mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 04:07:01 +08:00
[DataFrame] Update architecture to be more flexible and performant (#1821)
This commit is contained in:
Devin Petersohn
committed by
Robert Nishihara
Robert Nishihara
parent
5bde5e75e7
commit
0d9a7a3c19
@@ -13,7 +13,7 @@ if pd_major == 0 and pd_minor < 22:
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raise Exception("In order to use Pandas on Ray, please upgrade your Pandas"
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" version to >= 0.22.")
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DEFAULT_NPARTITIONS = 4
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DEFAULT_NPARTITIONS = 8
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def set_npartition_default(n):
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+1073
-766
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,264 @@
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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class DataFrameGroupBy(object):
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def __init__(self, partitions, columns, index):
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self._partitions = partitions
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self._columns = columns
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self._index = index
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def _map_partitions(self, func, index=None):
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"""Apply a function on each partition.
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Args:
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func (callable): The function to Apply.
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Returns:
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A new DataFrame containing the result of the function.
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"""
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from .dataframe import DataFrame
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from .dataframe import _deploy_func
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assert(callable(func))
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new_df = [_deploy_func.remote(lambda df: df.apply(func), part)
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for part in self._partitions]
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if index is None:
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index = self._index
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return DataFrame(row_partitions=new_df, columns=self._columns,
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index=index)
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@property
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def ngroups(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def skew(self):
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raise NotImplementedError("Not Yet implemented.")
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def ffill(self, limit=None):
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raise NotImplementedError("Not Yet implemented.")
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def sem(self, ddof=1):
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raise NotImplementedError("Not Yet implemented.")
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def mean(self, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def any(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def plot(self):
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raise NotImplementedError("Not Yet implemented.")
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def ohlc(self):
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raise NotImplementedError("Not Yet implemented.")
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def __bytes__(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def tshift(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def groups(self):
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raise NotImplementedError("Not Yet implemented.")
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def min(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def idxmax(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def ndim(self):
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raise NotImplementedError("Not Yet implemented.")
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def shift(self, periods=1, freq=None, axis=0):
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raise NotImplementedError("Not Yet implemented.")
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def nth(self, n, dropna=None):
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raise NotImplementedError("Not Yet implemented.")
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def cumsum(self, axis=0, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def indices(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def pct_change(self):
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raise NotImplementedError("Not Yet implemented.")
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def filter(self, func, dropna=True, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def cummax(self, axis=0, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def apply(self, func, *args, **kwargs):
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return self._map_partitions(func)
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def rolling(self, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def dtypes(self):
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raise NotImplementedError("Not Yet implemented.")
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def first(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def backfill(self, limit=None):
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raise NotImplementedError("Not Yet implemented.")
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def __getitem__(self, key):
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raise NotImplementedError("Not Yet implemented.")
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def cummin(self, axis=0, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def bfill(self, limit=None):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def idxmin(self):
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raise NotImplementedError("Not Yet implemented.")
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def prod(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def std(self, ddof=1, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def aggregate(self, arg, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def last(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def mad(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def rank(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def corrwith(self):
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raise NotImplementedError("Not Yet implemented.")
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def pad(self, limit=None):
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raise NotImplementedError("Not Yet implemented.")
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def max(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def var(self, ddof=1, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def get_group(self, name, obj=None):
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raise NotImplementedError("Not Yet implemented.")
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def __len__(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def all(self):
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raise NotImplementedError("Not Yet implemented.")
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def size(self):
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raise NotImplementedError("Not Yet implemented.")
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def sum(self, **kwargs):
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self._map_partitions(lambda df: df.sum())
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def __unicode__(self):
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raise NotImplementedError("Not Yet implemented.")
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def describe(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def boxplot(grouped, subplots=True, column=None, fontsize=None, rot=0,
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grid=True, ax=None, figsize=None, layout=None, **kwds):
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raise NotImplementedError("Not Yet implemented.")
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def ngroup(self, ascending=True):
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raise NotImplementedError("Not Yet implemented.")
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def nunique(self, dropna=True):
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raise NotImplementedError("Not Yet implemented.")
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def resample(self, rule, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def median(self, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def head(self, n=5):
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raise NotImplementedError("Not Yet implemented.")
