def save_test_performance(data, tfids, fold, args):
print "Predicting...", tfids
# Generate predictions for each model
predictions = util.predict(data, args.finaldir, args.reportdir)
print "done"
for tfname in predictions:
print tfname
Z = predictions[tfname]
# Restore the original (unpreprocessed) scale and normalization of the predictions
# Then add the opposite microarray's estimated probe value to each probe
with open(args.finaldir + "/" + tfname +"/preprocessors.pkl") as f:
normalizer = cPickle.load(f)['targets'][0]
Z *= normalizer.scales[0]
Z += normalizer.biases[0]
# Add per-probe multiplicative bias to predictions
probe_biases = load_probe_biases()[fold].reshape((-1,1))
if args.quick:
probe_biases = probe_biases[:len(Z),:]
M = ~np.isnan(probe_biases)
Z[M] *= probe_biases[M]
#with open("predictions/%s_%s.tsv" % (tfname, {"A":"B","B":"A"}[foldid]), 'w') as f:
# f.writelines([str(x)+"\n" for x in Z.ravel()])
z = Z.ravel()
y = data.Y[:,data.targetnames.index(tfname)].ravel()
rowidx = data.rowidx.ravel()
mask = ~np.isnan(y)
zscore4 = np.std(y[mask])*4+np.mean(y[mask])
util._update_metrics(args.finaldir, tfname, "test", rowidx, z, y, aucthresh=(zscore4,zscore4))
def save_chip_performance_table(args, tfids):
with open(args.reportdir + "/performance_chipseq.txt", "w") as f:
f.write("Background\tFile\tauc.mean\tauc.std\n")
chipseqids = ["TF_%d" % i for i in [23, 25, 31, 40, 44]]
for tfid in chipseqids:
if tfid not in tfids:
continue
for windowsize in [51, 100]:
for background in ["dinuc", "full_genomic", "genomic"]:
seqfile = "../data/dream5/chipseq/%s_CHIP_%d_%s.seq" % (
tfid, windowsize, background)
invivodata = util.datasource.fromtxt(seqfile + "[0]",
None,
seqfile + "[1]",
sequencenames=["seq"],
targetnames=[tfid])
invivodata.targetnames = [tfid]
predictions = util.predict(invivodata,
args.finaldir,
args.reportdir,
include=[tfid])
z = predictions[tfid].ravel()
y = invivodata.Y.ravel()
metrics = util.calc_metrics(z, y)
s = "%s\t%s\t%.3f\t%.3f\n" % (background, seqfile,
metrics['auc.mean'],
metrics['auc.std'])
print s,
f.write(s)
def save_chip_performance_table(args, tfids):
with open(args.reportdir+"/performance_chipseq.txt", "w") as f:
f.write("Background\tFile\tauc.mean\tauc.std\n")
chipseqids = ["TF_%d"%i for i in [23,25,31,40,44]]
for tfid in chipseqids:
if tfid not in tfids:
continue
for windowsize in [51,100]:
for background in ["dinuc", "full_genomic", "genomic"]:
seqfile = "../data/dream5/chipseq/%s_CHIP_%d_%s.seq" % (tfid, windowsize, background)
invivodata = util.datasource.fromtxt(seqfile+"[0]", None, seqfile+"[1]", sequencenames=["seq"], targetnames=[tfid])
invivodata.targetnames = [tfid]
predictions = util.predict(invivodata, args.finaldir, args.reportdir, include=[tfid])
z = predictions[tfid].ravel()
y = invivodata.Y.ravel()
metrics = util.calc_metrics(z, y)
s = "%s\t%s\t%.3f\t%.3f\n" % (background, seqfile, metrics['auc.mean'], metrics['auc.std'])
print s,
f.write(s)
def save_test_performance(data, tfids, fold, args):
print "Predicting...", tfids
# Generate predictions for each model
predictions = util.predict(data, args.finaldir, args.reportdir)
print "done"
for tfname in predictions:
print tfname
Z = predictions[tfname]
# Restore the original (unpreprocessed) scale and normalization of the predictions
# Then add the opposite microarray's estimated probe value to each probe
with open(args.finaldir + "/" + tfname + "/preprocessors.pkl") as f:
