def model_pred(NGRAM_NFILTER, weight_file, test_data, test_docids,
test_qids):
dump_modelplot(model.build(), detail_outdir + 'predplot_' + expid)
model_predict = model.build_from_dump(weight_file)
qid_cwid_pred = pred_label(model_predict, test_data, test_docids,
test_qids)
return qid_cwid_pred
def model_pred(NGRAM_NFILTER, weight_file, test_data, test_docids,
test_qids):
dump_modelplot(model.build(), detail_outdir + 'predplot_' + expid)
model_predict = model.build_from_dump(weight_file)
for layer in model.layers:
g = layer.get_config()
h = layer.get_weights()
print(g)
print(h)
exit(0)
qid_cwid_pred = pred_label(model_predict, test_data, test_docids,
test_qids)
return qid_cwid_pred
def main(_log, _config):
p = _config
modelname = file2name[p['modelfn']]
mod_model = importlib.import_module('models.%s' % p['modelfn'])
# load the model to be employed, say from models/pacrr.py
model_cls = getattr(mod_model, modelname)
model_params = {k: v for k, v in p.items() if k in model_cls.params or k == 'modelfn'}
model = model_cls(model_params, rnd_seed=p['seed'])
# create a expid based on the configured parameters
expid = model.params_to_string(model_params)
# the model files
outdir='%s/train_%s/%s/model_weight/%s'%(p['parentdir'], p['train_years'], p['expname'], expid)
# the plots for the model, the training loss etc..
detail_outdir='%s/train_%s/%s/model_detail/'%(p['parentdir'], p['train_years'], p['expname'])
if not os.path.isdir(detail_outdir + 'outs'):
print(detail_outdir + 'outs')
os.makedirs(detail_outdir + 'outs')
_log.info('Input parameters: {0}'.format(p))
label2tlabel={4:2,3:2,2:2,1:1,0:0,-2:0}
sample_label_prob=dict()
_log.info('{0} {1} {2}'.format(p['expname'], p['train_years'], sample_label_prob))
NGRAM_NFILTER, N_GRAMS = get_ngram_nfilter(p['winlen'], p['qproximity'], p['maxqlen'], p['xfilters'])
_log.info('process and output to %s'%outdir)
_log.info('{0} {1} {2} {3} {4}'.format(p['distill'], 'NGRAM_NFILTER', NGRAM_NFILTER, 'N_GRAMS', N_GRAMS))
if os.path.exists(outdir) and len(os.listdir(outdir)) == p['epochs']:
_log.info("outdir already seems to be full... exiting early")
return
# prepare train data
qids = get_train_qids(p['train_years'])
qrelf = get_qrelf(qrelfdir, p['train_years'])
qid_cwid_label = read_qrel(qrelf, qids, include_spam=False)
train_qids =[qid for qid in qids if qid in qid_cwid_label]
_log.info('%s train_num %d '%(p['train_years'], len(train_qids)))
def plot_curve_loss(epoch_train_loss, outdir, name, plot_id, series):
epochs, losses = zip(*list(enumerate(epoch_train_loss)))
argmin_loss_epoch = np.argmin(epoch_train_loss)
fig = plt.figure()
plt.plot(epochs, losses, 'k:')
plt.ylabel('Training Loss')
plt.tick_params('y')
plt.xlabel('epoches')
plt.title('loss:%d %.3f'%(argmin_loss_epoch, epoch_train_loss[argmin_loss_epoch]))
fig.savefig(trunc_dir(outdir) + trunc_file(name + '_' + plot_id + '.pdf'), format='pdf')
plt.close()
# dump model plot
built_model = model.build()
model.build_predict() # run build_predict to verify it's working
dump_modelplot(built_model, detail_outdir + 'model_' + expid)
# callback function, dump the model and compute ndcg/map
dump_weight = DumpWeight(outdir, batch_size=p['batch'], nb_sample=p['nsamples'])
# keras 2 steps per epoch is number of batches per epoch, not number of samples per epoch
steps_per_epoch = np.int(p['nsamples'] / p['batch'])
# the generator for training data
train_data_generator=\
load_train_data_generator(qids, rawdoc_mat_dir, qid_cwid_label, N_GRAMS, p,\
label2tlabel=label2tlabel, sample_label_prob=sample_label_prob)
history = built_model.fit_generator(train_data_generator, steps_per_epoch=steps_per_epoch, epochs=p['epochs'],
verbose=0, callbacks=[dump_weight], max_q_size=15, workers=1, pickle_safe=False)
epoch_train_loss = history.history['loss']
# plot the training loss for debugging
plot_curve_loss(epoch_train_loss, detail_outdir, 'train_', expid, ['loss'])
historyfile = detail_outdir + 'hist_' + expid + '.history'
with open(detail_outdir + 'hist_' + expid + '.p', 'wb') as handle:
pickle.dump(history.history, handle, protocol=pickle.HIGHEST_PROTOCOL)