spark-instrumented-optimizer/docs/ml-guide.md

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title: "Overview: estimators, transformers and pipelines - spark.ml"
displayTitle: "Overview: estimators, transformers and pipelines - spark.ml"
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The `spark.ml` package aims to provide a uniform set of high-level APIs built on top of
[DataFrames](sql-programming-guide.html#dataframes) that help users create and tune practical
machine learning pipelines.
See the [algorithm guides](#algorithm-guides) section below for guides on sub-packages of
`spark.ml`, including feature transformers unique to the Pipelines API, ensembles, and more.

**Table of contents**

* This will become a table of contents (this text will be scraped).
{:toc}


# Main concepts in Pipelines

Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
algorithms into a single pipeline, or workflow.
This section covers the key concepts introduced by the Spark ML API, where the pipeline concept is
mostly inspired by the [scikit-learn](http://scikit-learn.org/) project.

* **[`DataFrame`](ml-guide.html#dataframe)**: Spark ML uses `DataFrame` from Spark SQL as an ML
  dataset, which can hold a variety of data types.
  E.g., a `DataFrame` could have different columns storing text, feature vectors, true labels, and predictions.

* **[`Transformer`](ml-guide.html#transformers)**: A `Transformer` is an algorithm which can transform one `DataFrame` into another `DataFrame`.
E.g., an ML model is a `Transformer` which transforms `DataFrame` with features into a `DataFrame` with predictions.

* **[`Estimator`](ml-guide.html#estimators)**: An `Estimator` is an algorithm which can be fit on a `DataFrame` to produce a `Transformer`.
E.g., a learning algorithm is an `Estimator` which trains on a `DataFrame` and produces a model.

* **[`Pipeline`](ml-guide.html#pipeline)**: A `Pipeline` chains multiple `Transformer`s and `Estimator`s together to specify an ML workflow.

* **[`Parameter`](ml-guide.html#parameters)**: All `Transformer`s and `Estimator`s now share a common API for specifying parameters.

## DataFrame

Machine learning can be applied to a wide variety of data types, such as vectors, text, images, and structured data.
Spark ML adopts the `DataFrame` from Spark SQL in order to support a variety of data types.

`DataFrame` supports many basic and structured types; see the [Spark SQL datatype reference](sql-programming-guide.html#spark-sql-datatype-reference) for a list of supported types.
In addition to the types listed in the Spark SQL guide, `DataFrame` can use ML [`Vector`](mllib-data-types.html#local-vector) types.

A `DataFrame` can be created either implicitly or explicitly from a regular `RDD`.  See the code examples below and the [Spark SQL programming guide](sql-programming-guide.html) for examples.

Columns in a `DataFrame` are named.  The code examples below use names such as "text," "features," and "label."

## Pipeline components

### Transformers

A `Transformer` is an abstraction that includes feature transformers and learned models.
Technically, a `Transformer` implements a method `transform()`, which converts one `DataFrame` into
another, generally by appending one or more columns.
For example:

* A feature transformer might take a `DataFrame`, read a column (e.g., text), map it into a new
  column (e.g., feature vectors), and output a new `DataFrame` with the mapped column appended.
* A learning model might take a `DataFrame`, read the column containing feature vectors, predict the
  label for each feature vector, and output a new `DataFrame` with predicted labels appended as a
  column.

### Estimators

An `Estimator` abstracts the concept of a learning algorithm or any algorithm that fits or trains on
data.
Technically, an `Estimator` implements a method `fit()`, which accepts a `DataFrame` and produces a
`Model`, which is a `Transformer`.
For example, a learning algorithm such as `LogisticRegression` is an `Estimator`, and calling
`fit()` trains a `LogisticRegressionModel`, which is a `Model` and hence a `Transformer`.

### Properties of pipeline components

`Transformer.transform()`s and `Estimator.fit()`s are both stateless.  In the future, stateful algorithms may be supported via alternative concepts.

Each instance of a `Transformer` or `Estimator` has a unique ID, which is useful in specifying parameters (discussed below).

## Pipeline

In machine learning, it is common to run a sequence of algorithms to process and learn from data.
E.g., a simple text document processing workflow might include several stages:

* Split each document's text into words.
* Convert each document's words into a numerical feature vector.
* Learn a prediction model using the feature vectors and labels.

Spark ML represents such a workflow as a `Pipeline`, which consists of a sequence of
`PipelineStage`s (`Transformer`s and `Estimator`s) to be run in a specific order.
We will use this simple workflow as a running example in this section.

