Silence debug statements

miplearn/extractors.py

@@ -28,7 +28,7 @@ class InstanceIterator:
         result = self.instances[self.current]
         self.current += 1
         if isinstance(result, str):
-            logger.info("Read: %s" % result)
+            logger.debug("Read: %s" % result)
             if result.endswith(".gz"):
                 with gzip.GzipFile(result, "rb") as file:
                     result = pickle.load(file)

miplearn/solvers/gurobi.py

@@ -84,16 +84,17 @@ class GurobiSolver(InternalSolver):
                     self._bin_vars[name] = {}
                 self._bin_vars[name][idx] = var
 
-    def _apply_params(self):
+    def _apply_params(self, streams):
+        with RedirectOutput(streams):
             for (name, value) in self.params.items():
                 self.model.setParam(name, value)
 
     def solve_lp(self, tee=False):
         self._raise_if_callback()
-        self._apply_params()
         streams = [StringIO()]
         if tee:
             streams += [sys.stdout]
+        self._apply_params(streams)
         for (varname, vardict) in self._bin_vars.items():
             for (idx, var) in vardict.items():
                 var.vtype = self.GRB.CONTINUOUS
@@ -122,12 +123,12 @@ class GurobiSolver(InternalSolver):
 
         if lazy_cb:
             self.params["LazyConstraints"] = 1
-        self._apply_params()
         total_wallclock_time = 0
         total_nodes = 0
         streams = [StringIO()]
         if tee:
             streams += [sys.stdout]
+        self._apply_params(streams)
         if iteration_cb is None:
             iteration_cb = lambda: False
         while True:

miplearn/solvers/learning.py

@@ -12,6 +12,7 @@ from copy import deepcopy
 from typing import Optional, List
 from p_tqdm import p_map
 
+from . import RedirectOutput
 from .. import (
     ObjectiveValueComponent,
     PrimalSolutionComponent,
@@ -38,7 +39,6 @@ def _parallel_solve(idx):
     else:
         output = OUTPUTS[0][idx]
     instance = INSTANCES[0][idx]
-    print(instance)
     stats = solver.solve(instance, output=output)
     return (stats, instance)
 
@@ -219,6 +219,7 @@ class LearningSolver:
                     instance = pickle.load(file)
 
         if model is None:
+            with RedirectOutput([]):
             model = instance.to_model()
 
         self.tee = tee