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 <div class="header">
   <div class="headertitle">
 <div class="title">Term Frequency<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> &raquo; <a class="el" href="group__grp__text__analysis.html">Text Analysis</a></div></div>  </div>
 </div><!--header-->
 <div class="contents">
 <div class="toc"><b>Contents</b> </p><ul>
 <li>
 <a href="#term_frequency">Term Frequency</a> </li>
 <li>
 <a href="#examples">Examples</a> </li>
 <li>
 <a href="#rel;ated">Related Topics</a> </li>
 </ul>
 </div><p><a class="anchor" id="term_frequency"></a></p><dl class="section user"><dt>Term frequency</dt><dd>Term frequency <code>tf(t,d)</code> is to the raw frequency of a word/term in a document, i.e. the number of times that word/term <code>t</code> occurs in document <code>d</code>. For this function, 'word' and 'term' are used interchangeably. <b>Note:</b> the term frequency is not normalized by the document length. <pre class="syntax">
     term_frequency(input_table,
                    doc_id_col,
                    word_col,
                    output_table,
                    compute_vocab)
 </pre></dd></dl>
 <p><b>Arguments:</b> </p><dl class="arglist">
 <dt>input_table </dt>
 <dd><p class="startdd">TEXT. The name of the table storing the documents. Each row is in the form &lt;doc_id, word_vector&gt; where <code>doc_id</code> is an id, unique to each document, and <code>word_vector</code> is a text array containing the words in the document. The <code>word_vector</code> should contain multiple entries of a word if the document contains multiple occurrence of that word. </p>
 <p class="enddd"></p>
 </dd>
 <dt>id_col </dt>
 <dd><p class="startdd">TEXT. The name of the column containing the document id. </p>
 <p class="enddd"></p>
 </dd>
 <dt>word_col </dt>
 <dd><p class="startdd">TEXT. The name of the column containing the vector of words/terms in the document. This column should of type that can be cast to TEXT[].</p>
 <p class="enddd"></p>
 </dd>
 <dt>output_table </dt>
 <dd><p class="startdd">TEXT. The name of the table to store the term frequency output. The output table contains the following columns:</p><ul>
 <li><code>id_col:</code> This the document id column (same as the one provided as input).</li>
 <li><code>word:</code> A word/term present in a document. This is either the original word present in <code>word_col</code> or an id representing the word (depending on the value of compute_vocab below).</li>
 <li><code>count:</code> The number of times this word is found in the document. </li>
 </ul>
 <p class="enddd"></p>
 </dd>
 <dt>compute_vocab </dt>
 <dd>BOOLEAN. (Optional, Default=FALSE) Flag to indicate if a vocabulary is to be created. If TRUE, an additional output table is created containing the vocabulary of all words, with an id assigned to each word. The table is called <em>output_table</em>_vocabulary (suffix added to the <em>output_table</em> name) and contains the following columns:<ul>
 <li><code>wordid:</code> An id assignment for each word</li>
 <li><code>word:</code> The word/term  </li>
 </ul>
 </dd>
 </dl>
 <p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
 <ol type="1">
 <li>Prepare datasets with some example documents <pre class="example">
 DROP TABLE IF EXISTS documents;
 CREATE TABLE documents(docid INTEGER, doc_contents TEXT);
 INSERT INTO documents VALUES
 (1, 'I like to eat broccoli and banana. I ate a banana and spinach smoothie for breakfast.'),
 (2, 'Chinchillas and kittens are cute.'),
 (3, 'My sister adopted two kittens yesterday'),
 (4, 'Look at this cute hamster munching on a piece of broccoli');
