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https://issues.apache.org/jira/browse/MADLIB-1226?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan resolved MADLIB-1226.
-------------------------------------
Resolution: Fixed
> Add option for 1-hot encoding to minibatch preprocessor
> -------------------------------------------------------
>
> Key: MADLIB-1226
> URL: https://issues.apache.org/jira/browse/MADLIB-1226
> Project: Apache MADlib
> Issue Type: Improvement
> Components: Module: Utilities
> Reporter: Frank McQuillan
> Priority: Minor
> Fix For: v1.14
>
>
> I was testing MNIST dataset with minibatch preprocessor + MLP and could not
> get it to converge. It turned out to be user error (me) and not a problem
> with convergence at all, because I forgot to 1-hot encode the dependent
> variable.
> But I am wondering if other people might do the same thing that I did and get
> confused.
> Here's what I did. For this input data:
> {code}
> madlib=# \d+ public.mnist_train
> Table "public.mnist_train"
> Column | Type | Modifiers
> | Storage | Stats target | Description
> --------+-----------+----------------------------------------------------------+----------+--------------+-------------
> y | integer |
> | plain | |
> x | integer[] |
> | extended | |
> id | integer | not null default
> nextval('mnist_train_id_seq'::regclass) | plain | |
> {code}
> I called minibatch preprocessor:
> {code}
> SELECT madlib.minibatch_preprocessor('mnist_train', -- Source table
> 'mnist_train_packed', -- Output table
> 'y', -- Dependent
> variable
> 'x' -- Independent
> variables
> );
> {code}
> then mlp:
> {code}
> SELECT madlib.mlp_classification(
> 'mnist_train_packed', -- Source table from preprocessor output
> 'mnist_result', -- Destination table
> 'independent_varname', -- Independent
> 'dependent_varname', -- Dependent
> ARRAY[5], -- Hidden layer sizes
> 'learning_rate_init=0.01,
> n_iterations=20,
> learning_rate_policy=exp, n_epochs=20,
> lambda=0.0001, -- Regularization
> tolerance=0',
> 'tanh', -- Activation function
> '', -- No weights
> FALSE, -- No warmstart
> TRUE); -- Verbose
> {code}
> with the result:
> {code}
> INFO: Iteration: 2, Loss: <-79.5295531257>
> INFO: Iteration: 3, Loss: <-79.529408892>
> INFO: Iteration: 4, Loss: <-79.5291940436>
> INFO: Iteration: 5, Loss: <-79.5288964944>
> INFO: Iteration: 6, Loss: <-79.5285051451>
> INFO: Iteration: 7, Loss: <-79.5280094708>
> INFO: Iteration: 8, Loss: <-79.5273995189>
> INFO: Iteration: 9, Loss: <-79.5266665607>
> {code}
> So it did not error out but clearly is not working on data in the right
> format.
> I suggest 2 changes:
> 1) Add an explicit param in the mini-batch preprocessor for 1-hot encoding of
> scalar integer dependent variables (this JIRA)
> 2) Add a check to the MLP classification code to check that the dependent var
> has been 1-hot encoded, and error out if that is not the case.
> (https://issues.apache.org/jira/browse/MADLIB-1226)
> Proposed interface:
> {code}
> minibatch_preprocessor( source_table,
> output_table,
> dependent_varname,
> independent_varname,
> grouping_col,
> buffer_size,
> one_hot_encode_int_dep_var
> )
> {code}
> {code}
> one_hot_encode_int_dep_var (optional)
> BOOLEAN. default: FALSE. Whether to one-hot encode dependent variables that
> are scalar integer.
> This parameter is ignored if the dependent variable is not a scalar integer.
> More detail: the mini-batch preprocessor automatically encodes dependent
> variables that are
> Boolean and character types such as text, char and varchar. However, scalar
> integers are a
> special case because they can be used in both classification and regression
> problems, so
> you must tell the mini-batch preprocessor whether you want to encode them or
> not.
> In the case that you have already encoded the dependent variable yourself,
> you can ignore this parameter. Also, if you want to encode float values for
> some reason, cast them
> to text first.
> {code}
>
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