def test_onthefly_labels():
clusterer = BlockClustering(base_estimator=ScipyHierarchicalClustering(
n_clusters=1, method="complete"))
clusterer.fit(X)
assert_array_equal([100], np.bincount(clusterer.labels_))
clusterer.clusterers_[0].set_params(n_clusters=4)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
def test_onthefly_labels():
clusterer = BlockClustering(
base_estimator=ScipyHierarchicalClustering(n_clusters=1,
method="complete"))
clusterer.fit(X)
assert_array_equal([100], np.bincount(clusterer.labels_))
clusterer.clusterers_[0].set_params(n_clusters=4)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
class Clusterer(object):
def __init__(self, estimator):
# TODO get rid of this global
global distance_estimator
distance_estimator = estimator.distance_estimator
try:
distance_estimator.steps[-1][1].set_params(n_jobs=1)
except Exception:
pass
# threshold determines when to split blocks into smaller ones adding first initial
self.block_function = partial(block_phonetic,
threshold=0,
phonetic_algorithm='nysiis')
self.clustering_threshold = 0.709 # magic value taken from BEARD example
self.clustering_method = 'average'
def load_data(self, signatures_path, publications_path,
input_clusters_path):
signatures_by_uuid = load_signatures(signatures_path,
publications_path)
self.X = np.empty((len(signatures_by_uuid), 1), dtype=np.object)
self.y = -np.ones(len(self.X), dtype=np.int)
i = 0
with open(input_clusters_path, 'r') as fd:
for line in fd:
cluster = json.loads(line)
for signature_uuid in cluster['signature_uuids']:
if signature_uuid not in signatures_by_uuid:
continue # TODO figure out how this can happen
self.X[i, 0] = signatures_by_uuid[signature_uuid]
self.y[i] = cluster['cluster_id']
i += 1
def load_model(self, input_filename):
with open(input_filename, 'rb') as fd:
self.clusterer = pickle.load(fd)
def save_model(self, output_filename):
with open_file_in_folder(output_filename, 'wb') as fd:
pickle.dump(self.clusterer, fd, protocol=pickle.HIGHEST_PROTOCOL)
def fit(self, n_jobs=8):
self.clusterer = BlockClustering(
blocking=self.block_function,
base_estimator=ScipyHierarchicalClustering(
affinity=_affinity,
threshold=self.clustering_threshold,
method=self.clustering_method,
supervised_scoring=b3_f_score),
n_jobs=n_jobs,
verbose=True)
self.clusterer.fit(self.X, self.y)
def test_predict():
"""Test predict."""
clusterer = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X, blocks=(y <= 1))
pred = clusterer.predict(X, blocks=(y <= 1))
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
pred = clusterer.predict(X, blocks=10 * np.ones(len(X)))
assert_array_equal(-np.ones(len(X)), pred)
def fit(self, n_jobs=8):
self.clusterer = BlockClustering(
blocking=self.block_function,
base_estimator=ScipyHierarchicalClustering(
affinity=_affinity,
threshold=self.clustering_threshold,
method=self.clustering_method,
supervised_scoring=b3_f_score),
n_jobs=n_jobs,
verbose=True)
self.clusterer.fit(self.X, self.y)
def test_partial_fit():
"""Test partial_fit."""
blocks = (y <= 1)
clusterer1 = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer1.partial_fit(X[y <= 1], blocks=blocks[y <= 1])
assert_equal(len(clusterer1.clusterers_), 1)
clusterer1.partial_fit(X[y > 1], blocks=blocks[y > 1])
assert_equal(len(clusterer1.clusterers_), 2)
clusterer2 = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer2.fit(X, blocks=blocks)
c1 = clusterer1.predict(X, blocks=blocks)
c2 = clusterer2.labels_
assert_equal(paired_f_score(c1, c2), 1.0)
def test_predict():
"""Test predict."""