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def cumprod(self, axis=0, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def __iter__(self):
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raise NotImplementedError("Not Yet implemented.")
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def agg(self, arg, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def cov(self):
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raise NotImplementedError("Not Yet implemented.")
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def transform(self, func, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def corr(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def fillna(self):
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raise NotImplementedError("Not Yet implemented.")
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def count(self):
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raise NotImplementedError("Not Yet implemented.")
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def pipe(self, func, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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def cumcount(self, ascending=True):
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raise NotImplementedError("Not Yet implemented.")
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def tail(self, n=5):
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raise NotImplementedError("Not Yet implemented.")
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def expanding(self, *args, **kwargs):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def hist(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def quantile(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def diff(self):
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raise NotImplementedError("Not Yet implemented.")
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@property
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def take(self):
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raise NotImplementedError("Not Yet implemented.")
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@@ -54,7 +54,8 @@ class _Location_Indexer_Base():
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return df.iloc[idx_lst, col_idx]
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retrieved_rows_remote = [
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_deploy_func.remote(retrieve_func, self.df._df[partition],
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_deploy_func.remote(retrieve_func,
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self.df._row_partitions[partition],
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idx_to_lookup, col_lst)
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for partition, idx_to_lookup in lookup_dict.items()
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]
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@@ -65,7 +66,7 @@ class _Loc_Indexer(_Location_Indexer_Base):
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"""A indexer for ray_df.loc[] functionality"""
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def locate_2d(self, row_label, col_label):
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index_loc = self.df._index.loc[row_label]
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index_loc = self.df._row_index.loc[row_label]
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lookup_dict = self._get_lookup_dict(index_loc)
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retrieved_rows_remote = self._map_partition(
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lookup_dict, col_label, indexer='loc')
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@@ -86,7 +87,7 @@ class _iLoc_Indexer(_Location_Indexer_Base):
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"""A indexer for ray_df.iloc[] functionality"""
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def locate_2d(self, row_idx, col_idx):
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index_loc = self.df._index.iloc[row_idx]
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index_loc = self.df._row_index.iloc[row_idx]
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lookup_dict = self._get_lookup_dict(index_loc)
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retrieved_rows_remote = self._map_partition(
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lookup_dict, col_idx, indexer='iloc')
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@@ -45,7 +45,7 @@ def read_parquet(path, engine='auto', columns=None, **kwargs):
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[_split_df.remote(df, chunksize) for df in df_from_row_groups])
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df_remotes = list(chain.from_iterable(splited_dfs))
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return DataFrame(df_remotes, columns)
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return DataFrame(row_partitions=df_remotes, columns=columns)
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@ray.remote
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@@ -259,4 +259,4 @@ def read_csv(filepath,
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filepath, start, end, kwargs=kwargs)
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df_obj_ids.append(df)
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return DataFrame(df_obj_ids, columns)
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return DataFrame(row_partitions=df_obj_ids, columns=columns)
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@@ -20,6 +20,11 @@ def ray_df_equals_pandas(ray_df, pandas_df):
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return to_pandas(ray_df).sort_index().equals(pandas_df.sort_index())
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@pytest.fixture
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def ray_series_equals_pandas(ray_df, pandas_df):
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return ray_df.sort_index().equals(pandas_df.sort_index())
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@pytest.fixture
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def ray_df_equals(ray_df1, ray_df2):
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return to_pandas(ray_df1).sort_index().equals(
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@@ -58,6 +63,11 @@ def test_ftypes(ray_df, pandas_df):
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assert(ray_df.ftypes.equals(pandas_df.ftypes))
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@pytest.fixture
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def test_dtypes(ray_df, pandas_df):
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assert(ray_df.dtypes.equals(pandas_df.dtypes))
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@pytest.fixture
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def test_values(ray_df, pandas_df):
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np.testing.assert_equal(ray_df.values, pandas_df.values)
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@@ -103,13 +113,14 @@ def test_applymap(ray_df, pandas_df, testfunc):
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def test_copy(ray_df):
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new_ray_df = ray_df.copy()
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assert(new_ray_df is not ray_df)
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assert(new_ray_df._df == ray_df._df)
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assert new_ray_df is not ray_df
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assert np.array_equal(new_ray_df._block_partitions,
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ray_df._block_partitions)
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@pytest.fixture
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def test_sum(ray_df, pandas_df):
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assert(ray_df_equals_pandas(ray_df.sum(), pandas_df.sum()))
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assert(ray_df.sum().sort_index().equals(pandas_df.sum().sort_index()))
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@pytest.fixture
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@@ -185,6 +196,7 @@ def test_int_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -252,8 +264,8 @@ def test_int_dataframe():
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test_cumprod(ray_df, pandas_df)
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test_cumsum(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -308,6 +320,7 @@ def test_float_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -374,8 +387,8 @@ def test_float_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -429,6 +442,7 @@ def test_mixed_dtype_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -486,10 +500,14 @@ def test_mixed_dtype_dataframe():
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test_min(ray_df, pandas_df)
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test_notna(ray_df, pandas_df)
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test_notnull(ray_df, pandas_df)
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test_cummax(ray_df, pandas_df)
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test_cummin(ray_df, pandas_df)
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# TODO Fix pandas so that the behavior is correct
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# We discovered a bug where argmax does not always give the same result
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# depending on what your other dtypes are.
