def persistence_diagram(boundary_matrix_csc, dimensions, filtration):
""" Compute persistence diagram from a sparse matrix
:param boundary_matrix_csc: Sparse matrix
:param dimensions: Cell dimensions
:param filtration: Filtration
:returns: Persistence diagrams
"""
sort_order = np.lexsort(
(dimensions, filtration)) # Last key has higher sort priority
boundary_matrix_csc = boundary_matrix_csc[sort_order, :][:, sort_order]
dimensions = dimensions[sort_order]
filtration = filtration[sort_order]
col_count = boundary_matrix_csc.shape[1]
assert len(dimensions) == col_count
columns = [
boundary_matrix_csc.getcol(col).indices.tolist()
for col in range(col_count)
]
# Many representations (vector_vector, full_pivot_column, bit_tree_pivot_column)
# of PHAT seem to work incorrectly. vector_heap is ok.
bdry = phat.boundary_matrix(
representation=phat.representations.vector_heap,
columns=list(zip(dimensions, columns)))
pairs = bdry.compute_persistence_pairs(
reduction=phat.reductions.twist_reduction)
dgms = pairs_to_diagram(pairs, dimensions, filtration)
return dgms
def _compute_persistence_pairs(self, boundary_matrix=None):
boundary_matrix = boundary_matrix or self._boundary_matrix
self._reduced_boundary_matrix = phat.boundary_matrix(
columns=boundary_matrix,
representation=phat.representations.sparse_pivot_column,
)
pairs = self._reduced_boundary_matrix.compute_persistence_pairs()
pairs.sort()
self._pairs = list(pairs)
return self._pairs
def phat_diagrams(simplices, show_inf=False, verbose=True):
"""
Do a custom filtration wrapping around phat
Parameters
-----------
simplices: A list of lists of simplices and their distances
the kth element is itself a list of tuples ([idx1, ..., idxk], dist)
where [idx1, ..., idxk] is a list of vertices involved in the simplex
and "dist" is the distance at which the simplex is added
show_inf: Boolean
Whether or not to return points that never die
Returns
--------
dgms: A dictionary of persistence diagrams, where dgms[k] is
the persistence diagram for Hk
"""
## Convert simplices representation to sparse pivot column
# -- sort by birth time, if tie, use order of simplex
ordered_simplices = sorted(simplices, key=lambda x: (x[1], len(x[0])))
columns = _simplices_to_sparse_pivot_column(ordered_simplices, verbose)
## Setup boundary matrix and reduce
if verbose:
print("Computing persistence pairs...")
tic = time.time()
boundary_matrix = phat.boundary_matrix(
columns=columns, representation=phat.representations.sparse_pivot_column
)
pairs = boundary_matrix.compute_persistence_pairs()
pairs.sort()
if verbose:
print(
"Finished computing persistence pairs (Elapsed Time %.3g)"
% (time.time() - tic)
)
## Setup persistence diagrams by reading off distances
dgms = _process_distances(pairs, ordered_simplices)
## Add all unpaired simplices as infinite points
if show_inf:
dgms = _add_unpaired(dgms, pairs, simplices)
## Convert to arrays:
dgms = [np.array(dgm) for dgm in dgms.values()]
return dgms
def test_phat():
import numpy as np
import phat
columns = [[], [], [], [], [], [], [], [], [], [], [0, 7], [5, 9], [0, 2],
[4, 8], [7, 8], [2, 9], [0, 9], [16, 12, 15], [6, 8], [6, 7],
[14, 18, 19], [1, 6], [1, 4], [4, 6], [23, 18, 13], [7, 9],
[25, 16, 10], [0, 8], [27, 14, 10], [23, 21, 22], [6, 9],
[30, 25, 19], [5, 6], [30, 32, 11],
[3, 5], [3, 6], [35, 32, 34], [2, 8], [37, 27, 12], [1, 3],
[39, 21, 35], [2, 4], [41, 37, 13]]
dimensions = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1,
2, 1, 2, 1, 2, 2, 1, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2
]
bdry = phat.boundary_matrix(
representation=phat.representations.vector_heap,
columns=list(zip(dimensions, columns)))
pairs = np.array(
bdry.compute_persistence_pairs(
reduction=phat.reductions.twist_reduction))
assert np.all(pairs[:, 0] != 10), "First added edge should kill 7"
def rips_filtration(max_dim, max_scale, dist_mat):
"""
Builds a boundary matrix for the boundary-Rips filtration up to dimension
`max_dim`.
