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* @file encode_categorical.sql_in
*
* @brief SQL functions for encoding categorical variables to numerical values
* @date Dec 2016
*
* @sa Encodes categorical variables to numerical values
*
*//* ----------------------------------------------------------------------- */
m4_include(`SQLCommon.m4')
/**
@addtogroup grp_encode_categorical
<div class="toc"><b>Contents</b>
<ul>
<li><a href="#categorical">Coding Systems for Categorical Variables</a></li>
<li><a href="#examples">Examples</a></li>
<li><a href="#literature">Literature</a></li>
</ul>
</div>
@brief Functions to encode categorical variables to prepare data for
input into predictive algorithms.
@anchor categorical
@par Coding Systems for Categorical Variables
Categorical variables [1] require special attention in regression analysis because,
unlike dichotomous or continuous variables, they cannot be entered into the
regression equation just as they are. For example, if you have a variable
called race that is coded with 1=Hispanic, 2=Asian, 3=Black, 4=White, then
entering race in your regression will look at the linear effect of the race variable, which
is probably not what you intended. Instead, categorical variables like this need
to be coded into a series of indicator variables which can then be entered
into the regression model. There are a variety of coding systems that can
be used for coding categorical variables, including
one-hot, dummy, effects, orthogonal, and Helmert.
We currently support one-hot and dummy coding techniques.
Dummy coding is used when a researcher wants to compare other groups of the
predictor variable with one specific group of the predictor variable.
Often, the specific group to compare with is called the reference group.
One-hot encoding is similar to dummy coding except it builds indicator (0/1)
columns (cast as numeric) for each value of each category.
Only one of these columns could take on the value 1 for each row (data point).
There is no reference category for this function.
<pre class="syntax">
encode_categorical_variables (
source_table,
output_table,
categorical_cols,
categorical_cols_to_exclude, -- Optional
row_id, -- Optional
top, -- Optional
value_to_drop, -- Optional
encode_null, -- Optional
output_type, -- Optional
output_dictionary, -- Optional
distributed_by -- Optional
)
</pre>
\b Arguments
<dl class="arglist">
<dt>source_table</dt>
<dd>VARCHAR. Name of the table containing the source categorical data to encode.</dd>
<dt>output_table</dt>
<dd>VARCHAR. Name of the result table.
@note
If there are index columns in the 'source_table' specified by the parameter
'row_id' (see below), then the output table will contain only the index
columns 'row_id' and the encoded columns. If the parameter 'row_id' is
not specified, then all columns from the 'source_table', with the
exception of the original columns that have been encoded, will be
included in the 'output_table'.
</dd>
<dt>categorical_cols</dt>
<dd>VARCHAR. Comma-separated string of column names of categorical variables to encode.
Can also be '*' meaning all columns are to be encoded, except the ones
specified in 'categorical_cols_to_exclude' and 'row_id'. Please note that all Boolean,
integer and text columns are considered categorical columns and will be
encoded when ‘*’ is specified for this argument.
</dd>
<dt>categorical_cols_to_exclude (optional)</dt>
<dd>VARCHAR. Comma-separated string of column names to exclude from the
categorical variables to encode. Applicable only if 'categorical_cols' = '*'.
</dd>
<dt>row_id (optional)</dt>
<dd>VARCHAR. Comma-separated column name(s) corresponding to the primary key(s) of the
source table. This parameter determines the format of the 'output_table' as
described above. If 'categorical_cols' = '*', these columns will be
excluded from encoding (but will be included in the output table).
@note
If you want to see both the raw categorical variable and its encoded form
in the output_table, then include the categorical variable in the 'row_id' parameter.
However, this will not work if you specify '*' for the parameter 'categorical_cols',
because in this case 'row_id' columns will not be encoded at all.
</dd>
<dt>top (optional)</dt>
<dd>VARCHAR. default: NULL. If integer, encodes the top n values
by frequency. If float in the range (0.0, 1.0), encodes the specified fraction
of values by frequency (e.g., 0.1 means top 10%). Can be specified as a global
for all categorical columns, or as a dictionary with separate 'top' values for each
categorical variable. Set to NULL to encode all levels (values) for all categorical
columns.
</dd>
<dt>value_to_drop (optional)</dt>
<dd>VARCHAR. Default: NULL.
