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89 lines
3.2 KiB
89 lines
3.2 KiB
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2022, 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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import logging
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from typing import List, Dict, Any, Optional
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import numpy as np
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from miplearn.components.primal import _extract_bin_var_names_values
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from miplearn.components.primal.actions import PrimalComponentAction
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from miplearn.extractors.abstract import FeaturesExtractor
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from miplearn.solvers.abstract import AbstractModel
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from miplearn.h5 import H5File
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logger = logging.getLogger(__name__)
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class JointVarsPrimalComponent:
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def __init__(
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self, clf: Any, extractor: FeaturesExtractor, action: PrimalComponentAction
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):
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self.clf = clf
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self.extractor = extractor
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self.bin_var_names_: Optional[np.ndarray] = None
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self.action = action
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def fit(self, train_h5: List[str]) -> None:
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logger.info("Reading training data...")
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self.bin_var_names_ = None
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x, y, n_samples, n_features = [], [], len(train_h5), None
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for h5_filename in train_h5:
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with H5File(h5_filename, "r") as h5:
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bin_var_names, bin_var_values, _ = _extract_bin_var_names_values(h5)
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# Store/check variable names
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if self.bin_var_names_ is None:
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self.bin_var_names_ = bin_var_names
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else:
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assert np.all(bin_var_names == self.bin_var_names_)
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# Build x and y vectors
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x_sample = self.extractor.get_instance_features(h5)
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assert len(x_sample.shape) == 1
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if n_features is None:
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n_features = len(x_sample)
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else:
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assert len(x_sample) == n_features
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x.append(x_sample)
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y.append(bin_var_values)
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assert self.bin_var_names_ is not None
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logger.info("Constructing matrices...")
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x_np = np.vstack(x)
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y_np = np.array(y)
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assert len(x_np.shape) == 2
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assert x_np.shape[0] == n_samples
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assert x_np.shape[1] == n_features
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assert y_np.shape == (n_samples, len(self.bin_var_names_))
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logger.info(
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f"Dataset has {n_samples:,d} samples, "
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f"{n_features:,d} features and {y_np.shape[1]:,d} targets"
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)
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logger.info("Training classifier...")
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self.clf.fit(x_np, y_np)
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logger.info("Done fitting.")
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def before_mip(
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self, test_h5: str, model: AbstractModel, stats: Dict[str, Any]
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) -> None:
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assert self.bin_var_names_ is not None
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# Read features
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with H5File(test_h5, "r") as h5:
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x_sample = self.extractor.get_instance_features(h5)
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assert len(x_sample.shape) == 1
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x_sample = x_sample.reshape(1, -1)
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# Predict optimal solution
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logger.info("Predicting warm starts...")
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y_pred = self.clf.predict(x_sample)
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assert len(y_pred.shape) == 2
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assert y_pred.shape[0] == 1
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assert y_pred.shape[1] == len(self.bin_var_names_)
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# Construct warm starts, based on prediction
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self.action.perform(model, self.bin_var_names_, y_pred, stats)
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