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2.1.0
User Documentation for Apache MADlib
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2.1.0
User Documentation for Apache MADlib
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This module contains automated machine learning (autoML) methods for model architecture search and hyperparameter tuning.
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 Define Model Configurations.
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.
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.
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.
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
)
Arguments
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.
VARCHAR. Table containing model architectures and weights. For more information on this table refer to Define Model Architectures.
VARCHAR. Model selection table created by this method. A summary table named <model_selection_table>_summary is also created. Contents of both of these tables are described below.
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.
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:
$$
{'loss': ['categorical_crossentropy'],
'optimizer_params_list': [
{'optimizer': ['SGD'], 'lr': [0.0001, 0.001, 'log'], 'momentum': [0.95, 0.99, 'log_near_one']},
{'optimizer': ['Adam'], 'lr': [0.01, 0.1, 'log'], 'decay': [1e-6, 1e-4, 'log']}],
'metrics': ['accuracy']
}
$$
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.
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:
$$
{'batch_size': [32, 64, 128, 256],
'epochs': [10, 20, 30]
}
$$
VARCHAR, default 'hyperband'. Name of the autoML algorithm to run. Can be either 'hyperband' or 'hyperopt' (case insensitive).
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.
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'.
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.
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.
TEXT, default: NULL. Free text string to provide a name, if desired.
TEXT, default: NULL. Free text string to provide a description, if desired.
BOOLEAN, default: FALSE. Use caching of images in memory on the segment in order to speed up processing.
Output tables
The model selection output table <model_selection_table> has only one row containing the best model configuration from autoML, based on the training/validation loss. It contains the following columns:
| mst_key | INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters. |
|---|---|
| model_id | VARCHAR. Model architecture ID from the 'model_arch_table'. |
| compile_params | VARCHAR. Keras compile parameters. |
| fit_params | VARCHAR. Keras fit parameters. |
A summary table named <model_selection_table>_summary is also created, which contains the following columns:
| model_arch_table | VARCHAR. Name of the model architecture table containing the model architecture IDs. |
|---|---|
| object_table | 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. |
The model output table produced by autoML contains columns below. There is one row per model configuration generated:
| mst_key | INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, as defined in the 'model_selection_table'. |
|---|---|
| model_weights | BYTEA8. Byte array containing the weights of the neural net. |
| model_arch | TEXT. A JSON representation of the model architecture used in training. |
An info table named <model_output_table>_info is also created, which has the columns below. There is one row per model:
| mst_key | INTEGER. ID that defines a unique tuple for model architecture-compile parameters-fit parameters, for each model configuration generated. |
|---|---|
| model_id | INTEGER. ID that defines model in the 'model_arch_table'. |
| compile_params | Compile parameters passed to Keras. |
| fit_params | Fit parameters passed to Keras. |
| model_type | General identifier for type of model trained. Currently says 'madlib_keras'. |
| model_size | Size of the model in KB. Models are stored in 'bytea' data format which is used for binary strings in PostgreSQL type databases. |
| metrics_elapsed_time | 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. |
| metrics_type | Metric specified in the 'compile_params'. |
| training_metrics_final | Final value of the training metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter. |
| training_loss_final | Final value of the training loss after all iterations have completed. |
| training_metrics | 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. |
| training_loss | 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. |
| validation_metrics_final | Final value of the validation metric after all iterations have completed. The metric reported is the one specified in the 'metrics_type' parameter. |
| validation_loss_final | Final value of the validation loss after all iterations have completed. |
| validation_metrics | 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. |
| validation_loss | 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. |
| metrics_iters | 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. 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. |
| s | Bracket number from Hyperband schedule. This column is not present for Hyperopt. |
| i | Latest evaluated round number from Hyperband schedule. This column is not present for Hyperopt. |
A summary table named <model_output_table>_summary is also created, which has the following columns:
| source_table | Source table used for training. |
|---|---|
| validation_table | Name of the table containing the validation dataset (if specified). |
