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<div class="header">
  <div class="headertitle">
<div class="title">AutoML<div class="ingroups"><a class="el" href="group__grp__dl.html">Deep Learning</a> &raquo; <a class="el" href="group__grp__model__selection.html">Train Multiple Models</a></div></div>  </div>
</div><!--header-->
<div class="contents">
<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">Print 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="#literature">Literature</a> </li>
<li class="level1">
<a href="#related">Related Topics</a> </li>
</ul>
</div><p>This module contains automated machine learning (autoML) methods for model architecture search and hyperparameter tuning.</p>
<p>The goal of autoML when training deep nets is to reduce the amount of hand-tuning by data scientists to produce a model of acceptable accuracy, compared to manual methods like grid or random search. The two autoML methods implemented here are Hyperband and Hyperopt. If you want to use grid or random search, please refer to <a href="group__grp__keras__setup__model__selection.html">Define Model Configurations</a>.</p>
<p>Hyperband is an effective model selection algorithm that utilizes the idea of successive halving. It accelerates random search through adaptive resource allocation and early stopping [1]. The implementation here is designed to keep MPP database cluster resources as busy as possible when executing the Hyperband schedule.</p>
<p>There is also a utility function for printing out the Hyperband schedule for a given set of input parameters, to give you a sense of how long a run might take before starting.</p>
<p>Hyperopt is meta-modeling approach for automated hyperparameter optimization [2]. It intelligently explores the search space while narrowing down to the best estimated parameters. Within Hyperopt we support random search and Tree of Parzen Estimators (TPE) approach.</p>
<dl class="section note"><dt>Note</dt><dd>AutoML methods do not currently support multi-input or multi-output neural networks.</dd></dl>
<p><a class="anchor" id="madlib_keras_automl"></a></p><dl class="section user"><dt>AutoML</dt><dd></dd></dl>
<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,
    use_caching
    )
</pre><p><b>Arguments</b> </p><dl class="arglist">
<dt>source_table </dt>
<dd><p class="startdd">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. Configurations will be evaluated by the autoML methods on the basis of training loss, unless a validation table is specified below, in which case validation loss will be used. </p>
<p class="enddd"></p>
</dd>
<dt>model_output_table </dt>
<dd>TEXT. Name of the output table containing the multiple models created. <dl class="section note"><dt>Note</dt><dd>'pg_temp' is not allowed as an output table schema. Details of output tables are shown below. </dd></dl>
</dd>
<dt>model_arch_table </dt>
<dd><p class="startdd">VARCHAR. Table containing model architectures and weights. For more information on this table refer to <a href="group__grp__keras__model__arch.html">Define Model Architectures</a>. </p>
<p class="enddd"></p>
</dd>
<dt>model_selection_table </dt>
<dd><p class="startdd">VARCHAR. Model selection table created by this method. A summary table named &lt;model_selection_table&gt;_summary is also created. Contents of both of these tables are described below. </p>
<p class="enddd"></p>
</dd>
<dt>model_id_list </dt>
<dd><p class="startdd">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 try. </p>
<p class="enddd"></p>
</dd>
<dt>compile_params_grid </dt>
<dd><p class="startdd">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:</p>
<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><p>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. However, 'log_near_one' is only supported for Hyperband, not for Hyperopt.</p>
<dl class="section note"><dt>Note</dt><dd><ul>
<li>Custom loss functions and custom metrics can be used as defined in <a href="group__grp__custom__function.html">Define Custom Functions.</a> List the custom function name and provide the name of the table where the serialized Python objects reside using the parameter 'object_table' below.</li>
<li>The following loss function is not supported: <em>sparse_categorical_crossentropy</em>. The following metrics are not supported: <em>sparse_categorical_accuracy, sparse_top_k_categorical_accuracy</em>.</li>
<li>The Keras accuracy parameter <em>top_k_categorical_accuracy</em> returns top 5 accuracy by default. If you want a different top k value, use the helper function <a href="group__grp__custom__function.html#top_k_function">Top k Accuracy Function</a> to create a custom Python function to compute the top k accuracy that you want. </li>
</ul>
</dd></dl>
</dd>
<dt>fit_params_grid </dt>
<dd><p class="startdd">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:</p>
<pre class="example">
  $$
    {'batch_size': [32, 64, 128, 256],
     'epochs': [10, 20, 30]
    }
  $$
  </pre><dl class="section note"><dt>Note</dt><dd>Callbacks are not currently supported except for TensorBoard which you can specify in the usual way, e.g., 'callbacks': ['[TensorBoard(log_dir="/tmp/logs/fit")]'] </dd></dl>
</dd>
<dt>automl_method (optional) </dt>
<dd><p class="startdd">VARCHAR, default 'hyperband'. Name of the autoML algorithm to run. Can be either 'hyperband' or 'hyperopt' (case insensitive).</p>
<dl class="section note"><dt>Note</dt><dd>If you select 'hyperopt', then the Hyperopt package must be installed on the main node of the database cluster [3]. Hyperband does not need any separate package installation.</dd></dl>
<p class="enddd"></p>
</dd>
