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Remove {get,put}_set and deprecated functions

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This commit is contained in:
Alinson S. Xavier
2021-08-10 17:27:06 -05:00
parent ed58242b5c
commit 9cfb31bacb
11 changed files with 56 additions and 124 deletions
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13
miplearn/components/dynamic_common.py
View File

@@ -56,6 +56,11 @@ class DynamicConstraintsComponent(Component):
cids: Dict[ConstraintCategory, List[ConstraintName]] = {}
known_cids = np.array(self.known_cids, dtype="S")
enforced_cids = None
enforced_cids_np = sample.get_array(self.attr)
if enforced_cids_np is not None:
enforced_cids = list(enforced_cids_np)
# Get user-provided constraint features
(
constr_features,
@@ -72,13 +77,11 @@ class DynamicConstraintsComponent(Component):
constr_features,
]
)
assert len(known_cids) == constr_features.shape[0]
categories = np.unique(constr_categories)
for c in categories:
x[c] = constr_features[constr_categories == c].tolist()
cids[c] = known_cids[constr_categories == c].tolist()
enforced_cids = np.array(list(sample.get_set(self.attr)), dtype="S")
if enforced_cids is not None:
tmp = np.isin(cids[c], enforced_cids).reshape(-1, 1)
y[c] = np.hstack([~tmp, tmp]).tolist() # type: ignore
@@ -99,7 +102,7 @@ class DynamicConstraintsComponent(Component):
assert pre is not None
known_cids: Set = set()
for cids in pre:
known_cids |= cids
known_cids |= set(list(cids))
self.known_cids.clear()
self.known_cids.extend(sorted(known_cids))
@@ -128,7 +131,7 @@ class DynamicConstraintsComponent(Component):
@overrides
def pre_sample_xy(self, instance: Instance, sample: Sample) -> Any:
return sample.get_set(self.attr)
return sample.get_array(self.attr)
@overrides
def fit_xy(
@@ -150,7 +153,7 @@ class DynamicConstraintsComponent(Component):
instance: Instance,
sample: Sample,
) -> Dict[str, float]:
actual = sample.get_set(self.attr)
actual = sample.get_array(self.attr)
assert actual is not None
pred = set(self.sample_predict(instance, sample))
tp, tn, fp, fn = 0, 0, 0, 0

6
miplearn/components/dynamic_lazy.py
View File

@@ -3,6 +3,7 @@
# Released under the modified BSD license. See COPYING.md for more details.
import logging
import pdb
from typing import Dict, List, TYPE_CHECKING, Tuple, Any, Optional, Set
import numpy as np
@@ -78,7 +79,10 @@ class DynamicLazyConstraintsComponent(Component):
stats: LearningSolveStats,
sample: Sample,
) -> None:
sample.put_set("mip_constr_lazy_enforced", set(self.lazy_enforced))
sample.put_array(
"mip_constr_lazy_enforced",
np.array(list(self.lazy_enforced), dtype="S"),
)
@overrides
def iteration_cb(

5
miplearn/components/dynamic_user_cuts.py
View File

@@ -87,7 +87,10 @@ class UserCutsComponent(Component):
stats: LearningSolveStats,
sample: Sample,
) -> None:
sample.put_set("mip_user_cuts_enforced", set(self.enforced))
sample.put_array(
"mip_user_cuts_enforced",
np.array(list(self.enforced), dtype="S"),
)
stats["UserCuts: Added in callback"] = self.n_added_in_callback
if self.n_added_in_callback > 0:
logger.info(f"{self.n_added_in_callback} user cuts added in callback")

7
miplearn/components/static_lazy.py
View File

@@ -61,7 +61,10 @@ class StaticLazyConstraintsComponent(Component):
stats: LearningSolveStats,
sample: Sample,
) -> None:
sample.put_set("mip_constr_lazy_enforced", self.enforced_cids)
sample.put_array(
"mip_constr_lazy_enforced",
np.array(list(self.enforced_cids), dtype="S"),
)
stats["LazyStatic: Restored"] = self.n_restored
stats["LazyStatic: Iterations"] = self.n_iterations
@@ -212,7 +215,7 @@ class StaticLazyConstraintsComponent(Component):
constr_names = sample.get_array("static_constr_names")
constr_categories = sample.get_array("static_constr_categories")
constr_lazy = sample.get_array("static_constr_lazy")
lazy_enforced = sample.get_set("mip_constr_lazy_enforced")
lazy_enforced = sample.get_array("mip_constr_lazy_enforced")
if constr_features is None:
constr_features = sample.get_array("static_constr_features")

