def main():
usage = 'usage: %prog [options] <params_file> <model_file> <test_hdf5_file>'
parser = OptionParser(usage)
parser.add_option(
'--ai',
dest='accuracy_indexes',
help=
'Comma-separated list of target indexes to make accuracy plots comparing true versus predicted values'
)
parser.add_option(
'--clip',
dest='target_clip',
default=None,
type='float',
help=
'Clip targets and predictions to a maximum value [Default: %default]')
parser.add_option(
'-d',
dest='down_sample',
default=1,
type='int',
help=
'Down sample test computation by taking uniformly spaced positions [Default: %default]'
)
parser.add_option('-g',
dest='genome_file',
default='%s/assembly/human.hg19.genome' %
os.environ['HG19'],
help='Chromosome length information [Default: %default]')
parser.add_option('--mc',
dest='mc_n',
default=0,
type='int',
help='Monte carlo test iterations [Default: %default]')
parser.add_option(
'--peak',
'--peaks',
dest='peaks',
default=False,
action='store_true',
help='Compute expensive peak accuracy [Default: %default]')
parser.add_option(
'-o',
dest='out_dir',
default='test_out',
help='Output directory for test statistics [Default: %default]')
parser.add_option(
'--rc',
dest='rc',
default=False,
action='store_true',
help='Average the fwd and rc predictions [Default: %default]')
parser.add_option('-s',
dest='scent_file',
help='Dimension reduction model file')
parser.add_option('--sample',
dest='sample_pct',
default=1,
type='float',
help='Sample percentage')
parser.add_option('--save',
dest='save',
default=False,
action='store_true')
parser.add_option('--shifts',
dest='shifts',
default='0',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option(
'-t',
dest='track_bed',
help='BED file describing regions so we can output BigWig tracks')
parser.add_option(
'--ti',
dest='track_indexes',
help='Comma-separated list of target indexes to output BigWig tracks')
parser.add_option('--train',
dest='train',
default=False,
action='store_true',
help='Process the training set [Default: %default]')
parser.add_option('-v',
dest='valid',
default=False,
action='store_true',
help='Process the validation set [Default: %default]')
parser.add_option(
'-w',
dest='pool_width',
default=1,
type='int',
help=
'Max pool width for regressing nt predictions to predict peak calls [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error('Must provide parameters, model, and test data HDF5')
else:
params_file = args[0]
model_file = args[1]
test_hdf5_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(',')]
#######################################################
# load data
#######################################################
data_open = h5py.File(test_hdf5_file)
if options.train:
test_seqs = data_open['train_in']
test_targets = data_open['train_out']
if 'train_na' in data_open:
test_na = data_open['train_na']
elif options.valid:
test_seqs = data_open['valid_in']
test_targets = data_open['valid_out']
test_na = None
if 'valid_na' in data_open:
test_na = data_open['valid_na']
else:
test_seqs = data_open['test_in']
test_targets = data_open['test_out']
test_na = None
if 'test_na' in data_open:
test_na = data_open['test_na']
if options.sample_pct < 1:
sample_n = int(test_seqs.shape[0] * options.sample_pct)
print('Sampling %d sequences' % sample_n)
sample_indexes = sorted(
np.random.choice(np.arange(test_seqs.shape[0]),
size=sample_n,
replace=False))
test_seqs = test_seqs[sample_indexes]
test_targets = test_targets[sample_indexes]
if test_na is not None:
test_na = test_na[sample_indexes]
target_labels = [tl.decode('UTF-8') for tl in data_open['target_labels']]
#######################################################
# model parameters and placeholders
job = params.read_job_params(params_file)
job['seq_length'] = test_seqs.shape[1]
job['seq_depth'] = test_seqs.shape[2]
job['num_targets'] = test_targets.shape[2]
job['target_pool'] = int(np.array(data_open.get('pool_width', 1)))
t0 = time.time()
dr = seqnn.SeqNN()
dr.build(job)
print('Model building time %ds' % (time.time() - t0))
# adjust for fourier
job['fourier'] = 'train_out_imag' in data_open
if job['fourier']:
test_targets_imag = data_open['test_out_imag']
