Configuration

This page covers the runtime configuration options for KumoRFM, including run modes, temporal behavior, inference behavior, batch prediction, and retry handling.

Run Modes

The run_mode parameter controls the trade-off between prediction quality and speed by adjusting how much context data is sampled.

Run Mode	Context Size	Neighbor Sampling	Use Case
`DEBUG`	100	[16, 16, 4, 4, 1, 1]	Quick iteration, testing queries
`FAST`	1,000	[32, 32, 8, 8, 4, 4]	Default. Good balance of speed and quality
`NORMAL`	5,000	[64, 64, 8, 8, 4, 4]	Higher quality predictions
`BEST`	10,000	[64, 64, 8, 8, 4, 4]	Maximum quality

For forecasting tasks, when an entity has more historical rows than the Context Size cap, KumoRFM uses the most recent N rows (not the oldest). This ensures forecasts reflect the latest data patterns.

# Use the fastest mode for quick testing
result = model.predict(query, run_mode="DEBUG")

# Use the highest quality mode for production
result = model.predict(query, run_mode="BEST")

You can also fine-tune the neighbor sampling directly:

result = model.predict(
    query,
    num_neighbors=[64, 64, 8, 8, 4, 4],
)

Temporal and Context Timing

Use these parameters in KumoRFM.predict() and KumoRFM.evaluate() when you need to control the prediction timestamp or the historical examples used as model context.

Option	Default	Description
`anchor_time`	`None`	The anchor timestamp for the prediction. If set to `None`, KumoRFM uses the maximum timestamp in the data. If set to `"entity"`, KumoRFM uses each entity’s own timestamp.
`context_anchor_time`	`None`	The maximum anchor timestamp for context examples. If set to `None`, `anchor_time` determines the anchor time for context examples.
`use_prediction_time`	`False`	Whether to use the anchor timestamp as an additional feature during prediction. When `True`, the anchor time is included as a feature for all task types including forecasting.
`lag_timesteps`	`0`	Number of past timesteps to include as lagged target features for temporal predictive queries.

For example, set anchor_time when you want to predict as of a specific point in time:

result = model.predict(
    query,
    indices=[0, 1, 2],
    anchor_time=pd.Timestamp("2024-06-01"),
)

Use context_anchor_time when the prediction date and the latest available context data should differ:

result = model.predict(
    query,
    indices=[0, 1, 2],
    anchor_time=pd.Timestamp("2024-06-01"),
    context_anchor_time=pd.Timestamp("2024-03-01"),
)

Set lag_timesteps when recent historical target values should be available to the model as additional context. For example, lag_timesteps=3 adds the previous three target windows as lagged features:

result = model.predict(
    query,
    indices=[0, 1, 2],
    lag_timesteps=3,
)

Inference Configuration

The inference_config parameter controls inference-time model behavior, including ensembling. You can pass either a dictionary or a configuration object from kumoapi.rfm. When you pass a dictionary, KumoRFM casts it based on the task type:

Classification tasks use ClassificationInferenceConfig.
Regression and forecasting tasks use RegressionInferenceConfig.

If you omit inference_config, KumoRFM selects defaults automatically based on the task type. Common options:

Option	Description
`num_estimators`	Number of estimators to ensemble. Defaults to `1` and must be between `1` and `4`.
`column_shuffle`	Whether to shuffle column order across estimators.
`category_shuffle`	Whether to shuffle categories within categorical columns across estimators.
`hop_shuffle`	Whether to shuffle subgraph depth across estimators.

Classification option:

Option	Description
`class_shuffle`	Whether to shuffle class order across estimators.

Regression and forecasting options:

Option	Description
`target_transforms`	Target preprocessing transforms to vary across estimators. Supported values are `"clip"`, `"power"`, `"quantile"`, and `None`. Defaults to `["quantile"]`.
`output_type`	How to summarize the output distribution. Supported values are `"median"`, `"mean"`, and `"quantiles"`. Defaults to `"median"`.

When output_type="quantiles", the prediction output contains 27 quantile columns instead of a single TARGET_PRED column:

q_0.005 q_0.01  q_0.02  q_0.025 q_0.05  q_0.1   q_0.15
q_0.2   q_0.25  q_0.3   q_0.35  q_0.4   q_0.45  q_0.5
q_0.55  q_0.6   q_0.65  q_0.7   q_0.75  q_0.8   q_0.85
q_0.9   q_0.95  q_0.975 q_0.98  q_0.99  q_0.995

Classification example:

result = model.predict(
    query,
    indices=[0, 1, 2],
    inference_config=dict(
        num_estimators=4,
        column_shuffle=True,
        hop_shuffle=True,
        class_shuffle=True,
    ),
)

Regression example:

result = model.predict(
    regression_query,
    indices=[0, 1, 2],
    inference_config=dict(
        num_estimators=4,
        column_shuffle=True,
        target_transforms=["quantile", "clip", "power", None],
        output_type="median",
    ),
)

Output and Collection Controls

These options control what predict() returns and how KumoRFM collects valid context labels.

Option	Default	Description
`return_embeddings`	`False`	Whether to include embeddings for each prediction example in the output DataFrame.
`explain`	`False`	Whether to return an `Explanation` object instead of a plain prediction DataFrame. Explainability currently supports single-entity predictions with `run_mode="FAST"`. See Prediction Explainability.
`max_pq_iterations`	`10`	Maximum number of iterations used to collect valid labels. Increase this when a predictive query has strict entity filters and KumoRFM needs to sample more entities to find enough valid labels.
`random_seed`	`42`	Manual seed for pseudo-random sampling.
`verbose`	`True`	Whether to print progress output during prediction or evaluation.

result = model.predict(
    query,
    indices=[0, 1, 2],
    return_embeddings=True,
    max_pq_iterations=20,
    random_seed=42,
)

Batch Mode

For predictions over many entities, use KumoRFM.batch_mode() to automatically split the workload into batches:

with model.batch_mode(batch_size='max', num_retries=1):
    result = model.predict(
        "PREDICT COUNT(orders.*, 0, 30, days) > 0 FOR users.user_id=1",
        indices=list(range(1, 1001)),
    )

Parameters:

batch_size: The number of entities per batch. Set to "max" (default) to use the maximum applicable batch size for the task type.
num_retries: Number of retries for failed batches due to server issues.

The maximum prediction sizes per task type are:

Task Type	Max Prediction Size	Max Test Size
Classification / Regression / Forecasting	1,000	2,000
Temporal Link Prediction	200	400

Retry

Use KumoRFM.retry() to automatically retry failed queries due to transient server issues:

with model.retry(num_retries=2):
    result = model.predict(query, indices=[1, 2, 3])

This is useful for long-running batch predictions where occasional failures are expected.

Size Limits

KumoRFM enforces a 30 MB context size limit per prediction. If exceeded, you will see an error message suggesting:

Reducing the number of tables in the graph
Reducing the number of columns (e.g., large text columns)
Adjusting the neighborhood configuration
Using a lower run mode

The optimize parameter in KumoRFM can help with database backends by creating indices for faster sampling:

model = rfm.KumoRFM(graph, optimize=True)

Get Started

KumoRFM SDK

KumoRFM Fine-Tuning SDK

KumoRFM Fine-Tuning Platform

Run Modes

Temporal and Context Timing

Inference Configuration

Output and Collection Controls

Batch Mode

Retry

Size Limits

​Run Modes

​Temporal and Context Timing

​Inference Configuration

​Output and Collection Controls

​Batch Mode

​Retry

​Size Limits

Run Modes

Temporal and Context Timing

Inference Configuration

Output and Collection Controls

Batch Mode

Retry

Size Limits