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Replace Hashable by str

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This commit is contained in:
Alinson S. Xavier
2021-07-15 16:21:40 -05:00
parent 8d89285cb9
commit ef9c48d79a
21 changed files with 123 additions and 133 deletions
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8
miplearn/components/component.py
View File

@@ -2,7 +2,7 @@
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Hashable, Optional
from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Optional
import numpy as np
from p_tqdm import p_umap
@@ -101,8 +101,8 @@ class Component:
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
"""
Given two dictionaries x and y, mapping the name of the category to matrices
@@ -152,7 +152,7 @@ class Component:
self,
instance: Optional[Instance],
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
return {}
def sample_xy(

36
miplearn/components/dynamic_common.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import Dict, Hashable, List, Tuple, Optional, Any, Set
from typing import Dict, List, Tuple, Optional, Any, Set
import numpy as np
from overrides import overrides
@@ -32,8 +32,8 @@ class DynamicConstraintsComponent(Component):
assert isinstance(classifier, Classifier)
self.threshold_prototype: Threshold = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Hashable, Classifier] = {}
self.thresholds: Dict[Hashable, Threshold] = {}
self.classifiers: Dict[str, Classifier] = {}
self.thresholds: Dict[str, Threshold] = {}
self.known_cids: List[str] = []
self.attr = attr
@@ -42,14 +42,14 @@ class DynamicConstraintsComponent(Component):
instance: Optional[Instance],
sample: Sample,
) -> Tuple[
Dict[Hashable, List[List[float]]],
Dict[Hashable, List[List[bool]]],
Dict[Hashable, List[str]],
Dict[str, List[List[float]]],
Dict[str, List[List[bool]]],
Dict[str, List[str]],
]:
assert instance is not None
x: Dict[Hashable, List[List[float]]] = {}
y: Dict[Hashable, List[List[bool]]] = {}
cids: Dict[Hashable, List[str]] = {}
x: Dict[str, List[List[float]]] = {}
y: Dict[str, List[List[bool]]] = {}
cids: Dict[str, List[str]] = {}
constr_categories_dict = instance.get_constraint_categories()
constr_features_dict = instance.get_constraint_features()
instance_features = sample.get("instance_features_user")
@@ -111,8 +111,8 @@ class DynamicConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> List[Hashable]:
pred: List[Hashable] = []
) -> List[str]:
pred: List[str] = []
if len(self.known_cids) == 0:
logger.info("Classifiers not fitted. Skipping.")
return pred
@@ -137,8 +137,8 @@ class DynamicConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
for category in x.keys():
self.classifiers[category] = self.classifier_prototype.clone()
@@ -153,14 +153,14 @@ class DynamicConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
actual = sample.get(self.attr)
assert actual is not None
pred = set(self.sample_predict(instance, sample))
tp: Dict[Hashable, int] = {}
tn: Dict[Hashable, int] = {}
fp: Dict[Hashable, int] = {}
fn: Dict[Hashable, int] = {}
tp: Dict[str, int] = {}
tn: Dict[str, int] = {}
fp: Dict[str, int] = {}
fn: Dict[str, int] = {}
constr_categories_dict = instance.get_constraint_categories()
for cid in self.known_cids:
if cid not in constr_categories_dict:

14
miplearn/components/dynamic_lazy.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import Dict, List, TYPE_CHECKING, Hashable, Tuple, Any, Optional, Set
from typing import Dict, List, TYPE_CHECKING, Tuple, Any, Optional, Set
import numpy as np
from overrides import overrides
@@ -41,11 +41,11 @@ class DynamicLazyConstraintsComponent(Component):
self.classifiers = self.dynamic.classifiers
self.thresholds = self.dynamic.thresholds
self.known_cids = self.dynamic.known_cids
self.lazy_enforced: Set[Hashable] = set()
self.lazy_enforced: Set[str] = set()
@staticmethod
def enforce(
cids: List[Hashable],
cids: List[str],
instance: Instance,
model: Any,
solver: "LearningSolver",
@@ -117,7 +117,7 @@ class DynamicLazyConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> List[Hashable]:
) -> List[str]:
return self.dynamic.sample_predict(instance, sample)
@overrides
@@ -127,8 +127,8 @@ class DynamicLazyConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
self.dynamic.fit_xy(x, y)
@@ -137,5 +137,5 @@ class DynamicLazyConstraintsComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample)

