Source code for holoviews.core.data.cudf

from __future__ import absolute_import

import sys
import warnings

try:
    import itertools.izip as zip
except ImportError:
    pass

from itertools import product

import numpy as np

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


[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 import pandas as pd 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): column = dataset.data[dataset.get_dimension(dimension, strict=True).name] if column.dtype.kind == 'O': return np.NaN, np.NaN else: return (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.to_array() if compute else data.values elif keep_index: return data elif compute: return data.to_array() return data.values @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() 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, compute=False) if util.isdatetime(arr) and util.pd: try: sel = util.parse_datetime_selection(sel) except: 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[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('%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, agg.to_array()))) df = util.pd.DataFrame(data, columns=list(agg.index)) 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=[], reverse=False): 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)