<dt>automl_params (optional) </dt>
<dd><p class="startdd">VARCHAR, default depends on the method. Parameters for the chosen autoML method in a comma-separated string of key-value pairs. Please refer to references [1] and [2] for more details on the definition of these parameters.</p>
<dl class="arglist">
<dt><em>Hyperband params:</em></dt>
<dd></dd>
<dt>R </dt>
<dd>Default: 6. Maximum amount of resources (i.e., iterations) to allocate to a single configuration in a round of Hyperband.  </dd>
<dt>eta </dt>
<dd>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>Default: 0. The number of last rounds to skip. For example, 'skip_last=1' will skip the last round (i.e., last entry in each bracket), which is standard random search and can be expensive when run for the total R iterations. </dd>
</dl>
<dl class="arglist">
<dt><em>Hyperopt params:</em></dt>
<dd></dd>
<dt>num_configs </dt>
<dd>Default: 20. Number of trials to evaluate.  </dd>
<dt>num_iterations </dt>
<dd>Default: 5. Number of iterations to run for each trial. </dd>
<dt>algorithm </dt>
<dd>Default: 'tpe'. Name of the algorithm to explore the search space in Hyperopt ('rand' or 'tpe'). </dd>
</dl>
<p class="enddd"></p>
</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.  <dl class="section note"><dt>Note</dt><dd>Specifying a random state does not guarantee result reproducibility of the best configuration or the best train/validation accuracy/loss. It only guarantees that the same set of configurations will be chosen for evaluation. </dd></dl>
</dd>
<dt>object_table (optional) </dt>
<dd><p class="startdd">VARCHAR, default: NULL. Name of the table containing Python objects in the case that custom loss functions, metrics or top k categorical accuracy are specified in the 'compile_params_grid'. </p>
<p class="enddd"></p>
</dd>
<dt>validation_table (optional) </dt>
<dd><p class="startdd">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 in autoML will be evaluated on the basis of validation loss instead of training loss.</p>
<p class="enddd"></p>
</dd>
<dt>metrics_compute_frequency (optional) </dt>
<dd><p class="startdd">INTEGER, default: once at the end of training. 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. If you use the default, metrics will be computed only once after training has completed. </p>
<p class="enddd"></p>
</dd>
<dt>name (optional) </dt>
<dd><p class="startdd">TEXT, default: NULL. Free text string to provide a name, if desired. </p>
<p class="enddd"></p>
</dd>
<dt>description (optional) </dt>
<dd><p class="startdd">TEXT, default: NULL. Free text string to provide a description, if desired. </p>
<p class="enddd"></p>
</dd>
<dt>use_caching (optional) </dt>
<dd><p class="startdd">BOOLEAN, default: FALSE. Use caching of images in memory on the segment in order to speed up processing.</p>
<dl class="section note"><dt>Note</dt><dd>When set to TRUE, image byte arrays on each segment are maintained in cache (GD). This can speed up training significantly, however the memory usage per segment increases. In effect, it requires enough available memory on a segment so that all images residing on that segment can be read into memory. </dd></dl>
</dd>
</dl>
<p><b>Output tables</b> <br />
 The model selection output table &lt;model_selection_table&gt; has only one row containing the best model configuration from autoML, based on the training/validation loss. It contains the following columns: </p><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>
<p>A summary table named &lt;model_selection_table&gt;_summary is also created, which contains the following columns: </p><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, custom metrics and top k categorical accuracy. If there are none, this field will be blank.   </td></tr>
</table>
<p>The model output table produced by autoML contains columns below. There is one row per model configuration generated: </p><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>
<p>An info table named &lt;model_output_table&gt;_info is also created, which has the columns below. There is one row per model: </p><table class="output">
<tr>
<th>mst_key </th><td>INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, for each model configuration generated.  </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><p class="starttd">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.</p>
<p class="endtd">Note that 'metrics_iters' values are for the overall iterations. For some models, the count might start at a later iteration based on the schedule. This representation is selected to simplify representing the results in iteration-metric graphs.   </p>
</td></tr>
<tr>
<th>s </th><td>Bracket number from Hyperband schedule. This column is not present for Hyperopt.  </td></tr>
<tr>
<th>i </th><td>Latest evaluated round number from Hyperband schedule. This column is not present for Hyperopt.  </td></tr>
</table>
<p>A summary table named &lt;model_output_table&gt;_summary is also created, which has the following columns: </p><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 used.  </td></tr>
<tr>
<th>automl_params </th><td>AutoML parameter 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>&lt;dependent_varname&gt;_class_values </th><td>Array of actual class values used for a particular dependent variable. A column will be generated for each dependent variable.  </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>
<p><a class="anchor" id="hyperband_schedule"></a></p><dl class="section user"><dt>Print Hyperband Schedule</dt><dd></dd></dl>