clusterer = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X, blocks=(y <= 1))
pred = clusterer.predict(X, blocks=(y <= 1))
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
pred = clusterer.predict(X, blocks=10 * np.ones(len(X)))
assert_array_equal(-np.ones(len(X)), pred)
class Clusterer(object):
def __init__(self, estimator):
# TODO get rid of this global
global distance_estimator
distance_estimator = estimator.distance_estimator
try:
distance_estimator.steps[-1][1].set_params(n_jobs=1)
except Exception:
pass
# threshold determines when to split blocks
# into smaller ones adding first initial
self.block_function = partial(block_phonetic,
threshold=0,
phonetic_algorithm="nysiis")
self.clustering_threshold = 0.709 # magic value taken from BEARD example
self.clustering_method = "average"
def load_data(self, signatures, input_clusters):
"""Loads data to the estimator vectors
Args:
signatures (iterable): Signatures which should be processed
input_clusters (iterable): Input clusters built for provided signatures
see: `inspire_disambiguation.core.es.readers.get_input_clusters`
"""
signatures_by_uuid = load_signatures(signatures)
self.X = np.empty((len(signatures_by_uuid), 1), dtype=np.object)
self.y = -np.ones(len(self.X), dtype=np.int)
i = 0
for cluster in input_clusters:
for signature_uuid in cluster["signature_uuids"]:
if signature_uuid not in signatures_by_uuid:
continue # TODO figure out how this can happen
self.X[i, 0] = signatures_by_uuid[signature_uuid]
self.y[i] = cluster["cluster_id"]
i += 1
def load_model(self, input_filename):
"""Loads model dumped by pickle
Args:
input_filename (str): path to file with dumped ethnicity model.
"""
with open(input_filename, "rb") as fd:
self.clusterer = pickle.load(fd)
def save_model(self, output_filename):
"""Dump object to a file
Args:
output_filename (str): Path where model will be dumped
"""
with open_file_in_folder(output_filename, "wb") as fd:
pickle.dump(self.clusterer, fd, protocol=pickle.HIGHEST_PROTOCOL)
def fit(self, n_jobs=8):
"""Fit data using the estimator"""
self.clusterer = BlockClustering(
blocking=self.block_function,
base_estimator=ScipyHierarchicalClustering(
affinity=_affinity,
threshold=self.clustering_threshold,
method=self.clustering_method,
supervised_scoring=b3_f_score,
),
n_jobs=n_jobs,
verbose=True,
)
self.clusterer.fit(self.X, self.y)
def test_partial_fit():
"""Test partial_fit."""
blocks = (y <= 1)
clusterer1 = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer1.partial_fit(X[y <= 1], blocks=blocks[y <= 1])
assert_equal(len(clusterer1.clusterers_), 1)
clusterer1.partial_fit(X[y > 1], blocks=blocks[y > 1])
assert_equal(len(clusterer1.clusterers_), 2)
clusterer2 = BlockClustering(blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer2.fit(X, blocks=blocks)
c1 = clusterer1.predict(X, blocks=blocks)
c2 = clusterer2.labels_
assert_equal(paired_f_score(c1, c2), 1.0)
def test_fit(n_jobs):
"""Test fit."""
