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From Felix Schüler (JIRA) <>
Subject [jira] [Commented] (MAHOUT-1388) Add command line support and logging for MLP
Date Sun, 13 Jul 2014 14:31:05 GMT


Felix Schüler commented on MAHOUT-1388:

We played around with the existing implementation and came across some issues that might need
some clarification/fixes. We could try to fix some of the issues but would be glad to get
some feedback first, especially given that the mlp resides in the mrlegacy package and might
not be used any more as soon as an implementation in the spark DSL exists.

- First of all, it seems like the MLP CLI does not perform iterations of any kind during training.
 This is especially unpleasant in the case of a small dataset such as the iris data-set. In
the corresponding unit-test, 2000 iterations are performed on the input data whereas the command
line version only forwards the input once. This leads to wrong output on the validation data.
We think there should be a solution to this that either consists of an iteration parameter
or the possibility to define a train/validation split and use the technique of early stopping
where iteration stops if no significant improvement on the validation-set is observed.

- In the RunMultilayerperceptron case, the parameter -cr (column range) can not be set. Usually,
the classified data doesn't have labels, but we think it should still be possible to select
the columns of an input file for validation, especially if we split the same dataset into
training and validation parts, we don't want to remove all the labels by hand. The fix for
this is fairly easy since the functionality is already implemented and just has to be added
to the argument-parser (we will provide a patch for this).

- We are not sure if the CLI-MLP can be used for regression since all the labels have to be
provided as arguments. 

- small typo in momentumweight: "momemtumweight", we can provide the patch for this as well.

> Add command line support and logging for MLP
> --------------------------------------------
>                 Key: MAHOUT-1388
>                 URL:
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Classification
>    Affects Versions: 1.0
>            Reporter: Yexi Jiang
>            Assignee: Suneel Marthi
>              Labels: mlp, sgd
>             Fix For: 1.0
>         Attachments: Mahout-1388.patch, Mahout-1388.patch
> The user should have the ability to run the Perceptron from the command line.
> There are two programs to execute MLP, the training and labeling. The first one takes
the data as input and outputs the model, the second one takes the model and unlabeled data
as input and outputs the results.
> The parameters for training are as follows:
> ------------------------------------------------
> --input -i (input data)
> --skipHeader -sk // whether to skip the first row, this parameter is optional
> --labels -labels // the labels of the instances, separated by whitespace. Take the iris
dataset for example, the labels are 'setosa versicolor virginica'.
> --model -mo  // in training mode, this is the location to store the model (if the specified
location has an existing model, it will update the model through incremental learning), in
labeling mode, this is the location to store the result
> --update -u // whether to incremental update the model, if this parameter is not given,
train the model from scratch
> --output -o           // this is only useful in labeling mode
> --layersize -ls (no. of units per hidden layer) // use whitespace separated number to
indicate the number of neurons in each layer (including input layer and output layer), e.g.
'5 3 2'.
> --squashingFunction -sf // currently only supports Sigmoid
> --momentum -m 
> --learningrate -l
> --regularizationweight -r
> --costfunction -cf   // the type of cost function,
> ------------------------------------------------
> For example, train a 3-layer (including input, hidden, and output) MLP with 0.1 learning
rate, 0.1 momentum rate, and 0.01 regularization weight, the parameter would be:
> mlp -i /tmp/training-data.csv -labels setosa versicolor virginica -o /tmp/model.model
-ls 5,3,1 -l 0.1 -m 0.1 -r 0.01
> This command would read the training data from /tmp/training-data.csv and write the trained
model to /tmp/model.model.
> The parameters for labeling is as follows:
> -------------------------------------------------------------
> --input -i // input file path
> --columnRange -cr // the range of column used for feature, start from 0 and separated
by whitespace, e.g. 0 5
> --format -f // the format of input file, currently only supports csv
> --model -mo // the file path of the model
> --output -o // the output path for the results
> -------------------------------------------------------------
> If a user need to use an existing model, it will use the following command:
> mlp -i /tmp/unlabel-data.csv -m /tmp/model.model -o /tmp/label-result
> Moreover, we should be providing default values if the user does not specify any. 

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