normalizer = cPickle.load(f)['targets'][0]
Z *= normalizer.scales[0]
Z += normalizer.biases[0]
# Add per-probe multiplicative bias to predictions
probe_biases = load_probe_biases()[fold].reshape((-1, 1))
if args.quick:
probe_biases = probe_biases[:len(Z), :]
M = ~np.isnan(probe_biases)
Z[M] *= probe_biases[M]
#with open("predictions/%s_%s.tsv" % (tfname, {"A":"B","B":"A"}[foldid]), 'w') as f:
# f.writelines([str(x)+"\n" for x in Z.ravel()])
z = Z.ravel()
y = data.Y[:, data.targetnames.index(tfname)].ravel()
rowidx = data.rowidx.ravel()
mask = ~np.isnan(y)
zscore4 = np.std(y[mask]) * 4 + np.mean(y[mask])
util._update_metrics(args.finaldir,
tfname,
"test",
rowidx,
z,
y,
aucthresh=(zscore4, zscore4))
def save_test_predictions(args):
# In quick mode, only load a subset of the data
maxrows = 10000 if args.quick else None
chunktargets, chunkcols = get_chunktargets(args)
tedata = {}
abs_auc = lambda x: max(x, 1 - x)
if args.mode == "AB":
invivo_data_dir = "../data/rnac/invivo"
util.makepath(args.testdir + "/invivo")
aucdump = open(args.testdir + "/invivo/deepbind_all.txt", "w")
aucdump.write("Protein\tFile\tModel\tauc\tauc.mean\tauc.std\n")
for invivo_id in invivo_ids.keys():
rnac_ids, invivo_files = invivo_ids[invivo_id]
rnac_names = ["RNCMPT%05d" % id for id in rnac_ids]
rnac_names = [name for name in rnac_names if name in chunktargets]
if not rnac_names:
continue
for invivo_file in invivo_files:
if not os.path.exists(invivo_data_dir + "/" + invivo_file +
".txt"):
continue
print "File %s using models %s..." % (invivo_file,
",".join(rnac_names))
# Convert the invivo sequence file into a format that predict.py expects,
# i.e. with a Fold ID, Event ID, and Sequence column.
data = util.datasource.fromtxt(
invivo_data_dir + "/" + invivo_file + ".txt[1]",
None,
invivo_data_dir + "/" + invivo_file + ".txt[0]",
sequencenames=["bound", "seq"],
targetnames=["bound"])
# First generate predictions based on "trivial" features for this particular invivo file
sequences = [row[0] for row in data.sequences]
labels = data.targets
predictions = {}
predictions["len"] = np.asarray([len(s) for s in sequences],
np.float32).reshape((-1, 1))
predictions["A"] = np.asarray(
[s.upper().count("A") / float(len(s)) for s in sequences],
np.float32).reshape((-1, 1))
predictions["C"] = np.asarray(
[s.upper().count("C") / float(len(s)) for s in sequences],
np.float32).reshape((-1, 1))
predictions["G"] = np.asarray(
[s.upper().count("G") / float(len(s)) for s in sequences],
np.float32).reshape((-1, 1))
predictions["T"] = np.asarray(
[(s.upper().count("U") + s.upper().count("T")) /
float(len(s))
for s in sequences], np.float32).reshape((-1, 1))
predictions["GC"] = predictions["G"] + predictions["C"]
# Next, generate predictions for each model on this same data file
data.targetnames = rnac_names
data.Y = np.repeat(data.Y, len(rnac_names), 1)
data.Ymask = np.repeat(data.Ymask, len(rnac_names), 1)
data.targets = data.Y.copy()
#pred = util.predict(data, "../data/rnac/pfms", args.reportdir, scan=20)
pred = util.predict(data,
args.finaldir,
args.reportdir,
scan=20)
predictions.update(pred)
# Dump all performance stats to the file
for pname in sorted(
predictions.keys(),
key=lambda x: x if "RNCMPT" not in x else " " + x
): # Make sure RNCMPT items go first in each group, for readability of all_aucs.txt
