metric / processors.py
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import ast
import copy
import json
import re
import string
from difflib import get_close_matches
from typing import Any, Dict
import numpy as np
from .deprecation_utils import deprecation
from .error_utils import Documentation, UnitxtError
from .operator import MultiStreamOperator
from .operators import FieldOperator, InstanceFieldOperator
from .settings_utils import get_constants
from .type_utils import isoftype
constants = get_constants()
class PostProcess(MultiStreamOperator):
operator: InstanceFieldOperator
process_prediction: bool = True
process_references: bool = True
def prepare(self):
super().prepare()
if not isoftype(self.operator, InstanceFieldOperator):
raise UnitxtError(
f"PostProcess requires operator field to be of type InstanceFieldOperator. Got object of type <{type(self.operator).__name__}>.",
Documentation.POST_PROCESSORS,
)
self.prediction_operator = copy.copy(self.operator)
self.prediction_operator.field = "prediction"
self.references_operator = copy.copy(self.operator)
self.references_operator.field = "references"
self.references_operator.process_every_value = True
self.references_operator.dont_apply_to_streams = [constants.inference_stream]
def process(self, multi_stream):
if self.process_prediction:
multi_stream = self.prediction_operator(multi_stream)
if self.process_references:
multi_stream = self.references_operator(multi_stream)
return multi_stream
class ToString(FieldOperator):
def process_value(self, text: Any) -> Any:
return str(text)
class ToStringStripped(FieldOperator):
def process_value(self, text: Any) -> Any:
return str(text).strip()
class SplitStrip(FieldOperator):
delimiter: str = " "
strip_every_element: bool = False
def process_value(self, text: Any) -> Any:
return [
x.strip() if self.strip_every_element else x
for x in text.split(self.delimiter)
]
class ToListByComma(SplitStrip):
delimiter = ","
strip_every_element = True
class ToListByCommaSpace(SplitStrip):
delimiter = ", "
strip_every_element = True
class RegexParser(FieldOperator):
"""A processor that uses regex in order to parse a string."""
regex: str
termination_regex: str = None
def process_value(self, text: Any) -> Any:
if self.termination_regex is not None and re.fullmatch(
self.termination_regex, text
):
return []
return re.findall(self.regex, text)
class ExtractWithRegex(RegexParser):
def process_value(self, text: Any) -> Any:
matches = super().process_value(text)
if matches:
return matches[0]
return ""
class ListToEmptyEntitiesTuples(FieldOperator):
def process_value(self, lst: Any) -> Any:
try:
return [(str(item), "") for item in lst]
except json.JSONDecodeError:
return []
class DictOfListsToPairs(FieldOperator):
position_key_before_value: bool = True
def process_value(self, obj: Any) -> Any:
try:
result = []
for key, values in obj.items():
for value in values:
assert isinstance(value, str)
pair = (
(key, value) if self.position_key_before_value else (value, key)
)
result.append(pair)
return result
except:
return []
class TakeFirstNonEmptyLine(FieldOperator):
def process_value(self, text: Any) -> Any:
parts = str(text).strip().split("\n")
if len(parts) == 0:
return ""
return parts[0].strip()
class TakeLastNonEmptyLine(FieldOperator):
def process_value(self, text: Any) -> Any:
parts = str(text).strip().split("\n")
if len(parts) == 0:
return ""
return parts[-1].strip()
class ConvertToBoolean(FieldOperator):
def process_value(self, text: Any) -> Any:
clean_instance = str(text).strip().lower()
if any(w in clean_instance for w in ["no", "not", "wrong", "false"]):
return "FALSE"
if any(w in clean_instance for w in ["yes", "right", "correct", "true"]):
return "TRUE"
return "OTHER"
class LowerCaseTillPunc(FieldOperator):
def process_value(self, text: Any) -> Any:
non_empty_line = text.lower()
match = re.search(r"[.,!?;]", non_empty_line)
if match:
# Extract text up to the first punctuation
non_empty_line = non_empty_line[: match.start()]
return non_empty_line
class Lower(FieldOperator):
def process_value(self, text: Any) -> Any:
return text.lower()
class Upper(FieldOperator):
def process_value(self, text: Any) -> Any:
return str(text).upper()
@deprecation("2.0.0", alternative=Lower)
class LowerCase(Lower):
pass
class Capitalize(FieldOperator):
def process_value(self, text: Any) -> Any:
return text.capitalize()
class GetStringAfter(FieldOperator):
substring: str
def process_value(self, text: Any) -> Any:
