Train without loading all instances to memory

miplearn/components/primal.py

@@ -53,7 +53,6 @@ class PrimalSolutionComponent(Component):
 
         for category in tqdm(features.keys(),
                              desc="Fit (primal)",
-                             disable=not sys.stdout.isatty(),
                             ):
             x_train = features[category]
             for label in [0, 1]:
@@ -110,7 +109,6 @@ class PrimalSolutionComponent(Component):
               "Fix one": {}}
         for instance_idx in tqdm(range(len(instances)),
                                  desc="Evaluate (primal)",
-                                 disable=not sys.stdout.isatty(),
                                 ):
             instance = instances[instance_idx]
             solution_actual = instance.solution

miplearn/components/relaxation.py

@@ -8,10 +8,11 @@ from copy import deepcopy
 
 import numpy as np
 from miplearn.components import classifier_evaluation_dict
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from miplearn import Component
 from miplearn.classifiers.counting import CountingClassifier
+from miplearn.extractors import InstanceIterator
 
 logger = logging.getLogger(__name__)
 
@@ -60,16 +61,12 @@ class RelaxationComponent(Component):
         instance.slacks = solver.internal_solver.get_constraint_slacks()
 
     def fit(self, training_instances):
-        training_instances = [instance
-                              for instance in training_instances
-                              if hasattr(instance, "slacks")]
         logger.debug("Extracting x and y...")
         x = self.x(training_instances)
         y = self.y(training_instances)
         logger.debug("Fitting...")
         for category in tqdm(x.keys(),
-                             desc="Fit (relaxation)",
-                             disable=not sys.stdout.isatty()):
+                             desc="Fit (relaxation)"):
             if category not in self.classifiers:
                 self.classifiers[category] = deepcopy(self.classifier_prototype)
             self.classifiers[category].fit(x[category], y[category])
@@ -80,7 +77,9 @@ class RelaxationComponent(Component):
           return_constraints=False):
         x = {}
         constraints = {}
-        for instance in instances:
+        for instance in tqdm(InstanceIterator(instances),
+                             desc="Extract (relaxation:x)",
+                             disable=len(instances) < 5):
             if constraint_ids is not None:
                 cids = constraint_ids
             else:
@@ -101,7 +100,9 @@ class RelaxationComponent(Component):
 
     def y(self, instances):
         y = {}
-        for instance in instances:
+        for instance in tqdm(InstanceIterator(instances),
+                             desc="Extract (relaxation:y)",
+                             disable=len(instances) < 5):
             for (cid, slack) in instance.slacks.items():
                 category = instance.get_constraint_category(cid)
                 if category is None:
@@ -120,7 +121,7 @@ class RelaxationComponent(Component):
             if category not in self.classifiers:
                 continue
             y[category] = []
-            #x_cat = np.array(x_cat)
+            x_cat = np.array(x_cat)
             proba = self.classifiers[category].predict_proba(x_cat)
             for i in range(len(proba)):
                 if proba[i][1] >= self.threshold: