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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")