phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-07-29 07:14:20 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

address comments

Browse source
This commit is contained in:
Xi Yan 2024-10-23 22:17:38 -07:00
parent 59c93548bc
commit afa0c2b146
2 changed files with 15 additions and 11 deletions
Download patch file Download diff file Expand all files Collapse all files

16
llama_stack/providers/impls/meta_reference/datasetio/datasetio.py
View file

@ -61,13 +61,13 @@ class PandasDataframeDataset(BaseDataset):
else:
return self.df.iloc[idx].to_dict()
def _validate_dataset_schema(self) -> None:
assert self.df is not None, "Dataset not loaded. Please call .load() first"
def _validate_dataset_schema(self, df) -> pandas.DataFrame:
# note that we will drop any columns in dataset that are not in the schema
self.df = self.df[self.dataset_def.dataset_schema.keys()]
df = df[self.dataset_def.dataset_schema.keys()]
# check all columns in dataset schema are present
assert len(self.df.columns) == len(self.dataset_def.dataset_schema)
assert len(df.columns) == len(self.dataset_def.dataset_schema)
# TODO: type checking against column types in dataset schema
return df
def load(self) -> None:
if self.df is not None:
@ -99,8 +99,7 @@ class PandasDataframeDataset(BaseDataset):
else:
raise ValueError(f"Unsupported file type: {self.dataset_def.url}")
self.df = df
self._validate_dataset_schema()
self.df = self._validate_dataset_schema(df)
class MetaReferenceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
@ -136,7 +135,10 @@ class MetaReferenceDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
dataset_info = self.dataset_infos.get(dataset_id)
dataset_info.dataset_impl.load()
if page_token is None or not page_token.isnumeric():
if page_token and not page_token.isnumeric():
raise ValueError("Invalid page_token")
if page_token is None:
next_page_token = 0
else:
next_page_token = int(page_token)

10
llama_stack/providers/impls/meta_reference/scoring/scoring.py
View file

@ -59,11 +59,11 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
for required_column in ["generated_answer", "expected_answer", "input_query"]:
if required_column not in dataset_def.dataset_schema:
raise ValueError(
f"Dataset {dataset_id} does not have a '{required_column}' column. Please make sure '{required_column}' column is in the dataset."
f"Dataset {dataset_id} does not have a '{required_column}' column."
)
if dataset_def.dataset_schema[required_column].type != "string":
raise ValueError(
f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'. Please make sure '{required_column}' column is of type 'string'."
f"Dataset {dataset_id} does not have a '{required_column}' column of type 'string'."
)
async def score_batch(
@ -73,12 +73,12 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
save_results_dataset: bool = False,
) -> ScoreBatchResponse:
await self.validate_scoring_input_dataset_schema(dataset_id=dataset_id)
rows_paginated = await self.datasetio_api.get_rows_paginated(
all_rows = await self.datasetio_api.get_rows_paginated(
dataset_id=dataset_id,
rows_in_page=-1,
)
res = await self.score(
input_rows=rows_paginated.rows, scoring_functions=scoring_functions
input_rows=all_rows.rows, scoring_functions=scoring_functions
)
if save_results_dataset:
# TODO: persist and register dataset on to server for reading
@ -94,6 +94,8 @@ class MetaReferenceScoringImpl(Scoring, ScoringFunctionsProtocolPrivate):
) -> ScoreResponse:
res = {}
for scoring_fn_id in scoring_functions:
if scoring_fn_id not in SCORER_REGISTRY:
raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
scorer = SCORER_REGISTRY[scoring_fn_id]()
score_results = scorer.score(input_rows)
agg_results = scorer.aggregate(score_results)