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Select threshold

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neingeist 10 years ago
parent 4bc9a2b246
commit 87457bb7b4
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ex8/selectThreshold.m
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function [bestEpsilon bestF1] = selectThreshold(yval, pval) function [bestEpsilon bestF1] = selectThreshold(yval, pval)
%SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting %SELECTTHRESHOLD Find the best threshold (epsilon) to use for selecting
%outliers %outliers
% [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best % [bestEpsilon bestF1] = SELECTTHRESHOLD(yval, pval) finds the best
% threshold to use for selecting outliers based on the results from a % threshold to use for selecting outliers based on the results from a
% validation set (pval) and the ground truth (yval). % validation set (pval) and the ground truth (yval).
% %
bestEpsilon = 0; bestEpsilon = 0;
bestF1 = 0; bestF1 = 0;
F1 = 0; F1 = 0;
stepsize = (max(pval) - min(pval)) / 1000; stepsize = (max(pval) - min(pval)) / 1000;
for epsilon = min(pval):stepsize:max(pval) for epsilon = min(pval):stepsize:max(pval)
% ====================== YOUR CODE HERE ====================== % ====================== YOUR CODE HERE ======================
% Instructions: Compute the F1 score of choosing epsilon as the % Instructions: Compute the F1 score of choosing epsilon as the
% threshold and place the value in F1. The code at the % threshold and place the value in F1. The code at the
% end of the loop will compare the F1 score for this % end of the loop will compare the F1 score for this
% choice of epsilon and set it to be the best epsilon if % choice of epsilon and set it to be the best epsilon if
% it is better than the current choice of epsilon. % it is better than the current choice of epsilon.
% %
% Note: You can use predictions = (pval < epsilon) to get a binary vector % Note: You can use predictions = (pval < epsilon) to get a binary vector
% of 0's and 1's of the outlier predictions % of 0's and 1's of the outlier predictions
predictions = (pval < epsilon);
tp = sum((predictions == 1) & (yval == 1));
fp = sum((predictions == 1) & (yval == 0));
fn = sum((predictions == 0) & (yval == 1));
prec = tp/(tp+fp);
rec = tp/(tp+fn);
F1 = (2*prec*rec)/(prec+rec);
% =============================================================
% ============================================================= if F1 > bestF1
bestF1 = F1;
if F1 > bestF1 bestEpsilon = epsilon;
bestF1 = F1; end
bestEpsilon = epsilon; end
end
end end
end