def pred_evaluation(prepare_data, data, iterator):
"""
Compute recall@20 and mrr@20
prepare_data: usual prepare_data for that dataset.
"""
recall = 0.0
mrr = 0.0
evalutation_point_count = 0
# pred_res = []
# att = []
for _, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
np.array(data[1])[valid_index])
preds = sess.run(output_probs,feed_dict={input_items: x,target_items : y,input_item_mask :mask,keep_prob_1:1.0,keep_prob_2:1.0})
# weights = f_weight(x, mask)
targets = y
ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1
rank_ok = (ranks <= 20)
# pred_res += list(rank_ok)
recall += rank_ok.sum()
mrr += (1.0 / ranks[rank_ok]).sum()
evalutation_point_count += len(ranks)
# att.append(weights)
recall = numpy_floatX(recall) / evalutation_point_count
mrr = numpy_floatX(mrr) / evalutation_point_count
eval_score = (recall, mrr)
return eval_score
def predict(_args, lex_test, idxs_test, f_classify, groundtruth_test, batchsize=1, graph=False, dep=None, weighted=False, print_prediction=False, prediction_file=None):
''' On the test set predict the labels using f_classify.
Compare those labels against groundtruth.
It returns a dictionary 'results' that contains
f1 : F1 or Accuracy
p : Precision
r : Recall
'''
predictions_test = []
if print_prediction:
assert prediction_file is not None
pred_file = open(prediction_file, 'w')
if batchsize > 1:
nb_idxs = get_minibatches_idx(len(lex_test), batchsize, shuffle=False)
for i, tr_idxs in enumerate(nb_idxs):
words = [lex_test[ii] for ii in tr_idxs]
eidxs = [idxs_test[ii] for ii in tr_idxs]
#labels = [groundtruth_test[ii] for ii in tr_idxs]
orig_eidxs = eidxs
if graph:
assert dep is not None
masks = [dep[ii] for ii in tr_idxs]
else:
masks = None
x, masks, eidxs = prepare_data(words, eidxs, masks, maxlen=200)
if weighted or not graph:
pred_all = f_classify(x, masks, *eidxs)
predictions_test.extend(list(numpy.argmax(pred_all, axis=1))) #[0]))
else:
pred_all = f_classify(x, masks.sum(axis=-1), *eidxs)
predictions_test.extend(list(numpy.argmax(pred_all, axis=1)))
if print_prediction:
for idx, p in zip(tr_idxs, pred_all):
pred_file.write(str(idx) + '\t' + str(p[1]) + '\n')
else:
for i, (word, idxs) in enumerate(zip(lex_test, idxs_test)):
idxs = conv_idxs(idxs, len(word))
if graph:
assert dep is not None
if weighted:
predictions_test.append(f_classify(word, dep[i], *idxs)) #.sum(axis=-1)
else:
predictions_test.append(f_classify(word, dep[i].sum(axis=-1), *idxs)) #.sum(axis=-1)
else:
predictions_test.append(f_classify(word, *idxs))
print 'in predict,', len(predictions_test), len(groundtruth_test)
if print_prediction:
pred_file.close()
#results = eval_logitReg_F1(predictions_test, groundtruth_test)
results = eval_logitReg_accuracy(predictions_test, groundtruth_test)
return results, predictions_test
def construct_feeddict(self,
batch_data,
batch_label,
keepprob,
state,
starting=False):
x, mask, y, lengths = data_process.prepare_data(
batch_data, batch_label)
feed = {
self.x_input: x,
self.mask_x: mask,
self.y_target: y,
self.len_x: lengths,
self.keep_prob: keepprob,
self.state: state,
self.starting: starting
}
# feed the initialized state into placeholder
return feed
def pred_evaluation(f_pred_prob,
prepare_data,
data,
iterator,
item_freqs,
k=20):
"""
Compute recall@20 and mrr@20
f_pred_prob: Theano fct computing the prediction
prepare_data: usual prepare_data for that dataset.
"""
recall = 0.0
mrr = 0.0
evalutation_point_count = 0
preds_freqs = []
preds_items = []
#print('HEEELLLOOO', len(item_freqs))
for _, valid_index in iterator:
x, mask, y = prepare_data([data[0][t] for t in valid_index],
np.array(data[1])[valid_index])
preds = f_pred_prob(x, mask)
#print(preds.shape)
targets = y
ranks = (preds.T > np.diag(preds.T[targets])).sum(axis=0) + 1
rank_ok = (ranks <= k)
recall += rank_ok.sum()
mrr += (1.0 / ranks[rank_ok]).sum()
evalutation_point_count += len(ranks)
for i in range(preds.shape[0]):
series = pd.Series(data=preds[i])
s = series.nlargest(k).index.values
for r in s:
preds_items.append(r)
preds_freqs.append(item_freqs[r])
recall = numpy_floatX(recall) / evalutation_point_count
mrr = numpy_floatX(mrr) / evalutation_point_count
eval_score = (recall, mrr,
len(list(set(preds_items))) / len(item_freqs.keys()),
(np.mean(preds_freqs) / max(item_freqs.values())))
