def scatter_blake(a, b, which='circles', classes=[0,1], colors=['k','r'],
maxval=None, **kwargs):
if maxval:
a,b = filter_valpairs(a,b,maxval)
defaults = {'s': 50, 'alpha':.2, 'lw':0}
kwargs = ut.dict_set_defaults(kwargs, defaults)
if type(a[0]) == list or type(a[0]) == tuple:
# second value is presumed to be class--should be 0 or 1, which will be
# mapped to the colormap cmap.
# Also need to clean a and b to just be values rather than values and
# classes.
print 'using classes'
assert ut.i1(a) == ut.i1(b), "Classes not the same between a and b"
kwargs['c'] = [colors[0] if x==classes[0] else colors[1] for x in
ut.i1(a)]
a,b = ut.i0(a), ut.i0(b)
else:
c = 'k'
if which=='pointcloud':
scatter(a, b, s=50, alpha=0.08, lw=0)
scatter(a, b, **kwargs)
elif which=='points':
scatter(a, b, **kwargs)
elif which=='fadepoints':
scatter(a, b, **kwargs)
elif which=='circles':
del kwargs['lw']
scatter(a, b, facecolors='none', edgecolors=c, **kwargs)
title('R-squared: %0.3f' % ut.r_squared(a,b))
def result_gold(splits, species, split_inds, make_unmerged=False,
consv_sp='Dm'):
if make_unmerged:
print "Converting to unmerged using conserved:", (consv_sp if consv_sp
else "None")
ppi_corum,_,_ = ppi.load_training_complexes(species,'',consv_sp)
splits = unmerged_splits_from_merged_splits(ut.i1(ppi_corum),
[[ut.i1(s) for s in split] for split in splits])
gold = ut.i1(reduce(operator.add, [splits[i] for i in split_inds]))
return gold
def unmerged_splits_from_merged_splits(unmerged, merged_splits):
"""
Unmerged: list of enumerated/named complex sets, usually from
ppi.load_training_complexes.
Merged_splits: A list of sets for each split (often 3 splits).
"""
merged_splits = [ut.i1(ms) for ms in merged_splits]
unmerged = ut.i1(unmerged)
usplits = [[] for ms in merged_splits] + [[]] # extra for non matches
for u in unmerged:
matches = [best_match(u, ms, sensitivity) for ms in merged_splits]
which_split = np.argmax(matches) if max(matches) > 0 else -1
usplits[which_split].append(u)
return usplits
def triple_venn(three_ppis, names=['a','b','c']):
# Can send output to venn3(sizes, names) for plotting
ppis_names = zip(three_ppis, names)
full_sizes = [len(p) for p in three_ppis]
print zip(names, full_sizes)
trip = ints_overlap(three_ppis)
print names, trip
intersects_2 = []
for (a,namea),(b,nameb) in it.combinations(ppis_names,2):
intersect = ints_overlap([a,b])
print namea, nameb, "--", intersect
intersect_2 = intersect - trip
print namea, nameb, "-only-", intersect_2
intersects_2.append((set([namea,nameb]), intersect_2))
only_singles = []
for i,(a,namea) in enumerate(ppis_names):
#print namea, len(a), trip, intersects_2 #debug
only_single = (len(a) - trip - sum([x[1] for x in intersects_2 if namea in x[0]]))
print namea, "only:", only_single
only_singles.append(only_single)
# for output into matplotlib_venn format
set_sizes = [0] * 7
set_sizes[6] = trip
set_sizes[:2], set_sizes[3] = only_singles[:2], only_singles[2]
set_sizes[2], set_sizes[4], set_sizes[5] = ut.i1(intersects_2)
return set_sizes, names
def pds_alloverlaps(named_pds):
"""
input: [(name, pairdict), ...]
"""
for num in range(2,len(named_pds)+1):
for n_pd in it.combinations(named_pds, num):
print ut.i0(n_pd), pds_overlap(ut.i1(n_pd))
def triple_venn_consv():
hints = co.load_havug_ints()
ppi_cxs, clean_cxs, corconsv = ppi.load_training_complexes("Hs", "Dm")
cints = co.pairs_from_complexes(ut.i1(ppi_cxs)) # exclude huge ones
ints23 = ut.loadpy(ut.bigd("../23_collapsenodes/Hs_filtorth025_withsc_2sp_refilt2sp_cxs_cxppis_clust27_532cxs"))[1]
ints3 = [cp.consv_pairs(i, h2d) for i in ints23, hints, cints]
cp.triple_venn(ints3, ["map23", "havug", "corum"])
def ppis_gold_standard(ppis, cxs_splits, species):
pdppis = pd.PairDict([p[:3] for p in ppis])
print len(pdppis.d), "predicted interactions"
ppi_cxs,_,all_cxs = ppi.load_training_complexes(species, None,'') #conv doesn't matter
pdcorum = pd.PairDict([(i[0],i[1],'gold') for i in
co.pairs_from_complexes(ut.i1(all_cxs))])
print len(pdcorum.d), "total gold standard"
pdcomb = pd.pd_union_disjoint_vals(pdppis, pdcorum)
unmr_splits = cp.unmerged_splits_from_merged_splits(ppi_cxs,cxs_splits)
print "unmerged split assignment lengths", [len(s) for s in unmr_splits]
pdtrainpos = pd.PairDict([(t[0],t[1]) for t in
co.pairs_from_complexes(unmr_splits[0])])
print len(pdtrainpos.d), "total train interactions"
counterrs = 0
for tpair in pdtrainpos.d:
cpair = pdcomb.find(tpair)
#assert cpair is not None, "Gold standard problem--filter_methods changed since run?"