<p>This utility prints out the schedule for a set of input parameters. It does not run the Hyperband method, rather it just prints out the schedule so you can see what the brackets look like. Refer to [1] for information on Hyperband schedules. </p><pre class="syntax">
hyperband_schedule(
    schedule_table,
    R,
    eta,
    skip_last
    )
</pre><p><b>Arguments</b> </p><dl class="arglist">
<dt>schedule_table </dt>
<dd><p class="startdd">VARCHAR. Name of output table containing hyperband schedule. </p>
<p class="enddd"></p>
</dd>
<dt>R </dt>
<dd><p class="startdd">INTEGER. Maximum number of resources (i.e., iterations) to allocate to a single configuration in a round of Hyperband. </p>
<p class="enddd"></p>
</dd>
<dt>eta </dt>
<dd><p class="startdd">INTEGER. 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. </p>
<p class="enddd"></p>
</dd>
<dt>skip_last </dt>
<dd><p class="startdd">INTEGER. The number of last rounds to skip. For example, 'skip_last=1' will skip the last round (i.e., last entry in each bracket), which is standard random search and can be expensive when run for the total R iterations. </p>
<p class="enddd"></p>
</dd>
</dl>
<p><b>Output table</b> <br />
 The hyperband schedule output table contains the following columns: </p><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>
<p><a class="anchor" id="example"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
<dl class="section note"><dt>Note</dt><dd>Deep learning works best on very large datasets, but that is not convenient for a quick introduction to the syntax. So in this example we use an MLP on the well known iris data set from <a href="https://archive.ics.uci.edu/ml/datasets/iris">https://archive.ics.uci.edu/ml/datasets/iris</a>. For more realistic examples with images please refer to the deep learning notebooks at <a href="https://github.com/apache/madlib-site/tree/asf-site/community-artifacts">https://github.com/apache/madlib-site/tree/asf-site/community-artifacts</a>.</dd></dl>
<h4>Setup</h4>
<ol type="1">
<li>Create an input data set. <pre class="example">
DROP TABLE IF EXISTS iris_data;
CREATE TABLE iris_data(
    id serial,
    attributes numeric[],
    class_text varchar
);
INSERT INTO iris_data(id, attributes, class_text) VALUES
(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa'),
(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa'),
(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa'),
(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa'),
(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa'),
(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa'),
(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa'),
(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa'),
(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa'),
(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa'),
(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa'),
(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa'),
(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa'),
(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa'),
(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa'),
(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa'),
(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa'),
(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa'),
(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa'),
(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa'),
(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa'),
(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa'),
(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa'),
(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa'),
(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa'),
(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa'),
(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa'),
(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa'),
(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa'),
(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa'),
(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa'),
(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa'),
(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa'),
(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa'),
(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa'),
(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa'),
(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa'),
(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa'),
(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa'),
(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa'),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa'),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa'),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa'),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa'),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa'),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa'),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa'),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa'),
(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor'),
(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor'),
(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor'),
(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor'),
(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor'),
(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor'),
(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor'),
(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor'),
(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor'),
(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor'),
(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor'),
(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor'),