if options.valid:
test_targets_imag = data_open['valid_out_imag']
# adjust for factors
if options.scent_file is not None:
t0 = time.time()
test_targets_full = data_open['test_out_full']
model = joblib.load(options.scent_file)
#######################################################
# test
# initialize batcher
if job['fourier']:
batcher_test = batcher.BatcherF(test_seqs, test_targets,
test_targets_imag, test_na,
dr.hp.batch_size, dr.hp.target_pool)
else:
batcher_test = batcher.Batcher(test_seqs, test_targets, test_na,
dr.hp.batch_size, dr.hp.target_pool)
# initialize saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# test
t0 = time.time()
test_acc = dr.test(sess,
batcher_test,
rc=options.rc,
shifts=options.shifts,
mc_n=options.mc_n)
if options.save:
np.save('%s/preds.npy' % options.out_dir, test_acc.preds)
np.save('%s/targets.npy' % options.out_dir, test_acc.targets)
test_preds = test_acc.preds
print('SeqNN test: %ds' % (time.time() - t0))
# compute stats
t0 = time.time()
test_r2 = test_acc.r2(clip=options.target_clip)
# test_log_r2 = test_acc.r2(log=True, clip=options.target_clip)
test_pcor = test_acc.pearsonr(clip=options.target_clip)
test_log_pcor = test_acc.pearsonr(log=True, clip=options.target_clip)
#test_scor = test_acc.spearmanr() # too slow; mostly driven by low values
print('Compute stats: %ds' % (time.time() - t0))
# print
print('Test Loss: %7.5f' % test_acc.loss)
print('Test R2: %7.5f' % test_r2.mean())
# print('Test log R2: %7.5f' % test_log_r2.mean())
print('Test PearsonR: %7.5f' % test_pcor.mean())
print('Test log PearsonR: %7.5f' % test_log_pcor.mean())
# print('Test SpearmanR: %7.5f' % test_scor.mean())
acc_out = open('%s/acc.txt' % options.out_dir, 'w')
for ti in range(len(test_r2)):
print('%4d %7.5f %.5f %.5f %.5f %s' %
(ti, test_acc.target_losses[ti], test_r2[ti], test_pcor[ti],
test_log_pcor[ti], target_labels[ti]),
file=acc_out)
acc_out.close()
# print normalization factors
target_means = test_preds.mean(axis=(0, 1), dtype='float64')
target_means_median = np.median(target_means)
target_means /= target_means_median
norm_out = open('%s/normalization.txt' % options.out_dir, 'w')
print('\n'.join([str(tu) for tu in target_means]), file=norm_out)
norm_out.close()
# clean up
del test_acc
# if test targets are reconstructed, measure versus the truth
if options.scent_file is not None:
compute_full_accuracy(dr, model, test_preds, test_targets_full,
options.out_dir, options.down_sample)
#######################################################
# peak call accuracy
if options.peaks:
# sample every few bins to decrease correlations
ds_indexes_preds = np.arange(0, test_preds.shape[1], 8)
ds_indexes_targets = ds_indexes_preds + (dr.hp.batch_buffer //
dr.hp.target_pool)
aurocs = []
auprcs = []
peaks_out = open('%s/peaks.txt' % options.out_dir, 'w')
for ti in range(test_targets.shape[2]):
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
# subset and flatten
test_targets_ti_flat = test_targets_ti[:,
ds_indexes_targets].flatten(
).astype('float32')
test_preds_ti_flat = test_preds[:, ds_indexes_preds,
ti].flatten().astype('float32')
# call peaks
test_targets_ti_lambda = np.mean(test_targets_ti_flat)
test_targets_pvals = 1 - poisson.cdf(
np.round(test_targets_ti_flat) - 1, mu=test_targets_ti_lambda)
test_targets_qvals = np.array(ben_hoch(test_targets_pvals))
test_targets_peaks = test_targets_qvals < 0.01
if test_targets_peaks.sum() == 0:
aurocs.append(0.5)
auprcs.append(0)
else:
# compute prediction accuracy
aurocs.append(
roc_auc_score(test_targets_peaks, test_preds_ti_flat))
auprcs.append(
average_precision_score(test_targets_peaks,
test_preds_ti_flat))
print('%4d %6d %.5f %.5f' %
(ti, test_targets_peaks.sum(), aurocs[-1], auprcs[-1]),
file=peaks_out)
peaks_out.close()
print('Test AUROC: %7.5f' % np.mean(aurocs))
print('Test AUPRC: %7.5f' % np.mean(auprcs))
#######################################################
# BigWig tracks
# NOTE: THESE ASSUME THERE WAS NO DOWN-SAMPLING ABOVE
# print bigwig tracks for visualization
if options.track_bed:
if options.genome_file is None:
parser.error(
'Must provide genome file in order to print valid BigWigs')
if not os.path.isdir('%s/tracks' % options.out_dir):
