def test_5_default():
"""
test all plotting embbeding algorithm
"""
granatum = GranatumClustering(
n_components=5)
granatum.fit(TEST_DATASET)
for embbed in PLOTTING_EMBEDDING:
print('#### plotting embbeding used tested: {0}'.format(embbed))
plots = granatum.plot(embedding=embbed)
assert('embedding_for_plotting' in plots)
assert('plot_figure_html' in plots)
assert('plot_figure_png' in plots)
def test_0_default():
"""
test default parameter on the default test dataset and the plotting
"""
granatum = GranatumClustering(n_components=5, selected_clustering='DBSCAN')
results = granatum.fit(TEST_DATASET)
assert('clusters' in results)
assert('n_clusters' in results)
assert('n_components' in results)
assert('embedding' in results)
assert('clustering_algorithm' in results)
plots = granatum.plot(plot_figure_png=True,
plot_figure_html=True,
embedding='PCA',
jsonify=False)
assert('embedding_for_plotting' in plots)
assert('plot_figure_html' in plots)
assert('plot_figure_png' in plots)
def main():
""" """
from sklearn.datasets import make_blobs
test_dataset, ref_array = make_blobs(n_samples=300,
n_features=200,
centers=8)
metadata = {
i: {
'dummy': 'dummy:{0}'.format(i)
}
for i in range(len(test_dataset))
}
sample_ids = list(range(len(test_dataset)))
granatum = GranatumClustering(selected_embedding='PCA',
selected_clustering='WARD')
results = granatum.fit(matrix=test_dataset,
metadata=metadata,
sample_ids=sample_ids)
assert ('clusters' in results)
assert ('n_clusters' in results)
assert ('n_components' in results)
assert ('embedding' in results)
assert ('clustering_algorithm' in results)
plots = granatum.plot(figsize=(600, 600),
embedding='PCA',
plot_figure_png=True,
plot_figure_html=True,
jsonify=False)
assert ('plot_figure_html' in plots)
assert ('plot_figure_png' in plots)