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| <div class="title">Encoding Categorical Variables<div class="ingroups"><a class="el" href="group__grp__datatrans.html">Data Types and Transformations</a></div></div> </div> |
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| <div class="contents"> |
| <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> |
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| </div><p><a class="anchor" id="categorical"></a></p><dl class="section user"><dt>Coding Systems for Categorical Variables</dt><dd>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.</dd></dl> |
| <p>We currently support one-hot and dummy coding techniques.</p> |
| <p>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.</p> |
| <p>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.</p> |
| <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><p> <b>Arguments</b> </p><dl class="arglist"> |
| <dt>source_table </dt> |
| <dd><p class="startdd">VARCHAR. Name of the table containing the source categorical data to encode.</p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>output_table </dt> |
| <dd><p class="startdd">VARCHAR. Name of the result table.</p> |
| <dl class="section note"><dt>Note</dt><dd>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></dl> |
| </dd> |
| <dt>categorical_cols </dt> |
| <dd><p class="startdd">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. </p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>categorical_cols_to_exclude (optional) </dt> |
| <dd><p class="startdd">VARCHAR. Comma-separated string of column names to exclude from the categorical variables to encode. Applicable only if 'categorical_cols' = '*'. </p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>row_id (optional) </dt> |
| <dd><p class="startdd">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).</p> |
| <dl class="section note"><dt>Note</dt><dd>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></dl> |
| </dd> |
| <dt>top (optional) </dt> |
| <dd><p class="startdd">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. </p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>value_to_drop (optional) </dt> |
| <dd><p class="startdd">VARCHAR. Default: NULL.</p> |
| <ul> |
| <li>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.</li> |
| <li>Set to NULL for one-hot encoding (default)</li> |
| </ul> |
| <dl class="section note"><dt>Note</dt><dd>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></dl> |
| </dd> |
| <dt>encode_null (optional) </dt> |
| <dd><p class="startdd">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. </p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>output_type (optional) </dt> |
| <dd><p class="startdd">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.</p> |
| <p>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.</p> |
| <p>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'. </p> |
| <p class="enddd"></p> |
| </dd> |
| <dt>output_dictionary (optional) </dt> |
| <dd><p class="startdd">BOOLEAN. default: FALSE. This parameter is used to handle auto-generated column names that exceed the PostgreSQL limit of 63 bytes.</p> |
| <ul> |
| <li>If TRUE, column names will include numerical IDs and will create a dictionary table called 'output_table_dictionary' ('output_table' appended with '_dictionary').</li> |
| <li>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. </li> |
| </ul> |
| <p class="enddd"></p> |
| </dd> |
| <dt>distributed_by (optional) </dt> |
| <dd><p class="startdd">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.</p> |
| <ul> |
| <li>NULL: By default, the distribution policy of the source_table will be used.</li> |
| <li>Comma-separated column names: Column(s) to be used for the distribution key.</li> |
| <li>RANDOMLY: Use random distribution policy (only if there does not exist a column named 'randomly').</li> |
| </ul> |
| <p class="enddd"></p> |
| </dd> |
| </dl> |
| <p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl> |
| <ol type="1"> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| <li>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></li> |
| </ol> |
| <p><a class="anchor" id="literature"></a></p><dl class="section user"><dt>Literature</dt><dd></dd></dl> |
| <p><a class="anchor" id="svm-lit-1"></a>[1] <a href="https://en.wikipedia.org/wiki/Categorical_variable">https://en.wikipedia.org/wiki/Categorical_variable</a></p> |
| <p>[2] <a href="https://archive.ics.uci.edu/ml/datasets/Abalone">https://archive.ics.uci.edu/ml/datasets/Abalone</a> </p> |
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