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 *
 * @file madlib_keras_automl.sql_in
 *
 * @brief SQL functions for training with AutoML methods
 * @date August 2020
 *
 *
 *//* ----------------------------------------------------------------------- */

m4_include(`SQLCommon.m4')


/**
@addtogroup grp_automl


@brief Functions to run automated machine learning (AutoML) algorithms to automate
and speed-up the model selection and training processes for model architecture search,
hyperparameter tuning, and model evaluation.

\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="#madlib_keras_automl">AutoML Function</a></li>
<li class="level1"><a href="#hyperband_schedule">Hyperband Schedule</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 sets up the AutoML algorithms to automate and accelerate
the model selection and training processes, involving hyperparameter optimization,
model architecture search, and model training.

The module also has a a utility function for viewing the Hyperband schedule of
evaluating configurations for use by the Keras AutoML of MADlib.
By configuration we mean both hyperparameter tuning and
model architecture search.

@anchor madlib_keras_automl
@par AutoML

<pre class="syntax">
madlib_keras_automl(
    source_table,
    model_output_table,
    model_arch_table,
    model_selection_table,
    model_id_list,
    compile_params_grid,
    fit_params_grid,
    automl_method,
    automl_params,
    random_state,
    object_table,
    use_gpus,
    validation_table,
    metrics_compute_frequency,
    name,
    description
    )
</pre>

\b Arguments
<dl class="arglist">
  <dt>source_table</dt>
  <dd>TEXT. Name of the table containing the training data.
  This is the name of the output table from the image preprocessor. Independent
  and dependent variables are specified in the preprocessor
  step which is why you do not need to explictly state
  them here as part of the fit function. The configurations would be evaluated on the basis the training loss,
  unless a validation table is specified below.
  </dd>

  <dt>model_output_table</dt>
  <dd>TEXT. Name of the output table containing the
  multiple models created.
  @note pg_temp is not allowed as an output table schema for fit multiple.
  Details of output tables are shown below.
  </dd>

  <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 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 test.
  </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>automl_method (optional)</dt>
  <dd>VARCHAR, default 'hyperband'. Name of the automl algorithm to run.
  Can be either 'hyperband' or hyperopt'. Prefixing is not supported but arg value can be case insensitive.
  </dd>

  <dt>automl_params (optional)</dt>
  <dd>VARCHAR, default 'R=6, eta=3, skip_last=0' (for Hyperband). Parameters for the chosen automl method in a
  comma-separated string of key-value pairs. For eg - 'num_configs=20, num_iterations=5, algorithm=tpe' for Hyperopt
  Hyperband params are:
  R - the maximum amount of resources/iterations allocated to a single configuration
  in a round of hyperband,
  eta - factor controlling the proportion of configurations discarded in each
  round of successive halving,
  skip_last - number of last diagonal brackets to skip running
  in the algorithm.
  We encourage setting an low R value (i.e. 2 to 10), or a high R value and a high skip_last value to evaluate
  a variety of configurations with decent number of iterations. See the description below for details.
  Hyperopt params are:
  num_configs - total number of model configurations to evaluate,
  num_iterations - fixed number of iterations for evaluating each model configurations,
  algorithm - name of algorithm to explore search space in hyperopt ('rand', 'tpe', 'atpe').
  </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 to evaluate a fixed set of configurations.

  @note
    Specifying a random state doesn't not guarantee result reproducibility of the best configuration or the best
    train/validation accuracy/loss. It guarantees that the same set of configurations will be chosen for evaluation.

