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202 lines
8.2 KiB
202 lines
8.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from .component import Component
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from ..extractors import *
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from abc import ABC, abstractmethod
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from copy import deepcopy
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import numpy as np
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from sklearn.pipeline import make_pipeline
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from sklearn.linear_model import LogisticRegression
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import cross_val_score
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from sklearn.metrics import roc_curve
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from sklearn.neighbors import KNeighborsClassifier
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from tqdm.auto import tqdm
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import pyomo.environ as pe
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import logging
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logger = logging.getLogger(__name__)
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class AdaptivePredictor:
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def __init__(self,
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predictor=None,
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min_samples_predict=1,
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min_samples_cv=100,
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thr_fix=0.999,
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thr_alpha=0.50,
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thr_balance=0.95,
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):
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self.min_samples_predict = min_samples_predict
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self.min_samples_cv = min_samples_cv
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self.thr_fix = thr_fix
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self.thr_alpha = thr_alpha
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self.thr_balance = thr_balance
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self.predictor_factory = predictor
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def fit(self, x_train, y_train):
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n_samples = x_train.shape[0]
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# If number of samples is too small, don't predict anything.
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if n_samples < self.min_samples_predict:
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logger.debug(" Too few samples (%d); always predicting false" % n_samples)
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self.predictor = 0
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return
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# If vast majority of observations are false, always return false.
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y_train_avg = np.average(y_train)
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if y_train_avg <= 1.0 - self.thr_fix:
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logger.debug(" Most samples are negative (%.3f); always returning false" % y_train_avg)
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self.predictor = 0
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return
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# If vast majority of observations are true, always return true.
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if y_train_avg >= self.thr_fix:
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logger.debug(" Most samples are positive (%.3f); always returning true" % y_train_avg)
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self.predictor = 1
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return
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# If classes are too unbalanced, don't predict anything.
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if y_train_avg < (1 - self.thr_balance) or y_train_avg > self.thr_balance:
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logger.debug(" Classes are too unbalanced (%.3f); always returning false" % y_train_avg)
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self.predictor = 0
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return
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# Select ML model if none is provided
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if self.predictor_factory is None:
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if n_samples < 30:
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self.predictor_factory = KNeighborsClassifier(n_neighbors=n_samples)
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else:
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self.predictor_factory = make_pipeline(StandardScaler(), LogisticRegression())
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# Create predictor
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if callable(self.predictor_factory):
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pred = self.predictor_factory()
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else:
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pred = deepcopy(self.predictor_factory)
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# Skip cross-validation if number of samples is too small
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if n_samples < self.min_samples_cv:
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logger.debug(" Too few samples (%d); skipping cross validation" % n_samples)
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self.predictor = pred
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self.predictor.fit(x_train, y_train)
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return
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# Calculate cross-validation score
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cv_score = np.mean(cross_val_score(pred, x_train, y_train, cv=5))
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dummy_score = max(y_train_avg, 1 - y_train_avg)
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cv_thr = 1. * self.thr_alpha + dummy_score * (1 - self.thr_alpha)
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# If cross-validation score is too low, don't predict anything.
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if cv_score < cv_thr:
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logger.debug(" Score is too low (%.3f < %.3f); always returning false" % (cv_score, cv_thr))
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self.predictor = 0
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else:
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logger.debug(" Score is acceptable (%.3f > %.3f); training classifier" % (cv_score, cv_thr))
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self.predictor = pred
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self.predictor.fit(x_train, y_train)
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def predict_proba(self, x_test):
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if isinstance(self.predictor, int):
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y_pred = np.zeros((x_test.shape[0], 2))
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y_pred[:, self.predictor] = 1.0
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return y_pred
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else:
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return self.predictor.predict_proba(x_test)
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class PrimalSolutionComponent(Component):
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"""
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A component that predicts primal solutions.
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"""
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def __init__(self,
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predictor=AdaptivePredictor(),
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mode="exact",
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max_fpr=[1e-3, 1e-3],
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min_threshold=[0.75, 0.75],
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dynamic_thresholds=True,
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):
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self.mode = mode
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self.predictors = {}
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self.is_warm_start_available = False
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self.max_fpr = max_fpr
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self.min_threshold = min_threshold
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self.thresholds = {}
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self.predictor_factory = predictor
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self.dynamic_thresholds = dynamic_thresholds
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def before_solve(self, solver, instance, model):
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solution = self.predict(instance)
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if self.mode == "heuristic":
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solver.internal_solver.fix(solution)
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else:
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solver.internal_solver.set_warm_start(solution)
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def after_solve(self, solver, instance, model):
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pass
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def fit(self, training_instances):
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logger.debug("Extracting features...")
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features = VariableFeaturesExtractor().extract(training_instances)
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solutions = SolutionExtractor().extract(training_instances)
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for category in tqdm(features.keys(), desc="Fit (Primal)"):
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x_train = features[category]
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y_train = solutions[category]
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for label in [0, 1]:
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logger.debug("Fitting predictors[%s, %s]:" % (category, label))
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if callable(self.predictor_factory):
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pred = self.predictor_factory(category, label)
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else:
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pred = deepcopy(self.predictor_factory)
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self.predictors[category, label] = pred
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y = y_train[:, label].astype(int)
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pred.fit(x_train, y)
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# If y is either always one or always zero, set fixed threshold
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y_avg = np.average(y)
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if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
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self.thresholds[category, label] = self.min_threshold[label]
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logger.debug(" Setting threshold to %.4f" % self.min_threshold[label])
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continue
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# Calculate threshold dynamically using ROC curve
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y_scores = pred.predict_proba(x_train)[:, 1]
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fpr, tpr, thresholds = roc_curve(y, y_scores)
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k = 0
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while True:
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if (k + 1) > len(fpr):
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break
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if fpr[k + 1] > self.max_fpr[label]:
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break
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if thresholds[k + 1] < self.min_threshold[label]:
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break
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k = k + 1
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logger.debug(" Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)"%
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(thresholds[k], fpr[k], tpr[k]))
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self.thresholds[category, label] = thresholds[k]
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def predict(self, instance):
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x_test = VariableFeaturesExtractor().extract([instance])
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solution = {}
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var_split = Extractor.split_variables(instance)
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for category in var_split.keys():
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for (i, (var, index)) in enumerate(var_split[category]):
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if var not in solution.keys():
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solution[var] = {}
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solution[var][index] = None
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for label in [0, 1]:
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if (category, label) not in self.predictors.keys():
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continue
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ws = self.predictors[category, label].predict_proba(x_test[category])
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logger.debug("%s[%s] ws=%.6f threshold=%.6f" %
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(var, index, ws[i, 1], self.thresholds[category, label]))
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if ws[i, 1] >= self.thresholds[category, label]:
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solution[var][index] = label
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return solution
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