os.mkdir('%s/tracks' % options.out_dir)
track_indexes = range(test_preds.shape[2])
if options.track_indexes:
track_indexes = [
int(ti) for ti in options.track_indexes.split(',')
]
bed_set = 'test'
if options.valid:
bed_set = 'valid'
for ti in track_indexes:
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
# make true targets bigwig
bw_file = '%s/tracks/t%d_true.bw' % (options.out_dir, ti)
bigwig_write(bw_file,
test_targets_ti,
options.track_bed,
options.genome_file,
bed_set=bed_set)
# make predictions bigwig
bw_file = '%s/tracks/t%d_preds.bw' % (options.out_dir, ti)
bigwig_write(bw_file,
test_preds[:, :, ti],
options.track_bed,
options.genome_file,
dr.hp.batch_buffer,
bed_set=bed_set)
# make NA bigwig
bw_file = '%s/tracks/na.bw' % options.out_dir
bigwig_write(bw_file,
test_na,
options.track_bed,
options.genome_file,
bed_set=bed_set)
#######################################################
# accuracy plots
if options.accuracy_indexes is not None:
accuracy_indexes = [
int(ti) for ti in options.accuracy_indexes.split(',')
]
if not os.path.isdir('%s/scatter' % options.out_dir):
os.mkdir('%s/scatter' % options.out_dir)
if not os.path.isdir('%s/violin' % options.out_dir):
os.mkdir('%s/violin' % options.out_dir)
if not os.path.isdir('%s/roc' % options.out_dir):
os.mkdir('%s/roc' % options.out_dir)
if not os.path.isdir('%s/pr' % options.out_dir):
os.mkdir('%s/pr' % options.out_dir)
for ti in accuracy_indexes:
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
############################################
# scatter
# sample every few bins (adjust to plot the # points I want)
ds_indexes_preds = np.arange(0, test_preds.shape[1], 8)
ds_indexes_targets = ds_indexes_preds + (dr.hp.batch_buffer //
dr.hp.target_pool)
# subset and flatten
test_targets_ti_flat = test_targets_ti[:,
ds_indexes_targets].flatten(
).astype('float32')
test_preds_ti_flat = test_preds[:, ds_indexes_preds,
ti].flatten().astype('float32')
# take log2
test_targets_ti_log = np.log2(test_targets_ti_flat + 1)
test_preds_ti_log = np.log2(test_preds_ti_flat + 1)
# plot log2
sns.set(font_scale=1.2, style='ticks')
out_pdf = '%s/scatter/t%d.pdf' % (options.out_dir, ti)
plots.regplot(test_targets_ti_log,
test_preds_ti_log,
out_pdf,
poly_order=1,
alpha=0.3,
sample=500,
figsize=(6, 6),
x_label='log2 Experiment',
y_label='log2 Prediction',
table=True)
############################################
# violin
# call peaks
test_targets_ti_lambda = np.mean(test_targets_ti_flat)
test_targets_pvals = 1 - poisson.cdf(
np.round(test_targets_ti_flat) - 1, mu=test_targets_ti_lambda)
test_targets_qvals = np.array(ben_hoch(test_targets_pvals))
test_targets_peaks = test_targets_qvals < 0.01
test_targets_peaks_str = np.where(test_targets_peaks, 'Peak',
'Background')
# violin plot
sns.set(font_scale=1.3, style='ticks')
plt.figure()
df = pd.DataFrame({
'log2 Prediction':
np.log2(test_preds_ti_flat + 1),
'Experimental coverage status':
test_targets_peaks_str
})
ax = sns.violinplot(x='Experimental coverage status',
y='log2 Prediction',
data=df)
ax.grid(True, linestyle=':')
plt.savefig('%s/violin/t%d.pdf' % (options.out_dir, ti))
plt.close()
# ROC
plt.figure()
fpr, tpr, _ = roc_curve(test_targets_peaks, test_preds_ti_flat)
auroc = roc_auc_score(test_targets_peaks, test_preds_ti_flat)
plt.plot([0, 1], [0, 1],
c='black',
linewidth=1,
linestyle='--',
alpha=0.7)
plt.plot(fpr, tpr, c='black')
ax = plt.gca()
ax.set_xlabel('False positive rate')
ax.set_ylabel('True positive rate')
ax.text(0.99,
0.02,
'AUROC %.3f' % auroc,
horizontalalignment='right') # , fontsize=14)
ax.grid(True, linestyle=':')
plt.savefig('%s/roc/t%d.pdf' % (options.out_dir, ti))
plt.close()
# PR
plt.figure()
prec, recall, _ = precision_recall_curve(test_targets_peaks,
test_preds_ti_flat)
auprc = average_precision_score(test_targets_peaks,
test_preds_ti_flat)
plt.axhline(y=test_targets_peaks.mean(),
c='black',
linewidth=1,
linestyle='--',
alpha=0.7)
plt.plot(recall, prec, c='black')
ax = plt.gca()
ax.set_xlabel('Recall')
ax.set_ylabel('Precision')
ax.text(0.99,
0.95,
'AUPRC %.3f' % auprc,
horizontalalignment='right') # , fontsize=14)
ax.grid(True, linestyle=':')
