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/* ----------------------------------------------------------------------- *//**
*
* @file summary.sql_in
*
* @brief Summary function for descriptive statistics
* @date Mar 2013
*
*//* ------------------------------------------------------------------------*/
m4_include(`SQLCommon.m4')
/**
@addtogroup grp_summary
<div class="toc"><b>Contents</b>
<ul>
<li><a href="#usage">Summary Function Syntax</a></li>
<li><a href="#examples">Examples</a></li>
<li><a href="#notes">Notes</a></li>
<li><a href="#related">Related Topics</a></li>
</ul>
</div>
@brief Calculates general descriptive statistics for any data table.
The MADlib \b summary() function produces summary
statistics for any data table. The function invokes various methods from
the MADlib library to provide the data overview.
@anchor usage
@par Summary Function Syntax
The \b summary() function has the following syntax:
<pre class="syntax">
summary ( source_table,
output_table,
target_cols,
grouping_cols,
get_distinct,
get_quartiles,
ntile_array,
how_many_mfv,
get_estimates,
n_cols_per_run
)
</pre>
The \b summary() function returns a composite type containing three fields:
<table class="output">
<tr>
<th>output_table</th>
<td>TEXT. The name of the output table.</td>
</tr>
<tr>
<th>num_col_summarized</th>
<td>INTEGER. The number of columns from the source table that have been summarized.</td>
</tr>
<tr>
<th>duration</th>
<td>FLOAT8. The time taken (in seconds) to compute the summary.</td>
</tr>
</table>
\b Arguments
<DL class="arglist">
<dt>source_table</dt>
<dd>TEXT. Name of the table containing the input data.</dd>
<dt>output_table</dt>
<dd>TEXT. Name of the table for the output summary statistics.
This table contains the following columns:
<table class="output">
<tr>
<th>group_by</th>
<td>Group-by column name. NULL if none provided.</td>
</tr>
<tr>
<th>group_by_value</th>
<td>Value of the group-by column. NULL if there is no grouping.</td>
</tr>
<tr>
<th>target_column</th>
<td>Targeted column values for which summary is requested.</td>
</tr>
<tr>
<th>column_number</th>
<td>Physical column number for the target column, as described in \e pg_attribute</td> catalog.
</tr>
<tr>
<th>data_type</th>
<td>Data type of the target column. Standard GPDB type descriptors are displayed.</td>
</tr>
<tr>
<th>row_count</th>
<td>Number of rows for the target column.</td>
</tr>
<tr>
<th>distinct_values</th>
<td>Number of distinct values in the target column.
If the summary() function is called with the <em>get_estimates</em>
argument set to TRUE (default), then this is an estimated statistic based on the
Flajolet-Martin distinct count estimator. If the <em>get_estimates</em>
argument set to FALSE, will use PostgreSQL COUNT DISTINCT.</td>
</tr>
<tr>
<th>missing_values</th>
<td>Number of missing values in the target column.</td>
</tr>
<tr>
<th>blank_values</th>
<td>Number of blank values. Blanks are defined by this regular expression: \verbatim '^\w*$'\endverbatim</td>
</tr>
<tr>
<th>fraction_missing</th>
<td>Percentage of total rows that are missing, as a decimal value, e.g. 0.3.</td>
</tr>
<tr>
<th>fraction_blank</th>
<td>Percentage of total rows that are blank, as a decimal value, e.g. 0.3.</td>
</tr>
<tr>
<th>positive_values</th>
<td>Number of positive values in the target column if target is numeric, otherwise NULL.</td>
</tr>
<tr>
<th>negative_values</th>
<td>Number of negative values in the target column if target is numeric, otherwise NULL.</td>
</tr>
<tr>
<th>zero_values</th>
<td>Number of zero values in the target column if target is numeric, otherwise NULL.
Note that we are reporting exact equality to 0.0 here, so even if you have a
float value that is extremely small (say due to rounding), it will not be
reported as a zero value.</td>
</tr>
<tr>
<th>mean</th>
<td>Mean value of target column if target is numeric, otherwise NULL.</td>
</tr>
<tr>
<th>variance</th>
<td>Variance of target column if target is numeric, otherwise NULL.</td>
</tr>
<tr>
<th>confidence_interval</th>
<td>Confidence interval (95% using z-score) of the mean value for the target column
if target is numeric, otherwise NULL.
Presented as an array of two elements in the form {lower bound, upper bound}.</td>
</tr>
<tr>
<th>min</th>
<td>Minimum value of target column. For strings this is the length of the shortest string.</td>
</tr>
<tr>
<th>max</th>
<td>Maximum value of target column. For strings this is the length of the longest string.</td>
</tr>
<tr>
<th>first_quartile</th>
<td>First quartile (25th percentile), only for numeric columns.