(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor'),
(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor'),
(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor'),
(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor'),
(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor'),
(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor'),
(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor'),
(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor'),
(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor'),
(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor'),
(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor'),
(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor'),
(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor'),
(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor'),
(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor'),
(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor'),
(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor'),
(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor'),
(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor'),
(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor'),
(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor'),
(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor'),
(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor'),
(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor'),
(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor'),
(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor'),
(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor'),
(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor'),
(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor'),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor'),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor'),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor'),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor'),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor'),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor'),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor'),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor'),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor'),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica'),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica'),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica'),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica'),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica'),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica'),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica'),
(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica'),
(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica'),
(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica'),
(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica'),
(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica'),
(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica'),
(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica'),
(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica'),
(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica'),
(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica'),
(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica'),
(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica'),
(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica'),
(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica'),
(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica'),
(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica'),
(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica'),
(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica'),
(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica'),
(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica'),
(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica'),
(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica'),
(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica'),
(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica'),
(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica'),
(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica'),
(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica'),
(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica'),
(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica'),
(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica'),
(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica'),
(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica'),
(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica'),
(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica'),
(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica'),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica'),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica'),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica'),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica'),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica'),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica'),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica');
</pre> Create a test/validation dataset from the training data: <pre class="example">
DROP TABLE IF EXISTS iris_train, iris_test;
-- Set seed so results are reproducible
SELECT setseed(0);
SELECT madlib.train_test_split('iris_data',     -- Source table
                               'iris',          -- Output table root name
                                0.8,            -- Train proportion
