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 *
 * @file madlib_keras_model_selection.sql_in
 *
 * @brief Generate configurations for model selection (hyperparams, architectures)
 * @date August 2020
 *
 *
 *//* ----------------------------------------------------------------------- */

m4_include(`SQLCommon.m4')
/**
@addtogroup grp_keras_setup_model_selection

@brief Utility function to generate configurations for model architecture search
and hyperparameter tuning.

\warning <em> This MADlib method is still in early stage development.
Interface and implementation are subject to change. </em>

<div class="toc"><b>Contents</b><ul>
<li class="level1"><a href="#gen_mst_configs">Generate Model Configurations</a></li>
<li class="level1"><a href="#load_mst_table">Load Model Selection Table [Deprecated]</a></li>
<li class="level1"><a href="#example">Examples</a></li>
<li class="level1"><a href="#notes">Notes</a></li>
<li class="level1"><a href="#related">Related Topics</a></li>
</ul></div>

This module generates model configurations 
for training multiple models at the same time
using <a href="group__grp__keras__run__model__selection.html">Run Model Selection</a>.
By model configurations we mean both hyperparameters and
model architectures. Grid search or random search 
can be used to generate the configurations. 
The output table from this module
defines the combinations of model architectures, 
compile and fit parameters to be trained in parallel.

<!-- --------------------------------------START-------------------------------------------------------------- -->
@anchor gen_mst_configs
@par Generate Model Configurations

<pre class="syntax">
generate_model_configs(
    model_arch_table,
    model_selection_table,
    model_id_list,
    compile_params_grid,
    fit_params_grid,
    search_type,
    num_configs,
    random_state,
    object_table
    )
</pre>

\b Arguments
<dl class="arglist">
  <dt>model_arch_table</dt>
  <dd>VARCHAR. Table containing model architectures and weights.
  For more information on this table
  refer to the module <a href="group__grp__keras__model__arch.html">Load Models</a>.
  </dd>

  <dt>model_selection_table</dt>
  <dd>VARCHAR. Model selection table created by this module.  A summary table
  named <model_selection_table>_summary is also created.  Contents of both output
  tables are described below.
  </dd>

  <dt>model_id_list</dt>
  <dd>INTEGER[]. Array of model IDs from the 'model_arch_table' to be included
  in the run combinations.  For hyperparameter search, this will typically be
  one model ID.  For model architecture search, this will be the different model IDs
  that you want to compare.
  </dd>

  <dt>compile_params_grid</dt>
  <dd>VARCHAR. String representation of a Python dictionary 
  of compile parameters to be tested. Each entry
  of the dictionary should consist of keys as compile parameter names, 
  and values as a Python list of compile parameter values to be passed to Keras.
  Also, optimizer parameters are a nested dictionary to allow different
  optimizer types to have different parameters or ranges of parameters.
  Here is an example:

  <pre class="example">
  $$
    {'loss': ['categorical_crossentropy'], 
     'optimizer_params_list': [ 
        {'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']}, 
        {'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}], 
     'metrics': ['accuracy']
    }
  $$
  </pre>

  The following types of sampling are supported:  'linear', 'log' and 'log_near_one'.
  The 'log_near_one' sampling is useful for exponentially weighted average types of parameters like momentum, 
  which are very sensitive to changes near 1.  It has the effect of producing more values near 1 
  than regular log-based sampling. 
  
  In the case of grid search, omit the sample type and just put the grid points in the list.
  For custom loss functions or custom metrics,
  list the custom function name in the usual way, and provide the name of the
  table where the serialized Python objects reside using the 
  parameter 'object_table' below. See the examples section later on this page for more examples. 
  </dd>

  <dt>fit_params_grid</dt>
  <dd>VARCHAR.  String representation of a Python dictionary 
  of fit parameters to be tested. Each entry
  of the dictionary should consist of keys as fit parameter names, 
  and values as a Python list of fit parameter values
  to be passed to Keras. Here is an example:

  <pre class="example">
  $$ 
    {'batch_size': [32, 64, 128, 256],
     'epochs': [10, 20, 30] 
    } 
  $$
  </pre>
  See the examples section later on this page for more examples.
  </dd>