(Unavailable for PostgreSQL 9.3 or lower.)</td>
</tr>
<tr>
<th>median</th>
<td>Median value of target column, if target is numeric, otherwise NULL.
(Unavailable for PostgreSQL 9.3 or lower.)</td>
</tr>
<tr>
<th>third_quartile</th>
<td>Third quartile (25th percentile), only for numeric columns.
(Unavailable for PostgreSQL 9.3 or lower.)</td>
</tr>
<tr>
<th>quantile_array</th>
<td>Percentile values corresponding to \e ntile_array.
(Unavailable for PostgreSQL 9.3 or lower.)</td>
</tr>
<tr>
<th>most_frequent_values</th>
<td>An array containing the most frequently occurring values. The \e
how_many_mfv argument determines the length of the array, which is 10 by
default. If the summary() function is called with the \e
get_estimates argument set to TRUE (default), the frequent values
computation is performed using a parallel aggregation method that is
faster, but in some cases may fail to detect the exact most frequent
values.</td>
</tr>
<tr>
<th>mfv_frequencies</th>
<td>Array containing the frequency count for each of the most frequent values. </td>
</tr>
</table></dd>
<dt>target_columns (optional)</dt>
<dd>TEXT, default NULL. A comma-separated list of columns to summarize.
If NULL, summaries are produced for all columns.</dd>
<dt>grouping_cols (optional)</dt>
<dd>TEXT, default: null. A comma-separated list of columns on which to
group results. If NULL, summaries are produced for the complete table.</dd>
@note Please note that summary statistics are calculated for each grouping
column independently. That is, grouping columns are not combined together
as in the regular PostgreSQL style GROUP BY directive. (This was done
to reduce long run time and huge output table size which would otherwise
result in the case of large input tables with a lot of grouping_cols and
target_cols specified.)
<dt>get_distinct (optional)</dt>
<dd>BOOLEAN, default TRUE. If true, distinct values are counted.
The method for computing distinct values depends on the setting of
the 'get_estimates' parameter below.</dd>
<dt>get_quartiles (optional)</dt>
<dd>BOOLEAN, default TRUE. If TRUE, quartiles are computed.</dd>
<dt>ntile_array (optional)</dt>
<dd>FLOAT8[], default NULL. An array of quantile values to compute.
If NULL, quantile values are not computed.</dd>
@note Quartile and quantile functions are not available in PostgreSQL 9.3 or
lower. If you are using PostgreSQL 9.3 or lower, the output table will not
contain these values, even if you set 'get_quartiles' = TRUE or
provide an array of quantile values for the parameter 'ntile_array'.
<dt>how_many_mfv (optional)</dt>
<dd>INTEGER, default: 10. The number of most-frequent-values to compute.
The method for computing MFV depends on the setting of
the 'get_estimates' parameter below.</dd>
<dt>get_estimates (optional)</dt>
<dd>BOOLEAN, default TRUE. If TRUE, estimated values are produced for
distinct values and most frequent values. If FALSE, exact values are
calculated which will take longer to run, with the impact depending on
data size.</dd>
<dt>n_cols_per_run (optional)</dt>
<dd>INTEGER, default: 15. The number of columns to collect summary statistics in
one pass of the data.
This parameter determines the number of passes through the data. For e.g.,
with a total of 40 columns to summarize and 'n_cols_per_run = 15', there will be
3 passes through the data, with each pass summarizing a maximum of 15 columns.
@note This parameter should be used with caution. Increasing this parameter could
decrease the total run time (if number of passes decreases), but will increase
the memory consumption during each run. Since PostgreSQL limits the memory available
for a single aggregate run, this increased memory consumption could result in an
out-of-memory termination error.
</dd>
</DL>
@anchor examples
@examp
-# View online help for the summary() function.
<pre class="example">
SELECT * FROM madlib.summary();
</pre>
-# Create an input data table using part of the well known
iris data set.