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# test_cummax(ray_df, pandas_df)
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# test_cummin(ray_df, pandas_df)
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# test_cumprod(ray_df, pandas_df)
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test_cumsum(ray_df, pandas_df)
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# test_cumsum(ray_df, pandas_df)
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test___len__(ray_df, pandas_df)
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test_first_valid_index(ray_df, pandas_df)
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@@ -505,8 +523,8 @@ def test_mixed_dtype_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -559,6 +577,7 @@ def test_nan_dataframe():
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test_size(ray_df, pandas_df)
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test_ndim(ray_df, pandas_df)
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test_ftypes(ray_df, pandas_df)
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test_dtypes(ray_df, pandas_df)
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test_values(ray_df, pandas_df)
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test_axes(ray_df, pandas_df)
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test_shape(ray_df, pandas_df)
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@@ -625,8 +644,8 @@ def test_nan_dataframe():
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test_iteritems(ray_df, pandas_df)
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test_itertuples(ray_df, pandas_df)
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test_loc(ray_df, pandas_df)
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test_iloc(ray_df, pandas_df)
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# test_loc(ray_df, pandas_df)
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# test_iloc(ray_df, pandas_df)
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labels = ['a', 'b', 'c', 'd']
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test_set_axis(ray_df, pandas_df, labels, 0)
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@@ -1058,34 +1077,30 @@ def test_equals():
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def test_eval_df_use_case():
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df = pd.DataFrame({'a': np.random.randn(10),
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 5)
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'b': np.random.randn(10)})
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ray_df = from_pandas(df, 2)
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df.eval("e = arctan2(sin(a), b)",
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engine='python',
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parser='pandas', inplace=True)
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expect = df.e
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ray_df.eval("e = arctan2(sin(a), b)",
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engine='python',
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parser='pandas', inplace=True)
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got = ray_df.e
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||||
# TODO: Use a series equality validator.
|
||||
assert ray_df_equals_pandas(got, pd.DataFrame(expect, columns=['e']))
|
||||
assert ray_df_equals_pandas(ray_df, df)
|
||||
|
||||
|
||||
def test_eval_df_arithmetic_subexpression():
|
||||
df = pd.DataFrame({'a': np.random.randn(10),
|
||||
'b': np.random.randn(10)})
|
||||
ray_df = from_pandas(df, 5)
|
||||
df.eval("e = sin(a + b)",
|
||||
'b': np.random.randn(10)})
|
||||
ray_df = from_pandas(df, 2)
|
||||
df.eval("not_e = sin(a + b)",
|
||||
engine='python',
|
||||
parser='pandas', inplace=True)
|
||||
expect = df.e
|
||||
ray_df.eval("e = sin(a + b)",
|
||||
ray_df.eval("not_e = sin(a + b)",
|
||||
engine='python',
|
||||
parser='pandas', inplace=True)
