Merge pull request #26259 from Qazalbash:scipy-expon

PiperOrigin-RevId: 723576962

docs/jax.scipy.rst

@@ -303,6 +303,11 @@ jax.scipy.stats.expon
 
    logpdf
    pdf
+   logcdf
+   cdf
+   logsf
+   sf
+   ppf
 
 jax.scipy.stats.gamma
 ~~~~~~~~~~~~~~~~~~~~~

jax/_src/scipy/stats/expon.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from jax import lax
+
 import jax.numpy as jnp
+from jax import lax
 from jax._src.numpy.util import promote_args_inexact
 from jax._src.typing import Array, ArrayLike
 
@@ -41,7 +42,13 @@ def logpdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
     array of logpdf values.
 
   See Also:
+    :func:`jax.scipy.stats.expon.cdf`
     :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
   """
   x, loc, scale = promote_args_inexact("expon.logpdf", x, loc, scale)
   log_scale = lax.log(scale)
@@ -73,6 +80,190 @@ def pdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
     array of pdf values.
 
   See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
     :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
   """
   return lax.exp(logpdf(x, loc, scale))
+
+
+def cdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
+  r"""Exponential cumulative density function.
+
+  JAX implementation of :obj:`scipy.stats.expon` ``cdf``.
+
+  The cdf is defined as
+
+  .. math::
+
+     f_{cdf}(x) = \int_{-\infty}^x f_{pdf}(y)\mathrm{d}y
+
+  where :math:`f_{pdf}` is the exponential distribution probability density function,
+  :func:`jax.scipy.stats.expon.pdf`.
+
+  Args:
+    x: arraylike, value at which to evaluate the PDF
+    loc: arraylike, distribution offset parameter
+    scale: arraylike, distribution scale parameter
+
+  Returns:
+    array of pdf values.
+
+  See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
+  """
+  x, loc, scale = promote_args_inexact("expon.cdf", x, loc, scale)
+  neg_scaled_x = lax.div(lax.sub(loc, x), scale)
+  return jnp.where(
+    lax.lt(x, loc),
+    jnp.zeros_like(neg_scaled_x),
+    lax.neg(lax.expm1(neg_scaled_x)),
+  )
+
+
+def logcdf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
+  r"""Exponential log cumulative density function.
+
+  JAX implementation of :obj:`scipy.stats.expon` ``logcdf``.
+
+  The cdf is defined as
+
+  .. math::
+
+     f_{cdf}(x) = \int_{-\infty}^x f_{pdf}(y)\mathrm{d}y
+
+  where :math:`f_{pdf}` is the exponential distribution probability density function,
+  :func:`jax.scipy.stats.expon.pdf`.
+
+  Args:
+    x: arraylike, value at which to evaluate the PDF
+    loc: arraylike, distribution offset parameter
+    scale: arraylike, distribution scale parameter
+
+  Returns:
+    array of pdf values.
+
+  See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
+  """
+  return lax.log1p(lax.neg(sf(x, loc, scale)))
+
+
+def logsf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
+  r"""Exponential log survival function.
+
+  JAX implementation of :obj:`scipy.stats.expon` ``logsf``.
+
+  The survival function is defined as
+
+  .. math::
+
+     f_{sf}(x) = 1 - f_{cdf}(x)
+
+  where :math:`f_{cdf}(x)` is the exponential cumulative distribution function,
+  :func:`jax.scipy.stats.expon.cdf`.
+
+  Args:
+    x: arraylike, value at which to evaluate the PDF
+    loc: arraylike, distribution offset parameter
+    scale: arraylike, distribution scale parameter
+
+  Returns:
+    array of pdf values.
+
+  See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
+  """
+  x, loc, scale = promote_args_inexact("expon.sf", x, loc, scale)
+  neg_scaled_x = lax.div(lax.sub(loc, x), scale)
+  return jnp.where(lax.lt(x, loc), jnp.zeros_like(neg_scaled_x), neg_scaled_x)
+
+
+def sf(x: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
+  r"""Exponential survival function.
+
+  JAX implementation of :obj:`scipy.stats.expon` ``sf``.
+
+  The survival function is defined as
+
+  .. math::
+
+     f_{sf}(x) = 1 - f_{cdf}(x)
+
+  where :math:`f_{cdf}(x)` is the exponential cumulative distribution function,
+  :func:`jax.scipy.stats.expon.cdf`.
+
+  Args:
+    x: arraylike, value at which to evaluate the PDF
+    loc: arraylike, distribution offset parameter
+    scale: arraylike, distribution scale parameter
+
+  Returns:
+    array of pdf values.
+
+  See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
