role, "owner"
+        # specify one or multiple authors and maintainers, only for skpro contribution
+        # remove maintainer tag if maintained by skpro/sktim core team
+        #
+        "python_version": None,  # PEP 440 python version specifier to limit versions
+        "python_dependencies": None,  # PEP 440 python dependencies specifier,
+        # e.g., "numba>0.53", or a list, e.g., ["numba>0.53", "numpy>=1.19.0"]
+        # delete if no python dependencies or version limitations
+        #
+        # estimator tags
+        # --------------
+        "distr:measuretype": "continuous",  # one of "discrete", "continuous", "mixed"
+        # these tags should correspond to which methods are numerically exact
+        # and which are approximations, e.g., using Monte Carlo
+        "capabilities:approx": ["pdfnorm", "energy"],
+        "capabilities:exact": ["mean", "var", "pdf", "log_pdf", "cdf", "ppf"],
+        # leave the broadcast_init tag as-is, this tag exists for compatibility with
+        # distributions deviating from assumptions on input parameters, e.g., Empirical
+        "broadcast_init": "on",
+    }
+
+    # todo: fill init
+    # params should be written to self and never changed
+    # super call must not be removed, change class name
+    # parameter checks can go after super call
+    def __init__(self, param1, param2="param2default", index=None, columns=None):
+        # all distributions must have index and columns arg with None defaults
+        # this is to ensure pandas-like behaviour
+
+        # todo: write any hyper-parameters and components to self
+        self.param1 = param1
+        self.param2 = param2
+
+        # leave this as is
+        super().__init__(index=index, columns=columns)
+
+        # 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: implement as many of the following methods as possible
+    # if not implemented, the base class will try to fill it in
+    # from the other implemented methods
+    # at least _ppf, or sample should be implemented for the distribution to be usable
+    # if _ppf is implemented, sample does not need to be implemented (uses ppf sampling)
+
+    # todo: consider implementing
+    # if not implemented, uses Monte Carlo estimate via sample
+    def _mean(self):
+        """Return expected value of the distribution.
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            expected value of distribution (entry-wise)
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing
+    # if not implemented, uses Monte Carlo estimate via sample
+    def _var(self):
+        r"""Return element/entry-wise variance of the distribution.
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            variance of the distribution (entry-wise)
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing - only for continuous or mixed distributions
+    # at least one of _pdf and _log_pdf should be implemented
+    # if not implemented, returns exp of log_pdf
+    def _pdf(self, x):
+        """Probability density function.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to evaluate the pdf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            pdf values at the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing - only for continuous or mixed distributions
+    # at least one of _pdf and _log_pdf should be implemented
+    # if not implemented, returns log of pdf
+    def _log_pdf(self, x):
+        """Logarithmic probability density function.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to evaluate the pdf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            log pdf values at the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing - only for discrete or mixed distributions
+    # at least one of _pmf and _log_pmf should be implemented
+    # if not implemented, returns exp of log_pmf
+    def _pmf(self, x):
+        """Probability mass function.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to evaluate the pmf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            pmf values at the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing - only for discrete or mixed distributions
+    # at least one of _pmf and _log_pmf should be implemented
+    # if not implemented, returns log of pmf
+    def _log_pmf(self, x):
+        """Logarithmic probability mass function.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to evaluate the pmf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            log pmf values at the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing
+    # at least one of _ppf and sample must be implemented
+    # if not implemented, uses Monte Carlo estimate based on sample
+    def _cdf(self, x):
+        """Cumulative distribution function.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to evaluate the cdf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            cdf values at the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing
+    # at least one of _ppf and sample must be implemented
+    # if not implemented, uses bisection method on cdf
+    def _ppf(self, p):
+        """Quantile function = percent point function = inverse cdf.
+
+        Parameters
+        ----------
+        p : 2D np.ndarray, same shape as ``self``
+            values to evaluate the ppf at
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            ppf values at the given points
+        """
+        pass
+
+    # todo: consider implementing
+    # if not implemented, uses Monte Carlo estimate via sample
+    def _energy_self(self):
+        r"""Energy of self, w.r.t. self.
+
+        :math:`\mathbb{E}[|X-Y|]`, where :math:`X, Y` are i.i.d. copies of self.
+
+        Private method, to be implemented by subclasses.
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            energy values w.r.t. the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing
+    # if not implemented, uses Monte Carlo estimate via sample
+    def _energy_x(self, x):
+        r"""Energy of self, w.r.t. a constant frame x.
+
+        :math:`\mathbb{E}[|X-x|]`, where :math:`X` is a copy of self,
+        and :math:`x` is a constant.
+
+        Private method, to be implemented by subclasses.
+
+        Parameters
+        ----------
+        x : 2D np.ndarray, same shape as ``self``
+            values to compute energy w.r.t. to
+
+        Returns
+        -------
+        2D np.ndarray, same shape as ``self``
+            energy values w.r.t. the given points
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # todo: consider implementing
+    # at least one of _ppf and sample must be implemented
+    # if not implemented, uses _ppf for sampling (inverse cdf on uniform)
+    def sample(self, n_samples=None):
+        """Sample from the distribution.
+
+        Parameters
+        ----------
+        n_samples : int, optional, default = None
+
+        Returns
+        -------
+        if `n_samples` is `None`:
+        returns a sample that contains a single sample from `self`,
+        in `pd.DataFrame` mtype format convention, with `index` and `columns` as `self`
+        if n_samples is `int`:
+        returns a `pd.DataFrame` that contains `n_samples` i.i.d. samples from `self`,
+        in `pd-multiindex` mtype format convention, with same `columns` as `self`,
+        and `MultiIndex` that is product of `RangeIndex(n_samples)` and `self.index`
+        """
+        param1 = self._bc_params["param1"]  # returns broadcast params to x.shape
+        param2 = self._bc_params["param2"]  # returns broadcast params to x.shape
+
+        res = "do_sth_with(" + param1 + param2 + ")"  # replace this by internal logic
+        return res
+
+    # 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.
+
+        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
+        #
+        # this can, if required, use:
+        #   class properties (e.g., inherited); parent class test case
+        #   imported objects such as estimators from skpro or sklearn
+        # important: all such imports should be *inside get_test_params*, not at the top
+        #            since imports are used only at testing time
+        #
+        # 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}]
+        #
+        # 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
+        # params = {"est": value3, "parama": value4}
+        # return params

extension_templates/regression.py

@@ -319,8 +319,6 @@ def get_test_params(cls, parameter_set="default"):
         #
         # The parameter_set argument is not used for most automated, module level tests.
         #   It can be used in custom, estimator specific tests, for "special" settings.
-        #   For classification, this is also used in tests for reference settings,
-        #       such as published in benchmarking studies, or for identity testing.
         # A parameter dictionary must be returned *for all values* of parameter_set,
         #   i.e., "parameter_set not available" errors should never be raised.
         #

extension_templates/survival.py

@@ -306,8 +306,6 @@ def get_test_params(cls, parameter_set="default"):
         #
         # The parameter_set argument is not used for most automated, module level tests.
         #   It can be used in custom, estimator specific tests, for "special" settings.
-        #   For classification, this is also used in tests for reference settings,
-        #       such as published in benchmarking studies, or for identity testing.
         # A parameter dictionary must be returned *for all values* of parameter_set,
         #   i.e., "parameter_set not available" errors should never be raised.
         #