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Source code for catalyst.contrib.utils.thresholds

from typing import Callable, List, Tuple
from collections import defaultdict
import enum
from functools import partial

import numpy as np
from sklearn.model_selection import RepeatedStratifiedKFold

METRIC_FN = Callable[[np.ndarray, np.ndarray], float]


class ThresholdMode(str, enum.Enum):
    """Available threshold search strategies types."""

    NOOP = noop = "noop"  # noqa: WPS115
    MULTILABEL = multilabel = "multilabel"  # noqa: WPS115
    MULTICLASS = multiclass = "multiclass"  # noqa: WPS115


[docs]def get_baseline_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Returns baseline thresholds for multiclass/multilabel classification. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] thresholds = [0.5] * num_classes predictions = np.greater(scores, thresholds).astype(np.int32) best_metric = objective(labels, predictions) return best_metric, thresholds
[docs]def get_binary_threshold( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_thresholds: int = 100, ) -> Tuple[float, float]: """Finds best threshold for binary classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ] labels: ground truth labels, numpy array with shape [num_examples, ] objective: callable function, metric which we want to maximize num_thresholds: number of thresholds ot try for each class Returns: tuple with best found objective score and threshold """ thresholds = np.linspace(scores.min(), scores.max(), num=num_thresholds) metric_values = [] for threshold in thresholds: predictions = (scores >= threshold).astype(np.int32) if np.sum(predictions) > 0: metric_value = objective(labels, predictions) metric_values.append(metric_value) else: metric_values.append(0.0) if np.max(metric_values) == 0.0: best_metric_value = 0.0 best_threshold = 1.0 else: best_metric_value = metric_values[np.argmax(metric_values)] best_threshold = thresholds[np.argmax(metric_values)] return best_metric_value, best_threshold
[docs]def get_multiclass_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[List[float], List[float]]: """Finds best thresholds for multiclass classification task. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes # score threshold -> classes with such score classes_by_threshold = defaultdict(list) for class_index in range(num_classes): for score in np.unique(scores[:, class_index]): classes_by_threshold[score].append(class_index) for threshold in sorted(classes_by_threshold): for class_index in classes_by_threshold[threshold]: metric_value = objective(labels[:, class_index], scores[:, class_index] >= threshold) if metric_value > metrics[class_index]: metrics[class_index] = metric_value thresholds[class_index] = threshold return metrics, thresholds
[docs]def get_multilabel_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ): """Finds best thresholds for multilabel classification task. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = labels.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes for class_index in range(num_classes): best_metric, best_threshold = get_binary_threshold( labels=labels[:, class_index], scores=scores[:, class_index], objective=objective, ) metrics[class_index] = best_metric thresholds[class_index] = best_threshold return metrics, thresholds
[docs]def get_binary_threshold_cv( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_splits: int = 5, num_repeats: int = 1, random_state: int = 42, ): """Finds best threshold for binary classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, ] labels: ground truth labels, numpy array with shape [num_examples, ] objective: callable function, metric which we want to maximize num_splits: number of splits to use for cross-validation num_repeats: number of repeats to use for cross-validation random_state: random state to use for cross-validation Returns: tuple with best found objective score and threshold """ rkf = RepeatedStratifiedKFold( n_splits=num_splits, n_repeats=num_repeats, random_state=random_state ) fold_metrics, fold_thresholds = [], [] for train_index, valid_index in rkf.split(labels, labels): labels_train, labels_valid = labels[train_index], labels[valid_index] scores_train, scores_valid = scores[train_index], scores[valid_index] _, best_threshold = get_binary_threshold( labels=labels_train, scores=scores_train, objective=objective, ) valid_predictions = (scores_valid >= best_threshold).astype(np.int32) best_metric_value = objective(labels_valid, valid_predictions) fold_metrics.append(best_metric_value) fold_thresholds.append(best_threshold) return np.mean(fold_metrics), np.mean(fold_thresholds)
[docs]def get_multilabel_thresholds_cv( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_splits: int = 5, num_repeats: int = 1, random_state: int = 42, ): """Finds best thresholds for multilabel classification task based on cross-validation estimates. Args: scores: estimated per-class scores/probabilities predicted by the model, numpy array with shape [num_examples, num_classes] labels: ground truth labels, numpy array with shape [num_examples, num_classes] objective: callable function, metric which we want to maximize num_splits: number of splits to use for cross-validation num_repeats: number of repeats to use for cross-validation random_state: random state to use for cross-validation Returns: tuple with best found objective score and per-class thresholds """ num_classes = labels.shape[1] metrics = [0.0] * num_classes thresholds = [0.0] * num_classes for class_index in range(num_classes): best_metric, best_threshold = get_binary_threshold_cv( labels=labels[:, class_index], scores=scores[:, class_index], objective=objective, num_splits=num_splits, num_repeats=num_repeats, random_state=random_state, ) metrics[class_index] = best_metric thresholds[class_index] = best_threshold return metrics, thresholds
