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

A model's training summary: a second server-side object, and the one the cache really drops.

A model is offloaded to disk under memory pressure and comes back; a summary is *dropped*,
and the fetch that finds it gone answers `CONNECT_ML.MODEL_SUMMARY_LOST`. That is not a
failure the caller caused and not one they can prevent, so this package recovers from it the
way PySpark does — it sends `CreateSummary` with the frame the model was fitted on and asks
again. `dataset` is that frame, and carrying it is the whole reason this struct exists rather
than a bare reference.

**The reference is the model's own.** Spark has no separate cache key for a summary: it is
reached by asking the model for one, so a `Fetch` names the model and puts `summary` at the
head of its method chain. PySpark spells that as the string `"<model_ref>.summary"` and
splits it apart again on the way out; here the parts are just kept apart.

    summary = Latu.ML.summary(model)
    {:ok, auc} = Latu.ML.Classification.BinaryLogisticRegressionSummary.area_under_roc(summary)
    roc = Latu.ML.Classification.BinaryLogisticRegressionSummary.roc(summary)

`class` is nil for `LogisticRegressionModel` and `RandomForestClassificationModel`, whose
summary class depends on how many classes the *fitted* model found — `_summaryCls` branches
on `numClasses`, and no client knows the answer before it asks. `candidates` is what PySpark
can return, both of them, in its own order; `class` is set only where that list has one
entry, so a class check is made where one can be and skipped where it cannot.

The candidates are what an argument's type is looked up in, and the two never disagree:
4.2.0's binary summary is a *subclass* of its multiclass one, so every attribute they share
has the same signature and the binary one only adds. `Latu.ML.attributes/1` asks a narrower
question — what is allowed whichever class this turns out to be — and so reports what they
share; name `Latu.ML.Classification.BinaryLogisticRegressionTrainingSummary` to see the rest.

# `t`

```elixir
@type t() :: %Latu.ML.Summary{
  candidates: [String.t()],
  class: String.t() | nil,
  dataset: term(),
  methods: [String.t()],
  ref: Latu.ML.Plan.object_ref(),
  session: Latu.Session.t()
}
```

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