data_designer.interface.results

Bases: data_designer.config.utils.visualization.WithRecordSamplerMixin

Results container for a Data Designer dataset creation run.

This class provides access to the generated dataset, profiling analysis, and visualization utilities. It is returned by the DataDesigner.create() method and implements ResultsProtocol of the DataDesigner interface.

Resume scope: methods that read from the artifact directory (load_dataset, count_records, load_analysis, export, push_to_hub) reflect the full dataset on disk, including rows produced by earlier create() calls that the current invocation resumed. Per-run observability — task_traces and any model-usage / telemetry side effects emitted during the call — is scoped to the current invocation only, because the original run’s in-memory state is not persisted across process boundaries.

Initialization:

Creates a new instance with results based on a dataset creation run.

Parameters:

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load_analysis() -> data_designer.config.analysis.dataset_profiler.DatasetProfilerResults

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load_dataset() -> pandas.DataFrame

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count_records() -> int

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load_processor_dataset(processor_name: str) -> pandas.DataFrame

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get_path_to_processor_artifacts(processor_name: str) -> pathlib.Path

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export(
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    path: pathlib.Path | str,
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    *,
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    format: data_designer.interface.results.ExportFormat | None = None
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) -> pathlib.Path

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>>> results = data_designer.create(config, num_records=1000)
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>>> results.export("output.jsonl")
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PosixPath('output.jsonl')
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>>> results.export("output.csv")
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PosixPath('output.csv')
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>>> results.export("output.txt", format="jsonl")
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PosixPath('output.txt')

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push_to_hub(
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    repo_id: str,
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    description: str,
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    *,
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    token: str | None = None,
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    private: bool = False,
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    tags: list[str] | None = None
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) -> str

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>>> results = data_designer.create(config, num_records=1000)
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>>> description = "This dataset contains synthetic conversations for training chatbots."
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>>> results.push_to_hub("username/my-synthetic-dataset", description, tags=["chatbot", "conversation"])
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'https://huggingface.co/datasets/username/my-synthetic-dataset'

Write batch_files to output as JSONL, one record per line.

Each batch is appended in turn so peak memory stays proportional to one batch.

Write batch_files to output as CSV with a single header row.

Write batch_files to output as a single Parquet file.

Schemas are unified across batches before writing so that columns with minor type drift (e.g. int64 vs float64 across batches) are cast to a consistent schema rather than causing a write error.

Raises: