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https://issues.apache.org/jira/browse/MADLIB-1227?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16436605#comment-16436605
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Frank McQuillan commented on MADLIB-1227:
-----------------------------------------
looks Ok, I got this error as expected when I tried to run mlp classification
w/minibatch w/integer dependent variable that had not been encoded:
{code}
InternalError: (psycopg2.InternalError) plpy.Error: MiniBatch expects the
variable dependent_varname to be one hot encoded. You might need to re run the
minibatch_preprocessor function and make sure that the variable is encoded
(plpython.c:4960)
CONTEXT: Traceback (most recent call last):
PL/Python function "mlp_classification", line 36, in <module>
grouping_col
PL/Python function "mlp_classification", line 103, in mlp
PL/Python function "mlp_classification", line 734, in
_validate_params_based_on_minibatch
PL/Python function "mlp_classification", line 685, in _validate_dependent_var
PL/Python function "mlp_classification", line 28, in
is_var_one_hot_encoded_for_minibatch
PL/Python function "mlp_classification"
[SQL: "SELECT madlib.mlp_classification(\n 'mnist_train_packed', --
Packed table from preprocessor\n 'mnist_result', -- Destination
table\n 'independent_varname', -- Independent\n
'dependent_varname', -- Dependent\n ARRAY[20,20],
-- Hidden layer sizes\n 'learning_rate_init=0.1,\n n_iterations=25,\n
learning_rate_policy=const,\n lambda=0.0001, --
Regularization\n tolerance=0',\n 'tanh', --
Activation function\n '', -- No weights\n FALSE,
-- No warmstart\n FALSE); --
Verbose"]
{code}
> In MLP classification with mini-batch, check for 1-hot encoding of dependent
> variable
> -------------------------------------------------------------------------------------
>
> Key: MADLIB-1227
> URL: https://issues.apache.org/jira/browse/MADLIB-1227
> Project: Apache MADlib
> Issue Type: Improvement
> Reporter: Frank McQuillan
> Priority: Minor
> Fix For: v1.14
>
>
> Related to
> https://issues.apache.org/jira/browse/MADLIB-1226
> Add check to MLP classification code to check that the dependent var has been
> 1-hot encoded by accident, and error out if that is not the case.
> This is to avoid the case of passing INTs as dep var to MLP that have not
> been 1-hot encoded and having it run classification and not converge, or give
> erroneous results, and giving no notification to the user about the problem.
> OK to pick 1st row and check one that y array value for encoding looks like:
> {code}
> [0.0, 1.0, 0.0 ...]
> {code}
> i.e., all 0's and just one 1
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