Created
October 30, 2020 00:21
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sf_crime_17.py
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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() |
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