Also builds the corresponding list of bigrades follows closely
the "incremental algorithm" in the paper on fast Vietoris-Rips comptuation
by Zomorodian, with some modification to store boundary matrix and
filtration info. That in turn is based on a version of Bron-Kerbosch
algorithm.
Parameters
----------
max_dim: int >= 0
the highest dimension to compute
max_scale: float
the highest scale (distance) to consider
dist_mat: 2D array
an n x n distance matrix, which may be lower-triangular.
Returns
-------
pairs: list of (column, grade) pairs
The barcodes up to dimension max_dim, for the truncated Vietoris-Rips
filtration, including only simplices whose index of appearance
is <= max_scale. The barcodes are output as a list of three-element
lists. Each three-element lists represents one interval of in barcode
and has the form [birth,death,dimension]
"""
max_face_dim = max_dim+1
sorted_simplices = _rips_simplices(max_face_dim, max_scale, dist_mat)
len_minus_one = len(sorted_simplices) - 1
cobdy_matrix_pre = _create_coboundary_matrix(sorted_simplices,
max_face_dim)
# print(cobdy_matrix_pre)
# print(sorted_simplices)
# print(bdy_matrix_pre)
cobdy_matrix = phat.boundary_matrix(
representation=phat.representations.bit_tree_pivot_column)
cobdy_matrix.columns = cobdy_matrix_pre
# call Bryn's PHAT wrapper for the persistence computation
pairs = cobdy_matrix.compute_persistence_pairs()
# next, rescale the pairs to their original filtration values, eliminating
# pairs with the same birth and death time. In keeping with our chosen
# output format, we also add the dimension to the pair.
scaled_pairs = []
for i in range(len(pairs)):
cobirth = sorted_simplices[len_minus_one - pairs[i][0]][1]
codeath = sorted_simplices[len_minus_one - pairs[i][1]][1]
if codeath < cobirth:
dimension = len(
sorted_simplices[len_minus_one - pairs[i][1]][0]) - 1
scaled_pairs.append((codeath, cobirth, dimension))
# add in the intervals with endpoint inf
# To do this, we first construct an array paired_indices such that
# if the j^th simplex (in the coboundary order) appears in a pair,
# paired_indices[j] = 1 otherwise paired_incides[j] = 0.
paired_indices = np.zeros(len(sorted_simplices))
for i in range(len(pairs)):
paired_indices[pairs[i][0]] = 1
paired_indices[pairs[i][1]] = 1
for i in range(len(paired_indices)):
if paired_indices[i] == 0:
birth = sorted_simplices[len_minus_one - i][1]
dimension = len(sorted_simplices[len_minus_one - i][0]) - 1
# we don't report the infinite bars in degree max_dim+1
if dimension < max_face_dim:
scaled_pairs.append((birth, float("inf"), dimension))
return scaled_pairs
def build(self, **kwargs):
'''
Apply persistent homology using the combination of Cechmate and PHAT.
On successful run, this function constructs:
1. A list of dictionaries. Each dictionary corresponds to a
birth/death feature containing keys:
'pair', 'birth', 'death', 'H_i', 'generator_ptr'
The ordering here has no significance outside of the internals
of the cechmate/PHAT algorithms.
2. A collection of useful statistics in the same order for
visualization/access of top-level information. These are
flat numpy arrays for easy slicing/access without having to work
with dictionary/list logic. Available attributes:
self.births
self.deaths
self.H_i
self.generator_ptrs
3. A collection of common orderings of the topological features
based on the above information generated using by numpy.argsort.
Available orderings:
self._persistence_order (death-birth; sorted from largest first)
self._birth_order (sorted from smallest first)
self._death_order (sorted from smallest first)
Inputs: None; but you must instantiate the class with the data matrix first.
Optional inputs:
verbosity: controls amount of print statements. Currently only two levels;
0 : no print statements (default)
1 : reports progress along pipeline.
Outputs: None; but the above attributes are stored in the object.
'''
verbosity = kwargs.get('verbosity', 0)
# this part loosely follows https://cechmate.scikit-tda.org/notebooks/BasicUsage.html
rips = cechmate.Rips(maxdim=self.maxdim)
if verbosity > 0: print('Building complex...')
compl = rips.build(self.X) # TODO: this is the second slowest part
if verbosity > 0: print('ordering simplices...')
ordered_simplices = sorted(compl, key=lambda x: (x[1], len(x[0])))
# cast as numpy array for handy array slicing later
o_s2 = np.array(ordered_simplices)
#
# TODO: This is the bottleneck right now in terms of speed!
# It's written in python; if there's a C++ version sitting around it could
# be sped up. The python code doesn't look too crazy...
if verbosity > 0: print('Casting to sparse pivot column form...')