<pre class="example">
DROP TABLE IF EXISTS iris;
CREATE TABLE iris (id INT, sepal_length FLOAT, sepal_width FLOAT,
petal_length FLOAT, petal_width FLOAT,
class_name text);
INSERT INTO iris VALUES
(1,5.1,3.5,1.4,0.2,'Iris-setosa'),
(2,4.9,3.0,1.4,0.2,'Iris-setosa'),
(3,4.7,3.2,1.3,0.2,'Iris-setosa'),
(4,4.6,3.1,1.5,0.2,'Iris-setosa'),
(5,5.0,3.6,1.4,0.2,'Iris-setosa'),
(6,5.4,3.9,1.7,0.4,'Iris-setosa'),
(7,4.6,3.4,1.4,0.3,'Iris-setosa'),
(8,5.0,3.4,1.5,0.2,'Iris-setosa'),
(9,4.4,2.9,1.4,0.2,'Iris-setosa'),
(10,4.9,3.1,1.5,0.1,'Iris-setosa'),
(11,7.0,3.2,4.7,1.4,'Iris-versicolor'),
(12,6.4,3.2,4.5,1.5,'Iris-versicolor'),
(13,6.9,3.1,4.9,1.5,'Iris-versicolor'),
(14,5.5,2.3,4.0,1.3,'Iris-versicolor'),
(15,6.5,2.8,4.6,1.5,'Iris-versicolor'),
(16,5.7,2.8,4.5,1.3,'Iris-versicolor'),
(17,6.3,3.3,4.7,1.6,'Iris-versicolor'),
(18,4.9,2.4,3.3,1.0,'Iris-versicolor'),
(19,6.6,2.9,4.6,1.3,'Iris-versicolor'),
(20,5.2,2.7,3.9,1.4,'Iris-versicolor'),
(21,6.3,3.3,6.0,2.5,'Iris-virginica'),
(22,5.8,2.7,5.1,1.9,'Iris-virginica'),
(23,7.1,3.0,5.9,2.1,'Iris-virginica'),
(24,6.3,2.9,5.6,1.8,'Iris-virginica'),
(25,6.5,3.0,5.8,2.2,'Iris-virginica'),
(26,7.6,3.0,6.6,2.1,'Iris-virginica'),
(27,4.9,2.5,4.5,1.7,'Iris-virginica'),
(28,7.3,2.9,6.3,1.8,'Iris-virginica'),
(29,6.7,2.5,5.8,1.8,'Iris-virginica'),
(30,7.2,3.6,6.1,2.5,'Iris-virginica');
</pre>
-# Run the \b summary() function using all defaults.
<pre class="example">
DROP TABLE IF EXISTS iris_summary;
SELECT * FROM madlib.summary( 'iris', -- Source table
'iris_summary' -- Output table
);
</pre>
Result:
<pre class="result">
output_table | num_col_summarized | duration
--------------+--------------------+-------------------
iris_summary | 6 | 0.574938058853149
(1 row)
</pre>
View the summary data.
<pre class=example>
-- Turn on expanded display for readability.
\\x on
SELECT * FROM iris_summary;
</pre>
Result (partial):
<pre class="result">
...
&nbsp;-[ RECORD 2 ]--------+---------------------------------------------
group_by |
group_by_value |
target_column | sepal_length
column_number | 2
data_type | float8
row_count | 30
distinct_values | 22
missing_values | 0
blank_values |
fraction_missing | 0
fraction_blank |
positive_values | 30
negative_values | 0
zero_values | 0
mean | 5.84333333333333
variance | 0.929436781609188
confidence_interval | {5.49834423494374,6.18832243172292}
min | 4.4
max | 7.6
first_quartile | 4.925
median | 5.75
third_quartile | 6.575
most_frequent_values | {4.9,6.3,5,6.5,4.6,7.2,5.5,5.7,7.3,6.7}
mfv_frequencies | {4,3,2,2,2,1,1,1,1,1}
...
&nbsp;-[ RECORD 6 ]--------+---------------------------------------------
group_by |
group_by_value |
target_column | class_name
column_number | 6
data_type | text
row_count | 30
distinct_values | 3
missing_values | 0
blank_values | 0
fraction_missing | 0
fraction_blank | 0
positive_values |
negative_values |
zero_values |
mean |
variance |
confidence_interval |
min | 11
max | 15
first_quartile |
median |
third_quartile |
most_frequent_values | {Iris-setosa,Iris-versicolor,Iris-virginica}
mfv_frequencies | {10,10,10}
</pre>
Note that for the text column in record 6, some statistics are n/a,
and the min and max values represent the length of the shortest and
longest strings respectively.
