268 lines
9.3 KiB
Python
268 lines
9.3 KiB
Python
from statistics import mean
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from typing import Dict
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import numpy as np
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from quapy.data import LabelledCollection
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from sklearn.base import BaseEstimator
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from sklearn.model_selection import cross_validate
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import sklearn.metrics as metrics
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from quapy.protocol import (
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AbstractStochasticSeededProtocol,
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OnLabelledCollectionProtocol,
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)
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from .report import EvaluationReport
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import elsahar19_rca.rca as rca
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import garg22_ATC.ATC_helper as atc
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import guillory21_doc.doc as doc
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import jiang18_trustscore.trustscore as trustscore
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def kfcv(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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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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scoring = ["accuracy", "f1_macro"]
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scores = cross_validate(c_model, validation.X, validation.y, scoring=scoring)
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acc_score = mean(scores["test_accuracy"])
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f1_score = mean(scores["test_f1_macro"])
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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="kfcv")
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for test in protocol():
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test_preds = c_model_predict(test.X)
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meta_acc = abs(acc_score - metrics.accuracy_score(test.y, test_preds))
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meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds))
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report.append_row(
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test.prevalence(),
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acc_score=(1. - acc_score),
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f1_score=f1_score,
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acc=meta_acc,
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f1=meta_f1,
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)
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return report
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def reference(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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):
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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c_model_predict = getattr(c_model, "predict_proba")
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report = EvaluationReport(prefix="ref")
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for test in protocol():
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test_probs = c_model_predict(test.X)
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test_preds = np.argmax(test_probs, axis=-1)
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report.append_row(
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test.prevalence(),
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acc_score=(1 - metrics.accuracy_score(test.y, test_preds)),
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f1_score=metrics.f1_score(test.y, test_preds),
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)
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return report
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def atc_mc(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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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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## 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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_, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds)
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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="atc_mc")
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for test in protocol():
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## Load OOD test data probs
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test_probs = c_model_predict(test.X)
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test_preds = np.argmax(test_probs, axis=-1)
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test_scores = atc.get_max_conf(test_probs)
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atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores)
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meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds))
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f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs)
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meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds))
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report.append_row(
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test.prevalence(),
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acc=meta_acc,
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acc_score=1.0 - atc_accuracy,
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f1_score=f1_score,
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f1=meta_f1,
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)
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return report
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def atc_ne(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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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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## 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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_, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds)
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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="atc_ne")
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for test in protocol():
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## Load OOD test data probs
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test_probs = c_model_predict(test.X)
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test_preds = np.argmax(test_probs, axis=-1)
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test_scores = atc.get_entropy(test_probs)
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atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores)
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meta_acc = abs(atc_accuracy - metrics.accuracy_score(test.y, test_preds))
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f1_score = atc.get_ATC_f1(atc_thres, test_scores, test_probs)
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meta_f1 = abs(f1_score - metrics.f1_score(test.y, test_preds))
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report.append_row(
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test.prevalence(),
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acc=meta_acc,
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acc_score=(1.0 - atc_accuracy),
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f1_score=f1_score,
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f1=meta_f1,
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)
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return report
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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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protocol: AbstractStochasticSeededProtocol,
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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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val_scores = np.max(val_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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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="doc_feat")
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for test in protocol():
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test_probs = c_model_predict(test.X)
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test_preds = np.argmax(test_probs, axis=-1)
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test_scores = np.max(test_probs, axis=-1)
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score = (v1acc + doc.get_doc(val_scores, test_scores)) / 100.0
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meta_acc = abs(score - metrics.accuracy_score(test.y, test_preds))
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report.append_row(test.prevalence(), acc=meta_acc, acc_score=(1.0 - score))
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return report
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def rca_score(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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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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val_pred1 = c_model_predict(validation.X)
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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="rca")
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for test in protocol():
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try:
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test_pred = c_model_predict(test.X)
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c_model2 = rca.clone_fit(c_model, test.X, test_pred)
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c_model2_predict = getattr(c_model2, predict_method)
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val_pred2 = c_model2_predict(validation.X)
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rca_score = rca.get_score(val_pred1, val_pred2, validation.y)
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meta_score = abs(
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rca_score - (1 - metrics.accuracy_score(test.y, test_pred))
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)
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report.append_row(test.prevalence(), acc=meta_score, acc_score=rca_score)
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except ValueError:
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report.append_row(
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test.prevalence(), acc=float("nan"), acc_score=float("nan")
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)
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return report
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def rca_star_score(
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c_model: BaseEstimator,
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validation: LabelledCollection,
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protocol: AbstractStochasticSeededProtocol,
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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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validation1, validation2 = validation.split_stratified(
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train_prop=0.5, random_state=0
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)
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val1_pred = c_model_predict(validation1.X)
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c_model1 = rca.clone_fit(c_model, validation1.X, val1_pred)
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c_model1_predict = getattr(c_model1, predict_method)
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val2_pred1 = c_model1_predict(validation2.X)
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# ensure that the protocol returns a LabelledCollection for each iteration
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protocol.collator = OnLabelledCollectionProtocol.get_collator("labelled_collection")
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report = EvaluationReport(prefix="rca_star")
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for test in protocol():
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try:
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test_pred = c_model_predict(test.X)
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c_model2 = rca.clone_fit(c_model, test.X, test_pred)
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c_model2_predict = getattr(c_model2, predict_method)
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val2_pred2 = c_model2_predict(validation2.X)
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rca_star_score = rca.get_score(val2_pred1, val2_pred2, validation2.y)
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meta_score = abs(
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rca_star_score - (1 - metrics.accuracy_score(test.y, test_pred))
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)
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report.append_row(
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test.prevalence(), acc=meta_score, acc_score=rca_star_score
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)
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except ValueError:
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report.append_row(
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test.prevalence(), acc=float("nan"), acc_score=float("nan")
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)
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return report
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