### How it works

A `Pipeline` is specified as a sequence of stages, and each stage is either a `Transformer` or an `Estimator`.
These stages are run in order, and the input `DataFrame` is transformed as it passes through each stage.
For `Transformer` stages, the `transform()` method is called on the `DataFrame`.
For `Estimator` stages, the `fit()` method is called to produce a `Transformer` (which becomes part of the `PipelineModel`, or fitted `Pipeline`), and that `Transformer`'s `transform()` method is called on the `DataFrame`.

We illustrate this for the simple text document workflow.  The figure below is for the *training time* usage of a `Pipeline`.

<p style="text-align: center;">
  <img
    src="img/ml-Pipeline.png"
    title="Spark ML Pipeline Example"
    alt="Spark ML Pipeline Example"
    width="80%"
  />
</p>

Above, the top row represents a `Pipeline` with three stages.
The first two (`Tokenizer` and `HashingTF`) are `Transformer`s (blue), and the third (`LogisticRegression`) is an `Estimator` (red).
The bottom row represents data flowing through the pipeline, where cylinders indicate `DataFrame`s.
The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw text documents and labels.
The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`.
The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`.
Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`.
If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()`
method on the `DataFrame` before passing the `DataFrame` to the next stage.

A `Pipeline` is an `Estimator`.
Thus, after a `Pipeline`'s `fit()` method runs, it produces a `PipelineModel`, which is a
`Transformer`.
This `PipelineModel` is used at *test time*; the figure below illustrates this usage.

<p style="text-align: center;">
  <img
    src="img/ml-PipelineModel.png"
    title="Spark ML PipelineModel Example"
    alt="Spark ML PipelineModel Example"
    width="80%"
  />
</p>

In the figure above, the `PipelineModel` has the same number of stages as the original `Pipeline`, but all `Estimator`s in the original `Pipeline` have become `Transformer`s.
When the `PipelineModel`'s `transform()` method is called on a test dataset, the data are passed
through the fitted pipeline in order.
Each stage's `transform()` method updates the dataset and passes it to the next stage.

`Pipeline`s and `PipelineModel`s help to ensure that training and test data go through identical feature processing steps.

### Details

*DAG `Pipeline`s*: A `Pipeline`'s stages are specified as an ordered array.  The examples given here are all for linear `Pipeline`s, i.e., `Pipeline`s in which each stage uses data produced by the previous stage.  It is possible to create non-linear `Pipeline`s as long as the data flow graph forms a Directed Acyclic Graph (DAG).  This graph is currently specified implicitly based on the input and output column names of each stage (generally specified as parameters).  If the `Pipeline` forms a DAG, then the stages must be specified in topological order.

*Runtime checking*: Since `Pipeline`s can operate on `DataFrame`s with varied types, they cannot use
compile-time type checking.
`Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.

*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances.  E.g., the same instance
`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
unique IDs.  However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
can be put into the same `Pipeline` since different instances will be created with different IDs.

## Parameters

Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.

A `Param` is a named parameter with self-contained documentation.
A `ParamMap` is a set of (parameter, value) pairs.

There are two main ways to pass parameters to an algorithm:

1. Set parameters for an instance.  E.g., if `lr` is an instance of `LogisticRegression`, one could
   call `lr.setMaxIter(10)` to make `lr.fit()` use at most 10 iterations.
   This API resembles the API used in `spark.mllib` package.
2. Pass a `ParamMap` to `fit()` or `transform()`.  Any parameters in the `ParamMap` will override parameters previously specified via setter methods.

Parameters belong to specific instances of `Estimator`s and `Transformer`s.
For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.

## Saving and Loading Pipelines

Often times it is worth it to save a model or a pipeline to disk for later use. In Spark 1.6, a model import/export functionality was added to the Pipeline API. Most basic transformers are supported as well as some of the more basic ML models. Please refer to the algorithm's API documentation to see if saving and loading is supported.

# Code examples

This section gives code examples illustrating the functionality discussed above.
For more info, please refer to the API documentation
([Scala](api/scala/index.html#org.apache.spark.ml.package),
[Java](api/java/org/apache/spark/ml/package-summary.html),
and [Python](api/python/pyspark.ml.html)).
Some Spark ML algorithms are wrappers for `spark.mllib` algorithms, and the
[MLlib programming guide](mllib-guide.html) has details on specific algorithms.

## Example: Estimator, Transformer, and Param

This example covers the concepts of `Estimator`, `Transformer`, and `Param`.

<div class="codetabs">

<div data-lang="scala">
{% include_example scala/org/apache/spark/examples/ml/EstimatorTransformerParamExample.scala %}
</div>

<div data-lang="java">
{% include_example java/org/apache/spark/examples/ml/JavaEstimatorTransformerParamExample.java %}
</div>

<div data-lang="python">
{% include_example python/ml/estimator_transformer_param_example.py %}
</div>

</div>

## Example: Pipeline

This example follows the simple text document `Pipeline` illustrated in the figures above.