 </pre></li>
 <li>Add a new column containing the words (lower-cased) in a text array <pre class="example">
 ALTER TABLE documents ADD COLUMN words TEXT[];
 UPDATE documents SET words = regexp_split_to_array(lower(doc_contents), E'[\\s+\\.]');
 </pre></li>
 <li>Compute the frequency of each word in each document <pre class="example">
 DROP TABLE IF EXISTS documents_tf;
 SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf');
 SELECT * FROM documents_tf order by docid;
 </pre> <pre class="result">
  docid |    word    | count
 -------+------------+-------
      1 | ate        |     1
      1 | like       |     1
      1 | breakfast  |     1
      1 | to         |     1
      1 | broccoli   |     1
      1 | spinach    |     1
      1 | i          |     2
      1 | and        |     2
      1 | a          |     1
      1 |            |     2
      1 | smoothie   |     1
      1 | eat        |     1
      1 | banana     |     2
      1 | for        |     1
      2 | cute       |     1
      2 | are        |     1
      2 | kitten     |     1
      2 | and        |     1
      2 | chinchilla |     1
      3 | kitten     |     1
      3 | my         |     1
      3 | a          |     1
      3 | sister     |     1
      3 | adopted    |     1
      3 | yesterday  |     1
      4 | at         |     1
      4 | of         |     1
      4 | piece      |     1
      4 | this       |     1
      4 | a          |     1
      4 | broccoli   |     1
      4 | hamster    |     1
      4 | munching   |     1
      4 | cute       |     1
      4 | look       |     1
 (35 rows)
 </pre></li>
 <li>We also can create a vocabulary of the words and store a wordid in the output table instead of the actual word. <pre class="example">
 DROP TABLE IF EXISTS documents_tf;
 DROP TABLE IF EXISTS documents_tf_vocabulary;
 SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf', TRUE);
 -- Output with wordid instead of the actual words
 SELECT * FROM documents_tf order by docid;
 </pre>  <pre class="result">
  docid | wordid | count
 -------+--------+-------
      1 |      0 |     2
      1 |      1 |     1
      1 |      3 |     2
      1 |      6 |     1
      1 |      7 |     2
      1 |      8 |     1
      1 |      9 |     1
      1 |     12 |     1
      1 |     13 |     1
      1 |     15 |     2
      1 |     17 |     1
      1 |     24 |     1
      1 |     25 |     1
      1 |     27 |     1
      2 |     16 |     1
      2 |      3 |     1
      2 |      4 |     1
      2 |     10 |     1
      2 |     11 |     1
      3 |      1 |     1
      3 |     16 |     1
      3 |     28 |     1
      3 |     23 |     1
      3 |      2 |     1
      3 |     20 |     1
      4 |      9 |     1
      4 |     11 |     1
      4 |     22 |     1
      4 |     14 |     1
      4 |     26 |     1
      4 |      1 |     1
      4 |      5 |     1
      4 |     18 |     1
      4 |     19 |     1
      4 |     21 |     1
 (35 rows)
 </pre>  <pre class="example">
 -- Vocabulary
 SELECT * FROM documents_tf_vocabulary order by wordid;
 </pre> <pre class="result">
  wordid |    word
 --------+------------
       0 |
       1 | a
       2 | adopted
       3 | and
       4 | are
       5 | at
       6 | ate
       7 | banana
       8 | breakfast
       9 | broccoli
      10 | chinchilla
      11 | cute
      12 | eat
      13 | for
      14 | hamster
      15 | i
      16 | kitten
      17 | like
      18 | look
      19 | munching
      20 | my
      21 | of
      22 | piece
      23 | sister
      24 | smoothie
      25 | spinach
      26 | this
      27 | to
