8000 [MRG+1] Modify gaussian_process examples for matplotlib v2 comp by rishikksh20 · Pull Request #8394 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Modify gaussian_process examples for matplotlib v2 comp #8394

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Merged
merged 7 commits into from
Jun 19, 2017
Merged

[MRG+1] Modify gaussian_process examples for matplotlib v2 comp #8394

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00e7aea
Modify plot_gpc_xor.py for matplotlib v2 comp
rishikksh20 Feb 18, 2017
eb5e587
Modify plot_gpr_noisy.py for matplotlib v2 comp
rishikksh20 Feb 18, 2017
50c1330
Modify plot_gpr_prior_posterior for matplotlibv2
rishikksh20 Feb 18, 2017
8919161
Modify plot_gpc.py for matplotlib v2 comp
rishikksh20 Feb 18, 2017
aa847b3
Modify plot_gpr_noisy.py to remove flake8 error
rishikksh20 Feb 18, 2017
d010fb2
Cosmetic change in plot_gpr_prior_posterior
rishikksh20 Feb 20, 2017
f8648b5
Modify plot_gpr_noisy.py for better visualization
rishikksh20 Feb 22, 2017
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6 changes: 4 additions & 2 deletions examples/gaussian_process/plot_gpc.py
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Expand Up @@ -64,8 +64,10 @@

# Plot posteriors
plt.figure(0)
plt.scatter(X[:train_size, 0], y[:train_size], c='k', label="Train data")
plt.scatter(X[train_size:, 0], y[train_size:], c='g', label="Test data")
plt.scatter(X[:train_size, 0], y[:train_size], c='k', label="Train data",
edgecolors=(0, 0, 0))
plt.scatter(X[train_size:, 0], y[train_size:], c='g', label="Test data",
edgecolors=(0, 0, 0))
X_ = np.linspace(0, 5, 100)
plt.plot(X_, gp_fix.predict_proba(X_[:, np.newaxis])[:, 1], 'r',
label="Initial kernel: %s" % gp_fix.kernel_)
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3 changes: 2 additions & 1 deletion examples/gaussian_process/plot_gpc_xor.py
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Expand Up @@ -44,7 +44,8 @@
aspect='auto', origin='lower', cmap=plt.cm.PuOr_r)
contours = plt.contour(xx, yy, Z, levels=[0], linewidths=2,
linetypes='--')
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired)
plt.scatter(X[:, 0], X[:, 1], s=30, c=Y, cmap=plt.cm.Paired,
edgecolors=(0, 0, 0))
plt.xticks(())
plt.yticks(())
plt.axis([-3, 3, -3, 3])
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8 changes: 4 additions & 4 deletions examples/gaussian_process/plot_gpr_noisy.py
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Expand Up @@ -47,7 +47,7 @@
y_mean + np.sqrt(np.diag(y_cov)),
alpha=0.5, color='k')
plt.plot(X_, 0.5*np.sin(3*X_), 'r', lw=3, zorder=9)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10, edgecolors=(0, 0, 0))
plt.title("Initial: %s\nOptimum: %s\nLog-Marginal-Likelihood: %s"
% (kernel, gp.kernel_,
gp.log_marginal_likelihood(gp.kernel_.theta)))
Expand All @@ -66,7 +66,7 @@
y_mean + np.sqrt(np.diag(y_cov)),
alpha=0.5, color='k')
plt.plot(X_, 0.5*np.sin(3*X_), 'r', lw=3, zorder=9)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10, edgecolors=(0, 0, 0))
plt.title("Initial: %s\nOptimum: %s\nLog-Marginal-Likelihood: %s"
% (kernel, gp.kernel_,
gp.log_marginal_likelihood(gp.kernel_.theta)))
Expand All @@ -83,9 +83,9 @@

vmin, vmax = (-LML).min(), (-LML).max()
vmax = 50
level = np.around(np.logspace(np.log10(vmin), np.log10(vmax), 50), decimals=1)
plt.contour(Theta0, Theta1, -LML,
levels=np.logspace(np.log10(vmin), np.log10(vmax), 50),
norm=LogNorm(vmin=vmin, vmax=vmax))
levels=level, norm=LogNorm(vmin=vmin, vmax=vmax))
plt.colorbar()
plt.xscale("log")
plt.yscale("log")
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6 changes: 3 additions & 3 deletions examples/gaussian_process/plot_gpr_prior_posterior.py
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Expand Up @@ -44,7 +44,7 @@
y_mean, y_std = gp.predict(X_[:, np.newaxis], return_std=True)
plt.plot(X_, y_mean, 'k', lw=3, zorder=9)
plt.fill_between(X_, y_mean - y_std, y_mean + y_std,
alpha=0.5, color='k')
alpha=0.2, color='k')
y_samples = gp.sample_y(X_[:, np.newaxis], 10)
plt.plot(X_, y_samples, lw=1)
plt.xlim(0, 5)
Expand All @@ -63,11 +63,11 @@
y_mean, y_std = gp.predict(X_[:, np.newaxis], return_std=True)
plt.plot(X_, y_mean, 'k', lw=3, zorder=9)
plt.fill_between(X_, y_mean - y_std, y_mean + y_std,
alpha=0.5, color='k')
alpha=0.2, color='k')

y_samples = gp.sample_y(X_[:, np.newaxis], 10)
plt.plot(X_, y_samples, lw=1)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10)
plt.scatter(X[:, 0], y, c='r', s=50, zorder=10, edgecolors=(0, 0, 0))
plt.xlim(0, 5)
plt.ylim(-3, 3)
plt.title("Posterior (kernel: %s)\n Log-Likelihood: %.3f"
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