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