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Use roc auc instead of accuracy #268

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5167ddc
use roc auc instead of accuracy
ziw-liu Jul 9, 2025
039b05f
do not require z in annotation
ziw-liu Jul 16, 2025
b276c7e
add classical sampling results
ziw-liu Jul 16, 2025
f282132
update legend
ziw-liu Jul 16, 2025
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43 changes: 32 additions & 11 deletions applications/contrastive_phenotyping/evaluation/knowledge_distillation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,12 @@
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import accuracy_score, classification_report, f1_score
from sklearn.metrics import (
accuracy_score,
classification_report,
f1_score,
roc_auc_score,
)

# %%
# Mantis
Expand All @@ -22,15 +27,22 @@
prediction_from_scratch = pd.read_csv(
"/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/bootstrap-labels/test/from-scratch-last-1126.csv"
)
prediction_from_scratch["pretraining"] = "ImageNet"
prediction_from_scratch["pretraining"] = "ImageNet encoder"

prediction_classical = pd.read_csv(
"/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/bootstrap-labels/test/fine-tune-classical-last-1126.csv"
)
prediction_classical["pretraining"] = "DynaCLR (classical sampling)"

prediction_finetuned = pd.read_csv(
"/hpc/projects/intracellular_dashboard/viral-sensor/infection_classification/models/bootstrap-labels/test/fine-tune-last-1126.csv"
)
pretrained_name = "DynaCLR"
pretrained_name = "DynaCLR (cell- and time-aware sampling)"
prediction_finetuned["pretraining"] = pretrained_name

prediction = pd.concat([prediction_from_scratch, prediction_finetuned], axis=0)
prediction = pd.concat(
[prediction_from_scratch, prediction_classical, prediction_finetuned], axis=0
)

# %%
prediction = prediction[prediction["fov_name"].isin(VAL_FOVS)]
Expand Down Expand Up @@ -82,14 +94,22 @@
lambda x: float(f1_score(x["label"], x["prediction_binary"]))
)

roc_auc_by_t = prediction.groupby(id_vars).apply(
lambda x: float(roc_auc_score(x["label"], x["prediction"]))
)

metrics_df = pd.DataFrame(
data={"accuracy": accuracy_by_t.values, "F1": f1_by_t.values},
data={
"accuracy": accuracy_by_t.values,
"F1": f1_by_t.values,
"ROC AUC": roc_auc_by_t.values,
},
index=f1_by_t.index,
).reset_index()

metrics_long = metrics_df.melt(
id_vars=id_vars,
value_vars=["accuracy"],
value_vars=["accuracy", "F1", "ROC AUC"],
var_name="metric",
value_name="score",
)
Expand All @@ -104,18 +124,19 @@
kind="point",
linewidth=1.5,
linestyles="--",
col="metric",
)
g.set_axis_labels("HPI", "accuracy")
sns.move_legend(g, "upper left", bbox_to_anchor=(0.35, 1.1))
g.figure.set_size_inches(3.5, 1.5)
g.set(xlim=(-1, 7), ylim=(0.6, 1.0))
g.set_axis_labels("HPI", "ROC AUC")
# sns.move_legend(g, "upper left", bbox_to_anchor=(0.35, 1.1))
# g.figure.set_size_inches(3.5, 1.5)
# g.set(xlim=(-1, 7), ylim=(0.6, 1.0))
plt.show()


# %%
g.figure.savefig(
Path.home()
/ "gdrive/publications/dynaCLR/2025_dynaCLR_paper/fig_manuscript_svg/figure_knowledge_distillation/figure_parts/accuracy_students.pdf",
/ "gdrive/publications/dynaCLR/2025_dynaCLR_paper/fig_manuscript_svg/figure_knowledge_distillation/figure_parts/roc_auc_students.pdf",
dpi=300,
)

Expand Down
19 changes: 15 additions & 4 deletions applications/contrastive_phenotyping/evaluation/knowledge_distillation_teacher.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,13 @@
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from iohub.ngff import open_ome_zarr
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, f1_score
from sklearn.metrics import (
accuracy_score,
classification_report,
f1_score,
roc_auc_score,
)

from viscy.representation.embedding_writer import read_embedding_dataset

Expand Down Expand Up @@ -69,12 +73,14 @@ def load_features_and_annotations(embedding_path, annotation_path, filter_fn):
model = model.fit(train_features, train_annotation)
train_prediction = model.predict(train_features)
val_prediction = model.predict(val_features)
val_prediction_score = model.predict_proba(val_features)

print("Training\n", classification_report(train_annotation, train_prediction))
print("Validation\n", classification_report(val_annotation, val_prediction))

val_selected["label"] = val_selected["infection_state"].cat.codes
val_selected["prediction_binary"] = val_prediction
val_selected["prediction"] = val_prediction_score[:, 1]

# %%
prediction = val_selected
Expand Down Expand Up @@ -107,17 +113,22 @@ def load_features_and_annotations(embedding_path, annotation_path, filter_fn):
lambda x: float(f1_score(x["label"], x["prediction_binary"]))
)

roc_auc_by_t = prediction.groupby(["stage"]).apply(
lambda x: float(roc_auc_score(x["label"], x["prediction"]))
)

metrics_df = pd.DataFrame(
data={
"accuracy": accuracy_by_t.values,
"F1": f1_by_t.values,
"ROC AUC": roc_auc_by_t.values,
},
index=f1_by_t.index,
).reset_index()

metrics_long = metrics_df.melt(
id_vars=["stage"],
value_vars=["accuracy"],
value_vars=["ROC AUC"],
var_name="metric",
value_name="score",
)
Expand All @@ -133,7 +144,7 @@ def load_features_and_annotations(embedding_path, annotation_path, filter_fn):
legend=False,
color="gray",
)
g.set_axis_labels("HPI", "accuracy")
g.set_axis_labels("HPI", "ROC AUC")
g.figure.set_size_inches(3.5, 0.75)
g.set(xlim=(-1, 7), ylim=(0.9, 1.0))
plt.show()
Expand Down
13 changes: 11 additions & 2 deletions viscy/data/cell_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@
from torch.utils.data import DataLoader, Dataset

from viscy.data.hcs import _read_norm_meta
from viscy.data.triplet import INDEX_COLUMNS


class ClassificationDataset(Dataset):
Expand Down Expand Up @@ -42,6 +41,16 @@ def __init__(
annotation["y"].between(*y_range, inclusive="neither")
& annotation["x"].between(*x_range, inclusive="neither")
]
self._index_columns = [
"fov_name",
"track_id",
"t",
"id",
"parent_track_id",
"parent_id",
"y",
"x",
]

def __len__(self):
return len(self.annotation)
Expand All @@ -68,7 +77,7 @@ def __getitem__(
img = self.transform(img)
label = torch.tensor(row["infection_state"]).float()[None]
if self.return_indices:
return img, label, row[INDEX_COLUMNS].to_dict()
return img, label, row[self._index_columns].to_dict()
else:
return img, label

Expand Down