  <dt>search_type</dt>
  <dd>VARCHAR, 'grid' or 'random'. Search strategy
  for generating model configurations.  For grid search, will generate
  all combinations of model IDs + compile params + fit params.  For
  random search, specify the number of configs you want to 
  generate using the 'num_configs' parameter below. 
  Note that you can also use short forms
  for the 'grid' or 'random' keywords, e.g.,'rand' or 'r' instead 
  of writing out 'random' in full.  
  </dd>

  <dt>num_configs (optional)</dt>
  <dd>INTEGER, default: NULL. Number of model configs to generate.
  Only applies when search_type='random'.
  </dd>

  <dt>random_state (optional)</dt>
  <dd>INTEGER, default: NULL.  Pseudo random number generator 
  state used for random uniform sampling from lists of possible 
  values. Pass an integer for reproducible output across multiple
  function calls.  Only applies when search_type='random'.
  </dd>

  <dt>object_table (optional)</dt>
  <dd>VARCHAR, default: NULL. Name of the table containing 
  Python objects in the case that custom loss functions or 
  custom metrics are specified in the 'compile_params_grid'.
  </dd>

</dl>

<b>Output table</b>
<br>
    The model selection output table contains the following columns:
    <table class="output">
      <tr>
        <th>mst_key</th>
        <td>INTEGER. ID that defines a unique tuple for
        model architecture-compile parameters-fit parameters.
        </td>
      </tr>
      <tr>
        <th>model_id</th>
        <td>VARCHAR. Model architecture ID from the 'model_arch_table'.
        </td>
      </tr>
      <tr>
        <th>compile_params</th>
        <td>VARCHAR. Keras compile parameters.
        </td>
      </tr>
      <tr>
        <th>fit_params</th>
        <td>VARCHAR. Keras fit parameters.
        </td>
      </tr>
    </table>
    A summary table named <model_selection_table>_summary is
    also created, which contains the following column:
    <table class="output">
      <tr>
        <th>model_arch_table</th>
        <td>VARCHAR. Name of the model architecture table containing the
        model architecture IDs.
        </td>
      </tr>
      <tr>
        <th>object_table</th>
        <td>VARCHAR. Name of the object table containing the serialized
        Python objects for custom loss functions and custom metrics.
        If there are none, this field will be blank.
        </td>
      </tr>
    </table>
</br>

<!-- --------------------------------------END-------------------------------------------------------------- -->

@anchor load_mst_table
@par Load Model Selection Table [Deprecated]

This method is deprecated and replaced by the method 'generate_model_configs()' described above.

<pre class="syntax">
load_model_selection_table(
    model_arch_table,
    model_selection_table,
    model_id_list,
    compile_params_list,
    fit_params_list,
    object_table
    )
</pre>

\b Arguments
<dl class="arglist">
  <dt>model_arch_table</dt>
  <dd>VARCHAR. Table containing model architectures and weights.
  For more information on this table
  refer to <a href="group__grp__keras__model__arch.html">Load Model</a>.
  </dd>

  <dt>model_selection_table</dt>
  <dd>VARCHAR. Model selection table created by this utility.  A summary table
  named <model_selection_table>_summary is also created.  Contents of both output
  tables are the same as described above for the method 'generate_model_configs()'.
  </dd>

  <dt>model_id_list</dt>
  <dd>INTEGER[]. Array of model IDs from the 'model_arch_table' to be included
  in the run combinations.  For hyperparameter search, this will typically be
  one model ID.  For model architecture search, this will be the different model IDs
  that you want to test.
  </dd>

  <dt>compile_params_list</dt>
  <dd>VARCHAR[]. Array of compile parameters to be tested.  Each element
  of the array should consist of a string of compile parameters
  exactly as it is to be passed to Keras. For custom loss functions or custom metrics,
  list the custom function name in the usual way, and also provide the name of the
  table where the serialized objects reside in the parameter 'object_table'
  below.
  </dd>

  <dt>fit_params_list</dt>
  <dd>VARCHAR[].  Array of fit parameters to be tested.  Each element
  of the array should consist of a string of fit parameters
  exactly as it is to be passed to Keras.
  </dd>

  <dt>object_table (optional)</dt>
  <dd>VARCHAR, default: NULL. Name of the table containing Python objects in the case that
  custom loss functions or custom metrics are specified in the
  parameter 'compile_params_list'.
  </dd>

