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name: deep-feature-synthesis description: "Generate Featuretools feature matrices with DFS, cutoff times, encoding, and optional Dask-backed calculation." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Deep Feature Synthesis

Use this sub-skill when the task is about turning a valid EntitySet into a feature matrix, tuning DFS parameters, applying cutoff-time logic, or encoding the resulting matrix for downstream ML.

Route Here For

• Running dfs or DeepFeatureSynthesis.
• Calling calculate_feature_matrix directly on feature definitions.
• Setting cutoff_time, training_window, include_cutoff_time, or cutoff_time_in_index.
• Generating or debugging encode_features output.
• Choosing primitives with get_valid_primitives.
• Bounded time-series and approximate feature-matrix workflows.
• Dask-backed parallel feature calculation when n_jobs > 1 is needed.

Start With These References

• ../../references/api-reference.md: DFS, feature-matrix, cutoff, and time-helper signatures.
• references/workflows.md: the recommended DFS workflow and tuning order.
• references/time-and-cutoffs.md: windowing, temporal cutoffs, and time-series helper notes.
• references/troubleshooting.md: invalid cutoff, Dask, and feature-matrix recovery notes.
• scripts/dfs_smoke.py: a tiny end-to-end DFS smoke check.

Boundaries

• Stay inside feature generation and feature-matrix calculation.
• Route raw EntitySet modeling to ../entitysets-and-data/.
• Route primitive discovery, feature pruning, and show_info to ../feature-inspection-and-selection/.
• Route custom primitive authoring, feature descriptions, and serialization to ../primitives-and-feature-definitions/.

Minimal Workflow

1. Start with a valid EntitySet and a clear target dataframe.
2. Choose primitives and depth with dfs or DeepFeatureSynthesis.
3. Add cutoff-time or training-window constraints if the task is time-aware.
4. Use calculate_feature_matrix when you already have feature definitions.
5. Apply encode_features only after the matrix exists.

Common Decision Points

• Use features_only=True when you only need definitions for a later calculation step.
• Use get_valid_primitives when you want to inspect what DFS can build before generating the full matrix.
• Use make_temporal_cutoffs when you need a compact cutoff table for a time-series or rolling-window task.
• Keep n_jobs=1 unless the user explicitly wants Dask-backed parallelism and the featuretools[dask] extras are available.

Quality Bar

Future agents should be able to reproduce a small feature matrix and explain how the cutoff logic changed it without reopening the original repository.