ml_multilayer_perceptron_classifier {sparklyr} | R Documentation

## Spark ML – Multilayer Perceptron

### Description

Classification model based on the Multilayer Perceptron. Each layer has sigmoid activation function, output layer has softmax.

### Usage

    ml_multilayer_perceptron_classifier(x, formula = NULL, layers,
      max_iter = 100L, step_size = 0.03, tol = 1e-06, block_size = 128L,
      solver = "l-bfgs", seed = NULL, initial_weights = NULL,
      features_col = "features", label_col = "label",
      prediction_col = "prediction",
      uid = random_string("multilayer_perceptron_classifier_"), ...)

    ml_multilayer_perceptron(x, formula = NULL, layers, max_iter = 100L,
      step_size = 0.03, tol = 1e-06, block_size = 128L, solver = "l-bfgs",
      seed = NULL, initial_weights = NULL, features_col = "features",
      label_col = "label", prediction_col = "prediction",
      uid = random_string("multilayer_perceptron_classifier_"), response = NULL,
      features = NULL, ...)

### Arguments

`x` | A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
`formula` | Used when `x` is a `tbl_spark`. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
`layers` | A numeric vector describing the layers – each element in the vector gives the size of a layer. For example, `c(4, 5, 2)` would imply three layers, with an input (feature) layer of size 4, an intermediate layer of size 5, and an output (class) layer of size 2.
`max_iter` | The maximum number of iterations to use.
`step_size` | Step size to be used for each iteration of optimization (> 0).
`tol` | Param for the convergence tolerance for iterative algorithms.
`block_size` | Block size for stacking input data in matrices to speed up the computation. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data. Recommended size is between 10 and 1000. Default: 128
`solver` | The solver algorithm for optimization. Supported options: "gd" (minibatch gradient descent) or "l-bfgs". Default: "l-bfgs"
`seed` | A random seed. Set this value if you need your results to be reproducible across repeated calls.
`initial_weights` | The initial weights of the model.
`features_col` | Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by `ft_r_formula`.
`label_col` | Label column name. The column should be a numeric column. Usually this column is output by `ft_r_formula`.
`prediction_col` | Prediction column name.
`uid` | A character string used to uniquely identify the ML estimator.
`...` | Optional arguments; see Details.
`response` | (Deprecated) The name of the response column (as a length-one character vector.)
`features` | (Deprecated) The name of features (terms) to use for the model fit.

### Details

When `x` is a `tbl_spark` and `formula` (alternatively, `response` and `features`) is specified, the function returns a `ml_model` object wrapping a `ml_pipeline_model` which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument `predicted_label_col` (defaults to `"predicted_label"`) can be used to specify the name of the predicted label column. In addition to the fitted `ml_pipeline_model`, `ml_model` objects also contain a `ml_pipeline` object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by `ml_save` with `type = "pipeline"` to faciliate model refresh workflows.

`ml_multilayer_perceptron()` is an alias for `ml_multilayer_perceptron_classifier()` for backwards compatibility.

### Value

The object returned depends on the class of `x`.

- `spark_connection`: When `x` is a `spark_connection`, the function returns an instance of a `ml_predictor` object. The object contains a pointer to a Spark `Predictor` object and can be used to compose `Pipeline` objects.

- `ml_pipeline`: When `x` is a `ml_pipeline`, the function returns a `ml_pipeline` with the predictor appended to the pipeline.

- `tbl_spark`: When `x` is a `tbl_spark`, a predictor is constructed then immediately fit with the input `tbl_spark`, returning a prediction model.

- `tbl_spark`, with `formula`: specified When `formula` is specified, the input `tbl_spark` is first transformed using a `RFormula` transformer before being fit by the predictor. The object returned in this case is a `ml_model` which is a wrapper of a `ml_pipeline_model`.

### See Also

See http://spark.apache.org/docs/latest/ml-classification-regression.html for more information on the set of supervised learning algorithms.

Other ml algorithms: `ml_aft_survival_regression`, `ml_decision_tree_classifier`, `ml_gbt_classifier`, `ml_generalized_linear_regression`, `ml_isotonic_regression`, `ml_linear_regression`, `ml_linear_svc`, `ml_logistic_regression`, `ml_naive_bayes`, `ml_one_vs_rest`, `ml_random_forest_classifier`

### Examples

    ## Not run:
    sc <-  spark_connect(master = "local")

    iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)
    partitions <- iris_tbl %>%
      sdf_partition(training = 0.7, test = 0.3, seed = 1111)

    iris_training <- partitions$training
    iris_test <- partitions$test

    mlp_model <- iris_training %>%
      ml_multilayer_perceptron(Species ~ ., layers = c(4,3,3))

    pred <- sdf_predict(iris_test, mlp_model)

    ml_multiclass_classification_evaluator(pred)

    ## End(Not run)

[Package sparklyr version 0.8.4 Index]