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@ -6,6 +6,7 @@ from math import log, isfinite
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from typing import TYPE_CHECKING, List, Tuple, Optional
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import numpy as np
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from scipy.sparse import coo_matrix
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from miplearn.features.sample import Sample
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from miplearn.solvers.internal import LPSolveStats
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@ -15,6 +16,7 @@ if TYPE_CHECKING:
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from miplearn.instance.base import Instance
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# noinspection PyPep8Naming
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class FeaturesExtractor:
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def __init__(
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self,
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@ -23,6 +25,7 @@ class FeaturesExtractor:
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) -> None:
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self.with_sa = with_sa
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self.with_lhs = with_lhs
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self.var_features_user: Optional[np.ndarray] = None
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def extract_after_load_features(
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self,
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@ -65,6 +68,7 @@ class FeaturesExtractor:
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vars_features_user,
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var_categories,
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) = self._extract_user_features_vars(instance, sample)
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self.var_features_user = vars_features_user
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sample.put_array("static_var_categories", var_categories)
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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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@ -74,12 +78,11 @@ class FeaturesExtractor:
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np.hstack(
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[
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vars_features_user,
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self._extract_var_features_AlvLouWeh2017(
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obj_coeffs=variables.obj_coeffs,
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self._compute_AlvLouWeh2017(
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A=constraints.lhs,
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b=constraints.rhs,
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c=variables.obj_coeffs,
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),
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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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@ -112,11 +115,13 @@ class FeaturesExtractor:
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# Variable 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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self._extract_var_features_AlvLouWeh2017(
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obj_coeffs=sample.get_array("static_var_obj_coeffs"),
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obj_sa_up=variables.sa_obj_up,
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obj_sa_down=variables.sa_obj_down,
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self.var_features_user,
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self._compute_AlvLouWeh2017(
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A=sample.get_sparse("static_constr_lhs"),
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b=sample.get_array("static_constr_rhs"),
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c=sample.get_array("static_var_obj_coeffs"),
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c_sa_up=variables.sa_obj_up,
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c_sa_down=variables.sa_obj_down,
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values=variables.values,
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),
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]:
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@ -315,90 +320,108 @@ class FeaturesExtractor:
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], f"Instance features have unsupported {features.dtype}"
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sample.put_array("static_instance_features", features)
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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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# noinspection PyPep8Naming
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def _extract_var_features_AlvLouWeh2017(
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self,
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obj_coeffs: Optional[np.ndarray] = None,
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obj_sa_down: Optional[np.ndarray] = None,
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obj_sa_up: Optional[np.ndarray] = None,
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@classmethod
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def _compute_AlvLouWeh2017(
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cls,
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A: Optional[coo_matrix] = None,
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b: Optional[np.ndarray] = None,
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c: Optional[np.ndarray] = None,
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c_sa_down: Optional[np.ndarray] = None,
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c_sa_up: Optional[np.ndarray] = None,
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values: Optional[np.ndarray] = None,
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) -> np.ndarray:
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assert obj_coeffs is not None
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obj_coeffs = obj_coeffs.astype(float)
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_fix_infinity(obj_coeffs)
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nvars = len(obj_coeffs)
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if obj_sa_down is not None:
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obj_sa_down = obj_sa_down.astype(float)
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_fix_infinity(obj_sa_down)
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if obj_sa_up is not None:
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obj_sa_up = obj_sa_up.astype(float)
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_fix_infinity(obj_sa_up)
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if values is not None:
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values = values.astype(float)
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_fix_infinity(values)
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pos_obj_coeffs_sum = obj_coeffs[obj_coeffs > 0].sum()
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neg_obj_coeffs_sum = -obj_coeffs[obj_coeffs < 0].sum()
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"""
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Computes static variable features described in:
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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),
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185-195.
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"""
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assert b is not None
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assert c is not None
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nvars = len(c)
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c_pos_sum = c[c > 0].sum()
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c_neg_sum = -c[c < 0].sum()
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curr = 0
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max_n_features = 8
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max_n_features = 30
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features = np.zeros((nvars, max_n_features))
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def push(v: np.ndarray) -> None:
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nonlocal curr
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features[:, curr] = v
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curr += 1
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with np.errstate(divide="ignore", invalid="ignore"):
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# Feature 1
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features[:, curr] = np.sign(obj_coeffs)
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curr += 1
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push(np.sign(c))
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# Feature 2
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if abs(pos_obj_coeffs_sum) > 0:
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features[:, curr] = np.abs(obj_coeffs) / pos_obj_coeffs_sum
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curr += 1
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push(np.abs(c) / c_pos_sum)
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# Feature 3
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if abs(neg_obj_coeffs_sum) > 0:
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features[:, curr] = np.abs(obj_coeffs) / neg_obj_coeffs_sum
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curr += 1
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push(np.abs(c) / c_neg_sum)
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if A is not None:
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M1 = A.T.multiply(1.0 / np.abs(b)).T.tocsr()
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M1_pos = M1[b > 0, :]
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if M1_pos.shape[0] > 0:
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M1_pos_max = M1_pos.max(axis=0).todense()
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M1_pos_min = M1_pos.min(axis=0).todense()
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else:
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M1_pos_max = np.zeros(nvars)
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M1_pos_min = np.zeros(nvars)
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M1_neg = M1[b < 0, :]
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if M1_neg.shape[0] > 0:
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M1_neg_max = M1_neg.max(axis=0).todense()
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M1_neg_min = M1_neg.min(axis=0).todense()
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else:
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M1_neg_max = np.zeros(nvars)
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M1_neg_min = np.zeros(nvars)
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# Features 4-11
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push(np.sign(M1_pos_min))
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push(np.sign(M1_pos_max))
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push(np.abs(M1_pos_min))
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push(np.abs(M1_pos_max))
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push(np.sign(M1_neg_min))
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push(np.sign(M1_neg_max))
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push(np.abs(M1_neg_min))
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push(np.abs(M1_neg_max))
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# Feature 37
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if values is not None:
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features[:, curr] = np.minimum(
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values - np.floor(values),
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np.ceil(values) - values,
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push(
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np.minimum(
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values - np.floor(values),
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np.ceil(values) - values,
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)
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)
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curr += 1
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# Feature 44
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if obj_sa_up is not None:
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features[:, curr] = np.sign(obj_sa_up)
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curr += 1
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if c_sa_up is not None:
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push(np.sign(c_sa_up))
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# Feature 46
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if obj_sa_down is not None:
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features[:, curr] = np.sign(obj_sa_down)
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curr += 1
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if c_sa_down is not None:
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push(np.sign(c_sa_down))
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# Feature 47
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if obj_sa_down is not None:
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features[:, curr] = np.log(
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obj_coeffs - obj_sa_down / np.sign(obj_coeffs)
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)
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curr += 1
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if c_sa_down is not None:
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push(np.log(c - c_sa_down / np.sign(c)))
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# Feature 48
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if obj_sa_up is not None:
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features[:, curr] = np.log(obj_coeffs - obj_sa_up / np.sign(obj_coeffs))
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curr += 1
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if c_sa_up is not None:
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push(np.log(c - c_sa_up / np.sign(c)))
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features = features[:, 0:curr]
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_fix_infinity(features)
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return features
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def _fix_infinity(m: np.ndarray) -> None:
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def _fix_infinity(m: Optional[np.ndarray]) -> None:
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if m is None:
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return
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masked = np.ma.masked_invalid(m)
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max_values = np.max(masked, axis=0)
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min_values = np.min(masked, axis=0)
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