plt.savefig('%s/pr/t%d.pdf' % (options.out_dir, ti))
plt.close()
data_open.close()
def main():
usage = "usage: %prog [options] <params_file> <model_file> <test_hdf5_file>"
parser = OptionParser(usage)
parser.add_option(
"--ai",
dest="accuracy_indexes",
help=
"Comma-separated list of target indexes to make accuracy plots comparing true versus predicted values",
)
parser.add_option(
"--clip",
dest="target_clip",
default=None,
type="float",
help=
"Clip targets and predictions to a maximum value [Default: %default]",
)
parser.add_option(
"-d",
dest="down_sample",
default=1,
type="int",
help=
"Down sample test computation by taking uniformly spaced positions [Default: %default]",
)
parser.add_option(
"-g",
dest="genome_file",
default="%s/data/human.hg19.genome" % os.environ["BASENJIDIR"],
help="Chromosome length information [Default: %default]",
)
parser.add_option(
"--mc",
dest="mc_n",
default=0,
type="int",
help="Monte carlo test iterations [Default: %default]",
)
parser.add_option(
"--peak",
"--peaks",
dest="peaks",
default=False,
action="store_true",
help="Compute expensive peak accuracy [Default: %default]",
)
parser.add_option(
"-o",
dest="out_dir",
default="test_out",
help="Output directory for test statistics [Default: %default]",
)
parser.add_option(
"--rc",
dest="rc",
default=False,
action="store_true",
help="Average the fwd and rc predictions [Default: %default]",
)
parser.add_option("-s",
dest="scent_file",
help="Dimension reduction model file")
parser.add_option("--sample",
dest="sample_pct",
default=1,
type="float",
help="Sample percentage")
parser.add_option("--save",
dest="save",
default=False,
action="store_true")
parser.add_option(
"--shifts",
dest="shifts",
default="0",
help="Ensemble prediction shifts [Default: %default]",
)
parser.add_option(
"-t",
dest="track_bed",
help="BED file describing regions so we can output BigWig tracks",
)
parser.add_option(
"--ti",
dest="track_indexes",
help="Comma-separated list of target indexes to output BigWig tracks",
)
parser.add_option(
"--train",
dest="train",
default=False,
action="store_true",
help="Process the training set [Default: %default]",
)
parser.add_option(
"-v",
dest="valid",
default=False,
action="store_true",
help="Process the validation set [Default: %default]",
)
parser.add_option(
"-w",
dest="pool_width",
default=1,
type="int",
help=
"Max pool width for regressing nt predictions to predict peak calls [Default: %default]",
)
(options, args) = parser.parse_args()
if len(args) != 3:
parser.error("Must provide parameters, model, and test data HDF5")
else:
params_file = args[0]
model_file = args[1]
test_hdf5_file = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
options.shifts = [int(shift) for shift in options.shifts.split(",")]
#######################################################
# load data
#######################################################
data_open = h5py.File(test_hdf5_file)
if options.train:
test_seqs = data_open["train_in"]
test_targets = data_open["train_out"]
if "train_na" in data_open:
test_na = data_open["train_na"]
elif options.valid:
test_seqs = data_open["valid_in"]
test_targets = data_open["valid_out"]
test_na = None
if "valid_na" in data_open:
test_na = data_open["valid_na"]
else:
test_seqs = data_open["test_in"]
test_targets = data_open["test_out"]
test_na = None
if "test_na" in data_open:
test_na = data_open["test_na"]
if options.sample_pct < 1:
sample_n = int(test_seqs.shape[0] * options.sample_pct)
print("Sampling %d sequences" % sample_n)
sample_indexes = sorted(
np.random.choice(np.arange(test_seqs.shape[0]),
size=sample_n,
replace=False))
test_seqs = test_seqs[sample_indexes]
test_targets = test_targets[sample_indexes]
if test_na is not None:
test_na = test_na[sample_indexes]
target_labels = [tl.decode("UTF-8") for tl in data_open["target_labels"]]
#######################################################
# model parameters and placeholders
job = params.read_job_params(params_file)
job["seq_length"] = test_seqs.shape[1]
job["seq_depth"] = test_seqs.shape[2]
job["num_targets"] = test_targets.shape[2]
job["target_pool"] = int(np.array(data_open.get("pool_width", 1)))
t0 = time.time()