-# Now group by the class of iris:
<pre class="example">
DROP TABLE IF EXISTS iris_summary;
SELECT * FROM madlib.summary( 'iris', -- Source table
'iris_summary', -- Output table
'sepal_length, sepal_width', -- Columns to summarize
'class_name' -- Grouping column
);
SELECT * FROM iris_summary;
</pre>
Result (partial):
<pre class="result">
&nbsp;-[ RECORD 1 ]--------+----------------------------------------
group_by | class_name
group_by_value | Iris-setosa
target_column | sepal_length
column_number | 2
data_type | float8
row_count | 10
distinct_values | 7
missing_values | 0
blank_values |
fraction_missing | 0
fraction_blank |
positive_values | 10
negative_values | 0
zero_values | 0
mean | 4.86
variance | 0.0848888888888875
confidence_interval | {4.67941507384182,5.04058492615818}
min | 4.4
max | 5.4
first_quartile | 4.625
median | 4.9
third_quartile | 5
most_frequent_values | {4.9,5,4.6,5.1,4.7,5.4,4.4}
mfv_frequencies | {2,2,2,1,1,1,1}
...
&nbsp;-[ RECORD 3 ]--------+----------------------------------------
group_by | class_name
group_by_value | Iris-versicolor
target_column | sepal_length
column_number | 2
data_type | float8
row_count | 10
distinct_values | 10
missing_values | 0
blank_values |
fraction_missing | 0
fraction_blank |
positive_values | 10
negative_values | 0
zero_values | 0
mean | 6.1
variance | 0.528888888888893
confidence_interval | {5.64924734548141,6.55075265451859}
min | 4.9
max | 7
first_quartile | 5.55
median | 6.35
third_quartile | 6.575
most_frequent_values | {6.9,5.5,6.5,5.7,6.3,4.9,6.6,5.2,7,6.4}
mfv_frequencies | {1,1,1,1,1,1,1,1,1,1}
...
</pre>
-# Trying some other parameters:
<pre class="example">
DROP TABLE IF EXISTS iris_summary;
SELECT * FROM madlib.summary( 'iris', -- Source table
'iris_summary', -- Output table
'sepal_length, sepal_width', -- Columns to summarize
NULL, -- No grouping
TRUE, -- Get distinct values
FALSE, -- Dont get quartiles
ARRAY[0.33, 0.66], -- Get ntiles
3, -- Number of MFV to compute
FALSE -- Get exact values
);
SELECT * FROM iris_summary;
</pre>
Result:
<pre class="result">
&nbsp;-[ RECORD 1 ]--------+------------------------------------
group_by |
group_by_value |
target_column | sepal_length
column_number | 2
data_type | float8
row_count | 30
distinct_values | 22
missing_values | 0
blank_values |
fraction_missing | 0
fraction_blank |
positive_values | 30
negative_values | 0
zero_values | 0
mean | 5.84333333333333
variance | 0.929436781609175
confidence_interval | {5.49834423494375,6.18832243172292}
min | 4.4
max | 7.6
quantile_array | {5.057,6.414}
most_frequent_values | {4.9,6.3,6.5}
mfv_frequencies | {4,3,2}
&nbsp;-[ RECORD 2 ]--------+------------------------------------
group_by |
group_by_value |
target_column | sepal_width
column_number | 3
data_type | float8
row_count | 30
distinct_values | 14
missing_values | 0
blank_values |
fraction_missing | 0
fraction_blank |
positive_values | 30
negative_values | 0
zero_values | 0
mean | 3.04
variance | 0.13903448275862
confidence_interval | {2.90656901047539,3.17343098952461}
min | 2.3
max | 3.9
quantile_array | {2.9,3.2}
most_frequent_values | {2.9,3,3.2}
mfv_frequencies | {4,4,3}
</pre>
@anchor notes
@par Notes
- Table names can be optionally schema qualified (current_schemas() would be
searched if a schema name is not provided) and table and column names
should follow case-sensitivity and quoting rules per the database.
(For instance, 'mytable' and 'MyTable' both resolve to the same entity, i.e. 'mytable'.
If mixed-case or multi-byte characters are desired for entity names then the
string should be double-quoted; in this case the input would be '"MyTable"').
- The <em>get_estimates</em> parameter controls computation for both distinct
count and most frequent values:
- If <em>get_estimates</em> is TRUE then the distinct value computation is
estimated using Flajolet-Martin. MFV is computed using a
fast method that does parallel aggregation in Greenplum Database at the expense
of missing or duplicating some of the most frequent values.
- If <em>get_estimates</em> is FALSE then the distinct values are computed
in a slower but exact method using PostgreSQL COUNT DISTINCT. MFV is computed using a
faithful implementation that preserves the approximation guarantees of
the Cormode/Muthukrishnan method (more information at \ref grp_mfvsketch).