- For dummy coding, indicate the desired value (reference) to drop for each
categorical variable. Can be specified as a global for all categorical columns,
or a comma-separated string containing items of the form 'name=value', where
'name' is the column name and 'value' is the reference value to be dropped.
- Set to NULL for one-hot encoding (default)
@note
If you specify both 'value_to_drop' and 'top' parameters,
the 'value_to_drop' will be applied first (takes priority),
then 'top' will be applied to the remaining values.
</dd>
<dt>encode_null (optional)</dt>
<dd>BOOLEAN. default: FALSE. Whether NULL should be treated as one of the
values of the categorical variable. If TRUE, then an indicator
variable is created corresponding to the NULL value. If FALSE, then
all encoded values for that variable will be set to 0.
</dd>
<dt>output_type (optional)</dt>
<dd>VARCHAR. default: 'column'. This parameter controls the output format
of the indicator variables. If 'column', a column is created for each indicator
variable. PostgreSQL limits the number of columns in a table.
If the total number of indicator columns exceeds the limit, then make this
parameter either 'array' to combine the indicator columns into an array or
'svec' to cast the array output to <em>'madlib.svec'</em> type.
Since the array output for any single tuple would be sparse
(only one non-zero entry for each categorical column), the 'svec' output would
be most efficient for storage. The 'array' output is useful if the array is
used for post-processing, including concatenating with other non-categorical
features.
The order of the array is the same as specified in 'categorical_cols'.
A dictionary will be created when 'output_type' is 'array' or 'svec' to
define an index into the array. The dictionary table will be given the name
of the 'output_table' appended by '_dictionary'.
</dd>
<dt>output_dictionary (optional)</dt>
<dd>BOOLEAN. default: FALSE.
This parameter is used to handle auto-generated column names that exceed the
PostgreSQL limit of 63 bytes.
- If TRUE, column names will include numerical IDs and will create a dictionary
table called 'output_table_dictionary'
('output_table' appended with '_dictionary').
- If FALSE, will auto-generate column names in the
usual way unless the limit of 63 bytes will be exceeded. In this case, a
dictionary output file will be created and a message given to the user.
</dd>
<dt>distributed_by (optional)</dt>
<dd>VARCHAR. default: NULL. Columns to use for the distribution policy of
the output table. When NULL, either 'row_id' is used as distribution policy
(when provided), or else the distribution policy of 'source_table' will be used.
This argument does not apply to PostgreSQL platforms.
- NULL: By default, the distribution policy of the source_table will be used.
- Comma-separated column names: Column(s) to be used for the distribution key.
- RANDOMLY: Use random distribution policy (only if there does not exist a column named 'randomly').
</dd>
</dl>
@anchor examples
@examp
-# Use a subset of the abalone dataset [2]:
<pre class="example">
DROP TABLE IF EXISTS abalone;
CREATE TABLE abalone (
id serial,
sex character varying,
length double precision,
diameter double precision,
height double precision,
rings int
);
INSERT INTO abalone (sex, length, diameter, height, rings) VALUES
('M', 0.455, 0.365, 0.095, 15),
('M', 0.35, 0.265, 0.09, 7),
('F', 0.53, 0.42, 0.135, 9),
('M', 0.44, 0.365, 0.125, 10),
('I', 0.33, 0.255, 0.08, 7),
('I', 0.425, 0.3, 0.095, 8),
('F', 0.53, 0.415, 0.15, 20),
('F', 0.545, 0.425, 0.125, 16),
('M', 0.475, 0.37, 0.125, 9),
(NULL, 0.55, 0.44, 0.15, 19),
('F', 0.525, 0.38, 0.14, 14),
('M', 0.43, 0.35, 0.11, 10),
('M', 0.49, 0.38, 0.135, 11),
('F', 0.535, 0.405, 0.145, 10),
('F', 0.47, 0.355, 0.1, 10),
('M', 0.5, 0.4, 0.13, 12),
('I', 0.355, 0.28, 0.085, 7),
('F', 0.44, 0.34, 0.1, 10),
('M', 0.365, 0.295, 0.08, 7),
(NULL, 0.45, 0.32, 0.1, 9);
</pre>
-# Create new table with one-hot encoding.
