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Results plots consistent with the data #1

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Results plots consistent with the data #1

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8 changes: 4 additions & 4 deletions timeeval_gui/_pages/results.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,13 +123,13 @@ def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, da
fig = make_subplots(2, 1)
if dataset_dim == "multivariate":
for i in range(1, df_dataset.shape[1] - 1):
fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f"channel-{i}"), 1, 1)
fig.add_trace(go.Scatter(x=df_dataset["timestamp"], y=df_dataset.iloc[:, i], name=df_dataset.columns[i]), 1, 1)
else:
fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name="timeseries"), 1, 1)
fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset["is_anomaly"], name="label"), 2, 1)
fig.add_trace(go.Scatter(x=df_dataset["timestamp"], y=df_dataset.iloc[:, 1], name="timeseries"), 1, 1)
fig.add_trace(go.Scatter(x=df_dataset["timestamp"], y=df_dataset["is_anomaly"], name="label"), 2, 1)

for algo in algorithms:
fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[algo], name=f"{algo}={auroc[algo]:.4f}"), 2, 1)
fig.add_trace(go.Scatter(x=df_dataset["timestamp"], y=df_scores[algo], name=f"{algo}={auroc[algo]:.4f}"), 2, 1)
fig.update_xaxes(matches="x")
fig.update_layout(
title=f"Results of {','.join(np.unique(algorithms))} on {dataset_name}",
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