# Single block
clusterer = BlockClustering(blocking="single",
base_estimator=AgglomerativeClustering(
n_clusters=4, linkage="complete"),
n_jobs=n_jobs)
clusterer.fit(X)
assert_equal(len(clusterer.clusterers_), 1)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Precomputed blocks
clusterer = BlockClustering(blocking="precomputed",
base_estimator=AgglomerativeClustering(
n_clusters=2, linkage="complete"),
n_jobs=n_jobs)
clusterer.fit(X, blocks=(y <= 1))
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Precomputed affinity
clusterer = BlockClustering(affinity="precomputed",
blocking="precomputed",
base_estimator=ScipyHierarchicalClustering(
affinity="precomputed",
n_clusters=2,
method="complete"),
n_jobs=n_jobs)
X_affinity = euclidean_distances(X)
clusterer.fit(X_affinity, blocks=(y <= 1))
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Custom blocking function
X_ids = np.arange(len(X)).reshape((-1, 1))
def _blocking(X_ids):
return y[X_ids.ravel()] <= 1 # block labels into {0,1} and {2,3}
clusterer = BlockClustering(blocking=_blocking,
base_estimator=AgglomerativeClustering(
n_clusters=2,
linkage="complete",
affinity=_distance))
clusterer.fit(X_ids)
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
def clustering(input_signatures,
input_records,
distance_model,
input_clusters=None,
output_clusters=None,
verbose=1,
n_jobs=-1,
clustering_method="average",
train_signatures_file=None,
clustering_threshold=None,
results_file=None,
blocking_function="block_phonetic",
blocking_threshold=1,
blocking_phonetic_alg="nysiis"):
"""Cluster signatures using a pretrained distance model.
Parameters
----------
:param input_signatures: string
Path to the file with signatures. The content should be a JSON array
of dictionaries holding metadata about signatures.
[{"signature_id": 0,
"author_name": "Doe, John",
"publication_id": 10, ...}, { ... }, ...]
:param input_records: string
Path to the file with records. The content should be a JSON array of
dictionaries holding metadata about records
[{"publication_id": 0,
"title": "Author disambiguation using Beard", ... }, { ... }, ...]
:param distance_model: string
Path to the file with the distance model. The file should be a pickle
created using the ``distance.py`` script.
:param input_clusters: string
Path to the file with knownn clusters. The file should be a dictionary,
where keys are cluster labels and values are the `signature_id` of the
signatures grouped in the clusters. Signatures assigned to the cluster
with label "-1" are not clustered.
{"0": [0, 1, 3], "1": [2, 5], ...}
:param output_clusters: string
Path to the file with output cluster. The file will be filled with
clusters, using the same format as ``input_clusters``.
:param verbose: int
If not zero, function will output scores on stdout.
:param n_jobs: int
Parameter passed to joblib. Number of threads to be used.
:param clustering_method: string
Parameter passed to ``ScipyHierarchicalClustering``. Used only if
``clustering_test_size`` is specified.
:param train_signatures_file: str
Path to the file with train set signatures. Format the same as in
``input_signatures``.
:param clustering_threshold: float
Threshold passed to ``ScipyHierarchicalClustering``.
:param results_file: str
Path to the file where the results will be output. It will give
additional information about pairwise variant of scores.
:param blocking_function: string
must be a defined blocking function. Defined functions are:
- "block_last_name_first_initial"
- "block_phonetic"
:param blocking_threshold: int or None
It determines the maximum allowed size of blocking on the last name
It can only be:
- None; if the blocking function is block_last_name_first_initial
- int; if the blocking function is block_phonetic
please check the documentation of phonetic blocking in
beard.clustering.blocking_funcs.py
:param blocking_phonetic_alg: string or None
If not None, determines which phonetic algorithm is used. Options:
- "double_metaphone"
- "nysiis" (only for Python 2)