The column 'sex' is replaced by three columns encoding the
values 'F', 'M' and 'I'. Null values are not encoded by default:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'sex' -- Categorical columns
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | length | diameter | height | rings | sex_F | sex_I | sex_M
----+--------+----------+--------+-------+-------+-------+-------
1 | 0.455 | 0.365 | 0.095 | 15 | 0 | 0 | 1
2 | 0.35 | 0.265 | 0.09 | 7 | 0 | 0 | 1
3 | 0.53 | 0.42 | 0.135 | 9 | 1 | 0 | 0
4 | 0.44 | 0.365 | 0.125 | 10 | 0 | 0 | 1
5 | 0.33 | 0.255 | 0.08 | 7 | 0 | 1 | 0
6 | 0.425 | 0.3 | 0.095 | 8 | 0 | 1 | 0
7 | 0.53 | 0.415 | 0.15 | 20 | 1 | 0 | 0
8 | 0.545 | 0.425 | 0.125 | 16 | 1 | 0 | 0
9 | 0.475 | 0.37 | 0.125 | 9 | 0 | 0 | 1
10 | 0.55 | 0.44 | 0.15 | 19 | 0 | 0 | 0
11 | 0.525 | 0.38 | 0.14 | 14 | 1 | 0 | 0
12 | 0.43 | 0.35 | 0.11 | 10 | 0 | 0 | 1
13 | 0.49 | 0.38 | 0.135 | 11 | 0 | 0 | 1
14 | 0.535 | 0.405 | 0.145 | 10 | 1 | 0 | 0
15 | 0.47 | 0.355 | 0.1 | 10 | 1 | 0 | 0
16 | 0.5 | 0.4 | 0.13 | 12 | 0 | 0 | 1
17 | 0.355 | 0.28 | 0.085 | 7 | 0 | 1 | 0
18 | 0.44 | 0.34 | 0.1 | 10 | 1 | 0 | 0
19 | 0.365 | 0.295 | 0.08 | 7 | 0 | 0 | 1
20 | 0.45 | 0.32 | 0.1 | 9 | 0 | 0 | 0
(20 rows)
</pre>
-# Now include NULL values in encoding (note the additional column 'sex_null'):
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'sex', -- Categorical columns
NULL, -- Categorical columns to exclude
NULL, -- Index columns
NULL, -- Top values
NULL, -- Value to drop for dummy encoding
TRUE -- Encode nulls
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | length | diameter | height | rings | sex_F | sex_I | sex_M | sex_null
----+--------+----------+--------+-------+-------+-------+-------+----------
1 | 0.455 | 0.365 | 0.095 | 15 | 0 | 0 | 1 | 0
2 | 0.35 | 0.265 | 0.09 | 7 | 0 | 0 | 1 | 0
3 | 0.53 | 0.42 | 0.135 | 9 | 1 | 0 | 0 | 0
4 | 0.44 | 0.365 | 0.125 | 10 | 0 | 0 | 1 | 0
5 | 0.33 | 0.255 | 0.08 | 7 | 0 | 1 | 0 | 0
6 | 0.425 | 0.3 | 0.095 | 8 | 0 | 1 | 0 | 0
7 | 0.53 | 0.415 | 0.15 | 20 | 1 | 0 | 0 | 0
8 | 0.545 | 0.425 | 0.125 | 16 | 1 | 0 | 0 | 0
9 | 0.475 | 0.37 | 0.125 | 9 | 0 | 0 | 1 | 0
10 | 0.55 | 0.44 | 0.15 | 19 | 0 | 0 | 0 | 1
11 | 0.525 | 0.38 | 0.14 | 14 | 1 | 0 | 0 | 0
12 | 0.43 | 0.35 | 0.11 | 10 | 0 | 0 | 1 | 0
13 | 0.49 | 0.38 | 0.135 | 11 | 0 | 0 | 1 | 0
14 | 0.535 | 0.405 | 0.145 | 10 | 1 | 0 | 0 | 0
15 | 0.47 | 0.355 | 0.1 | 10 | 1 | 0 | 0 | 0
16 | 0.5 | 0.4 | 0.13 | 12 | 0 | 0 | 1 | 0
17 | 0.355 | 0.28 | 0.085 | 7 | 0 | 1 | 0 | 0
18 | 0.44 | 0.34 | 0.1 | 10 | 1 | 0 | 0 | 0
19 | 0.365 | 0.295 | 0.08 | 7 | 0 | 0 | 1 | 0
20 | 0.45 | 0.32 | 0.1 | 9 | 0 | 0 | 0 | 1
(20 rows)
</pre>
-# Encode all categorical variables in the source table. Also, specify the
column 'id' as the index (primary key) which changes the output table
to include only the index and the encoded variables:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
NULL, -- Categorical columns to exclude
'id' -- Index columns
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex_F | sex_I | sex_M | rings_7 | rings_8 | rings_9 | rings_10 | rings_11 | rings_12 | rings_14 | rings_15 | rings_16 | rings_19 | rings_20
----+-------+-------+-------+---------+---------+---------+----------+----------+----------+----------+----------+----------+----------+----------
1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
4 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
5 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
6 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
9 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
11 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
12 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
13 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0
14 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
