Merge ae1b6c3 into 65827cc

gpkit/constraints/bounded.py

@@ -3,7 +3,7 @@
 import numpy as np
 from .. import Variable
 from .set import ConstraintSet
-from ..small_scripts import appendsolwarning
+from ..small_scripts import appendsolwarning, initsolwarning
 
 
 def varkey_bounds(varkeys, lower, upper):
@@ -77,6 +77,7 @@ def process_result(self, result):
     def check_boundaries(self, result):
         "Creates (and potentially prints) a dictionary of unbounded variables."
         out = defaultdict(set)
+        initsolwarning(result, "Arbitrarily Bounded Variables")
         for i, varkey in enumerate(self.bound_varkeys):
             value = result["variables"][varkey]
             c_senss = [result["sensitivities"]["constraints"].get(c, 0)

gpkit/constraints/gp.py

@@ -222,15 +222,17 @@ def solve(self, solver=None, *, verbosity=1, gen_result=True, **kwargs):
                 msg = ("The model ran to an infinitely low cost;"
                        " bounding the right variables would prevent this.")
             elif isinstance(infeasibility, UnknownInfeasible):
-                msg = "The solver failed for an unknown reason."
+                msg = ("Solver failed for an unknown reason. Relaxing"
+                       " constraints/constants, bounding variables, or"
+                       " using a different solver might fix it.")
             if (verbosity > 0 and solver_out["soltime"] < 1
                     and hasattr(self, "model")):  # fast, top-level model
                 print(msg + "\nSince the model solved in less than a second,"
                       " let's run `.debug()` to analyze what happened.\n`")
                 return self.model.debug(solver=solver)
             # else, raise a clarifying error
-            msg += (" Running `.debug()` may pinpoint the trouble. You can"
-                    " also try another solver, or increase the verbosity.")
+            msg += (" Running `.debug()` or increasing verbosity may pinpoint"
+                    " the trouble.")
             raise infeasibility.__class__(msg) from infeasibility
 
         if not gen_result:
@@ -330,8 +332,7 @@ def _compile_result(self, solver_out):
                 with warnings.catch_warnings():  # skip pesky divide-by-zeros
                     warnings.simplefilter("ignore")
                     dlogv_dlogc = dv_dc * result["constants"][c]/val
-                    before = gpv_ss.get(c, 0)
-                    gpv_ss[c] = before + dlogcost_dlogv*dlogv_dlogc
+                    gpv_ss[c] = gpv_ss.get(c, 0) + dlogcost_dlogv*dlogv_dlogc
                 if v in cost_senss:
                     if c in self.cost.vks:
                         dlogcost_dlogv = cost_senss.pop(v)

gpkit/constraints/loose.py

@@ -1,6 +1,6 @@
 "Implements Loose"
 from .set import ConstraintSet
-from ..small_scripts import appendsolwarning
+from ..small_scripts import appendsolwarning, initsolwarning
 
 
 class Loose(ConstraintSet):
@@ -15,6 +15,7 @@ def __init__(self, constraints, *, senstol=None):
     def process_result(self, result):
         "Checks that all constraints are satisfied with equality"
         super().process_result(result)
+        initsolwarning(result, "Unexpectedly Tight Constraints")
         for constraint in self.flat():
             c_senss = result["sensitivities"]["constraints"].get(constraint, 0)
             if c_senss >= self.senstol:

gpkit/constraints/prog_factories.py

@@ -15,6 +15,9 @@ def evaluate_linked(constants, linked):
                    for k, v in constants.items()})
     kdc_plain = None
     array_calulated = {}
+    for key in constants:  # remove gradients from constants
+        if key.gradients:
+            del key.descr["gradients"]
     for v, f in linked.items():
         try:
             if v.veckey and v.veckey.original_fn:
@@ -130,7 +133,7 @@ def run_sweep(genfunction, self, solution, skipsweepfailures,
                    for (var, grid) in zip(sweepvars, sweep_grids)}
 
     if verbosity > 0:
-        print("Sweeping over %i solves." % N_passes)
+        print("Sweeping with %i solves:" % N_passes)
         tic = time()
 
     self.program = []
@@ -143,6 +146,8 @@ def run_sweep(genfunction, self, solution, skipsweepfailures,
         program, solvefn = genfunction(self, constants)
         self.program.append(program)  # NOTE: SIDE EFFECTS
         try:
+            if verbosity > 1:
+                print("\nSolve %i:" % i)
             result = solvefn(solver, verbosity=verbosity-1, **solveargs)
             if solveargs.get("process_result", True):
                 self.process_result(result)
@@ -151,10 +156,13 @@ def run_sweep(genfunction, self, solution, skipsweepfailures,
             last_error = e
             if not skipsweepfailures:
                 raise RuntimeWarning(
-                    "Sweep halted! Progress saved to m.program. To skip over"
-                    " such failures, solve with skipsweepfailures=True.") from e
+                    "Solve %i was infeasible; progress saved to m.program."
+                    " To continue sweeping after failures, solve with"
+                    " skipsweepfailures=True." % i) from e
+            if verbosity > 0:
+                print("Solve %i was %s." % (i, e.__class__.__name__))
     if not solution:
-        raise RuntimeWarning("No sweeps solved successfully.") from last_error
+        raise RuntimeWarning("All solves were infeasible.") from last_error
 
     solution["sweepvariables"] = KeyDict()
     ksweep = KeyDict(sweep)

