[ENH] Student's t-distribution

sktime/proba/t.py

@@ -0,0 +1,166 @@
+# copyright: sktime developers, BSD-3-Clause License (see LICENSE file)
+"""Student's t-distribution."""
+
+__author__ = ["Alex-JG3"]
+
+import numpy as np
+import pandas as pd
+from scipy.special import betaincinv, gamma, hyp2f1, loggamma
+
+from sktime.proba.base import BaseDistribution
+
+
+class TDistribution(BaseDistribution):
+    """Student's t-distribution (sktime native).
+
+    Parameters
+    ----------
+    mean : float or array of float (1D or 2D)
+        mean of the t-distribution distribution
+    sd : float or array of float (1D or 2D), must be positive
+        standard deviation of the t-distribution distribution
+    df : float or array of float (1D or 2D), must be positive
+        Degrees of freedom of the t-distribution
+    index : pd.Index, optional, default = RangeIndex
+    columns : pd.Index, optional, default = RangeIndex
+
+    Example
+    -------
+    >>> from sktime.proba.t import TDistribution
+
+    >>> n = TDistribution(mu=[[0, 1], [2, 3], [4, 5]], sigma=1, df=10)
+    """
+
+    _tags = {
+        "capabilities:approx": ["pdfnorm", "energy"],
+        "capabilities:exact": ["mean", "var", "pdf", "log_pdf", "cdf", "ppf"],
+        "distr:measuretype": "continuous",
+    }
+
+    def __init__(self, mu, sigma, df=1, index=None, columns=None):
+        self.mu = mu
+        self.sigma = sigma
+        self.df = df
+        self.index = index
+        self.columns = columns
+
+        self._mu, self._sigma, self._df = self._get_bc_params()
+        shape = self._mu.shape
+
+        if index is None:
+            index = pd.RangeIndex(shape[0])
+
+        if columns is None:
+            columns = pd.RangeIndex(shape[1])
+
+        super().__init__(index=index, columns=columns)
+
+    def _get_bc_params(self):
+        """Fully broadcast parameters of self, given param shapes and index, columns."""
+        to_broadcast = [self.mu, self.sigma, self.df]
+        if hasattr(self, "index") and self.index is not None:
+            to_broadcast += [self.index.to_numpy().reshape(-1, 1)]
+        if hasattr(self, "columns") and self.columns is not None:
+            to_broadcast += [self.columns.to_numpy()]
+        bc = np.broadcast_arrays(*to_broadcast)
+        return bc[0], bc[1], bc[2]
+
+    def mean(self):
+        r"""Return expected value of the distribution.
+
+        Let :math:`X` be a random variable with the distribution of `self`.
+        Returns the expectation :math:`\mathbb{E}[X]`. The expectation,
+        :math:`\mathbb{E}[X]`, as infinite if :math:`\nu \le 1`.
+
+        Returns
+        -------
+        pd.DataFrame with same rows, columns as `self`
+        expected value of distribution (entry-wise)
+        """
+        mean_arr = self._mu.copy()
+        if (self._df <= 1).any():
+            mean_arr = mean_arr.astype(np.float32)
+            mean_arr[self._df <= 1] = np.inf
+        return pd.DataFrame(mean_arr, index=self.index, columns=self.columns)
+
+    def var(self):
+        r"""Return element/entry-wise variance of the distribution.
+
+        Let :math:`X` be a random variable with the distribution of `self`.
+        Returns,
+
+        .. math::
+            \mathbb{V}[X] = \begin{cases}
+                \frac{\nu}{\nu - 2} & \text{if} \nu > 2, \\
+                \infty              & \text{if} \nu \le 2, \\
+            \begin{cases}
+
+        Where :math:`\nu` is the degrees of freedom of the t-distribution.
+
+        Returns
+        -------
+        pd.DataFrame with same rows, columns as `self`
+        variance of distribution (entry-wise)
+        """
+        df_arr = self._df.copy()
+        df_arr = df_arr.astype(np.float32)
+        df_arr[df_arr <= 2] = np.inf
+        mask = (df_arr > 2) & (df_arr != np.inf)
+        df_arr[mask] = df_arr[mask] / (df_arr[mask] - 2)
+        return pd.DataFrame(df_arr, index=self.index, columns=self.columns)
+
+    def pdf(self, x):
+        """Probability density function."""
+        d = self.loc[x.index, x.columns]
+        pdf_arr = gamma((d._df + 1) / 2)
+        pdf_arr = pdf_arr / (np.sqrt(np.pi * d._df) * gamma(d._df / 2))
+        pdf_arr = pdf_arr * (1 + x**2 / d._df) ** (-(d._df + 1) / 2)
+        return pd.DataFrame(pdf_arr, index=x.index, columns=x.columns)
+
+    def log_pdf(self, x):
+        """Logarithmic probability density function."""
+        d = self.loc[x.index, x.columns]
+        lpdf_arr = loggamma((d._df + 1) / 2)
+        lpdf_arr = lpdf_arr - 0.5 * np.log(d._df * np.pi)
+        lpdf_arr = lpdf_arr - loggamma(d._df / 2)
+        lpdf_arr = lpdf_arr - ((d._df + 1) / 2) * np.log(1 + x**2 / d._df)
+        return pd.DataFrame(lpdf_arr, index=x.index, columns=x.columns)
+
+    def cdf(self, x):
+        """Cumulative distribution function."""
+        d = self.loc[x.index, x.columns]
+        cdf_arr = x * gamma((d._df + 1) / 2)
+        cdf_arr = cdf_arr * hyp2f1(0.5, (d._df + 1) / 2, 3 / 2, -(x**2) / d._df)
+        cdf_arr = 0.5 + cdf_arr / (np.sqrt(np.pi * d._df) * gamma(d._df / 2))
+        return pd.DataFrame(cdf_arr, index=x.index, columns=x.columns)
+
+    def ppf(self, p):
+        """Quantile function = percent point function = inverse cdf."""
+        d = self.loc[p.index, p.columns]
+        ppf_arr = p.to_numpy(copy=True)
+        ppf_arr[p.values == 0.5] = 0.0
+        ppf_arr[p.values <= 0] = -np.inf
+        ppf_arr[p.values >= 1] = np.inf
+
+        mask1 = (p.values < 0.5) & (p.values > 0)
+        mask2 = (p.values < 1) & (p.values > 0.5)
+        ppf_arr[mask1] = 1 / betaincinv(0.5 * d._df[mask1], 0.5, 2 * ppf_arr[mask1])
+        ppf_arr[mask2] = 1 / betaincinv(
+            0.5 * d._df[mask2], 0.5, 2 * (1 - ppf_arr[mask2])
+        )
+        ppf_arr[mask1 | mask2] = np.sqrt(ppf_arr[mask1 | mask2] - 1)
+        ppf_arr[mask1 | mask2] = np.sqrt(d._df[mask1 | mask2]) * ppf_arr[mask1 | mask2]
+        ppf_arr[mask1] = -ppf_arr[mask1]
+        return pd.DataFrame(ppf_arr, index=p.index, columns=p.columns)
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator."""
+        params1 = {"mu": [[0, 1], [2, 3], [4, 5]], "sigma": 1}
+        params2 = {
+            "mu": 0,
+            "sigma": 1,
+            "index": pd.Index([1, 2, 5]),
+            "columns": pd.Index(["a", "b"]),
+        }
+        return [params1, params2]