mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-08 02:18:51 -06:00
Make get_variable_{categories,features} return np.ndarray
This commit is contained in:
@@ -46,20 +46,25 @@ class FeaturesExtractor:
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vars_features_user, var_categories = self._extract_user_features_vars(
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instance, sample
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)
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sample.put_vector("static_var_categories", var_categories)
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sample.put_array("static_var_categories", var_categories)
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self._extract_user_features_constrs(instance, sample)
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self._extract_user_features_instance(instance, sample)
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alw17 = self._extract_var_features_AlvLouWeh2017(sample)
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sample.put_vector_list(
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# Build static_var_features
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assert variables.lower_bounds is not None
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assert variables.obj_coeffs is not None
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assert variables.upper_bounds is not None
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sample.put_array(
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"static_var_features",
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self._combine(
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np.hstack(
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[
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alw17,
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vars_features_user,
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sample.get_array("static_var_lower_bounds"),
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sample.get_array("static_var_obj_coeffs"),
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sample.get_array("static_var_upper_bounds"),
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],
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alw17,
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variables.lower_bounds.reshape(-1, 1),
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variables.obj_coeffs.reshape(-1, 1),
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variables.upper_bounds.reshape(-1, 1),
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]
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),
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)
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@@ -88,23 +93,29 @@ class FeaturesExtractor:
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sample.put_array("lp_constr_sa_rhs_up", constraints.sa_rhs_up)
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sample.put_array("lp_constr_slacks", constraints.slacks)
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alw17 = self._extract_var_features_AlvLouWeh2017(sample)
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sample.put_vector_list(
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"lp_var_features",
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self._combine(
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[
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alw17,
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sample.get_array("lp_var_reduced_costs"),
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sample.get_array("lp_var_sa_lb_down"),
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sample.get_array("lp_var_sa_lb_up"),
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sample.get_array("lp_var_sa_obj_down"),
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sample.get_array("lp_var_sa_obj_up"),
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sample.get_array("lp_var_sa_ub_down"),
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sample.get_array("lp_var_sa_ub_up"),
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sample.get_array("lp_var_values"),
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sample.get_vector_list("static_var_features"),
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],
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),
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)
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# Build lp_var_features
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lp_var_features_list = []
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for f in [
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sample.get_array("static_var_features"),
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alw17,
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]:
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if f is not None:
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lp_var_features_list.append(f)
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for f in [
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variables.reduced_costs,
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variables.sa_lb_down,
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variables.sa_lb_up,
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variables.sa_obj_down,
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variables.sa_obj_up,
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variables.sa_ub_down,
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variables.sa_ub_up,
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variables.values,
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]:
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if f is not None:
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lp_var_features_list.append(f.reshape(-1, 1))
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sample.put_array("lp_var_features", np.hstack(lp_var_features_list))
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sample.put_vector_list(
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"lp_constr_features",
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self._combine(
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@@ -148,60 +159,49 @@ class FeaturesExtractor:
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self,
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instance: "Instance",
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sample: Sample,
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) -> Tuple[List, List]:
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) -> Tuple[np.ndarray, np.ndarray]:
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# Query variable names
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var_names = sample.get_array("static_var_names")
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assert var_names is not None
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# Query variable features and categories
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var_features_dict = {
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v.encode(): f for (v, f) in instance.get_variable_features().items()
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}
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var_categories_dict = {
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v.encode(): f for (v, f) in instance.get_variable_categories().items()
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}
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# Query variable features
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var_features = instance.get_variable_features(var_names)
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assert isinstance(var_features, np.ndarray), (
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f"Variable features must be a numpy array. "
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f"Found {var_features.__class__} instead."
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)
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assert len(var_features.shape) == 2, (
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f"Variable features must be 2-dimensional array. "
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f"Found array with shape {var_features.shape} instead."
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)
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assert var_features.shape[0] == len(var_names), (
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f"Variable features must have exactly {len(var_names)} rows. "
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f"Found {var_features.shape[0]} rows instead."
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)
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assert var_features.dtype.kind in ["f"], (
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f"Variable features must be floating point numbers. "
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f"Found dtype: {var_features.dtype} instead."
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)
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# Assert that variables in user-provided dicts actually exist
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var_names_set = set(var_names)
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for keys in [var_features_dict.keys(), var_categories_dict.keys()]:
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for vn in cast(KeysView, keys):
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assert (
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vn in var_names_set
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), f"Variable {vn!r} not found in the problem; {var_names_set}"
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# Assemble into compact lists
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user_features: List[Optional[List[float]]] = []
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categories: List[Optional[str]] = []
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for (i, var_name) in enumerate(var_names):
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if var_name not in var_categories_dict:
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user_features.append(None)
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categories.append(None)
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continue
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category: str = var_categories_dict[var_name]
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assert isinstance(category, str), (
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f"Variable category must be a string. "
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f"Found {type(category).__name__} instead for var={var_name}."
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)
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categories.append(category)
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user_features_i: Optional[List[float]] = None
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if var_name in var_features_dict:
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user_features_i = var_features_dict[var_name]
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if isinstance(user_features_i, np.ndarray):
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user_features_i = user_features_i.tolist()
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assert isinstance(user_features_i, list), (
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f"Variable features must be a list. "
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f"Found {type(user_features_i).__name__} instead for "
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f"var={var_name}."
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)
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for v in user_features_i:
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assert isinstance(v, numbers.Real), (
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f"Variable features must be a list of numbers. "
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f"Found {type(v).__name__} instead "
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f"for var={var_name}."
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)
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user_features_i = list(user_features_i)
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user_features.append(user_features_i)
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return user_features, categories
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# Query variable categories
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var_categories = instance.get_variable_categories(var_names)
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assert isinstance(var_categories, np.ndarray), (
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f"Variable categories must be a numpy array. "
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f"Found {var_categories.__class__} instead."
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)
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assert len(var_categories.shape) == 1, (
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f"Variable categories must be a vector. "
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f"Found array with shape {var_categories.shape} instead."
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)
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assert len(var_categories) == len(var_names), (
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f"Variable categories must have exactly {len(var_names)} elements. "
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f"Found {var_features.shape[0]} elements instead."
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)
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assert var_categories.dtype.kind == "S", (
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f"Variable categories must be a numpy array with dtype='S'. "
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f"Found {var_categories.dtype} instead."
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)
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return var_features, var_categories
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def _extract_user_features_constrs(
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self,
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@@ -277,7 +277,7 @@ class FeaturesExtractor:
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# Alvarez, A. M., Louveaux, Q., & Wehenkel, L. (2017). A machine learning-based
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# approximation of strong branching. INFORMS Journal on Computing, 29(1), 185-195.
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def _extract_var_features_AlvLouWeh2017(self, sample: Sample) -> List:
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def _extract_var_features_AlvLouWeh2017(self, sample: Sample) -> np.ndarray:
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obj_coeffs = sample.get_array("static_var_obj_coeffs")
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obj_sa_down = sample.get_array("lp_var_sa_obj_down")
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obj_sa_up = sample.get_array("lp_var_sa_obj_up")
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@@ -351,7 +351,7 @@ class FeaturesExtractor:
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f[i] = 0.0
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features.append(f)
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return features
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return np.array(features, dtype=float)
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def _combine(
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self,
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