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import streamlit as st
import pickle
import plotly.express as px
import plotly.graph_objects as go
def load_evaluation_metrics(filepath: str):
with open(filepath, 'rb') as f:
evaluation_metrics = pickle.load(f)
return (
evaluation_metrics['confusion_matrices'],
evaluation_metrics['roc_curves'],
evaluation_metrics['auc_scores'],
evaluation_metrics['f1_scores']
)
def plot_roc_curve(selected_category, roc_curves, auc_scores):
fig = go.Figure()
roc_data = roc_curves[selected_category]
fig.add_trace(go.Scatter(
x=roc_data['fpr'],
y=roc_data['tpr'],
name=selected_category
))
fig.add_trace(go.Scatter(
x=[0, 1],
y=[0, 1],
mode='lines',
line=dict(dash='dash'),
name='Random'
))
fig.update_layout(
title=f"AUC-ROC Curve - {selected_category}, AUC={auc_scores[selected_category]:.3f}",
xaxis_title="False Positive Rate",
yaxis_title="True Positive Rate",
width=500,
height=450,
showlegend=False
)
return fig
def plot_confusion_matrix(selected_category, confusion_matrices, f1_scores):
labels = ['OK', 'NOK']
conf_matrix = confusion_matrices[selected_category]
f1_score = f1_scores[selected_category]
fig = px.imshow(
conf_matrix,
labels=dict(x="True Label", y="Predicted Label"),
x=labels,
y=labels,
color_continuous_scale='Reds',
width=500,
height=500,
)
for i in range(len(labels)):
for j in range(len(labels)):
fig.add_annotation(
x=j,
y=i,
text=str(conf_matrix[i, j]),
showarrow=False,
font=dict(size=14)
)
fig.update_layout(
title=f"Confusion Matrix - {selected_category}, F1: {f1_score:.3f}",
xaxis_title="True Label",
yaxis_title="Predicted Label",
coloraxis_showscale=False # This line removes the vertical color scale
)
return fig
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