Use np in Constraints.lazy; replace some get_vector

miplearn/components/primal.py

@@ -143,7 +143,7 @@ class PrimalSolutionComponent(Component):
         x: Dict = {}
         y: Dict = {}
         instance_features = sample.get_vector("static_instance_features")
-        mip_var_values = sample.get_vector("mip_var_values")
+        mip_var_values = sample.get_array("mip_var_values")
         var_features = sample.get_vector_list("lp_var_features")
         var_names = sample.get_array("static_var_names")
         var_categories = sample.get_vector("static_var_categories")

miplearn/components/static_lazy.py

@@ -183,7 +183,7 @@ class StaticLazyConstraintsComponent(Component):
         logger.info(f"Found {n_violated} violated lazy constraints found")
         if n_violated > 0:
             logger.info(
-                "Enforcing {n_violated} lazy constraints; "
+                f"Enforcing {n_violated} lazy constraints; "
                 f"{n_satisfied} left in the pool..."
             )
             solver.internal_solver.add_constraints(violated_constraints)
@@ -208,7 +208,7 @@ class StaticLazyConstraintsComponent(Component):
         constr_features = sample.get_vector_list("lp_constr_features")
         constr_names = sample.get_array("static_constr_names")
         constr_categories = sample.get_vector("static_constr_categories")
-        constr_lazy = sample.get_vector("static_constr_lazy")
+        constr_lazy = sample.get_array("static_constr_lazy")
         lazy_enforced = sample.get_set("mip_constr_lazy_enforced")
         if constr_features is None:
             constr_features = sample.get_vector_list("static_constr_features")

miplearn/features/extractor.py

@@ -241,7 +241,7 @@ class FeaturesExtractor:
             else:
                 lazy.append(False)
         sample.put_vector_list("static_constr_features", user_features)
-        sample.put_vector("static_constr_lazy", lazy)
+        sample.put_array("static_constr_lazy", np.array(lazy, dtype=bool))
         sample.put_array("static_constr_categories", np.array(categories, dtype="S"))
 
     def _extract_user_features_instance(
@@ -261,18 +261,18 @@ class FeaturesExtractor:
                 f"Instance features must be a list of numbers. "
                 f"Found {type(v).__name__} instead."
             )
-        constr_lazy = sample.get_vector("static_constr_lazy")
+        constr_lazy = sample.get_array("static_constr_lazy")
         assert constr_lazy is not None
         sample.put_vector("static_instance_features", user_features)
-        sample.put_scalar("static_constr_lazy_count", sum(constr_lazy))
+        sample.put_scalar("static_constr_lazy_count", int(sum(constr_lazy)))
 
     # Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
     # approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
     def _extract_var_features_AlvLouWeh2017(self, sample: Sample) -> List:
-        obj_coeffs = sample.get_vector("static_var_obj_coeffs")
-        obj_sa_down = sample.get_vector("lp_var_sa_obj_down")
-        obj_sa_up = sample.get_vector("lp_var_sa_obj_up")
-        values = sample.get_vector(f"lp_var_values")
+        obj_coeffs = sample.get_array("static_var_obj_coeffs")
+        obj_sa_down = sample.get_array("lp_var_sa_obj_down")
+        obj_sa_up = sample.get_array("lp_var_sa_obj_up")
+        values = sample.get_array("lp_var_values")
         assert obj_coeffs is not None
 
         pos_obj_coeff_sum = 0.0

miplearn/features/sample.py

@@ -94,21 +94,21 @@ class Sample(ABC):
     def _assert_is_scalar(self, value: Any) -> None:
         if value is None:
             return
-        if isinstance(value, (str, bool, int, float)):
+        if isinstance(value, (str, bool, int, float, np.bytes_)):
             return
-        assert False, f"scalar expected; found instead: {value}"
+        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}"
+        ), f"list or numpy array expected; found instead: {value} ({value.__class__})"
         for v in value:
             self._assert_is_scalar(v)
 
     def _assert_is_vector_list(self, value: Any) -> None:
         assert isinstance(
             value, (list, np.ndarray)
-        ), f"list or numpy array expected; found instead: {value}"
+        ), f"list or numpy array expected; found instead: {value} ({value.__class__})"
         for v in value:
             if v is None:
                 continue
@@ -125,7 +125,7 @@ class MemorySample(Sample):
     def __init__(
         self,
         data: Optional[Dict[str, Any]] = None,
-        check_data: bool = False,
+        check_data: bool = True,
     ) -> None:
         if data is None:
             data = {}
@@ -210,7 +210,7 @@ class Hdf5Sample(Sample):
         self,
         filename: str,
         mode: str = "r+",
-        check_data: bool = False,
+        check_data: bool = True,
     ) -> None:
         self.file = h5py.File(filename, mode, libver="latest")
         self._check_data = check_data

miplearn/solvers/internal.py

@@ -70,7 +70,7 @@ class Variables:
 class Constraints:
     basis_status: Optional[np.ndarray] = None
     dual_values: Optional[np.ndarray] = None
-    lazy: Optional[List[bool]] = None
+    lazy: Optional[np.ndarray] = None
     lhs: Optional[List[List[Tuple[bytes, float]]]] = None
     names: Optional[np.ndarray] = None
     rhs: Optional[np.ndarray] = None
@@ -83,15 +83,15 @@ class Constraints:
     def from_sample(sample: "Sample") -> "Constraints":
         return Constraints(
             basis_status=sample.get_array("lp_constr_basis_status"),
-            dual_values=sample.get_vector("lp_constr_dual_values"),
-            lazy=sample.get_vector("static_constr_lazy"),
+            dual_values=sample.get_array("lp_constr_dual_values"),
+            lazy=sample.get_array("static_constr_lazy"),
             # lhs=sample.get_vector("static_constr_lhs"),
             names=sample.get_array("static_constr_names"),
-            rhs=sample.get_vector("static_constr_rhs"),
-            sa_rhs_down=sample.get_vector("lp_constr_sa_rhs_down"),
-            sa_rhs_up=sample.get_vector("lp_constr_sa_rhs_up"),
+            rhs=sample.get_array("static_constr_rhs"),
+            sa_rhs_down=sample.get_array("lp_constr_sa_rhs_down"),
+            sa_rhs_up=sample.get_array("lp_constr_sa_rhs_up"),
             senses=sample.get_array("static_constr_senses"),
-            slacks=sample.get_vector("lp_constr_slacks"),
+            slacks=sample.get_array("lp_constr_slacks"),
         )
 
     def __getitem__(self, selected: List[bool]) -> "Constraints":
@@ -103,7 +103,7 @@ class Constraints:
                 None if self.dual_values is None else self.dual_values[selected]
             ),
             names=(None if self.names is None else self.names[selected]),
-            lazy=self._filter(self.lazy, selected),
+            lazy=(None if self.lazy is None else self.lazy[selected]),
             lhs=self._filter(self.lhs, selected),
             rhs=(None if self.rhs is None else self.rhs[selected]),
             sa_rhs_down=(