def calc_loss(scores, qd_labels, sigma):
# TODO: Fix this spaghetti shit
ranking_boi = np.float64(scores.squeeze().cpu().detach().numpy())
# Get the ranking
if not isinstance(ranking_boi, np.ndarray):
ranking_boi = np.array([ranking_boi])
ranking, inv_ranking = rnk.rank_and_invert(ranking_boi)
ranking = torch.tensor(ranking.copy()).cuda()
# Get rid of pesky 1-document queries and initialize loss
if len(scores) < 2:
return 0, 0
loss = 0
# Vectorize the loss calculation
# Calculate all score differences
scorediff = scores - scores.T
# Calculate all signs
squeeze_labels = qd_labels.unsqueeze(-1)
signs = torch.sign(squeeze_labels - qd_labels).float()
# Loss is just vectorized formula
# loss = (1/2) * (1-signs) * torch.sigmoid(scorediff) + torch.log(1 + torch.exp(-1 * sigma * scorediff))
loss = (1 / 2) * (1 - signs) * sigma * scorediff + torch.log(
1 + torch.exp(-1 * sigma * scorediff))
loss = loss.sum()
return loss, ranking
def validate_ndcg(validation_set):
with torch.no_grad():
total_ndcg = 0
for i, qid in enumerate(np.arange(validation_set.num_queries())):
qd_feats = validation_set.query_feat(qid)
qd_labels = validation_set.query_labels(qid)
scores = model.forward(torch.tensor(qd_feats).float().cuda())
# Make em torchy?
qd_feats = torch.tensor(qd_feats).cuda()
qd_labels = torch.tensor(qd_labels).cuda()
# TODO: Fix this spaghetti shit
ranking_boi = np.float64(scores.squeeze().cpu().detach().numpy())
# Get the ranking
if not isinstance(ranking_boi, np.ndarray):
ranking_boi = np.array([ranking_boi])
ranking, inv_ranking = rnk.rank_and_invert(ranking_boi)
ranking = torch.tensor(ranking.copy()).cuda()
# Get rid of pesky 1-document queries and initialize loss
if len(scores) < 2:
continue
total_ndcg += evaluate_model(model,
validation_set,
regression=True)
return total_ndcg / validation_set.num_queries()
def get_IRM(pred_scores, true_scores):
"""
Get an array with all delta_IRM(i,j) for all doc combinations
corresponding to a query.
"""
final_irm = []
# When all relevances are 0, we set all delta irm to 1
if sum(true_scores) != 0:
true_ranking, _ = rnk.rank_and_invert(true_scores)
before_irm = get_score(pred_scores, true_ranking)
delta_irm = []
prev_id = 0
# Loop through all permutations of the docs
for pair in itertools.permutations(enumerate(pred_scores), r=2):
swap_scores = list(pred_scores)
curr_id = pair[0][0]
compare_id = pair[1][0]
if curr_id == prev_id:
delta_ij = calculate_delta(swap_scores, curr_id, compare_id, true_ranking, before_irm)
delta_irm.append(delta_ij)
else:
final_irm.append(delta_irm)
delta_ij = calculate_delta(swap_scores, curr_id, compare_id, true_ranking, before_irm)
delta_irm = [delta_ij]
prev_id = curr_id
final_irm.append(delta_irm)
else:
final_irm = np.ones(((len(true_scores)), len(true_scores)-1))
return final_irm
def evaluate_model(model, validation_set, k = 5, regression = False):
"""
model: Neural network model that we are using
validation_set: Pointer to the validation set
k: Cutoff to be used to calculate NDCG
regression: Boolean variable to denote whether the model used
regression or classification. This changes the way
we feed the scores to the NDCG function
"""
total_ndcg = 0
# Main loop over all queries in validation set
for qid in np.arange(validation_set.num_queries()):
# Get features, prediction scores and labels
qd_feats = validation_set.query_feat(qid)
qd_labels = validation_set.query_labels(qid)
scores = model.forward(torch.tensor(qd_feats).cuda().float())
labels = torch.tensor(qd_labels).cuda()
