[
https://issues.apache.org/jira/browse/MADLIB-1387?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Frank McQuillan updated MADLIB-1387:
------------------------------------
Description:
Follow on from
https://issues.apache.org/jira/browse/MADLIB-1386
Need an easy way for user to pick winner from param search to run evaluate and
predict/inference.
Proposed change in signatures:
{code}
madlib_keras_evaluate(
model_table,
test_table,
output_table,
gpus_per_host, -- this might change based on other story
mst_key -- new optional param
)
{code}
{code}
madlib_keras_predict(
model_table,
test_table,
id_col,
independent_varname,
output_table,
pred_type,
gpus_per_host, -- this might change based on other story
mst_key -- new optional param
)
{code}
Also should use `model_weights` in the summary table. Currently in single
model is it `model_data` which is less descriptive.
Table formats:
{code}
(A)
madlib_keras_fit()
http://madlib.apache.org/docs/latest/group__grp__keras.html
produces these output files:
1) model table
Column | Type | Modifiers
------------+-------+-----------
model_data | bytea |
model_arch | json |
Distributed by: (model_data)
2) summary table
Column | Type | Modifiers
---------------------------+-----------------------------+-----------
source_table | text |
model | text |
dependent_varname | text |
independent_varname | text |
model_arch_table | text |
model_arch_id | integer |
compile_params | text |
fit_params | text |
num_iterations | integer |
validation_table | text |
metrics_compute_frequency | integer |
name | text |
description | text |
model_type | text |
model_size | double precision |
start_training_time | timestamp without time zone |
end_training_time | timestamp without time zone |
metrics_elapsed_time | double precision[] |
madlib_version | text |
num_classes | integer |
class_values | character varying[] |
dependent_vartype | text |
normalizing_const | real |
metrics_type | text[] |
training_metrics_final | double precision |
training_loss_final | double precision |
training_metrics | double precision[] |
training_loss | double precision[] |
validation_metrics_final | double precision |
validation_loss_final | double precision |
validation_metrics | double precision[] |
validation_loss | double precision[] |
metrics_iters | integer[] |
Distributed by: (source_table)
(B)
madlib_keras_fit_multiple_model()
produces these output files:
1) model table
Column | Type | Modifiers
---------------+---------+-----------
mst_key | integer | not null
model_weights | bytea |
model_arch | json |
Indexes:
"iris_multi_model_pkey" PRIMARY KEY, btree (mst_key)
Distributed by: (mst_key)
2) summary table
Column | Type | Modifiers
---------------------+-----------------------------+-----------
source_table | text |
validation_table | text |
model | text |
model_info | text |
dependent_varname | text |
independent_varname | text |
model_arch_table | text |
num_iterations | integer |
start_training_time | timestamp without time zone |
end_training_time | timestamp without time zone |
madlib_version | text |
num_classes | integer |
class_values | text[] |
dependent_vartype | text |
normalizing_const | real |
Distributed by: (source_table)
3) info table
Column | Type | Modifiers
--------------------------+--------------------+-----------
mst_key | integer | not null
model_id | integer |
compile_params | text |
fit_params | text |
model_type | text |
model_size | double precision |
metrics_elapsed_time | double precision[] |
metrics_type | text[] |
training_metrics_final | double precision |
training_loss_final | double precision |
training_metrics | double precision[] |
training_loss | double precision[] |
validation_metrics_final | double precision |
validation_loss_final | double precision |
validation_metrics | double precision[] |
validation_loss | double precision[] |
Indexes:
"iris_multi_model_info_pkey" PRIMARY KEY, btree (mst_key)
Distributed by: (mst_key)
{code}
was:
Follow on from
https://issues.apache.org/jira/browse/MADLIB-1386
Need an easy way for user to pick winner from param search to run evaluate and
predict/inference
> Make param search fit() function work with existing evaluate and predict
> ------------------------------------------------------------------------
>
> Key: MADLIB-1387
> URL: https://issues.apache.org/jira/browse/MADLIB-1387
> Project: Apache MADlib
> Issue Type: New Feature
> Reporter: Frank McQuillan
> Priority: Major
> Fix For: v1.17
>
>
> Follow on from
> https://issues.apache.org/jira/browse/MADLIB-1386
> Need an easy way for user to pick winner from param search to run evaluate
> and predict/inference.
