import sys
import warnings
from itertools import product
import numpy as np
import pandas as pd
from .. import util
from ..dimension import dimension_name
from ..element import Element
from ..ndmapping import NdMapping, item_check, sorted_context
from .interface import DataError, Interface
from .pandas import PandasInterface
from .util import finite_range
[docs]class cuDFInterface(PandasInterface):
"""
The cuDFInterface allows a Dataset objects to wrap a cuDF
DataFrame object. Using cuDF allows working with columnar
data on a GPU. Most operations leave the data in GPU memory,
however to plot the data it has to be loaded into memory.
The cuDFInterface covers almost the complete API exposed
by the PandasInterface with two notable exceptions:
1) Aggregation and groupby do not have a consistent sort order
(see https://github.com/rapidsai/cudf/issues/4237)
3) Not all functions can be easily applied to a cuDF so
some functions applied with aggregate and reduce will not work.
"""
datatype = 'cuDF'
types = ()
[docs] @classmethod
def loaded(cls):
return 'cudf' in sys.modules
[docs] @classmethod
def applies(cls, obj):
if not cls.loaded():
return False
import cudf
return isinstance(obj, (cudf.DataFrame, cudf.Series))
@classmethod
def init(cls, eltype, data, kdims, vdims):
import cudf
element_params = eltype.param.objects()
kdim_param = element_params['kdims']
vdim_param = element_params['vdims']
if isinstance(data, (cudf.Series, pd.Series)):
data = data.to_frame()
if not isinstance(data, cudf.DataFrame):
data, _, _ = PandasInterface.init(eltype, data, kdims, vdims)
data = cudf.from_pandas(data)
columns = list(data.columns)
ncols = len(columns)
index_names = [data.index.name]
if index_names == [None]:
index_names = ['index']
if eltype._auto_indexable_1d and ncols == 1 and kdims is None:
kdims = list(index_names)
if isinstance(kdim_param.bounds[1], int):
ndim = min([kdim_param.bounds[1], len(kdim_param.default)])
else:
ndim = None
nvdim = vdim_param.bounds[1] if isinstance(vdim_param.bounds[1], int) else None
if kdims and vdims is None:
vdims = [c for c in columns if c not in kdims]
elif vdims and kdims is None:
kdims = [c for c in columns if c not in vdims][:ndim]
elif kdims is None:
kdims = list(columns[:ndim])
if vdims is None:
vdims = [d for d in columns[ndim:((ndim+nvdim) if nvdim else None)]
if d not in kdims]
elif kdims == [] and vdims is None:
vdims = list(columns[:nvdim if nvdim else None])
# Handle reset of index if kdims reference index by name
for kd in kdims:
kd = dimension_name(kd)
if kd in columns:
continue
if any(kd == ('index' if name is None else name)
for name in index_names):
data = data.reset_index()
break
if any(isinstance(d, (np.int64, int)) for d in kdims+vdims):
raise DataError("cudf DataFrame column names used as dimensions "
"must be strings not integers.", cls)
if kdims:
kdim = dimension_name(kdims[0])
if eltype._auto_indexable_1d and ncols == 1 and kdim not in columns:
data = data.copy()
data.insert(0, kdim, np.arange(len(data)))
for d in kdims+vdims:
d = dimension_name(d)
if len([c for c in columns if c == d]) > 1:
raise DataError('Dimensions may not reference duplicated DataFrame '
'columns (found duplicate %r columns). If you want to plot '
'a column against itself simply declare two dimensions '
'with the same name. '% d, cls)
return data, {'kdims':kdims, 'vdims':vdims}, {}
@classmethod
def range(cls, dataset, dimension):
dimension = dataset.get_dimension(dimension, strict=True)
column = dataset.data[dimension.name]
if dimension.nodata is not None:
column = cls.replace_value(column, dimension.nodata)
if column.dtype.kind == 'O':
return np.nan, np.nan
else:
return finite_range(column, column.min(), column.max())
@classmethod
def values(cls, dataset, dim, expanded=True, flat=True, compute=True,
keep_index=False):
dim = dataset.get_dimension(dim, strict=True)
data = dataset.data[dim.name]
if not expanded:
data = data.unique()
return data.values_host if compute else data.values
elif keep_index:
return data
elif compute:
return data.values_host
try:
return data.values
except Exception:
return data.values_host
@classmethod
def groupby(cls, dataset, dimensions, container_type, group_type, **kwargs):
# Get dimensions information
dimensions = [dataset.get_dimension(d).name for d in dimensions]
kdims = [kdim for kdim in dataset.kdims if kdim not in dimensions]
# Update the kwargs appropriately for Element group types
group_kwargs = {}
group_type = dict if group_type == 'raw' else group_type
if issubclass(group_type, Element):
group_kwargs.update(util.get_param_values(dataset))
group_kwargs['kdims'] = kdims
group_kwargs.update(kwargs)
# Propagate dataset
group_kwargs['dataset'] = dataset.dataset
# Find all the keys along supplied dimensions
keys = product(*(dataset.data[dimensions[0]].unique().values_host for d in dimensions))
# Iterate over the unique entries applying selection masks
grouped_data = []
for unique_key in util.unique_iterator(keys):
group_data = dataset.select(**dict(zip(dimensions, unique_key)))
if not len(group_data):
continue
group_data = group_type(group_data, **group_kwargs)
grouped_data.append((unique_key, group_data))
if issubclass(container_type, NdMapping):
with item_check(False), sorted_context(False):
kdims = [dataset.get_dimension(d) for d in dimensions]
return container_type(grouped_data, kdims=kdims)
else:
return container_type(grouped_data)
[docs] @classmethod
def select_mask(cls, dataset, selection):
"""
Given a Dataset object and a dictionary with dimension keys and
selection keys (i.e. tuple ranges, slices, sets, lists, or literals)
return a boolean mask over the rows in the Dataset object that
have been selected.
