Programming Tools based on Big Data and Conditional Random Fields


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1 Programming Tools based on Big Data and Conditional Random Fields Veselin Raychev Martin Vechev Andreas Krause Department of Computer Science ETH Zurich Zurich Machine Learning and Data Science Meetup, December 2014
2 Motivation Unprecedented access to massive codebases
3 Motivation ~16M repos ~ 7M users # of repositories year
4 Vision Statistical Programming Tools Probabilistically likely solutions to problems difficult or impossible to solve with traditional rulebased techniques
5 General Approach Find the right program representation for the task Find the right probabilistic model for the task Build a probabilistic model over the representation and existing code Use the probabilistic model to answer queries on new programs Programming languages + Machine learning
6 1,000+ Tweets (sample below):
7 JSNice: Popularity one of the top ranked tools for JavaScript in ,000 users in 1 st week of release used in 180 countries
8 JSNice Intuition: Image Denoising Original image Noisy Image Denoised Image
9 Image Denoising Noisy Image? Denoised Image
10 JSNice function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n;? function chunkdata(str, step) var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.substring(i, i + step)); colnames.push(str.substring(i, len)); return colnames;
11 Structured Prediction for Programs (V. Raychev, M. Vechev, A. Krause, ACM POPL 15, to appear) Bridges Program Analysis and Conditional Random Fields First connection between programs and CRFs JSNice is a special instance CRFs a key model in Computer Vision
12 Markov Random Fields Undirected graphical model Graph + factors define a joint probability distribution t i 1 r P i, r, t = 1 i, t 2 i, r Z(i, r, t) 2 Captures dependence between facts to be predicted Undirected models better suited for our than directed models (direction is hard to capture) More on graphical models in: Probabilistic Graphical Models for Image Analysis, ETH graduate course, McWilliams and Lucchi
13 Conditional Random Fields (McCallum et.al, 2001) Some facts are already known, denoted as x We would like to predict new facts, y, conditioned on the known facts x t i 1 K r P i, r t = 1 i, t 2 i, r Z(t) 2 Key advantage of CRFs over MRFs: no priors required.
14 MAP inference: joint prediction y best = argmax P y x y x Key: MAP inference over marginals! This is key for programs i 1 t K P i, r t = r 2 1 i, t 2 i, r Z(t) i, r best = argmax 1 i, t 2 i, r (i, r) x We use an iterative greedy algorithm
15 Learning CRFs from Data (via maxmargin training, Ratliff et.al., 2007) A convenient representation for learning from data is a loglinear CRF P y x = 1 Z(x) exp (wt f(y, x)) learned from data As we require only CRFs and MAP inference, we use the maxmargin training due to Ratliff et.al. (2007). Computes subgradient via MAP inference. Avoids computation of Z(x)!
16 Learning CRFs from Data (via maxmargin training, Ratliff et.al., 2007) A convenient representation for learning from data is a loglinear CRF P y x = 1 Z(x) exp (wt f(y, x)) learned from data y best = argmax w T f(y, x) y x As we require only CRFs and MAP inference, we use the maxmargin training due to Ratliff et.al. (2007). Computes subgradient via MAP inference. Avoids computation of Z(x)!
17 Recipe: From a Program to a CRF
18 Recipe: From a Program to a CRF Step 1: Define the elements and their properties of interest Elements become nodes in a network, node content ranges over properties Example: elements are variables, properties are their type
19 Recipe: From a Program to a CRF Step 1: Define the elements and their properties of interest Elements become nodes in a network, node content ranges over properties Example: elements are variables, properties are their type Step 2: Define feature functions between elements Feature functions become undirected edges in the network Example: aliasing between variables, shared function caller, etc.
20 Recipe: From a Program to a CRF Step 1: Define the elements and their properties of interest Elements become nodes in a network, node content ranges over properties Example: elements are variables, properties are their type Step 2: Define feature functions between elements Feature functions become undirected edges in the network Example: aliasing between variables, shared function caller, etc. Step 3: Build network via static program Automatically extract nodes and feature functions from the program Example: alias, call graph Key point: general problem undecidable, need good approximations! More on Program Analysis: Program Analysis, ETH graduate course, M. Vechev, Spring 2015
21 function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; MAP inference
22 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: t r i length
23 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: t r i length i t r length
24 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: t r i length i t w i step 0.5 j step 0.4 i t r length i t r length i r w i len 0.6 j length 0.3 r length w length length 0.5 len length
25 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: t r i length i t w i step 0.5 j step 0.4 i t r length i t r length i r w i len 0.6 j length 0.3 r length w length length 0.5 len length
26 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: i t r t r i length length i t w i step 0.5 j step 0.4 i i i r w i len 0.6 j length 0.3 t step r len length r length w length length 0.5 len length
27 MAP inference function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; function chunkdata(str, step) var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.substring(i, i + step)); colnames.push(str.substring(i, len)); return colnames; Unknown properties: Known properties: i t r t r i length length i t w i step 0.5 j step 0.4 i i i r w i len 0.6 j length 0.3 t step r len length r length w length length 0.5 len length
28 Structured Prediction for Programs var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs
29 Structured Prediction for Programs var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs alias,call
30 Structured Prediction for Programs var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs alias,call ~ 7M functions for names ~70K functions for type
31 Structured Prediction for Programs var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs maxmargin training alias,call ~ 7M functions for names ~70K functions for type
32 Structured Prediction for Programs var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform ~ 150MB var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs maxmargin training alias,call ~ 7M functions for names ~70K functions for type
33 Structured Prediction for Programs ~ 30 nodes, ~400 edges var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform ~ 150MB var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs maxmargin training alias,call ~ 7M functions for names ~70K functions for type
34 Structured Prediction for Programs ~ 30 nodes, ~400 edges Time: milliseconds var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase inference transform ~ 150MB var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Learning Phase Learned Weights and Feature Functions program learn weighs maxmargin training alias,call ~ 7M functions for names ~70K functions for type
35 Structured Prediction for Programs ~ 30 nodes, ~400 edges var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.subsi, i + t)); n.push(e.subsi, r)); return n; program Prediction Phase Learning Phase program Time: milliseconds inference learn weighs transform ~ 150MB Learned Weights and Feature Functions maxmargin training var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.subs(i, i + step)); colnames.push(str.substi, len)); return colnames; Names: 63% Types: 81% (helps typechecking) alias,call ~ 7M functions for names ~70K functions for type
36 Structured Prediction for Programs function chunkdata(e, t) var n = []; var r = e.length; for (; i < r; i += t) if (i + t < r) n.push(e.substring(i, i + t)); n.push(e.substring(i, r)); return n; Unknown properties: Known properties: i t r t r i length length function chunkdata(str, step) var colnames = []; var len = str.length; for (; i < len; i += step) if (i + step < len) colnames.push(str.substring(i, i + step)); Bridges Program Analysis and CRFs First application of CRFs to programs CRFs learned from data Fast and Precise colnames.push(str.substring(i, len)); return colnames; i t w i step 0.5 j step 0.4 i r w i len 0.6 j length 0.3 i i t step step r len length r length w length length 0.5 len length
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