                                NULL,           -- Test proportion (0.2)
                                NULL,           -- Strata definition
                                NULL,           -- Output all columns
                                NULL,           -- Sample without replacement
                                TRUE            -- Separate output tables
                              );
SELECT COUNT(*) FROM iris_train;
</pre> <pre class="result">
 count
------+
   120
</pre></li>
<li>Call the preprocessor for deep learning. For the training dataset: <pre class="example">
\x on
DROP TABLE IF EXISTS iris_train_packed, iris_train_packed_summary;
SELECT madlib.training_preprocessor_dl('iris_train',         -- Source table
                                       'iris_train_packed',  -- Output table
                                       'class_text',         -- Dependent variable
                                       'attributes'          -- Independent variable
                                        );
SELECT * FROM iris_train_packed_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------+---------------------------------------------
source_table        | iris_train
output_table        | iris_train_packed
dependent_varname   | class_text
independent_varname | attributes
dependent_vartype   | character varying
class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
buffer_size         | 60
normalizing_const   | 1.0
num_classes         | 3
</pre> For the validation dataset: <pre class="example">
DROP TABLE IF EXISTS iris_test_packed, iris_test_packed_summary;
SELECT madlib.validation_preprocessor_dl('iris_test',          -- Source table
                                         'iris_test_packed',   -- Output table
                                         'class_text',         -- Dependent variable
                                         'attributes',         -- Independent variable
                                         'iris_train_packed'   -- From training preprocessor step
                                          );
SELECT * FROM iris_test_packed_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------+---------------------------------------------
source_table        | iris_test
output_table        | iris_test_packed
dependent_varname   | class_text
independent_varname | attributes
dependent_vartype   | character varying
class_values        | {Iris-setosa,Iris-versicolor,Iris-virginica}
buffer_size         | 15
normalizing_const   | 1.0
num_classes         | 3
</pre></li>
<li>Define and load model architecture. Use Keras to define the model architecture with 1 hidden layer: <pre class="example">
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.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()
<pre class="fragment">_________________________________________________________________
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
</pre>
</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> Define 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()
<pre class="fragment">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
</pre>
</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 into model 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
);
</pre></li>
</ol>
<h4>Hyperband</h4>
<ol type="1">
<li>Print Hyperband schedule for example input parameters 'R=9' and 'eta=3': <pre class="example">
DROP TABLE IF EXISTS hb_schedule;
SELECT madlib.hyperband_schedule ('hb_schedule',
                                   81,
                                   3,
                                   0);
SELECT * FROM hb_schedule ORDER BY s DESC, i;
</pre> <pre class="result">
 s | i | n_i | r_i
---+---+-----+-----
 4 | 0 |  81 |   1
 4 | 1 |  27 |   3
 4 | 2 |   9 |   9
 4 | 3 |   3 |  27
 4 | 4 |   1 |  81
 3 | 0 |  27 |   3
 3 | 1 |   9 |   9
 3 | 2 |   3 |  27
 3 | 3 |   1 |  81
 2 | 0 |   9 |   9
 2 | 1 |   3 |  27
 2 | 2 |   1 |  81
 1 | 0 |   6 |  27
 1 | 1 |   2 |  81
 0 | 0 |   5 |  81
(15 rows)
</pre></li>
<li>Run Hyperband method with 'R=9' and 'eta=3': <pre class="example">
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table, automl_mst_table_summary;
SELECT madlib.madlib_keras_automl('iris_train_packed',                -- source table
                                  'automl_output',                    -- model output table
                                  'model_arch_library',               -- model architecture table
                                  'automl_mst_table',                 -- model selection output table
                                  ARRAY[1,2],                         -- model IDs
                                  $${
                                      'loss': ['categorical_crossentropy'],
                                      'optimizer_params_list': [
                                          {'optimizer': ['Adam'],'lr': [0.001, 0.1, 'log']},
                                          {'optimizer': ['RMSprop'],'lr': [0.001, 0.1, 'log']}
                                      ],
                                      'metrics': ['accuracy']
                                  } $$,                               -- compile param grid
                                  $${'batch_size': [4, 8], 'epochs': [1]}$$,  -- fit params grid
                                  'hyperband',                        -- autoML method
                                  'R=9, eta=3, skip_last=0',          -- autoML params
                                  NULL,                               -- random state
                                  NULL,                               -- object table
