Merge pull request scipy#12573 from rgommers/remove-frechet

DEP: remove `frechet_l` and `frechet_r`

doc/source/conf.py

@@ -142,8 +142,6 @@
         'The integral is probably divergent',  # stats.mielke example
         'underflow encountered in square',  # signal.filtfilt underflow
         'underflow encountered in multiply',  # scipy.spatial.HalfspaceIntersection
-        '`frechet_l` is deprecated',  # stats.frechet_l
-        '`frechet_r` is deprecated',  # stats.frechet_r
         'underflow encountered in nextafter',  # tuterial/interpolate.rst
         # stats.skewnorm, stats.norminvgauss, stats.gaussian_kde,
         # tutorial/stats.rst (twice):

doc/source/tutorial/stats.rst

@@ -98,7 +98,7 @@ introspection:
     >>> dist_discrete = [d for d in dir(stats) if
     ...                  isinstance(getattr(stats, d), stats.rv_discrete)]
     >>> print('number of continuous distributions: %d' % len(dist_continu))
-    number of continuous distributions: 101
+    number of continuous distributions: 99
     >>> print('number of discrete distributions:   %d' % len(dist_discrete))
     number of discrete distributions:   15
 

scipy/stats/__init__.py

@@ -52,8 +52,6 @@
    fisk              -- Fisk
    foldcauchy        -- Folded Cauchy
    foldnorm          -- Folded Normal
-   frechet_r         -- Deprecated. Alias for weibull_min
-   frechet_l         -- Deprecated. Alias for weibull_max
    genlogistic       -- Generalized Logistic
    gennorm           -- Generalized normal
    genpareto         -- Generalized Pareto

scipy/stats/_continuous_distns.py

@@ -2042,7 +2042,7 @@ class weibull_min_gen(rv_continuous):
     """
 
     def _pdf(self, x, c):
-        # frechet_r.pdf(x, c) = c * x**(c-1) * exp(-x**c)
+        # weibull_min.pdf(x, c) = c * x**(c-1) * exp(-x**c)
         return c*pow(x, c-1)*np.exp(-pow(x, c))
 
     def _logpdf(self, x, c):
@@ -2108,7 +2108,7 @@ class weibull_max_gen(rv_continuous):
 
     """
     def _pdf(self, x, c):
-        # frechet_l.pdf(x, c) = c * (-x)**(c-1) * exp(-(-x)**c)
+        # weibull_max.pdf(x, c) = c * (-x)**(c-1) * exp(-(-x)**c)
         return c*pow(-x, c-1)*np.exp(-pow(-x, c))
 
     def _logpdf(self, x, c):
@@ -2140,250 +2140,6 @@ def _entropy(self, c):
 
 weibull_max = weibull_max_gen(b=0.0, name='weibull_max')
 
