Created
November 4, 2022 16:49
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| def auc_recall_at_k(y_true, y_conf): | |
| """ | |
| Compute AUC under the Recall@k curve. | |
| y_true: A numpy array of expected predictions | |
| y_conf: A numpy array of the model's confidence | |
| scores for each datapoint | |
| Returns: AUC-Recall@k (float) | |
| """ | |
| # if there are no positive targets (good leads), | |
| # auc becomes invalid | |
| if y_true.count(1) == 0: | |
| return np.nan | |
| conf_df = pd.DataFrame() | |
| conf_df["conf"] = y_conf | |
| conf_df["expected"] = y_true | |
| conf_df.columns = ["conf", "expected"] | |
| conf_df = conf_df.sort_values("conf", ascending=False) | |
| recall_at_k = [] | |
| # calculating recall@k | |
| for i in range(len(conf_df)): | |
| recall_at_k.append( | |
| conf_df.iloc[:i+1, :]["expected"].to_list().count(1) | |
| / conf_df["expected"].to_list().count(1) | |
| ) | |
| # calculating ideal recall@k | |
| ideal_recall_at_k = np.minimum( | |
| np.ones(len(conf_df)), | |
| np.array(list(range(1, len(conf_df["expected"]) + 1)))/ conf_df["expected"].to_list().count(1) | |
| ) | |
| # Computing our final metric by getting the proportion of the areas | |
| # under these two curves | |
| return np.trapz(recall_at_k) / np.trapz(ideal_recall_at_k) |
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