return eval_score
# Get new shuffled index for the training set.
kf = get_minibatches_idx(len(train[0]), batch_size, shuffle=True)
kf_valid = get_minibatches_idx(len(valid[0]), valid_batch_size)
kf_test = get_minibatches_idx(len(test[0]), valid_batch_size)
for _, train_index in kf:
uidx += 1
# Select the random examples for this minibatch
y = [train[1][t] for t in train_index]
x = [train[0][t]for t in train_index]
# Get the data in numpy.ndarray format
# This swap the axis!
# Return something of shape (minibatch maxlen, n samples)
x, mask, y = prepare_data(x, y)
n_samples += x.shape[0]
_,loss = sess.run([optimizer,ce_loss],feed_dict={input_items: x,target_items : y,input_item_mask :mask,keep_prob_1:0.75,keep_prob_2:0.5})
epoch_loss.append(loss)
if np.isnan(loss) or np.isinf(loss):
print('bad loss detected: ', loss)
if np.mod(uidx, dispFreq) == 0:
print('Epoch ', eidx, 'Update ', uidx, 'Loss ', np.mean(epoch_loss))
if is_valid:
valid_evaluation = pred_evaluation(prepare_data, valid, kf_valid)
test_evaluation = pred_evaluation(prepare_data, test, kf_test)
history_errs.append([valid_evaluation, test_evaluation])
def train_single(train_lex,
train_idxs,
train_y,
_args,
f_cost,
f_update,
epoch_id,
learning_rate,
nsentences,
batchsize=1,
dep=None,
weighted=False):
''' This function is called from the main method. and it is primarily responsible for updating the
parameters. Because of the way that create_relation_circuit works that creates f_cost, f_update etc. this function
needs to be flexible and can't be put in a lib.
Look at lstm_dependency_parsing_simplification.py for more pointers.
'''
# None-batched version
def train_instance(words, idxs, sample_weights, label, learning_rate,
f_cost, f_update):
' Since function is called only for side effects, it is likely useless anywhere else'
if words.shape[0] < 2:
return 0.0
# need to change here, add sample weights
inputs = idxs + [words, sample_weights, label]
iter_cost = f_cost(*inputs) #words, id1, id2, labels)
f_update(learning_rate)
return iter_cost
# Mini-batch version
def train_batch(words, masks, idxs, sample_weights, label, learning_rate,
f_cost, f_update):
if words.shape[0] < 2:
return 0.0
# need to change here, add sample weights
inputs = idxs + [words, masks, sample_weights, label]
iter_cost = f_cost(*inputs) #words, id1, id2, labels)
f_update(learning_rate)
return iter_cost
## main body of train
# generate the weights according to the train label distribution
total_pos = 0
total_neg = 0
for y in train_y:
if y[0] == 0 and y[1] == 1:
total_pos += 1
else:
total_neg += 1
print("total pos: %d neg:%d \n" % (total_pos, total_neg))
sample_weights = [0] * (total_neg + total_pos)
for idx, y in enumerate(train_y):
if y[0] == 0 and y[1] == 1:
sample_weights[idx] = 0.5 * (total_neg + total_pos) / (total_pos)
else:
sample_weights[idx] = 0.5 * (total_neg + total_pos) / (total_neg)
if dep:
shuffle([train_lex, train_idxs, train_y, sample_weights, dep],
_args.seed)
else:
shuffle([train_lex, train_idxs, train_y, sample_weights], _args.seed)
if nsentences < len(train_lex):
train_lex = train_lex[:nsentences]
train_idxs = train_idxs[:nsentences]
train_y = train_y[:nsentences]
sample_weights = sample_weights[:nsentences]
tic = time.time()
