[ENH] concat operation on distributions

.all-contributorsrc

@@ -140,6 +140,15 @@
         "code"
       ]
     },
+    {
+      "login": "sairevanth25",
+      "name": "Sai Revanth Gowravajhala",
+      "avatar_url": "https://avatars.githubusercontent.com/SaiRevanth25",
+      "profile": "https://github.com/SaiRevanth25",
+      "contributions": [
+        "code"
+      ]
+    },
     {
       "login": "malikrafsan",
       "name": "Malik Akbar Hashemi Rafsanjani",

skpro/distributions/__init__.py

@@ -8,6 +8,8 @@
     "Beta",
     "Binomial",
     "ChiSquared",
+    "concat",
+    "ConcatDistr",
     "Delta",
     "Empirical",
     "Exponential",
@@ -39,11 +41,13 @@
     "Weibull",
 ]
 
+from skpro.distributions._concat import ConcatDistr
 from skpro.distributions.alpha import Alpha
 from skpro.distributions.beta import Beta
 from skpro.distributions.binomial import Binomial
 from skpro.distributions.chi_squared import ChiSquared
 from skpro.distributions.compose import IID
+from skpro.distributions.concat import concat
 from skpro.distributions.delta import Delta
 from skpro.distributions.empirical import Empirical
 from skpro.distributions.exponential import Exponential

skpro/distributions/_concat.py

@@ -0,0 +1,82 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Base class for concat operation."""
+
+__author__ = ["SaiRevanth25"]
+
+import pandas as pd
+from skbase.base._meta import _MetaObjectMixin
+
+
+class ConcatDistr(_MetaObjectMixin):
+    """Concatenate the given distributions along specified axis.
+
+    Parameters
+    ----------
+    distributions : list
+        list of distributions
+    axis : {0/'index', 1/'columns'}, default 0
+        The axis to concatenate along
+    """
+
+    def __init__(self, distributions, axis=0):
+        """Initialize concat with list of distributions and axis for concatenation."""
+        self.distributions = distributions
+        self.axis = axis
+        self.distribution_names = [dist.name for dist in distributions]
+
+    def mean(self):
+        """Calculate and concatenate means for each distribution."""
+        means = [dist.mean() for dist in self.distributions]
+        concatenated = pd.concat(means, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def var(self):
+        """Calculate and concatenate variances for each distribution."""
+        variances = [dist.var() for dist in self.distributions]
+        concatenated = pd.concat(variances, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def pdf(self, x):
+        """Concatenate PDFs of the distributions for a given value of `x`."""
+        pdfs = []
+        for dist in self.distributions:
+            try:
+                pdf_values = dist.pdf(x)
+                pdfs.append(pdf_values)
+            except ValueError as e:
+                raise ValueError(
+                    f"Error in pdf computation for distribution {dist.name}: {str(e)}"
+                )
+
+        concatenated = pd.concat(pdfs, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def cdf(self, x):
+        """Concatenate CDFs for each distribution at a given value of `x`."""
+        cdfs = []
+        for dist in self.distributions:
+            try:
+                cdf_values = dist.cdf(x)
+                cdfs.append(cdf_values)
+            except ValueError as e:
+                raise ValueError(
+                    f"Error in cdf computation for distribution {dist.name}: {str(e)}"
+                )
+
+        concatenated = pd.concat(cdfs, axis=self.axis, ignore_index=True)
+        concatenated.index = self._generate_index(len(concatenated))
+        return concatenated
+
+    def _generate_index(self, length):
+        """Generate index for concatenated result."""
+        if length != len(self.distribution_names):
+            return pd.RangeIndex(start=0, stop=length)
+        return self.distribution_names
+
+    # todo: Constructing a new distribution when the two distributions are same.
+    def _constr_distribution(self):
+        """Construct a new distrbution when the distributions are same."""
+        pass

skpro/distributions/concat.py

@@ -0,0 +1,54 @@
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+"""Concat operation."""
+
+__author__ = ["SaiRevanth25"]
+
+from skpro.distributions._concat import ConcatDistr
+
+
+def concat(objs, axis=0):
+    """
+    Concatenate a list of distributions into a ConcatDistr.
+
+    Parameters
+    ----------
+    objs : list
+        List of distribution-like objects to concatenate.
+    axis : {0/'index', 1/'columns'}, default 0
+        Axis to concatenate along.
+
+    Returns
+    -------
+    ConcatDistr
+        An object representing the concatenation of the given distributions.
+
+    Examples
+    --------
+    >>> import skpro.distributions as skpro
+    >>> d1 = Normal(mu=[[1, 2], [3, 4]], sigma=1)
+    >>> d2 = Normal(mu=0, sigma = [[2, 42]])
+    >>> skpro.concat([d1,d2]).mean()
+            0	1
+        0	1	2
+        1	3	4
+        2	0	0
+    >>> skpro.concat([d1,d2]).var()
+
+            0	1
+        0	1	1
+        1	1	1
+        2	4	1764
+    >>> d3 = Gamma(alpha=[[5, 2]], beta=4)
+    >>> d4 = Laplace(mu= [5,7], scale=[2,8])
+    >>> skpro.concat([d2,d3,d4]).pdf(x=1)
+                    0	 1
+        Normal	4.0000	1764.000
+        Gamma	0.3125	0.125
+        Laplace	8.0000	128.000
+    """
+    if not isinstance(objs, list):
+        raise ValueError("`objs` must be a list of distribution-like objects.")
+    if axis not in [0, 1, "index", "columns"]:
+        raise ValueError("`axis` must be one of {0, 1, 'index', 'columns'}.")
+
+    return ConcatDistr(objs, axis=axis)