[ENH] Gamma Distribution (#355)

Implements Gamma distribution. Towards #22

docs/source/api_reference/distributions.rst

@@ -39,6 +39,7 @@ Continuous support
     ChiSquared
     Exponential
     Fisk
+    Gamma
     Laplace
     Logistic
     Normal

skpro/distributions/__init__.py

@@ -10,6 +10,7 @@
     "Empirical",
     "Exponential",
     "Fisk",
+    "Gamma",
     "IID",
     "Laplace",
     "Logistic",
@@ -34,6 +35,7 @@
 from skpro.distributions.empirical import Empirical
 from skpro.distributions.exponential import Exponential
 from skpro.distributions.fisk import Fisk
+from skpro.distributions.gamma import Gamma
 from skpro.distributions.laplace import Laplace
 from skpro.distributions.logistic import Logistic
 from skpro.distributions.lognormal import LogNormal

skpro/distributions/gamma.py

@@ -0,0 +1,78 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Exponential probability distribution."""
+
+__author__ = ["ShreeshaM07"]
+
+import pandas as pd
+from scipy.stats import gamma, rv_continuous
+
+from skpro.distributions.adapters.scipy import _ScipyAdapter
+
+
+class Gamma(_ScipyAdapter):
+    r"""Gamma Distribution.
+
+    The Gamma Distribution is parameterized by shape :math:`\alpha` and
+    rate :math:`\beta`, such that the pdf is
+
+    .. math:: f(x) = \frac{x^{\alpha-1}\exp\left(-\beta x\right) \beta^{\alpha}}{\tau(\alpha)}
+
+    where :math:`\tau(\alpha)` is the Gamma function.
+    For all positive integers, :math:`\tau(\alpha) = (\alpha-1)!`.
+
+    Parameters
+    ----------
+    alpha : float or array of float (1D or 2D)
+        It represents the shape parameter.
+    beta : float or array of float (1D or 2D)
+        It represents the rate parameter which is also
+        inverse of the scale parameter.
+    index : pd.Index, optional, default = RangeIndex
+    columns : pd.Index, optional, default = RangeIndex
+
+    Example
+    -------
+    >>> from skpro.distributions.gamma import Gamma
+
+    >>> d = Gamma(beta=[[1, 1], [2, 3], [4, 5]], alpha=2)
+    """  # noqa: E501
+
+    _tags = {
+        "capabilities:approx": ["energy", "pdfnorm"],
+        "capabilities:exact": ["mean", "var", "pdf", "log_pdf", "cdf", "ppf"],
+        "distr:measuretype": "continuous",
+        "distr:paramtype": "parametric",
+        "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 gamma
+
+    def _get_scipy_param(self):
+        alpha = self._bc_params["alpha"]
+        beta = self._bc_params["beta"]
+        scale = 1 / beta
+
+        return [], {"a": alpha, "scale": scale}
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator."""
+        # array case examples
+        params1 = {"alpha": [6, 2.5], "beta": [[1, 1], [2, 3], [4, 5]]}
+        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]