<div class="codetabs">

<div data-lang="scala">
{% include_example scala/org/apache/spark/examples/ml/PipelineExample.scala %}
</div>

<div data-lang="java">
{% include_example java/org/apache/spark/examples/ml/JavaPipelineExample.java %}
</div>

<div data-lang="python">
{% include_example python/ml/pipeline_example.py %}
</div>

</div>

## Example: model selection via cross-validation

An important task in ML is *model selection*, or using data to find the best model or parameters for a given task.  This is also called *tuning*.
`Pipeline`s facilitate model selection by making it easy to tune an entire `Pipeline` at once, rather than tuning each element in the `Pipeline` separately.

Currently, `spark.ml` supports model selection using the [`CrossValidator`](api/scala/index.html#org.apache.spark.ml.tuning.CrossValidator) class, which takes an `Estimator`, a set of `ParamMap`s, and an [`Evaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.Evaluator).
`CrossValidator` begins by splitting the dataset into a set of *folds* which are used as separate training and test datasets; e.g., with `$k=3$` folds, `CrossValidator` will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing.
`CrossValidator` iterates through the set of `ParamMap`s. For each `ParamMap`, it trains the given `Estimator` and evaluates it using the given `Evaluator`.

The `Evaluator` can be a [`RegressionEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.RegressionEvaluator)
for regression problems, a [`BinaryClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.BinaryClassificationEvaluator)
for binary data, or a [`MultiClassClassificationEvaluator`](api/scala/index.html#org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator)
for multiclass problems. The default metric used to choose the best `ParamMap` can be overridden by the `setMetricName`
method in each of these evaluators.

The `ParamMap` which produces the best evaluation metric (averaged over the `$k$` folds) is selected as the best model.
`CrossValidator` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.

The following example demonstrates using `CrossValidator` to select from a grid of parameters.
To help construct the parameter grid, we use the [`ParamGridBuilder`](api/scala/index.html#org.apache.spark.ml.tuning.ParamGridBuilder) utility.

Note that cross-validation over a grid of parameters is expensive.
E.g., in the example below, the parameter grid has 3 values for `hashingTF.numFeatures` and 2 values for `lr.regParam`, and `CrossValidator` uses 2 folds.  This multiplies out to `$(3 \times 2) \times 2 = 12$` different models being trained.
In realistic settings, it can be common to try many more parameters and use more folds (`$k=3$` and `$k=10$` are common).
In other words, using `CrossValidator` can be very expensive.
However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning.

<div class="codetabs">

<div data-lang="scala">
{% include_example scala/org/apache/spark/examples/ml/ModelSelectionViaCrossValidationExample.scala %}
</div>

<div data-lang="java">
{% include_example java/org/apache/spark/examples/ml/JavaModelSelectionViaCrossValidationExample.java %}
</div>

<div data-lang="python">

{% include_example python/ml/cross_validator.py %}
</div>

</div>

## Example: model selection via train validation split
In addition to  `CrossValidator` Spark also offers `TrainValidationSplit` for hyper-parameter tuning.
`TrainValidationSplit` only evaluates each combination of parameters once as opposed to k times in
 case of `CrossValidator`. It is therefore less expensive,
 but will not produce as reliable results when the training dataset is not sufficiently large.

`TrainValidationSplit` takes an `Estimator`, a set of `ParamMap`s provided in the `estimatorParamMaps` parameter,
and an `Evaluator`.
It begins by splitting the dataset into two parts using `trainRatio` parameter
which are used as separate training and test datasets. For example with `$trainRatio=0.75$` (default),
`TrainValidationSplit` will generate a training and test dataset pair where 75% of the data is used for training and 25% for validation.
Similar to `CrossValidator`, `TrainValidationSplit` also iterates through the set of `ParamMap`s.
For each combination of parameters, it trains the given `Estimator` and evaluates it using the given `Evaluator`.
The `ParamMap` which produces the best evaluation metric is selected as the best option.
`TrainValidationSplit` finally fits the `Estimator` using the best `ParamMap` and the entire dataset.

<div class="codetabs">

<div data-lang="scala" markdown="1">
{% include_example scala/org/apache/spark/examples/ml/ModelSelectionViaTrainValidationSplitExample.scala %}
</div>

<div data-lang="java" markdown="1">
{% include_example java/org/apache/spark/examples/ml/JavaModelSelectionViaTrainValidationSplitExample.java %}
</div>

</div>