Frank McQuillan created MADLIB-1471:
---------------------------------------
Summary: MLP weights param not working
Key: MADLIB-1471
URL: https://issues.apache.org/jira/browse/MADLIB-1471
Project: Apache MADlib
Issue Type: New Feature
Components: Module: Neural Networks
Reporter: Frank McQuillan
Weights function not working for MLP:
{code}
DROP TABLE IF EXISTS temp1;
CREATE TABLE temp1(
id serial,
attributes numeric[],
class_text varchar,
row_weight numeric
);
INSERT INTO temp1(id, attributes, class_text, row_weight) VALUES
(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa', 1.0),
(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa', 1.0),
(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa', 1.0),
(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa', 1.0),
(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa', 1.0),
(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa', 1.0),
(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa', 1.0),
(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa', 1.0),
(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa', 1.0),
(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa', 1.0),
(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa', 1.0),
(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa', 1.0),
(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa', 1.0),
(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa', 1.0),
(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa', 1.0),
(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa', 1.0),
(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa', 1.0),
(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa', 1.0),
(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa', 1.0),
(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa', 1.0),
(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa', 1.0),
(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa', 1.0),
(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa', 1.0),
(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa', 1.0),
(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa', 1.0),
(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa', 1.0),
(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa', 1.0),
(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa', 1.0),
(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa', 1.0),
(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa', 1.0),
(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa', 1.0),
(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa', 1.0),
(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa', 1.0),
(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa', 1.0),
(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa', 1.0),
(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa', 1.0),
(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa', 1.0),
(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa', 1.0),
(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa', 1.0),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa', 1.0),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa', 1.0),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa', 1.0),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa', 1.0),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa', 1.0),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa', 1.0),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa', 1.0),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa', 1.0),
(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor', 1.0),
(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor', 1.0),
(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor', 1.0),
(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor', 1.0),
(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor', 1.0),
(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor', 1.0),
(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor', 1.0),
(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor', 1.0),
(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor', 1.0),
(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor', 1.0),
(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor', 1.0),
(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor', 1.0),
(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor', 1.0),
(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor', 1.0),
(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor', 1.0),
(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor', 1.0),
(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor', 1.0),
(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor', 1.0),
(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor', 1.0),
(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor', 1.0),
(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor', 1.0),
(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor', 1.0),
(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor', 1.0),
(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor', 1.0),
(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor', 1.0),
(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor', 1.0),
(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor', 1.0),
(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor', 1.0),
(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor', 1.0),
(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor', 1.0),
(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor', 1.0),
(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor', 1.0),
(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor', 1.0),
(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor', 1.0),
(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor', 1.0),
(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor', 1.0),
(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor', 1.0),
(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor', 1.0),
(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor', 1.0),
(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor', 1.0),
(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor', 1.0),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor', 1.0),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor', 1.0),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor', 1.0),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor', 1.0),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor', 1.0),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor', 1.0),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor', 1.0),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor', 1.0),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor', 1.0),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica', 1.0),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica', 1.0),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica', 1.0),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica', 1.0),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica', 1.0),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica', 1.0),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica', 1.0),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica', 1.0),
(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica', 1.0),
(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica', 1.0),
(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica', 1.0),
(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica', 1.0),
(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica', 1.0),
