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Python library and tools for working with term-document matrices

This project is just beginning, but a useful data structure, HTDM (hierarchical term-document matrix), is already available and TF-IDF calculations can be performed.

I am also interested in standardizing the exchange format for HTDMs.

pip install termdoc

Python 3.10 or higher is required.

The core data structure is a term-document matrix supporting hierarchical documents. Documents are labelled with a delimited string such as "1.7.5" or "Plato.Republic.5". This hierarchy could represent divisions of a work, grouping of multiple works, or some combination. Counts are aggregated at each level of the hierarchy (including at the top level to get totals across all documents).

HTDMs can be loaded withload:

>>>importtermdoc>>>c=termdoc.HTDM()>>>c.load("test_data/test1.tsv")

where the file looks something like:

1.1foo71.1bar41.2foo21.3bar12.1baz52.1foo1

with a period-separated hierarchical address / document ID, term, and count all separated with tabs.

Both the period and tab are just defaults and can be override by passingaddress_sep and/orfield_sep toload.

The HTDM can then give counts at any level of the document hierarchy:

>>>c.get_counts()["foo"]10>>>c.get_counts("1")["foo"]9>>>c.get_counts("1.2")["foo"]2

Note the separator used in the address / document ID defaults to a period (regardless of what was used inload) but can be override by passingaddress_sep to the HTDM constructor.

HTDMs can also be built up programmatically.

Here is an example with a single-level of documents (a traditional TDM):

>>>importtermdoc>>>c=termdoc.HTDM()>>>c.increment_count("1","foo",3)# document 1 contains the term "foo" 3 times>>>c.increment_count("1","bar",2)>>>c.increment_count("2","foo",2)>>>c.increment_count("2","bar",1)>>>c.get_counts()["foo"]5>>>c.get_counts()["bar"]3

And here is an example with a two-level hierarchy:

>>>importtermdoc>>>c=termdoc.HTDM()>>>c.increment_count("1.1","foo",3)>>>c.increment_count("1.2","foo",3)>>>c.increment_count("1.1","bar",2)>>>c.increment_count("1.2","bar",2)>>>c.increment_count("2.1","foo",2)>>>c.increment_count("2.2","foo",2)>>>c.increment_count("2.1","bar",1)>>>c.increment_count("2.2","bar")>>>c.get_counts()["foo"]10>>>c.get_counts()["bar"]6>>>c.get_counts("2")["foo"]4

Note that if thecount is1 you can omit it.

Entire lists of tokens can be added for a particular address in one go usingadd(address, term_list):

>>>importtermdoc>>>c=termdoc.HTDM()>>>c.add("1.1", ["foo","bar","bar","baz"])>>>c.add("1.2", ["foo","foo"])>>>c.get_counts()["bar"]2>>>c.get_counts()["foo"]3>>>c.get_counts("1.2")["foo"]2

You canprune a HTDM to justn levels with the methodprune(n).

You can iterate over the document-term counts at the leaves of the HTDM with the methodleaf_entries() (this returns a generator yielding(document_address, term, count) tuples). This is effectively a traditional TDM (the document IDs will still reflect the hierarchy but the aggregate counts aren't present).

You can optionally pass aprefix toleaf_entries() in each case only that subtree will be returns (with the prefix removed from the document IDs).

You cangraft one HTDM under another by using thegraft(prefix, subtree) method, specifying asprefix the document address you want to add the subtree under. This is useful if you have an HTDM for, say, a single work by an author, with chapters as documents and you want to incorporate that into a higher-level HTDM of multiple works by the author, or a collection of works by different authors.

Alternatively you can provide aprefix toload to load the file under a particular point in the tree. This effectively prepends the givenprefix (plus address separator) to the document IDs.

The third (count) field in a loaded file can be omitted if the count is 1 and a document ID + term may be repeated with the counts accumulating.

You can deep copy an HTDM withcopy(). You can also pass a prefix tocopy() if you want to clone just a subtree.

You can save out an HTDM withsave() which takes afilename and optionalfield_sep (defaulting to tab) andprefix (if you just want to save out a subtree).

You can get a term frequency withtf(term) ortf(term, address).

>>>c=termdoc.HTDM()>>>c.increment_count("1","foo")>>>c.increment_count("1","bar",3)>>>c.increment_count("2","foo",3)>>>c.increment_count("2","bar")>>>c.tf("foo")0.5>>>c.tf("foo","2")0.75

You can also get document frequency withdf(term).

>>>c=termdoc.HTDM()>>>c.add("1.1", ["foo","bar"])>>>c.add("1.2", ["bar"])>>>c.add("2.1", ["foo"])>>>c.add("2.2", ["foo","bar","baz"])>>>c.df("foo")0.75>>>c.df("bar")0.75>>>c.df("baz")0.25

By default this treats the leaves of the tree at the documents but you can instead specify an explicit number of levels to go down. For example this following will only tree the1 and2 as the documents (not1.1,1.2,2.1,2.2):

>>>c.df("foo",level=1)1.0>>>c.df("bar",level=1)1.0>>>c.df("baz",level=1)0.5

Furthermore you can scope the calculate to a subtree, in this case just the documents1.1 and1.2 under1:

>>>c.df("foo","1")0.5>>>c.df("bar","1")1.0>>>c.df("baz","1")0.0

This scoping can be combined with the level limit.

The TF-IDF of a term and document can be calculated withtf_idf(term, address):

>>>c=termdoc.HTDM()>>>c.add("1", ["this","is","a","a","sample"])>>>c.add("2", ["this","is","another","another","example","example","example"])>>>c.tf_idf("example","1")0.0>>>c.tf_idf("example","2")0.12901285528456335

You can also scope the TF-IDF calculation to a subtree.

>>>c=termdoc.HTDM()>>>c.add("1.1", ["foo","bar"])>>>c.add("1.2", ["bar"])>>>c.add("2.1", ["foo"])>>>c.add("2.2", ["foo","bar","baz"])>>>c.tf_idf("foo","1.1","1")0.1505149978319906

Note that the address must start with the subtree prefix if the latter is given.

You can optionally pass in aduplicates setting to the constructor indicating the policy you want to follow if a term-document count is updated more than once.

>>>c=termdoc.HTDM()>>>c.increment_count("","foo",3)>>>c.increment_count("","foo",2)>>>c.get_counts()["foo"]5

is the same as

>>>c=termdoc.HTDM(duplicates=termdoc.Duplicates.ALLOW)>>>c.increment_count("","foo",3)>>>c.increment_count("","foo",2)>>>c.get_counts()["foo"]5

But you can tell an HTDM to ignore attempts to update an existing count:

>>>c=termdoc.HTDM(duplicates=termdoc.Duplicates.IGNORE)>>>c.increment_count("","foo",3)>>>c.increment_count("","foo",2)>>>c.get_counts()["foo"]3

or to raise an exception:

>>>c=termdoc.HTDM(duplicates=termdoc.Duplicates.ERROR)>>>c.increment_count("","foo",3)>>>c.increment_count("","foo",2)# this will raise a ValueErrorTraceback (mostrecentcalllast):ValueError:'foo'alreadyin''

Note that duplicates are only checked in the leaves of the document tree.