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[Code Quality] Replace wildcard imports with explicit imports #13

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[Code Quality] Replace wildcard imports with explicit imports #13

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5 changes: 4 additions & 1 deletion MAEs/Hybrid_Transformer_Thanh_Nguyen/src/engine/jetclass_trainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,10 @@
from .trainer import Trainer
from ..utils import cleanup_ddp
from ..utils.data import JetClassDistributedSampler
from ..utils.viz import *
from ..utils.viz import (
plot_roc_curve,
plot_confusion_matrix
)


class JetClassTrainer(Trainer):
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1 change: 0 additions & 1 deletion MAEs/Hybrid_Transformer_Thanh_Nguyen/src/engine/mm_trainer.py
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Expand Up @@ -11,7 +11,6 @@
from .trainer import Trainer
from ..utils import cleanup_ddp
from ..utils.data import JetClassDistributedSampler
from ..utils.viz import *


class MaskedModelTrainer(Trainer):
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6 changes: 5 additions & 1 deletion MAEs/Hybrid_Transformer_Thanh_Nguyen/src/engine/trainer.py
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Expand Up @@ -30,7 +30,11 @@
cleanup_ddp
)
from ..utils.data import JetClassDistributedSampler
from ..utils.viz import *
from ..utils.viz import (
plot_roc_curve,
plot_confusion_matrix,
plot_history
)


class Trainer:
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18 changes: 17 additions & 1 deletion MAEs/Hybrid_Transformer_Thanh_Nguyen/src/utils/viz/__init__.py
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@@ -1 +1,17 @@
from .viz import *
from .viz import (
plot_feature_distribution,
plot_particle_reconstruction,
plot_history,
plot_ssl_history,
plot_confusion_matrix,
plot_roc_curve
)

__all__ = [
'plot_feature_distribution',
'plot_particle_reconstruction',
'plot_history',
'plot_ssl_history',
'plot_confusion_matrix',
'plot_roc_curve'
]
10 changes: 10 additions & 0 deletions MAEs/Hybrid_Transformer_Thanh_Nguyen/src/utils/viz/viz.py
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Expand Up @@ -6,6 +6,16 @@
from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score


__all__ = [
'plot_feature_distribution',
'plot_particle_reconstruction',
'plot_history',
'plot_ssl_history',
'plot_confusion_matrix',
'plot_roc_curve'
]


# Function to visualize the distribution of particle features (pT, eta, phi, E)
def plot_feature_distribution(X_jets: np.ndarray) -> None:
feature_names = ['pT', 'eta', 'phi', 'energy']
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