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Polaris000/numpy_implementation.py

Created November 4, 2022 16:51
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  1. Polaris000 created this gist Nov 4, 2022.
    36 changes: 36 additions & 0 deletions numpy_implementation.py
    View file
    Original file line number Diff line number Diff line change
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    def auc_recall_at_k_np(y_true, y_conf):
    """
    Experiment #2:
    --------------
    Compute AUC under the Recall@k curve using numpy's
    functions.
    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)

    ranking = conf_df["expected"].to_numpy()

    recall_at_k = (ranking == 1).cumsum() / (ranking == 1).sum()

    # calculating ideal recall@k
    ideal_recall_at_k = np.minimum(
    np.ones(len(ranking)),
    np.array(list(range(1, len(ranking) + 1)))/ (ranking == 1).sum()
    )

    return np.trapz(recall_at_k) / np.trapz(ideal_recall_at_k)