[docs]def get_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, score_fn: Callable, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels score_fn: callable function, based on (scores, labels, thresholds) num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] if thresholds is None: thresholds = [0.5] * num_classes best_metric = score_fn(scores, labels, thresholds) iteration_metrics = [] for i in range(num_iterations): if len(iteration_metrics) >= patience: if best_metric < iteration_metrics[i - patience] + atol: break for class_index in range(num_classes): current_thresholds = thresholds.copy() class_scores = [] class_thresholds = np.linspace( scores[:, class_index].min(), scores[:, class_index].max(), num=num_thresholds, ) for threshold in class_thresholds: current_thresholds[class_index] = threshold class_score = score_fn(scores, labels, current_thresholds) class_scores.append(class_score) best_class_score = np.max(class_scores) best_score_index = np.argmax(class_scores) if best_class_score > best_metric: best_metric = best_class_score thresholds[class_index] = class_thresholds[best_score_index] iteration_metrics.append(best_metric) return best_metric, thresholds
def _multilabel_score_fn(scores, labels, thresholds, objective): predictions = np.greater(scores, thresholds).astype(np.int32) return objective(labels, predictions)
[docs]def get_multilabel_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for multilabel classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ best_metric, thresholds = get_thresholds_greedy( scores=scores, labels=labels, score_fn=partial(_multilabel_score_fn, objective=objective), num_iterations=num_iterations, num_thresholds=num_thresholds, thresholds=thresholds, patience=patience, atol=atol, ) return best_metric, thresholds
def _multiclass_score_fn(scores, labels, thresholds, objective): scores_copy = scores.copy() scores_copy[np.less(scores, thresholds)] = 0 predictions = scores_copy.argmax(axis=1) return objective(labels, predictions)
[docs]def get_multiclass_thresholds_greedy( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, num_iterations: int = 100, num_thresholds: int = 100, thresholds: np.ndarray = None, patience: int = 3, atol: float = 0.01, ) -> Tuple[float, List[float]]: """Finds best thresholds for multiclass classification task with brute-force algorithm. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize num_iterations: number of iteration for brute-force algorithm num_thresholds: number of thresholds ot try for each class thresholds: baseline thresholds, which we want to optimize patience: maximum number of iteration before early stop exit atol: minimum required improvement per iteration for early stop exit Returns: tuple with best found objective score and per-class thresholds """ best_metric, thresholds = get_thresholds_greedy( scores=scores, labels=labels, score_fn=partial(_multiclass_score_fn, objective=objective), num_iterations=num_iterations, num_thresholds=num_thresholds, thresholds=thresholds, patience=patience, atol=atol, ) return best_metric, thresholds
[docs]def get_best_multilabel_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Finds best thresholds for multilabel classification task. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] best_metric, best_thresholds = 0.0, [] for baseline_thresholds_fn in [ get_baseline_thresholds, get_multiclass_thresholds, get_binary_threshold, get_multilabel_thresholds, ]: _, baseline_thresholds = baseline_thresholds_fn( labels=labels, scores=scores, objective=objective, ) if isinstance(baseline_thresholds, (int, float)): baseline_thresholds = [baseline_thresholds] * num_classes metric_value, thresholds_value = get_multilabel_thresholds_greedy( labels=labels, scores=scores, objective=objective, thresholds=baseline_thresholds, ) if metric_value > best_metric: best_metric = metric_value best_thresholds = thresholds_value return best_metric, best_thresholds
[docs]def get_best_multiclass_thresholds( scores: np.ndarray, labels: np.ndarray, objective: METRIC_FN, ) -> Tuple[float, List[float]]: """Finds best thresholds for multiclass classification task. Args: scores: estimated per-class scores/probabilities predicted by the model labels: ground truth labels objective: callable function, metric which we want to maximize Returns: tuple with best found objective score and per-class thresholds """ num_classes = scores.shape[1] best_metric, best_thresholds = 0.0, [] labels_onehot = np.zeros((labels.size, labels.max() + 1)) labels_onehot[np.arange(labels.size), labels] = 1 for baseline_thresholds_fn in [ get_baseline_thresholds, get_multiclass_thresholds, get_binary_threshold, get_multilabel_thresholds, ]: _, baseline_thresholds = baseline_thresholds_fn( labels=labels_onehot, scores=scores, objective=objective, ) if isinstance(baseline_thresholds, (int, float)): baseline_thresholds = [baseline_thresholds] * num_classes metric_value, thresholds_value = get_multiclass_thresholds_greedy( labels=labels, scores=scores, objective=objective, thresholds=baseline_thresholds, ) if metric_value > best_metric: best_metric = metric_value best_thresholds = thresholds_value return best_metric, best_thresholds
__all__ = [ "get_baseline_thresholds", "get_binary_threshold", "get_multiclass_thresholds", "get_multilabel_thresholds", "get_binary_threshold_cv", "get_multilabel_thresholds_cv", "get_thresholds_greedy", "get_multilabel_thresholds_greedy", "get_multiclass_thresholds_greedy", "get_best_multilabel_thresholds", "get_best_multiclass_thresholds", ]