return text.split(self.substring, 1)[-1].strip()
class MatchClosestOption(InstanceFieldOperator):
options_field: str = "options"
def process_instance_value(self, value: Any, instance: Dict[str, Any]):
options = instance["task_data"][self.options_field]
return get_close_matches(value, options, n=1, cutoff=0.0)[0]
def process_instance_value(self, value, instance):
options = instance[self.options_field]
# Get the closest match; n=1 returns the single closest match
closest_match = get_close_matches(value, options, n=1, cutoff=0)
return closest_match[0] if closest_match else None
class Substring(FieldOperator):
begin: int = 0
end: int = None
def process_value(self, text: Any) -> Any:
if self.end is None:
return text[self.begin :]
return text[self.begin : self.end]
class FirstCharacter(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"\s*(\w)", text)
if match:
return match.groups(0)[0]
return ""
class TakeFirstWord(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"([-]*[0-9]+(\.([0-9]+))*)|([\w]+)", text)
if match:
return text[match.start() : match.end()]
return ""
class YesNoToInt(FieldOperator):
def process_value(self, text: Any) -> Any:
if text == "yes":
return "1"
if text == "no":
return "0"
return text
class YesToOneElseZero(FieldOperator):
def process_value(self, text: Any) -> Any:
if text == "yes":
return "1"
return "0"
class StrToFloatFormat(FieldOperator):
def process_value(self, text: Any) -> Any:
try:
return str(float(text))
except Exception:
return str(text)
class ToYesOrNone(FieldOperator):
def process_value(self, text: Any) -> Any:
if text == "yes":
return "yes"
return "none"
class StanceToProCon(FieldOperator):
def process_value(self, text: Any) -> Any:
if text == "positive":
return "PRO"
if text in ["negative", "suggestion"]:
return "CON"
return "none"
class StringEquals(FieldOperator):
string: str
def process_value(self, text: Any) -> Any:
if "not " + self.string.lower() in text.lower():
return "not " + self.string.lower()
if self.string.lower() in text.lower():
return self.string.lower()
return text
@deprecation("2.0.0", alternative=StringEquals)
class StringOrNotString(StringEquals):
pass
class ExtractMtBenchRatingJudgment(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"\[\[([\d]+\.?[\d]*)\]\]", text)
try:
return float(match.group(1)) / 10
except:
return 0.0
class ExtractHarmRatingJudgement(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"\[\[([\d]+\.?[\d]*)\]\]", text)
try:
return float(match.group(1))*0.25 - 0.25
except:
return np.NaN
class ExtractMtBenchLabelJudgment(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"\[\[([^\]]+)\]\]", text)
try:
return str(match.group(1))
except:
return "None"
class LiteralEval(FieldOperator):
def process_value(self, text: Any) -> Any:
if text is not None and not isinstance(text, str):
raise ValueError(
f"LiteralEval: field '{self.field}' is expected to be of 'str' input type, got: {type(text)}"
)
if text is None or text == "":
return text
return ast.literal_eval(text.strip())
class ExtractSafeUnsafeJudgment(FieldOperator):
def process_value(self, text: Any) -> Any:
first_line = str(text).strip().split("\n")[0].lower()
if first_line == "safe":
return 1.0
return 0.0
class ExtractArenaHardNumericalJudgment(FieldOperator):
def process_value(self, text: Any) -> Any:
match = re.search(r"\[\[([^\]]+)\]\]", text)
try:
res = str(match.group(1))
if res == "A>B":
return 1
if res == "A>>B":
return 3
if res == "B>A":
return -1
if res == "B>>A":
return -3
return 0
except:
return 0
class InferDictsToBinaryLogprobs(FieldOperator):
neg_class_name: str
pos_class_name: str
take_logprobs_from_end: bool = False
num_logprobs_to_take: int = 3
min_probability_mass = 0.0001
def verify(self):
super().verify()
if (
self.neg_class_name.lower() in self.pos_class_name.lower()
or self.pos_class_name.lower() in self.neg_class_name.lower()
):
raise ValueError(
f"""Class names in {self.__class__.__name__} should not overlap, got "{self.pos_class_name}" and "{self.neg_class_name}"""
)
def process_value(self, obj: Any) -> Any:
for i in self.get_token_range(obj):
try:
pos_probs, neg_probs = self.get_pos_neg_probs(pred_dict=obj[i])
if pos_probs or neg_probs:
sum_probs = sum(pos_probs) + sum(neg_probs)
if sum_probs > self.min_probability_mass:
return sum(pos_probs) / sum_probs
except:
pass
return 0
def get_pos_neg_probs(self, pred_dict):
token_logprobs = pred_dict["top_tokens"]
pos_and_neg_probs = []
for class_name in [self.pos_class_name, self.neg_class_name]:
# We need to capture different variants of model behavior and tokenizers, for example with opening space,
# punctuation etc. but avoid longer words that contain the class name.
# For example, for class "yes" we would capture "YES," and " Yes" but not "yesterday".
name_regex = re.compile(
rf"(\W|Ġ|_)*{class_name}(\W|Ġ|_)*", flags=re.IGNORECASE
)
class_probs = [
np.exp(d["logprob"])
for d in token_logprobs
if name_regex.fullmatch(d["text"])
]
pos_and_neg_probs.append(class_probs)
return pos_and_neg_probs
def get_token_range(self, obj: Any) -> range:
n_tokens = min([self.num_logprobs_to_take, len(obj)])
if self.take_logprobs_from_end:
return range(-1, -(n_tokens + 1), -1)
return range(n_tokens)
class RemoveArticles(FieldOperator):
def process_value(self, text: Any) -> Any:
return re.sub(r"\b(a|an|the)\b", " ", text)
class RemovePunctuations(FieldOperator):
def process_value(self, text: Any) -> Any:
puncs_to_exclude = set(string.punctuation)
return "".join(c for c in text if c not in puncs_to_exclude)
class FixWhiteSpace(FieldOperator):
def process_value(self, text: Any) -> Any:
return " ".join(text.split())
class AddPrefix(FieldOperator):
prefix: str
def process_value(self, text: str) -> str:
text = text.strip()
if text.startswith(self.prefix):
return text
return self.prefix + text.strip()
class GetSQL(FieldOperator):
"""Operator to extract the most likely SQL query from text, often generated by language models.
It prioritizes SQL within markdown code blocks (```sql or ```)
and defaults to finding the last SELECT statement in the text
if no code blocks are found. It attempts to remove trailing text
after the first semicolon in the identified query.
"""
def process_value(self, text: str) -> str:
"""Extracts the most plausible SQL query from the given text.
Args:
text: The input string potentially containing an SQL query
and other text (e.g., explanations, markdown).
Returns:
The extracted SQL query string, or a message indicating
no query was found.
"""
if not isinstance(text, str):
return "Input must be a string" # Basic type check
sql_query_candidate = None # Renamed to indicate it might need cleanup
# 1. Try to find ```sql ... ``` code blocks
sql_blocks = re.findall(
r"```sql\s*(.*?)\s*```", text, re.DOTALL | re.IGNORECASE
)
if sql_blocks:
# Use the content of the last ```sql block
sql_query_candidate = sql_blocks[-1].strip()
else:
# 2. If no ```sql blocks, try to find generic ``` ... ``` blocks
generic_blocks = re.findall(r"```\s*(.*?)\s*```", text, re.DOTALL)
if generic_blocks:
# Check if the last block looks like SQL (starts with SELECT, INSERT, etc.)
last_block_content = generic_blocks[-1].strip()
# Allow common SQL starting keywords
sql_keywords = (
r"^(SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|WITH|DROP|TRUNCATE)\b"
)
if re.match(sql_keywords, last_block_content, re.IGNORECASE):
sql_query_candidate = last_block_content
# 3. If no suitable code blocks found, search the entire text for the last relevant SQL keyword
if sql_query_candidate is None:
# Find the start index of the *last* common SQL keyword (case-insensitive)
last_match = None
# Expand search beyond just SELECT for better fallback
sql_keywords_search = (
r"\b(SELECT|INSERT|UPDATE|DELETE|CREATE|ALTER|WITH|DROP|TRUNCATE)\b"
)
for match in re.finditer(sql_keywords_search, text, re.IGNORECASE):
last_match = match
if last_match:
# Extract from the last keyword to the end of the string
sql_query_candidate = text[last_match.start() :].strip()
# 4. Cleanup: Truncate at first semicolon and strip whitespace
if sql_query_candidate:
# Find the first semicolon in the candidate string
first_semicolon_index = sql_query_candidate.find(";")
if first_semicolon_index != -1:
# If found, take everything before it
sql_query = sql_query_candidate[:first_semicolon_index].strip()
else:
# If no semicolon, use the candidate as is (after stripping)
sql_query = sql_query_candidate.strip()
# clean the ```sql\n from the start and the \n``` in case it is there
sql_query = sql_query.replace("```sql", "").replace("```", "").strip()
else:
sql_query = None # Ensure sql_query is None if no candidate was found
# 5. Return result or 'not found' message
return (
sql_query if sql_query is not None else "No query found in generation"
) # Check for None explicitly
class ScaleNumberToZeroOneReturnZeroIfFails(FieldOperator):
max_val = 10
min_val = 0
def process_value(self, text: Any) -> Any:
try:
text = float(text)
return (text - self.min_val) / self.max_val
except Exception:
return 0
class ExtractVerbalJudgment(FieldOperator):
classes = ["not", "somewhat", "mostly", "completely"]
def process_value(self, text: Any) -> Any:
max_val = len(self.classes) - 1
for i, c in enumerate(self.classes):
if text.strip().lower().startswith(c):
return i / (max_val)
return 0
class ExtractVerbalJudgementBadGood(ExtractVerbalJudgment):
classes = ["very bad", "bad", "mediocre", "good", "very good"]