# Change scores to correct format for regression or classification
if regression:
prediction = scores
prediction = prediction.squeeze(1).detach().cpu()
else:
softmax = F.softmax(scores)
prediction = torch.argmax(softmax, dim=1)
prediction = prediction.detach().cpu()
# Get rankings based on scores
pred_rank, _ = rnk.rank_and_invert(prediction)
label_rank, _ = rnk.rank_and_invert(labels.detach().cpu())
# Convert ranking to sorting usable for calculating NDCG
sorted_pred = np.array([qd_labels[idx] for idx in pred_rank])
label_rank = np.array(sorted(qd_labels, reverse=True))
# Get rid of pesky 1-doc queries and naively prevent division by zero
if len(sorted_pred) > 1 and np.count_nonzero(qd_labels) != 0:
# k to len(sorted_pred) does NDCG over entire ranking
k = len(sorted_pred)
total_ndcg += ndcg_at_k(sorted_pred, label_rank, k)
return total_ndcg/validation_set.num_queries()
def get_score(pred_scores, true_ranking):
"""
Get the NDCG or ERR score of predicted scores vs true scores.
"""
pred_ranking, _ = rnk.rank_and_invert(pred_scores)
if config.eval_metric == 'ndcg':
score = evl.ndcg_at_k(pred_ranking, true_ranking, 0)
elif config.eval_metric == 'err':
score = evl.ERR(pred_ranking, true_ranking)
else:
raise Exception('Evaluation metric should be either ndcg or err.')
return score
import dataset
import ranking as rnk
import numpy as np
# we will rank 5 items with the scores:
scores = np.array([10., 8., 12., 9., 5.])
ranking, inverted_ranking = rnk.rank_and_invert(scores)
print('Ranking the scores: %s' % scores)
print('Resulting ranking: %s' % ranking)
print('This orders the scores as: %s' % scores[ranking])
print('an results in the inverted ranking: %s' % inverted_ranking)
print('The inverted ranking allows us to quickly see that:')
for i in range(scores.shape[0]):
print('Item %d with score %0.02f has rank: %d' %
(i, scores[i], inverted_ranking[i]))
print('It is also very useful for computing rank differences,')
print('for instance:')
for i, j in [(1, 2), (2, 4), (0, 3)]:
print('the difference between item %d (at rank %d)'
' and item %d (at rank %d) is %d' %
(i, inverted_ranking[i], j, inverted_ranking[j],
inverted_ranking[i] - inverted_ranking[j]))
print('Number of documents in test set: %d' % data.test.num_docs())
# initialize a random model
random_model = np.random.uniform(size=data.num_features)
# one score for every document (1d vector in ordering of the dataset)
all_scores = np.dot(data.train.feature_matrix, random_model)
# rank every query for all scores (1d vector ordered by query ordering in dataset)
all_rankings, all_inverted_rankings = rnk.data_split_rank_and_invert(all_scores, data.train)
qid = 1
s_i, e_i = data.train.query_range(qid)
# to rank only a single query use rank_and_invert
query_ranking, query_inverted_ranking = rnk.rank_and_invert(all_scores[s_i:e_i])
assert np.all(np.equal(query_ranking, all_rankings[s_i:e_i]))
assert np.all(np.equal(query_inverted_ranking, all_inverted_rankings[s_i:e_i]))
print('-------')
print('Looking at query with id: %d' % qid)
print('Number of documents in query %d: %d' % (qid, data.train.query_size(qid)))
print('Scores for query %d: %s' % (qid, all_scores[s_i:e_i]))
print('Ranking for query %d: %s' % (qid, all_rankings[s_i:e_i]))
print('Inverted ranking for query %d: %s' % (qid, all_inverted_rankings[s_i:e_i]))
validation_scores = np.dot(data.validation.feature_matrix, random_model)
print('------')
print('Evaluation on entire validation partition.')
