def matrix_type(request):
    return request.param
def aggregate(cls, dataset, dimensions, function, **kwargs):
        data = dataset.data
        cols = [d.name for d in dataset.kdims if d in dimensions]
        vdims = dataset.dimensions('value', label='name')
        reindexed = data[cols+vdims]
        agg = function.__name__
        if len(dimensions):
            agg_map = {'amin': 'min', 'amax': 'max'}
            agg = agg_map.get(agg, agg)
            grouped = reindexed.groupby(cols, sort=False)
            if not hasattr(grouped, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            df = getattr(grouped, agg)().reset_index()
        else:
            agg_map = {'amin': 'min', 'amax': 'max', 'size': 'count'}
            agg = agg_map.get(agg, agg)
            if not hasattr(reindexed, agg):
                raise ValueError('%s aggregation is not supported on cudf DataFrame.' % agg)
            agg = getattr(reindexed, agg)()
            data = dict(((col, [v]) for col, v in zip(agg.index.values_host, agg.to_array())))
            df = util.pd.DataFrame(data, columns=list(agg.index.values_host))

        dropped = []
        for vd in vdims:
            if vd not in df.columns:
                dropped.append(vd)
        return df, dropped