15 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
16 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
17 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
18 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
19 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
20 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
(20 rows)
</pre>
-# Now let's encode only the top values and group others into a
miscellaneous bucket column. Top values can be global across all
columns or specified by column. As an example of the latter, here
are the top 2 'sex' values and the top 50% of 'rings' values:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
NULL, -- Categorical columns to exclude
'id', -- Index columns
'sex=2, rings=0.5' -- Top values
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex_M | sex_F | sex__misc__ | rings_10 | rings_7 | rings_9 | rings__misc__
----+-------+-------+-------------+----------+---------+---------+---------------
1 | 1 | 0 | 0 | 0 | 0 | 0 | 1
2 | 1 | 0 | 0 | 0 | 1 | 0 | 0
3 | 0 | 1 | 0 | 0 | 0 | 1 | 0
4 | 1 | 0 | 0 | 1 | 0 | 0 | 0
5 | 0 | 0 | 1 | 0 | 1 | 0 | 0
6 | 0 | 0 | 1 | 0 | 0 | 0 | 1
7 | 0 | 1 | 0 | 0 | 0 | 0 | 1
8 | 0 | 1 | 0 | 0 | 0 | 0 | 1
9 | 1 | 0 | 0 | 0 | 0 | 1 | 0
10 | 0 | 0 | 0 | 0 | 0 | 0 | 1
11 | 0 | 1 | 0 | 0 | 0 | 0 | 1
12 | 1 | 0 | 0 | 1 | 0 | 0 | 0
13 | 1 | 0 | 0 | 0 | 0 | 0 | 1
14 | 0 | 1 | 0 | 1 | 0 | 0 | 0
15 | 0 | 1 | 0 | 1 | 0 | 0 | 0
16 | 1 | 0 | 0 | 0 | 0 | 0 | 1
17 | 0 | 0 | 1 | 0 | 1 | 0 | 0
18 | 0 | 1 | 0 | 1 | 0 | 0 | 0
19 | 1 | 0 | 0 | 0 | 1 | 0 | 0
20 | 0 | 0 | 0 | 0 | 0 | 1 | 0
(20 rows)
</pre>
-# If you want to see both the raw categorical variable and its
encoded form in the output_table, then include the categorical
variable(s) in the index parameter. (Remember that this will
not work if you specify '*' for the parameter 'categorical_cols',
because in this case 'row_id' columns will not be encoded at all.)
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'sex, rings', -- Categorical columns
NULL, -- Categorical columns to exclude
'id, sex, rings' -- Index columns
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex | rings | sex_F | sex_I | sex_M | rings_7 | rings_8 | rings_9 | rings_10 | rings_11 | rings_12 | rings_14 | rings_15 | rings_16 | rings_19 | rings_20
----+-----+-------+-------+-------+-------+---------+---------+---------+----------+----------+----------+----------+----------+----------+----------+----------
1 | M | 15 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
2 | M | 7 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
3 | F | 9 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
4 | M | 10 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
5 | I | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
6 | I | 8 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
7 | F | 20 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
8 | F | 16 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
9 | M | 9 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
10 | | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
11 | F | 14 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
12 | M | 10 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
13 | M | 11 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0
14 | F | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
15 | F | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
16 | M | 12 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
17 | I | 7 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
18 | F | 10 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
19 | M | 7 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
20 | | 9 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
(20 rows)
</pre>
-# For dummy encoding, let's make the 'I' value from the 'sex' variable as the reference.