106
miplearn/features/sample.py
View File

@@ -46,15 +46,6 @@ class Sample(ABC):
def put_scalar(self, key: str, value: Scalar) -> None:
pass
@abstractmethod
def get_vector(self, key: str) -> Optional[Any]:
warnings.warn("Deprecated", DeprecationWarning)
return None
@abstractmethod
def put_vector(self, key: str, value: Vector) -> None:
warnings.warn("Deprecated", DeprecationWarning)
@abstractmethod
def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
pass
@@ -71,19 +62,6 @@ class Sample(ABC):
def get_sparse(self, key: str) -> Optional[coo_matrix]:
pass
def get_set(self, key: str) -> Set:
warnings.warn("Deprecated", DeprecationWarning)
v = self.get_vector(key)
if v:
return set(v)
else:
return set()
def put_set(self, key: str, value: Set) -> None:
warnings.warn("Deprecated", DeprecationWarning)
v = list(value)
self.put_vector(key, v)
def _assert_is_scalar(self, value: Any) -> None:
if value is None:
return
@@ -91,20 +69,13 @@ class Sample(ABC):
return
assert False, f"scalar expected; found instead: {value} ({value.__class__})"
def _assert_is_vector(self, value: Any) -> None:
assert isinstance(
value, (list, np.ndarray)
), f"list or numpy array expected; found instead: {value} ({value.__class__})"
for v in value:
self._assert_is_scalar(v)
def _assert_supported(self, value: np.ndarray) -> None:
def _assert_is_array(self, value: np.ndarray) -> None:
assert isinstance(value, np.ndarray)
assert value.dtype.kind in "biufS", f"Unsupported dtype: {value.dtype}"
def _assert_is_sparse(self, value: Any) -> None:
assert isinstance(value, coo_matrix)
self._assert_supported(value.data)
self._assert_is_array(value.data)
class MemorySample(Sample):
@@ -113,35 +84,20 @@ class MemorySample(Sample):
def __init__(
self,
data: Optional[Dict[str, Any]] = None,
check_data: bool = True,
) -> None:
if data is None:
data = {}
self._data: Dict[str, Any] = data
self._check_data = check_data
@overrides
def get_scalar(self, key: str) -> Optional[Any]:
return self._get(key)
@overrides
def get_vector(self, key: str) -> Optional[Any]:
return self._get(key)
@overrides
def put_scalar(self, key: str, value: Scalar) -> None:
if value is None:
return
if self._check_data:
self._assert_is_scalar(value)
self._put(key, value)
@overrides
def put_vector(self, key: str, value: Vector) -> None:
if value is None:
return
if self._check_data:
self._assert_is_vector(value)
self._assert_is_scalar(value)
self._put(key, value)
def _get(self, key: str) -> Optional[Any]:
@@ -157,7 +113,7 @@ class MemorySample(Sample):
def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
if value is None:
return
self._assert_supported(value)
self._assert_is_array(value)
self._put(key, value)
@overrides
@@ -188,10 +144,8 @@ class Hdf5Sample(Sample):
self,
filename: str,
mode: str = "r+",
check_data: bool = True,
) -> None:
self.file = h5py.File(filename, mode, libver="latest")
self._check_data = check_data
@overrides
def get_scalar(self, key: str) -> Optional[Any]:
@@ -206,66 +160,20 @@ class Hdf5Sample(Sample):
else:
return ds[()].tolist()
@overrides
def get_vector(self, key: str) -> Optional[Any]:
if key not in self.file:
return None
ds = self.file[key]
assert (
len(ds.shape) == 1
), f"1-dimensional array expected; found shape {ds.shape}"
if h5py.check_string_dtype(ds.dtype):
result = ds.asstr()[:].tolist()
result = [r if len(r) > 0 else None for r in result]
return result
else:
return ds[:].tolist()
@overrides
def put_scalar(self, key: str, value: Any) -> None:
if value is None:
return
if self._check_data:
self._assert_is_scalar(value)
self._put(key, value)
@overrides
def put_vector(self, key: str, value: Vector) -> None:
if value is None:
return
if self._check_data:
self._assert_is_vector(value)
for v in value:
# Convert strings to bytes
if isinstance(v, str) or v is None:
value = np.array(
[u if u is not None else b"" for u in value],
dtype="S",
)
break
# Convert all floating point numbers to half-precision
if isinstance(v, float):
value = np.array(value, dtype=np.dtype("f2"))
break
self._put(key, value, compress=True)
def _put(self, key: str, value: Any, compress: bool = False) -> Dataset:
self._assert_is_scalar(value)
if key in self.file:
del self.file[key]
if compress:
ds = self.file.create_dataset(key, data=value, compression="gzip")
else:
ds = self.file.create_dataset(key, data=value)
return ds
self.file.create_dataset(key, data=value)
@overrides
def put_array(self, key: str, value: Optional[np.ndarray]) -> None:
if value is None:
return
self._assert_supported(value)
self._assert_is_array(value)
if key in self.file:
del self.file[key]
return self.file.create_dataset(key, data=value, compression="gzip")