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Stop reputation MF (diligence) training earlier #185

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Dec 19, 2023
Merged

Stop reputation MF (diligence) training earlier #185

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17 changes: 8 additions & 9 deletions sourcecode/scoring/reputation_matrix_factorization/reputation_matrix_factorization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,8 @@ def _train_one_round(
learningRate=0.2,
logRate=15,
device=torch.device("cpu"),
convergence=10**-7,
convergence=10**-5,
stablePeriod=5,
):
# Identify tensors for training and testing
notes, raters, _ = dataset
Expand All @@ -98,11 +99,12 @@ def _train_one_round(
loss += model.get_regularization_loss()
if logRate and epoch % logRate == 0:
print(f"epoch={epoch:03d} | loss={loss.item():7.4f} | time={time.time() - start:.1f}s")
if priorLoss is not None and (priorLoss - loss).abs() < convergence:
if logRate:
print(f"epoch={epoch:03d} | loss={loss.item():7.4f} | time={time.time() - start:.1f}s")
break
priorLoss = loss
if convergence > 0 and epoch % stablePeriod == 0:
if priorLoss is not None and (priorLoss - loss).abs() < convergence:
if logRate:
print(f"epoch={epoch:03d} | loss={loss.item():7.4f} | time={time.time() - start:.1f}s")
break
priorLoss = loss
# Perform backward pass
loss.backward()
# Update parameters
Expand Down Expand Up @@ -154,7 +156,6 @@ def train_model(
learningRate=learningRate,
logRate=logRate,
device=device,
convergence=-1,
)

# train round 1
Expand All @@ -169,7 +170,6 @@ def train_model(
learningRate=learningRate,
logRate=logRate,
device=device,
convergence=-1,
)

# train round 2
Expand Down Expand Up @@ -204,6 +204,5 @@ def train_model(
learningRate=learningRate,
logRate=logRate,
device=device,
convergence=-1,
)
return loss0, loss1, loss2
5 changes: 3 additions & 2 deletions sourcecode/scoring/tag_consensus.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ def train_tag_model(
helpfulModelNoteParams: pd.DataFrame = None,
helpfulModelRaterParams: pd.DataFrame = None,
useSigmoidCrossEntropy: bool = True,
name: str = "harassment",
):
print(f"-------------------Training for tag {tag}-------------------")
ratingDataForTag, labelColName = prepare_tag_data(ratings, tag)
Expand Down Expand Up @@ -61,8 +62,8 @@ def train_tag_model(
noteInit=helpfulModelNoteParams,
)

noteParams.columns = [col.replace("internal", "harassment") for col in noteParams.columns]
raterParams.columns = [col.replace("internal", "harassment") for col in raterParams.columns]
noteParams.columns = [col.replace("internal", name) for col in noteParams.columns]
raterParams.columns = [col.replace("internal", name) for col in raterParams.columns]
return noteParams, raterParams, globalBias


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