def preprocess(data):
"""Preprocess text data.
"""
# Constructing corpus:
textfiles = database.select("textfiles")
documents = utils.get_documents(textfiles)
corpus = cophi.model.Corpus(documents)
num_tokens = corpus.num_tokens
database.update("textfiles", num_tokens.to_dict())
# Get paramter:
D, W = corpus.dtm.shape
N = num_tokens.sum()
# Cleaning corpus:
stopwords = utils.get_stopwords(data, corpus)
hapax = corpus.hapax
features = set(stopwords).union(set(hapax))
logging.info("Cleaning corpus...")
dtm = corpus.drop(corpus.dtm, features)
# Save stopwords:
database.insert_into("stopwords", json.dumps(stopwords))
# Save parameters:
parameters = {
"n_topics": int(data["topics"]),
"n_iterations": int(data["iterations"]),
"n_documents": int(D),
"n_stopwords": int(len(stopwords)),
"n_hapax": int(len(hapax)),
"n_tokens": int(N),
"n_types": int(W)
}
return dtm, num_tokens.tolist(), parameters
def export_data():
"""Export model output to ZIP archive.
"""
logging.info("Creating data archive...")
if DATA_EXPORT.exists():
unlink_content(DATA_EXPORT)
else:
DATA_EXPORT.mkdir()
model, stopwords = database.select("data_export")
document_topic, topics, document_similarities, topic_similarities = model
logging.info("Preparing document-topic distributions...")
document_topic = pd.read_json(document_topic, orient="index")
document_topic.columns = [
col.replace(",", "").replace(" ...", "")
for col in document_topic.columns
]
logging.info("Preparing topics...")
topics = pd.read_json(topics, orient="index")
topics.index = ["Topic {}".format(n) for n in range(topics.shape[0])]
topics.columns = ["Word {}".format(n) for n in range(topics.shape[1])]
logging.info("Preparing topic similarity matrix...")
topic_similarities = pd.read_json(topic_similarities)
topic_similarities.columns = [
col.replace(",", "").replace(" ...", "")
for col in topic_similarities.columns
]
topic_similarities.index = [
ix.replace(",", "").replace(" ...", "")
for ix in topic_similarities.index
]
logging.info("Preparing document similarity matrix...")
document_similarities = pd.read_json(document_similarities)
data_export = {
"document-topic-distribution": document_topic,
"topics": topics,
"topic-similarities": topic_similarities,
"document-similarities": document_similarities,
"stopwords": json.loads(stopwords)
}
for name, data in data_export.items():
if name in {"stopwords"}:
with Path(DATA_EXPORT,
"{}.txt".format(name)).open("w",
encoding="utf-8") as file:
for word in data:
file.write("{}\n".format(word))
else:
path = Path(DATA_EXPORT, "{}.csv".format(name))
data.to_csv(path, sep=";", encoding="utf-8")
shutil.make_archive(DATA_EXPORT, "zip", DATA_EXPORT)
def get_textfile_sizes():
"""Textfile sizes.
"""
return database.select("textfile_sizes")
def get_parameters():
"""Model parameters.
"""
return json.dumps(database.select("parameters"))
def get_token_frequencies():
"""Token frequencies per document.
"""
return database.select("token_freqs")
def get_stopwords():
"""Stopwords.
"""
return database.select("stopwords")
def get_textfile(title):
"""Textfiles.
"""
return database.select("textfile", title=title)
def get_topic_similarities():
"""Topic similarity matrix.
"""
return database.select("topic_similarities")
def get_document_similarities():
"""Document similarity matrix.
"""
return database.select("document_similarities")
def get_topics():
"""Topics.
"""
return database.select("topics")
def get_document_topic_distributions():
"""Document-topics distributions.
"""
return database.select("document_topic_distributions")