  </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>

  <dt>validation_table (optional)</dt>
  <dd>TEXT, default: none. Name of the table containing
  the validation dataset.
  Note that the validation dataset must be preprocessed
  in the same way as the training dataset, so this
  is the name of the output
  table from running the image preprocessor on the validation dataset.
  Using a validation dataset can mean a
  longer training time depending on its size, and the configurations would be evaluated on the basis of validation
  loss instead of training loss.
  This can be controlled using the 'metrics_compute_frequency'
  parameter described below.</dd>

  <DT>metrics_compute_frequency (optional)</DT>
  <DD>INTEGER, default: once at the end of training
  after 'num_iterations'.  Frequency to compute per-iteration
  metrics for the training dataset and validation dataset
  (if specified).  There can be considerable cost to
  computing metrics every iteration, especially if the
  training dataset is large.  This parameter is a way of
  controlling the frequency of those computations.
  For example, if you specify 5, then metrics will be computed
  every 5 iterations as well as at the end of training
  after 'num_iterations'.  If you use the default,
  metrics will be computed only
  once after 'num_iterations' have completed.
  </DD>

  <DT>name (optional)</DT>
  <DD>TEXT, default: NULL.
    Free text string to identify a name, if desired.
  </DD>

  <DT>description (optional)</DT>
  <DD>TEXT, default: NULL.
    Free text string to provide a description, if desired.
  </DD>

</dl>

<b>Output tables</b>
<br>

    The model selection output table has exactly 1 row of the best model configuration based on the
    training/validation loss and 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>

    The model output table produced by fit contains the following columns.
    There is one row per model configuration generated:
    <table class="output">
      <tr>
        <th>mst_key</th>
        <td>INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters,
        as defined in the 'model_selection_table'.</td>
      </tr>
      <tr>
        <th>model_weights</th>
        <td>BYTEA8. Byte array containing the weights of the neural net.</td>
      </tr>
      <tr>
        <th>model_arch</th>
        <td>TEXT. A JSON representation of the model architecture
        used in training.</td>
      </tr>
    </table>

    An info table named \<model_output_table\>_info is also created, which has the following columns.
    There is one row per model as per the rows in the 'model_selection_table':
    <table class="output">
      <tr>
        <th>mst_key</th>
        <td>INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters,
        as defined in the 'model_selection_table'.</td>
      </tr>
      <tr>
        <th>model_id</th>
        <td>INTEGER. ID that defines model in the 'model_arch_table'.</td>
      </tr>
      <tr>
        <th>compile_params</th>
        <td>Compile parameters passed to Keras.</td>
    </tr>
    <tr>
        <th>fit_params</th>
        <td>Fit parameters passed to Keras.</td>
    </tr>
    <tr>
        <th>model_type</th>
        <td>General identifier for type of model trained.
        Currently says 'madlib_keras'.</td>
    </tr>
    <tr>
        <th>model_size</th>
        <td>Size of the model in KB.  Models are stored in
        'bytea' data format which is used for binary strings
        in PostgreSQL type databases.</td>
    </tr>
    <tr>
        <th>metrics_elapsed_time</th>
        <td> Array of elapsed time for metric computations as
        per the 'metrics_compute_frequency' parameter.
        Useful for drawing a curve showing loss, accuracy or
        other metrics as a function of time.
        For example, if 'metrics_compute_frequency=5'
        this would be an array of elapsed time for every 5th
        iteration, plus the last iteration.</td>
    </tr>
    <tr>
        <th>metrics_type</th>
        <td>Metric specified in the 'compile_params'.</td>
    </tr>
    <tr>
        <th>training_metrics_final</th>
        <td>Final value of the training
        metric after all iterations have completed.
        The metric reported is the one
        specified in the 'metrics_type' parameter.</td>
    </tr>
    <tr>
        <th>training_loss_final</th>
        <td>Final value of the training loss after all
        iterations have completed.</td>
    </tr>
    <tr>
        <th>training_metrics</th>
        <td>Array of training metrics as
        per the 'metrics_compute_frequency' parameter.
        For example, if 'metrics_compute_frequency=5'
        this would be an array of metrics for every 5th
        iteration, plus the last iteration.</td>
    </tr>
    <tr>
        <th>training_loss</th>
        <td>Array of training losses as
        per the 'metrics_compute_frequency' parameter.
        For example, if 'metrics_compute_frequency=5'
        this would be an array of losses for every 5th
        iteration, plus the last iteration.</td>
    </tr>
    <tr>
        <th>validation_metrics_final</th>
        <td>Final value of the validation
        metric after all iterations have completed.
        The metric reported is the one
        specified in the 'metrics_type' parameter.</td>
    </tr>
    <tr>
        <th>validation_loss_final</th>
        <td>Final value of the validation loss after all
        iterations have completed.</td>
    </tr>
    <tr>
        <th>validation_metrics</th>
        <td>Array of validation metrics as
        per the 'metrics_compute_frequency' parameter.
        For example, if 'metrics_compute_frequency=5'
        this would be an array of metrics for every 5th
        iteration, plus the last iteration.</td>
    </tr>
    <tr>
        <th>validation_loss</th>
        <td>Array of validation losses as
        per the 'metrics_compute_frequency' parameter.
        For example, if 'metrics_compute_frequency=5'
        this would be an array of losses for every 5th
        iteration, plus the last iteration.</td>
    </tr>
    <tr>
        <th>metrics_iters</th>
        <td>Array indicating the iterations for which
        metrics are calculated, as derived from the
        parameters 'metrics_compute_frequency' and iterations decided by the automl algorithm.
        For example, if 'num_iterations=5'
        and 'metrics_compute_frequency=2', then 'metrics_iters' value
        would be {2,4,5} indicating that metrics were computed
        at iterations 2, 4 and 5 (at the end).
        If 'num_iterations=5'
        and 'metrics_compute_frequency=1', then 'metrics_iters' value
        would be {1,2,3,4,5} indicating that metrics were computed
        at every iteration.</td>
    </tr>
    <tr>
        <th>s</th>
        <td>Bracket number</td>
    </tr>
    <tr>
        <th>i</th>
        <td>Latest evaluated round number</td>
    </tr>