columns = cechmate.solver._simplices_to_sparse_pivot_column(
ordered_simplices)
if verbosity > 0: print('Building boundary matrix...')
b_m = phat.boundary_matrix(
columns=columns,
representation=phat.representations.sparse_pivot_column)
if verbosity > 0: print('Computing persistence pairs...')
pairs = b_m.compute_persistence_pairs(
) # boundary matrix gets reduced in-place here
#
# OK, here's the sketch of what we're doing
# to get out generators:
#
# 1. Use some criterion to identify which birth/death pairs you want
# (e.g. lexsort by homological dimension, then lifetime)
# 2. Identify the "pairs" in the associated table (from b_m.compute_persistence_pairs)
# 3. Associate "pairs" (boundary matrix columns) with associated simplices
# by identifying nonzero entries (b_m.get_column(j)); these are indexes
# for ordered_simplices)
# 4. Visualize/attach information however you like.
#
# REVISITED: pass on #1 until visualization/processing stage.
# otherwise loosely following 2-4, but no visualization in this function.
#
pp = np.array(
list(pairs)) # cast to numpy array just to get array slicing
# Find homological dimension of each thing; following the lead of
# cechmate.solver._process_distances().
Hi = np.array([len(o_s2[ppi[0]][0]) - 1 for ppi in pp])
# Pull out simplex information from the processed boundary matrix.
if verbosity > 0: print('Identifying generators...')
sparse_reduced_b_m = {}
for j in range(b_m._matrix.get_num_cols()):
thing = b_m._matrix.get_col(j)
if len(thing) > 0:
sparse_reduced_b_m[j] = np.array(thing, dtype=np.int64)
#
if verbosity > 0: print('Putting a bow on everything...')
topo_features = []
for ii, pair in enumerate(pp):
idx = pair[1]
birth = o_s2[pair[0]][1]
death = o_s2[pair[1]][1]
# don't bother with trivial features.
if birth == death:
continue
#
gen_simplices = o_s2[sparse_reduced_b_m[idx]][:, 0]
# vertex indices in original ordering
g_v = np.unique(np.concatenate(gen_simplices))
topo_features.append({
'pair': pair,
'birth': birth,
'death': death,
'H_i': Hi[ii],
'generator_ptr': g_v
})
#
# numpy array just for slicing
self.topo_features = np.array(topo_features)
# store summary information in flat form for easier sorting/accessing by ptr.
self.births = np.zeros(len(self.topo_features), dtype=float)
self.deaths = np.zeros(len(self.topo_features), dtype=float)
self.H_i = np.zeros(len(self.topo_features), dtype=int)
generator_ptrs = []
for j, d in enumerate(self.topo_features):
self.births[j] = d['birth']
self.deaths[j] = d['death']
self.H_i[j] = d['H_i']
generator_ptrs.append(d['generator_ptr'])
#
self.generator_ptrs = np.array(generator_ptrs)
# create orderings for later use.
self._persistence_order = np.argsort(self.births -
self.deaths) # largest first
self._birth_order = np.argsort(self.births) # smallest first
self._death_order = np.argsort(self.deaths) # smallest first
if verbosity > 0: print('done.')
return
import numpy as np
import pandas as pd
import phat
from boundary_matrix import boundary_matrix
toy_data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
data = pd.DataFrame(data=toy_data,
index=['A', 'B', 'C', 'D'],
columns=['a', 'b', 'c'])
complex = boundary_matrix(data)
print "filtered complex: "
print complex
boundary_matrix = phat.boundary_matrix(
representation=phat.representations.vector_vector, columns=complex)
persistence_pairs = boundary_matrix.compute_persistence_pairs()
persistence_pairs.sort()
print persistence_pairs
print "There are {} persistence pairs: ".format(len(persistence_pairs))
for a, b in persistence_pairs:
print "Birth: {}, Death: {}, lifespan: {}".format(a, b, b - a)
import phat
from simplex_check import simplex_check
boundary_matrix_square = phat.boundary_matrix(
representation=phat.representations.vector_vector)
complex = [(0, []), (0, []), (0, []), (1, [0, 1]), (1, [0, 2]), (1, [1, 2]),
(0, []), (1, [2, 3]), (1, [1, 3]), (2, [5, 7, 8]), (2, [3, 4, 5])]
boundary_matrix_square.columns = complex
square_pairs = boundary_matrix_square.compute_persistence_pairs()
square_pairs.sort()
print("\nThere are %d persistence pairs: " % len(square_pairs))
for pair in square_pairs:
print("Birth: %d, Death: %d" % pair)
simplex_check(complex, 1)
print complex
| \\
| \\
| \\ 4
5| \\
| \\
| 6 \\
| \\
|________\\
0 2 1
""")
import phat
# define a boundary matrix with the chosen internal representation
boundary_matrix = phat.boundary_matrix(representation = phat.representations.vector_vector)
# set the respective columns -- (dimension, boundary) pairs
boundary_matrix.columns = [ (0, []),
(0, []),
(1, [0,1]),
(0, []),
(1, [1,3]),
(1, [0,3]),
(2, [2,4,5])]
