Ripser. Efficient Computation of Vietoris Rips Persistence Barcodes. Ulrich Bauer TUM

Transcription

1 Ripser Efficient Computation of Vietoris Rips Persistence Barcodes Ulrich Bauer TUM March 23, 2017 Computational and Statistical Aspects of Topological Data Analysis Alan Turing Institute 1 / 25

2 Persistent homology

3 2 / 25

4 2 / 25

5 2 / 25

6 2 / 25

7 2 / 25

8 2 / 25

9 2 / 25

10 2 / 25

11 2 / 25

12 2 / 25

13 3 / 25

14 δ 3 / 25

15 δ 3 / 25

16 δ 3 / 25

17 δ 3 / 25

18 δ 3 / 25

19 δ 3 / 25

20 Vietoris Rips persistence

21 Vietoris Rips filtrations Consider a finite metric space (X, d). The Vietoris Rips complex is the simplicial complex Rips t (X) = {S X diam S t} 1-skeleton: all edges with pairwise distance t all possible higher simplices (flag complex) 4 / 25

22 Vietoris Rips filtrations Consider a finite metric space (X, d). The Vietoris Rips complex is the simplicial complex Rips t (X) = {S X diam S t} Goal: 1-skeleton: all edges with pairwise distance t all possible higher simplices (flag complex) compute persistence barcodes for H d (Rips t (X)) (in dimensions 0 d k) 4 / 25

23 Demo: Ripser Example data set: 192 points on S 2 persistent homology barcodes up to dimension 2 over 56 mio. simplices in 3-skeleton 5 / 25

24 Demo: Ripser Example data set: 192 points on S 2 persistent homology barcodes up to dimension 2 over 56 mio. simplices in 3-skeleton Comparison with other software: javaplex: 3200 seconds, 12 GB Dionysus: 533 seconds, 3.4 GB GUDHI: 75 seconds, 2.9 GB DIPHA: 50 seconds, 6 GB Eirene: 12 seconds, 1.5 GB 5 / 25

25 Demo: Ripser Example data set: 192 points on S 2 persistent homology barcodes up to dimension 2 over 56 mio. simplices in 3-skeleton Comparison with other software: javaplex: 3200 seconds, 12 GB Dionysus: 533 seconds, 3.4 GB GUDHI: 75 seconds, 2.9 GB DIPHA: 50 seconds, 6 GB Eirene: 12 seconds, 1.5 GB Ripser: 1.2 seconds, 152 MB 5 / 25

26 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies 6 / 25

27 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p 6 / 25

28 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p sparse distance matrices for distance threshold 6 / 25

29 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p sparse distance matrices for distance threshold open source ( released in July / 25

30 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p sparse distance matrices for distance threshold open source ( released in July 2016 online version ( launched in August / 25

31 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p sparse distance matrices for distance threshold open source ( released in July 2016 online version ( launched in August 2016 most efficient software for Vietoris Rips persistence computes H 2 barcode for random points on a torus in 136 seconds / 9 GB (using distance threshold) 6 / 25

32 Ripser A software for computing Vietoris Rips persistence barcodes about 1000 lines of C++ code, no external dependencies support for coefficients in a prime field F p sparse distance matrices for distance threshold open source ( released in July 2016 online version ( launched in August 2016 most efficient software for Vietoris Rips persistence computes H 2 barcode for random points on a torus in 136 seconds / 9 GB (using distance threshold) 2016 ATMCS Best New Software Award (jointly with RIVET) 6 / 25

33 Design goals Goals for previous projects: PHAT [B, Kerber, Reininghaus, Wagner 2013]: fast persistence computation (matrix reduction only) DIPHA [B, Kerber, Reininghaus 2014]: distributed persistence computation 7 / 25

34 Design goals Goals for previous projects: PHAT [B, Kerber, Reininghaus, Wagner 2013]: fast persistence computation (matrix reduction only) DIPHA [B, Kerber, Reininghaus 2014]: distributed persistence computation Goals for Ripser: Use as little memory as possible Be reasonable about computation time 7 / 25

35 The four special ingredients The improved performance is based on 4 insights: Clearing inessential columns [Chen, Kerber 2011] Computing cohomology [de Silva et al. 2011] Implicit matrix reduction Apparent and emergent pairs 8 / 25

36 The four special ingredients The improved performance is based on 4 insights: Clearing inessential columns [Chen, Kerber 2011] Computing cohomology [de Silva et al. 2011] Implicit matrix reduction Apparent and emergent pairs Lessons from PHAT: Clearing and cohomology yield considerable speedup, but only when both are used in conjuction! 8 / 25

37 Matrix reduction

38 Matrix reduction algorithm Setting: finite metric space X, n points persistent homology H d (Rips t (X); F 2 ) in dimensions d k Notation: D: boundary matrix of filtration R i : ith column of R 9 / 25