dr = seqnn.SeqNN()
dr.build_feed(job)
print("Model building time %ds" % (time.time() - t0))
# adjust for fourier
job["fourier"] = "train_out_imag" in data_open
if job["fourier"]:
test_targets_imag = data_open["test_out_imag"]
if options.valid:
test_targets_imag = data_open["valid_out_imag"]
# adjust for factors
if options.scent_file is not None:
t0 = time.time()
test_targets_full = data_open["test_out_full"]
model = joblib.load(options.scent_file)
#######################################################
# test
# initialize batcher
if job["fourier"]:
batcher_test = batcher.BatcherF(
test_seqs,
test_targets,
test_targets_imag,
test_na,
dr.hp.batch_size,
dr.hp.target_pool,
)
else:
batcher_test = batcher.Batcher(test_seqs, test_targets, test_na,
dr.hp.batch_size, dr.hp.target_pool)
# initialize saver
saver = tf.train.Saver()
with tf.Session() as sess:
# load variables into session
saver.restore(sess, model_file)
# test
t0 = time.time()
test_acc = dr.test_h5_manual(sess,
batcher_test,
rc=options.rc,
shifts=options.shifts,
mc_n=options.mc_n)
if options.save:
np.save("%s/preds.npy" % options.out_dir, test_acc.preds)
np.save("%s/targets.npy" % options.out_dir, test_acc.targets)
test_preds = test_acc.preds
print("SeqNN test: %ds" % (time.time() - t0))
# compute stats
t0 = time.time()
test_r2 = test_acc.r2(clip=options.target_clip)
# test_log_r2 = test_acc.r2(log=True, clip=options.target_clip)
test_pcor = test_acc.pearsonr(clip=options.target_clip)
test_log_pcor = test_acc.pearsonr(log=True, clip=options.target_clip)
# test_scor = test_acc.spearmanr() # too slow; mostly driven by low values
print("Compute stats: %ds" % (time.time() - t0))
# print
print("Test Loss: %7.5f" % test_acc.loss)
print("Test R2: %7.5f" % test_r2.mean())
# print('Test log R2: %7.5f' % test_log_r2.mean())
print("Test PearsonR: %7.5f" % test_pcor.mean())
print("Test log PearsonR: %7.5f" % test_log_pcor.mean())
# print('Test SpearmanR: %7.5f' % test_scor.mean())
acc_out = open("%s/acc.txt" % options.out_dir, "w")
for ti in range(len(test_r2)):
print(
"%4d %7.5f %.5f %.5f %.5f %s" % (
ti,
test_acc.target_losses[ti],
test_r2[ti],
test_pcor[ti],
test_log_pcor[ti],
target_labels[ti],
),
file=acc_out,
)
acc_out.close()
# print normalization factors
target_means = test_preds.mean(axis=(0, 1), dtype="float64")
target_means_median = np.median(target_means)
target_means /= target_means_median
norm_out = open("%s/normalization.txt" % options.out_dir, "w")
print("\n".join([str(tu) for tu in target_means]), file=norm_out)
norm_out.close()
# clean up
del test_acc
# if test targets are reconstructed, measure versus the truth
if options.scent_file is not None:
compute_full_accuracy(
dr,
model,
test_preds,
test_targets_full,
options.out_dir,
options.down_sample,
)
#######################################################
# peak call accuracy
if options.peaks:
# sample every few bins to decrease correlations
ds_indexes_preds = np.arange(0, test_preds.shape[1], 8)
ds_indexes_targets = ds_indexes_preds + (dr.hp.batch_buffer //
dr.hp.target_pool)
aurocs = []
auprcs = []
peaks_out = open("%s/peaks.txt" % options.out_dir, "w")
for ti in range(test_targets.shape[2]):
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
# subset and flatten
test_targets_ti_flat = (test_targets_ti[:, ds_indexes_targets].
flatten().astype("float32"))
test_preds_ti_flat = (test_preds[:, ds_indexes_preds,
ti].flatten().astype("float32"))
# call peaks
test_targets_ti_lambda = np.mean(test_targets_ti_flat)
test_targets_pvals = 1 - poisson.cdf(
np.round(test_targets_ti_flat) - 1, mu=test_targets_ti_lambda)
test_targets_qvals = np.array(ben_hoch(test_targets_pvals))
test_targets_peaks = test_targets_qvals < 0.01
if test_targets_peaks.sum() == 0:
aurocs.append(0.5)
auprcs.append(0)
else:
# compute prediction accuracy
aurocs.append(
roc_auc_score(test_targets_peaks, test_preds_ti_flat))
auprcs.append(
average_precision_score(test_targets_peaks,
test_preds_ti_flat))
print(
"%4d %6d %.5f %.5f" %
(ti, test_targets_peaks.sum(), aurocs[-1], auprcs[-1]),
file=peaks_out,
)
peaks_out.close()
print("Test AUROC: %7.5f" % np.mean(aurocs))
print("Test AUPRC: %7.5f" % np.mean(auprcs))
