-
Notifications
You must be signed in to change notification settings - Fork 4.6k
/
readerwriter.py
245 lines (200 loc) · 8.34 KB
/
readerwriter.py
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
import abc
import json
from collections import OrderedDict
from pathlib import Path
import rasa.shared.nlu.training_data.util
from rasa.shared.constants import INTENT_MESSAGE_PREFIX
from rasa.shared.nlu.constants import (
INTENT,
ENTITY_ATTRIBUTE_VALUE,
ENTITY_ATTRIBUTE_START,
ENTITY_ATTRIBUTE_END,
ENTITY_ATTRIBUTE_TYPE,
ENTITY_ATTRIBUTE_GROUP,
ENTITY_ATTRIBUTE_ROLE,
)
import rasa.shared.utils.io
import typing
from typing import Text, Dict, Any, Union, List
from collections import defaultdict
if typing.TYPE_CHECKING:
from rasa.shared.nlu.training_data.training_data import TrainingData
def _raise_on_same_start_and_different_end_positions(
aggregated_entities: Dict[int, List[Dict[Text, Any]]],
) -> None:
"""Raises a ValueError iff two entities have overlapping but not identical spans.
Args:
aggregated_entities: Entities for each start position
"""
for entity_list in aggregated_entities.values():
end = entity_list[0]["end"]
for entity in entity_list[1:]:
# By construction, start positions of all entities in `entity_list` are
# identical
if entity["end"] != end:
raise ValueError(
f"Entities '{entity}' and "
f"'{entity_list[0]}' have identical "
f"start but different end positions"
)
class TrainingDataReader(abc.ABC):
"""Reader for NLU training data."""
def __init__(self) -> None:
"""Creates reader instance."""
self.filename: Text = ""
def read(self, filename: Union[Text, Path], **kwargs: Any) -> "TrainingData":
"""Reads TrainingData from a file."""
self.filename = str(filename)
return self.reads(rasa.shared.utils.io.read_file(filename), **kwargs)
@abc.abstractmethod
def reads(self, s: Text, **kwargs: Any) -> "TrainingData":
"""Reads TrainingData from a string."""
raise NotImplementedError
class TrainingDataWriter:
"""A class for writing training data to a file."""
def dump(self, filename: Text, training_data: "TrainingData") -> None:
"""Writes a TrainingData object to a file."""
s = self.dumps(training_data)
rasa.shared.utils.io.write_text_file(s, filename)
def dumps(self, training_data: "TrainingData") -> Text:
"""Turns TrainingData into a string."""
raise NotImplementedError
@staticmethod
def prepare_training_examples(
training_data: "TrainingData",
) -> Dict[Text, List[Union[Dict, Text]]]:
"""Pre-processes training data examples by removing not trainable entities."""
import rasa.shared.nlu.training_data.util as rasa_nlu_training_data_utils
training_examples: Dict[Text, List[Union[Dict, Text]]] = OrderedDict()
# Sort by intent while keeping basic intent order
for example in [e.as_dict_nlu() for e in training_data.training_examples]:
if not example.get(INTENT):
continue
rasa_nlu_training_data_utils.remove_untrainable_entities_from(example)
intent = example[INTENT]
training_examples.setdefault(intent, [])
training_examples[intent].append(example)
return training_examples
@staticmethod
def generate_list_item(text: Text) -> Text:
"""Generates text for a list item."""
return f"- {rasa.shared.nlu.training_data.util.encode_string(text)}\n"
@staticmethod
def generate_message(message: Dict[Text, Any]) -> Text:
"""Generates text for a message object.
Args:
message: A message
Returns:
The text of the message, annotated with the entity data that is contained
in the message
"""
md = ""
text = message.get("text", "")
pos = 0
# If a message was prefixed with `INTENT_MESSAGE_PREFIX` (this can only happen
# in end-to-end stories) then potential entities were provided in the json
# format (e.g. `/greet{"name": "Rasa"}) and we don't have to add the NLU
# entity annotation
if not text.startswith(INTENT_MESSAGE_PREFIX):
entities = message.get("entities", [])
entities_with_start_and_end = [
e for e in entities if "start" in e and "end" in e
]
# Multiple entities can share the same position span. To account for that,
# group all entities by their start positions
aggregated_entities: Dict[int, List[Dict[Text, Any]]] = defaultdict(list)
for entity in entities_with_start_and_end:
aggregated_entities[entity["start"]].append(entity)
_raise_on_same_start_and_different_end_positions(aggregated_entities)
for start, entities in sorted(aggregated_entities.items()):
md += text[pos:start]
md += TrainingDataWriter.generate_entity(
text,
entities[0] if len(entities) == 1 else entities,
)
pos = entities[0]["end"]
md += text[pos:]
return md
@staticmethod
def generate_entity_attributes(
text: Text, entity: Dict[Text, Any], short_allowed: bool = True
) -> Text:
"""Generates text for the entity attributes.
Args:
text: The text that is annotated with the entity
entity: Entity data
short_allowed: If `True`, allow shorthand annotation with parenthesis
Returns:
The annotation text that should follow the given text
"""
entity_text = text
entity_type = entity.get(ENTITY_ATTRIBUTE_TYPE)
entity_value = entity.get(ENTITY_ATTRIBUTE_VALUE)
entity_role = entity.get(ENTITY_ATTRIBUTE_ROLE)
entity_group = entity.get(ENTITY_ATTRIBUTE_GROUP)
if entity_value and entity_value == entity_text:
entity_value = None
use_short_syntax = (
short_allowed
and entity_value is None
and entity_role is None
and entity_group is None
)
if use_short_syntax:
return f"({entity_type})"
else:
entity_dict = OrderedDict(
[
(ENTITY_ATTRIBUTE_TYPE, entity_type),
(ENTITY_ATTRIBUTE_ROLE, entity_role),
(ENTITY_ATTRIBUTE_GROUP, entity_group),
(ENTITY_ATTRIBUTE_VALUE, entity_value),
]
)
entity_dict = OrderedDict(
[(k, v) for k, v in entity_dict.items() if v is not None]
)
return f"{json.dumps(entity_dict)}"
@staticmethod
def generate_entity(
text: Text, entity: Union[Dict[Text, Any], List[Dict[Text, Any]]]
) -> Text:
"""Generates text for one or multiple entity objects.
Args:
text: The un-annotated text
entity: One or multiple entity annotations for one part of this text
Returns:
Annotated part of the text
"""
if isinstance(entity, list):
entity_text = text[
entity[0][ENTITY_ATTRIBUTE_START] : entity[0][ENTITY_ATTRIBUTE_END]
]
return (
f"[{entity_text}]["
+ ", ".join(
[
TrainingDataWriter.generate_entity_attributes(
text=entity_text, entity=e, short_allowed=False
)
for e in entity
]
)
+ "]"
)
else:
entity_text = text[
entity[ENTITY_ATTRIBUTE_START] : entity[ENTITY_ATTRIBUTE_END]
]
return f"[{entity_text}]" + TrainingDataWriter.generate_entity_attributes(
text=entity_text, entity=entity, short_allowed=True
)
class JsonTrainingDataReader(TrainingDataReader):
"""A class for reading JSON files."""
def reads(self, s: Text, **kwargs: Any) -> "TrainingData":
"""Transforms string into json object and passes it on."""
js = json.loads(s)
return self.read_from_json(js, **kwargs)
def read_from_json(self, js: Dict[Text, Any], **kwargs: Any) -> "TrainingData":
"""Reads TrainingData from a json object."""
raise NotImplementedError