      28 | yesterday
 (29 rows)
 </pre></li>
 </ol>
 <p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
 <p>File <a class="el" href="text__utilities_8sql__in.html" title="SQL functions for carrying out routine text operations. ">text_utilities.sql_in</a> documenting the SQL functions. File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the utility functions for DB administration. </p>
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	<div class="header">
	<div class="headertitle">
	<div class="title">Term Frequency<div class="ingroups"><a class="el" href="group__grp__utility__functions.html">Utility Functions</a> » <a class="el" href="group__grp__text__analysis.html">Text Analysis</a></div></div> </div>
	</div><!--header-->
	<div class="contents">
	<div class="toc"><b>Contents</b> </p><ul>
	<li>
	<a href="#term_frequency">Term Frequency</a> </li>
	<li>
	<a href="#examples">Examples</a> </li>
	<li>
	<a href="#rel;ated">Related Topics</a> </li>
	</ul>
	</div><p><a class="anchor" id="term_frequency"></a></p><dl class="section user"><dt>Term frequency</dt><dd>Term frequency <code>tf(t,d)</code> is to the raw frequency of a word/term in a document, i.e. the number of times that word/term <code>t</code> occurs in document <code>d</code>. For this function, 'word' and 'term' are used interchangeably. <b>Note:</b> the term frequency is not normalized by the document length. <pre class="syntax">
	term_frequency(input_table,
	doc_id_col,
	word_col,
	output_table,
	compute_vocab)
	</pre></dd></dl>
	<p><b>Arguments:</b> </p><dl class="arglist">
	<dt>input_table </dt>
	<dd><p class="startdd">TEXT. The name of the table storing the documents. Each row is in the form <doc_id, word_vector> where <code>doc_id</code> is an id, unique to each document, and <code>word_vector</code> is a text array containing the words in the document. The <code>word_vector</code> should contain multiple entries of a word if the document contains multiple occurrence of that word. </p>
	<p class="enddd"></p>
	</dd>
	<dt>id_col </dt>
	<dd><p class="startdd">TEXT. The name of the column containing the document id. </p>
	<p class="enddd"></p>
	</dd>
	<dt>word_col </dt>
	<dd><p class="startdd">TEXT. The name of the column containing the vector of words/terms in the document. This column should of type that can be cast to TEXT[].</p>
	<p class="enddd"></p>
	</dd>
	<dt>output_table </dt>
	<dd><p class="startdd">TEXT. The name of the table to store the term frequency output. The output table contains the following columns:</p><ul>
	<li><code>id_col:</code> This the document id column (same as the one provided as input).</li>
	<li><code>word:</code> A word/term present in a document. This is either the original word present in <code>word_col</code> or an id representing the word (depending on the value of compute_vocab below).</li>
	<li><code>count:</code> The number of times this word is found in the document. </li>
	</ul>
	<p class="enddd"></p>
	</dd>
	<dt>compute_vocab </dt>
	<dd>BOOLEAN. (Optional, Default=FALSE) Flag to indicate if a vocabulary is to be created. If TRUE, an additional output table is created containing the vocabulary of all words, with an id assigned to each word. The table is called <em>output_table</em>_vocabulary (suffix added to the <em>output_table</em> name) and contains the following columns:<ul>
	<li><code>wordid:</code> An id assignment for each word</li>
	<li><code>word:</code> The word/term </li>
	</ul>
	</dd>
	</dl>
	<p><a class="anchor" id="examples"></a></p><dl class="section user"><dt>Examples</dt><dd></dd></dl>
	<ol type="1">
	<li>Prepare datasets with some example documents <pre class="example">