- "soundex" (only for Python 2)
"""
# Assumes that 'distance_estimator' lives in global, making things fast
global distance_estimator
distance_estimator = pickle.load(open(distance_model, "rb"))
try:
distance_estimator.steps[-1][1].set_params(n_jobs=1)
except:
pass
signatures, records = load_signatures(input_signatures, input_records)
indices = {}
X = np.empty((len(signatures), 1), dtype=np.object)
for i, signature in enumerate(
sorted(signatures.values(), key=lambda s: s["signature_id"])):
X[i, 0] = signature
indices[signature["signature_id"]] = i
if blocking_function == "block_last_name_first_initial":
block_function = block_last_name_first_initial
else:
block_function = partial(block_phonetic,
threshold=blocking_threshold,
phonetic_algorithm=blocking_phonetic_alg)
# Semi-supervised block clustering
if input_clusters:
true_clusters = json.load(open(input_clusters, "r"))
y_true = -np.ones(len(X), dtype=np.int)
for label, signature_ids in true_clusters.items():
for signature_id in signature_ids:
y_true[indices[signature_id]] = label
y = -np.ones(len(X), dtype=np.int)
if train_signatures_file:
train_signatures = json.load(open(train_signatures_file, "r"))
train_ids = [x['signature_id'] for x in train_signatures]
del train_signatures
y[train_ids] = y_true[train_ids]
test_ids = list(
set([x['signature_id']
for _, x in signatures.iteritems()]) - set(train_ids))
else:
y = y_true
else:
y = None
clusterer = BlockClustering(blocking=block_function,
base_estimator=ScipyHierarchicalClustering(
affinity=_affinity,
threshold=clustering_threshold,
method=clustering_method,
supervised_scoring=b3_f_score),
verbose=verbose,
n_jobs=n_jobs).fit(X, y)
labels = clusterer.labels_
# Save predicted clusters
if output_clusters:
clusters = {}
for label in np.unique(labels):
mask = (labels == label)
clusters[str(label)] = [r[0]["signature_id"] for r in X[mask]]
json.dump(clusters, open(output_clusters, "w"))
# Statistics
if verbose and input_clusters:
print("Number of blocks =", len(clusterer.clusterers_))
print("True number of clusters", len(np.unique(y_true)))
print("Number of computed clusters", len(np.unique(labels)))
b3_overall = b3_precision_recall_fscore(y_true, labels)
print("B^3 F-score (overall) =", b3_overall[2])
if train_signatures_file:
b3_train = b3_precision_recall_fscore(y_true[train_ids],
labels[train_ids])
b3_test = b3_precision_recall_fscore(y_true[test_ids],
labels[test_ids])
print("B^3 F-score (train) =", b3_train[2])
print("B^3 F-score (test) =", b3_test[2])
if results_file:
paired_overall = paired_precision_recall_fscore(y_true, labels)
paired_train = paired_precision_recall_fscore(
y_true[train_ids], labels[train_ids])
paired_test = paired_precision_recall_fscore(
y_true[test_ids], labels[test_ids])
json.dump(
{
"description": ["precision", "recall", "f_score"],
"b3": {
"overall": list(b3_overall),
"train": list(b3_train),
"test": list(b3_test)
},
"paired": {
"overall": list(paired_overall),
"train": list(paired_train),
"test": list(paired_test)
}
}, open(results_file, 'w'))
def test_single_signature(n_jobs):
"""Test clustering of a single signature."""
import numbers
clusterer = BlockClustering(base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(np.array([X[0]]))
assert isinstance(clusterer.predict(X[0])[0], numbers.Integral)
test_size=args.clustering_test_size,
random_state=args.clustering_random_state)
y = -np.ones(len(X), dtype=np.int)
y[train] = y_true[train]
else:
y = y_true
else:
y = None
clusterer = BlockClustering(blocking=blocking,
base_estimator=ScipyHierarchicalClustering(
affinity=affinity,
threshold=args.clustering_threshold,
method=args.clustering_method,
scoring=b3_f_score),
verbose=args.verbose,
n_jobs=args.n_jobs).fit(X, y)
labels = clusterer.labels_
# Save predicted clusters
if args.output_clusters:
clusters = {}
for label in np.unique(labels):
mask = (labels == label)
clusters[label] = [r[0]["signature_id"] for r in X[mask]]
json.dump(clusters, open(args.output_clusters, "w"))
def test_fit(n_jobs):
"""Test fit."""
