def test_multiple_measurements(par):
"""Multiple measurement vector (MMV) problem:
minimize ||Y||_1,2 subject to AW^{-1}Y = B
"""
# Create random m-by-n encoding matrix
m = par['m']
n = par['n']
k = par['k']
l = 6
A = np.random.randn(m, n)
p = np.random.permutation(n)[:k]
X = np.zeros((n, l))
X[p, :] = np.random.randn(k, l)
weights = 0.1 * np.random.rand(n) + 0.1
W = 1 / weights * np.eye(n)
B = A.dot(W).dot(X)
# Solve unweighted version
X_uw, _, _, _ = spg_mmv(A.dot(W), B, 0, verbosity=0)
# Solve weighted version
X_w, _, _, _ = spg_mmv(A, B, 0, weights=weights, verbosity=0)
X_w = spdiags(weights, 0, n, n).dot(X_w)
assert_array_almost_equal(X, X_uw, decimal=2)
assert_array_almost_equal(X, X_w, decimal=2)
def test_multiple_measurements_nonnegative(par):
"""Multiple measurement vector (MMV) problem with non-negative norm:
"""
np.random.seed(0)
# Create random m-by-n encoding matrix
m = par['m']
n = par['n']
k = par['k']
l = 6
A = np.random.randn(m, n)
A = A.astype(par['dtype'])
p = np.random.permutation(n)[:k]
X = np.zeros((n, l), dtype=par['dtype'])
X[p, :] = np.abs(np.random.randn(k, l))
B = A.dot(X)
Xnn, _, _, _ = spg_mmv(A,
B,
0,
project=norm_l12nn_project,
primal_norm=norm_l12nn_primal,
dual_norm=norm_l12nn_dual,
iter_lim=20,
verbosity=0)
assert Xnn.dtype == par['dtype']
assert np.any(Xnn < 0) == False
n = 150
k = 12
l = 6;
A = np.random.randn(m, n)
p = np.random.permutation(n)[:k]
X0 = np.zeros((n, l))
X0[p, :] = np.random.randn(k, l)
weights = 3 * np.random.rand(n) + 0.1
W = 1/weights * np.eye(n)
B = A.dot(W).dot(X0)
# Solve unweighted version
opts = spgSetParms({'verbosity': 1})
x_uw, _, _, _ = spg_mmv(A.dot(W), B, 0, opts)
# Solve weighted version
opts = spgSetParms({'verbosity': 1,
'weights': weights})
x_w, _, _, _ = spg_mmv(A, B, 0, opts)
x_w = spdiags(weights, 0, n, n).dot(x_w)
# Plot results
figure()
plot(x_uw[:, 0], 'b-')
plot(x_w[:, 0], 'b.')
plot(X0, 'ro');
plot(x_uw[:, 1:], '-')
plot(x_w[:, 1:], 'b.')
#legend('Coefficients (1)','Coefficients (2)','Original coefficients');
def strainSolver(
dataPath,
refStrains,
outputPath,
objectiveOption,
globalILP_option='all',
timelimit=600,
gap=8,
loci=["clpA", "clpX", "nifS", "pepX", "pyrG", "recG", "rplB", "uvrA"],
pathToDistMat=None,
eps=0.05):
# ------------------------- Data handling ----------------------------
# Parameters
propFormat = 1 # proportion in percentage or fraction
numLoci = len(loci)
# read data for samples and reference
data, numSamples = readData(dataPath, loci, globalILP_option)
newNameToOriName = dict()
namingIndex = 1
for i in sorted(data.keys()):
newNameToOriName["s{}".format(namingIndex)] = i
data["s{}".format(namingIndex)] = data.pop(i)
namingIndex += 1
reference = pd.read_csv(refStrains,
sep="\t",
usecols=range(1, numLoci + 1))
lociNames = list(reference.columns.values)
numReference = reference.shape[0]
allSamples = data.keys()
# check proportions sum to 100
checkProp(data, propFormat)
# round the proportions to 3 decimal places
data = roundProp(data)
# As reference only contains numbers as entries, add gene name to the variants for better identification
for name in lociNames:
reference["%s" %
name] = name + "_" + reference["%s" % name].astype(str)
# Get proportions of variants at different locus for each sample
varAndProp = returnVarAndProportions(data)
# Get the combinations at all loci across all samples
strains, numOfComb = returnCombinationsAndNumComb(data, numLoci, loci)
uniqueStrains = strains.drop_duplicates(loci)
uniqueStrains = (uniqueStrains[loci]).reset_index(drop=True)
uniqueStrains[
"ST"] = uniqueStrains.index.values + 1 # assign indices for strains or each unique combinations
strains = strains.merge(
uniqueStrains, indicator=True, how="left"
) # assign the index to the combinations(as strain data frame contains duplicated rows)
strains = strains.drop("_merge", 1)
# For each variants, get a mapping of which strains it maps to
varSampToST = mapVarAndSampleToStrain(strains, loci, allSamples)
