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# 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 miplearn import WarmStartComponent, LearningSolver
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from miplearn.problems.knapsack import KnapsackInstance
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import numpy as np
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import tempfile
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def _get_instances():
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return [
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KnapsackInstance(
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weights=[23., 26., 20., 18.],
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prices=[505., 352., 458., 220.],
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capacity=67.,
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),
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] * 2
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# def test_warm_start_save_load():
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# state_file = tempfile.NamedTemporaryFile(mode="r")
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# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
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# solver.parallel_solve(_get_instances(), n_jobs=2)
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# solver.fit()
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# comp = solver.components["warm-start"]
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# assert comp.x_train["default"].shape == (8, 6)
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# assert comp.y_train["default"].shape == (8, 2)
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# assert ("default", 0) in comp.predictors.keys()
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# assert ("default", 1) in comp.predictors.keys()
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# solver.save_state(state_file.name)
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# solver.solve(_get_instances()[0])
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# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
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# solver.load_state(state_file.name)
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# comp = solver.components["warm-start"]
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# assert comp.x_train["default"].shape == (8, 6)
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# assert comp.y_train["default"].shape == (8, 2)
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# assert ("default", 0) in comp.predictors.keys()
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# assert ("default", 1) in comp.predictors.keys()
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# 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 miplearn import KnnWarmStartPredictor
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from sklearn.metrics import accuracy_score, precision_score
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import numpy as np
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def test_knn_with_consensus():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.0],
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[0.0, 0.1],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[0., 1.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.0, 0.0]])
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y_test = np.array([[0, 1]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_without_consensus():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[1., 0.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[0, 0]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_always_true():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[1., 0.],
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[1., 0.],
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[1., 0.],
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[1., 0.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[1, 0]])
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assert (ws.predict(x_test) == y_test).all()
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def test_knn_always_false():
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x_train = np.array([
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[0.0, 0.0],
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[0.1, 0.1],
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[0.9, 0.9],
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[1.0, 1.0],
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])
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y_train = np.array([
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[0., 1.],
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[0., 1.],
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[0., 1.],
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[0., 1.],
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])
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ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
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ws.fit(x_train, y_train)
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x_test = np.array([[0.5, 0.5]])
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y_test = np.array([[0, 1]])
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assert (ws.predict(x_test) == y_test).all()
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# 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 miplearn import LogisticWarmStartPredictor
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from sklearn.metrics import accuracy_score, precision_score
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import numpy as np
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def _generate_dataset(ground_truth, n_samples=10_000):
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x_train = np.random.rand(n_samples,5)
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x_test = np.random.rand(n_samples,5)
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y_train = ground_truth(x_train)
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y_test = ground_truth(x_test)