# Single block
clusterer = BlockClustering(
blocking="single",
base_estimator=AgglomerativeClustering(n_clusters=4,
linkage="complete"),
n_jobs=n_jobs)
clusterer.fit(X)
assert_equal(len(clusterer.clusterers_), 1)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Precomputed blocks
clusterer = BlockClustering(
blocking="precomputed",
base_estimator=AgglomerativeClustering(n_clusters=2,
linkage="complete"),
n_jobs=n_jobs)
clusterer.fit(X, blocks=(y <= 1))
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Precomputed affinity
clusterer = BlockClustering(
affinity="precomputed",
blocking="precomputed",
base_estimator=ScipyHierarchicalClustering(affinity="precomputed",
n_clusters=2,
method="complete"),
n_jobs=n_jobs)
X_affinity = euclidean_distances(X)
clusterer.fit(X_affinity, blocks=(y <= 1))
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
# Custom blocking function
X_ids = np.arange(len(X)).reshape((-1, 1))
def _blocking(X_ids):
return y[X_ids.ravel()] <= 1 # block labels into {0,1} and {2,3}
clusterer = BlockClustering(
blocking=_blocking,
base_estimator=AgglomerativeClustering(n_clusters=2,
linkage="complete",
affinity=_distance))
clusterer.fit(X_ids)
assert_equal(len(clusterer.clusterers_), 2)
assert_array_equal([25, 25, 25, 25], np.bincount(clusterer.labels_))
def test_validation():
"""Test the validation of hyper-parameters and input data."""
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="foobar",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X)
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X)
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X, blocks=(y <= 1))
clusterer.predict(X)
distances[i, j] = 0.5
distances += distances.T
return distances
if __name__ == "__main__":
# Load data
data = np.load("data/author-disambiguation.npz")
X = data["X"]
truth = data["y"]
# Block clustering with fixed threshold
block_clusterer = BlockClustering(
blocking=block_last_name_first_initial,
base_estimator=ScipyHierarchicalClustering(
threshold=0.5,
affinity=affinity,
method="complete"),
verbose=3,
n_jobs=-1)
block_clusterer.fit(X)
labels = block_clusterer.labels_
# Print clusters
for cluster in np.unique(labels):
entries = set()
for name, affiliation in X[labels == cluster]:
entries.add((name, affiliation))
print("Cluster #%d = %s" % (cluster, entries))
print()
return distances
if __name__ == "__main__":
# Load data
data = np.load("data/author-disambiguation.npz")
X = data["X"]
truth = data["y"]
print("hello")
# Block clustering with fixed threshold
block_clusterer = BlockClustering(
blocking=block_last_name_first_initial,
base_estimator=ScipyHierarchicalClustering(threshold=0.5,
affinity=affinity,
method="complete"),
verbose=3,
n_jobs=-1)
block_clusterer.fit(X)
labels = block_clusterer.labels_
# Print clusters
for cluster in np.unique(labels):
entries = set()
for name, affiliation in X[labels == cluster]:
entries.add((name, affiliation))
print("Cluster #%d = %s" % (cluster, entries))
print()
def test_validation():
"""Test the validation of hyper-parameters and input data."""
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="foobar", base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X)
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X)
with pytest.raises(ValueError):
clusterer = BlockClustering(
blocking="precomputed",
base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(X, blocks=(y <= 1))
clusterer.predict(X)
class Clusterer(object):
def __init__(self, estimator):
# TODO get rid of this global
global distance_estimator
distance_estimator = estimator.distance_estimator
try:
distance_estimator.steps[-1][1].set_params(n_jobs=1)
except Exception:
pass
# threshold determines when to split blocks
# into smaller ones adding first initial
self.block_function = partial(block_phonetic,
threshold=0,
phonetic_algorithm="nysiis")
self.clustering_threshold = 0.709 # magic value taken from BEARD example
self.clustering_method = "average"
def load_data(self, signatures, input_clusters):
"""Loads data to the estimator vectors
Args:
signatures (iterable): Signatures which should be processed
input_clusters (iterable): Input clusters built for provided signatures
see: `inspire_disambiguation.core.es.readers.get_input_clusters`
"""
signatures_by_uuid = load_signatures(signatures)
self.X = np.empty((len(signatures_by_uuid), 1), dtype=np.object)
self.y = -np.ones(len(self.X), dtype=np.int)
i = 0
for cluster in input_clusters:
for signature_uuid in cluster["signature_uuids"]:
if signature_uuid not in signatures_by_uuid:
continue # TODO figure out how this can happen
self.X[i, 0] = signatures_by_uuid[signature_uuid]
self.y[i] = cluster["cluster_id"]
i += 1
def load_model(self, input_filename):
"""Loads model dumped by pickle
Args:
input_filename (str): path to file with dumped ethnicity model.