print "\n-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-\n\n"
# some data processing
vpdf = varAndProp.drop_duplicates(['Variant'])
loci = vpdf['Locus'].tolist()
from collections import OrderedDict
loci = list(OrderedDict.fromkeys(loci))
sampAlleles = [] # List for having each samples data
nsamp = 0 # Number of columns of X and columns of B
sampNames = []
for samp, numOfC in numOfComb.iteritems():
sampNames.append(int(samp[1:]))
nsamp += 1
sampNames = sorted(sampNames)
for samp in sampNames:
sampAlleles.append(varAndProp.loc[varAndProp['Sample'] == 's' +
str(samp)])
# for sa in sampAlleles:
# print sa
# print '\n'
# ~~~~~~~~~~~~ Solving for strains present in several samples ~~~~~~~~~~~~~~
# Below we detect the shared strains between all samples
strSet = set(strains.drop_duplicates(loci)['ST'].tolist())
chosenStrs = list()
sampsets = list()
for s in strSet:
# those strains which are present in more than two samples are detected by
# counting the number of samples they appear in
sampSet = set(strains.loc[strains['ST'] == s].drop_duplicates('Sample')
['Sample'].tolist())
if len(sampSet) > 1:
chosenStrs.append(s - 1)
sampsets.append(sampSet)
sharedStrs = strains.iloc[chosenStrs].reset_index(drop=True)
sharedStrs['Sample'] = sampsets
sharedStrs = sharedStrs.drop_duplicates(loci)
print '**SHARED STRAINS'
print sharedStrs, '\n'
# detecting the samples which have a shared strain
samplesSharing = []
for ss in sampsets:
for s in ss:
samplesSharing.append(int(s[1:]))
samplesSharing = sorted(list(set(samplesSharing)))
nsampS = len(samplesSharing)
# dictionary to hold output dataframes for each sample
outputdict = dict()
# matrix of residues of shared calculation allele proportions
BR = np.array([[0]])
variantsMixed = []
# In some iterations it's possible not to have any shared strains
if len(chosenStrs) > 0:
chosenVars = list()
dims = []
variants = [[], []]
for l in loci:
dims.append(len(set(sharedStrs[l].tolist())))
locusvars = list(set(sharedStrs[l].tolist()))
variants[0].append(locusvars)
variants[1].append((l, locusvars))
variantsMixed.extend(locusvars)
# A: Measurement matrix
A = createMeasureMatrix(sharedStrs, loci, variants[0], dims)
# B: Observation Matrix
nrows = len(variantsMixed) # Number of rows of A and rows of B
B = [[0 for x in range(nsampS)] for x in range(nrows)]
for ind in range(nsampS):
for indV in range(nrows):
if variantsMixed[indV] in sampAlleles[samplesSharing[ind] -
1]['Variant'].tolist():
B[indV][ind] = sampAlleles[samplesSharing[ind] - 1].loc[
sampAlleles[samplesSharing[ind] - 1]['Variant'] ==
variantsMixed[indV]]['Proportion'].values.tolist()[0]
# Now we solve the shared strains with mmv, then the remaining strains for each sample with single sample solution
# For solving the shared strains problem, we choose a range of sigmas, and use each of them as input to get the
# best result.
# calculating weights for shared strains
weights = []
for st in sharedStrs[loci].values.tolist():
weights.append(
min([
hamming(st, rst)
for rst in reference[loci].values.tolist()
]) * 8 + 1)
print "----------------------------------------------------------------------\n Solving the Joint Sparsity " \
"using SPGL1\n---------------------------------------------------------------------- "
print
sigmas = []
errs = []
min_err = 100.0
best_sig = 0
X1 = np.array([[0]])
# in order to find the best solution, we solve the join sparsity problem with different values of sigma
# to find the result with least error and then report that result.
for i in range(11):
sigmas.append(i * 0.05)
for sig in sigmas:
X, _, _, _ = spg_mmv(A,
np.array(B),
sig,
weights=np.array(weights))
errmat = np.dot(A, X) - np.array(B)
err = np.sqrt(sum([np.linalg.norm(row) for row in errmat]))
errs.append(err)
if min_err > err:
min_err = err
best_sig = sig
X1 = X
print '**BEST SIG AND ITS ERR ', best_sig, min_err
print '**RESULT column=samp row=strain:'
print pd.DataFrame(X1)
# setting the residue matrix for samples that were included in this part
BR = np.array(B) - np.dot(A, X1)
