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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
from typing import Callable, List, Union
from .choice import Choice
from .find_choices_options import FindChoicesOptions, FindValuesOptions
from .found_choice import FoundChoice
from .found_value import FoundValue
from .model_result import ModelResult
from .sorted_value import SortedValue
from .token import Token
from .tokenizer import Tokenizer
class Find:
"""Contains methods for matching user input against a list of choices"""
@staticmethod
def find_choices(
utterance: str,
choices: [Union[str, Choice]],
options: FindChoicesOptions = None,
):
"""Matches user input against a list of choices"""
if not choices:
raise TypeError(
"Find: choices cannot be None. Must be a [str] or [Choice]."
)
opt = options if options else FindChoicesOptions()
# Normalize list of choices
choices_list = [
Choice(value=choice) if isinstance(choice, str) else choice
for choice in choices
]
# Build up full list of synonyms to search over.
# - Each entry in the list contains the index of the choice it belongs to which will later be
# used to map the search results back to their choice.
synonyms: [SortedValue] = []
for index, choice in enumerate(choices_list):
if not opt.no_value:
synonyms.append(SortedValue(value=choice.value, index=index))
if (
getattr(choice, "action", False)
and getattr(choice.action, "title", False)
and not opt.no_value
):
synonyms.append(SortedValue(value=choice.action.title, index=index))
if choice.synonyms is not None:
for synonym in choice.synonyms:
synonyms.append(SortedValue(value=synonym, index=index))
def found_choice_constructor(value_model: ModelResult) -> ModelResult:
choice = choices_list[value_model.resolution.index]
return ModelResult(
start=value_model.start,
end=value_model.end,
type_name="choice",
text=value_model.text,
resolution=FoundChoice(
value=choice.value,
index=value_model.resolution.index,
score=value_model.resolution.score,
synonym=value_model.resolution.value,
),
)
# Find synonyms in utterance and map back to their choices_list
return list(
map(
found_choice_constructor, Find.find_values(utterance, synonyms, options)
)
)
@staticmethod
def find_values(
utterance: str, values: List[SortedValue], options: FindValuesOptions = None
) -> List[ModelResult]:
# Sort values in descending order by length, so that the longest value is searchd over first.
sorted_values = sorted(
values, key=lambda sorted_val: len(sorted_val.value), reverse=True
)
# Search for each value within the utterance.
matches: [ModelResult] = []
opt = options if options else FindValuesOptions()
tokenizer: Callable[[str, str], List[Token]] = (
opt.tokenizer if opt.tokenizer else Tokenizer.default_tokenizer
)
tokens = tokenizer(utterance, opt.locale)
max_distance = (
opt.max_token_distance if opt.max_token_distance is not None else 2
)
for entry in sorted_values:
# Find all matches for a value
# - To match "last one" in "the last time I chose the last one" we need
# to re-search the string starting from the end of the previous match.
# - The start & end position returned for the match are token positions.
start_pos = 0
searched_tokens = tokenizer(entry.value.strip(), opt.locale)
while start_pos < len(tokens):
match: Union[ModelResult, None] = Find._match_value(
tokens,
max_distance,
opt,
entry.index,
entry.value,
searched_tokens,
start_pos,
)
if match is not None:
start_pos = match.end + 1
matches.append(match)
else:
break
# Sort matches by score descending
sorted_matches = sorted(
matches,
key=lambda model_result: model_result.resolution.score,
reverse=True,
)
# Filter out duplicate matching indexes and overlapping characters
# - The start & end positions are token positions and need to be translated to
# character positions before returning. We also need to populate the "text"
# field as well.
results: List[ModelResult] = []
found_indexes = set()
used_tokens = set()
for match in sorted_matches:
# Apply filters.
add = match.resolution.index not in found_indexes
for i in range(match.start, match.end + 1):
if i in used_tokens:
add = False
break
# Add to results
if add:
# Update filter info
found_indexes.add(match.resolution.index)
for i in range(match.start, match.end + 1):
used_tokens.add(i)
# Translate start & end and populate text field
match.start = tokens[match.start].start
match.end = tokens[match.end].end
match.text = utterance[match.start : match.end + 1]
results.append(match)
# Return the results sorted by position in the utterance
return sorted(results, key=lambda model_result: model_result.start)
@staticmethod
def _match_value(
source_tokens: List[Token],
max_distance: int,
options: FindValuesOptions,
index: int,
value: str,
searched_tokens: List[Token],
start_pos: int,
) -> Union[ModelResult, None]:
# Match value to utterance and calculate total deviation.
# - The tokens are matched in order so "second last" will match in
# "the second from last one" but not in "the last from the second one".
# - The total deviation is a count of the number of tokens skipped in the
# match so for the example above the number of tokens matched would be
# 2 and the total deviation would be 1.
matched = 0
total_deviation = 0
start = -1
end = -1
for token in searched_tokens:
# Find the position of the token in the utterance.
pos = Find._index_of_token(source_tokens, token, start_pos)
if pos >= 0:
# Calculate the distance between the current token's position and the previous token's distance.
distance = pos - start_pos if matched > 0 else 0
if distance <= max_distance:
# Update count of tokens matched and move start pointer to search for next token
# after the current token
matched += 1
total_deviation += distance
start_pos = pos + 1
# Update start & end position that will track the span of the utterance that's matched.
if start < 0:
start = pos
end = pos
# Calculate score and format result
# - The start & end positions and the results text field will be corrected by the caller.
result: ModelResult = None
if matched > 0 and (
matched == len(searched_tokens) or options.allow_partial_matches
):
# Percentage of tokens matched. If matching "second last" in
# "the second form last one" the completeness would be 1.0 since
# all tokens were found.
completeness = matched / len(searched_tokens)
# Accuracy of the match. The accuracy is reduced by additional tokens
# occuring in the value that weren't in the utterance. So an utterance
# of "second last" matched against a value of "second from last" would
# result in an accuracy of 0.5.
accuracy = float(matched) / (matched + total_deviation)
# The final score is simply the compeleteness multiplied by the accuracy.
score = completeness * accuracy
# Format result
result = ModelResult(
text="",
start=start,
end=end,
type_name="value",
resolution=FoundValue(value=value, index=index, score=score),
)
return result
@staticmethod
def _index_of_token(tokens: List[Token], token: Token, start_pos: int) -> int:
for i in range(start_pos, len(tokens)):
if tokens[i].normalized == token.normalized:
return i
return -1
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