def test_instance_validation(self):
clf = self.Clusterer()
scikitplot.clustering_factory(clf)
not_clf = self.NotClusterer()
self.assertRaises(TypeError, scikitplot.clustering_factory, not_clf)
def test_instance_validation(self):
clf = self.Clusterer()
scikitplot.clustering_factory(clf)
not_clf = self.NotClusterer()
self.assertRaises(TypeError, scikitplot.clustering_factory, not_clf)
def test_copy(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_silhouette(self.X)
assert not hasattr(clf, "cluster_centers_")
ax = clf.plot_silhouette(self.X, copy=False)
assert hasattr(clf, "cluster_centers_")
def test_copy(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_silhouette(self.X)
assert not hasattr(clf, "cluster_centers_")
ax = clf.plot_silhouette(self.X, copy=False)
assert hasattr(clf, "cluster_centers_")
def test_ax(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
fig, ax = plt.subplots(1, 1)
out_ax = clf.plot_silhouette(self.X)
assert ax is not out_ax
out_ax = clf.plot_silhouette(self.X, ax=ax)
assert ax is out_ax
def test_ax(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
fig, ax = plt.subplots(1, 1)
out_ax = clf.plot_silhouette(self.X)
assert ax is not out_ax
out_ax = clf.plot_silhouette(self.X, ax=ax)
assert ax is out_ax
def test_n_clusters_in_clf(self):
np.random.seed(0)
class DummyClusterer:
def __init__(self):
pass
def fit(self):
pass
def fit_predict(self):
pass
clf = DummyClusterer()
scikitplot.clustering_factory(clf)
self.assertRaises(TypeError, clf.plot_elbow_curve, self.X)
def test_n_clusters_in_clf(self):
np.random.seed(0)
class DummyClusterer:
def __init__(self):
pass
def fit(self):
pass
def fit_predict(self):
pass
clf = DummyClusterer()
scikitplot.clustering_factory(clf)
self.assertRaises(TypeError, clf.plot_elbow_curve, self.X)
def test_method_insertion(self):
clf = self.Clusterer()
scikitplot.clustering_factory(clf)
assert hasattr(clf, 'plot_silhouette')
assert hasattr(clf, 'plot_elbow_curve')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
scikitplot.clustering_factory(clf)
assert len(w) >= 2
for warning in w[1:]:
assert issubclass(warning.category, UserWarning)
assert ' method already in clf. ' \
'Overriding anyway. This may ' \
'result in unintended behavior.' in str(warning.message)
def test_method_insertion(self):
clf = self.Clusterer()
scikitplot.clustering_factory(clf)
assert hasattr(clf, 'plot_silhouette')
assert hasattr(clf, 'plot_elbow_curve')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
scikitplot.clustering_factory(clf)
assert len(w) == 2
for warning in w:
assert issubclass(warning.category, UserWarning)
assert ' method already in clf. ' \
'Overriding anyway. This may ' \
'result in unintended behavior.' in str(warning.message)
def test_cmap(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_silhouette(self.X, cmap='Spectral')
ax = clf.plot_silhouette(self.X, cmap=plt.cm.Spectral)
def test_cluster_ranges(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_elbow_curve(self.X, cluster_ranges=range(1, 10))
ax = clf.plot_elbow_curve(self.X)
"""An example showing the plot_silhouette method used by a scikit-learn clusterer""" from __future__ import absolute_import import matplotlib.pyplot as plt from scikitplot import clustering_factory from sklearn.cluster import KMeans from sklearn.datasets import load_iris as load_data X, y = load_data(return_X_y=True) kmeans = clustering_factory(KMeans(n_clusters=4, random_state=1)) kmeans.plot_silhouette(X) plt.show()
"""An example showing the plot_silhouette method used by a scikit-learn clusterer""" from __future__ import absolute_import import matplotlib.pyplot as plt from scikitplot import clustering_factory from sklearn.cluster import KMeans from sklearn.datasets import load_iris as load_data X, y = load_data(return_X_y=True) kmeans = clustering_factory(KMeans(random_state=1)) kmeans.plot_elbow_curve(X, cluster_ranges=range(1, 11)) plt.show()
def test_cluster_ranges(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_elbow_curve(self.X, cluster_ranges=range(1, 10))
ax = clf.plot_elbow_curve(self.X)
def test_cmap(self):
np.random.seed(0)
clf = KMeans()
scikitplot.clustering_factory(clf)
ax = clf.plot_silhouette(self.X, cmap='Spectral')
ax = clf.plot_silhouette(self.X, cmap=plt.cm.Spectral)
