16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162 | 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()
|