if cpair is None or pdcomb.d[cpair][1] != 'gold':
#print 'error: train should be subset', tpair
counterrs += 1
else:
pdcomb.d[cpair][1] = 'train'
if counterrs: print "number of training not found in gold std:", counterrs
comblist = [list(k)+list(v) for k,v in pdcomb.d.items()]
print (len([1 for p in comblist if p[2] and p[3]=='gold']),
"ppis in gold not train")
print len([1 for p in comblist if p[2] and p[3]=='train']), "ppis in train"
# only return those that are predictions
return [p for p in comblist if p[2]]
def prot_counts_pep2prots(peplist, only_uniques, pep2prots):
"""
- pep2prots: use supplied dict of {peptide: set(proteinids)} instead of the
protein ids on the lines in the pep_list file, in which case sum up the
counts for a peptide-spectral combination.
"""
assert only_uniques, "Only handles only_uniques=True so far."
exclude_peps = (set([pep for pep,prots in pep2prots.items() if len(prots)>1])
if only_uniques else set([]))
print "%s non-unique peptides to exclude." % len(exclude_peps)
pep_samp_counts = defaultdict(float)
for _,sample,pep,count in peplist:
if pep not in exclude_peps:
pep_samp_counts[(pep,sample)] += float(count)
# Currently ignoring peptides without a mapping.
prots = sorted(list(reduce(set.union, [pep2prots[pep] for (pep,_),_ in
pep_samp_counts.items() if pep in pep2prots])))
samples = sorted(list(set(ut.i1(peplist))))
print "%s unique proteins. %s samples." % (len(prots), len(samples))
dprots, dsamples = [ut.list_inv_to_dict(lst) for lst in prots, samples]
counts = np.zeros((len(prots), len(samples)), dtype='float32')
for (pep,sample),count in pep_samp_counts.items():
if pep in pep2prots: # Currently ignoring peptides without a mapping.
assert len(pep2prots[pep])==1, "Non-unique peptide found"
counts[dprots[list(pep2prots[pep])[0]], dsamples[sample]] += count
totals = counts.sum(axis=1)
nonzero = totals > 0
prots, totals = [list(np.array(lst)[nonzero]) for lst in prots, totals]
counts = counts[nonzero,:]
return prots, samples, counts, totals
def hist_prot_counts(fs, in_prots_sets=[None],**kwargs):
"""
Usually set linewidth=3, histtype='step', range=(0,18), bins=36
"""
pcounts = prot_counts(fs).items()
for prots in in_prots_sets:
if prots:
pcounts = [(p,c) for p,c in pcounts if p in prots]
hist(np.log2(ut.i1(pcounts)), **kwargs)
def attr_to_sets(atts):
"""
Given a list of items with attributes, return a list of item sets.
Input: [(item1, attr1), (item2, attr2), (item3, attr1), ...]
Output: [{item1,item3}, {item2}, ...]
"""
datts = defaultdict(set)
for i,a in atts:
datts[a].add(i)
return ut.i1(datts.items())
def arrfeats_prep_all_data(arrfeats, ppis, sp="Hs", gold_consv="Dm", cutoff=0.5):
print "Adding species summary."
arrfeats = fe.arr_add_spsummary(arrfeats, cutoff)
print "Adding ppis."
arrfeats = fe.arrfeats_add_ppis(arrfeats, ppis)
_, _, all_cxs = ppi.load_training_complexes(sp, None, gold_consv)
pdgold = pd.PairDict(co.pairs_from_complexes(ut.i1(all_cxs)))
print "Setting trues."
