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5 changes: 4 additions & 1 deletion scikitplot/decomposition.py
Original file line number Diff line number Diff line change
Expand Up @@ -163,7 +163,10 @@ def plot_pca_2d_projection(clf, X, y, title='PCA 2-D Projection',
fig, ax = plt.subplots(1, 1, figsize=figsize)

ax.set_title(title, fontsize=title_fontsize)
classes = np.unique(np.array(y))

# Get unique classes from y, preserving order of class occurence in y
_, class_indexes = np.unique(np.array(y), return_index=True)
classes = np.array(y)[np.sort(class_indexes)]

colors = plt.cm.get_cmap(cmap)(np.linspace(0, 1, len(classes)))

Expand Down
27 changes: 27 additions & 0 deletions scikitplot/tests/test_decomposition.py
Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@

from scikitplot.decomposition import plot_pca_component_variance
from scikitplot.decomposition import plot_pca_2d_projection
import scikitplot


class TestPlotPCAComponentVariance(unittest.TestCase):
Expand Down Expand Up @@ -81,3 +82,29 @@ def test_biplot(self):
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)