# creating the output of shared strains. Latter output for individual samples will be appended to these.
output = sharedStrs.merge(reference, indicator=True, how="left")
output["_merge"].replace(to_replace="both",
value="Existing",
inplace=True)
output["_merge"].replace(to_replace="left_only",
value="New",
inplace=True)
output = output.rename(columns={"_merge": "New/Existing"})
out = output.drop_duplicates(loci)
# more output formatting and adding the results to proper dataframes
retainCol = ["ST", "New/Existing"]
out = out[retainCol].reset_index(drop=True)
samplecnt = 0
for sampStrains in X1.transpose():
persampout = out.copy(deep=True)
strainNums = np.nonzero(sampStrains)[0]
persampout = pd.merge(persampout,
sharedStrs.iloc[strainNums],
on='ST')
persampout['Proportions'] = [sampStrains[i] for i in strainNums]
persampout['Sample'] = 's' + str(samplesSharing[samplecnt])
columns = persampout.columns.tolist()
columns = columns[:-2] + columns[-1:] + columns[-2:-1]
persampout = persampout[columns]
#print persampout
outputdict['s' + str(samplesSharing[samplecnt])] = persampout
samplecnt += 1
# ------------------ Per-sample solution using single sample solver -------------------
# after subtraction of the results of shared strains, the remaining proportions are pas
# -sed to the function which solves the single sample problem.
for i in range(nsamp):
sampName = 's' + str(i + 1)
if i + 1 in samplesSharing:
persampout = single_sample_solver(
loci, sampAlleles[i], reference,
strains.loc[strains['Sample'] == sampName], eps, True,
BR.transpose().tolist()[samplesSharing.index(i + 1)],
variantsMixed)
outputdict[sampName] = outputdict[sampName].append(persampout)
else:
persampout = single_sample_solver(
loci, sampAlleles[i], reference,
strains.loc[strains['Sample'] == sampName], eps)
outputdict[sampName] = persampout
print outputdict[sampName]
outputdict[sampName].to_csv("{0}/{1}_strainsAndProportions.csv".format(
outputPath, newNameToOriName[sampName]))
return outputdict
from spgl1 import spg_mmv
from spgl1 import norm_l12nn_primal, norm_l12nn_dual, norm_l12nn_project
###############################################################################
# Let's first import our data, matrix operator and weights
data = np.load('../testdata/mmvnn.npz')
A = data['A']
B = data['B']
weights = data['weights']
###############################################################################
# We apply unweighted inversions with and without non-negative
# norms.
X, _, _, info = spg_mmv(A, B, 0.5, iter_lim=100, verbosity=0)
XNN, _, _, infoNN = spg_mmv(A,
B,
0.5,
iter_lim=100,
verbosity=0,
project=norm_l12nn_project,
primal_norm=norm_l12nn_primal,
dual_norm=norm_l12nn_dual)
print('Negative X - MMV:', np.any(X < 0))
print('Negative X - MMVNN:', np.any(XNN < 0))
print('Residual norm - MMV:', info['rnorm'])
print('Residual norm - MMVNN:', infoNN['rnorm'])
plt.figure()
# Create problem
m = 100
n = 150
k = 12
l = 6
A = np.random.randn(m, n)
p = np.random.permutation(n)[:k]
X0 = np.zeros((n, l))
X0[p, :] = np.random.randn(k, l)
weights = 3 * np.random.rand(n) + 0.1
W = 1 / weights * np.eye(n)
B = A.dot(W).dot(X0)
# Solve unweighted version
x_uw, _, _, _ = spgl1.spg_mmv(A.dot(W), B, 0, verbosity=1)
# Solve weighted version
x_w, _, _, _ = spgl1.spg_mmv(A, B, 0, weights=weights, verbosity=2)
x_w = spdiags(weights, 0, n, n).dot(x_w)
# Plot results
plt.figure()
plt.plot(x_uw[:, 0], 'b-', label='Coefficients (1)')
plt.plot(x_w[:, 0], 'b.', label='Coefficients (2)')
plt.plot(X0[:, 0], 'ro', label='Original coefficients')
plt.legend()
plt.title('Weighted Basis Pursuit with Multiple Measurement Vectors')
plt.figure()
plt.plot(x_uw[:, 1], 'b-', label='Coefficients (1)')
