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DOC improved example plot in plot_lda_qda.py #12942

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Merged
merged 9 commits into from
Jan 14, 2019
Merged

DOC improved example plot in plot_lda_qda.py #12942

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3cfe96b
improved example plot in plot_lda_qda.py
zyxue Jan 8, 2019
88a5555
added two empty lines after function
zyxue Jan 8, 2019
393b34f
shortened long lines
zyxue Jan 8, 2019
205afa8
plot misclassifications as x
zyxue Jan 8, 2019
303295c
made covariance ellipse transparent and added back suptitle
zyxue Jan 9, 2019
95a9be7
chopped long lines
zyxue Jan 9, 2019
74d0fe7
tuned down ellipse alpha to 0.2
zyxue Jan 9, 2019
5b6120b
removed redundant variable
zyxue Jan 9, 2019
8a669a4
corrected suptitle
zyxue Jan 9, 2019
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36 changes: 17 additions & 19 deletions examples/classification/plot_lda_qda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -72,19 +72,15 @@ def plot_data(lda, X, y, y_pred, fig_index):
X0_tp, X0_fp = X0[tp0], X0[~tp0]
X1_tp, X1_fp = X1[tp1], X1[~tp1]

alpha = 0.5

# class 0: dots
plt.plot(X0_tp[:, 0], X0_tp[:, 1], 'o', alpha=alpha,
color='red', markeredgecolor='k')
plt.plot(X0_fp[:, 0], X0_fp[:, 1], '*', alpha=alpha,
color='#990000', markeredgecolor='k') # dark red
plt.scatter(X0_tp[:, 0], X0_tp[:, 1], marker='.', color='red')
plt.scatter(X0_fp[:, 0], X0_fp[:, 1], marker='x',
s=20, color='#990000') # dark red

# class 1: dots
plt.plot(X1_tp[:, 0], X1_tp[:, 1], 'o', alpha=alpha,
color='blue', markeredgecolor='k')
plt.plot(X1_fp[:, 0], X1_fp[:, 1], '*', alpha=alpha,
color='#000099', markeredgecolor='k') # dark blue
plt.scatter(X1_tp[:, 0], X1_tp[:, 1], marker='.', color='blue')
plt.scatter(X1_fp[:, 0], X1_fp[:, 1], marker='x',
s=20, color='#000099') # dark blue

# class 0 and 1 : areas
nx, ny = 200, 100
Expand All @@ -95,14 +91,14 @@ def plot_data(lda, X, y, y_pred, fig_index):
Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])
Z = Z[:, 1].reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',
norm=colors.Normalize(0., 1.))
plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='k')
norm=colors.Normalize(0., 1.), zorder=0)
plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='white')

# means
plt.plot(lda.means_[0][0], lda.means_[0][1],
'o', color='black', markersize=10, markeredgecolor='k')
'*', color='yellow', markersize=15, markeredgecolor='grey')
plt.plot(lda.means_[1][0], lda.means_[1][1],
'o', color='black', markersize=10, markeredgecolor='k')
'*', color='yellow', markersize=15, markeredgecolor='grey')

return splot

Expand All @@ -115,10 +111,9 @@ def plot_ellipse(splot, mean, cov, color):
# filled Gaussian at 2 standard deviation
ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,
180 + angle, facecolor=color,
edgecolor='yellow',
linewidth=2, zorder=2)
edgecolor='black', linewidth=2)
ell.set_clip_box(splot.bbox)
ell.set_alpha(0.5)
ell.set_alpha(0.2)
splot.add_artist(ell)
splot.set_xticks(())
splot.set_yticks(())
Expand All @@ -133,6 +128,8 @@ def plot_qda_cov(qda, splot):
plot_ellipse(splot, qda.means_[0], qda.covariance_[0], 'red')
plot_ellipse(splot, qda.means_[1], qda.covariance_[1], 'blue')


plt.figure(figsize=(10, 8), facecolor='white')
for i, (X, y) in enumerate([dataset_fixed_cov(), dataset_cov()]):
# Linear Discriminant Analysis
lda = LinearDiscriminantAnalysis(solver="svd", store_covariance=True)
Expand All @@ -147,6 +144,7 @@ def plot_qda_cov(qda, splot):
splot = plot_data(qda, X, y, y_pred, fig_index=2 * i + 2)
plot_qda_cov(qda, splot)
plt.axis('tight')
plt.suptitle('Linear Discriminant Analysis vs Quadratic Discriminant'
'Analysis')
plt.suptitle('Linear Discriminant Analysis vs Quadratic Discriminant Analysis',
y=1.02, fontsize=15)
plt.tight_layout()
plt.show()