def main():
usage = 'usage: %prog [options] <params_file> <data1_dir> ...'
parser = OptionParser(usage)
parser.add_option('-k',
dest='keras_fit',
default=False,
action='store_true',
help='Train with Keras fit method [Default: %default]')
parser.add_option(
'-o',
dest='out_dir',
default='train_out',
help='Output directory for test statistics [Default: %default]')
parser.add_option(
'--restore',
dest='restore',
help='Restore model and continue training [Default: %default]')
parser.add_option('--trunk',
dest='trunk',
default=False,
action='store_true',
help='Restore only model trunk [Default: %default]')
parser.add_option(
'--tfr_train',
dest='tfr_train_pattern',
default=None,
help=
'Training TFR pattern string appended to data_dir/tfrecords for subsetting [Default: %default]'
)
parser.add_option(
'--tfr_eval',
dest='tfr_eval_pattern',
default=None,
help=
'Evaluation TFR pattern string appended to data_dir/tfrecords for subsetting [Default: %default]'
)
(options, args) = parser.parse_args()
if len(args) < 2:
parser.error('Must provide parameters and data directory.')
else:
params_file = args[0]
data_dirs = args[1:]
if options.keras_fit and len(data_dirs) > 1:
print('Cannot use keras fit method with multi-genome training.')
exit(1)
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
if params_file != '%s/params.json' % options.out_dir:
shutil.copy(params_file, '%s/params.json' % options.out_dir)
# read model parameters
with open(params_file) as params_open:
params = json.load(params_open)
params_model = params['model']
params_train = params['train']
# read datasets
train_data = []
eval_data = []
for data_dir in data_dirs:
# load train data
train_data.append(
dataset.SeqDataset(data_dir,
split_label='train',
batch_size=params_train['batch_size'],
mode='train',
tfr_pattern=options.tfr_train_pattern))
# load eval data
eval_data.append(
dataset.SeqDataset(data_dir,
split_label='valid',
batch_size=params_train['batch_size'],
mode='eval',
tfr_pattern=options.tfr_eval_pattern))
if params_train.get('num_gpu', 1) == 1:
########################################
# one GPU
# initialize model
# print('INITIALIZE MODEL')
seqnn_model = seqnn.SeqNN(params_model)
# seqnn_model = model_zoo.basenji_model((131072,4), 3)
# restore
if options.restore:
seqnn_model.restore(options.restore, trunk=options.trunk)
# initialize trainer
seqnn_trainer = trainer.Trainer(params_train, train_data, eval_data,
options.out_dir)
# compile model
seqnn_trainer.compile(seqnn_model)
# else:
########################################
# two GPU
# strategy = tf.distribute.MirroredStrategy()
#
# with strategy.scope():
#
# if not options.keras_fit:
# # distribute data
# for di in range(len(data_dirs)):
# train_data[di].distribute(strategy)
# eval_data[di].distribute(strategy)
#
# # initialize model
# seqnn_model = seqnn.SeqNN(params_model)
#
# # restore
# if options.restore:
# seqnn_model.restore(options.restore, options.trunk)
#
# # initialize trainer
# seqnn_trainer = trainer.Trainer(params_train, train_data, eval_data, options.out_dir,
# strategy, params_train['num_gpu'], options.keras_fit)
#
# # compile model
# seqnn_trainer.compile(seqnn_model)
# train model
if options.keras_fit:
seqnn_trainer.fit_keras(seqnn_model)
else:
if len(data_dirs) == 1:
seqnn_trainer.fit_tape(seqnn_model)
else:
seqnn_trainer.fit2(seqnn_model)
train_out = pd.read_csv(
"/mnt/scratch/ws/psbelokopytova/202103211631polina/nn_anopheles/dataset_like_Akita/data/Aalb_2048bp_repeat/train_out_test2/model2.out",
sep=" ",
names=range(24))
fasta_file = "/mnt/scratch/ws/psbelokopytova/202103211631polina/nn_anopheles/input/genomes/AalbS2_V4.fa"
params_file = model_dir + 'params.json'
for i in range(0, 48, 3):
model_file = model_dir + 'model_check_epoch' + str(i) + '.h5'
# model_file = model_dir+'model_best.h5'
with open(params_file) as params_open:
params = json.load(params_open)
params_model = params['model']
params_train = params['train']
seqnn_model = seqnn.SeqNN(params_model)
### restore model ###
seqnn_model.restore(model_file)
print('successfully loaded')
### names of targets ###
data_dir = '/mnt/scratch/ws/psbelokopytova/202103211631polina/nn_anopheles/dataset_like_Akita/data/Aalb_2048'
# data_dir ='/mnt/scratch/ws/psbelokopytova/202103211631polina/nn_anopheles/dataset_like_Akita/data/Aste_2048_globaloe'
hic_targets = pd.read_csv(data_dir + '/targets.txt', sep='\t')
hic_file_dict_num = dict(
zip(hic_targets['index'].values, hic_targets['file'].values))
hic_file_dict = dict(
zip(hic_targets['identifier'].values, hic_targets['file'].values))
hic_num_to_name_dict = dict(
zip(hic_targets['index'].values, hic_targets['identifier'].values))
def main():
usage = 'usage: %prog [options] <params_file> <model_file> <data_dir>'
parser = OptionParser(usage)
parser.add_option('--ai', dest='accuracy_indexes',
help='Comma-separated list of target indexes to make accuracy scatter plots.')