    </table>

    A summary table named \<model_output_table\>_summary is also created, which has the following columns:
    <table class="output">
    <tr>
        <th>source_table</th>
        <td>Source table used for training.</td>
    </tr>
    <tr>
        <th>validation_table</th>
        <td>Name of the table containing
        the validation dataset (if specified).</td>
    </tr>
    <tr>
        <th>model</th>
        <td>Name of the output table containing
        the model for each model selection tuple.</td>
    </tr>
    <tr>
        <th>model_info</th>
        <td>Name of the output table containing
        the model performance and other info for
        each model selection tuple.</td>
    </tr>
    <tr>
        <th>dependent_varname</th>
        <td>Dependent variable column from the original
        source table in the image preprocessing step.</td>
    </tr>
    <tr>
        <th>independent_varname</th>
        <td>Independent variables column from the original
        source table in the image preprocessing step.</td>
    </tr>
    <tr>
        <th>model_arch_table</th>
        <td>Name of the table containing
        the model architecture and (optionally) the
        initial model weights.</td>
    </tr>
    <tr>
        <th>model selection table</th>
        <td>Name of the mst table containing
        the best configuration.</td>
    </tr>
    <tr>
        <th>automl_method</th>
        <td>Name of the automl method</td>
    </tr>
    <tr>
        <th>automl_params</th>
        <td>AutoML param values</td>
    </tr>
    <tr>
        <th>random_state</th>
        <td>Chosen random seed</td>
    </tr>
    <tr>
        <th>metrics_compute_frequency</th>
        <td>Frequency that per-iteration metrics are computed
        for the training dataset and validation
        datasets.</td>
    </tr>
    <tr>
        <th>name</th>
        <td>Name of the training run (free text).</td>
    </tr>
    <tr>
        <th>description</th>
        <td>Description of the training run (free text).</td>
    </tr>
    <tr>
        <th>start_training_time</th>
        <td>Timestamp for start of training.</td>
    </tr>
    <tr>
        <th>end_training_time</th>
        <td>Timestamp for end of training.</td>
    </tr>
    <tr>
        <th>madlib_version</th>
        <td>Version of MADlib used.</td>
    </tr>
    <tr>
        <th>num_classes</th>
        <td>Count of distinct classes values used.</td>
    </tr>
    <tr>
        <th>class_values</th>
        <td>Array of actual class values used.</td>
    </tr>
    <tr>
        <th>dependent_vartype</th>
        <td>Data type of the dependent variable.</td>
    </tr>
    <tr>
        <th>normalizing_constant</th>
        <td>Normalizing constant used from the
        image preprocessing step.</td>
    </tr>
   </table>