# or equivalently, boundary_matrix = phat.boundary_matrix(representation = ..., columns = ...)
# would combine the creation of the matrix and the assignment of the columns
# print some information of the boundary matrix:
print("\nThe boundary matrix has %d columns:" % len(boundary_matrix.columns))
# this part loosely follows https://cechmate.scikit-tda.org/notebooks/BasicUsage.html rips = cechmate.Rips(maxdim=1) compl = rips.build(X) # this is the second slowest part ordered_simplices = sorted(compl, key=lambda x: (x[1], len(x[0]))) # cast as numpy array for handy array slicing later o_s2 = np.array(ordered_simplices) # # This is the bottleneck right now in terms of speed! # It's written in python; if there's a C++ version sitting around it could # be sped up. The python code doesn't look too crazy... columns = cechmate.solver._simplices_to_sparse_pivot_column(ordered_simplices) b_m = phat.boundary_matrix(columns=columns, representation=phat.representations.sparse_pivot_column) pairs = b_m.compute_persistence_pairs() # boundary matrix gets reduced in-place here dgms = cechmate.solver._process_distances(pairs,ordered_simplices) # get largest non-infinite time/radius. dgms_cat = np.concatenate(list(dgms.values())) dgms_max = dgms_cat[np.logical_not(np.isinf(dgms_cat))].max() # # OK, here's the sketch of what we're doing # to get out generators: # # 1. Use some criterion to identify which birth/death pairs you want # (e.g. lexsort by homological dimension, then lifetime)
def bit_tree_mat():
return phat.boundary_matrix(phat.representations.bit_tree_pivot_column, boundary_matrix)
from __future__ import print_function
import sys
import phat
if __name__=='__main__':
test_data = (sys.argv[1:] and sys.argv[1]) or "../../examples/torus.bin"
print("Reading test data %s in binary format ..." % test_data)
boundary_matrix = phat.boundary_matrix()
# This is broken for some reason
if not boundary_matrix.load(test_data):
print("Error: test data %s not found!" % test_data)
sys.exit(1)
error = False
def compute_chunked(mat):
return mat.compute_persistence_pairs(phat.reductions.chunk_reduction)
print("Comparing representations using Chunk algorithm ...")
print("Running Chunk - Sparse ...")
sparse_boundary_matrix = phat.boundary_matrix(phat.representations.sparse_pivot_column, boundary_matrix)
sparse_pairs = compute_chunked(sparse_boundary_matrix)
print("Running Chunk - Heap ...")
heap_boundary_matrix = phat.boundary_matrix(phat.representations.vector_heap, boundary_matrix)
heap_pairs = compute_chunked(heap_boundary_matrix)
print("Running Chunk - Full ...")
full_boundary_matrix = phat.boundary_matrix(phat.representations.full_pivot_column, boundary_matrix)
def bit_tree_mat():
return phat.boundary_matrix(phat.representations.bit_tree_pivot_column,
boundary_matrix)
from __future__ import print_function
import sys
import phat
if __name__ == '__main__':
test_data = (sys.argv[1:] and sys.argv[1]) or "../../examples/torus.bin"
print("Reading test data %s in binary format ..." % test_data)
boundary_matrix = phat.boundary_matrix()
# This is broken for some reason
if not boundary_matrix.load(test_data):
print("Error: test data %s not found!" % test_data)
sys.exit(1)
error = False
def compute_chunked(mat):
return mat.compute_persistence_pairs(phat.reductions.chunk_reduction)
print("Comparing representations using Chunk algorithm ...")
print("Running Chunk - Sparse ...")
sparse_boundary_matrix = phat.boundary_matrix(
phat.representations.sparse_pivot_column, boundary_matrix)
sparse_pairs = compute_chunked(sparse_boundary_matrix)
print("Running Chunk - Heap ...")
heap_boundary_matrix = phat.boundary_matrix(
phat.representations.vector_heap, boundary_matrix)
heap_pairs = compute_chunked(heap_boundary_matrix)
