Merge pull request #4951 from ev-br/distn_fit

MAINT: do not ignore invalid kwargs in distributions fit method

scipy/stats/_continuous_distns.py

@@ -419,8 +419,10 @@ def fit(self, data, *args, **kwds):
         # Override rv_continuous.fit, so we can more efficiently handle the
         # case where floc and fscale are given.
 
-        f0 = kwds.get('f0', None)
-        f1 = kwds.get('f1', None)
+        f0 = (kwds.get('f0', None) or kwds.get('fa', None) or
+              kwds.get('fix_a', None))
+        f1 = (kwds.get('f1', None) or kwds.get('fb', None) or
+              kwds.get('fix_b', None))
         floc = kwds.get('floc', None)
         fscale = kwds.get('fscale', None)
 
@@ -1885,7 +1887,8 @@ def _fitstart(self, data):
 
     @inherit_docstring_from(rv_continuous)
     def fit(self, data, *args, **kwds):
-        f0 = kwds.get('f0', None)
+        f0 = (kwds.get('f0', None) or kwds.get('fa', None) or
+              kwds.get('fix_a', None))
         floc = kwds.get('floc', None)
         fscale = kwds.get('fscale', None)
 

scipy/stats/_distn_infrastructure.py

@@ -1967,15 +1967,15 @@ def _reduce_func(self, args, kwds):
         # shapes='a, b'. To fix `a`, can specify either `f1` or `fa`.
         # Convert the latter into the former.
         if self.shapes:
-            fshapes = ['f%s' % s for s in self.shapes.replace(',', ' ').split()]
-            for j, fs in enumerate(fshapes):
-                if fs in kwds:
+            shapes = self.shapes.replace(',', ' ').split()
+            for j, s in enumerate(shapes):
+                val = kwds.pop('f' + s, None) or kwds.pop('fix_' + s, None)
+                if val is not None:
                     key = 'f%d' % j
                     if key in kwds:
-                        raise ValueError("Cannot specify both %s and %s" %
-                                         (fs, key))
+                        raise ValueError("Duplicate entry for %s." % key)
                     else:
-                        kwds.update({key: kwds[fs]})
+                        kwds[key] = val
 
         args = list(args)
         Nargs = len(args)
@@ -1985,7 +1985,7 @@ def _reduce_func(self, args, kwds):
         for n, key in enumerate(names):
             if key in kwds:
                 fixedn.append(n)
-                args[n] = kwds[key]
+                args[n] = kwds.pop(key)
             else:
                 x0.append(args[n])
 
@@ -2043,8 +2043,9 @@ def fit(self, data, *args, **kwds):
 
             - f0...fn : hold respective shape parameters fixed.
               Alternatively, shape parameters to fix can be specified by name.
-              For example, if ``self.shapes == "a, b"``, ``fa`` is equivalent to
-              ``f0`` and ``fb`` is equivalent to ``f1``.
+              For example, if ``self.shapes == "a, b"``, ``fa``and ``fix_a``
+              are equivalent to ``f0``, and ``fb`` and ``fix_b`` are
+              equivalent to ``f1``.
 
             - floc : hold location parameter fixed to specified value.
 
@@ -2110,12 +2111,12 @@ def fit(self, data, *args, **kwds):
             # get distribution specific starting locations
             start = self._fitstart(data)
             args += start[Narg:-2]
-        loc = kwds.get('loc', start[-2])
-        scale = kwds.get('scale', start[-1])
+        loc = kwds.pop('loc', start[-2])
+        scale = kwds.pop('scale', start[-1])
         args += (loc, scale)
         x0, func, restore, args = self._reduce_func(args, kwds)
 
-        optimizer = kwds.get('optimizer', optimize.fmin)
+        optimizer = kwds.pop('optimizer', optimize.fmin)
         # convert string to function in scipy.optimize
         if not callable(optimizer) and isinstance(optimizer, string_types):
             if not optimizer.startswith('fmin_'):
@@ -2126,6 +2127,11 @@ def fit(self, data, *args, **kwds):
                 optimizer = getattr(optimize, optimizer)
             except AttributeError:
                 raise ValueError("%s is not a valid optimizer" % optimizer)
+
+        # by now kwds must be empty, since everybody took what they needed
+        if kwds:
+            raise TypeError("Unknown arguments: %s." % kwds)
+
         vals = optimizer(func, x0, args=(ravel(data),), disp=0)
         if restore is not None:
             vals = restore(args, vals)

scipy/stats/tests/test_distributions.py

@@ -1249,17 +1249,42 @@ def test_fshapes(self):
         res_2 = stats.beta.fit(x, fa=3.)
         assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
 
+        res_2 = stats.beta.fit(x, fix_a=3.)
+        assert_allclose(res_1, res_2, atol=1e-12, rtol=1e-12)
+
         res_3 = stats.beta.fit(x, f1=4.)
         res_4 = stats.beta.fit(x, fb=4.)
         assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
 
+        res_4 = stats.beta.fit(x, fix_b=4.)
+        assert_allclose(res_3, res_4, atol=1e-12, rtol=1e-12)
+
         # cannot specify both positional and named args at the same time
         assert_raises(ValueError, stats.beta.fit, x, fa=1, f0=2)
 
         # check that attempting to fix all parameters raises a ValueError
         assert_raises(ValueError, stats.beta.fit, x, fa=0, f1=1,
                                                      floc=2, fscale=3)
 
+        # check that specifying floc, fscale and fshapes works for
+        # beta and gamma which override the generic fit method
+        res_5 = stats.beta.fit(x, fa=3., floc=0, fscale=1)
+        aa, bb, ll, ss = res_5
+        assert_equal([aa, ll, ss], [3., 0, 1])
+
+        # gamma distribution
+        a = 3.
+        data = stats.gamma.rvs(a, size=100)
+        aa, ll, ss = stats.gamma.fit(data, fa=a)
+        assert_equal(aa, a)
+
+    def test_extra_params(self):
+        # unknown parameters should raise rather than be silently ignored
+        dist = stats.exponnorm
+        data = dist.rvs(K=2, size=100)
+        dct = dict(enikibeniki=-101)
+        assert_raises(TypeError, dist.fit, data, **dct)
+
 
 class TestFrozen(TestCase):
     # Test that a frozen distribution gives the same results as the original object.