14
miplearn/components/dynamic_user_cuts.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import Any, TYPE_CHECKING, Hashable, Set, Tuple, Dict, List, Optional
from typing import Any, TYPE_CHECKING, Set, Tuple, Dict, List, Optional
import numpy as np
from overrides import overrides
@@ -34,7 +34,7 @@ class UserCutsComponent(Component):
threshold=threshold,
attr="user_cuts_enforced",
)
self.enforced: Set[Hashable] = set()
self.enforced: Set[str] = set()
self.n_added_in_callback = 0
@overrides
@@ -71,7 +71,7 @@ class UserCutsComponent(Component):
for cid in cids:
if cid in self.enforced:
continue
assert isinstance(cid, Hashable)
assert isinstance(cid, str)
instance.enforce_user_cut(solver.internal_solver, model, cid)
self.enforced.add(cid)
self.n_added_in_callback += 1
@@ -110,7 +110,7 @@ class UserCutsComponent(Component):
self,
instance: "Instance",
sample: Sample,
) -> List[Hashable]:
) -> List[str]:
return self.dynamic.sample_predict(instance, sample)
@overrides
@@ -120,8 +120,8 @@ class UserCutsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
self.dynamic.fit_xy(x, y)
@@ -130,5 +130,5 @@ class UserCutsComponent(Component):
self,
instance: "Instance",
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
return self.dynamic.sample_evaluate(instance, sample)

16
miplearn/components/objective.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import List, Dict, Any, TYPE_CHECKING, Tuple, Hashable, Optional
from typing import List, Dict, Any, TYPE_CHECKING, Tuple, Optional
import numpy as np
from overrides import overrides
@@ -53,8 +53,8 @@ class ObjectiveValueComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
for c in ["Upper bound", "Lower bound"]:
if c in y:
@@ -76,20 +76,20 @@ class ObjectiveValueComponent(Component):
self,
_: Optional[Instance],
sample: Sample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
lp_instance_features = sample.get("lp_instance_features")
if lp_instance_features is None:
lp_instance_features = sample.get("instance_features_user")
assert lp_instance_features is not None
# Features
x: Dict[Hashable, List[List[float]]] = {
x: Dict[str, List[List[float]]] = {
"Upper bound": [lp_instance_features],
"Lower bound": [lp_instance_features],
}
# Labels
y: Dict[Hashable, List[List[float]]] = {}
y: Dict[str, List[List[float]]] = {}
mip_lower_bound = sample.get("mip_lower_bound")
mip_upper_bound = sample.get("mip_upper_bound")
if mip_lower_bound is not None:
@@ -104,7 +104,7 @@ class ObjectiveValueComponent(Component):
self,
instance: Instance,
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
def compare(y_pred: float, y_actual: float) -> Dict[str, float]:
err = np.round(abs(y_pred - y_actual), 8)
return {
@@ -114,7 +114,7 @@ class ObjectiveValueComponent(Component):
"Relative error": err / y_actual,
}
result: Dict[Hashable, Dict[str, float]] = {}
result: Dict[str, Dict[str, float]] = {}
pred = self.sample_predict(sample)
actual_ub = sample.get("mip_upper_bound")
actual_lb = sample.get("mip_lower_bound")

