Multi-Layer Perceptron (MLP) Regressor

hanaml.MLPRegressor is a R wrapper for SAP HANA PAL Multi-layer Perceptron algorithm for Regression.

hanaml.MLPRegressor(
  data = NULL,
  key = NULL,
  features = NULL,
  label = NULL,
  formula = NULL,
  hidden.layer.size = NULL,
  activation = NULL,
  output.activation = NULL,
  learning.rate = NULL,
  momentum = NULL,
  training.style = NULL,
  max.iter = NULL,
  normalization = NULL,
  weight.init = NULL,
  thread.ratio = NULL,
  categorical.variable = NULL,
  batch.size = NULL,
  resampling.method = NULL,
  evaluation.metric = "RMSE",
  fold.num = NULL,
  repeat.times = NULL,
  param.search.strategy = NULL,
  random.search.times = NULL,
  random.state = NULL,
  timeout = NULL,
  progress.indicator.id = NULL,
  parameter.range = NULL,
  parameter.values = NULL
)

Arguments

data	`DataFrame` DataFrame containting the data.
key	`character, optional` Name of the ID column. If not provided, the data is assumed to have no ID column. No default value.
features	`character of list of characters, optional` Name of feature columns. If not provided, it defaults all non-key, non-label columns of data.
label	`character, optional` Name of the column which specifies the dependent variable. Defaults to the last column of data if not provided.
formula	`formula type, optional` Formula to be used for model generation. format = label~<feature_list> e.g.: formula=CATEGORY~V1+V2+V3 You can either give the formula, or a feature and label combination, but do not provide both. Defaults to NULL.
hidden.layer.size	`vector/list of integers, mandatory` Specifies the sizes of hidden layers. The value 0 will be ignored, for example, c(2, 0, 3) is equivalent to c(2, 3).
activation	`character` Activation function for the hidden layer options: `"tanh", "linear", "sigmoid-asymmetric", "sigmoid-symmetric", "gaussian-asymmetric", "gaussian-symmetric", "elliot-asymmetric", "elliot-symmetric", "sin-asymmetric", "sin-symmetric", "cos-asymmetric", "cos-symmetric", "relu"`
output.activation	`character` Output activation function for the hidden layer options: `"tanh", "linear", "sigmoid-asymmetric", "sigmoid-symmetric", "gaussian-asymmetric", "gaussian-symmetric", "elliot-asymmetric", "elliot-symmetric", "sin-asymmetric", "sin-symmetric", "cos-asymmetric", "cos-symmetric", "relu"`
learning.rate	`double, optional` Specifies the learning rate. Only valid when training.style is "stochastic". No default value.
momentum	`double, optional` Specifies the momentum for gradient descent update. Only valid when training.style is "stochastic". No default value.
training.style	`{"batch", "stochastic"}, optional` Specifies the training style. Defaults to "stochastic".
max.iter	`integer, optional` The maximum number of iterations. Defaults to 100.
normalization	`{"no", "z-transform", "scalar"}, optional` Specifies the normalization type. Defaults to 'no' .
weight.init	`character, optional` Specifies the initial value of weight from the options below. `"all-zeros", "normal", "uniform", "variance-scale-normal", "variance-scale-uniform"` Defaults to "all-zeros".
thread.ratio	`double, optional` Controls the proportion of available threads to use. The value range is from 0 to 1, where 0 indicates a single thread, and 1 indicates up to all available threads. Values between 0 and 1 will use that percentage of available threads. Values outside this range tell PAL to heuristically determine the number of threads to use. Defaults to 0.
categorical.variable	`character or list of characters, optional` Column names in the data table used as category variable. No default value.
batch.size	`integer, optional` Specifies the size of mini batch. Only valid when training.style is stochastic. Defaults to 1.
resampling.method	`character, optional` Specifies the resampling values. `"cv", "stratified_cv", "bootstrap", "stratified_bootstrap"` If no value is specified for this parameter, neither model evaluation nor parameter selection is activated. No default value.
evaluation.metric	`character, optional` Specifies the evaluation metric for model evaluation or parameter selection. Valid values include: `'RMSE'` No default value.
fold.num	`integer, optional` Specifies the fold number for the cross validation method. Mandatory and valid only when resampling.method is set to "cv" or "stratified_cv". No default value.
repeat.times	`integer, optional` Specifies the number of repeat times for resampling. Defaults to 1.
param.search.strategy	`character, optional` Specifies the method to activate parameter selection. values should either be "grid" or "random" No default value.
random.search.times	`integer, optional` Specifies the number of times to randomly select candidate parameters for selection. No default value.
random.state	`integer, optional` Specifies the seed for random generation. Use system time when 0 is specified.
timeout	`integer, optional` Specifies maximum running time for model evaluation or parameter selection, in seconds. No timeout when 0 is specified. Default value is 0.
progress.indicator.id	`character, optional` Sets an ID of progress indicator for model evaluation or parameter selection. No progress indicator is active if no value is provided. No default value.
parameter.range	`list, optional` Specifies range of the following parameters for parameter selection: `learning.rate`, `momentum`, `batch.size`.
parameter.values	`list, optional` Specifies values of the following parameters for parameter selection: `action`, `output.action`, `hidden.layer.size`, `learning.rate`, `momentum`, `batch.size`.

Value

A "MLPRegressor" object with the following attributes:

model: DataFrame

ROW_INDEX - model row index.
MODEL_CONTENT - model content.

log: DataFrame

ITERATION - iteration number.
ERROR - Mean squared error between predicted values and target values for each iteration.

statistics: DataFrame

STAT_NAME - statistics name.
STAT_VALUE - values of the statistics.

optim.param: DataFrame

Optimal parameters selected.

Examples

Input DataFrame df:

> df$Collect()
    V000  V001 V002  V003  T001  T002  T003
1     1  1.71   AC     0  12.7   2.8  3.06
2    10  1.78   CA     5  12.1   8.0  2.65
3    17  2.36   AA     6  10.1   2.8  3.24
4    12  3.15   AA     2  28.1   5.6  2.24
5     7  1.05   CA     3  19.8   7.1  1.98
6     6  1.50   CA     2  23.2   4.9  2.12
7     9  1.97   CA     6  24.5   4.2  1.05
8     5  1.26   AA     1  13.6   5.1  2.78
9    12  2.13   AC     4  13.2   1.9  1.34
10   18  1.87   AC     6  25.5   3.6  2.14

Training the model:

> mlpr <- hanaml.MLPRegressor(data = df,
                              label= c("T001", "T002", "T003"),
                              hidden.layer.size = c(10,5),
                              activation = "SIN-ASYMMETRIC",
                              output.activation = "SIN-ASYMMETRIC",
                              learning.rate = 0.001, momentum = 0.00001,
                              training.style = "batch",
                              max.iter = 10000, normalization = "z-transform",
                              weight.init = "normal", thread.ratio = 0.3)

Training result may look different from the following results due to model randomness.

> mlpr$train.log$Collect()
       ITERATION       ERROR
  1            1   34.525655
  2            2   82.656301
  3            3   67.289241
  4            4  162.768062
  5            5   38.988242
  6            6  142.239468
  7            7   34.467742
  ..         ...         ...
  732        732   11.891081
  733        733   11.891081
  734        734   11.891081
  735        735   11.891081

Arguments

Value

Examples

See also