(df, order=None)
return out
Compute effect size across datasets

    Returns kxk matrix of effect sizes, order defines the order of rows/columns

    Parameters
    ----------
    df: DataFrame
        Aggregated results, samples are index, columns are pipelines, and values are
        scores
    order: list
        list of length (num algorithms) with names corresponding to df columns

    Returns
    -------
    effect: ndarray of shape (n_pipelines, n_pipelines)
        array of effect size
Compute effect size across datasets
Compute effect size across datasets
def compute_effect(df, order=None):
    """Compute effect size across datasets

    Returns kxk matrix of effect sizes, order defines the order of rows/columns

    Parameters
    ----------
    df: DataFrame
        Aggregated results, samples are index, columns are pipelines, and values are
        scores
    order: list
        list of length (num algorithms) with names corresponding to df columns

    Returns
    -------
    effect: ndarray of shape (n_pipelines, n_pipelines)
        array of effect size
    """
    if order is None:
        order = df.columns
    else:
        errormsg = "provided order does not have all columns of dataframe"
        assert set(order) == set(df.columns), errormsg

    out = np.zeros((len(df.columns), len(df.columns)))
    for i, pipe1 in enumerate(order):
        for j, pipe2 in enumerate(order):
            if i != j:
                # for now it's just the standardized difference
                diffs = df.loc[:, pipe1] - df.loc[:, pipe2]
                diffs = diffs.mean() / diffs.std()
                out[i, j] = diffs
    return out
def compute_effect ( df , order = None ) : if order is None : order = df . columns else : errormsg = "provided order does not have all columns of dataframe" assert set ( order ) == set ( df . columns ) , errormsg out = np . zeros ( ( len ( df . columns ) , len ( df . columns ) ) ) for i , pipe1 in enumerate ( order ) : for j , pipe2 in enumerate ( order ) : if i != j : # for now it's just the standardized difference diffs = df . loc [ : , pipe1 ] - df . loc [ : , pipe2 ] diffs = diffs . mean ( ) / diffs . std ( ) out [ i , j ] = diffs return out