26
miplearn/components/primal.py
View File

@@ -3,15 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import (
Dict,
List,
Hashable,
Any,
TYPE_CHECKING,
Tuple,
Optional,
)
from typing import Dict, List, Any, TYPE_CHECKING, Tuple, Optional
import numpy as np
from overrides import overrides
@@ -55,8 +47,8 @@ class PrimalSolutionComponent(Component):
assert isinstance(threshold, Threshold)
assert mode in ["exact", "heuristic"]
self.mode = mode
self.classifiers: Dict[Hashable, Classifier] = {}
self.thresholds: Dict[Hashable, Threshold] = {}
self.classifiers: Dict[str, Classifier] = {}
self.thresholds: Dict[str, Threshold] = {}
self.threshold_prototype = threshold
self.classifier_prototype = classifier
@@ -128,7 +120,7 @@ class PrimalSolutionComponent(Component):
# Convert y_pred into solution
solution: Solution = {v: None for v in var_names}
category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
category_offset: Dict[str, int] = {cat: 0 for cat in x.keys()}
for (i, var_name) in enumerate(var_names):
category = var_categories[i]
if category not in category_offset:
@@ -194,7 +186,7 @@ class PrimalSolutionComponent(Component):
self,
_: Optional[Instance],
sample: Sample,
) -> Dict[Hashable, Dict[str, float]]:
) -> Dict[str, Dict[str, float]]:
mip_var_values = sample.get("mip_var_values")
var_names = sample.get("var_names")
assert mip_var_values is not None
@@ -221,13 +213,13 @@ class PrimalSolutionComponent(Component):
pred_one_negative = vars_all - pred_one_positive
pred_zero_negative = vars_all - pred_zero_positive
return {
0: classifier_evaluation_dict(
"0": classifier_evaluation_dict(
tp=len(pred_zero_positive & vars_zero),
tn=len(pred_zero_negative & vars_one),
fp=len(pred_zero_positive & vars_one),
fn=len(pred_zero_negative & vars_zero),
),
1: classifier_evaluation_dict(
"1": classifier_evaluation_dict(
tp=len(pred_one_positive & vars_one),
tn=len(pred_one_negative & vars_zero),
fp=len(pred_one_positive & vars_zero),
@@ -238,8 +230,8 @@ class PrimalSolutionComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
for category in x.keys():
clf = self.classifier_prototype.clone()

30
miplearn/components/static_lazy.py
View File

@@ -3,7 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
from typing import Dict, Tuple, List, Hashable, Any, TYPE_CHECKING, Set, Optional
from typing import Dict, Tuple, List, Any, TYPE_CHECKING, Set, Optional
import numpy as np
from overrides import overrides
@@ -44,11 +44,11 @@ class StaticLazyConstraintsComponent(Component):
assert isinstance(classifier, Classifier)
self.classifier_prototype: Classifier = classifier
self.threshold_prototype: Threshold = threshold
self.classifiers: Dict[Hashable, Classifier] = {}
self.thresholds: Dict[Hashable, Threshold] = {}
self.classifiers: Dict[str, Classifier] = {}
self.thresholds: Dict[str, Threshold] = {}
self.pool: Constraints = Constraints()
self.violation_tolerance: float = violation_tolerance
self.enforced_cids: Set[Hashable] = set()
self.enforced_cids: Set[str] = set()
self.n_restored: int = 0
self.n_iterations: int = 0
@@ -105,8 +105,8 @@ class StaticLazyConstraintsComponent(Component):
@overrides
def fit_xy(
self,
x: Dict[Hashable, np.ndarray],
y: Dict[Hashable, np.ndarray],
x: Dict[str, np.ndarray],
y: Dict[str, np.ndarray],
) -> None:
for c in y.keys():
assert c in x
@@ -136,9 +136,9 @@ class StaticLazyConstraintsComponent(Component):
) -> None:
self._check_and_add(solver)
def sample_predict(self, sample: Sample) -> List[Hashable]:
def sample_predict(self, sample: Sample) -> List[str]:
x, y, cids = self._sample_xy_with_cids(sample)
enforced_cids: List[Hashable] = []
enforced_cids: List[str] = []
for category in x.keys():
if category not in self.classifiers:
continue
@@ -156,7 +156,7 @@ class StaticLazyConstraintsComponent(Component):
self,
_: Optional[Instance],
sample: Sample,
) -> Tuple[Dict[Hashable, List[List[float]]], Dict[Hashable, List[List[float]]]]:
) -> Tuple[Dict[str, List[List[float]]], Dict[str, List[List[float]]]]:
x, y, __ = self._sample_xy_with_cids(sample)
return x, y
@@ -197,13 +197,13 @@ class StaticLazyConstraintsComponent(Component):
def _sample_xy_with_cids(
self, sample: Sample
) -> Tuple[
Dict[Hashable, List[List[float]]],
Dict[Hashable, List[List[float]]],
Dict[Hashable, List[str]],
Dict[str, List[List[float]]],
Dict[str, List[List[float]]],
Dict[str, List[str]],
]:
x: Dict[Hashable, List[List[float]]] = {}
y: Dict[Hashable, List[List[float]]] = {}
cids: Dict[Hashable, List[str]] = {}
x: Dict[str, List[List[float]]] = {}
y: Dict[str, List[List[float]]] = {}
cids: Dict[str, List[str]] = {}
instance_features = sample.get("instance_features_user")
constr_features = sample.get("lp_constr_features")
constr_names = sample.get("constr_names")