</dl>

@anchor example
@par Examples
-# The model selection table works in conjunction with a model architecture table,
so we first create a model architecture table with two different models.  Use Keras to define
a model architecture with 1 hidden layer:
<pre class="example">
import keras
from keras.models import Sequential
from keras.layers import Dense
model1 = Sequential()
model1.add(Dense(10, activation='relu', input_shape=(4,)))
model1.add(Dense(10, activation='relu'))
model1.add(Dense(3, activation='softmax'))
model1.summary()
\verbatim

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_1 (Dense)              (None, 10)                50
_________________________________________________________________
dense_2 (Dense)              (None, 10)                110
_________________________________________________________________
dense_3 (Dense)              (None, 3)                 33
=================================================================
Total params: 193
Trainable params: 193
Non-trainable params: 0
\endverbatim
</pre>
Export the model to JSON:
<pre class="example">
model1.to_json()
</pre>
<pre class="result">
'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
</pre>
Now use Keras to define
a model architecture with 2 hidden layers:
<pre class="example">
model2 = Sequential()
model2.add(Dense(10, activation='relu', input_shape=(4,)))
model2.add(Dense(10, activation='relu'))
model2.add(Dense(10, activation='relu'))
model2.add(Dense(3, activation='softmax'))
model2.summary()
\verbatim

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_4 (Dense)              (None, 10)                50
_________________________________________________________________
dense_5 (Dense)              (None, 10)                110
_________________________________________________________________
dense_6 (Dense)              (None, 10)                110
_________________________________________________________________
dense_7 (Dense)              (None, 3)                 33
=================================================================
Total params: 303
Trainable params: 303
Non-trainable params: 0
\endverbatim
</pre>
Export the model to JSON:
<pre class="example">
model2.to_json()
</pre>
<pre class="result">
'{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}'
</pre>

-# Load both models into the architecture table:
<pre class="example">
DROP TABLE IF EXISTS model_arch_library;
SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
$$
{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_1", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_2", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_3", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
$$
::json,         -- JSON blob
                               NULL,                  -- Weights
                               'Sophie',              -- Name
                               'MLP with 1 hidden layer'       -- Descr
);
SELECT madlib.load_keras_model('model_arch_library',  -- Output table,
$$
{"class_name": "Sequential", "keras_version": "2.1.6", "config": [{"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_4", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "dtype": "float32", "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "batch_input_shape": [null, 4], "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_5", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_6", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "relu", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 10, "use_bias": true, "activity_regularizer": null}}, {"class_name": "Dense", "config": {"kernel_initializer": {"class_name": "VarianceScaling", "config": {"distribution": "uniform", "scale": 1.0, "seed": null, "mode": "fan_avg"}}, "name": "dense_7", "kernel_constraint": null, "bias_regularizer": null, "bias_constraint": null, "activation": "softmax", "trainable": true, "kernel_regularizer": null, "bias_initializer": {"class_name": "Zeros", "config": {}}, "units": 3, "use_bias": true, "activity_regularizer": null}}], "backend": "tensorflow"}
$$
::json,         -- JSON blob
                               NULL,                  -- Weights
                               'Maria',               -- Name
                               'MLP with 2 hidden layers'       -- Descr
);
SELECT model_id, name, description FROM model_arch_library ORDER BY model_id;
</pre>
<pre class="result">
 model_id |  name  |       description
----------+--------+--------------------------
        1 | Sophie | MLP with 1 hidden layer
        2 | Maria  | MLP with 2 hidden layers
(2 rows)
</pre>

