def _calc_performance_stats(cost, batches):
Z = []
Y = []
L = []
rowidx = []
for batch in batches:
args = { key : val for key,val in batch.data().items() if hasattr(cost,key) }
cost.eval(clear=False,**args)
Zi = cost.loss.Z.fpval.asnumpy()
Yi = sm.asnumpy(batch.Y)
Li = cost.loss.loss.fpval.asnumpy()
rowidx_i = sm.asnumpy(batch.rowidx) if hasattr(batch,"rowidx") else None
# Deal with possibility that only a subset of predictions contributed to the loss, due to some test performed after prediction (e.g. max of sequence and reverse_complement of sequence)
if cost.Zmask.fpval is not None:
Zmask = cost.Zmask.fpval.asnumpy()
Zi = np.hstack([Zi[Zmask[:,col],col].reshape((-1,1)) for col in range(Zi.shape[1])])
Yi = np.hstack([Yi[Zmask[:,col],col].reshape((-1,1)) for col in range(Yi.shape[1])])
if rowidx_i is not None:
rowidx_i = rowidx_i[Zmask[:,0],0].reshape((-1,1))
Z.append(Zi)
Y.append(Yi)
L.append(Li)
if rowidx_i is not None:
rowidx.append(rowidx_i)
cost.clear()
Z = np.vstack(Z)
Y = np.vstack(Y)
L = np.vstack(L)
L = np.mean(L,axis=0)
rowidx = np.vstack(rowidx) if rowidx else None
return { "L" : L, "Z" : Z, "Y" : Y, "I" : rowidx }
def count_errors(model,datasrc):
# If model.ninst > datasrc.ntask, then that means multiple models
# were trained on the original data, so we need to replicate the task.
# For each task, we generate model.ninst/data.ntask copies
if isinstance(datasrc,datasource):
datasrc = datasrc.asbatches(128)
Z = make_predictions(model,datasrc)["Z"]
Y = np.vstack([sm.asnumpy(batch.Y) for batch in datasrc])
error_counts = []
for i in range(model.ninst):
s = slice(i*Z.shape[1]//model.ninst,(i+1)*Z.shape[1]//model.ninst)
count = np.sum(np.argmax(Z[:,s],axis=1) != np.argmax(Y[:,s],axis=1))
error_counts.append(count)
error_counts = np.asarray(error_counts)
return error_counts
def count_errors(model, datasrc):
# If model.ninst > datasrc.ntask, then that means multiple models
# were trained on the original data, so we need to replicate the task.
# For each task, we generate model.ninst/data.ntask copies
if isinstance(datasrc, datasource):
datasrc = datasrc.asbatches(128)
Z = make_predictions(model, datasrc)["Z"]
Y = np.vstack([sm.asnumpy(batch.Y) for batch in datasrc])
error_counts = []
for i in range(model.ninst):
s = slice(i * Z.shape[1] // model.ninst,
(i + 1) * Z.shape[1] // model.ninst)
count = np.sum(
np.argmax(Z[:, s], axis=1) != np.argmax(Y[:, s], axis=1))
error_counts.append(count)
error_counts = np.asarray(error_counts)
return error_counts