18
miplearn/features/extractor.py
View File

@@ -5,7 +5,7 @@
import collections
import numbers
from math import log, isfinite
from typing import TYPE_CHECKING, Dict, Optional, List, Hashable, Any
from typing import TYPE_CHECKING, Dict, Optional, List, Any
import numpy as np
@@ -142,7 +142,7 @@ class FeaturesExtractor:
instance: "Instance",
sample: Sample,
) -> None:
categories: List[Optional[Hashable]] = []
categories: List[Optional[str]] = []
user_features: List[Optional[List[float]]] = []
var_features_dict = instance.get_variable_features()
var_categories_dict = instance.get_variable_categories()
@@ -153,9 +153,9 @@ class FeaturesExtractor:
user_features.append(None)
categories.append(None)
continue
category: Hashable = var_categories_dict[var_name]
assert isinstance(category, collections.Hashable), (
f"Variable category must be be hashable. "
category: str = var_categories_dict[var_name]
assert isinstance(category, str), (
f"Variable category must be a string. "
f"Found {type(category).__name__} instead for var={var_name}."
)
categories.append(category)
@@ -187,7 +187,7 @@ class FeaturesExtractor:
) -> None:
has_static_lazy = instance.has_static_lazy_constraints()
user_features: List[Optional[List[float]]] = []
categories: List[Optional[Hashable]] = []
categories: List[Optional[str]] = []
lazy: List[bool] = []
constr_categories_dict = instance.get_constraint_categories()
constr_features_dict = instance.get_constraint_features()
@@ -195,15 +195,15 @@ class FeaturesExtractor:
assert constr_names is not None
for (cidx, cname) in enumerate(constr_names):
category: Optional[Hashable] = cname
category: Optional[str] = cname
if cname in constr_categories_dict:
category = constr_categories_dict[cname]
if category is None:
user_features.append(None)
categories.append(None)
continue
assert isinstance(category, collections.Hashable), (
f"Constraint category must be hashable. "
assert isinstance(category, str), (
f"Constraint category must be a string. "
f"Found {type(category).__name__} instead for cname={cname}.",
)
categories.append(category)

