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import random
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
__all__ = [
"ConcatDataFramesPipe",
"DataFramesAsTuplesPipe",
"ExampleAggregateAsDataFrames",
"FilterDataFramesPipe",
"PerRowDataFramesPipe",
"ShuffleDataFramesPipe",
]
@functional_datapipe('_dataframes_as_tuples')
class DataFramesAsTuplesPipe(IterDataPipe):
def __init__(self, source_datapipe):
self.source_datapipe = source_datapipe
def __iter__(self):
for df in self.source_datapipe:
# for record in df.to_records(index=False):
for record in df_wrapper.iterate(df):
yield record
@functional_datapipe('_dataframes_per_row', enable_df_api_tracing=True)
class PerRowDataFramesPipe(DFIterDataPipe):
def __init__(self, source_datapipe):
self.source_datapipe = source_datapipe
def __iter__(self):
for df in self.source_datapipe:
# TODO(VitalyFedyunin): Replacing with TorchArrow only API, as we are dropping pandas as followup
for i in range(len(df)):
yield df[i:i + 1]
@functional_datapipe('_dataframes_concat', enable_df_api_tracing=True)
class ConcatDataFramesPipe(DFIterDataPipe):
def __init__(self, source_datapipe, batch=3):
self.source_datapipe = source_datapipe
self.n_batch = batch
def __iter__(self):
buffer = []
for df in self.source_datapipe:
buffer.append(df)
if len(buffer) == self.n_batch:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
@functional_datapipe('_dataframes_shuffle', enable_df_api_tracing=True)
class ShuffleDataFramesPipe(DFIterDataPipe):
def __init__(self, source_datapipe):
self.source_datapipe = source_datapipe
def __iter__(self):
size = None
all_buffer = []
for df in self.source_datapipe:
if size is None:
size = df_wrapper.get_len(df)
for i in range(df_wrapper.get_len(df)):
all_buffer.append(df_wrapper.get_item(df, i))
random.shuffle(all_buffer)
buffer = []
for df in all_buffer:
buffer.append(df)
if len(buffer) == size:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
@functional_datapipe('_dataframes_filter', enable_df_api_tracing=True)
class FilterDataFramesPipe(DFIterDataPipe):
def __init__(self, source_datapipe, filter_fn):
self.source_datapipe = source_datapipe
self.filter_fn = filter_fn
def __iter__(self):
size = None
all_buffer = []
filter_res = []
for df in self.source_datapipe:
if size is None:
size = len(df.index)
for i in range(len(df.index)):
all_buffer.append(df[i:i + 1])
filter_res.append(self.filter_fn(df.iloc[i]))
buffer = []
for df, res in zip(all_buffer, filter_res):
if res:
buffer.append(df)
if len(buffer) == size:
yield df_wrapper.concat(buffer)
buffer = []
if len(buffer):
yield df_wrapper.concat(buffer)
@functional_datapipe('_to_dataframes_pipe', enable_df_api_tracing=True)
class ExampleAggregateAsDataFrames(DFIterDataPipe):
def __init__(self, source_datapipe, dataframe_size=10, columns=None):
self.source_datapipe = source_datapipe
self.columns = columns
self.dataframe_size = dataframe_size
def _as_list(self, item):
try:
return list(item)
except Exception: # TODO(VitalyFedyunin): Replace with better iterable exception
return [item]
def __iter__(self):
aggregate = []
for item in self.source_datapipe:
aggregate.append(self._as_list(item))
if len(aggregate) == self.dataframe_size:
yield df_wrapper.create_dataframe(aggregate, columns=self.columns)
aggregate = []
if len(aggregate) > 0:
yield df_wrapper.create_dataframe(aggregate, columns=self.columns)
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