1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
|
# mypy: allow-untyped-defs
import random
from typing import Any, List
from torch.utils.data.datapipes._decorator import functional_datapipe
from torch.utils.data.datapipes.dataframe import dataframe_wrapper as df_wrapper
from torch.utils.data.datapipes.datapipe import DFIterDataPipe, IterDataPipe
__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):
yield from df_wrapper.iterate(df)
@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: List[Any] = []
for df in self.source_datapipe:
if size is None:
size = df_wrapper.get_len(df)
all_buffer.extend(
df_wrapper.get_item(df, i) for i in range(df_wrapper.get_len(df))
)
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)
|
