topik.simple_run package¶
Subpackages¶
Submodules¶
topik.simple_run.cli module¶
topik.simple_run.run module¶
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topik.simple_run.run.
run_pipeline
(data_source, source_type='auto', year_field=None, start_year=None, stop_year=None, content_field=None, tokenizer='simple', vectorizer='bag_of_words', ntopics=10, dir_path='./topic_model', model='lda', termite_plot=False, output_file=False, lda_vis=True, seed=42, **kwargs)[source]¶ Run your data through all topik functionality and save all results to a specified directory.
Parameters: data_source : str
Input data (e.g. file or folder or solr/elasticsearch instance).
source_type : {‘json_stream’, ‘folder_files’, ‘json_large’, ‘solr’, ‘elastic’}.
The format of your data input. Currently available a json stream or a folder containing text files. Default is ‘json_stream’
year_field : str
The field name (if any) that contains the year associated with each document (for filtering).
start_year : int
For beginning of range filter on year_field values
stop_year : int
For beginning of range filter on year_field values
content_field : string
The primary text field to parse.
tokenizer : {‘simple’, ‘collocations’, ‘entities’, ‘mixed’}
The type of tokenizer to use. Default is ‘simple’.
vectorizer : {‘bag_of_words’, ‘tfidf’}
The type of vectorizer to use. Default is ‘bag_of_words’.
ntopics : int
Number of topics to find in your data
dir_path : str
Directory path to store all topic modeling results files. Default is ./topic_model.
model : {‘LDA’, ‘PLSA’}.
Statistical modeling algorithm to use. Default ‘LDA’.
termite_plot : bool
Generate termite plot of your model if True. Default is True.
ldavis : bool
Generate an interactive data visualization of your topics. Default is False.
seed : int
Set random number generator to seed, to be able to reproduce results. Default 42.
**kwargs : additional keyword arguments, passed through to each individual step