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164 | class EvaluatorBenchmarkerPack(BaseLlamaPack):
"""
A pack for benchmarking/evaluating your own evaluator.
Args:
evaluator (BaseEvaluator): The evaluator to evaluate/benchmark.
eval_dataset (LabelledEvaluatorDataset | LabelledPairwiseEvaluatorDataset): The
labelled evaluation dataset to run benchmarks against.
"""
def __init__(
self,
evaluator: BaseEvaluator,
eval_dataset: Union[LabelledEvaluatorDataset, LabelledPairwiseEvaluatorDataset],
show_progress: bool = True,
):
self.evaluator = evaluator
self.eval_dataset = eval_dataset
self._num_examples = len(self.eval_dataset.examples)
self.show_progress = show_progress
self.prediction_dataset = None
async def _amake_predictions(
self,
batch_size: int = 20,
sleep_time_in_seconds: int = 1,
):
"""Async make predictions with evaluator."""
self.prediction_dataset: Union[
EvaluatorPredictionDataset, PairwiseEvaluatorPredictionDataset
] = await self.eval_dataset.amake_predictions_with(
predictor=self.evaluator,
show_progress=self.show_progress,
batch_size=batch_size,
sleep_time_in_seconds=sleep_time_in_seconds,
)
def make_predictions(self, batch_size: int = 20, sleep_time_in_seconds: int = 1):
"""Sync make predictions with evaluator."""
self.prediction_dataset: Union[
EvaluatorPredictionDataset, PairwiseEvaluatorPredictionDataset
] = self.eval_dataset.make_predictions_with(
predictor=self.evaluator,
show_progress=self.show_progress,
batch_size=batch_size,
sleep_time_in_seconds=sleep_time_in_seconds,
)
def _prepare_and_save_benchmark_results_pairwise_grading(self) -> pd.DataFrame:
"""Compute benchmark metrics for pairwise evaluation."""
inconclusive_counts = 0
agreements_with_ties = 0
agreements_without_ties = 0
ties = 0
invalid_counts = 0
for example, prediction in zip(
self.eval_dataset[:], self.prediction_dataset[:]
):
if prediction.invalid_prediction:
invalid_counts += 1
continue
# don't count inconclusive results
if prediction.evaluation_source == "neither":
inconclusive_counts += 1
continue
if prediction.score == 0.5 or example.reference_score == 0.5:
ties += 1
else:
agreements_without_ties += int(
example.reference_score == prediction.score
)
agreements_with_ties += int(example.reference_score == prediction.score)
agreement_rate_with_ties = agreements_with_ties / (
len(self.prediction_dataset[:]) - inconclusive_counts - invalid_counts
)
agreement_rate_without_ties = agreements_without_ties / (
len(self.prediction_dataset[:])
- inconclusive_counts
- ties
- invalid_counts
)
df_data = {
"number_examples": [len(self.prediction_dataset[:])],
"invalid_predictions": [invalid_counts],
"inconclusives": [inconclusive_counts],
"ties": [ties],
"agreement_rate_with_ties": [agreement_rate_with_ties],
"agreement_rate_without_ties": [agreement_rate_without_ties],
}
benchmark_df = pd.DataFrame(df_data)
benchmark_df.to_csv("benchmark.csv")
return benchmark_df
def _prepare_and_save_benchmark_results_single_grading(self) -> pd.DataFrame:
"""Compute benchmark metrics for single grading evaluation."""
invalid_counts = sum([p.invalid_prediction for p in self.prediction_dataset[:]])
np_preds = np.array([p.score for p in self.prediction_dataset[:]])
np_refs = np.array([e.reference_score for e in self.eval_dataset[:]])
invalid_mask = ~np.array(
[p.invalid_prediction for p in self.prediction_dataset[:]]
)
# metrics
mae = np.mean(np.abs(np_preds[invalid_mask] - np_refs[invalid_mask]))
corr = np.corrcoef(
np_preds[invalid_mask].astype(float), np_refs[invalid_mask].astype(float)
)[0, 1]
hamming = np.sum(np_preds[invalid_mask] == np_refs[invalid_mask])
df_data = {
"number_examples": [len(self.prediction_dataset[:])],
"invalid_predictions": [invalid_counts],
"correlation": [corr],
"mae": [mae],
"hamming": [hamming],
}
benchmark_df = pd.DataFrame(df_data)
benchmark_df.to_csv("benchmark.csv")
return benchmark_df
def _make_evaluations(self) -> pd.DataFrame:
"""Returns benchmark_df."""
if isinstance(self.eval_dataset, LabelledPairwiseEvaluatorDataset):
return self._prepare_and_save_benchmark_results_pairwise_grading()
else:
return self._prepare_and_save_benchmark_results_single_grading()
async def arun(self, batch_size: int = 10, sleep_time_in_seconds: int = 1):
if batch_size > 10:
warnings.warn(
"You've set a large batch_size (>10). If using OpenAI GPT-4 as "
" `judge_llm` (which is the default judge_llm),"
" you may experience a RateLimitError. Previous successful eval "
" responses are cached per batch. So hitting a RateLimitError"
" would mean you'd lose all of the current batches successful "
" GPT-4 calls."
)
# make predictions
if self.prediction_dataset is None:
await self._amake_predictions(batch_size, sleep_time_in_seconds)
# produce metrics
return self._make_evaluations()
|