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name: data-and-feature-columns description: "Use this DeepCTR sub-skill for SparseFeat, DenseFeat, VarLenSparseFeat, hashing, embedding sharing, input dictionaries, and tabular or sequence feature-column validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Feature Columns

Use this sub-skill when the task is about preparing DeepCTR inputs before a model is chosen or trained: categorical ids, dense numerical vectors, variable-length sequences, feature hashing, vocabulary files, shared embeddings, and get_feature_names.

When to use this sub-skill

• The user asks how to create SparseFeat, DenseFeat, or VarLenSparseFeat objects.
• The user has a tabular CTR/recommender dataset and needs model input dictionaries.
• The user is debugging missing input keys, bad shapes, string categorical values, sequence padding, length_name, weight_name, or shared embedding errors.
• The user wants to validate a feature-column plan before routing to Keras, sequence, multitask, or Estimator workflows.

Route map

• Read references/feature-columns.md for constructor signatures, hashing, vocabulary files, embedding sharing, groups, and get_feature_names.
• Read references/data-formats.md for tabular CTR, multi-value MovieLens-style features, DIN/BST history fields, and DSIN session fields.
• Read references/troubleshooting.md when an error mentions use_hash=True, missing feature names, shape mismatch, padding id 0, or incompatible shared embeddings.
• Run scripts/validate_feature_spec.py when the user can express the intended features as a small JSON spec and wants preflight validation.

Minimal workflow

1. List every raw feature and classify it as one categorical id, a dense scalar/vector, or a variable-length list of categorical ids.
2. Encode one-id categorical fields as integers and use SparseFeat, or set use_hash=True with dtype="string" when hashing string values on the fly.
3. Use DenseFeat(name, dimension) for dense vectors; make each input array shape match the declared dimension.
4. Use VarLenSparseFeat(SparseFeat(...), maxlen=...) for multi-value or sequence ids. Pad every row to (batch_size, maxlen).
5. Keep 0 reserved for padding when a sequence field relies on masking instead of an explicit length_name.
6. Use the same embedding_name only when fields truly share one embedding table and have the same vocabulary size, embedding dimension, and trainability.
7. Call get_feature_names(linear_feature_columns + dnn_feature_columns) and build model_input = {name: array for name in feature_names}.
8. Route to:
   • ../keras-model-workflows/SKILL.md for ordinary Keras CTR/regression models.
   • ../sequence-models/SKILL.md for DIN, DIEN, DSIN, or BST history/session fields.
   • ../multitask-models/SKILL.md for multi-output labels.
   • ../estimator-workflows/SKILL.md for TensorFlow tf.feature_column Estimator workflows.

JSON validation helper

For a quick preflight, create a JSON file like:

{
  "features": [
    {"type": "SparseFeat", "name": "item_id", "vocabulary_size": 1001, "embedding_dim": 8},
    {"type": "DenseFeat", "name": "score", "dimension": 1},
    {
      "type": "VarLenSparseFeat",
      "name": "hist_item_id",
      "maxlen": 4,
      "length_name": "seq_length",
      "sparsefeat": {"name": "hist_item_id", "vocabulary_size": 1001, "embedding_dim": 8, "embedding_name": "item_id"}
    }
  ]
}

Then run:

python sub-skills/data-and-feature-columns/scripts/validate_feature_spec.py feature_spec.json

The helper catches common structural mistakes; it does not replace a real TensorFlow model smoke test.

Guardrails

• Do not use dtype="string" on SparseFeat or VarLenSparseFeat unless use_hash=True or values are pre-encoded.
• Do not set maxlen to the vocabulary size; maxlen is the number of ids in one row's list.
• Do not use 0 as a real category id for padded sequence features unless using an explicit length convention that still keeps masking consistent.
• Do not tell future agents to run source repository examples. Use this skill's bundled references and scripts, then route to the owning workflow sub-skill.