-
Notifications
You must be signed in to change notification settings - Fork 704
Expand file tree
/
Copy pathcolumn.py
More file actions
598 lines (477 loc) · 18.1 KB
/
Copy pathcolumn.py
File metadata and controls
598 lines (477 loc) · 18.1 KB
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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
# Copyright 2020 The SQLFlow Authors. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import six
from runtime.feature.field_desc import DataType, FieldDesc
class FeatureColumn(object):
"""
FeatureColumn corresponds to the COLUMN clause in the TO TRAIN statement.
It is the base class of all feature column classes.
"""
def get_field_desc(self):
"""
Get the underlying FieldDesc object list that the feature
column object holds.
Returns:
A list of the FieldDesc objects.
"""
raise NotImplementedError()
def new_feature_column_from(self, field_desc):
"""
Create a new feature column object of the same type
that holds the given FieldDesc object.
Args:
field_desc (FieldDesc): the given FieldDesc object.
Returns:
A new feature column object which is of the same type,
and holds the given FieldDesc object.
"""
raise NotImplementedError()
@classmethod
def to_dict(cls, feature_column):
"""
Convert the FeatureColumn object to a Python dict, which can be
serialized to a JSON string.
Args:
feature_column (FeatureColumn): a FeatureColumn object.
Returns:
A Python dict which represents the FeatureColumn object.
"""
return {
"type": type(feature_column).__name__,
"value": feature_column._to_dict(),
}
def _to_dict(self):
"""
The underlying implementation of `FeatureColumn.to_dict`.
Returns:
A Python dict which represents the FeatureColumn object.
"""
raise NotImplementedError()
@classmethod
def from_dict_or_feature_column(cls, obj):
"""
If obj is of type dict, create a FeatureColumn object from a Python
dict. If obj is of type FeatureColumn, return itself. This method
can be used to deserialize a FeatureColumn object from a JSON string.
Args:
obj (dict|FeatureColumn): a Python dict or FeatureColumn object.
Returns:
A FeatureColumn object.
"""
if isinstance(obj, dict):
typ = obj.get("type")
return eval(typ)._from_dict(obj.get("value"))
elif isinstance(obj, FeatureColumn):
return obj
else:
raise TypeError("not supported type %s" % type(obj))
@classmethod
def _from_dict(self, d):
"""
The underlying implementation of `FeatureColumn.from_dict`.
Args:
d (dict): a Python dict object.
Returns:
A FeatureColumn object.
"""
raise NotImplementedError()
class CategoryColumn(FeatureColumn):
"""
CategoryColumn corresponds to the categorical feature column.
It is the base class of all categorical feature column classes.
"""
def num_class(self):
"""
Get the class number of the categorical feature column.
Returns:
An integer which represents the class number.
"""
raise NotImplementedError()
class NumericColumn(FeatureColumn):
"""
NumericColumn represents a dense or sparse numeric feature.
Args:
field_desc (FieldDesc): the underlying FieldDesc object that the
NumericColumn object holds.
"""
def __init__(self, field_desc):
assert isinstance(field_desc, FieldDesc)
self.field_desc = field_desc
def get_field_desc(self):
return [self.field_desc]
def new_feature_column_from(self, field_desc):
return NumericColumn(field_desc)
def _to_dict(self):
return {
"field_desc": self.field_desc.to_dict(),
}
@classmethod
def _from_dict(cls, d):
fd = FieldDesc.from_dict(d["field_desc"])
return NumericColumn(fd)
class BucketColumn(CategoryColumn):
"""
BucketColumn represents a bucketized feature column.
Args:
source_column (NumericColumn): the underlying NumericColumn object.
boundaries (list[int|float]): the boundaries of the buckets.
"""
def __init__(self, source_column, boundaries):
assert isinstance(
source_column,
NumericColumn), "source_column of BUCKET must be of numeric type"
self.source_column = source_column
self.boundaries = boundaries
def get_field_desc(self):
return self.source_column.get_field_desc()
def new_feature_column_from(self, field_desc):
source_column = self.source_column.new_feature_column_from(field_desc)
return BucketColumn(source_column, self.boundaries)
def num_class(self):
return len(self.boundaries) + 1
def _to_dict(self):
return {
"source_column": FeatureColumn.to_dict(self.source_column),
"boundaries": self.boundaries,
}
@classmethod
def _from_dict(cls, d):
source_column = FeatureColumn.from_dict_or_feature_column(
d["source_column"])
boundaries = d["boundaries"]
return BucketColumn(source_column, boundaries)
class CategoryIDColumn(CategoryColumn):
"""
CategoryIDColumn represents a categorical id feature column.
Args:
field_desc (FieldDesc): the underlying FieldDesc object.
bucket_size (int): the bucket size.