aggregate_cost = 0.0
temp_cost_arr = [0.0] * 2
# make the judge on whether use mini-batch or not.
# No mini-batch
if batchsize == 1:
for i, (words, idxs, label, weight) in enumerate(
zip(train_lex, train_idxs, train_y, sample_weights)):
if len(words) < 2:
continue
#assert len(words) == len(labels) #+ 2
idxs = conv_idxs(idxs, len(words))
if _args.graph:
assert dep is not None
if weighted:
aggregate_cost += train_batch(words, dep[i], idxs, weight,
label, learning_rate, f_cost,
f_update)
else:
aggregate_cost += train_batch(words, dep[i].sum(axis=-1),
idxs, weight, label,
learning_rate, f_cost,
f_update)
else:
aggregate_cost += train_instance(words, idxs, weight, label,
learning_rate, f_cost,
f_update)
if _args.verbose == 2 and i % 10 == 0:
print '[learning] epoch %i >> %2.2f%%' % (epoch_id, (i + 1) *
100. / nsentences),
print 'completed in %.2f (sec). << avg loss: %.2f <<\r' % (
time.time() - tic, aggregate_cost / (i + 1)),
sys.stdout.flush()
# Mini-batch
else:
nb_idxs = get_minibatches_idx(len(train_lex), batchsize, shuffle=False)
nbatches = len(nb_idxs)
for i, tr_idxs in enumerate(nb_idxs):
words = [train_lex[ii] for ii in tr_idxs]
eidxs = [train_idxs[ii] for ii in tr_idxs]
labels = [train_y[ii] for ii in tr_idxs]
weights = [sample_weights[ii] for ii in tr_idxs]
orig_eidxs = eidxs
if _args.graph:
assert dep is not None
masks = [dep[ii] for ii in tr_idxs]
else:
masks = None
x, masks, eidxs, weight = prepare_data(words,
eidxs,
masks,
weights,
maxlen=200)
#print 'mask shape:', masks.shape
if weighted or dep is None:
iter_cost = train_batch(x, masks, eidxs, weight, labels,
learning_rate, f_cost, f_update)
aggregate_cost += iter_cost #[0]
else:
aggregate_cost += train_batch(x, masks.sum(axis=-1), eidxs,
weight, labels, learning_rate,
f_cost, f_update)
if _args.verbose == 2:
print '[learning] epoch %i >> %2.2f%%' % (epoch_id, (i + 1) *
100. / nbatches),
print 'completed in %.2f (sec). << avg loss: %.2f <<\r' % (
time.time() - tic, aggregate_cost / (i + 1)),
#print 'completed in %.2f (sec). << avg loss: %.2f <<%%' % (time.time() - tic, aggregate_cost/(i+1)),
#print 'average cost for each part: (%.2f, %.2f) <<\r' %(temp_cost_arr[0]/(i+1), temp_cost_arr[1]/(i+1)),
sys.stdout.flush()
if _args.verbose == 2:
print '\n>> Epoch completed in %.2f (sec) <<' % (
time.time() - tic), 'training cost: %.2f' % (aggregate_cost)
def train_gru(args):
#patience=100, saveto='gru_model.npz',
#is_valid=True, is_save=False, reload_model=None, test_size=-1
exps = pd.read_csv('exp.csv')
for i, row in exps.iterrows():
gc.collect()
args['expname'] = row['name']
args['sessionid'] = row['SessionID']
args['itemid'] = row['ItemID']
args['data_folder'] = row['path']
args['valid_data'] = row['test']
args['train_data'] = row['train']
args['freq'] = row['freq']
print('Train:', args['train_data'], ' -- Test:', args['valid_data'],
' -- Freq:', args['freq'])
with open("LOGGER_" + args['expname'] + ".txt", "a") as myfile:
myfile.write(row['train'] + ", " + row['test'] + "\n")
# split patterns to train_patterns and test_patterns
print('Start Data Preprocessing: Training Set')
train, itemsIDs, freqs, old_new = load_sequence(
args['data_folder'] + '/' + args['train_data'],
args['itemid'],
args['sessionid'],
itemsIDs=[])
args['n_items'] = len(itemsIDs) + 1
freqs[0] = 0
print('Start Data Preprocessing: Testing Set')
valid, _, _, _ = load_sequence(args['data_folder'] + '/' +
args['valid_data'],
args['itemid'],
args['sessionid'],
Train=False,
itemsIDs=itemsIDs,
freq=args['freq'],
old_new=old_new)
print("%d train examples." % len(train[0]))
print("%d valid examples." % len(valid[0]))
# Model options
params = init_params(args)
tparams = init_tparams(params)
(use_noise, x, mask, y, f_pred_prob, cost) = build_model(tparams, args)
all_params = list(tparams.values())
updates = adam(cost, all_params, args['lr'])
train_function = theano.function(inputs=[x, mask, y],
outputs=cost,
updates=updates)
uidx = 0 # the number of update done
cPid = os.getpid()
command_memory = "python memoryLogger.py " + str(
cPid) + " " + args['expname'] + "train"
#memory_task = subprocess.Popen(command_memory, stdout=subprocess.PIPE, shell=True)
try:
t1 = time.time()
for eidx in range(args['epoch']):
n_samples = 0
epoch_loss = []
# Get new shuffled index for the training set.
kf = get_minibatches_idx(len(train[0]),
int(args['batch_size']),
shuffle=True)
for _, train_index in kf:
uidx += 1
use_noise.set_value(1.)
