def test_ax(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
fig, ax = plt.subplots(1, 1)
out_ax = plot_pca_2d_projection(clf, self.X, self.y)
assert ax is not out_ax
out_ax = plot_pca_2d_projection(clf, self.X, self.y, ax=ax)
assert ax is out_ax
def test_biplot(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
ax = plot_pca_2d_projection(clf,
self.X,
self.y,
biplot=True,
feature_labels=load_data().feature_names)
def test_label_order(self):
'''
Plot labels should be in the same order as the classes in the provided y-array
'''
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
# define y such that the first entry is 1
y = np.copy(self.y)
y[0] = 1 # load_iris is be default orderer (i.e.: 0 0 0 ... 1 1 1 ... 2 2 2)
# test with len(y) == X.shape[0] with multiple rows belonging to the same class
ax = plot_pca_2d_projection(clf, self.X, y, cmap='Spectral')
legend_labels = ax.get_legend_handles_labels()[1]
self.assertListEqual(['1', '0', '2'], legend_labels)
# test with len(y) == #classes with each row belonging to an individual class
y = list(range(len(y)))
np.random.shuffle(y)
ax = plot_pca_2d_projection(clf, self.X, y, cmap='Spectral')
legend_labels = ax.get_legend_handles_labels()[1]
self.assertListEqual([str(v) for v in y], legend_labels)
def test_biplot(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
ax = plot_pca_2d_projection(clf, self.X, self.y, biplot=True,
feature_labels=load_data().feature_names)
def test_cmap(self):
np.random.seed(0)
clf = PCA()
clf.fit(self.X)
plot_pca_2d_projection(clf, self.X, self.y, cmap='Spectral')
plot_pca_2d_projection(clf, self.X, self.y, cmap=plt.cm.Spectral)