-# Generate model configurations using grid search. The output table for grid search 
contains the unique combinations of model architectures, compile and fit parameters.
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
                                        'model_arch_library', -- model architecture table
                                        'mst_table',          -- model selection table output
                                         ARRAY[1,2],          -- model ids from model architecture table
                                         $$
                                            {'loss': ['categorical_crossentropy'],
                                             'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ],
                                             'metrics': ['accuracy']}
                                         $$,                  -- compile_param_grid
                                         $$
                                         { 'batch_size': [64, 128],
                                           'epochs': [10]
                                         }
                                         $$,                  -- fit_param_grid
                                         'grid'               -- search_type
                                         );
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id |                                 compile_params                                  |        fit_params
---------+----------+---------------------------------------------------------------------------------+--------------------------
       1 |        1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
       2 |        1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
       3 |        1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
       4 |        1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       5 |        1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
       6 |        1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       7 |        1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
       8 |        1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
       9 |        2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
      10 |        2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      11 |        2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      12 |        2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      13 |        2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      14 |        2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      15 |        2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      16 |        2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
(16 rows)
</pre>
Note that above uses the same learning rate for the two optimizers. If you wanted to
use different learning rates and different parameters for different optimizers (common):
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
                                        'model_arch_library', -- model architecture table
                                        'mst_table',          -- model selection table output
                                         ARRAY[1,2],          -- model ids from model architecture table
                                         $$
                                            {'loss': ['categorical_crossentropy'], 
                                             'optimizer_params_list': [
                                                 {'optimizer': ['SGD']}, 
                                                 {'optimizer': ['SGD'], 'lr': [0.0001, 0.001], 'momentum': [0.95]}, 
                                                 {'optimizer': ['Adam'], 'lr': [0.01, 0.1], 'decay': [1e-4]}], 
                                             'metrics': ['accuracy']}
                                         $$,                  -- compile_param_grid    
                                         $$ 
                                         { 'batch_size': [64, 128],
                                           'epochs': [10] 
                                         } 
                                         $$,                  -- fit_param_grid                                          
                                         'grid'               -- search_type 
                                         );
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id |                                        compile_params                                         |        fit_params        
---------+----------+-----------------------------------------------------------------------------------------------+--------------------------
       1 |        1 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'                        | epochs=10,batch_size=64
       2 |        1 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'                        | epochs=10,batch_size=128
       3 |        2 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'                        | epochs=10,batch_size=64
       4 |        2 | optimizer='SGD()',metrics=['accuracy'],loss='categorical_crossentropy'                        | epochs=10,batch_size=128
       5 |        1 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
       6 |        1 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
       7 |        1 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
       8 |        1 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       9 |        2 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
      10 |        2 | optimizer='SGD(lr=0.0001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      11 |        2 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      12 |        2 | optimizer='SGD(lr=0.001,momentum=0.95)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      13 |        1 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      14 |        1 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
      15 |        1 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'    | epochs=10,batch_size=64
      16 |        1 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'    | epochs=10,batch_size=128
      17 |        2 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      18 |        2 | optimizer='Adam(lr=0.01,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
      19 |        2 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'    | epochs=10,batch_size=64
      20 |        2 | optimizer='Adam(lr=0.1,decay=0.0001)',metrics=['accuracy'],loss='categorical_crossentropy'    | epochs=10,batch_size=128
(20 rows)
</pre>

-# Generate model configurations using random search. The output table for random search 
contains the specified number of model architectures, compile and fit parameters, 
sampled from the specified distributions.
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
                                        'model_arch_library', -- model architecture table
                                        'mst_table',          -- model selection table output
                                         ARRAY[1,2],          -- model ids from model architecture table
                                         $$
                                            {'loss': ['categorical_crossentropy'],
                                             'optimizer_params_list': [
                                                 {'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']},
                                                 {'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}],
                                             'metrics': ['accuracy']}
                                         $$,                  -- compile_param_grid
                                         $$
                                         { 'batch_size': [64, 128],
                                           'epochs': [10]
                                         }
                                         $$,                  -- fit_param_grid
                                         'random',            -- search_type
                                         20
                                         );
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id |                                                       compile_params                                                        |        fit_params        
---------+----------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------
       1 |        1 | optimizer='SGD(lr=0.000195784477708685,momentum=0.9768159513291526)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
       2 |        2 | optimizer='SGD(lr=0.0002499200066875511,momentum=0.9807877269510826)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
       3 |        1 | optimizer='SGD(lr=0.0009097798285407916,momentum=0.9706029152411938)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
       4 |        1 | optimizer='SGD(lr=0.0001272842475986666,momentum=0.9858583458057799)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       5 |        1 | optimizer='SGD(lr=0.0001367874444015989,momentum=0.9772674033475668)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       6 |        2 | optimizer='SGD(lr=0.0002233708561319785,momentum=0.9743315606145182)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       7 |        1 | optimizer='SGD(lr=0.0009066689970530365,momentum=0.9835897505288803)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
       8 |        1 | optimizer='SGD(lr=0.0007589416356572876,momentum=0.958751411608181)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
       9 |        2 | optimizer='Adam(lr=0.057814228170084386,decay=1.0641718595377929e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      10 |        1 | optimizer='Adam(lr=0.01927466297833838,decay=1.039476442716842e-06)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
      11 |        2 | optimizer='Adam(lr=0.014718555287257804,decay=9.947768661882175e-05)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      12 |        1 | optimizer='Adam(lr=0.010397686133595378,decay=2.5730580994358942e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      13 |        1 | optimizer='SGD(lr=0.0008624562426613621,momentum=0.989134963527059)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      14 |        2 | optimizer='SGD(lr=0.00010555974470031461,momentum=0.980489419269402)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      15 |        2 | optimizer='Adam(lr=0.05041699703418617,decay=4.685540619995589e-05)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
      16 |        1 | optimizer='Adam(lr=0.034295140601304126,decay=1.6034699865163222e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
      17 |        1 | optimizer='Adam(lr=0.06888969005355218,decay=1.6318109152382423e-05)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      18 |        2 | optimizer='SGD(lr=0.0008225712651952847,momentum=0.9819748008695103)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      19 |        1 | optimizer='Adam(lr=0.0819110285922332,decay=1.6912312124827899e-06)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      20 |        1 | optimizer='Adam(lr=0.011688026325555774,decay=2.9315437856404027e-05)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
(20 rows)
</pre>