"""
def __init__(self, field_desc, bucket_size):
assert isinstance(field_desc, FieldDesc)
self.field_desc = field_desc
self.bucket_size = bucket_size
def get_field_desc(self):
return [self.field_desc]
def new_feature_column_from(self, field_desc):
return CategoryIDColumn(field_desc, self.bucket_size)
def num_class(self):
return self.bucket_size
def _to_dict(self):
return {
"field_desc": self.field_desc.to_dict(),
"bucket_size": self.bucket_size,
}
@classmethod
def _from_dict(cls, d):
field_desc = FieldDesc.from_dict(d["field_desc"])
bucket_size = d["bucket_size"]
return CategoryIDColumn(field_desc, bucket_size)
class CategoryHashColumn(CategoryColumn):
"""
CategoryHashColumn represents a categorical hash feature column.
Args:
field_desc (FieldDesc): the underlying FieldDesc object.
bucket_size (int): the bucket size for hashing.
"""
def __init__(self, field_desc, bucket_size):
assert isinstance(field_desc, FieldDesc)
self.field_desc = field_desc
self.bucket_size = bucket_size
def get_field_desc(self):
return [self.field_desc]
def new_feature_column_from(self, field_desc):
return CategoryHashColumn(field_desc, self.bucket_size)
def num_class(self):
return self.bucket_size
def _to_dict(self):
return {
"field_desc": self.field_desc.to_dict(),
"bucket_size": self.bucket_size,
}
@classmethod
def _from_dict(cls, d):
field_desc = FieldDesc.from_dict(d["field_desc"])
bucket_size = d["bucket_size"]
return CategoryHashColumn(field_desc, bucket_size)
class SeqCategoryIDColumn(CategoryColumn):
"""
SeqCategoryIDColumn represents a sequential categorical id feature column.
Args:
field_desc (FieldDesc): the underlying FieldDesc object.
bucket_size (int): the bucket size.
"""
def __init__(self, field_desc, bucket_size):
assert isinstance(field_desc, FieldDesc)
self.field_desc = field_desc
self.bucket_size = bucket_size
def get_field_desc(self):
return [self.field_desc]
def new_feature_column_from(self, field_desc):
return SeqCategoryIDColumn(field_desc, self.bucket_size)
def num_class(self):
return self.bucket_size
def _to_dict(self):
return {
"field_desc": self.field_desc.to_dict(),
"bucket_size": self.bucket_size,
}
@classmethod
def _from_dict(cls, d):
field_desc = FieldDesc.from_dict(d["field_desc"])
bucket_size = d["bucket_size"]
return SeqCategoryIDColumn(field_desc, bucket_size)
class CrossColumn(CategoryColumn):
"""
CrossColumn represents a crossed feature column.
Args:
keys (str|NumericColumn): the underlying feature column name or
NumericColumn object.
hash_bucket_size (int): the bucket size for hashing.
"""
def __init__(self, keys, hash_bucket_size):
for k in keys:
assert isinstance(k, (six.string_types, NumericColumn)), \
"keys of CROSS must be of either string or numeric type"
self.keys = keys
self.hash_bucket_size = hash_bucket_size
def get_field_desc(self):
descs = []
for k in self.keys:
if isinstance(k, six.string_types):
descs.append(
FieldDesc(name=k, dtype=DataType.STRING, shape=[1]))
elif isinstance(k, NumericColumn):
descs.extend(k.get_field_desc())
else:
raise ValueError("unsupported type %s" % type(k))
return descs
def new_feature_column_from(self, field_desc):
raise NotImplementedError("CROSS does not support apply_to method")
def num_class(self):
return self.hash_bucket_size
def _to_dict(self):
keys = []
for k in self.keys:
if isinstance(k, six.string_types):
keys.append(k)
else:
keys.append(FeatureColumn.to_dict(k))
return {
"keys": keys,
"hash_bucket_size": self.hash_bucket_size,
}
@classmethod
def _from_dict(cls, d):
keys = []
for k in d["keys"]:
if isinstance(k, six.string_types):
keys.append(k)
else:
keys.append(FeatureColumn.from_dict_or_feature_column(k))
hash_bucket_size = d["hash_bucket_size"]
return CrossColumn(keys, hash_bucket_size)
class WeightedCategoryColumn(CategoryColumn):
def __init__(self, category_column=None, name=""):
if category_column is not None:
assert isinstance(category_column, CategoryColumn)
self.category_column = category_column
self.name = name
def get_field_desc(self):
return self.category_column.get_field_desc()
def new_feature_column_from(self, field_desc):
if self.category_column is not None:
category_column = self.category_column.new_feature_column_from(
field_desc)
assert isinstance(category_column, CategoryColumn)
else:
category_column = None
return WeightedCategoryColumn(category_column=category_column,
name=self.name)
def num_class(self):
return self.category_column.num_class()
def _to_dict(self):
category_column = None
if self.category_column is not None:
category_column = FeatureColumn.to_dict(self.category_column)
return {
"category_column": category_column,
"name": self.name,
}
@classmethod
def _from_dict(cls, d):
category_column = d["category_column"]
if category_column is not None:
category_column = FeatureColumn.from_dict_or_feature_column(
category_column)
return WeightedCategoryColumn(category_column=category_column,
name=d["name"])
class EmbeddingColumn(FeatureColumn):
"""
EmbeddingColumn represents an embedding feature column.