@anchor related
@par Related Topics
File summary.sql_in documenting the \b summary() function
\ref grp_fmsketch <br/>
\ref grp_mfvsketch <br/>
\ref grp_countmin
*/
DROP TYPE IF EXISTS MADLIB_SCHEMA.summary_result CASCADE;
CREATE TYPE MADLIB_SCHEMA.summary_result AS
(
output_table TEXT,
num_col_summarized INT4,
duration FLOAT8
);
-----------------------------------------------------------------------
-- Main function for summary
-----------------------------------------------------------------------
/*
* @brief Compute a summary statistics on a table with optional grouping support
*
* @param source_table Name of source relation containing the data
* @param output_table Name of output table name to store the summary
* @param target_cols String with comma separated list of columns on which summary is desired
* @param grouping_cols String with comma separated list of columns on which to group the data by
* @param get_distinct Should distinct values count be included in result
* @param get_quartiles Should first, second (median), and third quartiles be included in result
* @param ntile_array Array of percentiles to compute
* @param how_many_mfv How many most frequent values to compute?
* @param get_estimates Should distinct counts be an estimated (faster) or exact count?
* @param n_cols_per_run Number of columns to use per run of summary
*
* @usage
*
* <pre> SELECT MADLIB_SCHEMA.summary (
* '<em>source_table</em>', '<em>output_table</em>',
* '<em>target_cols</em>', '<em>grouping_cols</em>',
* '<em>get_distinct</em>', '<em>get_quartiles</em>',
* '<em>ntile_array</em>', '<em>how_many_mfv</em>',
* '<em>get_estimates</em>', '<em>n_cols_per_run</em>'
* );
* SELECT * FROM '<em>output_table</em>'
* </pre>
*/
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT, -- source table name
output_table TEXT, -- output table name
target_cols TEXT, -- comma separated list of output cols
grouping_cols TEXT, -- comma separated names of grouping cols
get_distinct BOOLEAN, -- Are distinct values required
get_quartiles BOOLEAN, -- Are quartiles required
ntile_array FLOAT8[], -- Array of quantiles to compute
how_many_mfv INTEGER, -- How many most frequent values to compute?
get_estimates BOOLEAN, -- Should we produce exact or estimated
-- values for distinct computation
n_cols_per_run INTEGER -- Number of columns to use per run of summary
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
PythonFunctionBodyOnly(`summary', `summary')
with AOControl(False):
return summary.summary(
schema_madlib, source_table, output_table, target_cols, grouping_cols,
get_distinct, get_quartiles, ntile_array, how_many_mfv,
get_estimates, n_cols_per_run)
$$ LANGUAGE plpythonu VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-----------------------------------------------------------------------
--- Overloaded functions to support optional parameters
-----------------------------------------------------------------------
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT,
get_distinct BOOLEAN,
get_quartiles BOOLEAN,
ntile_array FLOAT8[],
how_many_mfv INTEGER,
get_estimates BOOLEAN
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, $5, $6, $7, $8, $9, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT,
get_distinct BOOLEAN,
get_quartiles BOOLEAN,
ntile_array FLOAT8[],
how_many_mfv INTEGER
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, $5, $6, $7, $8, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT,
get_distinct BOOLEAN,
get_quartiles BOOLEAN,
ntile_array FLOAT8[]
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, $5, $6, $7, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT,
get_distinct BOOLEAN,
get_quartiles BOOLEAN
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, $5, $6, NULL, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT,
get_distinct BOOLEAN
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, $5, True, NULL, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT,
grouping_cols TEXT
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, $4, True, True, NULL, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT,
target_cols TEXT
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, $3, NULL, True, True, NULL, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
CREATE OR REPLACE FUNCTION
MADLIB_SCHEMA.summary
(
source_table TEXT,
output_table TEXT
)
RETURNS MADLIB_SCHEMA.summary_result AS $$
SELECT MADLIB_SCHEMA.summary(
$1, $2, NULL, NULL, True, True, NULL, 10, True, 15)
$$ LANGUAGE sql VOLATILE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `MODIFIES SQL DATA', `');
-----------------------------------------------------------------------
-- Help functions
-----------------------------------------------------------------------
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.summary(
input_message TEXT
)
RETURNS TEXT AS $$
PythonFunctionBodyOnly(`summary', `summary')
with AOControl(False):
return summary.summary_help_message(schema_madlib, input_message)
$$ LANGUAGE plpythonu IMMUTABLE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.summary()
RETURNS TEXT AS $$
PythonFunctionBodyOnly(`summary', `summary')
with AOControl(False):
return summary.summary_help_message(schema_madlib, None)
$$ LANGUAGE plpythonu IMMUTABLE
m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');