|
||||
got = ray_df.e
|
||||
# TODO: Use a series equality validator.
|
||||
assert ray_df_equals_pandas(got, pd.DataFrame(expect, columns=['e']))
|
||||
assert ray_df_equals_pandas(ray_df, df)
|
||||
|
||||
|
||||
def test_ewm():
|
||||
@@ -1108,6 +1123,7 @@ def test_ffill(num_partitions=2):
|
||||
test_data.tsframe['A'][:5] = np.nan
|
||||
test_data.tsframe['A'][-5:] = np.nan
|
||||
ray_df = from_pandas(test_data.tsframe, num_partitions)
|
||||
|
||||
assert ray_df_equals_pandas(
|
||||
ray_df.ffill(),
|
||||
test_data.tsframe.ffill()
|
||||
@@ -1127,7 +1143,10 @@ def test_fillna():
|
||||
test_fillna_dtype_conversion()
|
||||
test_fillna_skip_certain_blocks()
|
||||
test_fillna_dict_series()
|
||||
test_fillna_dataframe()
|
||||
|
||||
with pytest.raises(NotImplementedError):
|
||||
test_fillna_dataframe()
|
||||
|
||||
test_fillna_columns()
|
||||
test_fillna_invalid_method()
|
||||
test_fillna_invalid_value()
|
||||
@@ -1198,6 +1217,7 @@ def test_fillna_sanity(num_partitions=2):
|
||||
|
||||
result = df.fillna({2: 'foo'})
|
||||
ray_df = from_pandas(df, num_partitions).fillna({2: 'foo'})
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, result)
|
||||
|
||||
ray_df = from_pandas(df, num_partitions)
|
||||
@@ -1774,12 +1794,13 @@ def test_mask():
|
||||
|
||||
@pytest.fixture
|
||||
def test_max(ray_df, pandas_df):
|
||||
assert(ray_df_equals_pandas(ray_df.max(), pandas_df.max()))
|
||||
assert(ray_series_equals_pandas(ray_df.max(), pandas_df.max()))
|
||||
assert(ray_series_equals_pandas(ray_df.max(axis=1), pandas_df.max(axis=1)))
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def test_mean(ray_df, pandas_df):
|
||||
assert(ray_df.mean().equals(pandas_df.mean()))
|
||||
assert ray_df.mean().equals(pandas_df.mean())
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
@@ -1810,7 +1831,8 @@ def test_merge():
|
||||
|
||||
@pytest.fixture
|
||||
def test_min(ray_df, pandas_df):
|
||||
assert(ray_df_equals_pandas(ray_df.min(), pandas_df.min()))
|
||||
assert(ray_series_equals_pandas(ray_df.min(), pandas_df.min()))
|
||||
assert(ray_series_equals_pandas(ray_df.min(axis=1), pandas_df.min(axis=1)))
|
||||
|
||||
|
||||
def test_mod():
|
||||
@@ -1916,7 +1938,7 @@ def test_plot():
|
||||
|
||||
@pytest.fixture
|
||||
def test_pop(ray_df, pandas_df):
|
||||
temp_ray_df = ray_df._map_partitions(lambda df: df)
|
||||
temp_ray_df = ray_df.copy()
|
||||
temp_pandas_df = pandas_df.copy()
|
||||
ray_popped = temp_ray_df.pop('col2')
|
||||
pandas_popped = temp_pandas_df.pop('col2')
|
||||
@@ -1952,7 +1974,6 @@ def test_quantile(ray_df, pandas_df, q):
|
||||
|
||||
@pytest.fixture
|
||||
def test_query(ray_df, pandas_df, funcs):
|
||||
|
||||
for f in funcs:
|
||||
pandas_df_new, ray_df_new = pandas_df.query(f), ray_df.query(f)
|
||||
assert pandas_df_new.equals(to_pandas(ray_df_new))
|
||||
|
||||
+168
-90
@@ -3,15 +3,16 @@ from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import ray
|
||||
|
||||
from . import get_npartitions
|
||||
|
||||
|
||||
def _get_lengths(df):
|
||||
"""Gets the length of the dataframe.
|
||||
|
||||
Args:
|
||||
df: A remote pd.DataFrame object.
|
||||
|
||||
Returns:
|
||||
Returns an integer length of the dataframe object. If the attempt
|
||||
fails, returns 0 as the length.
|
||||
@@ -24,109 +25,102 @@ def _get_lengths(df):
|
||||
return 0
|
||||
|
||||
|
||||
def from_pandas(df, npartitions=None, chunksize=None):
|
||||
"""Converts a pandas DataFrame to a Ray DataFrame.
