Train without loading all instances to memory

miplearn/benchmark.py

@@ -37,7 +37,8 @@ class BenchmarkRunner:
         for (solver_name, solver) in self.solvers.items():
             results = solver.parallel_solve(trials,
                                             n_jobs=n_jobs,
-                                            label="Solve (%s)" % solver_name)
+                                            label="Solve (%s)" % solver_name,
+                                            output=None)
             for i in range(len(trials)):
                 idx = (i % len(instances)) + index_offset
                 self._push_result(results[i],

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)",
+                             desc="Fit (relaxation)"):
-                             disable=not sys.stdout.isatty()):
             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:

miplearn/extractors.py

@@ -3,14 +3,41 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from abc import ABC, abstractmethod
+import pickle
+import gzip
 
 import numpy as np
-from tqdm import tqdm
+
+from tqdm.auto import tqdm
+from abc import ABC, abstractmethod
 
 logger = logging.getLogger(__name__)
 
 
+class InstanceIterator:
+    def __init__(self, instances):
+        self.instances = instances
+        self.current = 0
+        
+    def __iter__(self):
+        return self
+    
+    def __next__(self):
+        if self.current >= len(self.instances):
+            raise StopIteration
+        result = self.instances[self.current]
+        self.current += 1
+        if isinstance(result, str):
+            logger.info("Read: %s" % result)
+            if result.endswith(".gz"):
+                with gzip.GzipFile(result, "rb") as file:
+                    result = pickle.load(file)
+            else:
+                with open(result, "rb") as file:
+                    result = pickle.load(file)
+        return result
+
+
 class Extractor(ABC):
     @abstractmethod
     def extract(self, instances,):
@@ -34,7 +61,7 @@ class Extractor(ABC):
 class VariableFeaturesExtractor(Extractor):
     def extract(self, instances):
         result = {}
-        for instance in tqdm(instances,
+        for instance in tqdm(InstanceIterator(instances),
                              desc="Extract (vars)",
                              disable=len(instances) < 5):
             instance_features = instance.get_instance_features()
@@ -59,7 +86,7 @@ class SolutionExtractor(Extractor):
         
     def extract(self, instances):
         result = {}
-        for instance in tqdm(instances,
+        for instance in tqdm(InstanceIterator(instances),
                              desc="Extract (solution)",
                              disable=len(instances) < 5):
             var_split = self.split_variables(instance)
@@ -87,7 +114,7 @@ class InstanceFeaturesExtractor(Extractor):
                 instance.get_instance_features(),
                 instance.lp_value,
             ])
-            for instance in instances
+            for instance in InstanceIterator(instances)
         ])
     
     
@@ -98,8 +125,11 @@ class ObjectiveValueExtractor(Extractor):
         
     def extract(self, instances):
         if self.kind == "lower bound":
-            return np.array([[instance.lower_bound] for instance in instances])
+            return np.array([[instance.lower_bound]
+                             for instance in InstanceIterator(instances)])
         if self.kind == "upper bound":
-            return np.array([[instance.upper_bound] for instance in instances])
+            return np.array([[instance.upper_bound]
+                             for instance in InstanceIterator(instances)])
         if self.kind == "lp":
-            return np.array([[instance.lp_value] for instance in instances])
+            return np.array([[instance.lp_value]
+                             for instance in InstanceIterator(instances)])

miplearn/solvers/learning.py

@@ -6,6 +6,7 @@ import logging
 import pickle
 import os
 import tempfile
+import gzip
 
 from copy import deepcopy
 from typing import Optional, List
@@ -198,11 +199,18 @@ class LearningSolver:
         """
         
         filename = None
+        fileformat = None
         if isinstance(instance, str):
             filename = instance
             logger.info("Reading: %s" % filename)
-            with open(filename, "rb") as file:
+            if filename.endswith(".gz"):
-                instance = pickle.load(file)
+                fileformat = "pickle-gz"
+                with gzip.GzipFile(filename, "rb") as file:
+                    instance = pickle.load(file)
+            else:
+                fileformat = "pickle"
+                with open(filename, "rb") as file:
+                    instance = pickle.load(file)
                 
         if model is None:
             model = instance.to_model()
@@ -260,9 +268,12 @@ class LearningSolver:
             if len(output) == 0:
                 output_filename = filename
             logger.info("Writing: %s" % output_filename)
-            with tempfile.NamedTemporaryFile(delete=False) as tmp:
+            if fileformat == "pickle":
-                pickle.dump(instance, tmp)
+                with open(output_filename, "wb") as file:
-                os.replace(tmp.name, output_filename)
+                    pickle.dump(instance, file)
+            else:
+                with gzip.GzipFile(output_filename, "wb") as file:
+                    pickle.dump(instance, file)
 
         return results