def main():
usage = 'usage: %prog [options] <repr_hdf5> <data_hdf5> <target_index>'
parser = OptionParser(usage)
parser.add_option('-a', dest='add_only', default=False, action='store_true', help='Use additional features only; no sequence features')
parser.add_option('-b', dest='balance', default=False, action='store_true', help='Downsample the negative set to balance [Default: %default]')
parser.add_option('-o', dest='out_dir', default='postmodel', help='Output directory [Default: %default]')
parser.add_option('-r', dest='regression', default=False, action='store_true', help='Regression mode [Default: %default]')
parser.add_option('-s', dest='seq_only', default=False, action='store_true', help='Use sequence features only; no additional features [Default: %default]')
parser.add_option('--sample', dest='sample', default=None, type='int', help='Sample from the training set [Default: %default]')
parser.add_option('-t', dest='target_hdf5', default=None, help='Extract targets from this HDF5 rather than data_hdf5 argument')
parser.add_option('-x', dest='regex_add', default=None, help='Filter additional features using a comma-separated list of regular expressions')
(options,args) = parser.parse_args()
if len(args) != 3:
parser.error('Must provide full data HDF5, representation HDF5, and target index or filename')
else:
repr_hdf5_file = args[0]
data_hdf5_file = args[1]
target_i = args[2]
if not os.path.isdir(options.out_dir):
os.mkdir(options.out_dir)
random.seed(1)
#######################################################
# preprocessing
#######################################################
# load training targets
data_hdf5_in = h5py.File(data_hdf5_file, 'r')
if options.target_hdf5:
target_hdf5_in = h5py.File(options.target_hdf5, 'r')
else:
target_hdf5_in = data_hdf5_in
train_y = np.array(target_hdf5_in['train_out'])[:,target_i]
test_y = np.array(target_hdf5_in['test_out'])[:,target_i]
# load training representations
if not options.add_only:
repr_hdf5_in = h5py.File(repr_hdf5_file, 'r')
train_x = np.array(repr_hdf5_in['train_repr'])
test_x = np.array(repr_hdf5_in['test_repr'])
repr_hdf5_in.close()
if options.seq_only:
add_labels = []
else:
# load additional features
train_a = np.array(data_hdf5_in['train_add'])
test_a = np.array(data_hdf5_in['test_add'])
add_labels = np.array(data_hdf5_in['add_labels'])
if options.regex_add:
fi = filter_regex(options.regex_add, add_labels)
train_a, test_a, add_labels = train_a[:,fi], test_a[:,fi], add_labels[fi]
# append additional features
if options.add_only:
add_i = 0
train_x, test_x = train_a, test_a
else:
add_i = train_x.shape[1]
train_x = np.concatenate((train_x,train_a), axis=1)
test_x = np.concatenate((test_x,test_a), axis=1)
data_hdf5_in.close()
if options.target_hdf5:
target_hdf5_in.close()
# balance
if options.balance:
train_x, train_y = balance(train_x, train_y)
# sample
if options.sample is not None and options.sample < train_x.shape[0]:
sample_indexes = random.sample(range(train_x.shape[0]), options.sample)
train_x = train_x[sample_indexes]
train_y = train_y[sample_indexes]
#######################################################
# model
#######################################################
if options.regression:
# fit
model = BayesianRidge(fit_intercept=True)
model.fit(train_x, train_y)
# accuracy
acc_out = open('%s/r2.txt' % options.out_dir, 'w')
print >> acc_out, model.score(test_x, test_y)
acc_out.close()
test_preds = model.predict(test_x)
# plot a sample of predictions versus actual
plt.figure()
sns.jointplot(test_preds[:5000], test_y[:5000], joint_kws={'alpha':0.3})
plt.savefig('%s/scatter.pdf' % options.out_dir)
plt.close()
# plot the distribution of residuals
plt.figure()
sns.distplot(test_y-test_preds)
plt.savefig('%s/residuals.pdf' % options.out_dir)
plt.close()
else:
# fit
model = LogisticRegression(penalty='l2', C=1000)
model.fit(train_x, train_y)
# accuracy
test_preds = model.predict_proba(test_x)[:,1].flatten()
acc_out = open('%s/auc.txt' % options.out_dir, 'w')
print >> acc_out, roc_auc_score(test_y, test_preds)
acc_out.close()
# compute and print ROC curve
fpr, tpr, thresholds = roc_curve(test_y, test_preds)
roc_out = open('%s/roc.txt' % options.out_dir, 'w')
for i in range(len(fpr)):
print >> roc_out, '%f\t%f\t%f' % (fpr[i], tpr[i], thresholds[i])
roc_out.close()
# compute and print precision-recall curve
precision, recall, thresholds = precision_recall_curve(test_y, test_preds)
prc_out = open('%s/prc.txt' % options.out_dir, 'w')
for i in range(len(precision)):
print >> prc_out, '%f\t%f' % (precision[i], recall[i])
prc_out.close()
# save model
joblib.dump(model, '%s/model.pkl' % options.out_dir)
#######################################################
# analyze
#######################################################
# print coefficients table
coef_out = open('%s/add_coefs.txt' % options.out_dir, 'w')
for ai in range(len(add_labels)):
if options.regression:
coefi = model.coef_[add_i+ai]
else:
coefi = model.coef_[0,add_i+ai]
print >> coef_out, add_labels[ai], coefi
coef_out.close()
