Assuming that I have a dataset of the following size:
train = 500,000 * 960 %number of training samples (vector) each of 960 length
B_base = 1000000*960 %number of base samples (vector) each of 960 length
Query = 1000*960 %number of query samples (vector) each of 960 length
truth_nn = 1000*100
truth_nn contains the ground truth neighbors in the form of the
pre-computed k nearest neighbors and their square Euclidean distance. So, the columns of truth_nn represent the k = 100 nearest neighbors. I am finding difficult to apply nearest neighbor search in the code snippet. Can somebody please show how to apply the ground truth neighbors truth_nn in finding the mean average precision-recall?
It will be of immense help if somebody can show with any small example by creating any data matrix, query matrix, and the ground truth neighbors in the form of the pre-computed k nearest neighbors and their square Euclidean distance. I tried creating a sample database.
Assume, the base data is
B_base = [1 1; 2 2; 3 2; 4 4; 5 6];
Query data is
Query = [1 1; 2 1; 6 2];
[neighbors distances] = knnsearch(a,b,'k',2);
would find 2 nearest neighbors.
Question 1: how do I create the truth data containing the ground truth neighbors and pre-computed k nearest neighbor distances? This is called as the mean average precision recall. I tried implementing the knearest neighbor search and the average precision recall as follows but cannot understand (unsure) how to apply the ground truth table
Question 2:
I am trying to apply k nearest neighbor search by converting first the real-valued features into binary.
I am unable to apply the concept of k-nearest neighbor search for different values of k = 10,20,50 and to check how much data has been correctly recalled using the GIST database. In the GIST truth_nn() file, when I specify truth_nn(i,1:k) for a query vector i, the function AveragePrecision throws error. So, if somebody can show using any sample ground truth that is of similar structure to that in GIST, how to properly specify k and calculate the Average precision recall, then I shall be able to apply the solution to the GIST database. As of now, this is my approach and shall be of immense help if the correct way is provided using any example that will be easier for me to relate to the GIST database. The problem is on how I can find neighbors from the ground truth and compare it with the neighbors obtained after sorting the distances?
I am also interested on how I can apply pdist2() instead of the present distance calcualtion as it takes a long time.
numQueryVectors = size(Query,1);
%Calculate distances
for i=1:numQueryVectors,
queryMatrix(i,:)
dist = sum((repmat(queryMatrix(i,:),numDataVectors,1)-B_base ).^2,2);
[sortval sortpos] = sort(dist,'ascend');
neighborIds(i,:) = sortpos(1:k);
neighborDistances(i,:) = sqrt(sortval(1:k));
end
%Sorting calculated nearest neighbor distances for k = 50
%HOW DO I SPECIFY k = 50 in the ground truth, truth_nn
for i=1:numQueryVectors
AP(i) = AveragePrecision(neighborIds(i,:),truth_nn(i,:));
end
mAP = mean(AP);
function ap = AveragePrecision(rank_id, truth_id)
truth_num = length(truth_id);
truth_pos = zeros(truth_num,1);
for j=1:50 %% for k = 50 nearest neighbors
truth_pos(j) = find(rank_id == truth_id(j));
end
truth_pos = sort(truth_pos, 'ascend');
% compute average precision as the area below the recall-precision curve
ap = 0;
delta_recall = 1/truth_num;
for j=1:truth_num
p = j/truth_pos(j);
ap = ap + p*delta_recall;
end
end
end
UPDATE : Based on solution, I tried to calculate the mean average precision using the formula given formula hereand a reference code . But, not sure if my approach is correct because the theory says that I need to rank the returned queries based on the indices. I do not understand this fully. Mean average precision is required to judge the quality of the retrieval algortihm.
precision = positives/total_data;
recal = positives /(positives+negatives);
precision = positives/total_data;
recall = positives /(positives+negatives);
truth_pos = sort(positives, 'ascend');
truth_num = length(truth_pos);
ap = 0;
delta_recall = 1/truth_num;
for j=1:truth_num
p = j/truth_pos(j);
ap = ap + p*delta_recall;
end
ap
The value of ap = infinity , value of positive = 0 and negatives = 150. This means that knnsearch() does not work at all.
I think you are doing extra work. This process is very simple in matlab, you can also operate on entire arrays. This should be faster than for loops, and is a bit easier to read.
Your truth_nn and neighbors should have the same data, if there are no errors. There is one entry per row. Matlab already sorts the kmeans result in ascending order, so the column 1 is the closest neighbor, the second closest is column 2, 3rd closest is 3,.... There is no need to sort the data again.
Just compare truth_nn to neighbors to get your statistics. This is a simple example to show you how the program should go. It will not work on your data without some modification
%in your example this is provided, I created my own
truth_nn = [1,2;
1,3;
4,3];
B_base = [1 1; 2 2; 3 2; 4 4; 5 6];
Query = [1 1; 2 1; 6 2];
%performs k means
num_clusters = 2;
[neighbors distances] = knnsearch(B_base,Query,'k',num_clusters);
%--- output---
% neighbors = [1,2;
% 1,2; notice this doesn't match truth_nn 1,3
% 4,3]
% distances = [ 0 1.4142;
% 1.0000 1.0000;
% 2.8284 3.0000];
%computes statistics, nnz counts number of nonzero elements, in the first
%case every piece of data that matches
%NOTE1: the indexing on truth_nn (:,1:num_clusters ) it says all rows
% but only use the first num_clusters columns. This should
% prevent the dimension mistmatch error you were getting
positives = nnz(neighbors == truth_nn(:,1:num_clusters )); %result = 5
negatives = nnz(neighbors ~= truth_nn(:,1:num_clusters )); %result = 1
%NOTE1: I've switched this from truth_nn to neighbors, this helps
% when you cahnge num_neghbors
total_data = numel(neighbors); %result = 6
percent_incorrect = 100*(negatives / total_data); % 16.6666
percent_correct = 100*(positives / total_data); % 93.3333