Here we use the 'value_to_drop' parameter:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
'rings', -- Categorical columns to exclude
'id', -- Index columns
NULL, -- Top value
'sex=I' -- Value to drop for dummy encoding
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex_F | sex_M
----+-------+-------
1 | 0 | 1
2 | 0 | 1
3 | 1 | 0
4 | 0 | 1
5 | 0 | 0
6 | 0 | 0
7 | 1 | 0
8 | 1 | 0
9 | 0 | 1
10 | 0 | 0
11 | 1 | 0
12 | 0 | 1
13 | 0 | 1
14 | 1 | 0
15 | 1 | 0
16 | 0 | 1
17 | 0 | 0
18 | 1 | 0
19 | 0 | 1
20 | 0 | 0
(20 rows)
</pre>
-# Create an array output for the two categorical variables in the source table:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
NULL, -- Categorical columns to exclude
'id', -- Index columns
NULL, -- Top values
NULL, -- Value to drop for dummy encoding
NULL, -- Encode nulls
'array' -- Array output type
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | __encoded_variables__
----+-------------------------------
1 | {0,0,1,0,0,0,0,0,0,0,1,0,0,0}
2 | {0,0,1,1,0,0,0,0,0,0,0,0,0,0}
3 | {1,0,0,0,0,1,0,0,0,0,0,0,0,0}
4 | {0,0,1,0,0,0,1,0,0,0,0,0,0,0}
5 | {0,1,0,1,0,0,0,0,0,0,0,0,0,0}
6 | {0,1,0,0,1,0,0,0,0,0,0,0,0,0}
7 | {1,0,0,0,0,0,0,0,0,0,0,0,0,1}
8 | {1,0,0,0,0,0,0,0,0,0,0,1,0,0}
9 | {0,0,1,0,0,1,0,0,0,0,0,0,0,0}
10 | {0,0,0,0,0,0,0,0,0,0,0,0,1,0}
11 | {1,0,0,0,0,0,0,0,0,1,0,0,0,0}
12 | {0,0,1,0,0,0,1,0,0,0,0,0,0,0}
13 | {0,0,1,0,0,0,0,1,0,0,0,0,0,0}
14 | {1,0,0,0,0,0,1,0,0,0,0,0,0,0}
15 | {1,0,0,0,0,0,1,0,0,0,0,0,0,0}
16 | {0,0,1,0,0,0,0,0,1,0,0,0,0,0}
17 | {0,1,0,1,0,0,0,0,0,0,0,0,0,0}
18 | {1,0,0,0,0,0,1,0,0,0,0,0,0,0}
19 | {0,0,1,1,0,0,0,0,0,0,0,0,0,0}
20 | {0,0,0,0,0,1,0,0,0,0,0,0,0,0}
(20 rows)
</pre>
View the dictionary table that gives the index into the array:
<pre class="example">
SELECT * FROM abalone_out_dictionary;
</pre>
<pre class="result">
encoded_column_name | index | variable | value
-----------------------+-------+----------+-------
__encoded_variables__ | 1 | sex | F
__encoded_variables__ | 2 | sex | I
__encoded_variables__ | 3 | sex | M
__encoded_variables__ | 4 | rings | 7
__encoded_variables__ | 5 | rings | 8
__encoded_variables__ | 6 | rings | 9
__encoded_variables__ | 7 | rings | 10
__encoded_variables__ | 8 | rings | 11
__encoded_variables__ | 9 | rings | 12
__encoded_variables__ | 10 | rings | 14
__encoded_variables__ | 11 | rings | 15
__encoded_variables__ | 12 | rings | 16
__encoded_variables__ | 13 | rings | 19
__encoded_variables__ | 14 | rings | 20
(14 rows)
</pre>
-# Create a dictionary output:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
NULL, -- Categorical columns to exclude
'id', -- Index columns
NULL, -- Top values
NULL, -- Value to drop for dummy encoding
NULL, -- Encode nulls
NULL, -- Output type
TRUE -- Dictionary output
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex_1 | sex_2 | sex_3 | rings_1 | rings_2 | rings_3 | rings_4 | rings_5 | rings_6 | rings_7 | rings_8 | rings_9 | rings_10 | rings_11
----+-------+-------+-------+---------+---------+---------+---------+---------+---------+---------+---------+---------+----------+----------
1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
2 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
3 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
4 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
5 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
6 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
9 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
11 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
12 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
13 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0
14 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
15 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
16 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
17 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
18 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
19 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
20 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
(20 rows)
</pre>
View the dictionary table that defines the numerical columns in the output table:
<pre class="example">
SELECT * FROM abalone_out_dictionary ORDER BY encoded_column_name;
</pre>
<pre class="result">
encoded_column_name | index | variable | value
---------------------+-------+----------+-------
"rings_1" | 1 | rings | 7
"rings_10" | 10 | rings | 19
"rings_11" | 11 | rings | 20
"rings_2" | 2 | rings | 8
"rings_3" | 3 | rings | 9
"rings_4" | 4 | rings | 10
"rings_5" | 5 | rings | 11
"rings_6" | 6 | rings | 12
"rings_7" | 7 | rings | 14