# Calculate the AUC of this particular prediction, of this particular model,
# on this particular invivo file.
z = predictions[pname].ravel()
y = np.array(labels).ravel()
metrics = util.calc_metrics(z, y)
# Write out a row indicating performance of each model on this file
aucdump.write(
"%s\t%s\t%s\t%.4f\t%.4f\t%.6f\n" %
(invivo_id, invivo_file, pname, metrics["auc"],
metrics["auc.mean"], metrics["auc.std"]))
aucdump.close()
# Re-open the AUC dump file, pull out all experiments associated with a single protein,
# and collect only the best of each type
all_aucs = {}
with open(args.testdir + "/invivo/deepbind_all.txt") as f:
f.readline() # discard header line
for line in f:
protein_name, invivo_file, model_name, auc, auc_mean, auc_std = line.rstrip(
).split("\t")
model_suffix = ".deepbind" if "RNCMPT" in model_name else ""
if model_name == "GC":
continue
all_aucs.setdefault(protein_name, []).append({
"file":
invivo_file,
"model":
model_name + model_suffix,
"auc":
float(auc),
"auc.mean":
float(auc_mean),
"auc.std":
float(auc_std)
})
# Open the rnac_all.txt and pull out the AUCs for the PFMs from the RNAcompete paper
# The rnac_all.txt file is in a different format, for legacy reasons
head = rnac_all_aucs[0].rstrip().split("\t")
lines = [line.rstrip().split("\t") for line in rnac_all_aucs[1:]]
cols = {item: head.index(item) for item in head}
for line in lines:
protein_name = line[0]
for scantype in ("max", "sum", "avg", "direct"):
invivo_file = line[1].rsplit(".", 1)[0]
model_name = line[2] + "." + scantype
auc = "nan"
auc_mean = line[cols[scantype]]
auc_std = line[cols[scantype + ".std"]]
if protein_name in all_aucs:
all_aucs[protein_name].append({
"file": invivo_file,
"model": model_name + ".rnac",
"auc": float(auc),
"auc.mean": float(auc_mean),
"auc.std": float(auc_std)
})
for scantype in ("direct", "max", "sum", "avg"):
for modeltype in ("deepbind", "rnac"):
with open(
args.testdir + "/invivo/%s_best_%s.txt" %
(modeltype, scantype), "w") as f:
f.write("Protein\tFile")
f.write("\t" + "\t".join([
"deeputil.model", "deeputil.auc", "deeputil.auc.mean",
"deeputil.auc.std"
]))
f.write("\t" + "\t".join([
"rnac.model", "rnac.auc", "rnac.auc.mean",
"rnac.auc.std"
]))
f.write("\t" + "\t".join([
"trivial.model", "trivial.auc", "trivial.auc.mean",
"trivial.auc.std"
]))
f.write("\n")
for protein in sorted(all_aucs.keys()):
trials = all_aucs[protein]
# For this particular protein, first find the best row based on non-trivial models,
# e.g. among RNCMPTXXXXX.direct and RNCMPTXXXXX.max, while ignoring "A" and "len"
best = None
for trial in trials:
if trial["model"].endswith(scantype + "." +
modeltype):
if best is None or trial["auc.mean"] > best[
"auc.mean"]:
best = trial
# Also find the best trivial feature associated with this file
best_trivial = None
for trial in trials:
if trial["file"] == best[
"file"] and "RNCMPT" not in trial["model"]:
if best_trivial is None or abs_auc(
trial["auc.mean"]) > abs_auc(
best_trivial["auc.mean"]):
best_trivial = trial
# Also find the best competing feature associated with this file
best_other = None
if modeltype == "rnac":
other_scantype = "max"
elif modeltype == "deepbind":
other_scantype = "avg"
else:
other_scantype = scantype
for trial in trials:
if trial["file"] == best["file"] and (
"." + other_scantype +
".") in trial["model"] and not trial[
"model"].endswith(other_scantype +
"." + modeltype):
if best_other is None or trial[
"auc.mean"] > best_other["auc.mean"]:
best_other = trial
best_deepbind = best if modeltype == "deepbind" else best_other
best_rnac = best if modeltype == "rnac" else best_other
f.write("%s\t%s\t" % (protein, best["file"]))
f.write("%s\t%.4f\t%.4f\t%.6f\t" %
(best_deepbind["model"], best_deepbind["auc"],
best_deepbind["auc.mean"],
best_deepbind["auc.std"]))
f.write("%s\t%.4f\t%.4f\t%.6f\t" %
(best_rnac["model"], best_rnac["auc"],
best_rnac["auc.mean"], best_rnac["auc.std"]))
f.write("%s\t%.4f\t%.4f\t%.6f\n" %
(best_trivial["model"],
abs_auc(best_trivial["auc"]),
abs_auc(best_trivial["auc.mean"]),
best_trivial["auc.std"]))
elif args.mode in ("A", "B"):
# Generate predictions on PBM probes from test set (i.e. if mode="A", the training set was "A" so the test set is "B")
print "Loading PBM data...",
testfold = "A" if args.mode == "B" else "B"
pbmdata = util.datasource.fromtxt("../data/rnac/sequences.tsv.gz",
None,
"../data/rnac/targets.tsv.gz",
targetcols=[0],
foldfilter=testfold,
maxrows=maxrows)
print "done"
util.makepath(args.testdir + "/pbm")
for targetname in chunktargets:
print targetname
pbmdata.targetnames = [targetname]
predictions = util.predict(pbmdata,
args.finaldir,
args.reportdir,
include=[targetname])
Z = predictions[targetname].ravel()
with gzip.open(
args.testdir + "/pbm/%s-DB-%s.txt.gz" %
(targetname, testfold), "w") as f:
for z in Z:
f.write("%.4f\n" % z)
else:
quit("Unrecognized mode")
