import numpy as np
from sklearn.base import BaseEstimator
from sklearn.multiclass import OneVsRestClassifier
from modAL.models import ActiveLearner
from modAL.utils.data import modALinput
from modAL.utils.selection import multi_argmax, shuffled_argmax
from typing import Tuple, Optional
from itertools import combinations
def _SVM_loss(multiclass_classifier: ActiveLearner,
X: modALinput, most_certain_classes: Optional[int] = None) -> np.ndarray:
"""
Utility function for max_loss and mean_max_loss strategies.
Args:
multiclass_classifier: sklearn.multiclass.OneVsRestClassifier instance for which the loss
is to be calculated.
X: The pool of samples to query from.
most_certain_classes: optional, indexes of most certainly predicted class for each instance.
If None, loss is calculated for all classes.
Returns:
np.ndarray of shape (n_instances, ), losses for the instances in X.
"""
predictions = 2*multiclass_classifier.predict(X)-1
n_classes = len(multiclass_classifier.classes_)
if most_certain_classes is None:
cls_mtx = 2*np.eye(n_classes, n_classes) - 1
loss_mtx = np.maximum(1-np.dot(predictions, cls_mtx), 0)
return loss_mtx.mean(axis=1)
else:
cls_mtx = -np.ones(shape=(len(X), n_classes))
for inst_idx, most_certain_class in enumerate(most_certain_classes):
cls_mtx[inst_idx, most_certain_class] = 1
cls_loss = np.maximum(1 - np.multiply(cls_mtx, predictions), 0).sum(axis=1)
return cls_loss
[docs]def SVM_binary_minimum(classifier: ActiveLearner, X_pool: modALinput,
random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
SVM binary minimum multilabel active learning strategy. For details see the paper
Klaus Brinker, On Active Learning in Multi-label Classification
(https://link.springer.com/chapter/10.1007%2F3-540-31314-1_24)
Args:
classifier: The multilabel classifier for which the labels are to be queried. Must be an SVM model
such as the ones from sklearn.svm.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
decision_function = np.array([svm.decision_function(X_pool)
for svm in classifier.estimator.estimators_]).T
min_abs_dist = np.min(np.abs(decision_function), axis=1)
if not random_tie_break:
query_idx = np.argmin(min_abs_dist)
else:
query_idx = shuffled_argmax(min_abs_dist)
return query_idx, X_pool[query_idx]
[docs]def max_loss(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
Max Loss query strategy for SVM multilabel classification.
For more details on this query strategy, see
Li et al., Multilabel SVM active learning for image classification
(http://dx.doi.org/10.1109/ICIP.2004.1421535)
Args:
classifier: The multilabel classifier for which the labels are to be queried. Should be an SVM model
such as the ones from sklearn.svm. Although the function will execute for other models as well,
the mathematical calculations in Li et al. work only for SVM-s.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
assert len(X_pool) >= n_instances, 'n_instances cannot be larger than len(X_pool)'
most_certain_classes = classifier.predict_proba(X_pool).argmax(axis=1)
loss = _SVM_loss(classifier, X_pool, most_certain_classes=most_certain_classes)
if not random_tie_break:
query_idx = multi_argmax(loss, n_instances)
else:
query_idx = shuffled_argmax(loss, n_instances)
return query_idx, X_pool[query_idx]
[docs]def mean_max_loss(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
Mean Max Loss query strategy for SVM multilabel classification.
For more details on this query strategy, see
Li et al., Multilabel SVM active learning for image classification
(http://dx.doi.org/10.1109/ICIP.2004.1421535)
Args:
classifier: The multilabel classifier for which the labels are to be queried. Should be an SVM model
such as the ones from sklearn.svm. Although the function will execute for other models as well,
the mathematical calculations in Li et al. work only for SVM-s.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
assert len(X_pool) >= n_instances, 'n_instances cannot be larger than len(X_pool)'
loss = _SVM_loss(classifier, X_pool)
if not random_tie_break:
query_idx = multi_argmax(loss, n_instances)
else:
query_idx = shuffled_argmax(loss, n_instances)
return query_idx, X_pool[query_idx]
[docs]def min_confidence(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
MinConfidence query strategy for multilabel classification.
For more details on this query strategy, see
Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
(http://dx.doi.org/10.1007/978-3-642-00958-7_12)
Args:
classifier: The multilabel classifier for which the labels are to be queried.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
classwise_confidence = classifier.predict_proba(X_pool)
classwise_min = np.min(classwise_confidence, axis=1)
if not random_tie_break:
query_idx = multi_argmax(-classwise_min, n_instances)
else:
query_idx = shuffled_argmax(-classwise_min, n_instances)
return query_idx, X_pool[query_idx]
[docs]def avg_confidence(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
AvgConfidence query strategy for multilabel classification.
For more details on this query strategy, see
Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
(http://dx.doi.org/10.1007/978-3-642-00958-7_12)
Args:
classifier: The multilabel classifier for which the labels are to be queried.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
classwise_confidence = classifier.predict_proba(X_pool)
classwise_mean = np.mean(classwise_confidence, axis=1)
if not random_tie_break:
query_idx = multi_argmax(classwise_mean, n_instances)
else:
query_idx = shuffled_argmax(classwise_mean, n_instances)
return query_idx, X_pool[query_idx]
[docs]def max_score(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = 1) -> Tuple[np.ndarray, modALinput]:
"""
MaxScore query strategy for multilabel classification.
For more details on this query strategy, see
Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
(http://dx.doi.org/10.1007/978-3-642-00958-7_12)
Args:
classifier: The multilabel classifier for which the labels are to be queried.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
classwise_confidence = classifier.predict_proba(X_pool)
classwise_predictions = classifier.predict(X_pool)
classwise_scores = classwise_confidence*(classwise_predictions - 1/2)
classwise_max = np.max(classwise_scores, axis=1)
if not random_tie_break:
query_idx = multi_argmax(classwise_max, n_instances)
else:
query_idx = shuffled_argmax(classwise_max, n_instances)
return query_idx, X_pool[query_idx]
[docs]def avg_score(classifier: OneVsRestClassifier, X_pool: modALinput,
n_instances: int = 1, random_tie_break: bool = False) -> Tuple[np.ndarray, modALinput]:
"""
AvgScore query strategy for multilabel classification.
For more details on this query strategy, see
Esuli and Sebastiani., Active Learning Strategies for Multi-Label Text Classification
(http://dx.doi.org/10.1007/978-3-642-00958-7_12)
Args:
classifier: The multilabel classifier for which the labels are to be queried.
X_pool: The pool of samples to query from.
random_tie_break: If True, shuffles utility scores to randomize the order. This
can be used to break the tie when the highest utility score is not unique.
Returns:
The index of the instance from X_pool chosen to be labelled;
the instance from X_pool chosen to be labelled.
"""
classwise_confidence = classifier.predict_proba(X_pool)
classwise_predictions = classifier.predict(X_pool)
classwise_scores = classwise_confidence*(classwise_predictions-1/2)
classwise_mean = np.mean(classwise_scores, axis=1)
if not random_tie_break:
query_idx = multi_argmax(classwise_mean, n_instances)
else:
query_idx = shuffled_argmax(classwise_mean, n_instances)
return query_idx, X_pool[query_idx]