(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica', 1.0),
(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica', 1.0),
(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica', 1.0),
(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica', 1.0),
(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica', 1.0),
(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica', 1.0),
(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica', 1.0),
(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica', 1.0),
(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica', 1.0),
(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica', 1.0),
(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica', 1.0),
(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica', 1.0),
(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica', 1.0),
(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica', 1.0),
(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica', 1.0),
(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica', 1.0),
(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica', 1.0),
(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica', 1.0),
(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica', 1.0),
(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica', 1.0),
(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica', 1.0),
(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica', 1.0),
(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica', 1.0),
(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica', 1.0),
(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica', 1.0),
(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica', 1.0),
(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica', 1.0),
(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica', 1.0),
(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica', 1.0),
(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica', 1.0),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica', 1.0),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica', 1.0),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica', 1.0),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica', 1.0),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica', 1.0),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica', 1.0),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica', 1.0);
DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;
-- Set seed so results are reproducible
SELECT setseed(0);
SELECT madlib.mlp_classification(
'temp1', -- Source table
'mlp_model', -- Destination table
'attributes', -- Input features
'class_text', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.003,
n_iterations=500,
tolerance=0', -- Optimizer params
'tanh', -- Activation function
'row_weight', -- Weight
FALSE, -- No warm start
FALSE -- Not verbose
);
{code}
produces
{code}
ERROR: plpy.Error: MLP error: Weights should be a numeric type
CONTEXT: Traceback (most recent call last):
PL/Python function "mlp_classification", line 33, in <module>
grouping_col)
PL/Python function "mlp_classification", line 42, in wrapper
PL/Python function "mlp_classification", line 116, in mlp
PL/Python function "mlp_classification", line 740, in
_validate_params_based_on_minibatch
PL/Python function "mlp_classification", line 123, in _assert
PL/Python function "mlp_classification"
{code}
Also:
{code}
DROP TABLE IF EXISTS temp1;
CREATE TABLE temp1(
id serial,
attributes numeric[],
class_text varchar,
row_weight double precision
);
INSERT INTO temp1(id, attributes, class_text, row_weight) VALUES
(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa', 1.0),
(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa', 1.0),
(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa', 1.0),
(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa', 1.0),
(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa', 1.0),
(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa', 1.0),
(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa', 1.0),
(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa', 1.0),
(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa', 1.0),
(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa', 1.0),
(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa', 1.0),
(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa', 1.0),
(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa', 1.0),
(15,ARRAY[5.8,4.0,1.2,0.2],'Iris-setosa', 1.0),
(16,ARRAY[5.7,4.4,1.5,0.4],'Iris-setosa', 1.0),
(17,ARRAY[5.4,3.9,1.3,0.4],'Iris-setosa', 1.0),
(18,ARRAY[5.1,3.5,1.4,0.3],'Iris-setosa', 1.0),
(19,ARRAY[5.7,3.8,1.7,0.3],'Iris-setosa', 1.0),
(20,ARRAY[5.1,3.8,1.5,0.3],'Iris-setosa', 1.0),
(21,ARRAY[5.4,3.4,1.7,0.2],'Iris-setosa', 1.0),
(22,ARRAY[5.1,3.7,1.5,0.4],'Iris-setosa', 1.0),
(23,ARRAY[4.6,3.6,1.0,0.2],'Iris-setosa', 1.0),
(24,ARRAY[5.1,3.3,1.7,0.5],'Iris-setosa', 1.0),
(25,ARRAY[4.8,3.4,1.9,0.2],'Iris-setosa', 1.0),
(26,ARRAY[5.0,3.0,1.6,0.2],'Iris-setosa', 1.0),
(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa', 1.0),
(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa', 1.0),
(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa', 1.0),
(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa', 1.0),
(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa', 1.0),
(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa', 1.0),
(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa', 1.0),
(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa', 1.0),
(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa', 1.0),
(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa', 1.0),
(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa', 1.0),
(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa', 1.0),
(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa', 1.0),
(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa', 1.0),
(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa', 1.0),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa', 1.0),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa', 1.0),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa', 1.0),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa', 1.0),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa', 1.0),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa', 1.0),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa', 1.0),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa', 1.0),
(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor', 1.0),
(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor', 1.0),
(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor', 1.0),
(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor', 1.0),
(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor', 1.0),
(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor', 1.0),
(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor', 1.0),
(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor', 1.0),
(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor', 1.0),
(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor', 1.0),
(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor', 1.0),
(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor', 1.0),
(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor', 1.0),
(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor', 1.0),
(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor', 1.0),
(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor', 1.0),