-# Incremental loading for more complex combinations.  If it is easier to generate the model configurations
incrementally rather than all at once, you can do that by not dropping the model selection table and associated
summary table, in which case the new model configurations will be appended to the existing table.  Here we combine two
of the previous examples in to a single output table:
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.generate_model_configs(
                                        'model_arch_library', -- model architecture table
                                        'mst_table',          -- model selection table output
                                         ARRAY[1,2],          -- model ids from model architecture table
                                         $$
                                            {'loss': ['categorical_crossentropy'], 
                                             'optimizer_params_list': [ {'optimizer': ['Adam', 'SGD'], 'lr': [0.001, 0.01]} ], 
                                             'metrics': ['accuracy']}
                                         $$,                  -- compile_param_grid    
                                         $$ 
                                         { 'batch_size': [64, 128],
                                           'epochs': [10] 
                                         } 
                                         $$,                  -- fit_param_grid                                          
                                         'grid'               -- search_type 
                                         );
</pre>
Now add to the existing table and note that mst_key continues where it left off:
<pre class="example">
SELECT madlib.generate_model_configs(
                                        'model_arch_library', -- model architecture table
                                        'mst_table',          -- model selection table output
                                         ARRAY[1,2],          -- model ids from model architecture table
                                         $$
                                            {'loss': ['categorical_crossentropy'], 
                                             'optimizer_params_list': [ 
                                                 {'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']}, 
                                                 {'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}], 
                                             'metrics': ['accuracy']}
                                         $$,                  -- compile_param_grid    
                                         $$ 
                                         { 'batch_size': [64, 128],
                                           'epochs': [10] 
                                         } 
                                         $$,                  -- fit_param_grid                                          
                                         'random',            -- search_type
                                          20
                                         );
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id |                                                       compile_params                                                        |        fit_params        
---------+----------+-----------------------------------------------------------------------------------------------------------------------------+--------------------------
       1 |        1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                             | epochs=10,batch_size=64
       2 |        1 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                             | epochs=10,batch_size=128
       3 |        1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=64
       4 |        1 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=128
       5 |        1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=64
       6 |        1 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=128
       7 |        1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                               | epochs=10,batch_size=64
       8 |        1 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                               | epochs=10,batch_size=128
       9 |        2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                             | epochs=10,batch_size=64
      10 |        2 | optimizer='Adam(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                             | epochs=10,batch_size=128
      11 |        2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=64
      12 |        2 | optimizer='SGD(lr=0.001)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=128
      13 |        2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=64
      14 |        2 | optimizer='Adam(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                              | epochs=10,batch_size=128
      15 |        2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                               | epochs=10,batch_size=64
      16 |        2 | optimizer='SGD(lr=0.01)',metrics=['accuracy'],loss='categorical_crossentropy'                                               | epochs=10,batch_size=128
      17 |        2 | optimizer='SGD(lr=0.00013996842804647915,momentum=0.9677072493281305)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
      18 |        1 | optimizer='Adam(lr=0.04252873277972123,decay=9.503983307511243e-05)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      19 |        2 | optimizer='Adam(lr=0.06666969394323848,decay=1.5626668941131748e-05)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      20 |        2 | optimizer='SGD(lr=0.00016137313867804707,momentum=0.954293112127019)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      21 |        2 | optimizer='Adam(lr=0.019443570245321506,decay=1.2882524497407873e-06)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=64
      22 |        2 | optimizer='Adam(lr=0.06302317748060839,decay=6.238009849562074e-05)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=128
      23 |        2 | optimizer='SGD(lr=0.00010890482493011119,momentum=0.9826239169968034)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      24 |        1 | optimizer='SGD(lr=0.0009201966766121783,momentum=0.9896730563556151)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      25 |        2 | optimizer='SGD(lr=0.00028961522836420906,momentum=0.9859394149216544)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      26 |        1 | optimizer='SGD(lr=0.0001503249757866609,momentum=0.9777816636354879)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      27 |        2 | optimizer='SGD(lr=0.0008405326172626768,momentum=0.9538686498263182)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      28 |        1 | optimizer='SGD(lr=0.00011926989091387571,momentum=0.9876746918399469)',metrics=['accuracy'],loss='categorical_crossentropy' | epochs=10,batch_size=128
      29 |        1 | optimizer='Adam(lr=0.018794361633022855,decay=9.387826286694454e-06)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      30 |        2 | optimizer='SGD(lr=0.0009692977025027591,momentum=0.9878758592330659)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      31 |        2 | optimizer='SGD(lr=0.0006671929498585603,momentum=0.9786502962872058)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      32 |        2 | optimizer='Adam(lr=0.03948766165185474,decay=3.056584635386748e-06)',metrics=['accuracy'],loss='categorical_crossentropy'   | epochs=10,batch_size=64
      33 |        2 | optimizer='Adam(lr=0.020343961099103417,decay=1.183810228780669e-05)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      34 |        1 | optimizer='Adam(lr=0.016854644990148417,decay=3.561117893117444e-06)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=64
      35 |        2 | optimizer='SGD(lr=0.0004620089560788749,momentum=0.9887310587871919)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
      36 |        1 | optimizer='SGD(lr=0.0002493912675066962,momentum=0.9892077270385708)',metrics=['accuracy'],loss='categorical_crossentropy'  | epochs=10,batch_size=128
(36 rows)
</pre>

