def 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):
"""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
"""
np.random.seed(seed)
raw_data = read_input(data_source, content_field=content_field,
source_type=source_type, **kwargs)
raw_data = ((hash(item[content_field]), item[content_field]) for item in raw_data)
tokenized_data = tokenizers.registered_tokenizers[tokenizer](raw_data, **kwargs)
vectorized_data = vectorizers.registered_vectorizers[vectorizer](tokenized_data, **kwargs)
model = models.registered_models[model](vectorized_data, ntopics=ntopics, **kwargs)
if not os.path.exists(dir_path):
os.mkdir(dir_path)
if termite_plot:
termite_html(model, filename="termite.html", plot_title="Termite plot", topn=15)
if lda_vis:
visualizers.visualize(model, "lda_vis")
def 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):
"""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
"""
np.random.seed(seed)
raw_data = read_input(data_source,
content_field=content_field,
source_type=source_type,
**kwargs)
raw_data = ((hash(item[content_field]), item[content_field])
for item in raw_data)
tokenized_data = tokenizers.registered_tokenizers[tokenizer](raw_data,
**kwargs)
vectorized_data = vectorizers.registered_vectorizers[vectorizer](
tokenized_data, **kwargs)
model = models.registered_models[model](vectorized_data,
ntopics=ntopics,
**kwargs)
if not os.path.exists(dir_path):
os.mkdir(dir_path)
if termite_plot:
termite_html(model,
filename="termite.html",
plot_title="Termite plot",
topn=15)
if lda_vis:
visualizers.visualize(model, "lda_vis")
def test_termite():
termite_html(test_model_output, filename=os.path.join(module_path, 'termite_plot.html'),
plot_title="My lda results", topn=TOP_WORDS)
nt.assert_true(os.path.isfile(os.path.join(module_path, 'termite_plot.html')))
os.remove(os.path.join(module_path, 'termite_plot.html'))