# `Latu.ML.Pipeline`
[🔗](https://github.com/zero-one-group/latu_ml/blob/v0.4.2/lib/latu/ml/pipeline.ex#L1)

A sequence of stages, fitted as one.

**Not a server operator.** Spark has no `Fit` for a pipeline: PySpark loops over the stages in
Python and so does this package, which is why `Latu.ML.pipeline/1` needs no session and why a
pipeline has no cache entry of its own. What `Latu.ML.fit/2` hands back is a
`Latu.ML.PipelineModel` holding one entry per estimator it fitted.

    assembler = Feature.vector_assembler(input_cols: [:x1, :x2], output_col: :features)
    lr = Classification.logistic_regression(max_iter: 10)

    {:ok, model} = ML.fit(ML.pipeline([assembler, lr]), training)
    scored = ML.transform(model, test)
    :ok = ML.delete(model)

A stage is an estimator, a transformer, a model already fitted, or another pipeline. The fold
is PySpark's exactly, and its one surprise is worth knowing: **stages after the last estimator
are carried but never applied during the fit**. There is nothing left to learn from them, so
the frame stops being transformed at that point.

# `stage`

```elixir
@type stage() ::
  Latu.ML.Estimator.t()
  | Latu.ML.Transformer.t()
  | Latu.ML.Model.t()
  | t()
  | Latu.ML.PipelineModel.t()
```

Anything a pipeline may hold. A `Latu.ML.PipelineModel` is a transformer too.

# `t`

```elixir
@type t() :: %Latu.ML.Pipeline{stages: [stage()], uid: String.t()}
```

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*Consult [api-reference.md](api-reference.md) for complete listing*