minor fixes

Author: maximtrp

README.md

@@ -87,6 +87,11 @@ coherence = btm.coherence(model.matrix_topics_words_, X, M=20)
 # or
 perplexity = model.perplexity_
 coherence = model.coherence_
+
+# LABELS
+model.labels_
+# or
+btm.get_docs_top_topic(texts, model.matrix_docs_topics_)
 ```
 
 ### Results visualization

docs/source/bitermplus.util.rst

@@ -7,4 +7,5 @@ Utility functions
 .. autofunction:: get_vectorized_docs
 .. autofunction:: get_biterms
 .. autofunction:: get_top_topic_words
-.. autofunction:: get_top_topic_docs
+.. autofunction:: get_top_topic_docs
+.. autofunction:: get_docs_top_topic

docs/source/tutorial.rst

@@ -123,7 +123,8 @@ variation distance.
 Model loading and saving
 ------------------------
 
-Support for model serializing with `pickle <https://docs.python.org/3/library/pickle.html>`_ was implemented in v0.5.3.
+Support for model serializing with `pickle
+<https://docs.python.org/3/library/pickle.html>`_ was implemented in v0.5.3.
 Here is how you can save and load a model:
 
 .. code-block:: python

setup.cfg

@@ -13,6 +13,11 @@ classifiers =
     License :: OSI Approved :: MIT License
     Operating System :: OS Independent
     Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.6
+    Programming Language :: Python :: 3.7
+    Programming Language :: Python :: 3.8
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
     Topic :: Scientific/Engineering :: Information Analysis
     Topic :: Text Processing :: General
 

setup.py

@@ -1,5 +1,5 @@
-from setuptools import setup, Extension
 from platform import system
+from setuptools import setup, Extension
 from Cython.Build import cythonize
 # from numpy import get_include
 

src/bitermplus/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.6.11'
+__version__ = '0.6.12'
 
 from bitermplus._btm import BTM
 from bitermplus._util import *

src/bitermplus/_util.py

@@ -247,5 +247,15 @@ def get_docs_top_topic(
     -------
     DataFrame
         Documents and the most probable topic for each of them.
+
+    Example
+    -------
+    >>> import bitermplus as btm
+    >>> # Read documents from file
+    >>> # texts = ...
+    >>> # Build and train a model
+    >>> # model = ...
+    >>> # model.fit(...)
+    >>> btm.get_docs_top_topic(texts, model.matrix_docs_topics_)
     """
     return DataFrame({'documents': docs, 'label': p_zd.argmax(axis=1)})