#######################################################
# BigWig tracks
# NOTE: THESE ASSUME THERE WAS NO DOWN-SAMPLING ABOVE
# print bigwig tracks for visualization
if options.track_bed:
if options.genome_file is None:
parser.error(
"Must provide genome file in order to print valid BigWigs")
if not os.path.isdir("%s/tracks" % options.out_dir):
os.mkdir("%s/tracks" % options.out_dir)
track_indexes = range(test_preds.shape[2])
if options.track_indexes:
track_indexes = [
int(ti) for ti in options.track_indexes.split(",")
]
bed_set = "test"
if options.valid:
bed_set = "valid"
for ti in track_indexes:
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
# make true targets bigwig
bw_file = "%s/tracks/t%d_true.bw" % (options.out_dir, ti)
bigwig_write(
bw_file,
test_targets_ti,
options.track_bed,
options.genome_file,
bed_set=bed_set,
)
# make predictions bigwig
bw_file = "%s/tracks/t%d_preds.bw" % (options.out_dir, ti)
bigwig_write(
bw_file,
test_preds[:, :, ti],
options.track_bed,
options.genome_file,
dr.hp.batch_buffer,
bed_set=bed_set,
)
# make NA bigwig
# bw_file = '%s/tracks/na.bw' % options.out_dir
# bigwig_write(
# bw_file,
# test_na,
# options.track_bed,
# options.genome_file,
# bed_set=bed_set)
#######################################################
# accuracy plots
if options.accuracy_indexes is not None:
accuracy_indexes = [
int(ti) for ti in options.accuracy_indexes.split(",")
]
if not os.path.isdir("%s/scatter" % options.out_dir):
os.mkdir("%s/scatter" % options.out_dir)
if not os.path.isdir("%s/violin" % options.out_dir):
os.mkdir("%s/violin" % options.out_dir)
if not os.path.isdir("%s/roc" % options.out_dir):
os.mkdir("%s/roc" % options.out_dir)
if not os.path.isdir("%s/pr" % options.out_dir):
os.mkdir("%s/pr" % options.out_dir)
for ti in accuracy_indexes:
if options.scent_file is not None:
test_targets_ti = test_targets_full[:, :, ti]
else:
test_targets_ti = test_targets[:, :, ti]
############################################
# scatter
# sample every few bins (adjust to plot the # points I want)
ds_indexes_preds = np.arange(0, test_preds.shape[1], 8)
ds_indexes_targets = ds_indexes_preds + (dr.hp.batch_buffer //
dr.hp.target_pool)
# subset and flatten
test_targets_ti_flat = (test_targets_ti[:, ds_indexes_targets].
flatten().astype("float32"))
test_preds_ti_flat = (test_preds[:, ds_indexes_preds,
ti].flatten().astype("float32"))
# take log2
test_targets_ti_log = np.log2(test_targets_ti_flat + 1)
test_preds_ti_log = np.log2(test_preds_ti_flat + 1)
# plot log2
sns.set(font_scale=1.2, style="ticks")
out_pdf = "%s/scatter/t%d.pdf" % (options.out_dir, ti)
plots.regplot(
test_targets_ti_log,
test_preds_ti_log,
out_pdf,
poly_order=1,
alpha=0.3,
sample=500,
figsize=(6, 6),
x_label="log2 Experiment",
y_label="log2 Prediction",
table=True,
)
############################################
# violin
# call peaks
test_targets_ti_lambda = np.mean(test_targets_ti_flat)
test_targets_pvals = 1 - poisson.cdf(
np.round(test_targets_ti_flat) - 1, mu=test_targets_ti_lambda)
test_targets_qvals = np.array(ben_hoch(test_targets_pvals))
test_targets_peaks = test_targets_qvals < 0.01
test_targets_peaks_str = np.where(test_targets_peaks, "Peak",
"Background")
# violin plot
sns.set(font_scale=1.3, style="ticks")
plt.figure()
df = pd.DataFrame({
"log2 Prediction":
np.log2(test_preds_ti_flat + 1),
"Experimental coverage status":
test_targets_peaks_str,
})
ax = sns.violinplot(x="Experimental coverage status",
y="log2 Prediction",
data=df)
ax.grid(True, linestyle=":")
plt.savefig("%s/violin/t%d.pdf" % (options.out_dir, ti))
plt.close()
# ROC
plt.figure()
fpr, tpr, _ = roc_curve(test_targets_peaks, test_preds_ti_flat)
auroc = roc_auc_score(test_targets_peaks, test_preds_ti_flat)
plt.plot([0, 1], [0, 1],
c="black",
linewidth=1,
linestyle="--",
alpha=0.7)
plt.plot(fpr, tpr, c="black")
ax = plt.gca()
ax.set_xlabel("False positive rate")
ax.set_ylabel("True positive rate")
ax.text(0.99,
0.02,
"AUROC %.3f" % auroc,
horizontalalignment="right") # , fontsize=14)
ax.grid(True, linestyle=":")
plt.savefig("%s/roc/t%d.pdf" % (options.out_dir, ti))
plt.close()
# PR
plt.figure()
prec, recall, _ = precision_recall_curve(test_targets_peaks,
