def get_metrics()

in src/engine/step4/model_dev/utils/metrics.py [0:0]

• 22 lines of code
• 3 McCabe index (conditional complexity)

def get_metrics(tool, df, query2args_dict):
    """
    Computes the exact matching and execution accuracies for each inferred query.

    Args:
        tool(nlq2SqlTool): Tool with the functions for the query is be rendered and executed.
        df(pd.DataFrame): Pandas dataframe where the input and inferred queries are stored.
        query2args_dict(dict): Dictionary of default arguments for the queries.

    Returns:
        Pandas dataframe with the calculated accuracies.
    """

    df["exact_match_wiki"] = df.apply(exact_matching, args=("preds_wiki",), axis=1)

    df0 = df.drop_duplicates(
        subset=["base_question", "query", "preds_wiki"], inplace=False
    ).reset_index(drop=True)
    exec_match_wiki = []
    for i, (_, row) in enumerate(df0.iterrows()):
        if i % 10 == 0:
            print(f"Executing {i}/{df0.shape[0]}...")
        exec_match_wiki.append(
            execute_matching(
                tool, row, query2args_dict.get(row["query"], query2args_dict["default"])
            )
        )

    df0["exec_match_wiki"] = exec_match_wiki

    columns = ["base_question", "query", "preds_wiki", "exec_match_wiki"]
    df1 = df0[columns]
    common = ["base_question", "query", "preds_wiki"]
    df_final = pd.merge(
        left=df, right=df1, how="inner", left_on=common, right_on=common
    )

    return df_final