# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization # Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved. # Written by Alinson S. Xavier from . import Component from .transformers import PerVariableTransformer from abc import ABC, abstractmethod from copy import deepcopy import numpy as np from sklearn.pipeline import make_pipeline from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_score from sklearn.neighbors import KNeighborsClassifier class WarmStartPredictor(ABC): def __init__(self, thr_clip=[0.50, 0.50]): self.models = [None, None] self.thr_clip = thr_clip def fit(self, x_train, y_train): assert isinstance(x_train, np.ndarray) assert isinstance(y_train, np.ndarray) y_train = y_train.astype(int) assert y_train.shape[0] == x_train.shape[0] assert y_train.shape[1] == 2 for i in [0,1]: self.models[i] = self._fit(x_train, y_train[:, i], i) def predict(self, x_test): assert isinstance(x_test, np.ndarray) y_pred = np.zeros((x_test.shape[0], 2)) for i in [0,1]: if isinstance(self.models[i], int): y_pred[:, i] = self.models[i] else: y = self.models[i].predict_proba(x_test)[:,1] y[y < self.thr_clip[i]] = 0. y[y > 0.] = 1. y_pred[:, i] = y return y_pred.astype(int) @abstractmethod def _fit(self, x_train, y_train, label): pass class LogisticWarmStartPredictor(WarmStartPredictor): def __init__(self, min_samples=100, thr_fix=[0.95, 0.95], thr_balance=[0.95, 0.95], thr_score=[0.95, 0.95]): super().__init__() self.min_samples = min_samples self.thr_fix = thr_fix self.thr_balance = thr_balance self.thr_score = thr_score def _fit(self, x_train, y_train, label): y_train_avg = np.average(y_train) # If number of samples is too small, don't predict anything. if x_train.shape[0] < self.min_samples: return 0 # If vast majority of observations are true, always return true. if y_train_avg > self.thr_fix[label]: return 1 # If dataset is not balanced enough, don't predict anything. if y_train_avg < (1 - self.thr_balance[label]) or y_train_avg > self.thr_balance[label]: return 0 reg = make_pipeline(StandardScaler(), LogisticRegression()) reg_score = np.mean(cross_val_score(reg, x_train, y_train, cv=5)) # If cross-validation score is too low, don't predict anything. if reg_score < self.thr_score[label]: return 0 reg.fit(x_train, y_train.astype(int)) return reg class KnnWarmStartPredictor(WarmStartPredictor): def __init__(self, k=50, thr_clip=[0.90, 0.90], thr_fix=[0.99, 0.99]): super().__init__(thr_clip=thr_clip) self.k = k self.thr_fix = thr_fix def _fit(self, x_train, y_train, label): y_train_avg = np.average(y_train) # If number of training samples is too small, don't predict anything. if x_train.shape[0] < self.k: return 0 # If vast majority of observations are true, always return true. if y_train_avg > self.thr_fix[label]: return 1 # If vast majority of observations are false, always return false. if y_train_avg < (1 - self.thr_fix[label]): return 0 knn = KNeighborsClassifier(n_neighbors=self.k) knn.fit(x_train, y_train) return knn class WarmStartComponent(Component): def __init__(self, predictor_prototype=LogisticWarmStartPredictor(), mode="exact", ): self.mode = mode self.transformer = PerVariableTransformer() self.x_train = {} self.y_train = {} self.predictors = {} self.predictor_prototype = predictor_prototype def before_solve(self, solver, instance, model): var_split = self.transformer.split_variables(instance, model) x_test = {} # Collect training data (x_train) and build x_test for category in var_split.keys(): var_index_pairs = var_split[category] x = self.transformer.transform_instance(instance, var_index_pairs) x_test[category] = x if category not in self.x_train.keys(): self.x_train[category] = x else: assert x.shape[1] == self.x_train[category].shape[1] self.x_train[category] = np.vstack([self.x_train[category], x]) # Predict solutions for category in var_split.keys(): var_index_pairs = var_split[category] if category in self.predictors.keys(): ws = self.predictors[category].predict(x_test[category]) assert ws.shape == (len(var_index_pairs), 2) for i in range(len(var_index_pairs)): var, index = var_index_pairs[i] if self.mode == "heuristic": if ws[i,0] == 1: var[index].fix(0) if solver.is_persistent: solver.internal_solver.update_var(var[index]) elif ws[i,1] == 1: var[index].fix(1) if solver.is_persistent: solver.internal_solver.update_var(var[index]) else: if ws[i,0] == 1: var[index].value = 0 elif ws[i,1] == 1: var[index].value = 1 def after_solve(self, solver, instance, model): var_split = self.transformer.split_variables(instance, model) for category in var_split.keys(): var_index_pairs = var_split[category] y = self.transformer.transform_solution(var_index_pairs) if category not in self.y_train.keys(): self.y_train[category] = y else: self.y_train[category] = np.vstack([self.y_train[category], y]) def fit(self, solver): for category in self.x_train.keys(): x_train = self.x_train[category] y_train = self.y_train[category] self.predictors[category] = deepcopy(self.predictor_prototype) self.predictors[category].fit(x_train, y_train) def merge(self, other): for c in other.x_train.keys(): if c not in self.x_train: self.x_train[c] = other.x_train[c] self.y_train[c] = other.y_train[c] else: self.x_train[c] = np.vstack([self.x_train[c], other.x_train[c]]) self.y_train[c] = np.vstack([self.y_train[c], other.y_train[c]]) if (c in other.predictors.keys()) and (c not in self.predictors.keys()): self.predictors[c] = other.predictors[c]