"rings_8" | 8 | rings | 15
"rings_9" | 9 | rings | 16
"sex_1" | 1 | sex | F
"sex_2" | 2 | sex | I
"sex_3" | 3 | sex | M
(14 rows)
</pre>
-# We can chose from various distribution policies of the output table, for examply RANDOMLY:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'*', -- Categorical columns
NULL, -- Categorical columns to exclude
'id', -- Index columns
NULL, -- Top values
NULL, -- Value to drop for dummy encoding
NULL, -- Encode nulls
NULL, -- Output type
NULL, -- Dictionary output
'RANDOMLY' -- Distribution policy
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex_F | sex_I | sex_M | rings_10 | rings_11 | rings_12 | rings_14 | rings_15 | rings_16 | rings_19 | rings_20 | rings_7 | rings_8 | rings_9
----+-------+-------+-------+----------+----------+----------+----------+----------+----------+----------+----------+---------+---------+---------
1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0
2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
4 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
6 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
9 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
11 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
12 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
13 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
14 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
15 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
16 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
17 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
18 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
19 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
(20 rows)
</pre>
-# If you have a reason to encode FLOAT variables, you can cast them as TEXT
in the following way within the function call:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'height::TEXT' -- Categorical columns
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex | length | diameter | height | rings | height::TEXT_0.08 | height::TEXT_0.085 | height::TEXT_0.09 | height::TEXT_0.095 | height::TEXT_0.1 | height::TEXT_0.11 | height::TEXT_0.125 | height::TEXT_0.13 | height::TEXT_0.135 | height::TEXT_0.14 | height::TEXT_0.145 | height::TEXT_0.15
----+-----+--------+----------+--------+-------+-------------------+--------------------+-------------------+--------------------+------------------+-------------------+--------------------+-------------------+--------------------+-------------------+--------------------+-------------------
1 | M | 0.455 | 0.365 | 0.095 | 15 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
2 | M | 0.35 | 0.265 | 0.09 | 7 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
3 | F | 0.53 | 0.42 | 0.135 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
4 | M | 0.44 | 0.365 | 0.125 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
5 | I | 0.33 | 0.255 | 0.08 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
6 | I | 0.425 | 0.3 | 0.095 | 8 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
7 | F | 0.53 | 0.415 | 0.15 | 20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
8 | F | 0.545 | 0.425 | 0.125 | 16 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
9 | M | 0.475 | 0.37 | 0.125 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0
10 | | 0.55 | 0.44 | 0.15 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1
11 | F | 0.525 | 0.38 | 0.14 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0
12 | M | 0.43 | 0.35 | 0.11 | 10 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0
13 | M | 0.49 | 0.38 | 0.135 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0
14 | F | 0.535 | 0.405 | 0.145 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0
15 | F | 0.47 | 0.355 | 0.1 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
16 | M | 0.5 | 0.4 | 0.13 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0
17 | I | 0.355 | 0.28 | 0.085 | 7 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
18 | F | 0.44 | 0.34 | 0.1 | 10 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
19 | M | 0.365 | 0.295 | 0.08 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
20 | | 0.45 | 0.32 | 0.1 | 9 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0
(20 rows)
</pre>
-# You can also use a logical expression in the categorical columns, which will
be passed as boolean, and in the output table there will be two columns with
name logical_expression_true and logical_expression_false:
<pre class="example">
DROP TABLE IF EXISTS abalone_out, abalone_out_dictionary;
SELECT madlib.encode_categorical_variables (