test_preds_ti_flat)
auprc = average_precision_score(test_targets_peaks,
test_preds_ti_flat)
plt.axhline(
y=test_targets_peaks.mean(),
c="black",
linewidth=1,
linestyle="--",
alpha=0.7,
)
plt.plot(recall, prec, c="black")
ax = plt.gca()
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
ax.text(0.99,
0.95,
"AUPRC %.3f" % auprc,
horizontalalignment="right") # , fontsize=14)
ax.grid(True, linestyle=":")
plt.savefig("%s/pr/t%d.pdf" % (options.out_dir, ti))
plt.close()
data_open.close()
def run(params_file, data_file, train_epochs, train_epoch_batches,
test_epoch_batches):
#######################################################
# load data
#######################################################
data_open = h5py.File(data_file)
train_seqs = data_open['train_in']
train_targets = data_open['train_out']
train_na = None
if 'train_na' in data_open:
train_na = data_open['train_na']
valid_seqs = data_open['valid_in']
valid_targets = data_open['valid_out']
valid_na = None
if 'valid_na' in data_open:
valid_na = data_open['valid_na']
#######################################################
# model parameters and placeholders
#######################################################
job = params.read_job_params(params_file)
job['seq_length'] = train_seqs.shape[1]
job['seq_depth'] = train_seqs.shape[2]
job['num_targets'] = train_targets.shape[2]
job['target_pool'] = int(np.array(data_open.get('pool_width', 1)))
t0 = time.time()
model = seqnn.SeqNN()
model.build(job)
print('Model building time %f' % (time.time() - t0))
# adjust for fourier
job['fourier'] = 'train_out_imag' in data_open
if job['fourier']:
train_targets_imag = data_open['train_out_imag']
valid_targets_imag = data_open['valid_out_imag']
#######################################################
# prepare batcher
#######################################################
if job['fourier']:
batcher_train = batcher.BatcherF(train_seqs,
train_targets,
train_targets_imag,
train_na,
model.hp.batch_size,
model.hp.target_pool,
shuffle=True)
batcher_valid = batcher.BatcherF(valid_seqs, valid_targets,
valid_targets_imag, valid_na,
model.batch_size, model.target_pool)
else:
batcher_train = batcher.Batcher(train_seqs,
train_targets,
train_na,
model.hp.batch_size,
model.hp.target_pool,
shuffle=True)
batcher_valid = batcher.Batcher(valid_seqs, valid_targets, valid_na,
model.hp.batch_size,
model.hp.target_pool)
print('Batcher initialized')
#######################################################
# train
#######################################################
augment_shifts = [int(shift) for shift in FLAGS.augment_shifts.split(',')]
ensemble_shifts = [
int(shift) for shift in FLAGS.ensemble_shifts.split(',')
]
# checkpoints
saver = tf.train.Saver()
config = tf.ConfigProto()
if FLAGS.log_device_placement:
config.log_device_placement = True
with tf.Session(config=config) as sess:
t0 = time.time()
# set seed
tf.set_random_seed(FLAGS.seed)
if FLAGS.logdir:
train_writer = tf.summary.FileWriter(FLAGS.logdir + '/train',
sess.graph)
else:
train_writer = None
if FLAGS.restart:
# load variables into session
saver.restore(sess, FLAGS.restart)
else:
# initialize variables
print('Initializing...')
sess.run(tf.global_variables_initializer())
print('Initialization time %f' % (time.time() - t0))
train_loss = None
best_loss = None
early_stop_i = 0
epoch = 0
while (train_epochs is None
or epochs < train_epochs) and early_stop_i < FLAGS.early_stop:
t0 = time.time()
# alternate forward and reverse batches
fwdrc = True
if FLAGS.augment_rc and epoch % 2 == 1:
fwdrc = False
# cycle shifts
shift_i = epoch % len(augment_shifts)
# train
train_loss, steps = model.train_epoch(
sess,
batcher_train,
fwdrc=fwdrc,
shift=augment_shifts[shift_i],
sum_writer=train_writer,
epoch_batches=train_epoch_batches,
no_steps=FLAGS.no_steps)
# validate
valid_acc = model.test(sess,
batcher_valid,
mc_n=FLAGS.ensemble_mc,
rc=FLAGS.ensemble_rc,
shifts=ensemble_shifts,
test_batches=test_epoch_batches)
valid_loss = valid_acc.loss
valid_r2 = valid_acc.r2().mean()
del valid_acc
best_str = ''
if best_loss is None or valid_loss < best_loss:
best_loss = valid_loss
best_str = ', best!'
