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from functools import singledispatch
from typing import List, Optional
import pandas as pd
from .artifact import verbosed_fetch_artifact
from .metric_utils import get_remote_metrics_endpoint, get_remote_metrics_names
from .operator import SequentialOperator
from .stream import MultiStream
@singledispatch
def evaluate(
dataset, metric_names: List[str], compute_conf_intervals: Optional[bool] = False
):
"""Placeholder for overloading the function, supporting both dataframe input and list input."""
pass
@evaluate.register
def _(
dataset: list,
metric_names: List[str],
compute_conf_intervals: Optional[bool] = False,
):
global_scores = {}
remote_metrics = get_remote_metrics_names()
for metric_name in metric_names:
if metric_name in remote_metrics:
metric = verbosed_fetch_artifact(metric_name)
metric_step = as_remote_metric(metric)
else:
# The SequentialOperator below will handle the load of the metric from its name
metric_step = metric_name
metrics_operator = SequentialOperator(steps=[metric_step])
if not compute_conf_intervals:
first_step = metrics_operator.steps[0]
first_step.disable_confidence_interval_calculation()
multi_stream = MultiStream.from_iterables({"test": dataset}, copying=True)
instances = list(metrics_operator(multi_stream)["test"])
for entry, instance in zip(dataset, instances):
entry[metric_name] = instance["score"]["instance"]["score"]
if len(instances) > 0:
global_scores[metric_name] = instances[0]["score"].get("global", {})
return dataset, global_scores
@evaluate.register
def _(
dataset: pd.DataFrame,
metric_names: List[str],
compute_conf_intervals: Optional[bool] = False,
):
results, global_scores = evaluate(
dataset.to_dict("records"),
metric_names=metric_names,
compute_conf_intervals=compute_conf_intervals,
)
return pd.DataFrame(results), pd.DataFrame(global_scores)
def as_remote_metric(metric):
"""Wrap a metric with a RemoteMetric.
Currently supported is wrapping the inner metric within a MetricPipeline.
"""
from .metrics import MetricPipeline, RemoteMetric
remote_metrics_endpoint = get_remote_metrics_endpoint()
if isinstance(metric, MetricPipeline):
metric = RemoteMetric.wrap_inner_metric_pipeline_metric(
metric_pipeline=metric,
remote_metrics_endpoint=remote_metrics_endpoint,
)
else:
raise ValueError(
f"Unexpected remote metric type {type(metric)} for the metric named '{metric.__id__}'. "
f"Remotely executed metrics should be MetricPipeline objects."
)
return metric
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