108 lines
3.2 KiB
Python
108 lines
3.2 KiB
Python
from ast import get_docstring
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from statistics import mean
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from typing import Dict
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from sklearn.base import BaseEstimator
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from sklearn.model_selection import cross_validate
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from quapy.data import LabelledCollection
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import garg22_ATC.ATC_helper as atc
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import numpy as np
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import jiang18_trustscore.trustscore as trustscore
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import guillory21_doc.doc as doc
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def kfcv(c_model: BaseEstimator, validation: LabelledCollection) -> Dict:
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scoring = ["f1_macro"]
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scores = cross_validate(c_model, validation.X, validation.y, scoring=scoring)
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return {"f1_score": mean(scores["test_f1_macro"])}
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def atc_mc(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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test: LabelledCollection,
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predict_method="predict_proba",
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):
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c_model_predict = getattr(c_model, predict_method)
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## Load ID validation data probs and labels
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val_probs, val_labels = c_model_predict(validation.X), validation.y
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## Load OOD test data probs
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test_probs = c_model_predict(test.X)
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## score function, e.g., negative entropy or argmax confidence
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val_scores = atc.get_max_conf(val_probs)
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val_preds = np.argmax(val_probs, axis=-1)
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test_scores = atc.get_max_conf(test_probs)
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_, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds)
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atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores)
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return {
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"true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
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"pred_acc": atc_accuracy,
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}
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def atc_ne(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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test: LabelledCollection,
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predict_method="predict_proba",
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):
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c_model_predict = getattr(c_model, predict_method)
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## Load ID validation data probs and labels
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val_probs, val_labels = c_model_predict(validation.X), validation.y
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## Load OOD test data probs
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test_probs = c_model_predict(test.X)
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## score function, e.g., negative entropy or argmax confidence
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val_scores = atc.get_entropy(val_probs)
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val_preds = np.argmax(val_probs, axis=-1)
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test_scores = atc.get_entropy(test_probs)
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_, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds)
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atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores)
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return {
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"true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
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"pred_acc": atc_accuracy,
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}
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def trust_score(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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test: LabelledCollection,
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predict_method="predict",
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):
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c_model_predict = getattr(c_model, predict_method)
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test_pred = c_model_predict(test.X)
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trust_model = trustscore.TrustScore()
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trust_model.fit(validation.X, validation.y)
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return trust_model.get_score(test.X, test_pred)
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def doc_feat(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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test: LabelledCollection,
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predict_method="predict_proba",
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):
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c_model_predict = getattr(c_model, predict_method)
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val_probs, val_labels = c_model_predict(validation.X), validation.y
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test_probs = c_model_predict(test.X)
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val_scores = np.max(val_probs, axis=-1)
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test_scores = np.max(test_probs, axis=-1)
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val_preds = np.argmax(val_probs, axis=-1)
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v1acc = np.mean(val_preds == val_labels) * 100
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return v1acc + doc.get_doc(val_scores, test_scores)
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