BUG: stats: Fix a few issues with the distributions' fit method.

Fix the following problems:

* norm_gen.fit silently ignored unknown keyword arguments.
* beta_gen.fit silently ignored unknown keyword arguments if both
  floc and fscale were given.
* gamma_gen.fit silently ignored unknown keyword arguments if floc
  was given.
* A call such as `beta.fit(x, fa=0.5, fix_a=0.5)` generated the
  error `TypeError: Unknown arguments: {'fix_a': 0.5}`, when it
  should have generated an error about multiple keyword arguments
  setting the same fixed parameter.  After this change, the error is
  `ValueError: fit method got multiple keyword arguments to specify
  the same fixed parameter: fa, fix_a`.

scipy/stats/_continuous_distns.py

@@ -24,7 +24,8 @@
                                  tukeylambda_kurtosis as _tlkurt)
 from ._distn_infrastructure import (get_distribution_names, _kurtosis,
                                     _ncx2_cdf, _ncx2_log_pdf, _ncx2_pdf,
-                                    rv_continuous, _skew, valarray)
+                                    rv_continuous, _skew, valarray,
+                                    _get_fixed_fit_value)
 from ._constants import _XMIN, _EULER, _ZETA3, _XMAX, _LOGXMAX
 
 
@@ -36,6 +37,24 @@
     float_power = np.power
 
 
+def _remove_optimizer_parameters(kwds):
+    """
+    Remove the optimizer-related keyword arguments 'loc', 'scale' and
+    'optimizer' from `kwds`.  Then check that `kwds` is empty, and
+    raise `TypeError("Unknown arguments: %s." % kwds)` if it is not.
+
+    This function is used in the fit method of distributions that override
+    the default method and do not use the default optimization code.
+
+    `kwds` is modified in-place.
+    """
+    kwds.pop('loc', None)
+    kwds.pop('scale', None)
+    kwds.pop('optimizer', None)
+    if kwds:
+        raise TypeError("Unknown arguments: %s." % kwds)
+
+
 ## Kolmogorov-Smirnov one-sided and two-sided test statistics
 class ksone_gen(rv_continuous):
     r"""General Kolmogorov-Smirnov one-sided test.
@@ -252,8 +271,10 @@ def _entropy(self):
         estimation of the normal distribution parameters, so the
         `optimizer` argument is ignored.\n\n""")
     def fit(self, data, **kwds):
-        floc = kwds.get('floc', None)
-        fscale = kwds.get('fscale', None)
+        floc = kwds.pop('floc', None)
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
 
         if floc is not None and fscale is not None:
             # This check is for consistency with `rv_continuous.fit`.
@@ -528,17 +549,22 @@ 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) 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)
 
         if floc is None or fscale is None:
             # do general fit
             return super(beta_gen, self).fit(data, *args, **kwds)
 
+        # We already got these from kwds, so just pop them.
+        kwds.pop('floc', None)
+        kwds.pop('fscale', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        f1 = _get_fixed_fit_value(kwds, ['f1', 'fb', 'fix_b'])
+
+        _remove_optimizer_parameters(kwds)
+
         if f0 is not None and f1 is not None:
             # This check is for consistency with `rv_continuous.fit`.
             raise ValueError("All parameters fixed. There is nothing to "
@@ -1398,12 +1424,7 @@ def fit(self, data, *args, **kwds):
         floc = kwds.pop('floc', None)
         fscale = kwds.pop('fscale', None)
 
-        # Ignore the optimizer-related keyword arguments, if given.
-        kwds.pop('loc', None)
-        kwds.pop('scale', None)
-        kwds.pop('optimizer', None)
-        if kwds:
-            raise TypeError("Unknown arguments: %s." % kwds)
+        _remove_optimizer_parameters(kwds)
 
         if floc is not None and fscale is not None:
             # This check is for consistency with `rv_continuous.fit`.
@@ -2668,15 +2689,20 @@ def _fitstart(self, data):
         problem than the full ML optimization problem.  So in that case,
         the `optimizer`, `loc` and `scale` arguments are ignored.\n\n""")
     def fit(self, data, *args, **kwds):
-        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)
 
         if floc is None:
             # loc is not fixed.  Use the default fit method.
             return super(gamma_gen, self).fit(data, *args, **kwds)
 
+        # We already have this value, so just pop it from kwds.
+        kwds.pop('floc', None)
+
+        f0 = _get_fixed_fit_value(kwds, ['f0', 'fa', 'fix_a'])
+        fscale = kwds.pop('fscale', None)
+
+        _remove_optimizer_parameters(kwds)
+
         # Special case: loc is fixed.
 
         if f0 is not None and fscale is not None:
@@ -6723,12 +6749,7 @@ def fit(self, data, *args, **kwds):
         floc = kwds.pop('floc', None)
         fscale = kwds.pop('fscale', None)
 