results = evl.evaluate(data.validation, validation_scores, print_results=True)
def run_epoch(model, optimizer, data, eval_every=10000, sigma=1, IRM='ndcg'):
# Parameters
temp_loss = 0
overall_loss = 0
epoch_loss = 0
# Main Pairwise RankNet function
for i, qid in enumerate(np.arange(data.train.num_queries())):
# Zero the gradient buffer and get doc,query combinations, labels and scores
optimizer.zero_grad()
qd_feats = data.train.query_feat(qid)
qd_labels = data.train.query_labels(qid)
scores = model.forward(torch.tensor(qd_feats).float().cuda())
# Make em torchy?
qd_feats = torch.tensor(qd_feats).cuda()
qd_labels = torch.tensor(qd_labels).cuda()
# TODO: Fix this spaghetti shit
scores_d = scores.detach()
ranking_boi = np.float64(scores.squeeze().cpu().detach().numpy())
# Get the ranking
if not isinstance(ranking_boi, np.ndarray):
ranking_boi = np.array([ranking_boi])
ranking, inv_ranking = rnk.rank_and_invert(ranking_boi)
ranking = torch.tensor(ranking.copy()).cuda()
# Get rid of pesky 1-document queries and initialize loss
if len(scores) < 2:
continue
loss = 0
# Vectorize the loss calculation
# Calculate all score differences
scorediff = scores_d - scores_d.T
# Calculate all signs
squeeze_labels = qd_labels.unsqueeze(-1)
signs = torch.sign(squeeze_labels - qd_labels).float()
# Loss is just vectorized formula
lambdas_ij = sigma * ((1 / 2) * (1 - scorediff) -
(1 / (1 + torch.exp(sigma * scorediff))))
# Get labels and get ranking
np_labels = qd_labels.cpu().numpy()
np_ranking = ranking.cpu().numpy()
# Initialize permutations list and add initial permutation
pred_perms = []
sorted_pred = np.array([np_labels[idx] for idx in np_ranking])
pred_perms.append(sorted_pred)
# Add all vectors of possible permutations to the list
for p in range(len(sorted_pred)):
for q in range(p + 1, len(sorted_pred)):
perm_pred = sorted_pred.copy()
temp = sorted_pred[p]
perm_pred[p] = sorted_pred[q]
perm_pred[q] = temp
pred_perms.append(perm_pred)
pred_perms = np.array(pred_perms)
# Check correct IRM and calculate the scores for each permutation
if (IRM == 'err'):
ranking_measure = err(pred_perms)
elif (IRM == 'ndcg'):
label_rank = np.sort(np_labels)[::-1]
ranking_measure = ndcg(pred_perms, label_rank)
# Calculate all deltas for the IRM
deltas = np.abs(ranking_measure[0] - ranking_measure[1:])
delta_irm = np.zeros((len(ranking), len(ranking)))
delta_irm[np.triu_indices(len(ranking), 1)] = deltas
delta_irm = torch.from_numpy(delta_irm - delta_irm.T).float().cuda()
# Get the lambdas and multiply with the delta irm values
lambdas_ij = lambdas_ij * delta_irm
lambas_i = lambdas_ij.sum(dim=1)
loss = scores.squeeze() * lambas_i
loss = loss.sum()
# Keep track of rolling average
rolling_avg = loss / (len(ranking)**2)
overall_loss += rolling_avg
temp_loss += rolling_avg.item()
if (i + 1) % eval_every == 0:
avg_ndcg = evaluate_model(model, data.validation, regression=True)
print("NCDG: ", avg_ndcg, 'Loss: ', temp_loss / -eval_every)
temp_loss = 0
# Update gradients
loss.backward()
optimizer.step()
#break
#print(ranking)
print(ranking)
print("NDCG: ", evaluate_model(model, data.validation, regression=True))
print("epoch_loss: ", overall_loss / data.train.num_queries())