# Select the random examples for this minibatch
y = [train[1][t] for t in train_index]
x = [train[0][t] for t in train_index]
x, mask, y = prepare_data(x, y)
n_samples += x.shape[1]
loss = train_function(x, mask, y)
epoch_loss.append(loss)
if np.isnan(loss) or np.isinf(loss):
print('bad loss detected: ', loss)
return 1., 1., 1.
print('Epoch ', eidx, 'Loss ', np.mean(epoch_loss))
# Report intermediate result to the tuner
nni.report_intermediate_result(np.mean(epoch_loss))
logger.debug('test loss %g', np.mean(epoch_loss))
logger.debug('Pipe send intermediate result done')
use_noise.set_value(0.)
if eidx % 3 == 0:
kf_valid = get_minibatches_idx(len(valid[0]),
int(args['batch_size']))
valid_eval = pred_evaluation(f_pred_prob, prepare_data,
valid, kf_valid, freqs)
print('Valid Recall@20:', valid_eval[0], '\nValid Mrr@20:',
valid_eval[1])
# Report intermediate result to the tuner
nni.report_final_result(np.mean(epoch_loss))
logger.debug('Final loss is %g', np.mean(epoch_loss))
logger.debug('Send final result done')
except KeyboardInterrupt:
print("Training interupted")
#memory_task.kill()
train_time = time.time() - t1
use_noise.set_value(0.)
t1 = time.time()
Ks = [1, 3, 5, 10, 30]
hit = [0, 0, 0, 0, 0]
MRR = [0, 0, 0, 0, 0]
cov = [0, 0, 0, 0, 0]
pop = [0, 0, 0, 0, 0]
command_memory = "python memoryLogger.py " + str(
cPid) + " " + args['expname'] + "test"
#memory_task = subprocess.Popen(command_memory, stdout=subprocess.PIPE, shell=True)
for k in range(len(Ks)):
kf_valid = get_minibatches_idx(len(valid[0]),
int(args['batch_size']))
results = pred_evaluation(f_pred_prob, prepare_data, valid,
kf_valid, freqs, Ks[k])
hit[k] = results[0]
MRR[k] = results[1]
cov[k] = results[2]
pop[k] = results[3]
test_time = time.time() - t1
#memory_task.kill()
print('==================================================')
print('Recall:', hit, '\nMRR:', MRR, '\nCoverage:', cov,
'\nPopularity:', pop)
print('\ntrain_time:', train_time, '\nTest time:', test_time / len(Ks))
print('End Model Predictions')
# Print experiment to the logger
print('===================================================')
print("LOGGER_" + args['expname'])
print(
str(hit[0]) + ',' + str(hit[1]) + ',' + str(hit[2]) + ',' +
str(hit[3]) + ',' + str(hit[4]) + ',' + str(MRR[0]) + ',' +
str(MRR[1]) + ',' + str(MRR[2]) + ',' + str(MRR[3]) + ',' +
str(MRR[4]))
print("\nCOV:" + str(cov[0]) + ',' + str(cov[1]) + ',' + str(cov[2]) +
',' + str(cov[3]) + ',' + str(cov[4]))
print("\nPOP:" + str(pop[0]) + ',' + str(pop[1]) + ',' + str(pop[2]) +
',' + str(pop[3]) + ',' + str(pop[4]))
print("\nTrainTime:" + str(train_time))
print("\nTestTime:" + str(test_time))
with open("LOGGER_" + args['expname'] + ".txt", "a") as myfile:
myfile.write(
str(hit[0]) + ',' + str(hit[1]) + ',' + str(hit[2]) + ',' +
str(hit[3]) + ',' + str(hit[4]) + ',' + str(MRR[0]) + ',' +
str(MRR[1]) + ',' + str(MRR[2]) + ',' + str(MRR[3]) + ',' +
str(MRR[4]))
myfile.write("\nCOV:" + str(cov[0]) + ',' + str(cov[1]) + ',' +
str(cov[2]) + ',' + str(cov[3]) + ',' + str(cov[4]))
myfile.write("\nPOP:" + str(pop[0]) + ',' + str(pop[1]) + ',' +
str(pop[2]) + ',' + str(pop[3]) + ',' + str(pop[4]))
myfile.write("\nTrainTime:" + str(train_time))
myfile.write("\nTestTime:" + str(test_time))
myfile.write("\n############################################\n")