                                  FALSE,                              -- use GPUs
                                  'iris_test_packed',                 -- validation table
                                  1,                                  -- metrics compute freq
                                  NULL,                               -- name
                                  NULL);                              -- descr
</pre></li>
<li>View the model summary: <pre class="example">
SELECT * FROM automl_output_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------------+---------------------------------------------
source_table              | iris_train_packed
validation_table          | iris_test_packed
model                     | automl_output
model_info                | automl_output_info
dependent_varname         | class_text
independent_varname       | attributes
model_arch_table          | model_arch_library
model_selection_table     | automl_mst_table
automl_method             | hyperband
automl_params             | R=9, eta=3, skip_last=0
random_state              |
object_table              |
use_gpus                  | f
metrics_compute_frequency | 1
name                      |
description               |
start_training_time       | 2021-01-16 01:20:17
end_training_time         | 2021-01-16 01:21:47
madlib_version            | 1.21.0
num_classes               | 3
class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype         | character varying
normalizing_const         | 1
</pre></li>
<li>View results for a few models: <pre class="example">
SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final LIMIT 3;
</pre> <pre class="result">
-[ RECORD 1 ]------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
mst_key                  | 15
model_id                 | 1
compile_params           | optimizer='Adam(lr=0.005948073640447284)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=8
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {41.9598820209503,47.7600600719452,53.5559930801392,59.2904281616211,65.0303740501404,70.910637140274,76.6586999893188,82.3321261405945,88.0252130031586}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.975000023841858
training_loss_final      | 0.174209594726562
training_metrics         | {0.683333337306976,0.683333337306976,0.816666662693024,0.791666686534882,0.966666638851166,0.850000023841858,0.966666638851166,0.966666638851166,0.975000023841858}
training_loss            | {0.658287584781647,0.56329345703125,0.489711940288544,0.417204052209854,0.333063006401062,0.325938105583191,0.237209364771843,0.216858893632889,0.174209594726562}
validation_metrics_final | 0.933333337306976
validation_loss_final    | 0.282542854547501
validation_metrics       | {0.600000023841858,0.600000023841858,0.733333349227905,0.733333349227905,0.899999976158142,0.800000011920929,0.933333337306976,0.899999976158142,0.933333337306976}
validation_loss          | {0.844917356967926,0.739157736301422,0.651688754558563,0.567608654499054,0.458681106567383,0.461867392063141,0.344642341136932,0.335768848657608,0.282542854547501}
metrics_iters            | {5,6,7,8,9,10,11,12,13}
s                        | 0
i                        | 0
-[ RECORD 2 ]------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
mst_key                  | 10
model_id                 | 1
compile_params           | optimizer='RMSprop(lr=0.01152123686692268)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=8
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {21.1628739833832,27.9904689788818,34.9025909900665}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.933333337306976
training_loss_final      | 0.239687830209732
training_metrics         | {0.699999988079071,0.699999988079071,0.933333337306976}
training_loss            | {0.600760638713837,0.386314034461975,0.239687830209732}
validation_metrics_final | 0.899999976158142
validation_loss_final    | 0.369663149118423
validation_metrics       | {0.533333361148834,0.600000023841858,0.899999976158142}
validation_loss          | {0.723896682262421,0.539595663547516,0.369663149118423}
metrics_iters            | {2,3,4}
s                        | 1
i                        | 0
-[ RECORD 3 ]------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
mst_key                  | 2
model_id                 | 1
compile_params           | optimizer='RMSprop(lr=0.005464438486993435)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=4
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {11.6164019107819,20.9570059776306,27.7901480197906,34.7061359882355}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.925000011920929
training_loss_final      | 0.17901936173439
training_metrics         | {0.949999988079071,0.883333325386047,0.958333313465118,0.925000011920929}
training_loss            | {0.547602951526642,0.321837723255157,0.197886273264885,0.17901936173439}
validation_metrics_final | 0.866666674613953
validation_loss_final    | 0.325421392917633
validation_metrics       | {0.866666674613953,0.800000011920929,0.899999976158142,0.866666674613953}
validation_loss          | {0.723824441432953,0.462396681308746,0.326263695955276,0.325421392917633}
metrics_iters            | {1,2,3,4}
s                        | 2
i                        | 1
</pre></li>
</ol>
<h4>Hyperopt</h4>
<ol type="1">
<li>Run Hyperopt for a set number of trials, i.e., model configurations: <pre class="example">
DROP TABLE IF EXISTS automl_output, automl_output_info, automl_output_summary, automl_mst_table, automl_mst_table_summary;
SELECT madlib.madlib_keras_automl('iris_train_packed',                -- source table
