$M=0.5⋅(G+D)$
\n\nThe default rate captured at 4% is the percentage of the positive labels (defaults) captured within the highest-ranked 4% of the predictions, and represents a Sensitivity/Recall statistic.\n\nFor both of the sub-metrics $G$ and $D$, the negative labels are given a weight of 20 to adjust for downsampling.\n\nThis metric has a maximum value of 1.0.\n\nPython code for calculating this metric can be found in [this Notebook](https://www.kaggle.com/code/inversion/amex-competition-metric-python).","metadata":{}},{"cell_type":"code","source":"def amex_metric(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n\n def top_four_percent_captured(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n df = (pd.concat([y_true, y_pred], axis='columns')\n .sort_values('prediction', ascending=False))\n df['weight'] = df['target'].apply(lambda x: 20 if x==0 else 1)\n four_pct_cutoff = int(0.04 * df['weight'].sum())\n df['weight_cumsum'] = df['weight'].cumsum()\n df_cutoff = df.loc[df['weight_cumsum'] <= four_pct_cutoff]\n return (df_cutoff['target'] == 1).sum() / (df['target'] == 1).sum()\n \n def weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n df = (pd.concat([y_true, y_pred], axis='columns')\n .sort_values('prediction', ascending=False))\n df['weight'] = df['target'].apply(lambda x: 20 if x==0 else 1)\n df['random'] = (df['weight'] / df['weight'].sum()).cumsum()\n total_pos = (df['target'] * df['weight']).sum()\n df['cum_pos_found'] = (df['target'] * df['weight']).cumsum()\n df['lorentz'] = df['cum_pos_found'] / total_pos\n df['gini'] = (df['lorentz'] - df['random']) * df['weight']\n return df['gini'].sum()\n\n def normalized_weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n y_true_pred = y_true.rename(columns={'target': 'prediction'})\n return weighted_gini(y_true, y_pred) / weighted_gini(y_true, y_true_pred)\n\n g = normalized_weighted_gini(y_true, y_pred)\n d = top_four_percent_captured(y_true, y_pred)\n\n return 0.5 * (g + d)\n\n\ndef plot_roc(y_val,y_prob):\n colors=px.colors.qualitative.Prism\n fig=go.Figure()\n fig.add_trace(go.Scatter(x=np.linspace(0,1,11), y=np.linspace(0,1,11), \n name='Random Chance',mode='lines', showlegend=False,\n line=dict(color=\"Black\", width=1, dash=\"dot\")))\n for i in range(len(y_val)):\n y=y_val[i]\n prob=y_prob[i]\n fpr, tpr, _ = roc_curve(y, prob)\n roc_auc = auc(fpr,tpr)\n fig.add_trace(go.Scatter(x=fpr, y=tpr, line=dict(color=colors[::-1][i+1], width=3), \n hovertemplate = 'True positive rate = %{y:.3f}