-# Public methods to be deprecated in frechet_r and frechet_l:
-# ['__call__', 'cdf', 'entropy', 'expect', 'fit', 'fit_loc_scale', 'freeze',
-#  'interval', 'isf', 'logcdf', 'logpdf', 'logsf', 'mean', 'median', 'moment',
-#  'nnlf', 'pdf', 'ppf', 'rvs', 'sf', 'stats', 'std', 'var']
-
-_frechet_r_deprec_msg = """\
-The distribution `frechet_r` is a synonym for `weibull_min`; this historical
-usage is deprecated because of possible confusion with the (quite different)
-Frechet distribution.  To preserve the existing behavior of the program, use
-`scipy.stats.weibull_min`.  For the Frechet distribution (i.e. the Type II
-extreme value distribution), use `scipy.stats.invweibull`."""
-
-
-class frechet_r_gen(weibull_min_gen):
-    """A Frechet right (or Weibull minimum) continuous random variable.
-
-    %(before_notes)s
-
-    See Also
-    --------
-    weibull_min : The same distribution as `frechet_r`.
-
-    Notes
-    -----
-    %(after_notes)s
-
-    %(example)s
-    """
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def __call__(self, *args, **kwargs):
-        return weibull_min_gen.__call__(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def cdf(self, *args, **kwargs):
-        return weibull_min_gen.cdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def entropy(self, *args, **kwargs):
-        return weibull_min_gen.entropy(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def expect(self, *args, **kwargs):
-        return weibull_min_gen.expect(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def fit(self, *args, **kwargs):
-        return weibull_min_gen.fit(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def fit_loc_scale(self, *args, **kwargs):
-        return weibull_min_gen.fit_loc_scale(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def freeze(self, *args, **kwargs):
-        return weibull_min_gen.freeze(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def interval(self, *args, **kwargs):
-        return weibull_min_gen.interval(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def isf(self, *args, **kwargs):
-        return weibull_min_gen.isf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def logcdf(self, *args, **kwargs):
-        return weibull_min_gen.logcdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def logpdf(self, *args, **kwargs):
-        return weibull_min_gen.logpdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def logsf(self, *args, **kwargs):
-        return weibull_min_gen.logsf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def mean(self, *args, **kwargs):
-        return weibull_min_gen.mean(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def median(self, *args, **kwargs):
-        return weibull_min_gen.median(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def moment(self, *args, **kwargs):
-        return weibull_min_gen.moment(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def nnlf(self, *args, **kwargs):
-        return weibull_min_gen.nnlf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def pdf(self, *args, **kwargs):
-        return weibull_min_gen.pdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def ppf(self, *args, **kwargs):
-        return weibull_min_gen.ppf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def rvs(self, *args, **kwargs):
-        return weibull_min_gen.rvs(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def sf(self, *args, **kwargs):
-        return weibull_min_gen.sf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def stats(self, *args, **kwargs):
-        return weibull_min_gen.stats(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def std(self, *args, **kwargs):
-        return weibull_min_gen.std(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_r', message=_frechet_r_deprec_msg)
-    def var(self, *args, **kwargs):
-        return weibull_min_gen.var(self, *args, **kwargs)
-
-
-frechet_r = frechet_r_gen(a=0.0, name='frechet_r')
-
-
-_frechet_l_deprec_msg = """\
-The distribution `frechet_l` is a synonym for `weibull_max`; this historical
-usage is deprecated because of possible confusion with the (quite different)
-Frechet distribution.  To preserve the existing behavior of the program, use
-`scipy.stats.weibull_max`.  For the Frechet distribution (i.e. the Type II
-extreme value distribution), use `scipy.stats.invweibull`."""
-
-
-class frechet_l_gen(weibull_max_gen):
-    """A Frechet left (or Weibull maximum) continuous random variable.
-
-    %(before_notes)s
-
-    See Also
-    --------
-    weibull_max : The same distribution as `frechet_l`.
-
-    Notes
-    -----
-    %(after_notes)s
-
-    %(example)s
-    """
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def __call__(self, *args, **kwargs):
-        return weibull_max_gen.__call__(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def cdf(self, *args, **kwargs):
-        return weibull_max_gen.cdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def entropy(self, *args, **kwargs):
-        return weibull_max_gen.entropy(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def expect(self, *args, **kwargs):
-        return weibull_max_gen.expect(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def fit(self, *args, **kwargs):
-        return weibull_max_gen.fit(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def fit_loc_scale(self, *args, **kwargs):
-        return weibull_max_gen.fit_loc_scale(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def freeze(self, *args, **kwargs):
-        return weibull_max_gen.freeze(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def interval(self, *args, **kwargs):
-        return weibull_max_gen.interval(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def isf(self, *args, **kwargs):
-        return weibull_max_gen.isf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def logcdf(self, *args, **kwargs):
-        return weibull_max_gen.logcdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def logpdf(self, *args, **kwargs):
-        return weibull_max_gen.logpdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def logsf(self, *args, **kwargs):
-        return weibull_max_gen.logsf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def mean(self, *args, **kwargs):
-        return weibull_max_gen.mean(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def median(self, *args, **kwargs):
-        return weibull_max_gen.median(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def moment(self, *args, **kwargs):
-        return weibull_max_gen.moment(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def nnlf(self, *args, **kwargs):
-        return weibull_max_gen.nnlf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def pdf(self, *args, **kwargs):
-        return weibull_max_gen.pdf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def ppf(self, *args, **kwargs):
-        return weibull_max_gen.ppf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def rvs(self, *args, **kwargs):
-        return weibull_max_gen.rvs(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def sf(self, *args, **kwargs):
-        return weibull_max_gen.sf(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def stats(self, *args, **kwargs):
-        return weibull_max_gen.stats(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def std(self, *args, **kwargs):
-        return weibull_max_gen.std(self, *args, **kwargs)
-
-    @np.deprecate(old_name='frechet_l', message=_frechet_l_deprec_msg)
-    def var(self, *args, **kwargs):
-        return weibull_max_gen.var(self, *args, **kwargs)
-
-
-frechet_l = frechet_l_gen(b=0.0, name='frechet_l')
-
 
 class genlogistic_gen(rv_continuous):
     r"""A generalized logistic continuous random variable.

scipy/stats/_distr_params.py

@@ -29,8 +29,6 @@
     ['fisk', (3.0857548622253179,)],
     ['foldcauchy', (4.7164673455831894,)],
     ['foldnorm', (1.9521253373555869,)],
-    ['frechet_l', (3.6279911255583239,)],
-    ['frechet_r', (1.8928171603534227,)],
     ['gamma', (1.9932305483800778,)],
     ['gausshyper', (13.763771604130699, 3.1189636648681431,
                     2.5145980350183019, 5.1811649903971615)],  # veryslow

scipy/stats/tests/common_tests.py

@@ -265,7 +265,7 @@ def deriv(f, x, *arg):
         assert_allclose(deriv(distfn.sf, x, *arg), -pdf, rtol=1e-5)
         assert_allclose(deriv(distfn.logsf, x, *arg), -pdf/sf, rtol=1e-5)
 
-        assert_allclose(deriv(distfn.logpdf, x, *arg), 
+        assert_allclose(deriv(distfn.logpdf, x, *arg),
                         deriv(distfn.pdf, x, *arg) / distfn.pdf(x, *arg),
                         rtol=1e-5)
 
@@ -311,14 +311,10 @@ def check_freezing(distfn, args):
 
 def check_rvs_broadcast(distfunc, distname, allargs, shape, shape_only, otype):
     np.random.seed(123)
-    with suppress_warnings() as sup:
-        # frechet_l and frechet_r are deprecated, so all their
-        # methods generate DeprecationWarnings.
-        sup.filter(category=DeprecationWarning, message=".*frechet_")
-        sample = distfunc.rvs(*allargs)
-        assert_equal(sample.shape, shape, "%s: rvs failed to broadcast" % distname)
-        if not shape_only:
-            rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
-            np.random.seed(123)
-            expected = rvs(*allargs)
-            assert_allclose(sample, expected, rtol=1e-15)
+    sample = distfunc.rvs(*allargs)
+    assert_equal(sample.shape, shape, "%s: rvs failed to broadcast" % distname)
+    if not shape_only:
+        rvs = np.vectorize(lambda *allargs: distfunc.rvs(*allargs), otypes=otype)
+        np.random.seed(123)
+        expected = rvs(*allargs)
+        assert_allclose(sample, expected, rtol=1e-15)