| model | Name of the output table containing the model for each model selection tuple. |
| model_info | Name of the output table containing the model performance and other info for each model selection tuple. |
| dependent_varname | Dependent variable column from the original source table in the image preprocessing step. |
| independent_varname | Independent variables column from the original source table in the image preprocessing step. |
| model_arch_table | Name of the table containing the model architecture and (optionally) the initial model weights. |
| model selection table | Name of the mst table containing the best configuration. |
| automl_method | Name of the autoML method used. |
| automl_params | AutoML parameter values. |
| random_state | Chosen random seed. |
| metrics_compute_frequency | Frequency that per-iteration metrics are computed for the training dataset and validation datasets. |
| name | Name of the training run (free text). |
| description | Description of the training run (free text). |
| start_training_time | Timestamp for start of training. |
| end_training_time | Timestamp for end of training. |
| madlib_version | Version of MADlib used. |
| num_classes | Count of distinct classes values used. |
| <dependent_varname>_class_values | Array of actual class values used for a particular dependent variable. A column will be generated for each dependent variable. |
| dependent_vartype | Data type of the dependent variable. |
| normalizing_constant | Normalizing constant used from the image preprocessing step. |
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.
hyperband_schedule(
schedule_table,
R,
eta,
skip_last
)
Arguments
VARCHAR. Name of output table containing hyperband schedule.
INTEGER. Maximum number of resources (i.e., iterations) to allocate to a single configuration in a round of Hyperband.
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.
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.
Output table
The hyperband schedule output table contains the following columns:
| s | INTEGER. Bracket number. |
|---|---|
| i | INTEGER. Round (depth) in bracket. |
| n_i | INTEGER. Number of configurations in this round. |
| r_i | INTEGER. Resources (iterations) in this round. |
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');
Create a test/validation dataset from the training data:
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;
count ------+ 120
\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;
-[ 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
For the validation dataset:
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;
-[ 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
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()Export the model to JSON:_________________________________________________________________ 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
model1.to_json()
'{"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"}'
Define model architecture with 2 hidden layers: 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()Export the model to JSON: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
model2.to_json()
'{"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"}'
Load into model architecture table:
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
);
DROP TABLE IF EXISTS hb_schedule;
SELECT madlib.hyperband_schedule ('hb_schedule',
81,
3,
0);
SELECT * FROM hb_schedule ORDER BY s DESC, i;
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)
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
SELECT * FROM automl_output_summary;
-[ 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 | 2.1.0
num_classes | 3
class_values | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype | character varying
normalizing_const | 1
SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final LIMIT 3;
-[ RECORD 1 ]------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------
mst_key | 15
model_id | 1
compile_params | optimizer='Adam(lr=0.005948073640447284)',metrics=['categorical_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=['categorical_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=['categorical_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
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
SELECT * FROM automl_output_summary;
-[ 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 | 2.1.0
num_classes | 3
class_values | {Iris-setosa,Iris-versicolor,Iris-virginica}
dependent_vartype | character varying
normalizing_const | 1
SELECT * FROM automl_output_info ORDER BY validation_metrics_final DESC, validation_loss_final LIMIT 3;
-[ RECORD 1]----------------------------------------------------------------------------------------------------------
mst_key | 4
model_id | 1
compile_params | optimizer='Adam(lr=0.021044174547856155)',metrics=['categorical_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=['categorical_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=['categorical_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}
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;
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)
[1] Li et al., "Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization", Journal of Machine Learning Research 18 (2018) 1-52.
[2] J. Bergstra, D. Yamins, D. D. Cox, "Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures," Proceedings of the 30th International Conference on Machine Learning, Atlanta, Georgia, USA, 2013. JMLR: W&CP volume 28.
[3] Python catalog for Hyperopt https://pypi.org/project/hyperopt/
1.8.13