Fix various warnings

miplearn/components/relaxation.py

@@ -41,11 +41,11 @@ class RelaxationComponent(Component):
         If a constraint has slack greater than this threshold, then the constraint is
         considered loose. By default, this threshold equals a small positive number to
         compensate for numerical issues.
-    check_dropped : bool, optional
-        If `check_dropped` is true, then, after the problem is solved, the component
-        verifies that all dropped constraints are still satisfied, re-adds the violated
-        ones and resolves the problem. This loop continues until either no violations
-        are found, or a maximum number of iterations is reached.
+    check_feasibility : bool, optional
+        If true, after the problem is solved, the component verifies that all dropped
+        constraints are still satisfied, re-adds the violated ones and resolves the
+        problem. This loop continues until either no violations are found, or a maximum
+        number of iterations is reached.
     violation_tolerance : float, optional
         If `check_dropped` is true, a constraint is considered satisfied during the
         check if its violation is smaller than this tolerance.
@@ -61,7 +61,7 @@ class RelaxationComponent(Component):
         tight_classifier=CountingClassifier(),
         tight_threshold=0.95,
         slack_tolerance=1e-5,
-        check_dropped=False,
+        check_feasibility=False,
         violation_tolerance=1e-5,
         max_check_iterations=3,
     ):
@@ -73,7 +73,7 @@ class RelaxationComponent(Component):
                 slack_tolerance=slack_tolerance,
                 violation_tolerance=violation_tolerance,
                 max_iterations=max_check_iterations,
-                check_dropped=check_dropped,
+                check_feasibility=check_feasibility,
             ),
             ConvertTightIneqsIntoEqsStep(
                 classifier=tight_classifier,
@@ -86,8 +86,8 @@ class RelaxationComponent(Component):
     def before_solve(self, solver, instance, model):
         self.composite.before_solve(solver, instance, model)
 
-    def after_solve(self, solver, instance, model, results):
-        self.composite.after_solve(solver, instance, model, results)
+    def after_solve(self, solver, instance, model, stats, training_data):
+        self.composite.after_solve(solver, instance, model, stats, training_data)
 
     def fit(self, training_instances):
         self.composite.fit(training_instances)

miplearn/log.py

@@ -8,8 +8,9 @@ import time
 import sys
 
 
-class TimeFormatter:
+class TimeFormatter(logging.Formatter):
     def __init__(self, start_time, log_colors):
+        super().__init__()
         self.start_time = start_time
         self.log_colors = log_colors
 

miplearn/solvers/learning.py

@@ -284,7 +284,13 @@ class LearningSolver:
 
         return stats
 
-    def parallel_solve(self, instances, n_jobs=4, label="Solve", output=[]):
+    def parallel_solve(
+        self,
+        instances,
+        n_jobs=4,
+        label="Solve",
+        output=None,
+    ):
         """
         Solves multiple instances in parallel.
 
@@ -295,6 +301,12 @@ class LearningSolver:
 
         Parameters
         ----------
+        output: [str] or None
+            If instances are file names and output is provided, write the modified
+            instances to these files, instead of replacing the original files. If
+            output is None, discard modified instance.
+        label: str
+            Label to show in the progress bar.
         instances: [miplearn.Instance] or [str]
             The instances to be solved
         n_jobs: int
@@ -308,6 +320,8 @@ class LearningSolver:
             [solver.solve(p) for p in instances]
 
         """
+        if output is None:
+            output = []
         self.internal_solver = None
         self._silence_miplearn_logger()
         SOLVER[0] = self

miplearn/solvers/pyomo/base.py

@@ -2,12 +2,12 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import logging
 import re
 import sys
-import logging
-import pyomo
-from abc import abstractmethod
 from io import StringIO
+
+import pyomo
 from pyomo import environ as pe
 from pyomo.core import Var, Constraint
 

requirements.txt

@@ -14,3 +14,4 @@ scikit-learn~=0.23
 tqdm~=4.54
 black==20.8b1
 pre-commit~=2.9
+scipy~=1.6.0