def ranking(types, model, distfn):
lt = th.from_numpy(model.embedding())
embedding = Variable(lt, requires_grad=True)
with torch.no_grad():
ranks = []
ap_scores = []
for s, s_types in types.items():
s_e = Variable(lt[s].expand_as(embedding), requires_grad=True)
with torch.no_grad():
_dists = model.dist()(s_e, embedding).data.cpu().numpy().flatten()
_dists[s] = 1e+12
_labels = np.zeros(embedding.size(0))
_dists_masked = _dists.copy()
_ranks = []
for o in s_types:
_dists_masked[o] = np.Inf
_labels[o] = 1
ap_scores.append(average_precision_score(_labels, -_dists))
for o in s_types:
d = _dists_masked.copy()
d[o] = _dists[o]
r = np.argsort(d)
_ranks.append(np.where(r == o)[0][0] + 1)
ranks += _ranks
return np.mean(ranks), np.mean(ap_scores)
return tf.contrib.learn.InputFnOps(features, None,
feature_placeholders)
predictor = from_contrib_estimator(estimator=estimator,
prediction_input_fn=prediction_input_fn,
output_alternative_key="g_dvec")
sess = tf.Session()
print(predictor.fetch_tensors)
wvec = [[[1, 2, 3], [4, 5, 6]]]
dvec = [[[2, 3, 4], [5, 6, 7]]]
i = 0
g_wvec = [
[
model.dist(wvec[i][0], wvec[i][1]),
model.dist(dvec[i][0], dvec[i][1])
],
]
g_dvec = [
[
model.dist_r(wvec[i][0], wvec[i][1], dvec[i][0], dvec[i][1]),
model.dist_r(wvec[i][0], dvec[i][1], wvec[i][0], dvec[i][1])
],
]
features = {
'wvec': wvec,
'dvec': dvec,
}
print(predictor(features))