39 Matrix reduction algorithm Setting: finite metric space X, n points persistent homology H d (Rips t (X); F 2 ) in dimensions d k Notation: D: boundary matrix of filtration R i : ith column of R Algorithm: R = D, V = I while i < j with pivot R i = pivot R j add R i to R j, add V i to V j 9 / 25

40 Matrix reduction algorithm Setting: finite metric space X, n points persistent homology H d (Rips t (X); F 2 ) in dimensions d k Notation: D: boundary matrix of filtration R i : ith column of R Algorithm: R = D, V = I while i < j with pivot R i = pivot R j Result: add R i to R j, add V i to V j R = D V is reduced (unique pivots) V is full rank upper triangular 9 / 25

41 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} N = {j R j 0} E = P pivots R Then positive indices, negative indices, essential indices / 25

42 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} positive indices, N = {j R j 0} negative indices, E = P pivots R essential indices. Then ΣZ = {V i i P} is a basis of Z, 10 / 25

43 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} positive indices, N = {j R j 0} negative indices, E = P pivots R essential indices. Then ΣZ = {V i i P} is a basis of Z, Σ B = {R j j N} is a basis of B, 10 / 25

44 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} positive indices, N = {j R j 0} negative indices, E = P pivots R essential indices. Then ΣZ = {V i i P} is a basis of Z, Σ B = {R j j N} is a basis of B, Σ Z = Σ B {V i i E} is another basis of Z / 25

45 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} positive indices, N = {j R j 0} negative indices, E = P pivots R essential indices. Then ΣZ = {V i i P} is a basis of Z, Σ B = {R j j N} is a basis of B, Σ Z = Σ B {V i i E} is another basis of Z. Persistent homology is generated by the basis cycles Σ Z / 25

46 Compatible basis cycles For a reduced boundary matrix R = D V, call P = {i R i = 0} positive indices, N = {j R j 0} negative indices, E = P pivots R essential indices. Then ΣZ = {V i i P} is a basis of Z, Σ B = {R j j N} is a basis of B, Σ Z = Σ B {V i i E} is another basis of Z. Persistent homology is generated by the basis cycles Σ Z. Persistence intervals: {[i, j) i = pivot R j } {[i, ) i E} Columns with non-essential positive indices never used! 10 / 25

47 Clearing

48 Clearing non-essential positive columns Idea [Chen, Kerber 2011]: Don t reduce at non-essential positive indices Reduce boundary matrices of d C d C d 1 in decreasing dimension d = k + 1,..., 1 Whenever i = pivot R j (in matrix for d ) Set R i to 0 (in matrix for d 1 ) 11 / 25

49 Clearing non-essential positive columns Idea [Chen, Kerber 2011]: Don t reduce at non-essential positive indices Reduce boundary matrices of d C d C d 1 in decreasing dimension d = k + 1,..., 1 Whenever i = pivot R j (in matrix for d ) Set R i to 0 (in matrix for d 1 ) Set V i to R j 11 / 25

50 Clearing non-essential positive columns Idea [Chen, Kerber 2011]: Don t reduce at non-essential positive indices Reduce boundary matrices of d C d C d 1 in decreasing dimension d = k + 1,..., 1 Whenever i = pivot R j (in matrix for d ) Set R i to 0 (in matrix for d 1 ) Set V i to R j Still yields R = D V reduced, V full rank upper triangular 11 / 25

51 Clearing non-essential positive columns Idea [Chen, Kerber 2011]: Note: Don t reduce at non-essential positive indices Reduce boundary matrices of d C d C d 1 in decreasing dimension d = k + 1,..., 1 Whenever i = pivot R j (in matrix for d ) Set R i to 0 (in matrix for d 1 ) Set V i to R j Still yields R = D V reduced, V full rank upper triangular reducing positive columns typically harder than negative with clearing: need only reduce essential positive columns 11 / 25

52 Cohomology

53 Persistent cohomology We have seen: many columns of R = D V are not needed Skip those inessential columns in matrix reduction 12 / 25

54 Persistent cohomology We have seen: many columns of R = D V are not needed Skip those inessential columns in matrix reduction For persistence barcodes in low dimensions d k: Number of skipped indices for reducing D T (cohomology) is much larger than for D (homology) reducing boundary matrix produces basis for H k+1 (K k+1 ), which is not needed The resulting persistence barcode is the same [de Silva et al. 2011] 12 / 25

55 Counting homology column reductions standard matrix reduction: k+1 ( n d=1 d + 1 ) = dim C d (K) k+1 d=1 ( n 1 d ) dim B d 1 (K) k+1 + d=1 ( n 1 d + 1 ) dim Z d (K) 13 / 25

