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import json
from typing import Any, Dict, Iterable, List, Optional
from datasets import Features, Value
from .dataclass import Dataclass
from .operator import (
MultiStreamOperator,
SequentialOperatorInitilizer,
StreamInitializerOperator,
)
from .operators import (
Apply,
ApplyMetric,
ApplyOperatorsField,
FlattenInstances,
MergeStreams,
SplitByValue,
)
from .register import _reset_env_local_catalogs, register_all_artifacts
from .schema import UNITXT_DATASET_SCHEMA
from .settings_utils import get_settings
from .stream import MultiStream, Stream
class MultiStreamScoreMean(MultiStreamOperator):
def aggegate_results(self, multi_stream: MultiStream):
scores = []
for stream in multi_stream.values():
instance = stream.peek()
scores.append(instance["score"]["global"]["score"])
from statistics import mean
return mean(scores)
def spread_results(self, stream: Stream, score: float):
for instance in stream:
instance["score"]["global"]["groups_mean_score"] = score
yield instance
def spread_results_one_stream(self, stream: Stream):
for instance in stream:
instance["score"]["global"]["groups_mean_score"] = instance["score"][
"global"
]["score"]
yield instance
def process(self, multi_stream: MultiStream) -> MultiStream:
result = {}
# optimization in to avoid double calculation of metrics
# when aggregating results, if there is only one stream.
if len(multi_stream) == 1:
for stream_name, stream in multi_stream.items():
result[stream_name] = Stream(
self.spread_results_one_stream, gen_kwargs={"stream": stream}
)
return MultiStream(result)
mean_score = self.aggegate_results(multi_stream)
result = {}
for stream_name, stream in multi_stream.items():
result[stream_name] = Stream(
self.spread_results, gen_kwargs={"stream": stream, "score": mean_score}
)
return MultiStream(result)
class FromPredictionsAndOriginalData(StreamInitializerOperator):
def zip(self, predictions, references):
for prediction, original in zip(predictions, references):
yield {**original, "prediction": prediction}
def process(
self, predictions: List[str], references: Iterable, split_name: str = "all"
) -> MultiStream:
return MultiStream(
{
split_name: Stream(
self.zip,
gen_kwargs={"predictions": predictions, "references": references},
)
}
)
# The task_data field in the schema is defined as
# Sequence({"key": Value(dtype="string"), "value": Value("string")})
# When receiving instances from this scheme, the keys and values are returned as two separate
# lists, and are converted to a dictionary.
class MetricRecipe(SequentialOperatorInitilizer):
calc_confidence_intervals: bool = True
def prepare(self):
register_all_artifacts()
self.steps = [
FromPredictionsAndOriginalData(),
Apply(
"task_data",
function="json.loads",
to_field="task_data",
),
ApplyOperatorsField(
operators_field="postprocessors",
),
SplitByValue(["group"]),
ApplyMetric(
"metrics",
calc_confidence_intervals=self.calc_confidence_intervals,
),
MultiStreamScoreMean(),
MergeStreams(),
]
UNITXT_METRIC_SCHEMA = Features(
{"predictions": Value("string"), "references": dict(UNITXT_DATASET_SCHEMA)}
)
def _compute(
predictions: List[str],
references: Iterable,
flatten: bool = False,
split_name: str = "all",
calc_confidence_intervals: bool = True,
):
_reset_env_local_catalogs()
register_all_artifacts()
recipe = MetricRecipe(calc_confidence_intervals=calc_confidence_intervals)
multi_stream = recipe(
predictions=predictions, references=references, split_name=split_name
)
if flatten:
operator = FlattenInstances()
multi_stream = operator(multi_stream)
stream = multi_stream[split_name]
return list(stream)
"""
The API of a metric service:
- MetricRequest: A single input request to the metrics service.
- MetricResponse: A response returned from a metrics service.
"""
class InstanceInput(Dataclass):
"""A single instance inputted to a metric service."""
prediction: Any
references: List[Any]
additional_inputs: Optional[Dict] = None
class MetricRequest(Dataclass):
"""A request to a metrics service, includes a list of input instances."""
instance_inputs: List[InstanceInput]
class MetricResponse(Dataclass):
"""A response produced by a metrics service, includes the computed scores."""
# A list of instance score dictionaries. Each dictionary contains the
# score names and score values for a single instance.
instances_scores: List[Dict[str, Any]]
# The global scores dictionary, containing global score names and values.
# These are scores computed over the entire set of input instances, e.g.
# an average over a score computed per instance.
global_score: Dict[str, Any]
"""
Functionality for loading the remote metrics configuration from local environment variables.
"""
# A list of metrics to be executed remotely.
# For example: '["metrics.rag.context_relevance","metrics.rag.bert_k_precision"]'
# This value should be a valid json list
UNITXT_REMOTE_METRICS = "UNITXT_REMOTE_METRICS"
# The remote endpoint on which the remote metrics are available.
# For example, 'http://127.0.0.1:8000/compute'
UNITXT_REMOTE_METRICS_ENDPOINT = "UNITXT_REMOTE_METRICS_ENDPOINT"
def get_remote_metrics_names() -> List[str]:
"""Load the remote metrics names from an environment variable.
Returns:
List[str] - names of metrics to be executed remotely.
"""
settings = get_settings()
remote_metrics = settings.remote_metrics
if remote_metrics:
remote_metrics = json.loads(remote_metrics)
if not isinstance(remote_metrics, list):
raise RuntimeError(
f"Unexpected value {remote_metrics} for the '{UNITXT_REMOTE_METRICS}' environment variable. "
f"The value is expected to be a list of metric names in json format."
)
for remote_metric in remote_metrics:
if not isinstance(remote_metric, str):
raise RuntimeError(
f"Unexpected value {remote_metric} within the '{UNITXT_REMOTE_METRICS}' environment variable. "
f"The value is expected to be a string but its type is {type(remote_metric)}."
)
return remote_metrics
def get_remote_metrics_endpoint() -> str:
"""Load the remote metrics endpoint from an environment variable.
Returns:
str - The remote endpoint on which the remote metrics are available.
"""
settings = get_settings()
try:
remote_metrics_endpoint = settings.remote_metrics_endpoint
except AttributeError as e:
raise RuntimeError(
f"Unexpected None value for '{UNITXT_REMOTE_METRICS_ENDPOINT}'. "
f"Running remote metrics requires defining an "
f"endpoint in the environment variable '{UNITXT_REMOTE_METRICS_ENDPOINT}'."
) from e
return remote_metrics_endpoint
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