gpkit/constraints/relax.py

@@ -3,7 +3,7 @@
 from ..nomials import Variable, VectorVariable, parse_subs, NomialArray
 from ..keydict import KeyDict
 from .. import NamedVariables, SignomialsEnabled
-from ..small_scripts import appendsolwarning, mag
+from ..small_scripts import appendsolwarning, initsolwarning, mag
 
 
 class ConstraintsRelaxedEqually(ConstraintSet):
@@ -50,6 +50,7 @@ def process_result(self, result):
 
     def check_relaxed(self, result):
         "Adds relaxation warnings to the result"
+        initsolwarning(result, "Relaxed Constraints")
         for val, msg in get_relaxed([result["freevariables"][self.relaxvar]],
                                     ["All constraints relaxed by %i%%"]):
             appendsolwarning(msg % (0.9+(val-1)*100), self, result,
@@ -103,6 +104,7 @@ def check_relaxed(self, result):
         "Adds relaxation warnings to the result"
         relaxed = get_relaxed(result["freevariables"][self.relaxvars],
                               range(len(self["relaxed constraints"])))
+        initsolwarning(result, "Relaxed Constraints")
         for relaxval, i in relaxed:
             relax_percent = "%i%%" % (0.5+(relaxval-1)*100)
             oldconstraint = self.original_constraints[i]
@@ -224,6 +226,7 @@ def check_relaxed(self, result):
         "Adds relaxation warnings to the result"
         relaxed = get_relaxed([result["freevariables"][r]
                                for r in self.relaxvars], self.freedvars)
+        initsolwarning(result, "Relaxed Constants")
         for (_, freed) in relaxed:
             msg = ("  %s: relaxed from %-.4g to %-.4g"
                    % (freed,

gpkit/constraints/tight.py

@@ -1,7 +1,7 @@
 "Implements Tight"
 from .set import ConstraintSet
 from ..small_scripts import mag
-from ..small_scripts import appendsolwarning
+from ..small_scripts import appendsolwarning, initsolwarning
 from .. import SignomialsEnabled
 
 
@@ -18,6 +18,7 @@ def process_result(self, result):
         "Checks that all constraints are satisfied with equality"
         super().process_result(result)
         variables = result["variables"]
+        initsolwarning(result, "Unexpectedly Loose Constraints")
         for constraint in self.flat():
             with SignomialsEnabled():
                 leftval = constraint.left.sub(variables).value

gpkit/small_scripts.py

@@ -3,12 +3,16 @@
 import numpy as np
 
 
-def appendsolwarning(msg, data, result, category="uncategorized"):
-    "Append a particular category of warnings to a solution."
+def initsolwarning(result, category="uncategorized"):
+    "Creates a results dictionary for a particular category of warning."
     if "warnings" not in result:
         result["warnings"] = {}
     if category not in result["warnings"]:
         result["warnings"][category] = []
+
+
+def appendsolwarning(msg, data, result, category="uncategorized"):
+    "Append a particular category of warnings to a solution."
     result["warnings"][category].append((msg, data))
 
 

gpkit/solution_array.py

@@ -67,24 +67,30 @@ def msenss_table(data, _, **kwargs):
     lines = ["Model Sensitivities", "-------------------"]
     if kwargs["sortmodelsbysenss"]:
         lines[0] += " (sorts models in sections below)"
+    previousmsenssstr = ""
     for model, msenss in data:
         if not model:  # for now let's only do named models
             continue
         if (msenss < 0.1).all():
             msenss = np.max(msenss)
             if msenss:
-                msenssstr = "%6s" % ("<1E%i" % np.log10(msenss))
+                msenssstr = "%6s" % ("<1e%i" % np.log10(msenss))
             else:
                 msenssstr = "  =0  "
         elif not msenss.shape:
             msenssstr = "%+6.1f" % msenss
         else:
             meansenss = np.mean(msenss)
+            msenssstr = "%+6.1f" % meansenss
             deltas = msenss - meansenss
-            deltastrs = ["%+4.1f" % d if abs(d) >= 0.1 else "  - "
-                         for d in deltas]
-            msenssstr = "%+6.1f + [ %s ]" % (meansenss, "  ".join(deltastrs))
-
+            if np.max(np.abs(deltas)) > 0.1:
+                deltastrs = ["%+4.1f" % d if abs(d) >= 0.1 else "  - "
+                             for d in deltas]
+                msenssstr += " + [ %s ]" % "  ".join(deltastrs)
+        if msenssstr == previousmsenssstr:
+            msenssstr = "      "
+        else:
+            previousmsenssstr = msenssstr
         lines.append("%s : %s" % (msenssstr, model))
     return lines + [""] if len(lines) > 3 else []
 
@@ -234,9 +240,13 @@ def warnings_table(self, _, **kwargs):
         return []
     for wtype in sorted(self["warnings"]):
         data_vec = self["warnings"][wtype]
+        if len(data_vec) == 0:
+            continue
         if not hasattr(data_vec, "shape"):
             data_vec = [data_vec]
         for i, data in enumerate(data_vec):
+            if len(data) == 0:
+                continue
             data = sorted(data, key=lambda l: l[0])  # sort by msg
             title = wtype
             if len(data_vec) > 1:
@@ -650,8 +660,8 @@ def summary(self, showvars=(), ntopsenss=5, **kwargs):
         return out
 
     def table(self, showvars=(),
-              tables=("cost", "warnings", "sweepvariables",
-                      "model sensitivities", "freevariables",
+              tables=("cost", "warnings", "model sensitivities",
+                      "sweepvariables", "freevariables",
                       "constants", "sensitivities", "tightest constraints"),
               sortmodelsbysenss=True, **kwargs):
         """A table representation of this SolutionArray