SG separation challenge » History » Version 28

Ignacio Sevilla, 01/28/2014 03:24 AM

1 1 Ignacio Sevilla
h1. SG separation challenge
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*[[des-sci-verification:SG_separation_challenge_details|Details and results]]*
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Now that several people are testing their own approaches:
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7 19 William Wester
* Cut-based with DESDM info (Eli, Diego, Nacho, Ryan, William...).
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* Multi-class (Maayane)
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* Random Forests (Ryan)
10 20 Alex Drlica-Wagner
* Boosted Decision Trees (Nacho, Alex)
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* Alternative Neural Network with probabilistic output (Chris Bonnett).
12 13 Basilio Santiago
* Probability based on spread model and photometry (DES-Brazil)
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* Others...
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I think the time is right and the codes are mature to launch a specific SG separation challenge, mimicking the successful photo-z WG exercise.
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17 1 Ignacio Sevilla
We have to establish:
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19 1 Ignacio Sevilla
* The training/validation/testing sample (COSMOS, others).
20 17 Ignacio Sevilla
I have prepared a 70/30 training/testing with the deep COSMOS field matched to ACS imaging. About 280 parameters, up to each tester to choose which.
21 17 Ignacio Sevilla
Besides new datasets, also consider shallower COSMOS. Also consider fixed set of parameters as Eduardo suggests. Also need to add SLR corrections though I think not very important now.
22 6 Ignacio Sevilla
* Only stars and galaxies? What about QSOs, image artifacts?
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Star/galaxy for round 1.
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* The metrics (Fixed cut, Fixed purity, Fixed Efficiency, ROC -- see example below).
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I would prefer to use ROC, i.e., True Positive Rate vs False Positive Rate curve formed changing the threshold (thanks Alex for pointing out mistake in previous ROC!).
26 7 Eli Rykoff
* SVA1 systematics: correlations with depth, Galactic latitude, seeing, etc.
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* Who/how to run it.
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I suggest each group providing an output file with id (or ra,dec on first round) plus galaxy probability or binary value.
29 9 Maayane Soumagnac
* Is there any gain combining them (a committee)?
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* The schedule.
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h1. Comparison metrics
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There are a number of metrics that can be used for comparing the performance of classifiers. Some especially useful metrics are those defined in the DES star/galaxy separation (on simulation) paper "arXiv:1306.5236": and the "receiver operating characteristic (ROC)": generally used for classifier comparison.
35 9 Maayane Soumagnac
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h2. Completeness and Purity provided by a given classifier
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38 22 Alex Drlica-Wagner
We define the parameters used to quantify the quality of a star/galaxy classifier. For a given class of objects, X (stars or galaxies), we distinguish the surface density of properly classified objects, N_X , and the misclassified objects, M_X .
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* The galaxy completeness c^g is defined as the ratio of the number of true galaxies classified as galaxies to the total number of true galaxies. 
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* The stellar contamination f_s is defined as the ratio of stars classified as galaxies to the total amount of objects classified as galaxies. 
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* The purity p^g is defined as 1-f_s
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Bellow are three different plots we suggest to use to assess the performances of each classifier.
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h3. Histograms
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Example, on simulations, from arXiv:1306.5236
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h3. purity as a function of magnitude (for fixed completeness, the threshold/cut is let free)
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55 23 Alex Drlica-Wagner
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57 23 Alex Drlica-Wagner
!{width:300px}sg_separation_purity_vs_magauto_50.0_efficiency.png! !{width:300px}sg_separation_purity_vs_magauto_90.0_efficiency.png!
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59 1 Ignacio Sevilla
h3. completeness as a function of magnitude (for fixed purity, the threshold/cut is let free )
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61 24 Alex Drlica-Wagner
!{width:300px}sg_separation_efficiency_vs_magauto_95.0_purity.png! !{width:300px}sg_separation_efficiency_vs_magauto_99.0_purity.png!
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h2. Receiver operating characteristics
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The receiver operating characteristic (ROC) provides another tool for evaluating the performance of classifiers. The ROC provides some information orthogonal to that in the completeness vs purity plots:
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* Because ROCs compare the true positive rate to the false positive rate, they do not depend on relative composition of the test sample. Thus, unlike the purity, they contain information only about the intrinsic performance of the classifier and not the test sample.
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* ROCs allow classifiers to be compared without requiring a threshold/cut to be placed on the output. This is useful because different projects possess different requirements on object sample, completeness, purity, etc. The area under the ROC can serve as a very high-level scalar metric for classifier performance.
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* Once a threshold/cut is placed, we can generate magnitude dependent true positive vs false positive rate plots which would be intrinsic to the classifiers.
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71 27 Alex Drlica-Wagner