arrfeats = fe.arrfeats_set_gold(arrfeats, pdgold)
return arrfeats
def nonpairs_gen(pairs, n):
items = list(set(ut.i0(pairs) + ut.i1(pairs)))
exclude = set(pairs)
pdexclude = pd.PairDict(exclude)
count = 0
while count < n:
pair = (random.choice(items), random.choice(items))
if not pdexclude.contains(pair):
yield pair
count += 1
def hpa_stats(ppis, locs, max_set_size=None):
s = attr_to_sets(locs)
if max_set_size is not None:
s = [c for c in s if len(c) < max_set_size]
plocs = co.pairs_from_complexes(s)
ppiprots = set(ut.i0(ppis)+ut.i1(ppis))
anprots = set(ut.i0(locs))
intprots = set.intersection(ppiprots, anprots)
print len(ppiprots), len(anprots), len(intprots)
return ppis_stats(ppis, plocs, intprots)
def ppis_scatter(ppis1, ppis2, useinds=range(3)):
"""
useinds: set to [0,1,3,2] to take ppi.learning_examples output into (score,
t/f) tuples; [0,1,3] to exclude the class.
"""
pd1,pd2 = [pd.PairDict([[p[i] for i in useinds] for p in ppis])
for ppis in ppis1,ppis2]
nvals = len(useinds)-2
pdcomb = pd.pd_union_disjoint_vals(pd1, pd2, adefaults=[0]*nvals,
bdefaults=[0]*nvals)
vals = zip(*ut.i1(pdcomb.d.items()))
v1s,v2s = zip(*vals[:nvals]), zip(*vals[nvals:])
v1s,v2s = [ut.i0(x) for x in v1s,v2s]
return v1s,v2s
def stats(gold,cxs_list, cxppis_list=None, conv_to_sets=True, funcs=None):
if conv_to_sets:
gold = [set(c) for c in gold]
cxs_list = [[set(c) for c in cxs] for cxs in cxs_list]
funcs = funcs or ut.i0(cp_funcs)
use_funcs = [f for f in cp_funcs if f[0] in funcs]
print funcs
arr = np.zeros(len(cxs_list),dtype=','.join(['f8']*len(funcs)))
arr.dtype.names = funcs
print '%s total maps.' % len(cxs_list)
for i, (cxs, cxppis) in enumerate(zip(cxs_list, cxppis_list)):
#sys.stdout.write(str(i))
print i
arr[i] = np.array([f(gold, cxs, cxppis) for f in ut.i1(use_funcs)])
return arr
def merge_atonce(psets, cutoff, func, sep):
"""
Once difference seems to be that say a series of overlapping doubles can't
string together with this approach, whereas it can with the iter approach.
"""
to_merge = list(psets)
merged = []
while len(to_merge)>1:
c1,c1ps = random.choice(to_merge)
to_merge.remove((c1,c1ps))
matches = [(c,ps) for c,ps in to_merge if func(ps,c1ps)>cutoff]
for m in matches: to_merge.remove(m)
newname = sep.join([c1]+ut.i0(matches))
newps = reduce(set.union, [c1ps]+ut.i1(matches))
merged.append((newname,newps))
merged.append(to_merge[0])
return merged
def load_kegg_sequentials(fname, do_convert=True):
dkegg = load_kegg_brite(fname)
kegg_paths = [ut.i1(v) for v in dkegg.values() if v]
def path_pairs(list_path):
return [(list_path[i],list_path[i+1]) for i in range(len(list_path)-1)]
group_pairs = ut.flatten([path_pairs(lpath) for lpath in kegg_paths])
#if return_groups:
#if conv_dict:
#return convert_groups_singles(labeled_pairs, conv_dict)
#else:
#return labeled_pairs
single_pairs = [(xi,yi) for x,y in group_pairs for xi in x for yi in y]
unique_pairs = pu.dedupe(single_pairs)
print "%s total, %s single, %s unique pairs returned" % (
len(group_pairs), len(single_pairs), len(unique_pairs))
if do_convert:
conv_dict = ut.dict_inverse_sets(orth.convert_dict('Hs','Hs_entrez'))
conv_pairs = convert_pairs_singles(unique_pairs, conv_dict)
print "%s converted pairs with 1-1 matches" % len(conv_pairs)
return conv_pairs
else:
return unique_pairs
def load_havug_cxppis():
hints = load_havug_ppis()
hcxs = load_havug_cxs()
hints = cl._filter_ints(hints, ut.i1(hcxs))
return hints
def random_pairs(pairs, npairs):
ps = list(set(ut.i0(pairs) + ut.i1(pairs)))
rpairs = [(random.choice(ps), random.choice(ps)) for i in
range(int(npairs*1.5))]
return pu.dedupe(rpairs)[:npairs]
def set_from_pairs(ppis):
return set(ut.i0(ppis) + ut.i1(ppis))
def unique_items(pairs):
return set(ut.i0(pairs) + ut.i1(pairs))
def items_from_pairs(pairs):
return set(ut.i0(pairs)+ut.i1(pairs))