	DROP TABLE IF EXISTS documents;
	CREATE TABLE documents(docid INTEGER, doc_contents TEXT);
	INSERT INTO documents VALUES
	(1, 'I like to eat broccoli and banana. I ate a banana and spinach smoothie for breakfast.'),
	(2, 'Chinchillas and kittens are cute.'),
	(3, 'My sister adopted two kittens yesterday'),
	(4, 'Look at this cute hamster munching on a piece of broccoli');
	</pre></li>
	<li>Add a new column containing the words (lower-cased) in a text array <pre class="example">
	ALTER TABLE documents ADD COLUMN words TEXT[];
	UPDATE documents SET words = regexp_split_to_array(lower(doc_contents), E'[\\s+\\.]');
	</pre></li>
	<li>Compute the frequency of each word in each document <pre class="example">
	DROP TABLE IF EXISTS documents_tf;
	SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf');
	SELECT * FROM documents_tf order by docid;
	</pre> <pre class="result">
	docid \| word \| count
	-------+------------+-------
	1 \| ate \| 1
	1 \| like \| 1
	1 \| breakfast \| 1
	1 \| to \| 1
	1 \| broccoli \| 1
	1 \| spinach \| 1
	1 \| i \| 2
	1 \| and \| 2
	1 \| a \| 1
	1 \| \| 2
	1 \| smoothie \| 1
	1 \| eat \| 1
	1 \| banana \| 2
	1 \| for \| 1
	2 \| cute \| 1
	2 \| are \| 1
	2 \| kitten \| 1
	2 \| and \| 1
	2 \| chinchilla \| 1
	3 \| kitten \| 1
	3 \| my \| 1
	3 \| a \| 1
	3 \| sister \| 1
	3 \| adopted \| 1
	3 \| yesterday \| 1
	4 \| at \| 1
	4 \| of \| 1
	4 \| piece \| 1
	4 \| this \| 1
	4 \| a \| 1
	4 \| broccoli \| 1
	4 \| hamster \| 1
	4 \| munching \| 1
	4 \| cute \| 1
	4 \| look \| 1
	(35 rows)
	</pre></li>
	<li>We also can create a vocabulary of the words and store a wordid in the output table instead of the actual word. <pre class="example">
	DROP TABLE IF EXISTS documents_tf;
	DROP TABLE IF EXISTS documents_tf_vocabulary;
	SELECT madlib.term_frequency('documents', 'docid', 'words', 'documents_tf', TRUE);
	-- Output with wordid instead of the actual words
	SELECT * FROM documents_tf order by docid;
	</pre> <pre class="result">
	docid \| wordid \| count
	-------+--------+-------
	1 \| 0 \| 2
	1 \| 1 \| 1
	1 \| 3 \| 2
	1 \| 6 \| 1
	1 \| 7 \| 2
	1 \| 8 \| 1
	1 \| 9 \| 1
	1 \| 12 \| 1
	1 \| 13 \| 1
	1 \| 15 \| 2
	1 \| 17 \| 1
	1 \| 24 \| 1
	1 \| 25 \| 1
	1 \| 27 \| 1
	2 \| 16 \| 1
	2 \| 3 \| 1
	2 \| 4 \| 1
	2 \| 10 \| 1
	2 \| 11 \| 1
	3 \| 1 \| 1
	3 \| 16 \| 1
	3 \| 28 \| 1
	3 \| 23 \| 1
	3 \| 2 \| 1
	3 \| 20 \| 1
	4 \| 9 \| 1
	4 \| 11 \| 1
	4 \| 22 \| 1
	4 \| 14 \| 1
	4 \| 26 \| 1
	4 \| 1 \| 1
	4 \| 5 \| 1
	4 \| 18 \| 1
	4 \| 19 \| 1
	4 \| 21 \| 1
	(35 rows)
	</pre> <pre class="example">
	-- Vocabulary
	SELECT * FROM documents_tf_vocabulary order by wordid;
	</pre> <pre class="result">
	wordid \| word
	--------+------------
	0 \|
	1 \| a
	2 \| adopted
	3 \| and
	4 \| are
	5 \| at
	6 \| ate
	7 \| banana
	8 \| breakfast
	9 \| broccoli
	10 \| chinchilla
	11 \| cute
	12 \| eat
	13 \| for
	14 \| hamster
	15 \| i
	16 \| kitten
	17 \| like
	18 \| look
	19 \| munching
	20 \| my
	21 \| of
	22 \| piece
	23 \| sister
	24 \| smoothie
	25 \| spinach
	26 \| this
	27 \| to
	28 \| yesterday
	(29 rows)
	</pre></li>
	</ol>
	<p><a class="anchor" id="related"></a></p><dl class="section user"><dt>Related Topics</dt><dd></dd></dl>
	<p>File <a class="el" href="text__utilities_8sql__in.html" title="SQL functions for carrying out routine text operations. ">text_utilities.sql_in</a> documenting the SQL functions. File <a class="el" href="utilities_8sql__in.html" title="SQL functions for carrying out routine tasks. ">utilities.sql_in</a> documenting the utility functions for DB administration. </p>
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