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return x_train, y_train, x_test, y_test
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def _is_sum_greater_than_two(x):
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y = (np.sum(x, axis=1) > 2.0).astype(int)
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return np.vstack([y, 1 - y]).transpose()
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def _always_zero(x):
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y = np.zeros((1, x.shape[0]))
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return np.vstack([y, 1 - y]).transpose()
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def _random_values(x):
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y = np.random.randint(2, size=x.shape[0])
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return np.vstack([y, 1 - y]).transpose()
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def test_logistic_ws_with_balanced_labels():
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x_train, y_train, x_test, y_test = _generate_dataset(_is_sum_greater_than_two)
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ws = LogisticWarmStartPredictor()
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ws.fit(x_train, y_train)
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y_pred = ws.predict(x_test)
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assert accuracy_score(y_test[:,0], y_pred[:,0]) > 0.99
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assert accuracy_score(y_test[:,1], y_pred[:,1]) > 0.99
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def test_logistic_ws_with_unbalanced_labels():
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x_train, y_train, x_test, y_test = _generate_dataset(_always_zero)
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ws = LogisticWarmStartPredictor()
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ws.fit(x_train, y_train)
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y_pred = ws.predict(x_test)
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assert accuracy_score(y_test[:,0], y_pred[:,0]) == 1.0
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assert accuracy_score(y_test[:,1], y_pred[:,1]) == 1.0
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def test_logistic_ws_with_unpredictable_labels():
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x_train, y_train, x_test, y_test = _generate_dataset(_random_values)
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ws = LogisticWarmStartPredictor()
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ws.fit(x_train, y_train)
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y_pred = ws.predict(x_test)
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assert np.sum(y_pred) == 0
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def test_logistic_ws_with_small_sample_size():
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x_train, y_train, x_test, y_test = _generate_dataset(_random_values, n_samples=3)
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ws = LogisticWarmStartPredictor()
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ws.fit(x_train, y_train)
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y_pred = ws.predict(x_test)
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assert np.sum(y_pred) == 0
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# 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=1.0,
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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 WarmStartComponent(Component):
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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=[0.01, 0.01],
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min_threshold=[0.75, 0.75],
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dynamic_thresholds=False,
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):
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self.mode = mode
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self.x_train = {}
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self.y_train = {}
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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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# Build x_test
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x_test = CombinedExtractor([UserFeaturesExtractor(),
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SolutionExtractor(relaxation=True),
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]).extract([instance], [model])
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# Update self.x_train
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self.x_train = Extractor.merge([self.x_train, x_test],
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vertical=True)
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# Predict solutions
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count_total, count_fixed = 0, 0
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var_split = Extractor.split_variables(instance, model)
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for category in var_split.keys():
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var_index_pairs = var_split[category]
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# Clear current values
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for i in range(len(var_index_pairs)):
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var, index = var_index_pairs[i]
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var[index].value = None
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# Make predictions
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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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assert ws.shape == (len(var_index_pairs), 2)
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for i in range(len(var_index_pairs)):
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count_total += 1
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var, index = var_index_pairs[i]
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logger.debug("%s[%s] ws=%.6f threshold=%.6f" % (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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logger.debug("Setting %s[%s] to %d" % (var, index, label))
