Add error message explaining why probe count constraint cannot be sat…

…isfied

When the limit on the number of probes to design is not satisfied, this
now raises an custom exception (CannotSatisfyProbeCountConstraintError)
with a message explaining the likely reason. Previously, only an
assertion failed and it may have been unclear why. This also raises the
exception in the (n>2)-dimensional search, rather than only in the
standard search.

catch/pool/param_search.py

@@ -353,6 +353,12 @@ def _total_probe_count_without_interp(params, probe_counts):
     return s
 
 
+class CannotSatisfyProbeCountConstraintError(Exception):
+    """The search yielded a number of probes that exceeds the given constraint
+    """
+    pass
+
+
 def _round_params(params, probe_counts, max_total_count, loss_coeffs, weights,
         mismatches_eps=0.01, cover_extension_eps=0.1,
         mismatches_round=1, cover_extension_round=1):
@@ -434,12 +440,33 @@ def _round_params(params, probe_counts, max_total_count, loss_coeffs, weights,
 
     total_probe_count = ic._make_total_probe_count_across_datasets_fn(
         probe_counts, interp_fn_type='standard')
+
     # Verify that the probe count satisfies the constraint
     # Note that this assertion may fail if we are dealing with datasets
     # for which few actual probe counts have been computed; in these
     # cases, the interpolation may severely underestimate the number
     # of probes at a particular parameter choice
-    assert total_probe_count(params_rounded) < max_total_count
+    # It may also fail if it is just not possible to satisfy the desired
+    # constraint given the precomputed probe counts (e.g., the total probe
+    # count for the most tolerant precomputed parameter values still exceeds
+    # the constraint)
+    tpc = total_probe_count(params_rounded)
+    if tpc > max_total_count:
+        msg = ("The total probe count based on parameter values found "
+               "in the search (%d) exceeds the given limit (%d). This "
+               "is likely to happen if the range of the precomputed "
+               "parameter values is not as large as it needs to be to "
+               "satisfy the constraint. That is, one or more parameter "
+               "values may need to be more loose to obtain %d probes. To "
+               "fix this, try inputting probe counts for a larger range "
+               "(in particular, less stringent choices) of parameter "
+               "values. Also, note that the search interpolates probe "
+               "counts between precomputed parameter values (%d may be an "
+               "interpolated count) and, if the precomputed parameter values "
+               "are too sparse (i.e., too few actual probe counts were "
+               "input), it may be underestimating the true number of probes "
+               "required." % (tpc, max_total_count, max_total_count, tpc))
+        raise CannotSatisfyProbeCountConstraintError(msg)
 
     # Keep decreasing parameters while satisfying the constraint.
     # In particular, choose to decrease the parameter whose reduction
@@ -686,10 +713,22 @@ def higher_dimensional_search(param_names, probe_counts, max_total_count,
     x_sol_loss = loss_fn(x_sol, 0)
 
     # Verify that the probe count satisfies the constraint
-    # Note that this assertion may fail if we are dealing with datasets
-    # for which few actual probe counts have been computed; in these
-    # cases, the interpolation may severely underestimate the number
-    # of probes at a particular parameter choice
-    assert x_sol_count < max_total_count
+    if x_sol_count > max_total_count:
+        msg = ("The total probe count based on parameter values found "
+               "in the search (%d) exceeds the given limit (%d). This "
+               "is likely to happen if the range of the precomputed "
+               "parameter values is not as large as it needs to be to "
+               "satisfy the constraint. That is, one or more parameter "
+               "values may need to be more loose to obtain %d probes. To "
+               "fix this, try inputting probe counts for a larger range "
+               "(in particular, less stringent choices) of parameter "
+               "values. Also, note that the search interpolates probe "
+               "counts between precomputed parameter values (%d may be an "
+               "interpolated count) and, if the precomputed parameter values "
+               "are too sparse (i.e., too few actual probe counts were "
+               "input), it may be underestimating the true number of probes "
+               "required." % (x_sol_count, max_total_count, max_total_count,
+               x_sol_count))
+        raise CannotSatisfyProbeCountConstraintError(msg)
 
     return (x_sol_dict, x_sol_count, x_sol_loss)

catch/pool/tests/test_param_search.py

@@ -124,6 +124,12 @@ def _search_vwafr_typical_counts(self, search_fn):
             hiv1_mismatches, hiv1_cover_extension = opt_params['hiv1_without_ltr']
             self.assertTrue(hiv1_mismatches > 3 or hiv1_cover_extension > 20)
 
+    def _search_vwafr_too_small_counts(self, search_fn):
+        self.assertEqual(self.param_names_vwafr, ('mismatches', 'cover_extension'))
+        for max_total_count in [1, 1000, 10000]:
+            with self.assertRaises(param_search.CannotSatisfyProbeCountConstraintError):
+                search_fn(max_total_count)
+
     def test_standard_search_vwafr_typical_counts(self):
         """Integration test with the V-WAfr probe set data."""
         def search_fn(max_total_count):
@@ -146,6 +152,13 @@ def test_standard_search_vwafr_high_count(self):
             self.assertEqual(mismatches, 0)
             self.assertEqual(cover_extension, 0)
 
+    def test_standard_search_vwafr_too_small_counts(self):
+        """Integration test with the V-WAfr probe set data."""
+        def search_fn(max_total_count):
+            return param_search.standard_search(self.probe_counts_vwafr,
+                max_total_count)
+        self._search_vwafr_too_small_counts(search_fn)
+
     def test_higher_dimensional_search_vwafr_typical_counts(self):
         """Integration test with the V-WAfr probe set data."""
         def search_fn(max_total_count):
@@ -154,6 +167,14 @@ def search_fn(max_total_count):
                 max_total_count, loss_coeffs=(1.0, 1.0/100.0))
         self._search_vwafr_typical_counts(search_fn)
 
+    def test_higher_dimensional_search_vwafr_too_small_counts(self):
+        """Integration test with the V-WAfr probe set data."""
+        def search_fn(max_total_count):
+            return param_search.higher_dimensional_search(
+                self.param_names_vwafr, self.probe_counts_vwafr,
+                max_total_count, loss_coeffs=(1.0, 1.0/100.0))
+        self._search_vwafr_too_small_counts(search_fn)
+
     def test_higher_dimensional_search_vwafr_with_third_param(self):
         """Integration test with the V-WAfr probe set data.