[ENH] Beta Distribution (#298)

Towards #22

This PR implements a Beta distribution based on the Scipy Adapter

docs/source/api_reference/distributions.rst

@@ -35,6 +35,7 @@ Continuous support
     :toctree: auto_generated/
     :template: class.rst
 
+    Beta
     ChiSquared
     Fisk
     Laplace

skpro/distributions/__init__.py

@@ -3,6 +3,7 @@
 # adapted from sktime
 
 __all__ = [
+    "Beta",
     "ChiSquared",
     "Delta",
     "Empirical",
@@ -22,6 +23,7 @@
     "Weibull",
 ]
 
+from skpro.distributions.beta import Beta
 from skpro.distributions.chi_squared import ChiSquared
 from skpro.distributions.delta import Delta
 from skpro.distributions.empirical import Empirical

skpro/distributions/beta.py

@@ -0,0 +1,73 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Beta probability distribution."""
+
+__author__ = ["malikrafsan"]
+
+import pandas as pd
+from scipy.stats import beta, rv_continuous
+
+from skpro.distributions.adapters.scipy import _ScipyAdapter
+
+
+class Beta(_ScipyAdapter):
+    r"""Beta distribution.
+
+    The Beta distribution is parametrized by two shape parameters :math:`\alpha`
+    and :math:`\beta`, such that the probability density function (PDF) is given by:
+
+    .. math:: f(x) = \frac{x^{\alpha-1}(1-x)^{\beta-1}}{B(\alpha, \beta)}
+
+    where :math:`B(\alpha, \beta)` is the beta function. The beta function
+    is a normalization constant to ensure that the total probability is 1.
+
+    Parameters
+    ----------
+    alpha : float or array of float (1D or 2D), must be positive
+    beta : float or array of float (1D or 2D), must be positive
+    index : pd.Index, optional, default = RangeIndex
+    columns : pd.Index, optional, default = RangeIndex
+
+    Example
+    -------
+    >>> from skpro.distributions.beta import Beta
+
+    >>> d = Beta(beta=[[1, 1], [2, 3], [4, 5]], alpha=2)
+    """
+
+    _tags = {
+        "capabilities:approx": ["energy", "pdfnorm"],
+        "capabilities:exact": ["mean", "var", "pdf", "log_pdf", "cdf", "ppf"],
+        "distr:measuretype": "continuous",
+        "broadcast_init": "on",
+    }
+
+    def __init__(self, alpha, beta, index=None, columns=None):
+        self.alpha = alpha
+        self.beta = beta
+
+        super().__init__(index=index, columns=columns)
+
+    def _get_scipy_object(self) -> rv_continuous:
+        return beta
+
+    def _get_scipy_param(self):
+        alpha = self._bc_params["alpha"]
+        beta = self._bc_params["beta"]
+
+        return [], {"a": alpha, "b": beta}
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator."""
+        # array case examples
+        params1 = {"alpha": [[1, 1], [2, 3], [4, 5]], "beta": 3}
+        params2 = {
+            "alpha": 2,
+            "beta": 3,
+            "index": pd.Index([1, 2, 5]),
+            "columns": pd.Index(["a", "b"]),
+        }
+        # scalar case examples
+        params3 = {"alpha": 1.5, "beta": 2.1}
+
+        return [params1, params2, params3]