56 Counting homology column reductions standard matrix reduction: k+1 ( n d=1 d + 1 ) = dim C d (K) k+1 d=1 ( n 1 d ) dim B d 1 (K) k+1 + d=1 k = 2, n = 192: = ( n 1 d + 1 ) dim Z d (K) 13 / 25

57 Counting homology column reductions standard matrix reduction: k+1 ( n d=1 d + 1 ) = dim C d (K) k+1 d=1 ( n 1 d ) dim B d 1 (K) k+1 + d=1 k = 2, n = 192: = using clearing: k+1 d=1 ( n 1 d ) dim B d 1 (K) + ( n 1 k + 2 ) = dim H k+1 (K) k+2 d=1 ( n 1 d + 1 ) dim Z d (K) ( n 1 d ) 13 / 25

58 Counting homology column reductions standard matrix reduction: k+1 ( n d=1 d + 1 ) = dim C d (K) k+1 d=1 ( n 1 d ) dim B d 1 (K) k+1 + d=1 k = 2, n = 192: = using clearing: k+1 d=1 ( n 1 d ) dim B d 1 (K) + ( n 1 k + 2 ) = dim H k+1 (K) k+2 d=1 k = 2, n = 192: = ( n 1 d + 1 ) dim Z d (K) ( n 1 d ) 13 / 25

59 Counting cohomology column reductions standard matrix reduction: k ( n d=0 d + 1 ) = dim C d (K) k d=0 ( n 1 d + 1 ) dim B d+1 (K) k + d=0 ( n 1 d ) dim Z d (K) 14 / 25

60 Counting cohomology column reductions standard matrix reduction: k ( n d=0 d + 1 ) = dim C d (K) k d=0 ( n 1 d + 1 ) dim B d+1 (K) k = 2, n = 192: = k + d=0 ( n 1 d ) dim Z d (K) 14 / 25

61 Counting cohomology column reductions standard matrix reduction: k ( n d=0 d + 1 ) = dim C d (K) k d=0 ( n 1 d + 1 ) dim B d+1 (K) k = 2, n = 192: = using clearing: k d=0 ( n 1 d + 1 ) dim B d+1 (K) + ( n 1 0 ) + = dim H 0 (K) k + d=0 k+1 d=0 ( n 1 d ) dim Z d (K) ( n 1 d ) 14 / 25

62 Counting cohomology column reductions standard matrix reduction: k ( n d=0 d + 1 ) = dim C d (K) k d=0 ( n 1 d + 1 ) dim B d+1 (K) k = 2, n = 192: = using clearing: k d=0 ( n 1 d + 1 ) dim B d+1 (K) k = 2, n = 192: = ( n 1 0 ) + = dim H 0 (K) k + d=0 k+1 d=0 ( n 1 d ) dim Z d (K) ( n 1 d ) 14 / 25

63 Observations For a typical input: V has very few off-diagonal entries most negative columns of D are already reduced 15 / 25

64 Observations For a typical input: V has very few off-diagonal entries most negative columns of D are already reduced Previous example (k = 2, n = 192): Only 845 out of columns have to be reduced 15 / 25

65 Implicit matrix reduction

66 Implicit matrix reduction Standard approach: Boundary matrix D for filtration-ordered basis Explicitly generated and stored in memory 16 / 25

67 Implicit matrix reduction Standard approach: Boundary matrix D for filtration-ordered basis Explicitly generated and stored in memory Matrix reduction: store only reduced matrix R transform D into R by column operations 16 / 25

68 Implicit matrix reduction Standard approach: Boundary matrix D for filtration-ordered basis Explicitly generated and stored in memory Matrix reduction: store only reduced matrix R transform D into R by column operations Approach for Ripser: Boundary matrix D for lexicographically ordered basis Implicitly defined and recomputed when needed 16 / 25

69 Implicit matrix reduction Standard approach: Boundary matrix D for filtration-ordered basis Explicitly generated and stored in memory Matrix reduction: store only reduced matrix R transform D into R by column operations Approach for Ripser: Boundary matrix D for lexicographically ordered basis Implicitly defined and recomputed when needed Matrix reduction in Ripser: store only coefficient matrix V recompute previous columns of R = D V when needed Typically, V is much sparser and smaller than R 16 / 25

70 Oblivious matrix reduction Algorithm variant: R = D for j = 1,..., n while i < j with pivot R i = pivot R j add D i to R j 17 / 25

71 Oblivious matrix reduction Algorithm variant: R = D for j = 1,..., n while i < j with pivot R i = pivot R j add D i to R j Requires only current column R j pivots of previous columns R i to obtain the persistence intervals: {[i, j) i = pivot R j } {[i, ) i E} / 25