early_stop_i = 0
saver.save(sess, '%s/model_best.tf' % FLAGS.logdir)
else:
early_stop_i += 1
# measure time
et = time.time() - t0
if et < 600:
time_str = '%3ds' % et
elif et < 6000:
time_str = '%3dm' % (et / 60)
else:
time_str = '%3.1fh' % (et / 3600)
# print update
print(
'Epoch: %3d, Steps: %7d, Train loss: %7.5f, Valid loss: %7.5f, Valid R2: %7.5f, Time: %s%s'
% (epoch + 1, steps, train_loss, valid_loss, valid_r2,
time_str, best_str))
sys.stdout.flush()
if FLAGS.check_all:
saver.save(sess, '%s/model_check%d.tf' % (FLAGS.logdir, epoch))
# update epoch
epoch += 1
if FLAGS.logdir:
train_writer.close()
def run(params_file, data_file, num_train_epochs):
shifts = [int(shift) for shift in FLAGS.shifts.split(',')]
#######################################################
# load data
#######################################################
data_open = h5py.File(data_file)
train_seqs = data_open['train_in']
train_targets = data_open['train_out']
train_na = None
if 'train_na' in data_open:
train_na = data_open['train_na']
valid_seqs = data_open['valid_in']
valid_targets = data_open['valid_out']
valid_na = None
if 'valid_na' in data_open:
valid_na = data_open['valid_na']
#######################################################
# model parameters and placeholders
#######################################################
job = dna_io.read_job_params(params_file)
job['batch_length'] = train_seqs.shape[1]
job['seq_depth'] = train_seqs.shape[2]
job['num_targets'] = train_targets.shape[2]
job['target_pool'] = int(np.array(data_open.get('pool_width', 1)))
job['early_stop'] = job.get('early_stop', 16)
job['rate_drop'] = job.get('rate_drop', 3)
t0 = time.time()
dr = seqnn.SeqNN()
dr.build(job)
print('Model building time %f' % (time.time() - t0))
# adjust for fourier
job['fourier'] = 'train_out_imag' in data_open
if job['fourier']:
train_targets_imag = data_open['train_out_imag']
valid_targets_imag = data_open['valid_out_imag']
#######################################################
# train
#######################################################
# initialize batcher
if job['fourier']:
batcher_train = batcher.BatcherF(train_seqs,
train_targets,
train_targets_imag,
train_na,
dr.batch_size,
dr.target_pool,
shuffle=True)
batcher_valid = batcher.BatcherF(valid_seqs, valid_targets,
valid_targets_imag, valid_na,
dr.batch_size, dr.target_pool)
else:
batcher_train = batcher.Batcher(train_seqs,
train_targets,
train_na,
dr.batch_size,
dr.target_pool,
shuffle=True)
batcher_valid = batcher.Batcher(valid_seqs, valid_targets, valid_na,
dr.batch_size, dr.target_pool)
print('Batcher initialized')
# checkpoints
saver = tf.train.Saver()
config = tf.ConfigProto()
if FLAGS.log_device_placement:
config.log_device_placement = True
with tf.Session(config=config) as sess:
t0 = time.time()
# set seed
tf.set_random_seed(FLAGS.seed)
if FLAGS.logdir:
train_writer = tf.summary.FileWriter(FLAGS.logdir + '/train',
sess.graph)
else:
train_writer = None
if FLAGS.restart:
# load variables into session
saver.restore(sess, FLAGS.restart)
else:
# initialize variables
print('Initializing...')
sess.run(tf.global_variables_initializer())
print('Initialization time %f' % (time.time() - t0))
train_loss = None
best_loss = None
early_stop_i = 0
undroppable_counter = 3
max_drops = 8
num_drops = 0
for epoch in range(num_train_epochs):
if early_stop_i < job['early_stop'] or epoch < FLAGS.min_epochs:
t0 = time.time()
# save previous
train_loss_last = train_loss
# alternate forward and reverse batches
fwdrc = True
if FLAGS.rc and epoch % 2 == 1:
fwdrc = False
# cycle shifts
shift_i = epoch % len(shifts)
# train
train_loss = dr.train_epoch(sess, batcher_train, fwdrc,
shifts[shift_i], train_writer)
# validate
valid_acc = dr.test(sess,
batcher_valid,
mc_n=FLAGS.mc_n,
rc=FLAGS.rc,
shifts=shifts)
valid_loss = valid_acc.loss
valid_r2 = valid_acc.r2().mean()
del valid_acc
best_str = ''
if best_loss is None or valid_loss < best_loss:
best_loss = valid_loss
best_str = ', best!'
early_stop_i = 0
saver.save(
sess,
'%s/%s_best.tf' % (FLAGS.logdir, FLAGS.save_prefix))
else:
early_stop_i += 1
# measure time
et = time.time() - t0
if et < 600:
time_str = '%3ds' % et
elif et < 6000:
time_str = '%3dm' % (et / 60)
else:
time_str = '%3.1fh' % (et / 3600)
# print update
print(
'Epoch %3d: Train loss: %7.5f, Valid loss: %7.5f, Valid R2: %7.5f, Time: %s%s'
% (epoch + 1, train_loss, valid_loss, valid_r2, time_str,
best_str),
end='')
# if training stagnant
if FLAGS.learn_rate_drop and num_drops < max_drops and undroppable_counter == 0 and (
train_loss_last -
train_loss) / train_loss_last < 0.0002:
print(', rate drop', end='')
dr.drop_rate(2 / 3)
undroppable_counter = 1
num_drops += 1
else:
undroppable_counter = max(0, undroppable_counter - 1)
print('')
sys.stdout.flush()
if FLAGS.logdir:
train_writer.close()