parser.add_option('--head', dest='head_i',
default=0, type='int',
help='Parameters head to test [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('--save', dest='save',
default=False, action='store_true',
help='Save targets and predictions numpy arrays [Default: %default]')
parser.add_option('--shifts', dest='shifts',
default='0',
help='Ensemble prediction shifts [Default: %default]')
parser.add_option('-t', dest='targets_file',
default=None, type='str',
help='File specifying target indexes and labels in table format')
parser.add_option('--split', dest='split_label',
default='test',
help='Dataset split label for eg TFR pattern [Default: %default]')
parser.add_option('--tfr', dest='tfr_pattern',
default=None,
help='TFR pattern string appended to data_dir/tfrecords for subsetting [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]
data_dir = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
# parse shifts to integers
options.shifts = [int(shift) for shift in options.shifts.split(',')]
#######################################################
# inputs
# read targets
if options.targets_file is None:
options.targets_file = '%s/targets.txt' % data_dir
targets_df = pd.read_csv(options.targets_file, index_col=0, sep='\t')
# read model parameters
with open(params_file) as params_open:
params = json.load(params_open)
params_model = params['model']
params_train = params['train']
# construct eval data
eval_data = dataset.SeqDataset(data_dir,
split_label=options.split_label,
batch_size=params_train['batch_size'],
mode='eval',
tfr_pattern=options.tfr_pattern)
# initialize model
seqnn_model = seqnn.SeqNN(params_model)
seqnn_model.restore(model_file, options.head_i)
seqnn_model.build_ensemble(options.rc, options.shifts)
#######################################################
# evaluate
loss_label = params_train.get('loss', 'poisson').lower()
spec_weight = params_train.get('spec_weight', 1)
loss_fn = trainer.parse_loss(loss_label, spec_weight=spec_weight)
# evaluate
test_loss, test_metric1, test_metric2 = seqnn_model.evaluate(eval_data, loss=loss_fn)
# print summary statistics
print('\nTest Loss: %7.5f' % test_loss)
if loss_label == 'bce':
print('Test AUROC: %7.5f' % test_metric1.mean())
print('Test AUPRC: %7.5f' % test_metric2.mean())
# write target-level statistics
targets_acc_df = pd.DataFrame({
'index': targets_df.index,
'auroc': test_metric1,
'auprc': test_metric2,
'identifier': targets_df.identifier,
'description': targets_df.description
})
else:
print('Test PearsonR: %7.5f' % test_metric1.mean())
print('Test R2: %7.5f' % test_metric2.mean())
# write target-level statistics
targets_acc_df = pd.DataFrame({
'index': targets_df.index,
'pearsonr': test_metric1,
'r2': test_metric2,
'identifier': targets_df.identifier,
'description': targets_df.description
})
targets_acc_df.to_csv('%s/acc.txt'%options.out_dir, sep='\t',
index=False, float_format='%.5f')
#######################################################
# predict?
if options.save or options.peaks or options.accuracy_indexes is not None:
# compute predictions
test_preds = seqnn_model.predict(eval_data).astype('float16')
# read targets
test_targets = eval_data.numpy(return_inputs=False)
if options.save:
preds_h5 = h5py.File('%s/preds.h5' % options.out_dir, 'w')
preds_h5.create_dataset('preds', data=test_preds)
preds_h5.close()
targets_h5 = h5py.File('%s/targets.h5' % options.out_dir, 'w')
targets_h5.create_dataset('targets', data=test_targets)
targets_h5.close()
#######################################################
# peak call accuracy
if options.peaks:
peaks_out_file = '%s/peaks.txt' % options.out_dir
test_peaks(test_preds, test_targets, peaks_out_file)
#######################################################
# 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:
test_targets_ti = test_targets[:, :, ti]
############################################
# scatter
# sample every few bins (adjust to plot the # points I want)
ds_indexes = np.arange(0, test_preds.shape[1], 8)
# subset and flatten
test_targets_ti_flat = test_targets_ti[:, ds_indexes].flatten(
).astype('float32')
test_preds_ti_flat = test_preds[:, ds_indexes, 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()