"""
mask = None
for dim, sel in selection.items():
if isinstance(sel, tuple):
sel = slice(*sel)
arr = cls.values(dataset, dim, keep_index=True)
if util.isdatetime(arr):
try:
sel = util.parse_datetime_selection(sel)
except Exception:
pass
new_masks = []
if isinstance(sel, slice):
with warnings.catch_warnings():
warnings.filterwarnings('ignore', r'invalid value encountered')
if sel.start is not None:
new_masks.append(sel.start <= arr)
if sel.stop is not None:
new_masks.append(arr < sel.stop)
if not new_masks:
continue
new_mask = new_masks[0]
for imask in new_masks[1:]:
new_mask &= imask
elif isinstance(sel, (set, list)):
for v in sel:
new_masks.append(arr==v)
if not new_masks:
continue
new_mask = new_masks[0]
for imask in new_masks[1:]:
new_mask |= imask
elif callable(sel):
new_mask = sel(arr)
else:
new_mask = arr == sel
if mask is None:
mask = new_mask
else:
mask &= new_mask
return mask
@classmethod
def select(cls, dataset, selection_mask=None, **selection):
df = dataset.data
if selection_mask is None:
selection_mask = cls.select_mask(dataset, selection)
indexed = cls.indexed(dataset, selection)
if selection_mask is not None:
df = df.iloc[selection_mask]
if indexed and len(df) == 1 and len(dataset.vdims) == 1:
return df[dataset.vdims[0].name].iloc[0]
return df
@classmethod
def concat_fn(cls, dataframes, **kwargs):
import cudf
return cudf.concat(dataframes, **kwargs)
@classmethod
def add_dimension(cls, dataset, dimension, dim_pos, values, vdim):
data = dataset.data.copy()
if dimension.name not in data:
data[dimension.name] = values
return data
@classmethod
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(f'{agg} aggregation is not supported on cudf DataFrame.')
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(f'{agg} aggregation is not supported on cudf DataFrame.')
agg = getattr(reindexed, agg)()
try:
data = {col: [v] for col, v in zip(agg.index.values_host, agg.to_numpy())}
except Exception:
# Give FutureWarning: 'The to_array method will be removed in a future cuDF release.
# Consider using `to_numpy` instead.'
# Seen in cudf=21.12.01
data = {col: [v] for col, v in zip(agg.index.values_host, agg.to_array())}
df = 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
@classmethod
def iloc(cls, dataset, index):
import cudf
rows, cols = index
scalar = False
columns = list(dataset.data.columns)
if isinstance(cols, slice):
cols = [d.name for d in dataset.dimensions()][cols]
elif np.isscalar(cols):
scalar = np.isscalar(rows)
cols = [dataset.get_dimension(cols).name]
else:
cols = [dataset.get_dimension(d).name for d in index[1]]
col_index = [columns.index(c) for c in cols]
if np.isscalar(rows):
rows = [rows]
if scalar:
return dataset.data[cols[0]].iloc[rows[0]]
result = dataset.data.iloc[rows, col_index]
# cuDF does not handle single rows and cols indexing correctly
# as of cudf=0.10.0 so we have to convert Series back to DataFrame
if isinstance(result, cudf.Series):
if len(cols) == 1:
result = result.to_frame(cols[0])
else:
result = result.to_frame().T
return result
@classmethod
def sort(cls, dataset, by=None, reverse=False):
if by is None:
by = []
cols = [dataset.get_dimension(d, strict=True).name for d in by]
return dataset.data.sort_values(by=cols, ascending=not reverse)
@classmethod
def dframe(cls, dataset, dimensions):
if dimensions:
return dataset.data[dimensions].to_pandas()
else:
return dataset.data.to_pandas()
Interface.register(cuDFInterface)