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Documentation Index

Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt

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The kumoai.utils module provides helpers for loading sample datasets and visualizing model outputs.

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Datasets

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from_relbench()

Creates a Kumo Graph from a RelBench benchmark dataset. The function downloads the dataset, uploads its tables to the Kumo data plane, and constructs a Graph with inferred metadata and edges. 
from kumoai.utils.datasets import from_relbench

graph = from_relbench(dataset_name="rel-amazon")
This function is subject to the file size limits of FileUploadConnector. See the Connectors guide for details.
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dataset_name
str
required
The name of the RelBench dataset to load (e.g. "rel-amazon", "rel-trial").
Returns Graph — A Graph containing the dataset’s tables and inferred edges. Raises ValueError if the dataset cannot be retrieved or processed.

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Visualization

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ForecastVisualizer

An interactive visualization tool for inspecting forecast results produced by a trained Kumo model. Renders time series plots with actuals vs. predictions and residual diagnostics for each entity. 
from kumoai.utils.forecasting import ForecastVisualizer

viz = ForecastVisualizer(holdout_df=result.holdout_df())
viz.visualize()
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holdout_df
pd.DataFrame
required
The holdout dataset from a TrainingJobResult. Obtain via TrainingJobResult.holdout_df().

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visualize()

Renders an interactive Plotly figure with per-entity time series plots and residual diagnostics. Entity selection is available via dropdown buttons in the chart.