sf_crime_17.py

sf_crime_17.py

fpr, tpr, thresholds = roc_curve(y_validation, y_pred)

fig = make_subplots(
    rows=1, cols=2,
    subplot_titles=(
        "ROC Curve",
        "Precision vs Recall Curve"
    )
)

# ROC curve
# add dotted line to show the performance of randomly guessing (50%)
fig.add_trace(go.Scatter(
    x=[0, 1],
    y=[0, 1],
    line=dict(
        color='royalblue',
        width=2,
        dash='dash'
    )
), row=1, col=1)
fig.update_layout(showlegend=False)

# plot ROC curve, filling the margin above (or below!) the random guess line
fig.add_trace(go.Scatter(
    x=fpr,
    y=tpr,
    fill='tonexty',
    mode='lines',
), row=1, col=1)
fig['layout']['xaxis']['title'] = dict(text='FPR')
fig['layout']['yaxis']['title'] = dict(text='TPR')

# precision-recall curve
precision, recall, thresholds = precision_recall_curve(y_validation, y_pred)

fig_prc = px.area(
    x=recall, y=precision,
    title=f'Precision-Recall Curve (AUC={auc(fpr, tpr):.4f})',
    labels=dict(x='Recall', y='Precision'),
    width=700, height=500
)
fig.add_trace(fig_prc.data[0], row=1, col=2)
fig['layout']['xaxis2']['title'] = dict(text='Recall')
fig['layout']['yaxis2']['title'] = dict(text='Precision')

fig.show()