@anchor hyperband_schedule
@par Hyperband Schedule

<pre class="syntax">
hyperband_schedule(
    schedule_table,
    R,
    eta,
    skip_last
    )
</pre>

\b Arguments
<dl class="arglist">
  <dt>schedule_table</dt>
  <dd>VARCHAR. Name of output table containing hyperband schedule.
  </dd>

  <dt>R</dt>
  <dd>INTEGER. Maximum number of resources (iterations) that can be allocated
  to a single configuration.
  </dd>

  <dt>eta</dt>
  <dd>INTEGER, default 3. Controls the proportion of configurations discarded in
  each round of successive halving. For example, for eta=3 will keep the best 1/3
  the configurations for the next round.
  </dd>

  <dt>skip_last</dt>
  <dd>INTEGER, default 0. The number of last rounds to skip. For example, for skip_last=1 will skip the
  last round (i.e., last entry in each bracket), which is standard randomized search and can
  be expensive when run for the total R iterations.
  </dd>

</dl>

<b>Output table</b>
<br>
    The hyperband schedule output table contains the following columns:
    <table class="output">
      <tr>
        <th>s</th>
        <td>INTEGER. Bracket number
        </td>
      </tr>
      <tr>
        <th>i</th>
        <td>INTEGER. Round (depth) in bracket
        </td>
      </tr>
      <tr>
        <th>n_i</th>
        <td>INTEGER. Number of configurations in this round
        </td>
      </tr>
      <tr>
        <th>r_i</th>
        <td>INTEGER. Resources (iterations) in this round
        </td>
      </tr>
    </table>
</br>


@anchor example
@par Examples
TBD.


@anchor notes
@par Notes
TBD.


@anchor related
@par Related Topics
TBD.

*/

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.hyperband_schedule(
      schedule_table        VARCHAR,
      r                     INTEGER,
      eta                   INTEGER DEFAULT 3,
      skip_last             INTEGER DEFAULT 0
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_automl')
    with AOControl(False):
        schedule_loader = madlib_keras_automl.HyperbandSchedule(schedule_table, r, eta, skip_last)
        schedule_loader.load()
$$ LANGUAGE plpythonu VOLATILE
              m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');

CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.madlib_keras_automl(
    source_table                   VARCHAR,
    model_output_table             VARCHAR,
    model_arch_table               VARCHAR,
    model_selection_table          VARCHAR,
    model_id_list                  INTEGER[],
    compile_params_grid            VARCHAR,
    fit_params_grid                VARCHAR,
    automl_method                  VARCHAR DEFAULT 'hyperband',
    automl_params                  VARCHAR DEFAULT NULL,
    random_state                   INTEGER DEFAULT NULL,
    object_table                   VARCHAR DEFAULT NULL,
    use_gpus                       BOOLEAN DEFAULT FALSE,
    validation_table               VARCHAR DEFAULT NULL,
    metrics_compute_frequency      INTEGER DEFAULT NULL,
    name                           VARCHAR DEFAULT NULL,
    description                    VARCHAR DEFAULT NULL
) RETURNS VOID AS $$
    PythonFunctionBodyOnly(`deep_learning', `madlib_keras_automl')
    with AOControl(False):
        if automl_method is None or automl_method.lower() == 'hyperband':
            schedule_loader = madlib_keras_automl.AutoMLHyperband(**globals())
        elif automl_method.lower() == 'hyperopt':
            schedule_loader = madlib_keras_automl.AutoMLHyperopt(**globals())
        else:
            plpy.error("madlib_keras_automl: The chosen automl method must be 'hyperband' or 'hyperopt'")
$$ LANGUAGE plpythonu VOLATILE
    m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');