Move python files to root folder; remove built docs

miplearn/extractors.py

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+#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
+#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
+#  Released under the modified BSD license. See COPYING.md for more details.
+
+import logging
+from abc import ABC, abstractmethod
+
+import numpy as np
+from tqdm import tqdm
+
+logger = logging.getLogger(__name__)
+
+
+class Extractor(ABC):
+    @abstractmethod
+    def extract(self, instances,):
+        pass
+    
+    @staticmethod
+    def split_variables(instance):
+        assert hasattr(instance, "lp_solution")
+        result = {}
+        for var_name in instance.lp_solution:
+            for index in instance.lp_solution[var_name]:
+                category = instance.get_variable_category(var_name, index)
+                if category is None:
+                    continue
+                if category not in result:
+                    result[category] = []
+                result[category] += [(var_name, index)]
+        return result
+
+    
+class VariableFeaturesExtractor(Extractor):
+    def extract(self, instances):
+        result = {}
+        for instance in tqdm(instances,
+                             desc="Extract (vars)",
+                             disable=len(instances) < 5):
+            instance_features = instance.get_instance_features()
+            var_split = self.split_variables(instance)
+            for (category, var_index_pairs) in var_split.items():
+                if category not in result:
+                    result[category] = []
+                for (var_name, index) in var_index_pairs:
+                    result[category] += [
+                        instance_features.tolist() + \
+                        instance.get_variable_features(var_name, index).tolist() + \
+                        [instance.lp_solution[var_name][index]]
+                    ]
+        for category in result:
+            result[category] = np.array(result[category])
+        return result
+
+
+class SolutionExtractor(Extractor):
+    def __init__(self, relaxation=False):
+        self.relaxation = relaxation
+        
+    def extract(self, instances):
+        result = {}
+        for instance in tqdm(instances,
+                             desc="Extract (solution)",
+                             disable=len(instances) < 5):
+            var_split = self.split_variables(instance)
+            for (category, var_index_pairs) in var_split.items():
+                if category not in result:
+                    result[category] = []
+                for (var_name, index) in var_index_pairs:
+                    if self.relaxation:
+                        v = instance.lp_solution[var_name][index]
+                    else:
+                        v = instance.solution[var_name][index]
+                    if v is None:
+                        result[category] += [[0, 0]]
+                    else:
+                        result[category] += [[1 - v, v]]
+        for category in result:
+            result[category] = np.array(result[category])            
+        return result
+    
+    
+class InstanceFeaturesExtractor(Extractor):
+    def extract(self, instances):
+        return np.vstack([
+            np.hstack([
+                instance.get_instance_features(),
+                instance.lp_value,
+            ])
+            for instance in instances
+        ])
+    
+    
+class ObjectiveValueExtractor(Extractor):
+    def __init__(self, kind="lp"):
+        assert kind in ["lower bound", "upper bound", "lp"]
+        self.kind = kind
+        
+    def extract(self, instances):
+        if self.kind == "lower bound":
+            return np.array([[instance.lower_bound] for instance in instances])
+        if self.kind == "upper bound":
+            return np.array([[instance.upper_bound] for instance in instances])
+        if self.kind == "lp":
+            return np.array([[instance.lp_value] for instance in instances])