kolena.dataset

• Examples: kolena/examples/dataset ↗

upload_dataset(name, df, *, id_fields=None)

Create or update a dataset with the contents of the provided DataFrame df.

Updating id_fields

ID fields are used to associate model results (uploaded via upload_results) with datapoints in this dataset. When updating an existing dataset, update id_fields with caution.

Parameters:

Name	Type	Description	Default
name	str	The name of the dataset.	required
df	Union[DataFrame, Iterator[DataFrame]]	A DataFrame or iterator of DataFrames. Provide an iterator to perform batch upload (example: csv_reader = pd.read_csv("PathToDataset.csv", chunksize=10)).	required
id_fields	Optional[List[str]]	Optionally specify a list of ID fields that will be used to link model results with the datapoints within a dataset. When unspecified, a suitable value is inferred from the columns of the provided df. Note that id_fields must be hashable.	None

download_dataset(name, *, commit=None)

Download an entire dataset given its name.

Parameters:

Name	Type	Description	Default
name	str	The name of the dataset.	required
commit	Optional[str]	The commit hash for version control. Get the latest commit when this value is None.	None

Returns:

Type	Description
DataFrame	A DataFrame containing the specified dataset.

EvalConfig = Optional[Dict[str, Any]] module-attribute

User defined configuration for evaluating results, for example {"threshold": 7}.

DataFrame = Union[pd.DataFrame, Iterator[pd.DataFrame]] module-attribute

A type alias representing a DataFrame, which can be either a pandas DataFrame or an iterator of pandas DataFrames.

download_results(dataset, model)

Download results given dataset name and model name.

Concat dataset with results:

df_dp, results = download_results("dataset name", "model name")
for eval_config, df_result in results:
    df_combined = pd.concat([df_dp, df_result], axis=1)

Parameters:

Name	Type	Description	Default
dataset	str	The name of the dataset.	required
model	str	The name of the model.	required

Returns:

Type	Description
Tuple[DataFrame, List[Tuple[EvalConfig, DataFrame]]]	Tuple of DataFrame of datapoints and list of tuples, each containing an evaluation configuration and the corresponding DataFrame of results.

upload_results(dataset, model, results, thresholded_fields=None)

This function is used for uploading the results from a specified model on a given dataset.

Parameters:

Name	Type	Description	Default
dataset	str	The name of the dataset.	required
model	str	The name of the model.	required
results	Union[DataFrame, List[Tuple[EvalConfig, DataFrame]]]	Either a DataFrame or a list of tuples, where each tuple consists of an eval configuration and a DataFrame.	required
thresholded_fields	Optional[List[str]]	Columns in result DataFrame containing data associated with different thresholds.	None

Returns:

Type	Description
None	None