-# Create model selection table manually.  
If you want more control over the content of the model selection table, 
you could use grid or random search to generate a large number of combinations, 
then SELECT a subset of rows for training.  Alternatively, you could manually 
create the model selection table and the associated summary table. Both must be 
created since they are needed by the multiple model fit module.
For example, let's say we don't want all combinations but only want 
batch_size=4 for model_id=1 and batch_size=8 for model_id=2:
<pre class="example">
DROP TABLE IF EXISTS mst_table_manual;
CREATE TABLE mst_table_manual(
    mst_key serial,
    model_id integer,
    compile_params varchar,
    fit_params varchar
);
INSERT INTO mst_table_manual(model_id, compile_params, fit_params) VALUES
(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
(1, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=4,epochs=1'),
(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),
(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']$$, 'batch_size=8,epochs=1'),
(2, $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$, 'batch_size=8,epochs=1');
SELECT * FROM mst_table_manual ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id      |                                 compile_params                                  |      fit_params
---------+---------------+---------------------------------------------------------------------------------+-----------------------
       1 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
       2 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']  | batch_size=4,epochs=1
       3 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
       4 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
       5 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.01)',metrics=['accuracy']  | batch_size=8,epochs=1
       6 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
(6 rows)
</pre>
Create the summary table which must be named
with the model selection output table appended by "_summary":
<pre class="example">
DROP TABLE IF EXISTS mst_table_manual_summary;
CREATE TABLE mst_table_manual_summary (
    model_arch_table varchar
);
INSERT INTO mst_table_manual_summary(model_arch_table) VALUES
('model_arch_library');
SELECT * FROM mst_table_manual_summary;
</pre>
<pre class="result">
  model_arch_table
--------------------+
 model_arch_library
(1 row)
</pre>