(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor', 1.0),
(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor', 1.0),
(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor', 1.0),
(70,ARRAY[5.6,2.5,3.9,1.1],'Iris-versicolor', 1.0),
(71,ARRAY[5.9,3.2,4.8,1.8],'Iris-versicolor', 1.0),
(72,ARRAY[6.1,2.8,4.0,1.3],'Iris-versicolor', 1.0),
(73,ARRAY[6.3,2.5,4.9,1.5],'Iris-versicolor', 1.0),
(74,ARRAY[6.1,2.8,4.7,1.2],'Iris-versicolor', 1.0),
(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor', 1.0),
(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor', 1.0),
(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor', 1.0),
(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor', 1.0),
(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor', 1.0),
(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor', 1.0),
(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor', 1.0),
(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor', 1.0),
(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor', 1.0),
(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor', 1.0),
(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor', 1.0),
(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor', 1.0),
(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor', 1.0),
(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor', 1.0),
(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor', 1.0),
(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor', 1.0),
(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor', 1.0),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor', 1.0),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor', 1.0),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor', 1.0),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor', 1.0),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor', 1.0),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor', 1.0),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor', 1.0),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor', 1.0),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor', 1.0),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica', 1.0),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica', 1.0),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica', 1.0),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica', 1.0),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica', 1.0),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica', 1.0),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica', 1.0),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica', 1.0),
(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica', 1.0),
(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica', 1.0),
(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica', 1.0),
(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica', 1.0),
(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica', 1.0),
(114,ARRAY[5.7,2.5,5.0,2.0],'Iris-virginica', 1.0),
(115,ARRAY[5.8,2.8,5.1,2.4],'Iris-virginica', 1.0),
(116,ARRAY[6.4,3.2,5.3,2.3],'Iris-virginica', 1.0),
(117,ARRAY[6.5,3.0,5.5,1.8],'Iris-virginica', 1.0),
(118,ARRAY[7.7,3.8,6.7,2.2],'Iris-virginica', 1.0),
(119,ARRAY[7.7,2.6,6.9,2.3],'Iris-virginica', 1.0),
(120,ARRAY[6.0,2.2,5.0,1.5],'Iris-virginica', 1.0),
(121,ARRAY[6.9,3.2,5.7,2.3],'Iris-virginica', 1.0),
(122,ARRAY[5.6,2.8,4.9,2.0],'Iris-virginica', 1.0),
(123,ARRAY[7.7,2.8,6.7,2.0],'Iris-virginica', 1.0),
(124,ARRAY[6.3,2.7,4.9,1.8],'Iris-virginica', 1.0),
(125,ARRAY[6.7,3.3,5.7,2.1],'Iris-virginica', 1.0),
(126,ARRAY[7.2,3.2,6.0,1.8],'Iris-virginica', 1.0),
(127,ARRAY[6.2,2.8,4.8,1.8],'Iris-virginica', 1.0),
(128,ARRAY[6.1,3.0,4.9,1.8],'Iris-virginica', 1.0),
(129,ARRAY[6.4,2.8,5.6,2.1],'Iris-virginica', 1.0),
(130,ARRAY[7.2,3.0,5.8,1.6],'Iris-virginica', 1.0),
(131,ARRAY[7.4,2.8,6.1,1.9],'Iris-virginica', 1.0),
(132,ARRAY[7.9,3.8,6.4,2.0],'Iris-virginica', 1.0),
(133,ARRAY[6.4,2.8,5.6,2.2],'Iris-virginica', 1.0),
(134,ARRAY[6.3,2.8,5.1,1.5],'Iris-virginica', 1.0),
(135,ARRAY[6.1,2.6,5.6,1.4],'Iris-virginica', 1.0),
(136,ARRAY[7.7,3.0,6.1,2.3],'Iris-virginica', 1.0),
(137,ARRAY[6.3,3.4,5.6,2.4],'Iris-virginica', 1.0),
(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica', 1.0),
(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica', 1.0),
(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica', 1.0),
(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica', 1.0),
(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica', 1.0),
(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica', 1.0),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica', 1.0),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica', 1.0),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica', 1.0),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica', 1.0),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica', 1.0),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica', 1.0),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica', 1.0);
DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;
-- Set seed so results are reproducible
SELECT setseed(0);
SELECT madlib.mlp_classification(
'temp1', -- Source table
'mlp_model', -- Destination table
'attributes', -- Input features
'class_text', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.003,
n_iterations=500,
tolerance=0', -- Optimizer params
'tanh', -- Activation function
'row_weight', -- Weight
FALSE, -- No warm start
FALSE -- Not verbose
);
{code}
ERROR: spiexceptions.UndefinedColumn: column "row_weight" does not exist
LINE 15: (row_weight)::DOUBLE PRECISION,
^
QUERY:
SELECT
array_to_string(ARRAY[Null], ',') AS
__madlib_temp_col_grp_key37112385_1614190822_33660952__,
NULL,
1 AS
__madlib_temp_col_grp_iteration8055158_1614190822_5448717__,
(
madlib.mlp_igd_step(
(__madlib_temp_ind_var_norm9652667_1614190822_1337450__)::DOUBLE PRECISION[],
(ARRAY[(__madlib_temp_dep_var_norm15181682_1614190822_42979282__) =
'Iris-setosa', (__madlib_temp_dep_var_norm15181682_1614190822_42979282__) =
'Iris-versicolor', (__madlib_temp_dep_var_norm15181682_1614190822_42979282__) =
'Iris-virginica']::INTEGER[])::DOUBLE PRECISION[],
__madlib_temp_rel_state63146625_1614190822_28739242__.__madlib_temp_col_grp_state22029204_1614190822_47058640__,
ARRAY[ 4,5,3 ]::DOUBLE PRECISION[],
(0.003)::FLOAT8,
2,
1,
(row_weight)::DOUBLE PRECISION,
(NULL::DOUBLE PRECISION[])::DOUBLE PRECISION[],
0,
0.9::FLOAT8,
True::boolean
)
) AS
__madlib_temp_col_grp_state22029204_1614190822_47058640__
FROM (
SELECT *,
array_to_string(ARRAY[Null], ',') AS
__madlib_temp_col_grp_key37112385_1614190822_33660952__
FROM __madlib_temp_tbl_data_scaled79575459_1614190822_3285917__
) AS _src
JOIN ( SELECT grp_key AS
__madlib_temp_col_grp_key37112385_1614190822_33660952__,state AS
__madlib_temp_col_grp_state22029204_1614190822_47058640__ FROM
madlib._gen_state($1, NULL, $2) ) AS
__madlib_temp_rel_state63146625_1614190822_28739242__
ON TRUE
JOIN ( SELECT unnest($3) AS
__madlib_temp_col_grp_key37112385_1614190822_33660952__, unnest($4) AS
__madlib_temp_col_n_tuples70500775_1614190822_4678022__ ) AS _rel_n_tuples
ON TRUE
CONTEXT: Traceback (most recent call last):
PL/Python function "mlp_classification", line 33, in <module>
grouping_col)
PL/Python function "mlp_classification", line 42, in wrapper
PL/Python function "mlp_classification", line 334, in mlp
PL/Python function "mlp_classification", line 576, in update
PL/Python function "mlp_classification"
{code}
{code}
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