23
miplearn/instance/base.py
View File

@@ -4,7 +4,7 @@
import logging
from abc import ABC, abstractmethod
from typing import Any, List, Hashable, TYPE_CHECKING, Dict
from typing import Any, List, TYPE_CHECKING, Dict
from miplearn.features.sample import Sample
@@ -83,7 +83,7 @@ class Instance(ABC):
"""
return {}
def get_variable_categories(self) -> Dict[str, Hashable]:
def get_variable_categories(self) -> Dict[str, str]:
"""
Returns a dictionary mapping the name of each variable to its category.
@@ -91,7 +91,6 @@ class Instance(ABC):
internal ML model to predict the values of both variables. If a variable is not
listed in the dictionary, ML models will ignore the variable.
A category can be any hashable type, such as strings, numbers or tuples.
By default, returns {}.
"""
return {}
@@ -99,7 +98,7 @@ class Instance(ABC):
def get_constraint_features(self) -> Dict[str, List[float]]:
return {}
def get_constraint_categories(self) -> Dict[str, Hashable]:
def get_constraint_categories(self) -> Dict[str, str]:
return {}
def has_static_lazy_constraints(self) -> bool:
@@ -115,7 +114,7 @@ class Instance(ABC):
self,
solver: "InternalSolver",
model: Any,
) -> List[Hashable]:
) -> List[str]:
"""
Returns lazy constraint violations found for the current solution.
@@ -125,10 +124,10 @@ class Instance(ABC):
resolve the problem. The process repeats until no further lazy constraint
violations are found.
Each "violation" is simply a string, a tuple or any other hashable type which
allows the instance to identify unambiguously which lazy constraint should be
generated. In the Traveling Salesman Problem, for example, a subtour
violation could be a frozen set containing the cities in the subtour.
Each "violation" is simply a string which allows the instance to identify
unambiguously which lazy constraint should be generated. In the Traveling
Salesman Problem, for example, a subtour violation could be a string
containing the cities in the subtour.
The current solution can be queried with `solver.get_solution()`. If the solver
is configured to use lazy callbacks, this solution may be non-integer.
@@ -141,7 +140,7 @@ class Instance(ABC):
self,
solver: "InternalSolver",
model: Any,
violation: Hashable,
violation: str,
) -> None:
"""
Adds constraints to the model to ensure that the given violation is fixed.
@@ -167,14 +166,14 @@ class Instance(ABC):
def has_user_cuts(self) -> bool:
return False
def find_violated_user_cuts(self, model: Any) -> List[Hashable]:
def find_violated_user_cuts(self, model: Any) -> List[str]:
return []
def enforce_user_cut(
self,
solver: "InternalSolver",
model: Any,
violation: Hashable,
violation: str,
) -> Any:
return None

14
miplearn/instance/picklegz.py
View File

@@ -6,7 +6,7 @@ import gc
import gzip
import os
import pickle
from typing import Optional, Any, List, Hashable, cast, IO, TYPE_CHECKING, Dict
from typing import Optional, Any, List, cast, IO, TYPE_CHECKING, Dict
from overrides import overrides
@@ -52,7 +52,7 @@ class PickleGzInstance(Instance):
return self.instance.get_variable_features()
@overrides
def get_variable_categories(self) -> Dict[str, Hashable]:
def get_variable_categories(self) -> Dict[str, str]:
assert self.instance is not None
return self.instance.get_variable_categories()
@@ -62,7 +62,7 @@ class PickleGzInstance(Instance):
return self.instance.get_constraint_features()
@overrides
def get_constraint_categories(self) -> Dict[str, Hashable]:
def get_constraint_categories(self) -> Dict[str, str]:
assert self.instance is not None
return self.instance.get_constraint_categories()
@@ -86,7 +86,7 @@ class PickleGzInstance(Instance):
self,
solver: "InternalSolver",
model: Any,
) -> List[Hashable]:
) -> List[str]:
assert self.instance is not None
return self.instance.find_violated_lazy_constraints(solver, model)
@@ -95,13 +95,13 @@ class PickleGzInstance(Instance):
self,
solver: "InternalSolver",
model: Any,
violation: Hashable,
violation: str,
) -> None:
assert self.instance is not None
self.instance.enforce_lazy_constraint(solver, model, violation)
@overrides
def find_violated_user_cuts(self, model: Any) -> List[Hashable]:
def find_violated_user_cuts(self, model: Any) -> List[str]:
assert self.instance is not None
return self.instance.find_violated_user_cuts(model)
@@ -110,7 +110,7 @@ class PickleGzInstance(Instance):
self,
solver: "InternalSolver",
model: Any,
violation: Hashable,
violation: str,
) -> None:
assert self.instance is not None
self.instance.enforce_user_cut(solver, model, violation)