-# Custom loss functions and custom metrics.
TBD

-# <b>[Deprecated]</b> Load model selection table.  This method is replaced 
by the 'generate_model_configs()' method described above.
Select the model(s) from the model
architecture table that you want to run, along with the compile and
fit parameters.  Unique combinations will be created:
<pre class="example">
DROP TABLE IF EXISTS mst_table, mst_table_summary;
SELECT madlib.load_model_selection_table('model_arch_library', -- model architecture table
                                         'mst_table',          -- model selection table output
                                          ARRAY[1,2],              -- model ids from model architecture table
                                          ARRAY[                   -- compile params
                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']$$,
                                              $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy']$$,
                                              $$loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy']$$
                                          ],
                                          ARRAY[                    -- fit params
                                              $$batch_size=4,epochs=1$$,
                                              $$batch_size=8,epochs=1$$
                                          ]
                                         );
SELECT * FROM mst_table ORDER BY mst_key;
</pre>
<pre class="result">
 mst_key | model_id      |                                 compile_params                                  |      fit_params
---------+---------------+---------------------------------------------------------------------------------+-----------------------
       1 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
       2 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
       3 |             1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
       4 |             1 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
       5 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
       6 |             1 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
       7 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=4,epochs=1
       8 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.1)',metrics=['accuracy']   | batch_size=8,epochs=1
       9 |             2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=4,epochs=1
      10 |             2 | loss='categorical_crossentropy', optimizer='Adam(lr=0.01)',metrics=['accuracy'] | batch_size=8,epochs=1
      11 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=4,epochs=1
      12 |             2 | loss='categorical_crossentropy',optimizer='Adam(lr=0.001)',metrics=['accuracy'] | batch_size=8,epochs=1
(12 rows)
</pre>
The name of the model architecture table is stored in the summary table:
<pre class="example">
SELECT * FROM mst_table_summary;
</pre>
<pre class="result">
  model_arch_table
--------------------+
 model_arch_library
(1 row)
</pre>

@anchor notes
@par Notes

1. TBD

@anchor related
@par Related Topics

See keras_model_arch_table.sql_in

*/

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_model_selection_table(
    model_arch_table        VARCHAR,
    model_selection_table   VARCHAR,
    model_id_list           INTEGER[],
    compile_params_list     VARCHAR[],
    fit_params_list         VARCHAR[],
    object_table            VARCHAR
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
    with AOControl(False):
        mst_loader = madlib_keras_model_selection.MstLoader(**globals())
        mst_loader.load()
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.load_model_selection_table(
    model_arch_table        VARCHAR,
    model_selection_table   VARCHAR,
    model_id_list           INTEGER[],
    compile_params_list     VARCHAR[],
    fit_params_list         VARCHAR[]
) RETURNS VOID AS $$
SELECT MADLIB_SCHEMA.load_model_selection_table($1, $2, $3, $4, $5, NULL);
$$ LANGUAGE sql VOLATILE
    m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

/*
--------------------------------------------------------------------------
*/
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.generate_model_configs(
    model_arch_table        VARCHAR,
    model_selection_table   VARCHAR,
    model_id_list           INTEGER[],
    compile_params_grid     VARCHAR,
    fit_params_grid         VARCHAR,
    search_type             VARCHAR DEFAULT 'grid',
    num_configs             INTEGER DEFAULT NULL,
    random_state            INTEGER DEFAULT NULL,
    object_table            VARCHAR DEFAULT NULL
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
    with AOControl(False):
        mst_loader = madlib_keras_model_selection.MstSearch(**globals())
        mst_loader.load()
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

/*
--------------------------------------------------------------------------
*/
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.generate_model_configs(
    model_arch_table        VARCHAR,
    model_selection_table   VARCHAR,
    model_id_list           INTEGER[],
    compile_params_grid     VARCHAR,
    fit_params_grid         VARCHAR,
    search_type             VARCHAR DEFAULT 'grid',
    num_configs             INTEGER DEFAULT NULL,
    random_state            INTEGER DEFAULT NULL,
    object_table            VARCHAR DEFAULT NULL
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_model_selection')
    with AOControl(False):
        mst_loader = madlib_keras_model_selection.MstSearch(**globals())
        mst_loader.load()
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');