                                  'automl_output',                    -- model output table
                                  'model_arch_library',               -- model architecture table
                                  'automl_mst_table',                 -- model selection output table
                                  ARRAY[1,2],                         -- model IDs
                                  $${
                                      'loss': ['categorical_crossentropy'],
                                      'optimizer_params_list': [
                                          {'optimizer': ['Adam'],'lr': [0.001, 0.1, 'log']},
                                          {'optimizer': ['RMSprop'],'lr': [0.001, 0.1, 'log']}
                                      ],
                                      'metrics': ['accuracy']
                                  } $$,                               -- compile param grid
                                  $${'batch_size': [4, 8], 'epochs': [1]}$$,  -- fit params grid
                                  'hyperopt',                         -- autoML method
                                  'num_configs=20, num_iterations=10, algorithm=tpe',  -- autoML params
                                  NULL,                               -- random state
                                  NULL,                               -- object table
                                  FALSE,                              -- use GPUs
                                  'iris_test_packed',                 -- validation table
                                  1,                                  -- metrics compute freq
                                  NULL,                               -- name
                                  NULL);                              -- descr
</pre></li>
<li>View the model summary: <pre class="example">
SELECT * FROM automl_output_summary;
</pre> <pre class="result">
-[ RECORD 1 ]-------------+-------------------------------------------------
source_table              | iris_train_packed
validation_table          | iris_test_packed
model                     | automl_output
model_info                | automl_output_info
dependent_varname         | class_text
independent_varname       | attributes
model_arch_table          | model_arch_library
model_selection_table     | automl_mst_table
automl_method             | hyperopt
automl_params             | num_configs=20, num_iterations=10, algorithm=tpe
random_state              |
object_table              |
use_gpus                  | f
metrics_compute_frequency | 1
name                      |
description               |
start_training_time       | 2020-10-23 00:24:43
end_training_time         | 2020-10-23 00:28:41
madlib_version            | 1.21.0
num_classes               | 3
class_values              | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype         | character varying
normalizing_const         | 1
</pre></li>
<li>View results for a few models: <pre class="example">
SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final LIMIT 3;
</pre> <pre class="result">
-[ RECORD 1]----------------------------------------------------------------------------------------------------------
mst_key                  | 4
model_id                 | 1
compile_params           | optimizer='Adam(lr=0.021044174547856155)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=8
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {24.9291331768036,27.1591901779175,29.3875880241394,31.4712460041046,33.6599950790405,35.9415881633759,38.0477111339569,40.2351109981537,42.3932039737701,44.4729251861572}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.958333313465118
training_loss_final      | 0.116280987858772
training_metrics         | {0.658333361148834,0.658333361148834,0.733333349227905,0.816666662693024,0.949999988079071,0.949999988079071,0.949999988079071,0.875,0.958333313465118,0.958333313465118}
training_loss            | {0.681611657142639,0.50702965259552,0.41643014550209,0.349031865596771,0.2586330473423,0.234042942523956,0.204623967409134,0.337687611579895,0.116805233061314,0.116280987858772}
validation_metrics_final | 1
validation_loss_final    | 0.067971371114254
validation_metrics       | {0.699999988079071,0.699999988079071,0.733333349227905,0.766666650772095,0.899999976158142,0.899999976158142,0.899999976158142,0.899999976158142,1,1}
validation_loss          | {0.523795306682587,0.386897593736649,0.323715627193451,0.29447802901268,0.218715354800224,0.216124311089516,0.186037495732307,0.257792592048645,0.0693960413336754,0.067971371114254}
metrics_iters            | {1,2,3,4,5,6,7,8,9,10}
-[ RECORD 2]----------------------------------------------------------------------------------------------------------
mst_key                  | 8
model_id                 | 1
compile_params           | optimizer='RMSprop(lr=0.055711748803920255)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=4
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {68.9713232517242,71.1428651809692,73.0566282272339,75.2099182605743,77.4740402698517,79.4580070972443,81.5958452224731,83.6865520477295,85.6433861255646,87.8569240570068}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.966666638851166
training_loss_final      | 0.106823824346066
training_metrics         | {0.658333361148834,0.699999988079071,0.875,0.691666662693024,0.699999988079071,0.791666686534882,0.774999976158142,0.966666638851166,0.966666638851166,0.966666638851166}
training_loss            | {0.681002557277679,0.431159198284149,0.418115794658661,0.51969450712204,0.605500161647797,0.36535832285881,0.451890885829926,0.126570284366608,0.116986438632011,0.106823824346066}
