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

Frequent pattern mining.

Spark's own abbreviation, kept as one: `fpm` in PySpark, `FPM` here. Two operators, and both
answer with frames rather than predictions — the frequent itemsets and the association rules
a transaction log implies.

Every constructor here is generated from PySpark 4.2.0's own param table, and every
accessor module from the server's own attribute allowlist. Nothing in this file is
hand-written but the words you are reading — see `Latu.ML.operators/1` for the table it all
comes from.

# `fp_growth`

```elixir
@spec fp_growth(keyword()) :: Latu.ML.Estimator.t()
```

A parallel FP-growth algorithm to mine frequent itemsets.

`Latu.ML.fit/2` fits it, and hands back a `Latu.ML.Model` — a reference into the session's
ML cache, not a value. `Latu.ML.with_model/3` releases it for you; `Latu.ML.delete/1` is the
explicit form. Its attributes are on `Latu.ML.FPM.FPGrowthModel`.

*Status `:probed`* — `dev/probe_ml.exs` fitted it against a live Spark 4.2.0 server, and
every allowlisted attribute it could ask answered.

## Params

  * `:items_col` — items column name. Default `"items"`.
  * `:min_confidence` — Minimal confidence for generating Association Rule. [0.0, 1.0]. minConfidence will not affect the mining for frequent itemsets, but will affect the association rules generation. Default `0.8`.
  * `:min_support` — Minimal support level of the frequent pattern. [0.0, 1.0]. Any pattern that appears more than (minSupport * size-of-the-dataset) times will be output in the frequent itemsets. Default `0.3`.
  * `:num_partitions` — Number of partitions (at least 1) used by parallel FP-growth. By default the param is not set, and partition number of the input dataset is used.
  * `:prediction_col` — prediction column name. Default `"prediction"`.

Defaults are documented, never sent: a param the caller did not set and a param sent with
its default value are different requests, and only the first is right. A value's **kind** is
refused here; its **range** is Spark's own `ParamValidators` to refuse, with a better
message than this package could write.

# `prefix_span`

```elixir
@spec prefix_span(keyword()) :: Latu.ML.Helper.t()
```

A parallel PrefixSpan algorithm to mine frequent sequential patterns. The PrefixSpan
algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns Efficiently
by Prefix-Projected Pattern Growth (see [here](https://doi.org/10.1109/ICDE.2001.914830)).
This class is not yet an Estimator/Transformer, use `findFrequentSequentialPatterns` method
to run the PrefixSpan algorithm.

`Latu.ML.find_frequent_sequential_patterns/2` is the one thing it does, and it is a lazy
builder: it hands back a `Latu.DataFrame` and reaches no server until you collect one. There
is no fit and nothing cached — its params ride that call as **positional arguments**, so
every one is sent whether you set it or not, and the ones you leave alone are sent as the
defaults below.

*Status `:built`* — generated from PySpark 4.2.0's own param table, and not yet exercised
against a live server by `dev/probe_ml.exs`.

## Params

  * `:max_local_proj_db_size` — The maximum number of items (including delimiters used in the internal storage format) allowed in a projected database before local processing. If a projected database exceeds this size, another iteration of distributed prefix growth is run. Must be > 0. Default `32000000`.
  * `:max_pattern_length` — The maximal length of the sequential pattern. Must be > 0. Default `10`.
  * `:min_support` — The minimal support level of the sequential pattern. Sequential pattern that appears more than (minSupport * size-of-the-dataset) times will be output. Must be >= 0. Default `0.1`.
  * `:sequence_col` — The name of the sequence column in dataset, rows with nulls in this column are ignored. Default `"sequence"`.

Defaults are documented, never sent: a param the caller did not set and a param sent with
its default value are different requests, and only the first is right. A value's **kind** is
refused here; its **range** is Spark's own `ParamValidators` to refuse, with a better
message than this package could write.

---

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