Disable progress bars when stdout it not a TTY

src/python/miplearn/components/branching.py

@@ -4,6 +4,7 @@
 
 import logging
 import os
+import sys
 import subprocess
 import tempfile
 from copy import deepcopy
@@ -25,7 +26,8 @@ class BranchPriorityExtractor(Extractor):
         result = {}
         for instance in tqdm(instances,
                              desc="Extract (branch)",
-                             disable=len(instances) < 5):
+                             disable=len(instances) < 5 or (not sys.stdout.isatty()),
+                            ):
             var_split = self.split_variables(instance)
             for (category, var_index_pairs) in var_split.items():
                 if category not in result:
@@ -55,7 +57,10 @@ class BranchPriorityComponent(Component):
         pass
 
     def fit(self, training_instances, n_jobs=1):
-        for instance in tqdm(training_instances, desc="Fit (branch)"):
+        for instance in tqdm(training_instances,
+                             desc="Fit (branch)",
+                             disable=not sys.stdout.isatty(),
+                            ):
             if not hasattr(instance, "branch_priorities"):
                 instance.branch_priorities = self.compute_priorities(instance)
         x, y = self.x(training_instances), self.y(training_instances)

src/python/miplearn/components/cuts.py

@@ -2,6 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import sys
 from copy import deepcopy
 
 from miplearn.classifiers.counting import CountingClassifier
@@ -52,7 +53,10 @@ class UserCutsComponent(Component):
                     violation_to_instance_idx[v] = []
                 violation_to_instance_idx[v] += [idx]
 
-        for (v, classifier) in tqdm(self.classifiers.items(), desc="Fit (user cuts)"):
+        for (v, classifier) in tqdm(self.classifiers.items(),
+                                    desc="Fit (user cuts)",
+                                    disable=not sys.stdout.isatty(),
+                                   ):
             logger.debug("Training: %s" % (str(v)))
             label = np.zeros(len(training_instances))
             label[violation_to_instance_idx[v]] = 1.0
@@ -72,7 +76,10 @@ class UserCutsComponent(Component):
         all_violations = set()
         for instance in instances:
             all_violations |= set(instance.found_violated_user_cuts)
-        for idx in tqdm(range(len(instances)), desc="Evaluate (lazy)"):
+        for idx in tqdm(range(len(instances)),
+                        desc="Evaluate (lazy)",
+                        disable=not sys.stdout.isatty(),
+                       ):
             instance = instances[idx]
             condition_positive = set(instance.found_violated_user_cuts)
             condition_negative = all_violations - condition_positive

src/python/miplearn/components/lazy.py

@@ -2,6 +2,7 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import sys
 from copy import deepcopy
 
 from miplearn.classifiers.counting import CountingClassifier
@@ -47,12 +48,17 @@ class LazyConstraintsComponent(Component):
         violation_to_instance_idx = {}
         for (idx, instance) in enumerate(training_instances):
             for v in instance.found_violated_lazy_constraints:
+                if isinstance(v, list):
+                    v = tuple(v)
                 if v not in self.classifiers:
                     self.classifiers[v] = deepcopy(self.classifier_prototype)
                     violation_to_instance_idx[v] = []
                 violation_to_instance_idx[v] += [idx]
 
-        for (v, classifier) in tqdm(self.classifiers.items(), desc="Fit (lazy)"):
+        for (v, classifier) in tqdm(self.classifiers.items(),
+                                    desc="Fit (lazy)",
+                                    disable=not sys.stdout.isatty(),
+                                   ):
             logger.debug("Training: %s" % (str(v)))
             label = np.zeros(len(training_instances))
             label[violation_to_instance_idx[v]] = 1.0
@@ -72,7 +78,10 @@ class LazyConstraintsComponent(Component):
         all_violations = set()
         for instance in instances:
             all_violations |= set(instance.found_violated_lazy_constraints)
-        for idx in tqdm(range(len(instances)), desc="Evaluate (lazy)"):
+        for idx in tqdm(range(len(instances)),
+                        desc="Evaluate (lazy)",
+                        disable=not sys.stdout.isatty(),
+                       ):
             instance = instances[idx]
             condition_positive = set(instance.found_violated_lazy_constraints)
             condition_negative = all_violations - condition_positive

src/python/miplearn/components/primal.py

@@ -3,6 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from copy import deepcopy
+import sys
 
 from .component import Component
 from ..classifiers.adaptive import AdaptiveClassifier
@@ -49,7 +50,10 @@ class PrimalSolutionComponent(Component):
         features = VariableFeaturesExtractor().extract(training_instances)
         solutions = SolutionExtractor().extract(training_instances)
 
-        for category in tqdm(features.keys(), desc="Fit (primal)"):
+        for category in tqdm(features.keys(),
+                             desc="Fit (primal)",
+                             disable=not sys.stdout.isatty(),
+                            ):
             x_train = features[category]
             for label in [0, 1]:
                 y_train = solutions[category][:, label].astype(int)
@@ -104,7 +108,9 @@ class PrimalSolutionComponent(Component):
         ev = {"Fix zero": {},
               "Fix one": {}}
         for instance_idx in tqdm(range(len(instances)),
-                                 desc="Evaluate (primal)"):
+                                 desc="Evaluate (primal)",
+                                 disable=not sys.stdout.isatty(),
+                                ):
             instance = instances[instance_idx]
             solution_actual = instance.solution
             solution_pred = self.predict(instance)