ml_multilayer_perceptron_classifier

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 = 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

Argument	Description
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.

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_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.

Examples


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)