# `Latu.ML.FPM.FPGrowthModel`
[🔗](https://github.com/zero-one-group/latu_ml/blob/v0.4.2/lib/latu/ml/fpm.ex#L0)

Attributes of a fitted `FPGrowthModel`.

Every accessor here is one name on the server's allowlist for
`org.apache.spark.ml.fpm.FPGrowthModel` — 2 of them — so tab completion is that
allowlist. The server refuses anything else with `CONNECT_ML.ATTRIBUTE_NOT_ALLOWED`; these
refuse a model of the wrong class before it gets that far, and name the module that would
have taken it.

Allowed by the server but not here: `toString` is `Identifiable`'s, and tells you less than
the model's own uid. `Latu.ML.attribute/2` and `Latu.ML.attribute/3` will send any of these
names; it is what comes back that has nowhere to go.

The allowlist is inherited rather than per class: this one is the union of
`org.apache.spark.ml.fpm.FPGrowthModel`, `org.apache.spark.ml.util.Identifiable`.

# `association_rules`

```elixir
@spec association_rules(Latu.ML.Model.t()) :: Latu.DataFrame.t()
```

DataFrame with four columns: * `antecedent` - Array of the same type as the input column. *
`consequent` - Array of the same type as the input column. * `confidence` - Confidence for
the rule (`DoubleType`). * `lift` - Lift for the rule (`DoubleType`).

A lazy builder: the `Fetch` rides a relation, so this hands back a `Latu.DataFrame` and
nothing has run until you collect it.

# `class`

```elixir
@spec class() :: String.t()
```

The JVM class these accessors belong to.

# `freq_itemsets`

```elixir
@spec freq_itemsets(Latu.ML.Model.t()) :: Latu.DataFrame.t()
```

DataFrame with two columns: * `items` - Itemset of the same type as the input column. *
`freq` - Frequency of the itemset (`LongType`).

A lazy builder: the `Fetch` rides a relation, so this hands back a `Latu.DataFrame` and
nothing has run until you collect it.

---

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