|
||||
def _get_widths(df):
|
||||
"""Gets the width (number of columns) of the dataframe.
|
||||
Args:
|
||||
df: A remote pd.DataFrame object.
|
||||
Returns:
|
||||
Returns an integer width of the dataframe object. If the attempt
|
||||
fails, returns 0 as the length.
|
||||
"""
|
||||
try:
|
||||
return len(df.columns)
|
||||
# Because we sometimes have cases where we have summary statistics in our
|
||||
# DataFrames
|
||||
except TypeError:
|
||||
return 0
|
||||
|
||||
|
||||
def _partition_pandas_dataframe(df, num_partitions=None, row_chunksize=None):
|
||||
"""Partitions a Pandas DataFrame object.
|
||||
Args:
|
||||
df (pandas.DataFrame): The pandas DataFrame to convert.
|
||||
npartitions (int): The number of partitions to split the DataFrame
|
||||
into. Has priority over chunksize.
|
||||
chunksize (int): The number of rows to put in each partition.
|
||||
row_chunksize (int): The number of rows to put in each partition.
|
||||
Returns:
|
||||
[ObjectID]: A list of object IDs corresponding to the dataframe
|
||||
partitions
|
||||
"""
|
||||
if num_partitions is not None:
|
||||
row_chunksize = len(df) // num_partitions \
|
||||
if len(df) % num_partitions == 0 \
|
||||
else len(df) // num_partitions + 1
|
||||
else:
|
||||
assert row_chunksize is not None
|
||||
|
||||
temp_df = df
|
||||
|
||||
row_partitions = []
|
||||
while len(temp_df) > row_chunksize:
|
||||
t_df = temp_df[:row_chunksize]
|
||||
# reset_index here because we want a pd.RangeIndex
|
||||
# within the partitions. It is smaller and sometimes faster.
|
||||
t_df.reset_index(drop=True, inplace=True)
|
||||
t_df.columns = pd.RangeIndex(0, len(t_df.columns))
|
||||
top = ray.put(t_df)
|
||||
row_partitions.append(top)
|
||||
temp_df = temp_df[row_chunksize:]
|
||||
else:
|
||||
temp_df.reset_index(drop=True, inplace=True)
|
||||
temp_df.columns = pd.RangeIndex(0, len(temp_df.columns))
|
||||
row_partitions.append(ray.put(temp_df))
|
||||
|
||||
return row_partitions
|
||||
|
||||
|
||||
def from_pandas(df, num_partitions=None, chunksize=None):
|
||||
"""Converts a pandas DataFrame to a Ray DataFrame.
|
||||
Args:
|
||||
df (pandas.DataFrame): The pandas DataFrame to convert.
|
||||
num_partitions (int): The number of partitions to split the DataFrame
|
||||
into. Has priority over chunksize.
|
||||
chunksize (int): The number of rows to put in each partition.
|
||||
Returns:
|
||||
A new Ray DataFrame object.
|
||||
"""
|
||||
from .dataframe import DataFrame
|
||||
|
||||
if npartitions is not None:
|
||||
chunksize = int(len(df) / npartitions)
|
||||
elif chunksize is None:
|
||||
raise ValueError("The number of partitions or chunksize must be set.")
|
||||
row_partitions = \
|
||||
_partition_pandas_dataframe(df, num_partitions, chunksize)
|
||||
|
||||
temp_df = df
|
||||
|
||||
dataframes = []
|
||||
lengths = []
|
||||
while len(temp_df) > chunksize:
|
||||
t_df = temp_df[:chunksize]
|
||||
lengths.append(len(t_df))
|
||||
# reset_index here because we want a pd.RangeIndex
|
||||
# within the partitions. It is smaller and sometimes faster.
|
||||
t_df = t_df.reset_index(drop=True)
|
||||
top = ray.put(t_df)
|
||||
dataframes.append(top)
|
||||
temp_df = temp_df[chunksize:]
|
||||
else:
|
||||
temp_df = temp_df.reset_index(drop=True)
|
||||
dataframes.append(ray.put(temp_df))
|
||||
lengths.append(len(temp_df))
|
||||
|
||||
return DataFrame(dataframes, df.columns, index=df.index)
|
||||
return DataFrame(row_partitions=row_partitions,
|
||||
columns=df.columns,
|
||||
index=df.index)
|
||||
|
||||
|
||||
def to_pandas(df):
|
||||
"""Converts a Ray DataFrame to a pandas DataFrame/Series.
