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 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 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 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 load_pepcount(f):
lol = ut.load_lol(f)
print "Omitting header:", lol[0]
lol = lol[1:]
peps = ut.i0(lol[1:])
samples = lol[0][2:]
arr = np.zeros((len(peps), len(samples)))
for i,row in enumerate(lol[1:]):
arr[i,:] = row[2:]
return peps, samples, arr
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 collapse_alternatives(ppis):
"""
Simply go through the whole set of interactions, and whenever there are two
nodes with identical partners, and scores are all within 20% or 0.01,
collapse them to a single node.
"""
nodes = set([p[0] for p in ppis]+[p[1] for p in ppis])
d_ints = defaultdict(set)
for node1,node2,score in ppis:
d_ints[node1].add((node2,score)); d_ints[node2].add((node1,score))
alt_groups = []
for i,(node1,ints1) in enumerate(d_ints.items()):
# first go through the existing collections
found_group=False
for group in alt_groups:
anode1,an1ints = group[0]
if (len(ints1)==len(an1ints) and
set(ut.i0(ints1))==set(ut.i0(an1ints))):
group.append((node1,ints1))
found_group=True
break
if not found_group:
alt_groups.append([(node1,ints1)])
return alt_groups
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 load_paralogs(sp_base, sp_other, ogroup_max, other_ogroup_max):
"""
Get all pairwise base species paralogs from sharing orthogroups in the
inParanoid orthology files between the base and other species.
"""
ogs = orth.load_ogroups(sp_base, sp_other)
use_ogs = []
if ogroup_max:
for og_base, og_other in ogs:
if (len(og_base) <= ogroup_max
and (other_ogroup_max is None
or len(og_other) <= other_ogroup_max)):
use_ogs.append(og_base)
else:
use_ogs = ut.i0(base_ogs)
base_paralogs = pu.groups_to_pairs(use_ogs)
return base_paralogs
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 merge(proj_dir, dirnames, pq_new_path):
"""
Combine pepquant quantitation from project_1 (etc) PQ_FILE into
project+PQ_NEW.
"""
if not os.path.exists(proj_dir):
os.mkdir(proj_dir)
proj_name = ut.shortname(proj_dir)
assert not os.path.exists(pq_new_path), "%s exists. Exiting." % pq_new_path
dirnames = ut.i0(sort_numbered(dirnames))
#print "Sorted dirnames:", dirnames
pq_files = [os.path.join(d,PQ_FILE) for d in dirnames]
for f in pq_files:
if not os.path.exists(f):
print "No Elution File:", f
eluts = (el.load_elution(f) for f in pq_files if os.path.exists(f))
merged = reduce(el.combine_elutions, eluts)
el.write_elution(merged, pq_new_path)
def select_best(clstruct,
scorenames=['sensitivity','mmr','aupr','cliqueness_3_20','nonov_iter','n_proteins','n_complexes_3_20'],
rfunc=operator.add, use_norm=False, dispn=15, score_factors=None,
use_ranks=True, output_ranks=False, print_ranks=False,
require_scores=None):
cxstructs, stats = clstruct.cxstructs, clstruct.stats
clusts = [cxstr.cxs for cxstr in cxstructs]
scorenames = scorenames or list(stats.dtype.names)
stats = stats[scorenames]
ranks = rank_columns(stats)
if use_ranks:
stats = ranks
else:
if use_norm: stats = norm_columns(stats)
if score_factors: stats = rescale_columns(stats, score_factors)
inds = np.argsort(reduce(rfunc, [stats[n] for n in scorenames]))[::-1]
if require_scores is not None:
for req_name,thresh in require_scores:
thresh = (np.median(clstruct.stats[req_name]) if thresh is None
else thresh)
inds = [i for i in inds if clstruct.stats[req_name][i] > thresh]
nstats = len(stats)
def filt_params(s):
return " ".join([p[:2]+p.split('=')[1] for p in s.split(',')])
show_columns = (scorenames if require_scores is None else
scorenames+ut.i0(require_scores))
d = DataFrame(clstruct.stats[inds[:dispn]][show_columns],
index=["#%s: %sc %si %s" %
(i,len(clusts[i]),len(cxstructs[i].cxppis),
filt_params(cxstructs[i].params)) for i in inds[:dispn]])
print d.head(dispn)
for i in inds[:dispn]:
#print (i, ["%0.4f " % s for s in clstruct.stats[i]], len(clusts[i]),
#len(cxstructs[i].cxppis), cxstructs[i].params)
if print_ranks:
print i, [nstats-s for s in ranks[i]]
if output_ranks:
return inds
else:
return clusts[inds[0]], cxstructs[inds[0]].cxppis, inds[0]
def set_from_pairs(ppis):
return set(ut.i0(ppis) + ut.i1(ppis))
# Plot the feature importances of the trees and of the forest
if do_plot:
import pylab as pl
pl.figure()
pl.title("Feature importances")
for tree in forest.estimators_:
pl.plot(indnums, tree.feature_importances_[indices], "r")
pl.plot(indnums, importances[indices], "b")
pl.show()
feats, weights = zip(*ranked)
return list(feats), list(weights)
if __name__ == '__main__':
if len(sys.argv) < 4:
sys.exit("usage: python ml.py train_test feats_f clf_type \
donorm kwarg1_val1-kwarg2-val2")
ttf = sys.argv[1]
tt = np.load(ttf)
feats = ut.loadpy(sys.argv[2])
k = sys.argv[3]
do_norm = sys.argv[4]
kvs = sys.argv[5]
kwargs = dict([tuple(kv.split('_')) for kv in kvs.split('-')]) \
if kvs else {}
clf = tree(**kwargs) if k=='tree' else svm(kernel=k, **kwargs)
ts = [('%s features, %s kernel, norm: %s, %s' %(n,k,do_norm, kvs),
fit_and_test([fe.keep_cols(t, ut.i0(feats[:n])) for t in tt],
clf, norm=do_norm))
for n in 20,30,40,50]
ut.savepy(ts, 'ts_%s_%s_%s_%s' %(k,do_norm,kvs,ttf))
def items_from_pairs(pairs):
return set(ut.i0(pairs)+ut.i1(pairs))
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 unique_items(pairs):
return set(ut.i0(pairs) + ut.i1(pairs))