-        # Ignore the optimizer-related keyword arguments, if given.
-        kwds.pop('loc', None)
-        kwds.pop('scale', None)
-        kwds.pop('optimizer', None)
-        if kwds:
-            raise TypeError("Unknown arguments: %s." % kwds)
+        _remove_optimizer_parameters(kwds)
 
         if floc is not None and fscale is not None:
             # This check is for consistency with `rv_continuous.fit`.

scipy/stats/_distn_infrastructure.py

@@ -1350,6 +1350,23 @@ def support(self, *args, **kwargs):
         return _a * scale + loc, _b * scale + loc
 
 
+def _get_fixed_fit_value(kwds, names):
+    """
+    Given names such as `['f0', 'fa', 'fix_a']`, check that there is
+    at most one non-None value in `kwds` associaed with those names.
+    Return that value, or None if none of the names occur in `kwds`.
+    As a side effect, all occurrences of those names in `kwds` are
+    removed.
+    """
+    vals = [(name, kwds.pop(name)) for name in names if name in kwds]
+    if len(vals) > 1:
+        repeated = [name for name, val in vals]
+        raise ValueError("fit method got multiple keyword arguments to "
+                         "specify the same fixed parameter: " +
+                         ', '.join(repeated))
+    return vals[0][1] if vals else None
+
+
 ##  continuous random variables: implement maybe later
 ##
 ##  hf  --- Hazard Function (PDF / SF)
@@ -2102,22 +2119,23 @@ def _fitstart(self, data, args=None):
         loc, scale = self._fit_loc_scale_support(data, *args)
         return args + (loc, scale)
 
-    # Return the (possibly reduced) function to optimize in order to find MLE
-    #  estimates for the .fit method
     def _reduce_func(self, args, kwds):
-        # First of all, convert fshapes params to fnum: eg for stats.beta,
-        # shapes='a, b'. To fix `a`, can specify either `f1` or `fa`.
-        # Convert the latter into the former.
+        """
+        Return the (possibly reduced) function to optimize in order to find MLE
+        estimates for the .fit method.
+        """
+        # Convert fixed shape parameters to the standard numeric form: e.g. for
+        # stats.beta, shapes='a, b'. To fix `a`, the caller can give a value
+        # for `f0`, `fa` or 'fix_a'.  The following converts the latter two
+        # into the first (numeric) form.
         if self.shapes:
             shapes = self.shapes.replace(',', ' ').split()
             for j, s in enumerate(shapes):
-                val = kwds.pop('f' + s, None) or kwds.pop('fix_' + s, None)
+                key = 'f' + str(j)
+                names = [key, 'f' + s, 'fix_' + s]
+                val = _get_fixed_fit_value(kwds, names)
                 if val is not None:
-                    key = 'f%d' % j
-                    if key in kwds:
-                        raise ValueError("Duplicate entry for %s." % key)
-                    else:
-                        kwds[key] = val
+                    kwds[key] = val
 
         args = list(args)
         Nargs = len(args)

scipy/stats/tests/test_distributions.py

@@ -728,6 +728,12 @@ def test_mean_small_p(self):
         assert_allclose(m, 1.000000005)
 
 
+class TestNorm(object):
+    def test_bad_keyword_arg(self):
+        x = [1, 2, 3]
+        assert_raises(TypeError, stats.norm.fit, x, plate="shrimp")
+
+
 class TestPareto(object):
     def test_stats(self):
         # Check the stats() method with some simple values. Also check
@@ -1518,6 +1524,17 @@ def test_logpdf_ticket_1866(self):
         assert_allclose(b.logpdf(x).sum(), -1201.699061824062)
         assert_allclose(b.pdf(x), np.exp(b.logpdf(x)))
 
+    def test_fit_bad_keyword_args(self):
+        x = [0.1, 0.5, 0.6]
+        assert_raises(TypeError, stats.beta.fit, x, floc=0, fscale=1,
+                      plate="shrimp")
+
+    def test_fit_duplicated_fixed_parameter(self):
+        # At most one of 'f0', 'fa' or 'fix_a' can be given to the fit method.
+        # More than one raises a ValueError.
+        x = [0.1, 0.5, 0.6]
+        assert_raises(ValueError, stats.beta.fit, x, fa=0.5, fix_a=0.5)
+
 
 class TestBetaPrime(object):
     def test_logpdf(self):
@@ -1559,6 +1576,10 @@ def test_logpdf(self):
         logpdf = stats.gamma.logpdf(0, 1)
         assert_almost_equal(logpdf, 0)
 
+    def test_fit_bad_keyword_args(self):
+        x = [0.1, 0.5, 0.6]
+        assert_raises(TypeError, stats.gamma.fit, x, floc=0, plate="shrimp")
+
 
 class TestChi2(object):
     # regression tests after precision improvements, ticket:1041, not verified