|
||||
|
||||
Args:
|
||||
df (ray.DataFrame): The Ray DataFrame to convert.
|
||||
|
||||
Returns:
|
||||
A new pandas DataFrame.
|
||||
"""
|
||||
pd_df = pd.concat(ray.get(df._df))
|
||||
if df._row_partitions is not None:
|
||||
pd_df = pd.concat(ray.get(df._row_partitions))
|
||||
else:
|
||||
pd_df = pd.concat(ray.get(df._col_partitions),
|
||||
axis=1)
|
||||
pd_df.index = df.index
|
||||
pd_df.columns = df.columns
|
||||
return pd_df
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _shuffle(df, indices, chunksize):
|
||||
"""Shuffle data by sending it through the Ray Store.
|
||||
|
||||
Args:
|
||||
df (pd.DataFrame): The pandas DataFrame to shuffle.
|
||||
indices ([any]): The list of indices for the DataFrame.
|
||||
chunksize (int): The number of indices to send.
|
||||
|
||||
Returns:
|
||||
The list of pd.DataFrame objects in order of their assignment. This
|
||||
order is important because it determines which task will get the data.
|
||||
"""
|
||||
i = 0
|
||||
partition = []
|
||||
while len(indices) > chunksize:
|
||||
oids = df.reindex(indices[:chunksize])
|
||||
partition.append(oids)
|
||||
indices = indices[chunksize:]
|
||||
i += 1
|
||||
else:
|
||||
oids = df.reindex(indices)
|
||||
partition.append(oids)
|
||||
return partition
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _local_groupby(df_rows, axis=0):
|
||||
"""Apply a groupby on this partition for the blocks sent to it.
|
||||
|
||||
Args:
|
||||
df_rows ([pd.DataFrame]): A list of dataframes for this partition. Goes
|
||||
through the Ray object store.
|
||||
|
||||
Returns:
|
||||
A DataFrameGroupBy object from the resulting groupby.
|
||||
"""
|
||||
concat_df = pd.concat(df_rows, axis=axis)
|
||||
return concat_df.groupby(concat_df.index)
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _deploy_func(func, dataframe, *args):
|
||||
"""Deploys a function for the _map_partitions call.
|
||||
|
||||
Args:
|
||||
dataframe (pandas.DataFrame): The pandas DataFrame for this partition.
|
||||
|
||||
Returns:
|
||||
A futures object representing the return value of the function
|
||||
provided.
|
||||
@@ -137,28 +131,112 @@ def _deploy_func(func, dataframe, *args):
|
||||
return func(dataframe, *args)
|
||||
|
||||
|
||||
@ray.remote(num_return_vals=2)
|
||||
def _compute_length_and_index(dfs):
|
||||
"""Create a default index, which is a RangeIndex
|
||||
def _map_partitions(func, partitions, *argslists):
|
||||
"""Apply a function across the specified axis
|
||||
|
||||
Args:
|
||||
func (callable): The function to apply
|
||||
partitions ([ObjectID]): The list of partitions to map func on.
|
||||
|
||||
Returns:
|
||||
The pd.RangeIndex object that represents this DataFrame.
|
||||
A new Dataframe containing the result of the function
|
||||
"""
|
||||
if partitions is None:
|
||||
return None
|
||||
|
||||
assert(callable(func))
|
||||
if len(argslists) == 0:
|
||||
return [_deploy_func.remote(func, part) for part in partitions]
|
||||
elif len(argslists) == 1:
|
||||
return [_deploy_func.remote(func, part, argslists[0])
|
||||
for part in partitions]
|
||||
else:
|
||||
assert(all([len(args) == len(partitions) for args in argslists]))
|
||||
return [_deploy_func.remote(func, part, *args)
|
||||
for part, args in zip(partitions, *argslists)]
|
||||
|
||||
|
||||
@ray.remote(num_return_vals=2)
|
||||
def _build_columns(df_col, columns):
|
||||
"""Build columns and compute lengths for each partition."""