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count_fixed += 1
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if self.mode == "heuristic":
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var[index].fix(label)
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if solver.is_persistent:
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solver.internal_solver.update_var(var[index])
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else:
|
|
||||||
var[index].value = label
|
|
||||||
self.is_warm_start_available = True
|
|
||||||
|
|
||||||
# Clear current values
|
|
||||||
for i in range(len(var_index_pairs)):
|
|
||||||
var, index = var_index_pairs[i]
|
|
||||||
if var[index].value is None:
|
|
||||||
logger.debug("Variable %s[%s] not set" % (var, index))
|
|
||||||
else:
|
|
||||||
logger.debug("Varible %s[%s] set to %.2f" % (var, index, var[index].value))
|
|
||||||
|
|
||||||
|
|
||||||
logger.info("Setting values for %d variables (out of %d)" % (count_fixed, count_total // 2))
|
|
||||||
|
|
||||||
|
|
||||||
def after_solve(self, solver, instance, model):
|
|
||||||
y_test = SolutionExtractor().extract([instance], [model])
|
|
||||||
self.y_train = Extractor.merge([self.y_train, y_test], vertical=True)
|
|
||||||
|
|
||||||
def fit(self, solver, n_jobs=1):
|
|
||||||
for category in tqdm(self.x_train.keys(), desc="Fit (warm start)"):
|
|
||||||
x_train = self.x_train[category]
|
|
||||||
y_train = self.y_train[category]
|
|
||||||
for label in [0, 1]:
|
|
||||||
logger.debug("Fitting predictors[%s, %s]:" % (category, label))
|
|
||||||
|
|
||||||
if callable(self.predictor_factory):
|
|
||||||
pred = self.predictor_factory(category, label)
|
|
||||||
else:
|
|
||||||
pred = deepcopy(self.predictor_factory)
|
|
||||||
self.predictors[category, label] = pred
|
|
||||||
y = y_train[:, label].astype(int)
|
|
||||||
pred.fit(x_train, y)
|
|
||||||
|
|
||||||
# If y is either always one or always zero, set fixed threshold
|
|
||||||
y_avg = np.average(y)
|
|
||||||
if (not self.dynamic_thresholds) or y_avg <= 0.001 or y_avg >= 0.999:
|
|
||||||
self.thresholds[category, label] = self.min_threshold[label]
|
|
||||||
logger.debug(" Setting threshold to %.4f" % self.min_threshold[label])
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Calculate threshold dynamically using ROC curve
|
|
||||||
y_scores = pred.predict_proba(x_train)[:, 1]
|
|
||||||
fpr, tpr, thresholds = roc_curve(y, y_scores)
|
|
||||||
k = 0
|
|
||||||
while True:
|
|
||||||
if (k + 1) > len(fpr):
|
|
||||||
break
|
|
||||||
if fpr[k + 1] > self.max_fpr[label]:
|
|
||||||
break
|
|
||||||
if thresholds[k + 1] < self.min_threshold[label]:
|
|
||||||
break
|
|
||||||
k = k + 1
|
|
||||||
logger.debug(" Setting threshold to %.4f (fpr=%.4f, tpr=%.4f)" % (thresholds[k], fpr[k], tpr[k]))
|
|
||||||
self.thresholds[category, label] = thresholds[k]
|
|
||||||
|
|
||||||
|
|
||||||
def merge(self, other_components):
|
|
||||||
# Merge x_train and y_train
|
|
||||||
keys = set(self.x_train.keys())
|
|
||||||
for comp in other_components:
|
|
||||||
keys = keys.union(set(comp.x_train.keys()))
|
|
||||||
for key in keys:
|
|
||||||
x_train_submatrices = [comp.x_train[key]
|
|
||||||
for comp in other_components
|
|
||||||
if key in comp.x_train.keys()]
|
|
||||||
y_train_submatrices = [comp.y_train[key]
|
|
||||||
for comp in other_components
|
|
||||||
if key in comp.y_train.keys()]
|
|
||||||
if key in self.x_train.keys():
|
|
||||||
x_train_submatrices += [self.x_train[key]]
|
|
||||||
y_train_submatrices += [self.y_train[key]]
|
|
||||||
self.x_train[key] = np.vstack(x_train_submatrices)
|
|
||||||
self.y_train[key] = np.vstack(y_train_submatrices)
|
|
||||||
|
|
||||||
# Merge trained predictors
|
|
||||||
for comp in other_components:
|
|
||||||
for key in comp.predictors.keys():
|
|
||||||
if key not in self.predictors.keys():
|
|
||||||
self.predictors[key] = comp.predictors[key]
|
|
||||||
self.thresholds[key] = comp.thresholds[key]
|
|
||||||
|
|
||||||
|
|
||||||
# Deprecated
|
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
# Deprecated
|
|
||||||
class LogisticWarmStartPredictor(WarmStartPredictor):
|
|
||||||
def __init__(self,
|
|
||||||
min_samples=100,
|
|
||||||
thr_fix=[0.99, 0.99],
|
|
||||||
thr_balance=[0.80, 0.80],
|
|
||||||
thr_alpha=[0.50, 0.50],
|
|
||||||
):
|
|
||||||
super().__init__()
|
|
||||||
self.min_samples = min_samples
|
|
||||||
self.thr_fix = thr_fix
|
|
||||||
self.thr_balance = thr_balance
|
|
||||||
self.thr_alpha = thr_alpha
|
|
||||||
|
|
||||||
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))
|
|
||||||
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
|
||||||
reg_thr = 1. * self.thr_alpha[label] + dummy_score * (1 - self.thr_alpha[label])
|
|
||||||
|
|
||||||
# If cross-validation score is too low, don't predict anything.
|
|
||||||
if reg_score < reg_thr:
|
|
||||||
return 0
|
|
||||||
|
|
||||||
reg.fit(x_train, y_train.astype(int))
|
|
||||||
return reg
|
|
||||||
|
|
||||||
|
|
||||||
# Deprecated
|
|
||||||
class KnnWarmStartPredictor(WarmStartPredictor):
|
|
||||||
def __init__(self,
|
|
||||||
k=50,
|
|
||||||
min_samples=1,
|
|
||||||
thr_clip=[0.80, 0.80],
|
|
||||||
thr_fix=[1.0, 1.0],
|
|
||||||
):
|
|
||||||
super().__init__(thr_clip=thr_clip)
|
|
||||||
self.k = k
|
|
||||||
self.thr_fix = thr_fix
|
|
||||||
self.min_samples = min_samples
|
|
||||||
|
|
||||||
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.min_samples:
|
|
||||||
logger.debug("Too few samples; return 0")
|
|
||||||
return 0
|
|
||||||
|
|
||||||
# If vast majority of observations are true, always return true.
|
|
||||||
if y_train_avg >= self.thr_fix[label]:
|
|
||||||
logger.debug("Consensus reached; return 1")
|
|
||||||
return 1
|
|
||||||
|
|
||||||
# If vast majority of observations are false, always return false.
|
|
||||||
if y_train_avg <= (1 - self.thr_fix[label]):
|
|
||||||
logger.debug("Consensus reached; return 0")
|
|
||||||
return 0
|
|
||||||
|
|
||||||
logger.debug("Training classifier...")
|
|
||||||
k = min(self.k, x_train.shape[0])
|
|
||||||
knn = KNeighborsClassifier(n_neighbors=k)
|
|
||||||
knn.fit(x_train, y_train)
|
|
||||||
return knn
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Loading…
Reference in new issue