Args:
category_column (CategoryColumn): the underlying CategoryColumn object.
dimension (int): the dimension of the embedding.
combiner (str): how to reduce if there are multiple entries in a single
row. Currently 'mean', 'sqrtn' and 'sum' are supported.
initializer (str): the initializer of the embedding table.
name (str): only used when category_column=None. In this case, the
category_column would be filled automaticaly in the feature
derivation stage.
"""
def __init__(self,
category_column=None,
dimension=0,
combiner="",
initializer="",
name=""):
if category_column is not None:
assert isinstance(category_column, CategoryColumn)
self.category_column = category_column
self.dimension = dimension
self.combiner = combiner
self.initializer = initializer
self.name = name
def get_field_desc(self):
if self.category_column is None:
return []
return self.category_column.get_field_desc()
def new_feature_column_from(self, field_desc):
if self.category_column is not None:
category_column = self.category_column.new_feature_column_from(
field_desc)
else:
category_column = None
return EmbeddingColumn(category_column=category_column,
dimension=self.dimension,
combiner=self.combiner,
initializer=self.initializer,
name=self.name)
def _to_dict(self):
category_column = None
if self.category_column is not None:
category_column = FeatureColumn.to_dict(self.category_column)
return {
"category_column": category_column,
"dimension": self.dimension,
"combiner": self.combiner,
"initializer": self.initializer,
"name": self.name,
}
@classmethod
def _from_dict(cls, d):
category_column = d["category_column"]
if category_column is not None:
category_column = FeatureColumn.from_dict_or_feature_column(
category_column)
return EmbeddingColumn(category_column=category_column,
dimension=d["dimension"],
combiner=d["combiner"],
initializer=d["initializer"],
name=d["name"])
class IndicatorColumn(FeatureColumn):
"""
IndicatorColumn represents the one-hot feature column.
Args:
category_column (CategoryColumn): the underlying CategoryColumn object.
name (str): only used when category_column=None. In this case, the
category_column would be filled automaticaly in the feature
derivation stage.
"""
def __init__(self, category_column=None, name=""):
if category_column is not None:
assert isinstance(category_column, CategoryColumn)
self.category_column = category_column
self.name = name
def get_field_desc(self):
if self.category_column is None:
return []
return self.category_column.get_field_desc()
def new_feature_column_from(self, field_desc):
if self.category_column is not None:
category_column = self.category_column.new_feature_column_from(
field_desc)
else:
category_column = None
return IndicatorColumn(category_column, self.name)
def _to_dict(self):
category_column = None
if self.category_column is not None:
category_column = FeatureColumn.to_dict(self.category_column)
return {
"category_column": category_column,
"name": self.name,
}
@classmethod
def _from_dict(cls, d):
category_column = d["category_column"]
if category_column is not None:
category_column = FeatureColumn.from_dict_or_feature_column(
category_column)
return IndicatorColumn(category_column=category_column, name=d["name"])
class JSONEncoderWithFeatureColumn(json.JSONEncoder):
"""
A helper class to serialize FeatureColumn objects to JSON string.
"""
def default(self, obj):
"""
Convert obj to an object that `json.dumps` accepts.
If obj is of type FeatureColumn, convert it to a Python
dict.
Args:
obj: any Python object.
Returns:
A Python object that `json.dumps` accepts.
"""
if isinstance(obj, FeatureColumn):
return FeatureColumn.to_dict(obj)
# Use the default JSONEncoder if obj is not FeatureColumn
return json.JSONEncoder.default(self, obj)
SUPPORTED_CONCRETE_FEATURE_COLUMNS = [
'NumericColumn',
'BucketColumn',
'CategoryIDColumn',
'CategoryHashColumn',
'SeqCategoryIDColumn',
'CrossColumn',
'EmbeddingColumn',
'IndicatorColumn',
'WeightedCategoryColumn',
]
def feature_column_json_hook(obj):
"""
An object hook method that json.JSONDecoder accepts.
It is used to convert a Python dict to FeatureColumn object
if possible. See https://docs.python.org/3/library/json.html
for the usage of object hook.
Args:
obj: any Python object.
Returns:
If obj can be converted to a FeatureColumn object, convert
it. Otherwise, return itself.
"""
if isinstance(obj, dict):
typ = obj.get("type")
if typ in SUPPORTED_CONCRETE_FEATURE_COLUMNS:
return FeatureColumn.from_dict_or_feature_column(obj)
return obj
class JSONDecoderWithFeatureColumn(json.JSONDecoder):
"""
A helper class to deserialize JSON string to FeatureColumn objects.
"""
def __init__(self, *args, **kwargs):
# See here: https://docs.python.org/3/library/json.html
# for the usage of object_hook
kwargs['object_hook'] = feature_column_json_hook
super(JSONDecoderWithFeatureColumn, self).__init__(*args, **kwargs)