+  """
+  return lax.exp(logsf(x, loc, scale))
+
+
+def ppf(q: ArrayLike, loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
+  r"""Exponential survival function.
+
+  JAX implementation of :obj:`scipy.stats.expon` ``ppf``.
+
+  The percent point function is defined as the inverse of the
+  cumulative distribution function, :func:`jax.scipy.stats.expon.cdf`.
+
+  Args:
+    x: arraylike, value at which to evaluate the PDF
+    loc: arraylike, distribution offset parameter
+    scale: arraylike, distribution scale parameter
+
+  Returns:
+    array of pdf values.
+
+  See Also:
+    :func:`jax.scipy.stats.expon.cdf`
+    :func:`jax.scipy.stats.expon.pdf`
+    :func:`jax.scipy.stats.expon.ppf`
+    :func:`jax.scipy.stats.expon.sf`
+    :func:`jax.scipy.stats.expon.logcdf`
+    :func:`jax.scipy.stats.expon.logpdf`
+    :func:`jax.scipy.stats.expon.logsf`
+  """
+  q, loc, scale = promote_args_inexact("expon.ppf", q, loc, scale)
+  neg_scaled_q = lax.div(lax.sub(loc, q), scale)
+  return jnp.where(
+    jnp.isnan(q) | (q < 0) | (q > 1),
+    jnp.nan,
+    lax.neg(lax.log1p(neg_scaled_q)),
+  )

jax/scipy/stats/expon.py

@@ -16,6 +16,11 @@
 # See PEP 484 & https://github.com/jax-ml/jax/issues/7570
 
 from jax._src.scipy.stats.expon import (
+  cdf as cdf,
+  logcdf as logcdf,
   logpdf as logpdf,
+  logsf as logsf,
   pdf as pdf,
+  ppf as ppf,
+  sf as sf,
 )

tests/scipy_stats_test.py

@@ -523,6 +523,86 @@ def args_maker():
                               tol=1e-4)
       self._CompileAndCheck(lax_fun, args_maker)
 
+  @genNamedParametersNArgs(3)
+  def testExponLogCdf(self, shapes, dtypes):
+    rng = jtu.rand_positive(self.rng())
+    scipy_fun = osp_stats.expon.logcdf
+    lax_fun = lsp_stats.expon.logcdf
+
+    def args_maker():
+      x, loc, scale = map(rng, shapes, dtypes)
+      return [x, loc, scale]
+
+    with jtu.strict_promotion_if_dtypes_match(dtypes):
+      self._CheckAgainstNumpy(
+        scipy_fun, lax_fun, args_maker, check_dtypes=False, tol=5e-4
+      )
+      self._CompileAndCheck(lax_fun, args_maker)
+
+  @genNamedParametersNArgs(3)
+  def testExponCdf(self, shapes, dtypes):
+    rng = jtu.rand_positive(self.rng())
+    scipy_fun = osp_stats.expon.cdf
+    lax_fun = lsp_stats.expon.cdf
+
+    def args_maker():
+      x, loc, scale = map(rng, shapes, dtypes)
+      return [x, loc, scale]
+
+    with jtu.strict_promotion_if_dtypes_match(dtypes):
+      self._CheckAgainstNumpy(
+        scipy_fun, lax_fun, args_maker, check_dtypes=False, tol=5e-4
+      )
+      self._CompileAndCheck(lax_fun, args_maker)
+
+  @genNamedParametersNArgs(3)
+  def testExponSf(self, shapes, dtypes):
+    rng = jtu.rand_positive(self.rng())
+    scipy_fun = osp_stats.expon.sf
+    lax_fun = lsp_stats.expon.sf
+
+    def args_maker():
+      x, loc, scale = map(rng, shapes, dtypes)
+      return [x, loc, scale]
+
+    with jtu.strict_promotion_if_dtypes_match(dtypes):
+      self._CheckAgainstNumpy(
+        scipy_fun, lax_fun, args_maker, check_dtypes=False, tol=5e-4
+      )
+      self._CompileAndCheck(lax_fun, args_maker)
+
+  @genNamedParametersNArgs(3)
+  def testExponLogSf(self, shapes, dtypes):
+    rng = jtu.rand_positive(self.rng())
+    scipy_fun = osp_stats.expon.logsf
+    lax_fun = lsp_stats.expon.logsf
+
+    def args_maker():
+      x, loc, scale = map(rng, shapes, dtypes)
+      return [x, loc, scale]
+
+    with jtu.strict_promotion_if_dtypes_match(dtypes):
+      self._CheckAgainstNumpy(
+        scipy_fun, lax_fun, args_maker, check_dtypes=False, tol=5e-4
+      )
+      self._CompileAndCheck(lax_fun, args_maker)
+
+  @genNamedParametersNArgs(3)
+  def testExponPpf(self, shapes, dtypes):
+    rng = jtu.rand_positive(self.rng())
+    scipy_fun = osp_stats.expon.ppf
+    lax_fun = lsp_stats.expon.ppf
+
+    def args_maker():
+      q, loc, scale = map(rng, shapes, dtypes)
+      return [q, loc, scale]
+
+    with jtu.strict_promotion_if_dtypes_match(dtypes):
+      self._CheckAgainstNumpy(
+        scipy_fun, lax_fun, args_maker, check_dtypes=False, tol=5e-4
+      )
+      self._CompileAndCheck(lax_fun, args_maker)
+
   @genNamedParametersNArgs(4)
   def testGammaLogPdf(self, shapes, dtypes):
     rng = jtu.rand_positive(self.rng())