Module `miplearn.classifiers.adaptive`

Expand source code

#  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
#  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
#  Released under the modified BSD license. See COPYING.md for more details.

import logging
from copy import deepcopy
from typing import Any, Dict

from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.classifiers.evaluator import ClassifierEvaluator

logger = logging.getLogger(__name__)


class AdaptiveClassifier(Classifier):
    """
    A meta-classifier which dynamically selects what actual classifier to use
    based on its cross-validation score on a particular training data set.
    """

    def __init__(
        self,
        candidates: Dict[str, Any] = None,
        evaluator: ClassifierEvaluator = ClassifierEvaluator(),
    ) -> None:
        """
        Initializes the meta-classifier.
        """
        if candidates is None:
            candidates = {
                "knn(100)": {
                    "classifier": KNeighborsClassifier(n_neighbors=100),
                    "min samples": 100,
                },
                "logistic": {
                    "classifier": make_pipeline(StandardScaler(), LogisticRegression()),
                    "min samples": 30,
                },
                "counting": {
                    "classifier": CountingClassifier(),
                    "min samples": 0,
                },
            }
        self.candidates = candidates
        self.evaluator = evaluator
        self.classifier = None

    def fit(self, x_train, y_train):
        best_name, best_clf, best_score = None, None, -float("inf")
        n_samples = x_train.shape[0]
        for (name, clf_dict) in self.candidates.items():
            if n_samples < clf_dict["min samples"]:
                continue
            clf = deepcopy(clf_dict["classifier"])
            clf.fit(x_train, y_train)
            score = self.evaluator.evaluate(clf, x_train, y_train)
            if score > best_score:
                best_name, best_clf, best_score = name, clf, score
        logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
        self.classifier = best_clf

    def predict_proba(self, x_test):
        return self.classifier.predict_proba(x_test)

Classes

class AdaptiveClassifier (candidates=None, evaluator=)

A meta-classifier which dynamically selects what actual classifier to use based on its cross-validation score on a particular training data set.

Initializes the meta-classifier.

Expand source code

class AdaptiveClassifier(Classifier):
    """
    A meta-classifier which dynamically selects what actual classifier to use
    based on its cross-validation score on a particular training data set.
    """

    def __init__(
        self,
        candidates: Dict[str, Any] = None,
        evaluator: ClassifierEvaluator = ClassifierEvaluator(),
    ) -> None:
        """
        Initializes the meta-classifier.
        """
        if candidates is None:
            candidates = {
                "knn(100)": {
                    "classifier": KNeighborsClassifier(n_neighbors=100),
                    "min samples": 100,
                },
                "logistic": {
                    "classifier": make_pipeline(StandardScaler(), LogisticRegression()),
                    "min samples": 30,
                },
                "counting": {
                    "classifier": CountingClassifier(),
                    "min samples": 0,
                },
            }
        self.candidates = candidates
        self.evaluator = evaluator
        self.classifier = None

    def fit(self, x_train, y_train):
        best_name, best_clf, best_score = None, None, -float("inf")
        n_samples = x_train.shape[0]
        for (name, clf_dict) in self.candidates.items():
            if n_samples < clf_dict["min samples"]:
                continue
            clf = deepcopy(clf_dict["classifier"])
            clf.fit(x_train, y_train)
            score = self.evaluator.evaluate(clf, x_train, y_train)
            if score > best_score:
                best_name, best_clf, best_score = name, clf, score
        logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
        self.classifier = best_clf

    def predict_proba(self, x_test):
        return self.classifier.predict_proba(x_test)

Ancestors

Classifier
abc.ABC

Methods

def fit(self, x_train, y_train)

Expand source code

def fit(self, x_train, y_train):
    best_name, best_clf, best_score = None, None, -float("inf")
    n_samples = x_train.shape[0]
    for (name, clf_dict) in self.candidates.items():
        if n_samples < clf_dict["min samples"]:
            continue
        clf = deepcopy(clf_dict["classifier"])
        clf.fit(x_train, y_train)
        score = self.evaluator.evaluate(clf, x_train, y_train)
        if score > best_score:
            best_name, best_clf, best_score = name, clf, score
    logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
    self.classifier = best_clf

def predict_proba(self, x_test)

Expand source code

def predict_proba(self, x_test):
    return self.classifier.predict_proba(x_test)