def test_top_author_format(sample_data, zero_division_data) -> None:
"""
Test to ensure that the top_author is returning a tuple of two lists with
the appropriate formatting for the
output
:param sample_data: the sample data
:return: None
"""
# get the top authors for a set of documents
datasets = [sample_data['documents'], zero_division_data["documents"]]
for d in datasets:
# create an AuthorRank object
ar_graph = ar.Graph()
# fit to the data
ar_graph.fit(documents=d)
# get the top authors for a set of documents
top = ar_graph.top_authors()
# check that it returns a tuple
assert type(top) == tuple
# check to ensure each value in the responses are in the appropriate format
for k, v in zip(top[0], top[1]):
assert type(k) == tuple
assert type(v) == float
def test_speed(mls_data) -> None:
"""
While AuthorRank is not intended to be quick, we can create a benchmark
for its performance when generating the graph and ensure that any future
changes to the code base do not exceed this threshold.
This function could be used in the future to test speed improvements
to the approach by further constraining the maximum allowed time for the
test to pass or by testing speed differences between normal and parallel
processing modes.
:return: None
"""
# get the start time
t0 = time.time()
# calculate the top author graph
ar_graph = ar.Graph()
ar_graph.fit(documents=mls_data,
progress_bar=True,
authorship_key="author",
keys=set(["given", "family"]))
# get the finish time
t1 = time.time()
# assert the time is less than a particular amount
spread = t1 - t0
assert spread < 320.
def test_single_author() -> None:
"""
Tests the functionality of AuthorRank in the rare case when a single
author is present in the document set passed.
:return: None
"""
# first, create a single author dataset
data = [{
"title":
"PyNomaly: Anomaly detection using Local Outlier Probabilities (LoOP).",
"authors": [{
"first_name": "Valentino",
"last_name": "Constantinou",
"affiliation": {
"name": "NASA Jet Propulsion Laboratory",
"department": "Office of the Chief Information Officer"
}
}]
}]
# then attempt to fit to the data
# create an AuthorRank object
ar_graph = ar.Graph()
with pytest.warns(UserWarning) as record:
# fit to the data
ar_graph.fit(documents=data)
# check that the message matches
messages = [i.message.args[0] for i in record]
assert "Number of authors in document set must be greater than one. " \
"AuthorRank not fit to the data, please try again." in messages
def test_normalization(sample_data) -> None:
"""
Test to ensure that normalizing the author_rank scores returns values between
0 and 1.
:param sample_data: the sample data
:return: None
"""
# create an AuthorRank object
ar_graph = ar.Graph()
# fit to the data and use the progress bar
ar_graph.fit(documents=sample_data['documents'], progress_bar=True)
# get the top authors for a set of documents and normalize the scores
top = ar_graph.top_authors(normalize_scores=True)
# check that it returns a tuple
assert type(top) == tuple
# check to ensure each value in the responses are in the appropriate format
for v in top[1]:
assert 0. <= v <= 1.
# check to ensure that the last entry in the list is a value of 0
assert top[1][-1] == 0.
# check to ensure that the first entry in the list is a value of 1
assert top[1][0] == 1.0
def test_export_format(sample_data) -> None:
"""
Test to ensure that the graph is being effectively exported as a dictionary
which is valid JSON.
:param sample_data: the sample data
:return: None
"""
# create an AuthorRank object
ar_graph = ar.Graph()
# fit to the data
ar_graph.fit(documents=sample_data["documents"])
# export them
export = ar_graph.as_json()
assert type(export) == dict
def test_no_fit() -> None:
"""
Tests whether the AuthorRank approach has been fit to a set of documents
prior to calling top_authors, and checks for the correct UserWarning.
:return: None
"""
# create an AuthorRank object
ar_graph = ar.Graph()
with pytest.warns(UserWarning) as record:
# try to fit top authors
ar_graph.top_authors(normalize_scores=True)
# check that the message matches
messages = [i.message.args[0] for i in record]
assert "AuthorRank must first be fit on a set of documents " \
"prior to calling top_authors." in messages
# imports
import author_rank as ar
import json
# read in sample json
with open("../data/author_network.json", 'r') as f:
data = json.load(f)
# create an AuthorRank object
ar_graph = ar.Graph()
# fit to the data
ar_graph.fit(
documents=data["documents"]
)
# export them
export = ar_graph.as_json()
print(json.dumps(export, indent=4))