'abalone', -- Source table
'abalone_out', -- Output table
'height>.10' -- Categorical columns
);
SELECT * FROM abalone_out ORDER BY id;
</pre>
<pre class="result">
id | sex | length | diameter | height | rings | height>.10_false | height>.10_true
----+-----+--------+----------+--------+-------+------------------+-----------------
1 | M | 0.455 | 0.365 | 0.095 | 15 | 1 | 0
2 | M | 0.35 | 0.265 | 0.09 | 7 | 1 | 0
3 | F | 0.53 | 0.42 | 0.135 | 9 | 0 | 1
4 | M | 0.44 | 0.365 | 0.125 | 10 | 0 | 1
5 | I | 0.33 | 0.255 | 0.08 | 7 | 1 | 0
6 | I | 0.425 | 0.3 | 0.095 | 8 | 1 | 0
7 | F | 0.53 | 0.415 | 0.15 | 20 | 0 | 1
8 | F | 0.545 | 0.425 | 0.125 | 16 | 0 | 1
9 | M | 0.475 | 0.37 | 0.125 | 9 | 0 | 1
10 | | 0.55 | 0.44 | 0.15 | 19 | 0 | 1
11 | F | 0.525 | 0.38 | 0.14 | 14 | 0 | 1
12 | M | 0.43 | 0.35 | 0.11 | 10 | 0 | 1
13 | M | 0.49 | 0.38 | 0.135 | 11 | 0 | 1
14 | F | 0.535 | 0.405 | 0.145 | 10 | 0 | 1
15 | F | 0.47 | 0.355 | 0.1 | 10 | 1 | 0
16 | M | 0.5 | 0.4 | 0.13 | 12 | 0 | 1
17 | I | 0.355 | 0.28 | 0.085 | 7 | 1 | 0
18 | F | 0.44 | 0.34 | 0.1 | 10 | 1 | 0
19 | M | 0.365 | 0.295 | 0.08 | 7 | 1 | 0
20 | | 0.45 | 0.32 | 0.1 | 9 | 1 | 0
(20 rows)
</pre>
@anchor literature
@literature
@anchor svm-lit-1
[1] https://en.wikipedia.org/wiki/Categorical_variable
[2] https://archive.ics.uci.edu/ml/datasets/Abalone
*/
-------------------------------------------------------------------------
/**
* @brief Encode categorical columns using either one-hot encoding or dummy coding
*
* @param source_table Name of table containing categorical variable
* @param output_table Name of table to output dummy variables
* @param categorical_cols Comma-separated list of column names to dummy code (can be '*')
* @param categorical_cols_to_exclude Comma-separated list of column names to exclude (if categorical_cols = '*')
* @param row_id Columns from source table to index output table
* @param top Parameter to include only top values of a categorical variable
* @param value_to_drop Parameter to set reference column in dummy coding
* @param encode_null Boolean to determine the behavior for rows with NULL value
* @param output_type Parameter to set output data type: 'column', 'array' or 'svec'
* @param output_dictionary Boolean to simplify column naming and with a separate
* mapping table to actual values
* @param distributed_by Comma-separated list of column names to use for distribution of output
*
* @return Void
*
*/
-- Create the below function for PostgreSQL but ensure that distributed_by is a no-op
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR,
value_to_drop VARCHAR,
encode_null BOOLEAN,
output_type VARCHAR,
output_dictionary BOOLEAN,
distributed_by VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR,
value_to_drop VARCHAR,
encode_null BOOLEAN,
output_type VARCHAR,
output_dictionary BOOLEAN
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-- Overloaded functions --------------------------------------------------------
-- Default values are set by underlying Python function
--------------------------------------------------------------------------------
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR,
value_to_drop VARCHAR,
encode_null BOOLEAN,
output_type VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR,
value_to_drop VARCHAR,
encode_null BOOLEAN
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR,
value_to_drop VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR,
top VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR,
row_id VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR,
categorical_cols_to_exclude VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
source_table VARCHAR,
output_table VARCHAR,
categorical_cols VARCHAR
) RETURNS VOID AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_variables)
$$ LANGUAGE plpythonu
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-- Online help -----------------------------------------------------------------
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables(
message VARCHAR
) RETURNS VARCHAR AS $$
PythonFunction(utilities, encode_categorical, encode_categorical_help)
$$ LANGUAGE plpythonu IMMUTABLE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
--------------------------------------------------------------------------------
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.encode_categorical_variables()
RETURNS VARCHAR AS $$
SELECT MADLIB_SCHEMA.encode_categorical_variables('');
$$ LANGUAGE sql IMMUTABLE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
--------------------------------------------------------------------------------