|
||||
# Columns and width
|
||||
widths = ray.get([_deploy_func.remote(lambda df: len(df.columns), d)
|
||||
for d in df_col])
|
||||
dest_indices = [(p_idx, p_sub_idx) for p_idx in range(len(widths))
|
||||
for p_sub_idx in range(widths[p_idx])]
|
||||
|
||||
col_names = ("partition", "index_within_partition")
|
||||
column_df = pd.DataFrame(dest_indices, index=columns, columns=col_names)
|
||||
|
||||
return widths, column_df
|
||||
|
||||
|
||||
@ray.remote(num_return_vals=2)
|
||||
def _build_index(df_row, index):
|
||||
"""Build index and compute lengths for each partition."""
|
||||
# Rows and length
|
||||
lengths = ray.get([_deploy_func.remote(_get_lengths, d)
|
||||
for d in dfs])
|
||||
for d in df_row])
|
||||
|
||||
dest_indices = {"partition":
|
||||
[i for i in range(len(lengths))
|
||||
for j in range(lengths[i])],
|
||||
"index_within_partition":
|
||||
[j for i in range(len(lengths))
|
||||
for j in range(lengths[i])]}
|
||||
dest_indices = [(p_idx, p_sub_idx) for p_idx in range(len(lengths))
|
||||
for p_sub_idx in range(lengths[p_idx])]
|
||||
col_names = ("partition", "index_within_partition")
|
||||
index_df = pd.DataFrame(dest_indices, index=index, columns=col_names)
|
||||
|
||||
return lengths, pd.DataFrame(dest_indices)
|
||||
return lengths, index_df
|
||||
|
||||
|
||||
def _create_block_partitions(partitions, axis=0, length=None):
|
||||
|
||||
if length is not None and get_npartitions() > length:
|
||||
npartitions = length
|
||||
else:
|
||||
npartitions = get_npartitions()
|
||||
|
||||
x = [create_blocks._submit(args=(partition, npartitions, axis),
|
||||
num_return_vals=npartitions)
|
||||
for partition in partitions]
|
||||
|
||||
# In the case that axis is 1 we have to transpose because we build the
|
||||
# columns into rows. Fortunately numpy is efficent at this.
|
||||
return np.array(x) if axis == 0 else np.array(x).T
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _prepend_partitions(last_vals, index, partition, func):
|
||||
appended_df = last_vals[:index].append(partition)
|
||||
cum_df = func(appended_df)
|
||||
return cum_df[index:]
|
||||
def create_blocks(df, npartitions, axis):
|
||||
# Single partition dataframes don't need to be repartitioned
|
||||
if npartitions == 1:
|
||||
return df
|
||||
# In the case that the size is not a multiple of the number of partitions,
|
||||
# we need to add one to each partition to avoid losing data off the end
|
||||
block_size = df.shape[axis ^ 1] // npartitions \
|
||||
if df.shape[axis ^ 1] % npartitions == 0 \
|
||||
else df.shape[axis ^ 1] // npartitions + 1
|
||||
|
||||
# if not isinstance(df.columns, pd.RangeIndex):
|
||||
# df.columns = pd.RangeIndex(0, len(df.columns))
|
||||
|
||||
blocks = [df.iloc[:, i * block_size: (i + 1) * block_size]
|
||||
if axis == 0
|
||||
else df.iloc[i * block_size: (i + 1) * block_size, :]
|
||||
for i in range(npartitions)]
|
||||
|
||||
for block in blocks:
|
||||
block.columns = pd.RangeIndex(0, len(block.columns))
|
||||
return blocks
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _blocks_to_col(*partition):
|
||||
return pd.concat(partition, axis=0, copy=False)\
|
||||
.reset_index(drop=True)
|
||||
|
||||
|
||||
@ray.remote
|
||||
def _blocks_to_row(*partition):
|
||||
row_part = pd.concat(partition, axis=1, copy=False)\
|
||||
.reset_index(drop=True)
|
||||
# Because our block partitions contain different indices (for the
|
||||
# columns), this change is needed to ensure correctness.
|
||||
row_part.columns = pd.RangeIndex(0, len(row_part.columns))
|
||||
return row_part
|
||||
|
||||
Reference in New Issue
Block a user