validation_metrics_final | 1
validation_loss_final    | 0.0758842155337334
validation_metrics       | {0.699999988079071,0.699999988079071,0.966666638851166,0.699999988079071,0.699999988079071,0.800000011920929,0.766666650772095,0.966666638851166,0.966666638851166,1}
validation_loss          | {0.693905889987946,0.364648938179016,0.287941485643387,0.509377717971802,0.622031152248383,0.377092003822327,0.488217085599899,0.10258474200964,0.0973251685500145,0.0758842155337334}
metrics_iters            | {1,2,3,4,5,6,7,8,9,10}
-[ RECORD 3]----------------------------------------------------------------------------------------------------------
mst_key                  | 13
model_id                 | 1
compile_params           | optimizer='RMSprop(lr=0.006381376508189085)',metrics=['accuracy'],loss='categorical_crossentropy'
fit_params               | epochs=1,batch_size=4
model_type               | madlib_keras
model_size               | 0.7900390625
metrics_elapsed_time     | {141.029213190079,143.075024366379,145.330604314804,147.341159343719,149.579845190048,151.819869279861,153.939630270004,156.235336303711,158.536979198456,160.583434343338}
metrics_type             | {accuracy}
loss_type                | categorical_crossentropy
training_metrics_final   | 0.975000023841858
training_loss_final      | 0.0981351062655449
training_metrics         | {0.875,0.933333337306976,0.875,0.975000023841858,0.975000023841858,0.908333361148834,0.949999988079071,0.966666638851166,0.975000023841858,0.975000023841858}
training_loss            | {0.556384921073914,0.32896700501442,0.29009011387825,0.200998887419701,0.149432390928268,0.183790743350983,0.120595499873161,0.12202025949955,0.101290702819824,0.0981351062655449}
validation_metrics_final | 1
validation_loss_final    | 0.0775858238339424
validation_metrics       | {0.899999976158142,0.966666638851166,0.766666650772095,1,1,0.933333337306976,0.966666638851166,0.966666638851166,1,1}
validation_loss          | {0.442976772785187,0.249921068549156,0.268403559923172,0.167330235242844,0.134699374437332,0.140658855438232,0.0964709892868996,0.110730975866318,0.0810751244425774,0.0775858238339424}
metrics_iters            | {1,2,3,4,5,6,7,8,9,10}
</pre></li>
<li>Run inference on one of the models generated by Hyperopt. In this example we use the validation set to predict on: <pre class="example">
DROP TABLE IF EXISTS iris_predict;
SELECT madlib.madlib_keras_predict('automl_output',    -- model
                                   'iris_test',        -- test_table
                                   'id',               -- id column
                                   'attributes',       -- independent var
                                   'iris_predict',     -- output table
                                    'response',        -- prediction type
                                    FALSE,             -- use gpus
                                    4                 -- MST key
                                   );
SELECT * FROM iris_predict ORDER BY id;
</pre> <pre class="result">
 id  |   class_text    |    prob
-----+-----------------+------------
   5 | Iris-setosa     |  0.9998704
   7 | Iris-setosa     | 0.99953365
  10 | Iris-setosa     |  0.9993413
  16 | Iris-setosa     |  0.9999825
  17 | Iris-setosa     |  0.9999256
  21 | Iris-setosa     |  0.9995347
  23 | Iris-setosa     |  0.9999405
  27 | Iris-setosa     |  0.9989955
  30 | Iris-setosa     |  0.9990559
  31 | Iris-setosa     |  0.9986846
  32 | Iris-setosa     |  0.9992879
  37 | Iris-setosa     | 0.99987197
  39 | Iris-setosa     |  0.9989151
  46 | Iris-setosa     |  0.9981341
  47 | Iris-setosa     |  0.9999044
  53 | Iris-versicolor |  0.9745001
  54 | Iris-versicolor |  0.8989025
  56 | Iris-versicolor | 0.97066855
  63 | Iris-versicolor | 0.96652734
  71 | Iris-versicolor | 0.84569126
  77 | Iris-versicolor |  0.9564522
  83 | Iris-versicolor |  0.9664927
  85 | Iris-versicolor | 0.96553373
  93 | Iris-versicolor | 0.96748537
 103 | Iris-virginica  |  0.9343488
 108 | Iris-virginica  | 0.91668576
 117 | Iris-virginica  |  0.7323582
 124 | Iris-virginica  | 0.72906417
 132 | Iris-virginica  | 0.50430095
 144 | Iris-virginica  |  0.9487652
(30 rows)
</pre></li>
</ol>
<p><a class="anchor" id="notes"></a></p><dl class="section user"><dt>Notes</dt><dd></dd></dl>
<ol type="1">
<li>Hyperopt must be installed on the main node of the database cluster if you want to use the Hyperopt method of autoML. You can pip install it in the usual way [3]. Hyperband does not require any separate package installation.</li>
<li>In practice you may need to do more than one run of an autoML method to arrive at a model with adequate accuracy. One approach is to set the search space to be quite broad initially, then observe which hyperparameter ranges and model architectures seem to be doing the best. Subesquent runs can then zoom in on those good ones in order to fine tune the model.</li>
</ol>
<p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl>
<p>[1] Li <em>et al.</em>, "Hyperband: A Novel Bandit-Based Approach to
Hyperparameter Optimization", Journal of Machine Learning Research 18 (2018) 1-52.</p>
<p>[2] J. Bergstra, D. Yamins, D. D. Cox, "Making a Science of Model Search:
Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures," <em>Proceedings of the 30th International Conference on Machine Learning</em>, Atlanta, Georgia, USA, 2013. JMLR: W&amp;CP volume 28.</p>
<p>[3] Python catalog for Hyperopt <a href="https://pypi.org/project/hyperopt/">https://pypi.org/project/hyperopt/</a></p>
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