"""
with open(input_filename, "rb") as fd:
self.clusterer = pickle.load(fd)
def save_model(self, output_filename):
"""Dump object to a file
Args:
output_filename (str): Path where model will be dumped
"""
with open_file_in_folder(output_filename, "wb") as fd:
pickle.dump(self.clusterer, fd, protocol=pickle.HIGHEST_PROTOCOL)
def fit(self, n_jobs=8):
"""Fit data using the estimator"""
self.clusterer = BlockClustering(
blocking=self.block_function,
base_estimator=ScipyHierarchicalClustering(
affinity=_affinity,
threshold=self.clustering_threshold,
method=self.clustering_method,
supervised_scoring=b3_f_score,
),
n_jobs=n_jobs,
verbose=True,
)
self.clusterer.fit(self.X, self.y)
def prepare_test_data(self, test_uuids, labels):
"""
Returns the arrays used for scoring training and test datasets
Args:
test_uuids - set of signatures uuids used for testing
labels - list of labels (author id) for test dataset
"""
all_uuids = np.vectorize(lambda x: x.signature_uuid)(self.X).flatten()
test_uuids_array = np.array(list(test_uuids))
mask = np.isin(all_uuids, test_uuids_array)
y_train = self.y[~mask]
y_test = np.array(labels)
labels_train = self.clusterer.labels_[~mask]
labels_test = self.clusterer.labels_[mask]
return labels_train, y_train, labels_test, y_test
def nb_of_clusters_predicted_for_author(
self, input_clusters_with_all_author_labels,
test_signature_authors_ids):
author_ids = np.array([sample[0]['author_id'] for sample in self.X])
author_ids[author_ids == None] = test_signature_authors_ids
signatures_per_author = {
cluster['author_id']: set(cluster['signature_uuids'])
for cluster in input_clusters_with_all_author_labels
}
nb_of_clusters_per_author = {}
for author_id in signatures_per_author.keys():
author_mask = author_ids == author_id
signatures_predicted_in_one_cluster = self.clusterer.labels_[
author_mask]
nb_of_clusters_per_author[author_id] = np.unique(
signatures_predicted_in_one_cluster).size
return nb_of_clusters_per_author
def score(self, labels_train, y_train, labels_test, y_test):
"""
Return the clustering statistics (b3 precision, b3 recall, b3 f1 score)
and wrongly clustered samples for training, test and the whole dataset.
Args:
labels_train - array of labels predicted for training set
y_train - array of true labels for training set
labels_train - array of labels predicted for test set
y_test - array of true labels for test set
"""
return (b3_precision_recall_fscore(self.y, self.clusterer.labels_),
b3_precision_recall_fscore(y_train, labels_train)
if labels_train.size != 0 else None,
b3_precision_recall_fscore(y_test, labels_test)
if labels_test.size != 0 else None)
def test_single_signature(n_jobs):
"""Test clustering of a single signature."""
import numbers
clusterer = BlockClustering(base_estimator=MiniBatchKMeans(n_clusters=2))
clusterer.fit(np.array([X[0]]))
assert isinstance(clusterer.predict(X[0])[0], numbers.Integral)
