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Added predictions for classification task. Fixed the check for normalizer presence. #93

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Merged
CompRhys merged 1 commit into from
Jan 4, 2025
Merged

Added predictions for classification task. Fixed the check for normalizer presence. #93

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34 changes: 23 additions & 11 deletions aviary/predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,10 @@
import numpy as np
import pandas as pd
import torch
from torch.nn.functional import softmax
from tqdm import tqdm

from aviary.core import Normalizer
from aviary.core import Normalizer, sampled_softmax
from aviary.utils import get_metrics, print_walltime

if TYPE_CHECKING:
Expand Down Expand Up @@ -103,19 +104,30 @@ def make_ensemble_predictions(
pred_col = f"{target_col}_pred_{idx}" if target_col else f"pred_{idx}"

if model.robust:
preds, aleat_log_std = preds.T
ale_col = (
f"{target_col}_aleatoric_std_{idx}"
if target_col
else f"aleatoric_std_{idx}"
)
df[pred_col] = preds
df[ale_col] = aleatoric_std = np.exp(aleat_log_std)
if task_type == "regression":
preds, aleat_log_std = preds.T
ale_col = (
f"{target_col}_aleatoric_std_{idx}"
if target_col
else f"aleatoric_std_{idx}"
)
df[pred_col] = preds
df[ale_col] = aleatoric_std = np.exp(aleat_log_std)
elif task_type == "classification":
# need to convert to tensor to use `sampled_softmax`
preds = torch.from_numpy(preds).to(device)
pre_logits, log_std = preds.chunk(2, dim=1)
logits = sampled_softmax(pre_logits, log_std)
df[pred_col] = logits.argmax(dim=1).cpu().numpy()
else:
df[pred_col] = preds
if task_type == "regression":
df[pred_col] = preds
else:
logits = softmax(preds, dim=1)
df[pred_col] = logits.argmax(dim=1).cpu().numpy()

# denormalize predictions if a normalizer was used during training
if "normalizer_dict" in checkpoint:
if checkpoint["normalizer_dict"][target_name] is not None:
assert task_type == "regression", "Normalization only takes place for regression."
normalizer = Normalizer.from_state_dict(
checkpoint["normalizer_dict"][target_name]
Expand Down
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