4
miplearn/problems/knapsack.py
View File

@@ -1,7 +1,8 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import List, Dict, Optional, Hashable, Any
from typing import List, Dict, Optional
import numpy as np
import pyomo.environ as pe
@@ -10,7 +11,6 @@ from scipy.stats import uniform, randint, rv_discrete
from scipy.stats.distributions import rv_frozen
from miplearn.instance.base import Instance
from miplearn.types import VariableName, Category
class ChallengeA:

5
miplearn/problems/stab.py
View File

@@ -1,7 +1,7 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import List, Dict, Hashable
from typing import List, Dict
import networkx as nx
import numpy as np
@@ -12,7 +12,6 @@ from scipy.stats import uniform, randint
from scipy.stats.distributions import rv_frozen
from miplearn.instance.base import Instance
from miplearn.types import VariableName, Category
class ChallengeA:
@@ -85,7 +84,7 @@ class MaxWeightStableSetInstance(Instance):
return features
@overrides
def get_variable_categories(self) -> Dict[str, Hashable]:
def get_variable_categories(self) -> Dict[str, str]:
return {f"x[{v}]": "default" for v in self.nodes}

7
miplearn/problems/tsp.py
View File

@@ -1,7 +1,7 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import List, Tuple, FrozenSet, Any, Optional, Hashable, Dict
from typing import List, Tuple, FrozenSet, Any, Optional, Dict
import networkx as nx
import numpy as np
@@ -11,10 +11,9 @@ from scipy.spatial.distance import pdist, squareform
from scipy.stats import uniform, randint
from scipy.stats.distributions import rv_frozen
from miplearn.instance.base import Instance
from miplearn.solvers.learning import InternalSolver
from miplearn.solvers.pyomo.base import BasePyomoSolver
from miplearn.instance.base import Instance
from miplearn.types import VariableName, Category
class ChallengeA:
@@ -82,7 +81,7 @@ class TravelingSalesmanInstance(Instance):
return model
@overrides
def get_variable_categories(self) -> Dict[str, Hashable]:
def get_variable_categories(self) -> Dict[str, str]:
return {f"x[{e}]": f"x[{e}]" for e in self.edges}
@overrides

4
miplearn/solvers/gurobi.py
View File

@@ -6,7 +6,7 @@ import re
import sys
from io import StringIO
from random import randint
from typing import List, Any, Dict, Optional, Hashable, Tuple, TYPE_CHECKING
from typing import List, Any, Dict, Optional, Tuple, TYPE_CHECKING
from overrides import overrides
@@ -672,7 +672,7 @@ class GurobiTestInstanceKnapsack(PyomoTestInstanceKnapsack):
self,
solver: InternalSolver,
model: Any,
violation: Hashable,
violation: str,
) -> None:
x0 = model.getVarByName("x[0]")
model.cbLazy(x0 <= 0)

4
miplearn/solvers/pyomo/base.py
View File

@@ -6,7 +6,7 @@ import logging
import re
import sys
from io import StringIO
from typing import Any, List, Dict, Optional, Tuple, Hashable
from typing import Any, List, Dict, Optional, Tuple
import numpy as np
import pyomo
@@ -639,5 +639,5 @@ class PyomoTestInstanceKnapsack(Instance):
}
@overrides
def get_variable_categories(self) -> Dict[str, Hashable]:
def get_variable_categories(self) -> Dict[str, str]:
return {f"x[{i}]": "default" for i in range(len(self.weights))}

4
miplearn/types.py
View File

@@ -2,7 +2,7 @@
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Optional, Dict, Callable, Any, Union, TYPE_CHECKING, Hashable
from typing import Optional, Dict, Callable, Any, Union, TYPE_CHECKING
from mypy_extensions import TypedDict
@@ -10,7 +10,7 @@ if TYPE_CHECKING:
# noinspection PyUnresolvedReferences
from miplearn.solvers.learning import InternalSolver
Category = Hashable
Category = str
IterationCallback = Callable[[], bool]
LazyCallback = Callable[[Any, Any], None]
SolverParams = Dict[str, Any]