72 Oblivious matrix reduction Algorithm variant: R = D for j = 1,..., n while i < j with pivot R i = pivot R j add D i to R j Requires only current column R j pivots of previous columns R i to obtain the persistence intervals: {[i, j) i = pivot R j } {[i, ) i E}. Corollary The rank of an m n matrix can be computed in O(n) memory / 25

73 Apparent and emergent pairs

74 Natural filtration settings Typical assumptions on the filtration: general filtration persistence (in theory) filtration by singletons or pairs discrete Morse theory simplexwise filtration persistence (computation) 18 / 25

75 Natural filtration settings Typical assumptions on the filtration: general filtration filtration by singletons or pairs simplexwise filtration Conclusion: Discrete Morse theory sits in the middle between persistence and persistence (!) persistence (in theory) discrete Morse theory persistence (computation) 18 / 25

76 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ / 25

77 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

78 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

79 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

80 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

81 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

82 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and / 25

83 Discrete Morse theory Definition (Forman 1998) A discrete vector field on a cell complex is a partition of the set of simplices into singleton sets {ϕ} (critical cells), and pairs {σ, τ}, where σ is a facet of τ. A function f K R on a cell complex is a discrete Morse function if sublevel sets are subcomplexes, and level sets form a discrete vector field / 25

84 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K / 25

85 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K. Theorem (Forman 1998) If (s, t] contains no critical value of f, then the sublevel set K t collapses to K s / 25

86 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K. Theorem (Forman 1998) If (s, t] contains no critical value of f, then the sublevel set K t collapses to K s / 25

87 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K. Theorem (Forman 1998) If (s, t] contains no critical value of f, then the sublevel set K t collapses to K s / 25

88 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K. Theorem (Forman 1998) If (s, t] contains no critical value of f, then the sublevel set K t collapses to K s Corollary K M for some cell complex M built from the critical cells of f / 25

89 Fundamental theorem of discrete Morse theory Let f be a discrete Morse function on a cell complex K. Theorem (Forman 1998) If (s, t] contains no critical value of f, then the sublevel set K t collapses to K s Corollary K M for some cell complex M built from the critical cells of f. This homotopy equivalence is compatible with the filtration. Corollary K and M have isomorphic persistent homology (with regard to the sublevel sets of f ) / 25

90 Morse pairs and persistence pairs Consider a Morse filtration (one or two simplices at a time). Morse pair (σ, τ): inserting σ and τ simultaneously does not change the homotopy type 21 / 25

91 Morse pairs and persistence pairs Consider a Morse filtration (one or two simplices at a time). Morse pair (σ, τ): inserting σ and τ simultaneously does not change the homotopy type Consider a simplexwise filtration (one simplex at a time). Persistence pair (σ, τ): inserting simplex σ creates a new homological feature inserting τ destroys that feature again 21 / 25

92 Apparent pairs Definition In a simplexwise filtration, (σ, τ) is an apparent pair if σ is the youngest face of τ τ is the oldest coface of σ Lemma Any apparent pairs is a persistence pair. Lemma The apparent pairs form a discrete gradient. Generalizes a construction proposed by [Kahle 2011] for the study of random Rips filtrations 22 / 25

93 From Morse theory to persistence and back Proposition (from Morse to persistence) The pairs of a Morse filtration are apparent 0-persistence pairs for the canonical simplexwise refinement of the filtration / 25

94 From Morse theory to persistence and back Proposition (from Morse to persistence) The pairs of a Morse filtration are apparent 0-persistence pairs for the canonical simplexwise refinement of the filtration. Proposition (from persistence to Morse) Consider an arbitrary filtration with a simplexwise refinement. The apparent 0-persistence pairs yield a Morse filtration refining the original one, and refined by the simplexwise one / 25

95 Emergent persistent pairs Consider the lexicographically refined Rips filtration: increasing diameter, refined by lexicographic order This is the simplexwise filtration for computations in Ripser. Lemma Assume that τ is the lexicographically minimal proper coface of σ with diam(τ) = diam(σ), and there is no persistence pair (ρ, τ) with σ ρ. Then (σ, τ) is an emergent persistence pair / 25

96 Emergent persistent pairs Consider the lexicographically refined Rips filtration: increasing diameter, refined by lexicographic order This is the simplexwise filtration for computations in Ripser. Lemma Assume that τ is the lexicographically minimal proper coface of σ with diam(τ) = diam(σ), and there is no persistence pair (ρ, τ) with σ ρ. Then (σ, τ) is an emergent persistence pair. Includes all apparent persistence 0 pairs Can be identified without enumerating all cofaces of σ Provides a shortcut for computation 24 / 25

97 Ripser Live: users from 156 different cities 25 / 25