def _compute_metrics()

in covid19_spread/metrics.py [0:0]

• 58 lines of code
• 5 McCabe index (conditional complexity)

def _compute_metrics(df_true, df_pred, mincount=0, nanfill=False):
    if nanfill:
        cols = sorted(set(df_true.columns).difference(set(df_pred.columns)))
        zeros = pd.DataFrame(np.zeros((len(df_pred), len(cols))), columns=cols)
        zeros.index = df_pred.index
        df_pred = pd.concat([df_pred, zeros], axis=1)

    common_cols = list(set(df_true.columns).intersection(set(df_pred.columns)))
    df_pred = df_pred[common_cols]
    df_true = df_true[common_cols]
    z = len(df_pred)
    # print(df_pred.round(2))

    basedate = df_pred.index.min()
    pdate = basedate - timedelta(1)

    diff = df_true.loc[pdate] - df_true.loc[basedate - timedelta(2)]
    naive = [df_true.loc[pdate] + d * diff for d in range(1, z + 1)]
    naive = pd.DataFrame(naive)
    naive.index = df_pred.index

    ix = df_pred.index.intersection(df_true.index)

    df_pred = df_pred.loc[ix]
    naive = naive.loc[ix]
    gt = df_true.loc[ix]

    # compute state level MAE
    state_gt = gt.transpose().groupby(lambda x: x.split(", ")[-1]).sum()
    state_pred = df_pred.transpose().groupby(lambda x: x.split(", ")[-1]).sum()
    state_mae = (state_gt.sort_index() - state_pred.sort_index()).abs().mean(axis=0)

    metrics = pd.DataFrame(
        [
            rmse(df_pred, gt),
            mae(df_pred, gt),
            mape(df_pred, gt),
            rmse(naive, gt),
            mae(naive, gt),
            state_mae,
            max_mae(df_pred, gt),
            max_mae(naive, gt),
        ],
        columns=df_pred.index.to_numpy(),
    )
    metrics["Measure"] = [
        "RMSE",
        "MAE",
        "MAPE",
        "RMSE_NAIVE",
        "MAE_NAIVE",
        "STATE_MAE",
        "MAX_MAE",
        "MAX_NAIVE_MAE",
    ]
    metrics.set_index("Measure", inplace=True)
    if metrics.shape[1] > 0:
        metrics.loc["MAE_MASE"] = metrics.loc["MAE"] / metrics.loc["MAE_NAIVE"]
        metrics.loc["RMSE_MASE"] = metrics.loc["RMSE"] / metrics.loc["RMSE_NAIVE"]

        # Stack predictions onto last ground truth date.
        # We'll take the diff and compute MAE on the new daily counts
        stack = pd.concat(
            [df_true.loc[[df_pred.index.min() - timedelta(days=1)]], df_pred]
        )
        stack_diff = stack.diff().loc[ix]
        true_diff = df_true.diff().loc[ix]
        metrics.loc["MAE_DELTAS"] = mae(stack_diff, true_diff)
        metrics.loc["RMSE_DELTAS"] = rmse(stack_diff, true_diff)
    return metrics