> Proposed change in signatures:
> {code}
> madlib_keras_evaluate(
> model_table,
> test_table,
> output_table,
> gpus_per_host, -- this might change based on other story
> mst_key -- new optional param
> )
> {code}
> {code}
> madlib_keras_predict(
> model_table,
> test_table,
> id_col,
> independent_varname,
> output_table,
> pred_type,
> gpus_per_host, -- this might change based on other story
> mst_key -- new optional param
> )
> {code}
> Also should use `model_weights` in the summary table. Currently in single
> model is it `model_data` which is less descriptive.
> Table formats:
> {code}
> (A)
> madlib_keras_fit()
> http://madlib.apache.org/docs/latest/group__grp__keras.html
> produces these output files:
> 1) model table
> Column | Type | Modifiers
> ------------+-------+-----------
> model_data | bytea |
> model_arch | json |
> Distributed by: (model_data)
> 2) summary table
> Column | Type | Modifiers
> ---------------------------+-----------------------------+-----------
> source_table | text |
> model | text |
> dependent_varname | text |
> independent_varname | text |
> model_arch_table | text |
> model_arch_id | integer |
> compile_params | text |
> fit_params | text |
> num_iterations | integer |
> validation_table | text |
> metrics_compute_frequency | integer |
> name | text |
> description | text |
> model_type | text |
> model_size | double precision |
> start_training_time | timestamp without time zone |
> end_training_time | timestamp without time zone |
> metrics_elapsed_time | double precision[] |
> madlib_version | text |
> num_classes | integer |
> class_values | character varying[] |
> dependent_vartype | text |
> normalizing_const | real |
> metrics_type | text[] |
> training_metrics_final | double precision |
> training_loss_final | double precision |
> training_metrics | double precision[] |
> training_loss | double precision[] |
> validation_metrics_final | double precision |
> validation_loss_final | double precision |
> validation_metrics | double precision[] |
> validation_loss | double precision[] |
> metrics_iters | integer[] |
> Distributed by: (source_table)
> (B)
> madlib_keras_fit_multiple_model()
> produces these output files:
> 1) model table
> Column | Type | Modifiers
> ---------------+---------+-----------
> mst_key | integer | not null
> model_weights | bytea |
> model_arch | json |
> Indexes:
> "iris_multi_model_pkey" PRIMARY KEY, btree (mst_key)
> Distributed by: (mst_key)
> 2) summary table
> Column | Type | Modifiers
> ---------------------+-----------------------------+-----------
> source_table | text |
> validation_table | text |
> model | text |
> model_info | text |
> dependent_varname | text |
> independent_varname | text |
> model_arch_table | text |
> num_iterations | integer |
> start_training_time | timestamp without time zone |
> end_training_time | timestamp without time zone |
> madlib_version | text |
> num_classes | integer |
> class_values | text[] |
> dependent_vartype | text |
> normalizing_const | real |
> Distributed by: (source_table)
> 3) info table
> Column | Type | Modifiers
> --------------------------+--------------------+-----------
> mst_key | integer | not null
> model_id | integer |
> compile_params | text |
> fit_params | text |
> model_type | text |
> model_size | double precision |
> metrics_elapsed_time | double precision[] |
> metrics_type | text[] |
> training_metrics_final | double precision |
> training_loss_final | double precision |
> training_metrics | double precision[] |
> training_loss | double precision[] |
> validation_metrics_final | double precision |
> validation_loss_final | double precision |
> validation_metrics | double precision[] |
> validation_loss | double precision[] |
> Indexes:
> "iris_multi_model_info_pkey" PRIMARY KEY, btree (mst_key)
> Distributed by: (mst_key)
> {code}
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