# Spark ML -- Multilayer Perceptron

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

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

ml_multilayer_perceptron(x, formula = NULL, layers, max_iter = 100,
step_size = 0.03, tol = 1e-06, block_size = 128,
solver = "l-bfgs", seed = NULL, initial_weights = NULL,
features_col = "features", label_col = "label", thresholds = NULL,
prediction_col = "prediction", probability_col = "probability",
raw_prediction_col = "rawPrediction",
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. thresholds Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold. 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. probability_col Column name for predicted class conditional probabilities. raw_prediction_col Raw prediction (a.k.a. confidence) 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.

## 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_estimator 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.

## 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.

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

if (FALSE) {
sc <-  spark_connect(master = "local")

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

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

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

pred <- ml_predict(mlp_model, iris_test)

ml_multiclass_classification_evaluator(pred)
}